Self-assembly of pericentriolar material in interphase cells lacking centrioles

  1. Fangrui Chen
  2. Jingchao Wu
  3. Malina K Iwanski
  4. Daphne Jurriens
  5. Arianna Sandron
  6. Milena Pasolli
  7. Gianmarco Puma
  8. Jannes Z Kromhout
  9. Chao Yang
  10. Wilco Nijenhuis
  11. Lukas C Kapitein
  12. Florian Berger
  13. Anna Akhmanova  Is a corresponding author
  1. Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Netherlands
  2. Center for Living Technologies, Eindhoven‐Wageningen‐Utrecht Alliance, Netherlands

Abstract

The major microtubule-organizing center (MTOC) in animal cells, the centrosome, comprises a pair of centrioles surrounded by pericentriolar material (PCM), which nucleates and anchors microtubules. Centrosome assembly depends on PCM binding to centrioles, PCM self-association and dynein-mediated PCM transport, but the self-assembly properties of PCM components in interphase cells are poorly understood. Here, we used experiments and modeling to study centriole-independent features of interphase PCM assembly. We showed that when centrioles are lost due to PLK4 depletion or inhibition, dynein-based transport and self-clustering of PCM proteins are sufficient to form a single compact MTOC, which generates a dense radial microtubule array. Interphase self-assembly of PCM components depends on γ-tubulin, pericentrin, CDK5RAP2 and ninein, but not NEDD1, CEP152, or CEP192. Formation of a compact acentriolar MTOC is inhibited by AKAP450-dependent PCM recruitment to the Golgi or by randomly organized CAMSAP2-stabilized microtubules, which keep PCM mobile and prevent its coalescence. Linking of CAMSAP2 to a minus-end-directed motor leads to the formation of an MTOC, but MTOC compaction requires cooperation with pericentrin-containing self-clustering PCM. Our data reveal that interphase PCM contains a set of components that can self-assemble into a compact structure and organize microtubules, but PCM self-organization is sensitive to motor- and microtubule-based rearrangement.

Editor's evaluation

Microtubules are organized by microtubule organizing centers (MTOCs) such as the centrosome, which is composed of two centrioles surrounded by pericentriolar material (PCM). Despite a century of investigation, the mechanisms by which the centrosome organizes microtubules remains incompletely understood. Here, using genetic and pharmacological manipulations, as well as computer simulations, Chen and colleagues generate interphase cells with centriole-less PCM to investigate mechanisms by which PCM proteins cluster and nucleate and anchor microtubules. This manuscript will be of interest to cell biologists studying microtubule organization.

https://doi.org/10.7554/eLife.77892.sa0

Introduction

The centrosome is the major microtubule organizing center (MTOC) in animal cells. It consists of two centrioles surrounded by pericentriolar material (PCM) (reviewed in Conduit et al., 2015; Paz and Luders, 2017). Major PCM components are microtubule-nucleating and anchoring proteins, which can associate with centrioles and with each other. For a long time it was thought that the PCM is amorphous, but super-resolution microscopy studies have shown that it has a distinct organization, with some proteins likely attached to the centriole wall and others organized around them (Fu and Glover, 2012; Lawo et al., 2012; Mennella et al., 2014; Mennella et al., 2012). This distinct organization is more obvious in interphase than in mitosis, when the microtubule-organizing capacity of the centrosome increases due to enhanced PCM recruitment. Many PCM components are known to oligomerize and interact with each other, and recent work suggested that the phase separation of interacting PCM components might contribute to centrosome assembly during mitosis (Raff, 2019; Woodruff et al., 2017). This idea is underscored by data showing that various cell-type-specific assemblies of PCM components can form clusters that are able to nucleate and organize microtubules and serve as MTOCs in the absence of centrioles, particularly during the formation of mitotic spindle poles (Balestra et al., 2021; Chinen et al., 2021; Gartenmann et al., 2020; Meitinger et al., 2020; Watanabe et al., 2020; Yeow et al., 2020). Furthermore, an important centrosome component, cytoplasmic dynein, is a motor that can bind to different PCM proteins and transport them to the centrosome-anchored microtubule ends, where these PCM proteins can nucleate and anchor additional microtubules, thus generating a positive feedback loop in centrosome assembly (Balczon et al., 1999; Burakov et al., 2008; Purohit et al., 1999; Redwine et al., 2017). Dynein and its mitotic binding partner NuMA also strongly participate in the formation of mitotic and meiotic spindle poles (Chinen et al., 2020; Khodjakov et al., 2000; Kolano et al., 2012). The relative importance of different molecular pathways of PCM assembly varies between cell systems and phases of the cell cycle and has not been explored systematically in interphase cells.

Here, we set out to investigate the centriole-independent self-assembly properties of interphase PCM. These properties, such as the ability of PCM to cluster or form molecular condensates, nucleate and anchor microtubules and move with motor proteins, are relevant because in most differentiated cell types, centrosome function is suppressed, and some PCM components form acentrosomal MTOCs (Muroyama and Lechler, 2017; Sallee and Feldman, 2021). During mitotic exit, when mitotic kinases are inactivated, PCM complexes can be removed from the centrosomes as ‘fragments’ or ‘packets’ (Magescas et al., 2019; Rusan and Wadsworth, 2005), indicating that they maintain some degree of self-association. In other cases, complexes of PCM proteins may fully disassemble and then assemble at other locations, but their properties will likely still determine the composition and localization of acentrosomal MTOCs. To study centriole-independent function and dynamics of PCM proteins, we removed centrioles using the PLK4 kinase inhibitor centrinone B which blocks centriole duplication (Wong et al., 2015). Importantly, in most commonly studied cultured cell lines, such as fibroblasts, epithelial, endothelial or cancer cells, microtubule networks are dense, and the centrosome is not the only MTOC. In such cells, non-centrosomal microtubule minus ends are often stabilized by the members of CAMSAP family (Jiang et al., 2014; Meng et al., 2008; Tanaka et al., 2012), and the Golgi apparatus serves as a second MTOC which nucleates and anchors a very significant proportion of microtubules (Efimov et al., 2007; Rios, 2014; Wu et al., 2016; Zhu and Kaverina, 2013). If centrosomes are lost because centriole duplication is blocked by inhibiting PLK4 or depleting another essential centriole component, Golgi-dependent microtubule organization becomes predominant (Gavilan et al., 2018; Martin et al., 2018; Wu et al., 2016). The ability of the Golgi complex to serve as an MTOC critically depends on the Golgi adaptor AKAP450, which recruits several PCM components that nucleate microtubules including the γ-tubulin ring complex (γ-TuRC), CDK5RAP2 and pericentrin (Gavilan et al., 2018; Rivero et al., 2009; Wu et al., 2016). Moreover, AKAP450 also tethers microtubule minus ends stabilized by CAMSAP2 to the Golgi membranes (Gavilan et al., 2018; Rivero et al., 2009; Wu et al., 2016). In the absence of AKAP450, the Golgi ribbon is maintained, but neither PCM components nor CAMSAP-stabilized microtubule minus ends can be attached to the Golgi membranes and they are instead dispersed in cytoplasm, leading to a randomly organized microtubule network (Gavilan et al., 2018; Rivero et al., 2009; Wu et al., 2016). These data seem to suggest that centrioles and/or Golgi membranes are essential to assemble PCM into an MTOC in interphase. However, this notion appears to be inaccurate: in our previous study in RPE1 cells, we observed that a single compact acentriolar MTOC (caMTOC) can still form after centriole loss in AKAP450 knockout cells, if the stabilization of free minus ends in these cells is disabled by knocking out CAMSAP2 (Wu et al., 2016).

We used this observation as a starting point to investigate which properties of PCM components allow them to self-assemble in interphase mammalian cells and how the presence of non-centrosomal microtubules affects this process. AKAP450 knockout cells provided a system to study assembly of PCM proteins in the absence of competition with the Golgi-associated MTOC. caMTOC formation in AKAP450 knockout cells required microtubules and depended on dynein, which brought together small PCM clusters with attached minus ends. Experiments and modeling showed CAMSAP2-mediated minus-end stabilization strongly perturbed PCM coalescence, because in the absence of AKAP450, randomly oriented CAMSAP2-stabilized microtubules supported PCM motility and prevented PCM clustering. In the absence of CAMSAP2, caMTOCs did form, but were often cylindrical rather than spherical in shape and contained a subset of the major centrosome components. γ-tubulin, pericentrin, CDK5RAP2 and ninein were necessary for the formation of caMTOCs, whereas some other major PCM proteins, namely CEP192, CEP152 and NEDD1, were neither enriched in these structures nor required for their formation, indicating that not all PCM components associate with each other in the absence of centrioles and that interphase MTOC function does not strictly require these three proteins. A single caMTOC containing PCM components could also form in the presence of CAMSAP2 when this protein was directly linked to a microtubule minus-end-directed motor. Importantly, in the absence of pericentrin, minus-end-directed transport of CAMSAP2-stabilized minus ends organized these ends into a ring, indicating that self-associating PCM is required for the formation of a caMTOC. This conclusion was supported by modeling. Taken together, our data show that a subset of interphase PCM components can self-assemble and efficiently nucleate and tether microtubules, but PCM clustering is sensitive to microtubule- and motor-dependent rearrangements. These properties of interphase PCM may also be involved in the transition from a centrosomal to a non-centrosomal microtubule network, as typically occurs during cell differentiation.

Results

Assembly of microtubule-dependent caMTOCs in AKAP450/CAMSAP2 KO cells lacking PLK4 activity

To study centriole-independent PCM organization and dynamics in interphase cells, we induced centriole loss in RPE1 cells by treating them for 11 days with the PLK4 inhibitor centrinone B (Wong et al., 2015; Figure 1A and B). In wild type (WT) cells, PCM (detected with antibodies against pericentrin) relocalized to the Golgi apparatus, and the microtubule array reorganized around the Golgi membranes, as described previously (Gavilan et al., 2018; Wu et al., 2016; Figure 1C and D). In AKAP450 knockout cells, centriole loss led to the appearance of strongly dispersed PCM clusters, which could no longer bind to the Golgi, and a highly disorganized microtubule system, consistent with published work (Gavilan et al., 2018; Wu et al., 2016; Figure 1D and E). In contrast, in AKAP450/CAMSAP2 double knockout cells, a single caMTOC with microtubules focused around it was observed (Figure 1D–F). Formation of a single caMTOC was also observed in AKAP450 knockout cells transiently depleted of CAMSAP2 by siRNA transfection (Figure 1—figure supplement 1A, B). As an alternative approach to block the ability of the Golgi apparatus to recruit PCM, we treated the cells with Brefeldin A, which disrupts the Golgi (Klausner et al., 1992). As expected, Brefeldin A by itself had no effect on centrosome integrity but led to the dispersal of the Golgi marker GM130 (Figure 1—figure supplement 1C-E). In centrinone-treated wild-type cells, PCM was dispersed after Golgi disruption, and the same was true for the majority of CAMSAP2 knockout cells (Figure 1—figure supplement 1C-F). However, in 12% of CAMSAP2 knockout cells treated with both centrinone B and Brefeldin A, we observed PCM compaction (Figure 1—figure supplement 1D-F), similar to that seen in AKAP450/CAMSAP2 double knockout cells. The efficiency of caMTOC formation was low likely because dispersed AKAP450 could still recruit PCM to some extent or because the duration of the Brefeldin A treatment (2 hr) was too short to allow for PCM compaction in most cells (longer Brefeldin A treatments were not performed due to potential cell toxicity). The acentriolar PCM can thus form a compact structure if both the Golgi MTOC and CAMSAP2-mediated minus-end stabilization are disabled.

Figure 1 with 3 supplements see all
Formation and characterization of caMTOCs in AKAP450/CAMSAP2 knockout cells.

(A) Immunofluorescence images of control or centrinone-treated wild type (WT) RPE1 cells stained for centrioles (CEP135, red; centrin, green). The zooms of the boxed area show the centrioles stained with the indicated markers. (B) Quantification shows the percentage of cells with centrioles before and after the centrinone treatment. 350 cells (n=7 fields of view) analyzed for each measurement in three independent experiments. The statistical significance was determined by unpaired two-tailed Mann-Whitney test in Prism 9.1 (***p<0.001). Values represent mean ± SD. (C) Immunofluorescence images of centrinone-treated WT RPE1 cells stained for pericentrin (PCNT, green) and the Golgi marker GM130 (red). Inset shows the merged image of the boxed area. (D) Diagrams of the microtubule organization in WT and knockout (KO) cells used. (E) Immunofluorescence images of control and centrinone-treated WT and knockout RPE1 cell lines stained for pericentrin (green) and microtubules (α-tubulin, red). Enlargements on the right show the boxed areas. (F) Immunofluorescence images of centrinone-treated AKAP450/CAMSAP2 knockout RPE1 cells stained for different PCM components as indicated and imaged by STED microscopy. (G) Quantification of the length and width of cylindrical PCM clusters. n=65 cells analyzed in three independent experiments. Values represent mean ± SD. (H) (Top left) Two frames of time-lapse images of centrinone-treated AKAP450/CAMSAP2 knockout RPE1 cells stably expressing GFP-CDK5RAP2 prior to FRAP experiments. (Top right) Schemes show regions of caMTOC where photobleaching was performed. (Middle) Kymographs illustrating fluorescence of unbleached caMTOC (No FRAP), fully photobleached caMTOC (Whole FRAP) and partially photobleached caMTOC (Partial FRAP). (Bottom) Time-lapse images illustrating partial FRAP of a caMTOC. Time is min: s. (I) Normalized fluorescence intensity as a function of time. The blue line shows averaged intensity traces of unbleached caMTOCs (No FRAP), the black line shows averaged intensity traces of fully photobleached caMTOCs (Whole FRAP), the red line shows averaged intensity traces of whole caMTOC that were partially photobleached (whole caMTOC intensity, Partial FRAP) and the green line shows averaged intensity traces of the photobleached region of the partially photobleached caMTOC (FRAP region intensity, Partial FRAP). n=3 for No FRAP, 3 for Whole FRAP, 5 for Partial FRAP (whole caMTOC intensity) and 5 for Partial FRAP (FRAP region intensity); time-lapse images of ~1600 timepoints with 2 s interval were analyzed for each measurement. Values are mean ± SD.

Figure 1—source data 1

An Excel sheet with numerical data on the quantifications shown in panels B, G, and I.

https://cdn.elifesciences.org/articles/77892/elife-77892-fig1-data1-v2.xlsx

Unlike centrosomes, which always have a spherical shape, caMTOCs in AKAP450/CAMSAP2 knockout cells were cylindrical in ~35% of the cells, whereas in the remaining cells that lacked centrioles based on staining for centrin, MTOCs had a round shape (~38% of the cells); the rest of the centrin-negative cells either had dispersed PCM clusters (~7%) or no detectable PCM clusters (~11%) (Figure 1—figure supplement 2A, B). In contrast,~72% of acentriolar AKAP450 knockout cells had dispersed PCM, while caMTOCs were very rare (Figure 1E, Figure 1—figure supplement 2A, B). Analysis by Stimulated Emission Depletion (STED) microscopy revealed that cylindrical caMTOCs in AKAP450/CAMSAP2 knockout cells consisted of small clusters of PCM components, including pericentrin, CDK5RAP2, γ-tubulin, ninein and dynein heavy chain (Figure 1F). caMTOCs with a clearly elongated shape had an average length of ~11 µm and an average width of ~1.5 µm (Figure 1G).

To study PCM dynamics in caMTOCs, we generated cell lines stably expressing the PCM component CDK5RAP2 tagged with GFP. In control, untreated cells, GFP-CDK5RAP2 was localized to the centrosome and the Golgi apparatus as expected (Figure 1—figure supplement 3A). In centrinone-treated AKAP450/CAMSAP2 knockout cells, it was strongly enriched within caMTOCs and sometimes also present in small motile clusters around a caMTOC (Figure 1H, Figure 1—figure supplement 3B). Fluorescence recovery after photobleaching (FRAP) assays showed that when the whole caMTOC was bleached, the recovery was very slow and incomplete (Figure 1H, I). If only a part of a caMTOC was photobleached, the dynamics of recovery showed cell-to-cell variability. Highly condensed caMTOCs displayed a slow redistribution of GFP-CDK5RAP2 signal, suggesting that their components are largely immobile and do not rearrange. In more loosely organized caMTOCs, some rearrangement of small PCM clusters was observed (Figure 1H, I); however, the recovery was still far from complete. These data indicate that caMTOCs display variable degrees of compaction and are composed of PCM clusters that display limited exchange of GFP-CDK5RAP2 with the cytoplasmic pool, possibly because most of the GFP-CDK5RAP2 is accumulated within the caMTOC.

Next, we investigated whether centriole loss induced by means other than pharmacological PLK4 inhibition could also cause the formation of a single caMTOC in AKAP450/CAMSAP2 knockout cells. To achieve efficient protein depletion in RPE1 cells, they were transfected with siRNAs twice (on day 0 and day 2), treated with thymidine starting from day 4 to block cell cycle progression and fixed and stained on day 5 or day 7 (Figure 1—figure supplement 2C). Depletion of PLK4 using siRNAs caused the appearance of round or cylindrical caMTOCs, similar to those observed after PLK4 inhibition with centrinone B, indicating that catalytically inactive PLK4 had no scaffolding role within these structures (Figure 1—figure supplement 2C-F). The percentage of cells with caMTOCs increased over time (Figure 1—figure supplement 2D), possibly due to the gradual depletion of PLK4. In contrast, depletion of CPAP, an essential centriole biogenesis factor (Kohlmaier et al., 2009; Schmidt et al., 2009; Tang et al., 2009), which also led to centriole loss, was much less efficient in inducing caMTOCs, and cylindrical caMTOCs were never observed (Figure 1—figure supplement 2C-E). After CPAP depletion, cells in which pericentrin formed dispersed clusters or no visible clusters predominated (~67%, Figure 1—figure supplement 2D, E). Treatment of CPAP-depleted AKAP450/CAMSAP2 knockout cells with centrinone B for 1 day promoted the assembly of round or cylindrical caMTOCs, and the proportion of such cells increased to ~55% after 3 days of centrinone B treatment (Figure 1—figure supplement 2C-G). We also tested whether the inhibition of PLK1, a kinase that is known to be a major regulator of PCM self-assembly in mitosis (Haren et al., 2009; Joukov et al., 2014; Lee and Rhee, 2011), has an effect on the formation of caMTOCs by treating cells with BI2536 (a highly selective and potent inhibitor of PLK1), but found this not to be the case (Figure 1—figure supplement 2C,D). We conclude that PCM can assemble into a single stable MTOC in a centriole-independent manner if PLK4 is either inactivated or depleted and the two major pathways of microtubule nucleation and minus-end stabilization dependent on the Golgi membranes and CAMSAP2 are disabled.

Composition of caMTOCs and their effect on microtubule organization

PCM is composed of numerous proteins that can bind to each other and interact with microtubules, and we next set out to investigate which PCM components can self-assemble in the absence of centrioles. We first stained centrinone-treated AKAP450/CAMSAP2 knockout cells with antibodies against different centrosome and centriole markers and microtubule-associated proteins (MAPs). As indicated above, the abundant PCM components pericentrin, CDK5RAP2, ninein and γ-tubulin colocalized within caMTOCs (Figure 1F). In contrast, three other major PCM proteins, CEP152, CEP192 and NEDD1, could not be detected in these structures although they were present in centrosomes of AKAP450/CAMSAP2 knockout RPE1 cells that were not treated with centrinone B (Figure 2A–C) and were also expressed in centrinone-treated cells (Figure 2—figure supplement 1A). We then individually depleted all these proteins in centrinone-treated AKAP450/CAMSAP2 knockout cells using siRNAs. After the depletion of pericentrin, no clusters of other PCM components could be detected (Figure 2D, J and K, Figure 2—figure supplement 1B). To confirm this result, we also attempted to knock out pericentrin in AKAP450/CAMSAP2 knockout cells, but such cells were not viable, likely because centrosome defects in these cells caused prolonged mitosis and p53-dependent G1 arrest (Fong et al., 2016; Lambrus et al., 2016; Meitinger et al., 2016). However, we were able to knock out pericentrin in cells lacking AKAP450, CAMSAP2 and p53 (Figure 2—figure supplement 2), confirming that the loss of p53 makes the cells more tolerant to centrosome absence and allows them to divide even in the presence of centrinone B (Figure 2—figure supplement 3, Video 1). Similar to pericentrin-depleted cells, these quadruple knockout cells were unable to form a single caMTOC when treated with centrinone B (Figure 2E and J). In these acentriolar quadruple knockout cells, CDK5RAP2, γ-tubulin and cytoplasmic dynein displayed no clustering, while ninein and PCM1, a centriolar satellite protein that localizes closely around the centrosome in normal cells (Prosser and Pelletier, 2020), formed small clusters distributed throughout the cytoplasm (Figure 2—figure supplement 4A). Loss of pericentrin had no effect on the expression of CDK5RAP2, γ-tubulin and ninein (Figure 2—figure supplement 4B), indicating that clustering by pericentrin affects the organization but not the stability of these PCM components.

Figure 2 with 5 supplements see all
Molecular composition of caMTOCs in AKAP450/CAMSAP2 knockout cells.

All the cells used in this figure are AKAP450/CAMSAP2 knockout cells, except for panel E as indicated. (A–C) Immunofluorescence images of control or centrinone-treated AKAP450/CAMSAP2 knockout RPE1 cells stained for and depleted of the indicated proteins. (D, F–I) Immunofluorescence images of centrinone-treated AKAP450/CAMSAP2 knockout RPE1 cells stained for and depleted of the indicated proteins. (E) Immunofluorescence images of centrinone-treated AKAP450/CAMSAP2/p53 knockout and AKAP450/CAMSAP2/p53/pericentrin knockout RPE1 cells stained as indicated. In panels A-I, insets show enlargements of the merged channels of the boxed areas, and dashed lines indicate cell edges. (J) Quantification of the main PCM organization types, as described in Figure 1—figure supplement 2A, for cells prepared as described in panel A-C, E-I. Numbers on the histogram show the percentages. 1293 (-CentB), 1547(+CentB), 2021(siLuci), 1822(siCEP152), 1345(siCEP192), 1161(siNEDD1), 2302 (AKAP450/CAMSAP2/p53/PCNT knockout (PCNT KO)), 2264(siCDK5RAP2), 2510(siγ-tubulin), 2408(siNIN) and 2526(siDHC) cells were analyzed for each measurement in three independent experiments (n=3). Values represent mean ± SD. (K) Summarizing table of PCM localization and the depletion effects on caMTOC formation in AKAP450/CAMSAP2 knockout RPE1 cells. NT, not tested.

Figure 2—source data 1

An Excel sheet with numerical data on the quantifications shown in panel J.

https://cdn.elifesciences.org/articles/77892/elife-77892-fig2-data1-v2.xlsx
Video 1
Cell cycle progression in acentriolar AKAP450/CAMSAP2/p53 knockout cells.

Cell cycle progression visualized by phase-contrast imaging of centrinone-treated AKAP450/CAMSAP2/p53 knockout RPE1 cells. The cells were imaged for ~15 hr with 1 min interval. Time is hh:mm.

The depletion of CDK5RAP2, γ-tubulin or ninein in centrinone-treated AKAP450/CAMSAP2 knockout cells did not prevent the formation of small pericentrin clusters, but these failed to coalesce into a single caMTOC (Figure 2F–H and J, Figure 2—figure supplement 1B). In contrast, the depletion of CEP152, CEP192, or NEDD1 had no effect on the formation of caMTOCs (Figure 2A–C and J, Figure 2—figure supplement 1B), in agreement with the fact these proteins could not be detected within these structures. caMTOCs contained several centriole biogenesis factors, including CPAP, CP110, and CEP120, but lacked centrin and CEP135; however, the depletion of different centriolar proteins did not affect caMTOC formation (Figure 2K, Figure 2—figure supplement 1C). Within caMTOCs, we also detected a component of the HAUS complex (HAUS2), the centrosomal protein CEP170, dynein, dynactin, CLASP1/2, CLIP-115, CLIP-170, chTOG, KIF2A, and KIF1C (Figure 2K, Figure 2—figure supplement 1C). We tested the importance of some of these proteins for caMTOC formation by siRNA-mediated depletion (see Figure 2K for an overview), but among the tested proteins, only cytoplasmic dynein appeared essential for this process. In dynein-depleted cells, no clusters of pericentrin or other PCM components could be detected after centrinone B treatment (Figure 2I–K). It is important to note, however, that because we used siRNAs to reduce protein expression, we cannot exclude that the residual levels of some of the investigated proteins were sufficient to support caMTOC formation. Because we detected several microtubule plus-end tracking proteins (+TIPs) in the caMTOCs, such as CLIP-170, CLASP1/2 and the large subunit of dynactin, p150Glued, we also tested for the presence of the core components of the +TIP machinery, EB1 and EB3, but found that they were not enriched within the caMTOCs (Figure 2K, Figure 2—figure supplement 5A). Using the previously described cells that lack EB3, CAMSAP2 and the C-terminal partner-binding half of EB1 (Yang et al., 2017), we generated a knockout cell line that also lacks AKAP450 and found that caMTOCs could still form in these cells (Figure 2K, Figure 2—figure supplement 5B, C). We conclude that a subset of PCM components binds to each other in the absence of centrioles, and in AKAP450/CAMSAP2 knockout cells, these proteins form caMTOCs that recruit a number of additional PCM proteins and MAPs normally present in interphase centrosomes.

Despite containing only a subset of centrosomal proteins, caMTOCs strongly affected the organization and density of the microtubule network in acentriolar cells: microtubules were focused around caMTOCs if present and disorganized in cells lacking caMTOCs. The strongest loss of microtubule density was observed in cells lacking pericentrin, dynein or γ-tubulin, while milder phenotypes were observed in cells lacking CDK5RAP2 or ninein (Figure 3A, B and E). To further characterize microtubule organization after the loss of these proteins, we analyzed the proportion of the radial and non-radial microtubules. Whereas control cells (AKAP450/CAMSAP2 knockout cells treated with centrinone B and control siRNA) formed radial microtubule networks with ~12% non-radial microtubules, acentriolar cells lacking pericentrin or cytoplasmic dynein had ~46% non-radial microtubules, and the depletion of CDK5RAP2, ninein and γ-tubulin led to an intermediate phenotype with 25–30% non-radial microtubules (Figure 3C, D and E). An acentriolar PCM assembly containing pericentrin, CDK5RAP2, ninein, γ-tubulin and dynein is thus sufficient to form a radial microtubule array, similar to a centrosome, and PCM clustering promotes dense microtubule organization.

Microtubule organization in acentriolar cells missing different caMTOC components.

(A) Immunofluorescence images of centrinone-treated AKAP450/CAMSAP2 knockout RPE1 cells depleted of the indicated proteins and stained for microtubules (α-tubulin, red) and different PCM proteins (green). Insets show enlargements of the merged channels of the boxed areas and dashed lines show cell boundaries. (B) Quantification of the normalized overall microtubule intensity for the indicated conditions. The number of cells analyzed in three independent experiments: n=56 (siLuci), 45 (siPCNT), 33 (siCDK5RAP2), 36 (siNinein), 43 (siγ-tubulin), and 28 (siDHC). The statistical significance was determined by unpaired two-tailed Mann-Whitney test in Prism 9.1 (***P<0.001). Values represent mean ± SD. (C) Microtubule images were split into a radial and non-radial components (heat maps) based on microtubule orientation in relation to the PCM clusters or the brightest point, as described in Materials and methods. (D) Quantification of the proportion of the non-radial microtubules shown in panel C (see Materials and methods for details). The number of cells analyzed for each measurement in three independent experiments: n=25 (siLuci), 43 (siPCNT), 32 (siCDK5RAP2), 34 (siNinein), 37 (siγ-tubulin), and 25 (siDHC). The statistical significance was determined by unpaired two-tailed Mann-Whitney test in Prism 9.1 (***p<0.001). Values represent mean ± SD. (E) Diagram illustrating the distribution of PCM clusters and microtubule organization upon the depletion of the indicated proteins in centrinone-treated AKAP450/CAMSAP2 knockout cells.

Figure 3—source data 1

An Excel sheet with numerical data on the quantifications shown in panel B and D.

https://cdn.elifesciences.org/articles/77892/elife-77892-fig3-data1-v2.xlsx

Dynamics of caMTOC disassembly

To test whether the formation and maintenance of caMTOCs depends on microtubules, we depolymerized them by treating cells with nocodazole at 37 °C and found that caMTOCs fragmented into small clusters upon nocodazole treatment and reassembled into a single structure after nocodazole washout (Figure 4A–C). Because we found that caMTOC formation is dynein-dependent, we also included the dynein inhibitor dynapyrazole A in these experiments (Steinman et al., 2017). We confirmed that treatment with dynapyrazole A for 3 hr had no effect on dynein expression (Figure 4D) and found that the addition of this drug before nocodazole treatment prevented the disassembly of caMTOCs, whereas the treatment of cells with dynapyrazole A during nocodazole washout strongly inhibited caMTOC re-assembly (Figure 4A–C). These data indicate that both microtubule-dependent dispersal and coalescence of PCM clusters into caMTOCs are driven by dynein activity.

Figure 4 with 1 supplement see all
Microtubule- and dynein-dependent disassembly of caMTOCs.

(A) Diagram illustrating different order of cell treatments with nocodazole (Noco) and/or dynapyrazole A (Dyna, 5 µM) and the time points when the cells were fixed. (B) Immunofluorescence staining of centrinone-treated AKAP450/CAMSAP2 knockout cells treated as shown in panel A. Dashed red circles represent the areas occupied by PCM clusters in each condition. (C) Quantification of the area occupied by PCM clusters in each condition, as shown in panels A and B. n=35–53 cells analyzed for each measurement in three independent experiments. The statistical significance was determined by unpaired two-tailed Mann-Whitney test in Prism 9.1 (not significant (NS), p<0.12; *p<0.033; ***p<0.001). Values are represented as mean ± SD. (D) Western blot showing that 3 hr treatment with dynapyrazole A does not affect the expression of the endogenous dynein heavy chain and the dynactin large subunit p150Glued. (E) Time-lapse images of centrinone-treated AKAP450/CAMSAP2 knockout RPE1 cells stably expressing GFP-CDK5RAP2. Microtubules were visualized by treating cells with 100 nM SiR-tubulin overnight. Red arrows show the immobilized PCM clusters at indicated timepoints. Time is min: s. Time-lapse images of the same cell prior to the nocodazole treatment were shown in Figure 1—figure supplement 3B. (F) (Top) Kymograph illustrating the motility of PCM clusters during microtubule disassembly with nocodazole. (Bottom) Measurements of the normalized microtubule (SiR-tubulin) fluorescence intensity (red plot, left Y-axis) and the instantaneous velocity of GFP-CDK5RAP2 clusters (green plot, right Y-axis) during the movement of GFP-CDK5RAP2 clusters away from caMTOC. Microtubule density around each PCM cluster was determined by measuring mean fluorescence intensity of SiR-tubulin in a circular area with a 2 μm radius centered on the PCM cluster and normalizing it to the mean fluorescence intensity of 20 images prior to nocodazole addition (set as 100%). The moment when a PCM cluster started to move out of the caMTOC was set as the initial time point (0 min) for this cluster, and the subsequent PCM cluster motion velocity and the relative local microtubule density of 43 time points were calculated and averaged. n=12 clusters were analyzed in each condition. Values are represented as mean ± SD. (G) Motor-PAINT images of centrinone-treated AKAP450/CAMSAP2 knockout RPE1 cells before and after nocodazole treatment. Plus-end-out microtubules are shown in white whereas minus-end-out microtubules are shown in magenta. Asterisks represent the putative position of caMTOC. (H) Summarizing diagram illustrating microtubule organization and the motility of GFP-CDK5RAP2-positive PCM clusters during nocodazole treatment and dynapyrazole A (treat first) and nocodazole co-treatment. (I) Immunofluorescence images of centrinone-treated AKAP450/CAMSAP2 knockout RPE1 cells stained for microtubules (α-tubulin, red) and PCM (PCNT, green). Enlargements show the merged and single channels of the boxed areas.

Figure 4—source data 1

An Excel sheet with numerical data on the quantifications shown in panels C and F.

https://cdn.elifesciences.org/articles/77892/elife-77892-fig4-data1-v2.xlsx
Figure 4—source data 2

Full raw unedited western blots shown in panel D.

https://cdn.elifesciences.org/articles/77892/elife-77892-fig4-data2-v2.zip

We next studied PCM dynamics using stably expressed GFP-CDK5RAP2 as a marker in live cells where microtubules were labeled with SiR-tubulin. GFP-CDK5RAP2 was mostly immobile within caMTOCs before nocodazole treatment (Figure 1—figure supplement 3B, Video 2). After a few minutes of nocodazole treatment, when the microtubule density was significantly reduced, small PCM clusters started to move out of the caMTOC and undergo rapid directional motility with speeds of up to 2 µm/s, which is within the range characteristic for cytoplasmic dynein (Schlager et al., 2014; Figure 4E and F, Figure 4—figure supplement 1A, Video 2). Once microtubules were completely disassembled, the movement of GFP-CDK5RAP2-positive clusters stopped, indicating that it is microtubule-dependent but occurs only when the microtubule network is partially depolymerized. Since cluster dispersal toward the cell periphery could be blocked by a dynein inhibitor, and since cytoplasmic dynein is a minus-end-directed motor, these data indicate that during microtubule disassembly by nocodazole at 37 °C, there is a transient stage when PCM clusters interact with only a few microtubules, some of which have their minus-ends facing outwards, and these microtubules serve as tracks for PCM transport. To support this idea, we used motor-PAINT, a technique that employs nanometric tracking of purified kinesin motors on the extracted cytoskeleton of fixed cells to super-resolve microtubules and determine their orientation (Tas et al., 2017). Using this approach, we determined microtubule orientations in centrinone-treated AKAP450/CAMSAP2 knockout cells and in cells that were also treated with nocodazole for 15 min to induce partial microtubule disassembly (Figure 4G, Figure 4—figure supplement 1B). We found that the cells contained a significant number of minus-end-out microtubules, and their proportion increased during early stages of nocodazole treatment, possibly because minus-end-out microtubules are more stable (Figure 4G, for example, minus-end-out microtubules constituted ~23% of the total microtubule length determined from kinesin-1 trajectories in the untreated cell and ~46% in the nocodazole-treated cell). These microtubules could serve as tracks for outward movement of PCM, causing the disassembly of caMTOC when the overall microtubule density around the caMTOC was strongly reduced (Figure 4H). These data suggest that the dense network of PCM-anchored microtubule minus-ends around a caMTOC allows for its compaction via dynein-mediated forces, but that dynein can pull the caMTOC apart when microtubules are disorganized.

Video 2
caMTOC disassembly during nocodazole treatment of acentriolar AKAP450/CAMSAP2 knockout cells.

PCM dynamics visualized by stable expression of GFP-CDK5RAP2 (green) in centrinone-treated AKAP450/CAMSAP2 knockout RPE1 cells. Microtubules were labeled overnight with 100 nM SiR-tubulin (red). The cell was imaged for ~3.5 min (100 frames, 2 s interval) prior to the addition of 10 μM nocodazole. Time is min: s.

To further confirm that caMTOC disassembly is an active microtubule-dependent process, we also depolymerized microtubules by a combination of cold (4 °C) and nocodazole treatment. When all microtubules were depolymerized, caMTOCs did not fall apart, even when the cells were subsequently warmed to 37 °C in the presence of nocodazole, so that microtubules could not re-grow (Figure 4I). However, we noticed that in these conditions, the continuity and cylindrical organization of the PCM cluster were often perturbed. This raised the possibility that the elongated arrangement of PCM components within caMTOCs is microtubule-driven. Indeed, when cells were subjected to cold (4 °C) treatment in the absence of nocodazole, most of microtubules depolymerized, but some short cold-stable microtubules remained associated with the caMTOC (Figure 4—figure supplement 1C). These data indicate that PCM self-assembly in the absence of centrioles is microtubule-dependent, and microtubules are involved in shaping the PCM cluster. Once assembled, the PCM cluster is quite stable, unless microtubule organization is altered and dynein-driven microtubule-based transport pulls it apart.

Dynamics of caMTOC assembly

When nocodazole-mediated microtubule disassembly was carried out at 4 °C, the caMTOC remained intact and after nocodazole washout it served as the major microtubule nucleation site, similar to the centrosome in untreated wild-type cells (Figure 5—figure supplement 1A). However, when nocodazole-mediated disassembly of the caMTOC was carried out at 37 °C, the cluster fell apart and reassembled upon nocodazole washout (Figure 4A and B), providing a way to study the dynamics of PCM self-assembly and the roles of different PCM components during this process. Small PCM clusters positive for pericentrin, CDK5RAP2, γ-tubulin and the centriolar satellite protein PCM1 that co-localized with the plus-ends of microtubules (labeled with EB1) could be detected 30 s after nocodazole washout; these PCM clusters and nascent microtubules did not colocalize with the Golgi membranes (Figure 5A, Figure 5—figure supplement 1B). Ninein was not detected within the clusters at this early stage of microtubule regrowth but could be found 2 min after nocodazole washout. In contrast, no clusters of CEP192 or NEDD1 were observed even 10 min after nocodazole washout (Figure 5A, Figure 5—figure supplement 1B, C). The depletion of pericentrin, CDK5RAP2 and γ-tubulin strongly inhibited microtubule nucleation in these conditions, whereas the depletion of dynein heavy chain or ninein had a milder effect (Figure 5B and C). Live cell imaging with GFP-CDK5RAP2 and SiR-tubulin showed that CDK5RAP2 clusters with attached microtubules coalesced by undergoing microtubule-based movements (Figure 5D), and measurements in cells fixed at different time points after nocodazole washout showed that a partly radial microtubule system emerged already 2 min after nocodazole washout (Figure 5—figure supplement 1D). Reassembly of a single caMTOC in the central part of the cell occurred within ~15 min after nocodazole washout, though it was less compact than in cells that were not treated with nocodazole (Figure 5D–G). Depletion of pericentrin, γ-tubulin and dynein heavy chain strongly inhibited the reformation of a radial microtubule network during nocodazole washout, whereas the effect of depleting CDK5RAP2 and ninein was less strong (Figure 5—figure supplement 1D-F). Live cell imaging of acentriolar AKAP450/CAMSAP2 knockout RPE1 cells stably expressing GFP-CDK5RAP2 showed that when pericentrin was depleted, CDK5RAP2 clusters were not detectable, and the microtubule network, both before nocodazole treatment and after nocodazole washout, was disorganized (Figure 5—figure supplement 2, Video 3). Taken together, our data show that pericentrin and γ-tubulin form microtubule-nucleating and anchoring units, which are clustered by the self-association of pericentrin and assembled into larger structures by dynein-based transport. CDK5RAP2 contributes to microtubule nucleation efficiency, whereas ninein appears to act somewhat later and contributes to the formation of a compact PCM cluster and a radial microtubule network. Importantly, all these proteins can cluster in the absence of centrioles, and together they can efficiently nucleate and anchor microtubules.

Figure 5 with 2 supplements see all
Dynamics of microtubule nucleation and caMTOC re-assembly in acentriolar cells.

(A) Immunofluorescence images of microtubule regrowth after nocodazole washout at the indicated timepoints in centrinone-treated AKAP450/CAMSAP2 knockout RPE1 cells stained for PCM components (green) and newly nucleated microtubules (EB1, red). A Golgi marker, GM130 (blue), is included in the left row, and zooms of the boxed regions (numbered 1 and 2) show that microtubules nucleate from PCM clusters but not from the Golgi membranes. Dashed lines show cell boundaries. (B) (Top) Timeline shows the time course of protein depletion (siRNA transfection), nocodazole treatment, nocodazole washout and microtubule regrowth. (Bottom) Immunofluorescence images of microtubule regrowth experiments after depletion of the indicated proteins in centrinone-treated AKAP450/CAMSAP2 knockout RPE1 cells stained for the indicated PCM markers (green) and EB1 as a marker of nascent microtubules (red). Cell outlines are indicated with dashed lines and enlargements of the merged channels of the boxed areas are shown on the right. (C) Quantification of normalized microtubule intensity at 30 s after nocodazole washout in control cells and cells depleted of the indicated PCM proteins. n=40 (siLuci, siPCNT), 57 (siCDK5RAP2), 48 (siNIN), 45 (siγ-tubulin), and 50 (siDHC) cells analyzed for each measurement in three independent experiments. The statistical significance was determined by unpaired two-tailed Mann-Whitney test in Prism 9.1 (**p<0.002; ***p<0.001). Values are represented as mean ± SD. (D) Time-lapse images of centrinone-treated AKAP450/CAMSAP2 knockout RPE1 cells stably expressing GFP-CDK5RAP2 before and after nocodazole washout. Dispersed GFP-CDK5RAP2-positive PCM clusters (GFP, green) serve as microtubule nucleation sites (SiR-tubulin, red) and coalesce into a big cluster after nocodazole washout. Time is min: s. (E) Immunofluorescence images of centrinone-treated AKAP450/CAMSAP2 knockout RPE1 cells stained for pericentrin (green) and microtubules (α-tubulin, red) at the indicated timepoints after nocodazole washout. (F) Measurements of normalized fluorescence intensity of PCM clusters at the indicated distances in relation to the brightest point, as described in Materials and methods. The biggest PCM cluster (which normally also had the highest fluorescence intensity) was selected as the center, around which 10 concentric circles with 2 μm width were drawn. Fluorescence intensity of PCM clusters in these concentric circles was measured automatically and normalized by the sum of the total PCM intensity in each cell per condition. n=12 cells per plot per timepoint. Values represent mean ± SD. (G) Summarizing diagram illustrating microtubule organization and motility of GFP-CDK5RAP2-positive PCM clusters during nocodazole washout.

Figure 5—source data 1

An Excel sheet with numerical data on the quantifications shown in panels C and F.

https://cdn.elifesciences.org/articles/77892/elife-77892-fig5-data1-v2.xlsx
Video 3
Depletion of pericentrin inhibits PCM clustering in acentriolar AKAP450/CAMSAP2 knockout cells.

A pericentrin-depleted acentriolar AKAP450/CAMSAP2 knockout cell stably expressing GFP-CDK5RAP2 (green) and labeled overnight with 100 nM SiR-tubulin (red) was imaged for ~4.5 min (140 frames, 2 s time interval) prior to treatment with 10 μM nocodazole. Nocodazole was washed out at ~20 min (frame 591), when all microtubules were depolymerized. Time is hr: min:s.

The role of CAMSAP2-stabilized minus ends in defining microtubule network geometry

The results of nocodazole treatment and washout suggested that PCM can self-assemble into a caMTOC which nucleates and anchors microtubules, but this structure is sensitive to microtubule organization. This observation prompted us to investigate in more detail how the microtubules that are not anchored at PCM clusters affect PCM organization in steady state conditions. An abundant population of stable minus ends that do not attach to PCM is decorated by CAMSAP2. In centrinone-treated wild-type cells, CAMSAP2-bound microtubule minus ends were anchored at the Golgi (Wu et al., 2016; Figure 1D), whereas in centrinone-treated AKAP450 knockout cells they were distributed randomly (Figures 1D and 6A). Live imaging showed that CAMSAP2-decorated minus ends displayed only limited motility on the scale of hours and thus formed a relatively stationary, disorganized microtubule network (Video 4). Live imaging of GFP-CDK5RAP2 together with SiR-tubulin in these cells demonstrated that small PCM clusters were distributed throughout the cytoplasm (Figure 6B). These clusters moved along microtubules and encountered each other, but the direction of the movements was random and the clusters did not coalesce into a single structure (Figure 6B, Figure 6—figure supplement 1A, Video 5). Treatment with nocodazole and subsequent nocodazole washout confirmed that the motility of GFP-CDK5RAP2 clusters in centrinone-treated AKAP450 knockout cells was microtubule-dependent, and that these clusters could nucleate microtubules and move together with microtubule minus ends, but did not converge into a single caMTOC (Figure 6—figure supplement 1A-C, Video 5). Treatment with dynapyrazole A strongly inhibited the movements of small PCM clusters (Figure 6—figure supplement 1D, E, Video 6), indicating that they are dynein-driven. After the depletion of pericentrin, GFP-CDK5RAP2 became completely diffuse, and it failed to form clusters during nocodazole treatment or washout (Figure 6—figure supplement 2, Video 7), indicating that clustering of GFP-CDK5RAP2 in AKAP450 knockout cells is pericentrin-dependent. Based on these data, we conclude that in AKAP450 knockout cells, pericentrin still forms PCM clusters that can nucleate microtubules and can be moved by dynein along other microtubules, similar to what occurs in wild-type cells. However, in the absence of AKAP450, CAMSAP2-stabilized microtubules form a disorganized network, which imposes a random motility pattern on pericentrin-dependent PCM clusters and thus prevents their assembly into a single caMTOC, likely because PCM interactions are not sufficient to trigger their stable association (Figure 6C).

Figure 6 with 2 supplements see all
caMTOC formation in the presence of CAMSAP2 using inducible motor recruitment.

(A) Immunofluorescence images of control or centrinone treated AKAP450 knockout RPE1 cells stained for CAMSAP2 (green), PCM protein (γ-tubulin, cyan) and microtubules (α-tubulin, red). Enlargements show the boxed regions of the merged images. (B) Time lapse images of centrinone treated AKAP450 knockout RPE1 cells stably expressing GFP-CDK5RAP2 (green). Microtubules were labeled with 100 nM SiR-tubulin overnight (red, top row). The maximum intensity projection includes 200 frames, 200ms/frame. Red arrows show the motion directions of GFP-CDK5RAP2-positive PCM clusters. Time is min: s. (C) A diagram of microtubule organization and PCM motility in AKAP450 knockout cells. (D, E) Diagram of the inducible heterodimerization assay with ppKin14 and CAMSAP2. (D) CAMSAP2 was tagged with mCherry and fused to a tandemly repeated FKBP domain; tetramerized ppKin14 was tagged with TagBFP and fused to FRB. Heterodimerizer induces the binding of CAMSAP2 and ppKin14 by linking FKBP to FRB. (E) Heterodimerizer treatment induces the binding of CAMSAP2 (red) and ppKin14 (blue) and the formation of radial microtubule network. In this scheme, PCM-anchored microtubules are not shown. (F) Immunofluorescence images of centrinone-treated AKAP450/CAMSAP2 knockout RPE1 cells co-transfected with 2FKBP-mCherry-CAMSAP2 and FRB-TagBFP-GCN4-ppKin14 and stained for the indicated proteins in cells treated with DMSO or heterodimerizer. Zooms show the magnifications of boxed areas. (G) Quantification of the proportion of cells with a radial, whirlpool-like or non-radial microtubule organization with and without heterodimerizer treatment. Numbers on the histogram show the percentages. A total of 414 cells treated with DMSO (-Heterodimerizer) and 385 cells treated with heterodimerizer (+Heterodimerizer) analyzed for each measurement in three independent experiments (n=3). Values represent mean ± SD.

Figure 6—source data 1

An Excel sheet with numerical data on the quantifications shown in panel G.

https://cdn.elifesciences.org/articles/77892/elife-77892-fig6-data1-v2.xlsx
Video 4
CAMSAP2 distribution and dynamics in acentriolar AKAP450 knockout cells.

An acentriolar AKAP450 knockout RPE1 cell transiently expressing mCherry-CAMSAP2 was imaged for 15 hrs (900 frames, 1 min interval). Time is hr:min.

Video 5
PCM dynamics during nocodazole treatment and washout in acentriolar AKAP450 knockout cells.

An acentriolar AKAP450 knockout RPE1 cell stably expressing GFP-CDK5RAP2 (green) and labeled overnight with 100 nM SiR-tubulin (red) was imaged for ~7 min (200 frames, 2 s interval) prior to treatment with 10 μM nocodazole. Nocodazole was washed out at ~27 min (frame 801) when all microtubules were depolymerized. Time is min: s.

Video 6
PCM dynamics in acentriolar AKAP450 knockout cells are inhibited by dynapyrazole.

An acentriolar AKAP450 knockout RPE1 cell stably expressing GFP-CDK5RAP2 (green) and labeled overnight with 100 nM SiR-tubulin (red) was imaged for ~12 min (350 frames, 2 s interval) prior to the dynapyrazole treatment, treated with 5 μM Dynapyrazole A for 1 hr, and then the same cell was imaged for ~12 min. Time is min: s.

Video 7
Depletion of pericentrin inhibits PCM clustering in acentriolar AKAP450 knockout cells.

A pericentrin-depleted acentriolar AKAP450 knockout cell stably expressing GFP-CDK5RAP2 (green) and labeled overnight with 100 nM SiR-tubulin (red) was imaged for ~3 min (90 frames, 2 s time interval) prior to treatment with 10 μM nocodazole. Nocodazole was washed out at ~23 min (frame 701) when all microtubules were depolymerized. Time is hr: min:s.

If the geometry of the CAMSAP2-stabilized microtubule network determines PCM distribution, focusing CAMSAP2-bound minus ends is expected to bring PCM together. To test this idea, we linked CAMSAP2-stabilized minus ends to a minus-end-directed motor. In order to avoid potential cell toxicity associated with manipulating cytoplasmic dynein, we used the motor-containing part of a moss kinesin-14, type VI kinesin-14b from the spreading earthmoss Physcomitrella patens (termed here ppKin14). The C-terminal motor-containing part of this protein can efficiently induce minus-end-directed motility of different cargoes in mammalian cells when it is tetramerized through a fusion with the leucine zipper domain of GCN4 (GCN4-ppKin14-VIb (861–1321)) and recruited to cargoes using inducible protein heterodimerization (Jonsson et al., 2015; Nijenhuis et al., 2020). We employed a chemical heterodimerization system that is based on inducible binding of two protein domains, FRB and FKBP, upon the addition of a rapamycin analog (rapalog AP21967, also known as A/C heterodimerizer) (Clackson et al., 1998; Pollock et al., 2000). To ensure that all CAMSAP2-decorated microtubule minus ends were linked to kinesin-14, we rescued centrinone-treated AKAP450/CAMSAP2 knockout cells by expressing CAMSAP2 fused to a tandemly repeated FKBP domain (2FKBP-mCherry-CAMSAP2) (Figure 6D–F). This construct was co-expressed with the FRB-GCN4-tagBFP-ppKin14 fusion, which by itself localized quite diffusely, with only a weak enrichment along microtubules, as described previously (Nijenhuis et al., 2020; Figure 6D and F). In the absence of heterodimerizer, CAMSAP2-decorated microtubule minus ends were distributed randomly, similar to endogenous CAMSAP2 in AKAP450 knockout cells (Figure 6F). However, upon heterodimerizer addition, ppKin14 was rapidly recruited to CAMSAP2-decorated microtubule ends, and after 2 hr, more than 90% of cells acquired a radial microtubule organization (Figure 6E–G). In heterodimerizer-treated cells, CAMSAP2-bound microtubule minus ends formed either a tight cluster or a ‘whirlpool-like’ ring in the cell center (Figure 6D–G, Figure 6—figure supplement 1F). The whirlpool-like arrangement likely comes about when CAMSAP2-stretches are a bit longer and continue to slide against each other, forming a nematic circular bundle. The major caMTOC components, pericentrin, CDK5RAP2, γ-tubulin, and ninein were also concentrated within the CAMSAP2 cluster (Figure 6F, Figure 6—figure supplement 1F). These data indicate that the positioning of stabilized minus-ends is an important determinant of the overall microtubule organization and PCM localization in interphase cells.

The role of PCM in CAMSAP2-driven microtubule organization

Presence of PCM in the caMTOC induced by minus-end-directed transport of CAMSAP2 might be a passive consequence of microtubule reorganization but might also play an active role in forming this caMTOC. To distinguish between these possibilities, we attached CAMSAP2 to ppKin14 in centrinone-treated cells where both AKAP450 and pericentrin were knocked out (AKAP450/CAMSAP2/p53/pericentrin knockout). In the absence of heterodimerizer, CAMSAP2-stabilized minus ends and the whole microtubule network were disorganized, as expected, and the same was true when the two constructs were expressed separately, with or without heterodimerizer (Figure 7A and D, Figure 7—figure supplement 1). After heterodimerizer addition, microtubules in cells expressing both constructs acquired a radial organization, but their minus ends usually did not converge in a single spot but rather accumulated in a~30–70 µm-large ring-like structure (Figure 7A and D, Figure 7—figure supplement 2, Video 8). Staining for PCM markers showed that CDK5RAP2 and γ-tubulin were enriched in the vicinity of CAMSAP2-positive microtubule minus ends, whereas ninein appeared rather diffuse (Figure 7A). To determine the nature of the structure ‘corralled’ by the ring of CAMSAP2-decorated minus ends in heterodimerizer -treated cells, we stained for different membrane organelles and found that although there was no strong correlation with the nucleus, Golgi membranes, or lysosomes, the majority of mitochondria were found within the CAMSAP2 ring, and the endoplasmic reticulum (ER) displayed increased density overlapping with the CAMSAP2 ring (Figure 7B, Figure 7—figure supplement 2B). It therefore appeared that in the absence of pericentrin, CAMSAP2-decorated minus ends were brought together by ppKin14, but their convergence was inefficient and possibly impeded by membrane organelles enriched in the central, thicker part of the cell before heterodimerizer addition (see the upper panel of Figure 7—figure supplement 2B). Transient transfection of centrinone-treated AKAP450/CAMSAP2/p53/pericentrin knockout cells with GFP-pericentrin rescued the formation of a tight CAMSAP2 cluster upon heterodimerizer treatment (Figure 7C). Our data show that pericentrin-containing PCM contributes to the formation of a caMTOC driven by minus-end-directed transport of CAMSAP2-stabilized minus-ends.

Figure 7 with 3 supplements see all
The role of the PCM in CAMSAP2-driven formation of caMTOCs.

(A,B) Immunofluorescence images of centrinone-treated AKAP450/CAMSAP2/p53/pericentrin knockout RPE1 cells transfected with 2FKBP-mCherry-CAMSAP2 and FRB-TagBFP-GCN4-ppKin14 and stained for the indicated components before (top) or after an overnight heterodimerizer treatment. Zooms show magnifications of boxed areas. Black dashed lines show the position of the nucleus. (C) Cells treated as described for panel A were co-transfected with GFP-pericentrin and stained for mitochondria (cytochrome C, red) and CAMSAP2 (red) in same channel overnight after heterodimerizer addition. Zooms show magnifications of boxed areas. (D) Quantification of the proportion of cells with different types of microtubule minus end organization before and after overnight heterodimerizer treatment. Numbers on the histogram show the percentages. 334 (-Heterodimerizer), 424(+Heterodimerizer) cells of AKAP450/CAMSAP2/p53/pericentrin knockout RPE1 cells, 206(-Heterodimerizer), and 239(+Heterodimerizer) of AKAP450/CAMSAP2/CDK5RAP2/MMG/p53/pericentrin knockout RPE1 cells analyzed for each measurement in three independent experiments (n=3). Values represent mean ± SD. (E) Immunofluorescence images of centrinone-treated AKAP450/CAMSAP2/CDK5RAP2/MMG/p53/pericentrin knockout RPE1 cells transfected with 2FKBP-mCherry-CAMSAP2 and FRB-TagBFP-GCN4-ppKin14 and stained for microtubules (α-tubulin, green) after an overnight heterodimerizer treatment. Zooms show magnifications of boxed areas.

Figure 7—source data 1

An Excel sheet with numerical data on the quantifications shown in panel D.

https://cdn.elifesciences.org/articles/77892/elife-77892-fig7-data1-v2.xlsx
Video 8
Inducible CAMSAP2-driven radial microtubule rearrangement in an AKAP450/CAMSAP2/p53/pericentrin knockout cell.

An acentriolar AKAP450/CAMSAP2/p53/pericentrin knockout cell transiently expressing 2FKBP-mCherry-CAMSAP2 (red) and FRB-GFP-GCN4-ppKin14 (green) was imaged for 10 min (100 frames, 6 s interval) prior to treatment with 100 nM heterodimerizer. Subsequently, the cell was imaged for ~1 hr and 35 min after heterodimerizer addition. Time is hr: min: s.

To support this notion further, we also generated cells that were knockout for AKAP450, CAMSAP2, CDK5RAP2, myomegalin (MMG, homologue of CDK5RAP2), p53 and pericentrin. To achieve this, we used the previously described RPE1 cell line knockout for AKAP450, CAMSAP2, CDK5RAP2, and MMG (Wu et al., 2016), in which we sequentially knocked out p53 and pericentrin (Figure 7—figure supplement 3A-G). While it was not possible to induce centriole loss by centrinone B treatment in AKAP450/CAMSAP2/CDK5RAP2/MMG knockout cells because the proliferation of these cells was arrested in the absence of centrioles (Wu et al., 2016), centriole removal was successful in AKAP450/CAMSAP2/CDK5RAP2/MMG/p53/pericentrin knockout cells due to the absence of p53 and led to microtubule disorganization (Figure 7—figure supplement 3H). Interestingly, when these cells were co-transfected with FKBP-linked CAMSAP2 and FRB-linked ppKin14 and treated with heterodimerizer (Figure 6D), we observed that CAMSAP2 clustering was even less efficient than in AKAP450/CAMSAP2/p53/pericentrin knockout cells (Figure 7D and E). Forty-nine percent of AKAP450/CAMSAP2/CDK5RAP2/MMG/p53/pericentrin knockout cells had small bundles of CAMSAP2 stretches dispersed throughout the cytoplasm, and only 37% of these cells formed a ring of CAMSAP2-decorated minus ends, whereas 80% of AKAP450/CAMSAP2/p53/pericentrin knockout cells formed such a ring. We examined the ER and mitochondria in these cells and found that in cells that did form a CAMSAP2 ring, the ER displayed an overlapping ring-like density, although the mitochondria inside the CAMSAP2 ring were more scattered compared to those of AKAP450/CAMSAP2/p53/pericentrin knockout cells (Figure 7—figure supplement 2B). In cells with dispersed CAMSAP2-positive bundles, no increased ER density or central accumulation of mitochondria were observed (Figure 7—figure supplement 2B). These data further support the notion that minus-end-directed transport of stable minus ends alone is insufficient to generate a caMTOC, and that synergy with PCM is required.

Recapitulation of PCM self-organization by computer simulations

To rationalize the appearance of the different microtubule arrangements and to find a minimal set of interactions between filaments and motors that would lead to self-organization into structures that we observed experimentally, we set up agent-based computer simulations with Cytosim (Nedelec and Foethke, 2007). The numerical values for the biophysical parameters of the agents in our simulations have been taken from literature or reasonably estimated otherwise (see Table 1 for details). The self-organized structures that form in a simulation also depend on the number of certain components of the system, such as microtubules. Because these numbers can vary and are not easy to precisely determine experimentally, we systematically explored their variation in silico (Figure 8, Figure 8—figure supplement 1). For simplicity, we considered a two-dimensional circular cell with a radius of 10 μm. We described a mobile PCM complex as a bead with a radius of 50 nm from which one microtubule plus end could grow. Microtubule growth and shrinkage were simulated with the classical microtubule model from Cytosim. When the microtubule reached a maximal length of 7.5 μm, its growth was stopped. In this way, we limited the microtubule length to avoid long microtubules that push their minus end to the periphery of the cell. Additionally, one dynein molecule was attached to a PCM complex with its cargo-binding domain. With this configuration, dynein molecules could transport PCM complexes along microtubules growing from other PCM complexes. Once they were bound to a microtubule, they walked toward the minus end in a force-dependent manner and were able to stochastically unbind along the way. When a dynein motor reached a minus end, it detached. Furthermore, we implemented a reversible binding interaction between PCM complexes to make them adhere to each other. The details of such an adhesive interaction are unknown, therefore we assumed parameters in the range typical for the binding interaction of microtubules and motor proteins. Because we were interested in the spatial arrangement of the PCM complexes, we introduced steric interactions between them. However, we neglected steric interactions between microtubules to effectively account for the three-dimensional space that we projected to two dimensions.

Figure 8 with 2 supplements see all
Cytosim simulations of PCM and microtubule self-organization.

In all simulations, we considered a circular cell with a radius of 10 µm (gray line), containing microtubules (red), PCM complexes (green), and CAMSAP-kin14 complexes (blue). (A) Simulation of 300 PCM complexes; each PCM complex is attached to a dynein motor, and from each complex, a MT can grow. A weak adhesive interaction is introduced between the PCM complexes. A compact PCM cluster is observed. (B) The same simulation as in (A), but with the adhesive interaction disabled; PCM is organized in a ring-like structure. (C) The simulation in (A) modified in two ways: a randomly organized, dynamic microtubule network with 300 filaments is added, and only 50 of the 300 PCM complexes can nucleate microtubules. PCM cluster formation is disrupted. (D) The same simulation as in (C), but the strength of the adhesive interaction is increased. The formation of a compact cluster is recovered. (E) Cumulative distributions of distances of the PCM complexes to the average position (the center of mass), determined from the last frame of the simulations. (F) The mean and standard deviation (SD) of the distances of the PCM complexes to the center of mass from different repetitions of the simulations. Each system was simulated 10 times. Small values for the mean and for the standard deviation indicate a compact cluster. For a ring-like structure, the mean values are larger and describe the radius of the ring. A dispersed localization of PCM complexes is characterized by large values for the mean and the standard deviation. (G) Simulation of 300 CAMSAP-kin14 complexes, which consist of a microtubule nucleation site and five kinesin-14 motors, but do not adhere to each other. The complexes form a ring-like structure. (H) The same simulation as in (G), but with complexes that have the same weak adhesive interaction as for the PCM complexes in (A). The complexes form a central compact cluster. (I) Simulation of a mixed system with 150 CAMSAP-kin14 complexes that do not adhere to each other and 150 weakly adhesive PCM complexes. All complexes associate in a central compact cluster. (J) The same simulation as in (I), but with non-adhesive PCM complexes. A ring-like structure is formed. (K) Cumulative distributions of the distances from all complexes to the center of mass of the last frame of the simulation. (L) The mean and standard deviation of the distances from all complexes to the center of mass from different repetitions of the simulations. Each system was simulated 10 times. A compact cluster is characterized by a small value for the mean and for the standard deviation. A ring-like structure has a larger mean value.

Figure 8—source data 1

An Excel sheet with numerical data of the quantifications shown in panels E, F, K, and L.

https://cdn.elifesciences.org/articles/77892/elife-77892-fig8-data1-v2.xlsx
Table 1
Parameters with numerical values used in the Cytosim simulations.
SymbolValueCommentReference
Cell
Cell radius10 µmA typical RPE cell is ~50 µm wide. Therefore, the radius that we use is by a factor of ~2 smaller. We chose a smaller size to reduce the computational costs.Letort et al., 2016
Viscosity1 pN s/ µm2Typical value for Cytosim simulations based on measurements in C. elegans embryos.Kole et al., 2005; Letort et al., 2016; Daniels et al., 2006
Thermal energykT4.2 pN nmThermal energy at room temperature
DyneinTo parametrize dynein motors, we use the consensus numerical parameters discussed in Ohashi et al.
Binding ratekon5 s–1Ohashi et al., 2019
Force-free unbinding ratek0off0.1 s–1Ohashi et al., 2019
Detachment forceFd2Ohashi et al., 2019
Binding range75 nmEstimated value based on the length of the motor, typically used in Cytosim simulations.Letort et al., 2016
Force-free velocity v0500 nm/sBrenner et al., 2020
Stall forceFs4 pNBelyy et al., 2016
Kin14Because the biophysical parameters that describe a single kinesin-14 molecule are unknown, we assume numerical values as typical for kinesin-1
Binding ratekon1 s–1Klumpp et al., 2015
Force-free unbinding ratek0off1 s–1Berger et al., 2019
Detachment forceFd3 pNPyrpassopoulos et al., 2020
Binding range75 nmTypical length of a molecular motorLetort et al., 2016
Force-free velocity v0800 nm/sCarter and Cross, 2005
Stall forceFs7 pNCarter and Cross, 2005
MicrotubulesThe numerical values that we use to describe microtubule dynamics are typically used in Cytosim simulations and based on experimental measurements. The only new parameter that we introduced is the maximum length of a microtubule.
Rigidity20 pN µm2Typical value used for Cytosim simulations based on experiments.Gibeaux et al., 2017; Gittes et al., 1993
Catastrophe ratekcat0.026 s–1Published valueGibeaux et al., 2017
Rescue rate0We ignore rescues for simplicity
Growing force1.7 pNDogterom and Yurke, 1997
Growing speedvg0.13 µm/sBurakov et al., 2003; Letort et al., 2016
Shrinkage speed0.272 µm/sBurakov et al., 2003; Letort et al., 2016
Maximum length7.5 µmTo avoid boundary effects exerted by long microtubules pushing, we restrict microtubules to a maximum length.
Adhesive interactions of PCM complexesThe biophysical parameters describing the adhesive interaction of PCM complexes are unknown. We introduced an effective model based on the implemented agents in Cytosim, and therefore the used values don’t have a physical meaning. Overall, they generate an adhesive interaction between the PCM complexes which is not too weak and not too strong compared to the other forces that arise in the system.
Binding rate10 s–1
Binding range100 nm
Unbinding rate0.01 s–1
Detachment force3 pN
General parameters
Stiffness of all linking elements100 pN/ µmTypical value for molecular motorsGros et al., 2021; Letort et al., 2016
Total simulated time18,000 sTypical time scale of the experiments

First, we sought to recapitulate the formation of a single PCM cluster. Simulations of 300 of such PCM complexes reproducibly reached a steady state in which they formed a compact centrally located cluster (Figure 8A, Video 9A). Quantification of 10 simulations showed that the mean and standard deviation of the distance from each PCM complex to the average location of all complexes (the center of mass) were small compared to the radius of the cell (Figure 8E and F). However, if we disabled the adhesive interactions between PCM complexes, they formed a loose ring-like arrangement and not a compact cluster (Figure 8B, Video 9B), and the mean and standard deviation of the distance of PCM complexes to the center of mass were larger (Figure 8E and F). Systematic variation of the number of PCM complexes in the system indicated that approximately 150 PCM complexes were needed to observe robust clustering (Figure 8—figure supplement 1A), which fits with the fact that most cultured mammalian cells such as RPE1 typically contain several hundred microtubules. Increasing the number of microtubules nucleated from one PCM complex did not affect the properties of the system (Figure 8—figure supplement 1B). Taken together, our simulations support the idea that PCM components form a central cluster through positive feedback of dynein molecules carrying them toward the minus ends of microtubules attached to other PCM proteins, and that adhesive interactions between the PCM complexes promote cluster compaction. For such self-organization to emerge, a sufficient number of PCM complexes must be present in the system.

Video 9
Cytosim simulations of PCM and microtubule self-organization.

Example videos of our Cytosim simulations. Each simulation represents 1800 seconds of real time, and the last frame is shown in the panels of Figure 8. In all simulations, we considered a circular cell with a radius of 10 µm (gray line), containing microtubules (red), PCM complexes (green), and CAMSAP-kin14 complexes (blue). The videos correspond to panels A, B,C, D, I, and J of Figure 8, as indicated.

In our experiments, we saw that the presence of CAMSAP2-stabilized microtubule network prevented PCM clustering (Figure 1D and E, Figure 6A–C), and that CAMSAP2-bound minus ends appeared almost stationary on the scale of minutes (Video 4) compared to the rapid dynein-driven movement of small PCM clusters (Video 5). To simulate such a situation, we assumed that 300 microtubules could grow and shrink with random orientations from CAMSAP2-stabilized microtubule ends that were randomly distributed and stationary within the cell. Furthermore, we assumed that some of the moving PCM complexes were not efficient in nucleating microtubules, because the presence of a PCM-independent microtubule population would reduce the concentration of soluble tubulin and thus nucleation efficiency. To effectively account for such an effect, we allowed only 50 of the 300 PCM complexes to nucleate microtubules. In this system, the PCM cluster formation was disrupted, and PCM complexes randomly moved around the cell (Figure 8C, Video 9C). The distributions of the distances to the average position of the PCM complexes were broad and had a large mean and standard deviation, indicating the dispersed localization (Figure 8E and F). However, if we increased the strength of adhesive interactions between the PCM complexes, the randomly oriented microtubule network no longer suppressed the clustering of PCM complexes (Figure 8D, Video 9D). This outcome suggested that if self-association of pericentrin were increased, it would still form a cluster even in the presence of dispersed CAMSAP2-stabilized minus ends. To test this prediction, we have fused pericentrin to an inducible homodimerization domain FKBP, a variant of FKBP12 domain that homodimerizes in presence of rapamycin analog AP20187 (B/B homodimerizer) (Clackson et al., 1998; Pollock et al., 2000). We found that this modified pericentrin formed small dispersed clusters in centrinone-treated AKAP450 knockout protein, similar to endogenous pericentrin. However, the addition of the homodimerizer compound triggered strong pericentrin clustering, whereas CAMSAP2-decorated minus ends remained dispersed (Figure 8—figure supplement 2A). The results of this experiment were thus in line with our simulations.

Next, we varied the number of components in the system and found that when the number of immobile microtubules was less than 150, clustering of PCM complexes was still observed (Figure 8—figure supplement 1C). Likewise, when we increased the fraction of PCM complexes nucleating microtubules, 300 immobile microtubules were not sufficient any more to disperse the motion of PCM complexes. When 100 of the 300 PCM complexes could nucleate microtubules, the system became bistable: a cluster formed in some simulations, but not in others. A further increase of the fraction of PCM complexes nucleating microtubules led to robust clustering (Figure 8—figure supplement 1D). We conclude that the presence of a stationary randomly organized microtubule network can be sufficient to prevent the formation of a compact PCM cluster even though individual PCM complexes can still move with dynein and adhere to each other, provided that the interaction between PCM complexes is not too strong and that the number of immobile microtubules is sufficiently high.

We also explored a system where the localization of immobile minus ends was biased toward the cell periphery, by randomly placing their minus ends in a 1 µm-broad region adjacent to the cell boundary. Increasing the fraction of peripherally placed immobile microtubules led to the enrichment of the PCM complexes at the cell periphery (Figure 8—figure supplement 1E). To support this finding experimentally, we overexpressed mCherry-CAMSAP2 in centrinone-treated AKAP450 knockout cells. Overexpression of CAMSAP2 occasionally led to minus-end bundling and enrichment in certain cell areas, and in such cells, pericentrin clusters were enriched in the same cell regions (Figure 8—figure supplement 2B).

Next, we examined how CAMSAP2-stabilized microtubules would organize when minus-end-directed kinesin-14 motors could attach to them and carry the minus ends. CAMSAP2-stabilized microtubule ends bound to kinesin-14 motors (termed ‘CAMSAP-kin14 complexes’ in the text below) were modelled as a bead with a radius of 50 nm from which a microtubule could grow and to which 6 kinesin-14 molecules were attached. Because the biophysical properties of single plant kinesin-14 molecules used in our assays are poorly understood, we assumed typical kinesin-1 values with an opposite directionality. CAMSAP-kin14 complexes only interacted sterically but did not adhere to each other. In a simulation of 300 CAMSAP-kin14 complexes, a loose ring-like arrangement appeared (Figure 8G), similar to the one observed in our simulations of PCM complexes that could not bind to each other (Figure 8B). Similar to adhesive PCM complexes, CAMSAP-kin14 complexes could not self-organize when their number was low (Figure 8—figure supplement 1F). Introducing adhesive interactions between CAMSAP-kin14 complexes was sufficient to promote compact cluster formation (Figure 8H).

Finally, to investigate if a compact cluster of CAMSAP-kin14 complexes would emerge in the presence of self-associating PCM complexes, we set up simulations with 150 CAMSAP-kin14 complexes together with 150 adherent PCM complexes. The steady state of such a system displayed a compact central cluster, in which both types of complexes were mixed (Figure 8I, K and L, Video 9I), while making PCM complexes non-adhesive prevented cluster compaction (Figure 8J, K and L, Video 9J). To induce compaction, at least a half of microtubules had to be attached to adhesive PCM components rather than non-adhesive CAMSAP-kin14 complexes (Figure 8—figure supplement 1G). Taken together, our simulations suggest that PCM complexes can provide enough adhesive interactions to compact the cluster of non-interacting CAMSAP-kin14 and PCM complexes, very similar to our experimental observations.

Discussion

In this study, we explored the mechanisms of interphase PCM self-assembly in the absence of centrioles. Our experiments and agent-based Cytosim simulations support the idea that complexes of PCM proteins can form a single cluster through a positive feedback mechanism, whereby dynein motors carry microtubule minus-ends to other microtubule minus ends (Cytrynbaum et al., 2004). However, for a compact cluster to emerge, the minus ends must not only be able to move toward each other but also to bind to each other, and this idea is fully supported by our simulations. Interphase PCM is thus capable both of dynein binding and self-association sufficient to organize a compact microtubule-nucleating and anchoring structure in the absence of centrioles. However, the formation of a compact MTOC in acentriolar cells is slow and less robust than in centriole-containing cells, and the resulting structure is sensitive to the overall organization of microtubules and to dynein function. This means that interphase PCM self-association is by itself reversible and not sufficiently tight to resist dynein-driven forces. Centrioles can thus be regarded as catalysts of PCM assembly and stabilizers of interphase centrosomes, preventing PCM movement on microtubules oriented with their minus ends away from the centrosome.

The simulations that we developed allowed us not only to rationalize the emergence of the self-organized structures that we experimentally observed, but also to systematically change the relative numbers of the components of the system, a manipulation that is difficult or impossible in cells. We found that the emergence of specific organizations is robust if the number of components in the system is sufficiently high. For a fixed cell volume, a larger number of components implies a larger concentration and density. In low-density systems, the probability that components are close to each other and interact is small, and therefore structures cannot form or only form on very long time scales. All of the interactions that we defined were reversible, and therefore the observed structures were dynamic. Therefore, for persistent structures to emerge, the probability for reengagement between agents must be sufficiently large, which is only the case if the density of components is adequately high. This interplay of finding components, engaging in interactions, disengaging and finding again other components is the underlying process for the dynamic structures to form. It is challenging to quantitatively relate all parameters of the system to each other, because of their interdependence. Therefore, we expect that the absolute thresholds for structure formation that we report in this study depend on all other parameters of the system. Despite this complexity, we were able to identify robust parameter ranges, in which the experimentally observed structures could be recapitulated in computer simulations by only varying the number of components and keeping the specific interactions of components the same.

Our experimental system allowed us to examine which PCM components are capable of associating with each other and with dynein to promote microtubule nucleation and anchoring independently of centrioles. The major scaffold for interphase acentriolar PCM assembly is pericentrin, which can self-associate (Jiang et al., 2021) and form clusters that recruit CDK5RAP2 and ninein, two proteins known to bind to pericentrin (Chen et al., 2014; Delaval and Doxsey, 2010; Kim et al., 2014; Lawo et al., 2012). CDK5RAP2 is important for efficient microtubule nucleation, consistent with its role as an activator of γ-TuRC (Choi et al., 2010). The same may be true for pericentrin itself (Takahashi et al., 2002) and, to a lesser extent, for ninein (Delgehyr et al., 2005; Mogensen et al., 2000), which was less important for microtubule nucleation in our assays. Ninein might be required for promoting minus-end anchoring, as proposed by previous studies (Abal et al., 2002; Chong et al., 2020; Delgehyr et al., 2005; Goldspink et al., 2017; Lechler and Fuchs, 2007; Mogensen et al., 2000; Shinohara et al., 2013; Zheng et al., 2020). The importance of ninein for the formation of caMTOCs highlights its function within the PCM independent of its role at the centriolar appendages, a major site of ninein localization at the centrosome (Chong et al., 2020; Delgehyr et al., 2005; Sonnen et al., 2012). Pericentrin can also directly interact with dynein through the dynein light intermediate chain (Purohit et al., 1999). Ninein was shown to form a triple complex with dynein and dynactin and activate dynein motility (Redwine et al., 2017), and an interaction between CDK5RAP2 and dynein has also been reported (Jia et al., 2013; Lee and Rhee, 2010). All these interactions likely contribute to dynein-dependent PCM coalescence in the absence of centrioles and also to the function of PCM in microtubule organization, as in the absence of PCM clustering, microtubule density is strongly reduced even though all PCM proteins are expressed.

Pericentrin-dependent MTOC assembly has also been observed in acentriolar mitotic cells (Chinen et al., 2021; Watanabe et al., 2020), but the interphase pathway displays some interesting differences. Most notably, CEP192 and its binding partners CEP152 and NEDD1 (Gomez-Ferreria et al., 2012; Joukov et al., 2014; Kim et al., 2013; Sonnen et al., 2013) were not enriched at caMTOCs, and their depletion appeared to have no impact on caMTOC formation. This is in line with earlier observations showing that microtubule-nucleating clusters of PCM components in acentriolar AKAP450 knockout cells contain pericentrin, CDK5RAP2 and ninein, but not CEP192 (Gavilan et al., 2018). In contrast, in mitotic acentriolar cells that rely on pericentrin and CDK5RAP2 for spindle pole formation, CEP192 is recruited to pericentrin clusters (Chinen et al., 2021; Watanabe et al., 2020). Moreover, although NEDD1 is targeted to centrosomes by CEP192, pericentrin can also contribute to the centrosomal targeting of NEDD1 independently of CEP192 during mitosis (Chi et al., 2021). The interactions between the components of pericentrin- and CEP192 pathways thus seem to be stronger in mitosis than in interphase, where these proteins are brought together by the centrioles. The relative importance of the two pathways is also different depending on the phase of the cell cycle and the presence of centrioles: unlike pericentrin or CDK5RAP2, CEP192 is essential for cell division (Gomez-Ferreria et al., 2007; Joukov et al., 2014; Yang and Feldman, 2015; Zhu et al., 2008), and it is more important than pericentrin or CDK5RAP2 for microtubule nucleation at interphase centrosomes (Gavilan et al., 2018). However, the situation is different in differentiated cells, where centrosome function is suppressed. Pericentrin, ninein, CDK5RAP2 and γ-TuRC, but not CEP192 are present in non-centrosomal MTOCs at the nuclear envelope and the Golgi membranes in muscle cells, where they are targeted by AKAP450 (Gimpel et al., 2017; Oddoux et al., 2013; Vergarajauregui et al., 2020). Recent work also demonstrated that at the ciliary base of certain types of worm neurons, there is an acentriolar PCM-dependent MTOC that is formed by the functional counterparts of CDK5RAP2 (SPD-5), pericentrin (PCMD-1) and γ-tubulin, but lacks CEP192/SPD-2 (Garbrecht et al., 2021; Magescas et al., 2021). The co-assembly of pericentrin, ninein and CDK5RAP2 counterparts into structures that can promote γ-TuRC-dependent microtubule nucleation and also anchor minus ends may thus be a general property of interphase acentriolar MTOCs. Additionally, the participation of dynein is likely to be a general feature of acentrosomal microtubule organization, as exemplified by MTOCs formed by the Golgi apparatus or endosomal membranes (Liang et al., 2020; Zhu and Kaverina, 2013).

Our simple cellular system allowed us to dissect the molecular details of acentriolar PCM assemblies. We found that caMTOCs recruited multiple MAPs and a subset of centriolar proteins. For example, caMTOCs accumulated several +TIPs, such as CLASPs, CLIP170 and chTOG, though the depletion of these proteins had no effect on caMTOC formation or function. In contrast, the core components of the +TIP complexes, EB1 and EB3, were neither enriched in caMTOCs nor required for their assembly. While the negative results on other +TIPs might be due to their incomplete depletion, EB1 and EB3 function was tested using genetic knockouts, indicating that interphase PCM function is EB-independent. Among the centriolar proteins, we detected CPAP, CP110 and CEP120 in caMTOCs, and it will be interesting to test whether any of these microtubule-binding factors contribute to microtubule organization independently of their participation in centriole and centrosome assembly.

Furthermore, our work provided insight into the self-assembly properties of interphase PCM components. Previous work showed that some PCM proteins, such as pericentrin, CDK5RAP2 and ninein, or their counterparts, can form mobile clusters that contribute to MTOC maintenance (Dictenberg et al., 1998; Magescas et al., 2019; Megraw et al., 2002; Moss et al., 2007), but the nature of these clusters may differ. For example, oligomerization and clustering of pericentrin molecules is important for MTOC formation both in interphase and mitosis; during mitotic entry, pericentrin forms condensates (Jiang et al., 2021), while our observations in interphase show that pericentrin clusters display no hallmarks of liquid droplets or condensates. Moreover, the compaction and shape of acentriolar clusters of PCM proteins depend on dynein and microtubules. Our experiments showed that while caMTOCs were stable when all microtubules were disassembled, indicating that attractive interactions between PCM components are sufficient to keep them together, cold treatment experiments suggested that the cylindrical shape of caMTOCs is likely due to their association with some stable microtubules. Furthermore, when the number of PCM-unattached stable minus-end-out microtubules was increased by the presence of CAMSAP2, PCM clusters could no longer form a compact structure but continued to move along microtubules. Therefore, an important outcome of our study is the key role of stabilized minus ends in determining interphase organization of PCM components in the absence of the centrioles. Our simulations showed that in order to suppress clustering of the PCM complexes, the number of stabilized immobile minus ends should be relatively high compared to the number of PCM-complex-anchored microtubules. This explains why caMTOC falls apart during nocodazole treatment: PCM-anchored microtubules are lost first, while the minus-end-out microtubules persist longer, become relatively more abundant and drive dispersion of PCM complexes.

Our findings may help to explain how acentrosomal MTOCs form in cells where both PCM proteins and PCM-independent microtubule stabilizers such as CAMSAPs are present. Two well studied examples of such systems are the MTOCs at the Golgi membranes and the apical cortex of epithelial cells, where CAMSAPs and PCM proteins are present simultaneously and may cooperate with each other or play redundant roles (Goldspink et al., 2017; Nashchekin et al., 2016; Noordstra et al., 2016; Sanchez et al., 2021; Toya et al., 2016; Wang et al., 2015; Wu et al., 2016; Zhu and Kaverina, 2013). We found that CAMSAP2-stabilized minus ends can exert a highly dominant effect on PCM organization, and biases in the distribution of minus ends lead to similar biases in the distribution of PCM proteins. This property helps to explain how the Golgi, which anchors CAMSAP2-stabilized microtubules, recruits pericentrin and becomes the major MTOC in cells lacking centrosomes. Furthermore, mobility of PCM clusters may contribute to centrosome disassembly after mitosis, when the effect of mitotic kinases driving PCM coalescence is abolished. This might help to explain the appearance of PCM ‘packets’ or ‘fragments’ accompanying centrosome disassembly during mitotic exit in different cell types (Magescas et al., 2019; Rusan and Wadsworth, 2005). Re-emergence of stable non-centrosomal microtubules, for example, due to post-mitotic dephosphorylation of CAMSAP proteins (Jiang et al., 2014), might contribute to this process.

Importantly, our experiments and simulations showed that coupling stable minus-ends to a minus-end directed motor is by itself insufficient to form a compact MTOC, but self-clustering PCM can contribute to this process, when the clusters of PCM proteins that can anchor microtubules are sufficiently abundant. Altogether, self-association of interphase PCM appears to be strong enough to promote its clustering but is sufficiently dynamic to allow PCM reorganization dependent on other microtubule regulators present in the cell.

An interesting question that remains unanswered by our work is the inhibitory role of PLK4 in interphase caMTOC formation. We did observe caMTOCs in cells depleted of PLK4, indicating that, unlike cells lacking TRIM37, which form PCM clusters containing catalytically inactive PLK4 (Meitinger et al., 2020; Yeow et al., 2020), interphase cells studied here do not rely on enzymatically inactive PLK4 for PCM assembly. PLK4 is known to phosphorylate NEDD1 (Chi et al., 2021), and it is possible that the lack of phosphorylation prevents this γ-TuRC-binding protein and its partners, such as CEP192, from participating in interphase caMTOC assembly. It is, of course, also possible that PLK4 phosphorylation inhibits the interactions or activities of some of the players driving caMTOC formation. The easy-to-manipulate cellular model that we have described here will allow these questions to be addressed and facilitate detailed studies of the interactions and functions of PCM components in nucleating and stabilizing interphase microtubule minus ends.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
Antibodyanti-Pericentrin (mouse monoclonal)AbcamAbcam Cat# ab28144, RRID:AB_2160664(1:500) for IF
Antibodyanti-Pericentrin (rabbit polyclonal)AbcamAbcam Cat# ab4448, RRID:AB_304461(1:500) for IF; (1:1000) for WB
Antibodyanti-CDK5RAP2 (rabbit polyclonal)Bethyl LaboratoriesBethyl Cat# A300-554A, RRID:AB_477974(1:300) for IF; (1:1000) for WB
Antibodyanti-γ-tubulin (mouse monoclonal)Sigma-AldrichSigma-Aldrich: T6557; RRID:AB_477584(1:300) for IF; (1:2000) for WB
Antibodyanti-γ-tubulin (rabbit polyclonal)Sigma-AldrichSigma-Aldrich:T3559, RRID:AB_477575(1:300) for IF
Antibodyanti-NEDD1 (mouse monoclonal)AbnovaAbnova Corporation Cat# H00121441-M05, RRID:AB_534956(1:300) for IF
Antibodyanti-NEDD1 (rabbit polyclonal)RocklandRockland Cat# 109–401 C38S, RRID:AB_10893219(1:1000) for WB
Antibodyanti-Ninein (rabbit polyclonal)BETHYLBethyl Cat# A301-504A, RRID:AB_999627(1:300) for IF; (1:2000) for WB
Antibodyanti-Ninein (mouse monoclonal)Santa Cruz BiotechnologySanta Cruz Biotechnology Cat# sc-376420, RRID:AB_11151570(1:300) for IF
Antibodyanti-Dynein HC (rabbit polyclonal)Santa Cruz BiotechnologySanta Cruz Biotechnology Cat# sc-9115, RRID:AB_2093483(1:300) for IF; (1:500) for WB
Antibodyanti-p150Glued (mouse monoclonal)BD BiosciencesBD Biosciences Cat# 610473, RRID:AB_397845(1:100) for IF;
(1:500) for WB
Antibodyanti-PCM1 (mouse monoclonal)Santa Cruz BiotechnologySanta Cruz Biotechnology Cat# sc-398365, RRID:AB_2827155(1:300) for IF
Antibodyanti-PCM1 (rabbit polyclonal)Bethyl LaboratoriesBethyl Cat# A301-150A, RRID:AB_873100(1:300) for IF
Antibodyanti-AKAP450 (mouse monoclonal)BD BiosciencesBD Biosciences Cat# 611518, RRID:AB_398978(1:500) for WB
Antibodyanti-CAMSAP2 (rabbit polyclonal)ProteintechProteintech Cat# 17880–1-AP, RRID:AB_2068826(1:200) for IF; (1:1000) for WB
Antibodyanti-p53 (mouse monoclonal)Santa Cruz BiotechnologySanta Cruz Biotechnology Cat# sc-126, RRID:AB_628082(1:300) for IF;
(1:1000) for WB
Antibodyanti-p53 (rabbit polyclonal)BETHYLBethyl Cat# A300-248A, RRID:AB_263349(1:300) for IF
Antibodyanti-EB1 (mouse monoclonal)BD BiosciencesBD Biosciences:610535; RRID:AB_397892(1:400) for IF
Antibodyanti-EB3 (rabbit polyclonal)Martin et al., 2018;(1:300) for IF
Antibodyanti-Centrin (mouse monoclonal)MilliporeMillipore Cat# 041624, RRID:AB_10563501(1:500) for IF
Antibodyanti-CEP120 (rabbit polyclonal)Thermo Fisher ScientificThermo Fisher Scientific Cat# PA5-55985, RRID:AB_2639665(1:300) for IF
Antibodyanti-CEP135 (rabbit polyclonal)Sigma-AldrichSigma-Aldrich:SAB4503685; RRID:AB_10746232(1:300) for IF
Antibodyanti-CEP152 (rabbit polyclonal)AbcamAbcam, Cat # ab183911(1:300) for IF;
(1:1000) for WB
Antibodyanti-CEP170 (mouse monoclonal)Thermo Fisher ScientificThermo Fisher Scientific Cat# 413200, RRID:AB_2533502(1:200) for IF
Antibodyanti-CEP192 (rabbit polyclonal)Bethyl LaboratoriesBethyl Cat# A302-324A, RRID:AB_1850234(1:300) for IF; (1:1000) for WB
Antibodyanti-GM130 (mouse monoclonal)BD BiosciencesBD Biosciences:610823; RRID:AB_398142(1:300) for IF;
(1:2000) for WB
Antibodyanti-α-tubulin YL1/2 (rat monoclonal)PiercePierce: MA1-80017; RRID:AB_2210201(1:300) for IF
Antibodyanti-α-tubulin (mouse monoclonal)Sigma-AldrichSigma-Aldrich:T5168; RRID:AB_477579(1:400) for IF
Antibodyanti-α-tubulin (rabbit monoclonal antibody)AbcamAbcam Cat# ab52866, RRID:AB_869989(1:800) for IF
AntibodyAnti-β-tubulin (mouse monoclonal)Sigma-AldrichSigma-Aldrich Cat# T8660, RRID:AB_477590(1:2000) for WB
Antibodyanti-CLASP1 (rabbit polyclonal)Akhmanova et al., 2001(1:400) for IF
Antibodyanti-CLASP2 (rabbit polyclonal)Akhmanova et al., 2001(1:400) for IF
Antibodyanti-CLIP-115 #2,238 (rabbit polyclonal)Akhmanova et al., 2001(1:300) for IF
Antibodyanti-CLIP-170 #2,360 (rabbit polyclonal)Akhmanova et al., 2001(1:300) for IF
Antibodyanti-ch-TOG (rabbit polyclonal)Charrasse et al., 1998Dr. Lynne Cassimeris
(Lehigh University, USA)
(1:200) for IF
Antibodyanti-CPAP (rabbit polyclonal)Kohlmaier et al., 2009Dr. Pierre Gönczy
(EPFL, Switzerland)
(1:200) for IF
Antibodyanti-CP110 (rabbit monoclonal)ProteintechProteintech Cat# 127801-AP, RRID:AB_10638480(1:300) for IF
Antibodyanti-KIF1C (rabbit polyclonal)CytoskeletonCytoskeleton Cat# AKIN11-A, RRID:AB_10708792(1:300) for IF
Antibodyanti-KIF2A (rabbit polyclonal)Ganem and Compton, 2004Dr. Duane Compton
(Geisel School of Medicine at Dartmouth, USA)
(1:300) for IF
Antibodyanti-HAUS2 (rabbit polyclonal)Lawo et al., 2009Dr. Laurence Pelletier
(Lunenfeld-Tanenbaum Research Institute, Canada)
(1:200) for IF
Antibodyanti-BICD2 (rabbit polyclonal)Hoogenraad et al., 2003(1:2500) for WB
Antibodyanti-Actin (mouse monoclonal)MilliporeMillipore Cat# MAB1501, RRID:AB_2223041(1:4000) for WB
Antibodyanti-Ku80 (mouse monoclonal)BD BiosciencesBD Biosciences Cat# 611360, RRID:AB_398882(1:2000) for WB
Antibodyanti-LaminA/C (mouse monoclonal)BD BiosciencesBD Biosciences Cat# 612162, RRID:AB_399533(1:400) for IF
Antibodyanti-Cytochrome C (mouse monoclonal)BD BiosciencesBD Biosciences Cat# 556432, RRID:AB_396416(1:300) for IF
Antibodyanti-Calnexin (rabbit polyclonal)AbcamAbcam Cat# ab22595, RRID:AB_2069006(1:300) for IF
AntibodyAnti-Lamtor4 (rabbit monoclonal)Cell Signaling (CST)/BiokeCell Signaling Technology Cat# 12284, RRID:AB_2797870(1:800) for IF
AntibodyAnti-Tom20 (mouse monoclonal)BD BiosciencesBD Biosciences Cat# 612278, RRID:AB_399595(1:200) for IF
AntibodyIRDye 800CW/680LT secondariesLi-Cor BiosciencesLI-COR Biosciences Cat# 926–32219, RRID:AB_1850025
LI-COR Biosciences Cat# 926–68020, RRID:AB_10706161;
LI-COR Biosciences Cat# 926–32211, RRID:AB_621843;
LI-COR Biosciences Cat# 926–68021, RRID:AB_10706309
(1:5000) for WB
AntibodyAlexa Fluor 405–, 488–, and 594– secondariesMolecular Probes/
Thermo Fisher Scientific
Molecular Probes Cat# A-11007, RRID:AB_141374;
Cat# A-11034, RRID:AB_2576217; Cat# A32723,
RRID:AB_2633275; Cat# A-31553, RRID:AB_221604;
Cat# A-11029, RRID:AB_138404; Cat# A-11032,
RRID:AB_2534091; Cat# A-11006, RRID:AB_141373;
Thermo Fisher Scientific Cat# A-11012, RRID:AB_2534079
(1:500) for IF
Sequence-based reagentsiRNA against PCNT #1Gavilan et al., 20185’-AAAAGCUCUGAUU
UAUCAAAAGAAG-3’
Sequence-based reagentsiRNA against PCNT #2Gavilan et al., 20185’-UGAUUGGACGUCA
UCCAAUGAGAAA-3’
Sequence-based reagentsiRNA against PCNT #3Tibelius et al., 20095’-GCAGCUGAGCUGAAGGAGA-3’
Sequence-based reagentsiRNA against CDK5RAP2Fong et al., 20085’-UGGAAGAUCUCCUAACUAA-3’
Sequence-based reagentsiRNA against γ-tubulin #1Lüders et al., 20065’-GGAGGACAUGUUCAAGGAA-3’
Sequence-based reagentsiRNA against γ-tubulin #2Vinopal et al., 20125’-CGCAUCUCUUUCUCAUAU-3’
Sequence-based reagentsiRNA against NineinGoldspink et al., 20175’-CGGUACAAUGAGUGUAGAAUU-3’
Sequence-based reagentsiRNA against PCM1Wang et al., 20135’-UCAGCUUCGUGAUUCUCAG-3’
Sequence-based reagentsiRNA against CEP152Cizmecioglu et al., 2010; Komarova et al., 20055’-GCGGAUCCA
ACUGGAAAUCUA-3’
Sequence-based reagentsiRNA against CEP120Ganem et al., 2005; Lin et al., 20135’-AAUAUAUCUUCU
UGCAUCUCCUUCC-3’
Sequence-based reagentsiRNA against CEP192Sonnen et al., 20135’-CAGAGGAAUCAAUAAUAAA –3’
Sequence-based reagentsiRNA against NEDD1 #1Lüders et al., 20065’-GCAGACAUGUGUCAAUUUA-3’
Sequence-based reagentsiRNA against NEDD1 #2Haren et al., 20065’-GGGCAAAAGCAGACAUGUG-3’
Sequence-based reagentsiRNA against DHC #1Splinter et al., 20105’-CGUACUCCCGUGAUUGAUG-3’
Sequence-based reagentsiRNA against DHC #2Splinter et al., 20105’-GCCAAAAGUUACAGACUUU-3’
Sequence-based reagentsiRNA against CAMSAP2Jiang et al., 20145’-GUACUGGAUAAAUAAGGUA-3’
Sequence-based reagentsiRNA against CEP170Stolz et al., 20155’-GAAGGAAUCCUCCAAGUCA-3’
Sequence-based reagentsiRNA against CPAPTang et al., 20095’-AGAAUUAGCUCGAAUAGAA-3’
Sequence-based reagentsiRNA against CLIP170 #1Lansbergen et al., 2004; Mimori-Kiyosue et al., 20055’-GGAGAAGCAGCAGCACAUU-3’
Sequence-based reagentsiRNA against CLIP170 #2Lansbergen et al., 2004; Mimori-Kiyosue et al., 20055’-UGAAGAUGUCAGGAGAUAA-3’
Sequence-based reagentsiRNA against CLIP115 #1Lansbergen et al., 20045’-GGCACAGCAUGAGCAGUAU-3’
Sequence-based reagentsiRNA against CLIP115 #2Lansbergen et al., 20045’-CUGGAAAUCCAAGCUGGAC-3’
Sequence-based reagentsiRNA against ch-TOG:Cassimeris and Morabito, 2004; Lansbergen et al., 20045’-GAGCCCAGAGUGGUCCAAA-3’
Sequence-based reagentsiRNA against EB1Grigoriev et al., 2008; Lansbergen et al., 20045’-AUUCCAAGCUAAGCUAGAA-3’
Sequence-based reagentsiRNA against EB3Cassimeris and Morabito, 2004; Komarova et al., 20055’-CUAUGAUGGAAAGGAUUAC-3’
Sequence-based reagentsiRNA against KIF2AGanem et al., 2005; Grigoriev et al., 20085’-GGCAAAGAGAUUGACCUGG-3’
Sequence-based reagentsiRNA against CP110Cizmecioglu et al., 2010; Spektor et al., 20075'-AAGCAGCAUGAGUAUGCCAGU-3'
Sequence-based reagentsiRNA against LuciferaseLansbergen et al., 2004; Lin et al., 20135’-CGUACGCGGAAUACUUCGA-3’
Sequence-based reagentsgRNA target CAMSAP2Lansbergen et al., 2004; Wu et al., 20165’-gCATGATCGATACCCTCATGA-3
Sequence-based reagentsgRNA target p53 e2 #1This study5′-gCGTCGAGCCCCCTCTGAGTC-3′;
Sequence-based reagentsgRNA target p53 e4 #2This study5′-gCCATTGTTCAATATCGTCCG-3′;
Sequence-based reagentsgRNA target PCNT e5-1 #1This study5′-gAGACGGCATTGACGGAGCTG-3′;
Sequence-based reagentsgRNA target PCNT e5-2 #2This study5′-GCTCAACAGCCGGCGTGCCC-3′;
Sequence-based reagentp53 KO sequencing primer FThis study5′-TCAGACACTGGCATGGTGTT-3′;
Sequence-based reagentp53 KO sequencing primer RThis study5′-AGAAATGCAGGGGGATACGG-3′;
Sequence-based reagentPCNT KO sequencing primer FThis study5′-ATACAGCGAGGGAATTCGGG-3′;
Sequence-based reagentPCNT KO sequencing primer RThis study5′-TAGAATGCCCACACCGAGC-3′;
Sequence-based reagentForward primer for PCR of tagBFP to generate pB80-FRB-TagBFP-GCN4-ppKin14This study5′-TCTCAAAGCAATTGT
CGACAGGATCCGC
TGGCTCCGCTGCTG
GTTCTGGCGAATTCA
GCGAGCTGATTA
AGGAGAACA-3′;
Sequence-based reagentReverse primer for PCR of tagBFP to generate pB80-FRB-TagBFP-GCN4-ppKin14This study5′-ATAGCGGAGCC
TGCTTTTTTGTACA
CATTAAGCTTGTG
CCCCAGTTTG-3′;
Sequence-based reagentForward primer for PCR of tagBFP to generate pB80-FRB-HA-GCN4-ppKin14This study5′-TCTCAAAGCAAT
TGTCGACATACCCATA
CGATGTTCCAGAT
TACGCTGTGTAC
AAAAAAGCAGGCTCC-3′;
Sequence-based reagentReverse primer for PCR of tagBFP to generate pB80-FRB-HA-GCN4-ppKin14This study5′-GGAGCCTGCTTT
TTTGTACACAG
CGTAATCTGG
AACATCGTATGG
GTATGTCGACAA
TTGCTTTGAGA-3′;
Chemical compoundCentrinone BTocris BioscienceTocris Bioscience Cat # 5,690125 nM
Chemical compoundNocodazoleSigma-AldrichSigma-Aldrich, Cat # M1404-10MG10 μM
Chemical compoundRapalog (A/C Heterodimerizer)TakaraTakara, Cat # 635,05650 nM (fixation), 100 nM (live imaging).
Chemical compoundRapalog (B/B Homodimerizer)TakaraTakara, Cat # 635,060500 nM (fixation)
Chemical compoundDynapyrazole ASigma-AldrichSigma-Aldrich, Cat #
SML2127-25MG
5 μM
Chemical compoundBI2536SelleckchemSelleckchem, Cat # S1109500 nM
Chemical compoundThymidineSigma-AldrichSigma-Aldrich, Cat # T9250-25G5 mM
Chemical compoundproTAMEBoston BiochemBoston Biochem, Cat # I-4405 μM
Chemical compoundBrefeldin APeptrotechPeptrotech, Cat # 20315605 µg/ml
Chemical compoundSiR-tubulinTebu-bioTebu-bio, Cat # SC002100 nM
Software, algorithmImageJ radiality pluginKatrukha, 2019;
https://github.com/ekatrukha/radialitymap

Recombinant DNA reagentpLVX-IRES-puro
(plasmid)
Clontech
Recombinant DNA reagentpLVX-GFP-
CDK5RAP2-IRES-puro
(plasmid)
This work
Recombinant DNA reagentpB80-FRB-TagBFP-GCN4-ppKin14 (plasmid)This work
Recombinant DNA reagentpB80-FRB-GFP-GCN4-ppKin14 (plasmid)This work
Recombinant DNA reagentpB80-FRB-HA-GCN4-ppKin14 (plasmid)This work
Recombinant DNA reagent2FKBP-mCherry-CAMSAP2 (plasmid)This work
Recombinant DNA reagentGFP-PCNT (plasmid)This work
Recombinant DNA reagent2homoFKBP-mCherry-PCNT (plasmid)This work
Recombinant DNA reagentGST-DmKHC(1-421)-mNeonGreen (plasmid)This work
Cell line (Homo sapiens)hTERT-RPE-1ATCCCRL-4000
Cell line (Homo sapiens)hTERT-RPE-1
AKAP450 knockout
Wu et al., 2016
Cell line (Homo sapiens)hTERT-RPE-1
AKAP450/
CAMSAP2 knockout
Wu et al., 2016
Cell line (Homo sapiens)hTERT-RPE-1
AKAP450/CAMSAP2/p53 knockout
This work
Cell line (Homo sapiens)hTERT-RPE-1
AKAP450/CAMSAP2/
p53/Pericentrin knockout
This work
Cell line (Homo sapiens)hTERT-RPE-1
AKAP450/
CAMSAP2/EB1/EB3 mutant
This work
Cell line (Homo sapiens)hTERT-RPE-1
AKAP450/CAMSAP2/
CDK5RAP2/MMG/
p53/Pericentrin knockout
This work
Cell line (Homo sapiens)HEK 293TATCCCRL-11268

DNA constructs and protein purification

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To generate the lentiviral vector pLVX-GFP-CDK5RAP2-IRES-Puro, pLVX-IRES-Puro plasmid (Clontech) was digested with AgeI and NotI (FastDigest, Thermo Fisher), and then Gibson Assembly (NEB) was performed with gel-purified PCR product of GFP-CDK5RAP2 (Wu et al., 2016). To generate pB80-FRB-TagBFP-GCN4-ppKin14 and pB80-FRB-HA-GCN4-ppKin14, pB80-FRB-GFP-GCN4-ppKin14-VIb was digested with XbaI and BsrGI (FastDigest, Thermo Fisher), and then TagBFP and HA-tag encoding DNA fragments were subcloned into the linearized vector by Gibson Assembly. To generate 2FKBP-mCherry-CAMSAP2, a CAMSAP2-encoding DNA fragment was subcloned into a vector containing 2FKBP-mCherry digested by SalI and BamHI. To generate 2homoFKBP-mCherry-Pericentrin, a fragment contains two repeats of homodimerizing version of FKBP (homoFKBP) and mCherry was subcloned into the vector containing pericentrin fragment after digestion with NheI and NotI.

To generate the PX459 with single guide RNA (sgRNA) sequences, pSpCas9(BB)–2A-Puro (PX459) V2.0 (Ran et al., 2013; purchased from Addgene) was digested with FastDigest BbsI (Thermo Fisher), and the annealing product of single-strand sgRNA-encoding oligonucleotides was inserted into the linear PX459 linear vector by T4 ligation (Thermo Fisher). The sgRNA sequences that were used in this study are: sgRNA targeting AKAP450 5’- gAGGGTTACCTATGGGACTGA –3’; sgRNA targeting CAMSAP2 encoding gene 5’-gCATGATCGATACCCTCATGA-3’; sgRNA targeting p53-encoding gene exon 2 #1 5’-gCGTCGAGCCCCCTCTGAGTC-3’; sgRNA targeting p53 exon 4 #2 5’-gCCATTGTTCAATATCGTCCG-3’; sgRNA targeting pericentrin exon 5 #1 5’-gAGACGGCATTGACGGAGCTG-3’; sgRNA targeting pericentrin-encoding gene exon 5 #2 5’-GCTCAACAGCCGGCGTGCCC-3’.

To generate the GST-DmKHC(1-421)-mNeonGreen construct used for protein purification for motor-PAINT, the fragment containing amino acids 1–421 of the Drosophila melanogaster Kinesin Heavy Chain (DmKHC) was amplified from donor construct DmKHC(1-421)-GFP-6x-His with a C-terminal mNeonGreen tag by PCR and then cloned into a pGEX vector. The plasmid was transformed into E. coli BL21 cells for purification. Bacteria were cultured until OD600 ≈0.7 and cultures were cooled prior to inducing protein expression with 0.15 mM IPTG at 18 °C overnight. Cells were then pelleted by centrifugation, snap frozen in liquid nitrogen, and stored at –80 °C until use. Cells were rapidly thawed at 37 °C before being resuspended in chilled lysis buffer (phosphate buffered saline (PBS) supplemented with 5 mM MgCl2, 0.5 mM ATP, 0.1% Tween 20, 250 mM NaCl, and 1 x complete protease inhibitor; pH 7.4). Bacteria were lysed by sonication (5 rounds of 30 s) and supplemented with 5 mM DTT and 2 mg/mL lysozyme and then incubated on ice for 45 min. The lysate was clarified by centrifuging at 26,000 xg for 30 min before being incubated with equilibrated Glutathione Sepharose 4B resin for 1.75 hrs. Beads were then pelleted, resuspended in wash buffer (PBS supplemented with 5 mM MgCl2, 0.1% Tween 20, 250 mM NaCl, 1 mM DTT, and 0.5 mM ATP; pH 7.4), and transferred to a BioRad column. Once settled, the resin was washed with 2 × 10 column volumes (CV) wash buffer, followed by 1 × 10 CV PreScission buffer (50 mM Tris-HCl, 5 mM MgCl2, 100 mM NaCl, 1 mM DTT, 0.5 mM ATP; pH 8.0). The resin was then incubated overnight in 4CV PreScission buffer with 80 U PreScission protease to cleave off the GST tag. The following morning, after allowing the resin to settle, the eluent was collected, concentrated by spinning through a 3000 kDa MWCO filter, supplemented with an additional 0.1 mM ATP, 1 mM DTT, and 20% w/v sucrose before flash freezing in liquid nitrogen, and finally stored at –80 °C. Concentration was determined using a Nanodrop. All steps from lysis onwards were performed at 4 °C.

Cell culture and drug treatment hTERT immortalized RPE-1 (RPE1) cell lines were grown in an 1:1 mix of DMEM and F-10 (Lonza) and Human Embryonic Kidney (HEK) 293T cells line were cultured in DMEM, both supplemented with 10% fetal bovine serum (FBS, GE Healthcare) and 1% penicillin and streptomycin (Sigma-Aldrich). All cells were grown in tissue culture polystyrene flasks (Corning) and were maintained in a humidified incubator at 37 °C with 5% CO2. Mycoplasma contamination was routinely checked with LT07-518 Mycoalert assay (Lonza).

FuGENE 6 (Promega) was used to transfect RPE1 cells with plasmids for generating CRISPR/Cas9 knockouts, immunofluorescence staining and live cell imaging; RNAiMAX (Thermo Fisher Scientific) was used to transfect RPE1 cells with siRNAs at 20 nM; MaxPEI was used to transfect HEK293T cells for lentivirus packaging. Transfections were performed according to the manufacturer’s instructions within the recommended reagent/DNA or reagent/siRNA ratio range.

We used the following drugs: centrinone B (Tocris Bioscience), nocodazole (Sigma), rapalogs (A/C heterodimerizer and B/B homodimerizer, Takara), dynapyrazole A (Sigma-Aldrich), BI2536 (Selleckchem), Thymidine (Sigma-Aldrich), proTAME (Boston Biochem), and Brefeldin A (Peptrotech).

To remove centrioles, RPE1 cells were treated with 125 nM centrinone B containing complete medium for ~10 days, and drug-containing medium was refreshed every 24 hr; cell confluence was maintained around ~50–80% during the treatment.

For the microtubule disassembly and regrowth assay, the acentriolar RPE1 cells were seeded onto coverslips in 24-well plates and incubated for 24 hr, then cells were treated with 10 μM nocodazole for 1 hr in an incubator (37 °C, 5% CO2) and followed by another 1 hr treatment at 4 °C to achieve complete disassembly of stable microtubule fragments. Nocodazole washout was then carried out by at least six washes on ice with ice-cold complete medium; subsequently, plates were moved to a 37 °C water bath and pre-warmed medium was added to each well to allow microtubule regrowth.

For cell cycle synchronization, centrinone-treated AKAP450/CAMSAP2/P53 knockout cells were treated with 5 mM Thymidine (Sigma-Aldrich) overnight, released in centrinone containing medium for 4 hr and subsequently treated with 5 μM proTAME (Boston Biochem, I-440) for 2 hr before being released in centrinone containing medium for 1–4 hr followed by live imaging and fixation.

For the inducible ppKin14-CAMSAP2 heterodimerization experiment, acentriolar cells were seeded onto coverslips in 24-well plates, cultured with centrinone B containing medium and co-transfected with 2FKBP-mCherry-CAMSAP2 and FRB-TagBFP-GCN4-ppKin14 vectors. Twenty-four hr after transfection, rapalog AP21967 A/C heterodimerizer was added into the medium at a final concentration of 50 nM and incubated overnight for preparation of fixed cells. For live imaging, heterodimerizer was used at 100 nM.

For the inducible pericentrin homodimerization experiment, acentriolar AKAP450 KO cells were seeded onto coverslips in 24-well plates, cultured with centrinone B containing medium and transfected with 2homoFKBP-mCherry-pericentrin vector. Twenty-four hr after transfection, rapamycin analogue AP20187 (B/B homodimerizer) was added into the medium at a final concentration of 500 nM and incubated 2 hr before fixation.

Lentivirus packaging and generation of transgenic stable cell lines

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Lentiviruses were produced by MaxPEI-based co-transfection of HEK293T cells with the transfer vectors together with the packaging vector psPAX2 and envelope vector pMD2.G (psPAX2 and pMD2.G were a gift from Didier Trono, Addgene plasmid #12,259 and #12260; RRID:Addgene_12259 and RRID:Addgene_12260). Supernatant of packaging cells was harvested 48–72 hr after transfection, filtered through a 0.45 µm filter, incubated with a polyethylene glycol (PEG)–6000-based precipitation solution overnight at 4 °C and centrifuged for 30 min at 1500 rpm to concentrate the virus. Lentiviral pellet was resuspended in PBS.

Wild type, AKAP450 and AKAP450/CAMSAP2 knockout RPE1 cells were infected with lentivirus and incubated in complete medium supplemented with 8 μg/ml polybrene (Sigma-Aldrich). After 24 hr, the cell medium was replaced with fresh medium. Starting 72 hr after viral transduction, cells were subjected to selection with puromycin at a concentration of 25 μg/ml for wild-type, 20 μg/ml for AKAP450 knockout and 15 μg/ml for AKAP450/CASMAP2 knockout for up to 3 days (until most of the untransduced control cells, treated with the same concentration of antibiotic, were dead). After selection, cells were grown in normal medium for 3 days and individual colonies expressing GFP were isolated into 96-well plates by fluorescence-activated cell sorting (FACS). Sorted single transgenic stable cell lines were further confirmed by immunofluorescence staining to check the expression level of GFP-CDK5RAP2 and its colocalization with other centrosomal proteins.

Generation of CRIPSR/Cas9 knockout cell lines

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The CRISPR/Cas9-mediated knockout of p53-, pericentrin-, AKAP450- and CAMSAP2-encoding genes was performed as described previously (Ran et al., 2013). In brief, AKAP450/CAMSAP2 knockout RPE1 cells (Wu et al., 2016) were transfected with the vectors bearing the appropriate targeting sequences using FuGENE 6. One day after transfection, the transfected AKAP450/CAMSAP2 knockout RPE1 cells were subjected to selection with 15 µg/ml puromycin for up to 3 days. After selection, cells were allowed to recover in normal medium for ~7 days, and knockout efficiency was checked by immunofluorescence staining. Depending on the efficiency, 50–500 individual clones were isolated and confirmed by immunofluorescence staining, and the resulted single colonies were characterized by Western blotting, immunostaining and genome sequencing. AKAP450/CAMSAP2/p53 and AKAP450/CAMSAP2/MMG/CDK5RAP2/p53 knockout cell lines were generated first and subsequently, each of them was used to knock out the gene encoding pericentrin. The mutated portions of the p53- and pericentrin-encoding genes were sequenced using gel-purified PCR products obtained with primers located in the vicinity of the corresponding sgRNA targeting sites.

Antibodies, immunofluorescence staining, and western blotting

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Antibodies used for immunostaining and Western blotting are listed in the Key Reagent or Resource table. For immunofluorescence cell staining, cultured cells were fixed with –20 °C methanol for 5 min or with 4% paraformaldehyde (PFA) for 12 min at room temperature, rinsed in PBS for 5 min, permeabilized with 0.15% Triton X-100 in PBS for 2 min, washed 3 times for 5 min with 0.05% Tween-20 in PBS, sequentially incubated for 20 min in the blocking buffer (2% BSA and 0.05% Tween-20 in PBS), 1 hr with primary antibodies in the blocking buffer, washed 3 times for 5 min with 0.05% Tween-20 in PBS, then for 1 hr in secondary antibodies in the blocking buffer, washed 3 times for 5 min with 0.05% Tween-20 in PBS, and air-dried after a quick wash in 96% ethanol. Cells were mounted in Vectashield mounting medium with or without DAPI (Vector laboratories, Burlingame, CA). Alexa Fluor −405,–488, –594 and −647 conjugated goat antibodies against rabbit, rat and mouse IgG were used as secondary antibodies (Molecular Probes, Eugene, OR).

For Western blotting, cells were harvested from six-well plates or 10 cm dishes at 90% confluence and protein extracts were prepared using the lysis buffer containing 20 mM Tris-Cl, pH 7.5, 150 mM NaCl, 0.5 mM EDTA, 1 mM DTT, 1% Triton X-100 or RIPA buffer containing 50 mM Tris-HCl, pH 7.5, 150 mM NaCl, 1% Triton X-100, 0.5% Sodium Deoxycholate supplemented with protease inhibitor and phosphatase inhibitors (Roche). Samples were run on polyacrylamide gels, followed by transfer on 0.45 μm nitrocellulose membrane (Sigma-Aldrich). Blocking was performed in 2% BSA in PBS for 30 min at room temperature. The membrane was first incubated with the primary antibodies overnight at 4 °C and washed with 0.05% Tween-20 in PBS 3 times and subsequently incubated with secondary antibodies for 1 hr at room temperature and washed 3 times with 0.05% Tween-20 in PBS. IRDye 800CW/680 LT Goat anti-rabbit and anti-mouse were used as secondary antibodies (Li-Cor Biosciences, Lincoln, LE) and membranes were imaged on Odyssey CLx infrared imaging system (Image Studio version 5.2.5, Li-Cor Biosciences).

Imaging and analysis of fixed cells

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Images of fixed cells were collected with a Nikon Eclipse Ni upright fluorescence microscope equipped with a DS-Qi2 CMOS camera (Nikon), an Intensilight C-HGFI epi-fluorescence illuminator (Nikon), Plan Apo Lambda 100×NA 1.45 or Plan Apo Lambda 60 x N.A. 1.40 oil objectives (Nikon) and driven by NIS-Elements Br software (Nikon).

Gated STED imaging was performed with Leica TCS SP8 STED 3 X microscope driven by LAS X software using HC PL APO 100 x/1.4 oil STED WHITE objective, white laser (633 nm) for excitation and 775 nm pulsed lased for depletion. Images were acquired in 2D STED mode with vortex phase mask. Depletion laser power was equal to 90% of maximum power and an internal Leica HyD hybrid detector with a time gate of 1≤tg ≤ 8 ns was used.

ImageJ was used for adjustments of intensity levels and contrast, quantification of the immunofluorescence signal intensity and maximum intensity projections. To analyze PCM clustering after nocodazole washout in AKAP450/CAMSAP2 knockout RPE1 cells, images were separated into concentric circular areas using Concentric Circles plugin of ImageJ. The biggest PCM cluster (which normally also had the highest fluorescence intensity) was selected as the center, around which 20 circles with 2 μm inner radius and 20 μm outer radius were drawn. Fluorescence intensity of PCM clusters in these concentric circles was measured automatically and normalized by the sum of the total PCM intensity in each cell per condition. To quantify the areas occupied by PCM clusters, immunofluorescence images of fixed cells and time lapse images of live cells were analyzed by drawing the smallest circle that covered visible PCM clusters to indicate the area occupied by the PCM clusters, and the diameters of the circles were used for the quantification.

Measurements of microtubule radiality

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To analyze microtubule radiality, images of fluorescently labeled microtubules were separated into radial and non-radial components using a customized ImageJ macro (https://github.com/ekatrukha/radialitymap; Katrukha, 2019). First, a local orientation angle map was calculated for each pixel using the OrientationJ plugin. We used ‘cubic spline gradient’ method and tensor sigma parameter of 6 pixels (0.4 µm). The new origin of coordinates was specified by selecting the centrosome position in the corresponding channel, or the brightest spot in case of centrinone treatment. Radial local orientation angle was calculated as a difference between the local orientation angle and the angle of the vector drawn from the new origin of coordinates to the current pixel position. A radial map image was calculated as an absolute value of the cosine of the radial local orientation angle at each pixel providing values between zero and one. A non-radial map image was calculated as one minus the radial map. Both maps were multiplied with the original image to account for different signal intensities; the two maps illustrate separated radial and non-radial image components.

Live cell imaging and analysis

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Live fluorescent imaging was performed with spinning disk confocal microscopy on inverted research microscope Nikon EclipseTi-E (Nikon), equipped with the Perfect Focus System (Nikon), Nikon Plan Apo VC 60 x NA 1.4 and Nikon Plan Apo VC 100 x N.A. 1.40 oil objectives (Nikon) and spinning-disc confocal scanner unit (CSU-X1-A1, Yokogawa). The system was also equipped with ASI motorized stage with the piezo top plate MS-2000-XYZ (ASI), Photometrics Evolve 512 EMCCD camera (Photometrics) and controlled by the MetaMorph 7.8 software (Molecular Devices). Vortran Stradus lasers (405 nm 100 mW, 488 nm 150 mW and 642 nm 165 mW) and Cobolt Jive 561 nm 110 mW laser were used as the light sources. System was equipped with ET-DAPI (49000), ET-GFP (49002), ET-mCherry (49008) and ET-Cy5 (49006) filter sets (Chroma). 16-bit images were projected onto the EMCCD chip with the intermediate lens 2.0 X (Edmund Optics) at a magnification of 110 nm per pixel (60 x objective) and 67 nm per pixel (100 x objective). To keep cells at 37 °C and 5% CO2 we used stage top incubator (INUBG2E-ZILCS, Tokai Hit). Cells were plated on round 25 mm coverslips, which were mounted in Attofluor Cell Chamber (Thermo fisher). Cells were imaged with a 2 s interval and 200ms exposure for 1–3 hr at 10% laser power.

Phase-contrast live cell imaging was performed on a Nikon Ti equipped with a perfect focus system (Nikon), a super high pressure mercury lamp (C-SHG1, Nikon, Japan), a Plan Apo 60 x NA 1.4 (Ph3), a CoolSNAP HQ2 CCD camera (Photometrics, Tucson, AZ), a motorized stage MS-2000-XYZ with Piezo Top Plate (ASI, Eugene, OR) and a stage top incubator (Tokai Hit, Japan) for 37 °C/5% CO2 incubation. The microscope setup was controlled by Micro-manager software. Cells were plated on round 25 mm coverslips, which were mounted in Attofluor Cell Chamber (Thermo fisher), and imaged with a 1 min interval for ~24 hr.

For live imaging of nocodazole treatment and washout experiments, cells were incubated with the medium containing 100 nM SiR-tubulin (Tebu-bio) overnight to image the microtubule network. Centrinone-treated cells were imaged for a desired period of time prior to the nocodazole treatment, and then nocodazole was added into the medium at a final concentration of 10 μM while imaging simultaneously. Culture medium was carefully removed when microtubules were completely depolymerized and washed with prewarmed medium six times to let microtubules regrow. GFP-CDK5RAP2 and SiR-tubulin imaging was performed with a 2 s interval with 200ms exposure for 1–3 hr in total, and maximum intensity projections, contrast adjustment and further processing was performed using ImageJ.

FRAP

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FRAP experiments were performed on the spinning disc microscope describe above, equipped with iLas platform and using Targeted Laser Action options of iLas and controlled with iLas software (Roper Scientific, now Gataca Systems). Photobleaching in the GFP channel was performed with the 488 nm laser. For the FRAP analysis, Polygon ROIs were set in photobleached and non-bleached regions as well as in the background. The average fluorescence intensity was measured using ImageJ for each frame, the background intensity was subtracted from the bleached and non-bleached areas and normalized to the average of the frames acquired prior to the bleach. The mean fluorescence intensities of the images before photobleaching were set as 100%, and the subsequent relative recovery percentages were calculated. Time lapse acquisitions were corrected for drift with the ImageJ plugins Template Matching.

motor-PAINT and analysis

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For motor-PAINT, a protocol published previously (Tas et al., 2017) was used, with minor adjustments. Cells were incubated with 50 nM SiR-tubulin and 500 nM verapamil overnight to allow fields of view suitable for imaging to be located before the addition of purified GST-DmKHC(1-421)-mNeonGreen. For nocodazole-treated samples, cells were first incubated with 10 µM nocodazole for 15 min at 37 °C. A single nocodazole-treated or control sample was then transferred to an imaging chamber, and cells were subjected to extraction for 1 min in extraction buffer (BRB80: 80 mM K-Pipes, 1 mM MgCl2, 1 mM EGTA; pH 6.8, supplemented with 1 M sucrose and 0.15% TritonX-100) pre-warmed to 37 °C. Pre-warmed fixation buffer (BRB80 supplemented with 2% PFA) was added to this (i.e. final PFA concentration of 1%) and the solutions were mixed by gentle pipetting for 1 min. This buffer was removed and the chamber was washed for 4 times for 1 min in pre-warmed wash buffer (BRB80 supplemented with 1 µM Taxol) before adding imaging buffer (BRB80 supplemented with 583 µg/mL catalase, 42 µg/mL glucose oxidase, 1.7% w/v glucose, 1 mM DTT, 1 µM Taxol, and 5 mM ATP). An aliquot of GST-DmKHC(1-421)-mNeonGreen motors was warmed, spun in the Airfuge at 20 psi for 5 min in a pre-chilled rotor to remove any aggregates, and then transferred to a clean tube prior to use. Motors were kept on ice and added locally to cells in 0.3 µl increments.

Imaging was performed immediately after sample preparation at room temperature on a Nikon Ti-E microscope equipped with a 100 x Apo TIRF oil immersion objective (NA. 1.49) and Perfect Focus System 3 (Nikon). Excitation was achieved with a Lighthub-6 laser combiner (Omicron) containing a 647 nm laser (LuxX 140 mW, Omicron), a 488 nm laser (LuxX 200 mW, Omicron), and optics allowing for a tunable angle of incidence. Illumination was adjusted for (pseudo-) total internal reflection fluorescence (TIRF) microscopy. Emission light was separated from excitation light using a quad-band polychroic mirror (ZT405/488/561/640rpc, Chroma), a quad-band emission filter (ZET405/488/561/640 m, Chroma), and an additional single-band emission filter (ET525/50 m for mNeonGreen emission, Chroma). Detection was achieved using a Hamamatsu Flash 4.0v2 sCMOS camera. Image stacks were acquired with a 60ms exposure time, 7% laser power, and 15000–22000 images per field of view. Components were controlled using MicroManager (Edelstein et al., 2014).

Acquired stacks were pre-processed using the Faster Temporal Median ImageJ plugin (https://github.com/HohlbeinLab/FTM2; Jabermoradi et al., 2021) with a window size of 100 frames. These stacks were then analyzed using Detection of Molecules (DoM) plugin v.1.2.1 for ImageJ (https://github.com/ekatrukha/DoM_Utrecht), as has been described previously (Chazeau et al., 2016; Tas et al., 2017). Each image in an acquired stack is convoluted with a two-dimensional Mexican hat kernel. The resulting intensity histogram is used to create a thresholded mask based on a cut-off of three standard deviations above the mean. This mask is then subject to rounds of dilution and erosion to create a filtered mask used to calculate the centroids on the original image. These centroids are used as initial values to perform unweighted nonlinear least squares fitting with a Levenberg-Marquardt algorithm to an asymmetric two-dimensional Gaussian point spread function (PSF), allowing for the sub-pixel localization of particles.

Images were drift-corrected using DoM. The normalized cross-correlation between intermediate reconstructions of consecutive sub-stacks is used to calculate the drift in x and y between sub-stacks, which is then linearly interpolated to adjust each individual frame in the stack.

Detected particles were linked into tracks again using DoM, which performs a quicker variant of a nearest neighbor search, with a maximum distance of 5 pixels (~320 nm) between consecutive frames and no permitted frame gap. Tracks were later filtered to remove those shorter than 4 frames or longer than 200 frames, those in which an angle between parts of the trajectory exceeded 90 degrees, and those in which the speed of the motor was less than 100 nm/s or more than 1500 nm/s.

The particle table was then split into four particle tables corresponding to the four quadrants of the image with tracks sorted based on their net displacement (i.e., Δx>0 ∧ Δy>0; Δx>0 ∧ Δy<0; Δx<0 ∧ Δy>0; Δx<0 ∧ Δy<0), as described previously (Tas et al., 2017). These directionality-filtered particle tables were reconstructed using DoM, creating four super-resolved images of microtubule segments pointing in a similar direction. These were merged with the reconstructed image of all localizations to determine the direction of each microtubule segment. Each microtubule was manually assessed to assign it as being plus-end-in or plus-end-out. Microtubules were manually traced with lines 4 pixels (80 nm) wide, assigned a color based on their orientation, flattened onto the image, filtered with a Gaussian Blur of radius 2, and finally merged with the reconstructed image of all localizations.

To quantify the percentage of minus-end-out microtubule length to total microtubule length before and after nocodazole treatment, the length of each microtubule (determined from kinesin-1 trajectories) in the cell was measured by calculating the Euclidean distance between all subsequent pairs of points along the microtubule and summed. The ratio was calculated as the total minus-end-out microtubule length divided by the total microtubule length.

Analysis of PCM cluster dynamics

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To represent the motion of PCM clusters during nocodazole treatment, ImageJ plugin KymoResliceWide v.0.4 (https://github.com/ekatrukha/KymoResliceWide; Katrukha, 2020) was used for generating kymographs from the time lapse images. The velocity of PCM clusters was measured manually using kymographs starting from the time point when a small PCM cluster moved out of a caMTOC. Microtubule density around each PCM cluster was determined by measuring the mean fluorescence intensity of SiR-tubulin in a circular area with a 2 μm radius centered on the PCM cluster and normalizing it to the mean fluorescence intensity of 20 images prior to nocodazole addition (set as 100%). The moment when a PCM cluster started to move out of the caMTOC was set as the initial time point (0 min), and the subsequent PCM cluster motion velocity and the relative local microtubule density at 43 time points were calculated and averaged.

The movement trajectories of PCM clusters were generated using ImageJ plugin TrackMate (version is 6.0.2). The parameters and the settings used were as following: LoG (Laplacian of Gaussian) detector with estimated blob diameter: 14.9 µm; thresholding value 12.25; sub-pixel localization was selected. HyperStack Displayer was selected to overlay the spots and tracks on the current hyperstack window. Simple LAP tracker was selected to track the distance and time with the linking max distance: 32.0 µm, gap-closing max distance: 55.0 µm and gap-closing max frame gap: 2. All other parameters and settings were used as the default.

Computer simulations and analysis

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Simulations were performed with Cytosim (version June 2019). Cytosim solves a set of Langevin-equations that describe the movement of flexible cytoskeletal filaments and associated proteins, such as molecular motors (Nedelec and Foethke, 2007). The numerical values for the parameters are given in Table 1. The configuration file is provided as Supplementary file 1.

We defined the following components in the simulation:

Cell shape: We considered a two-dimensional system with a circular cell with a radius of 10 µm. As commonly used in Cytosim simulations, we set the intracellular viscosity to 1 pN s/ µm2.

Molecular motors: The binding process of a molecular motor to a microtubule was described by a binding rate kon and the unbinding from the microtubule by a force-dependent unbinding rate koff = k0off exp(F/Fd). When a motor was engaged with the microtubule it moved along the microtubule with a linear force-dependent velocity, characterized by v(F) = v0 (1 F/Fs). Dynein, as well as kinesin-14 motors moved to the minus end of microtubules.

Microtubule filaments: We used a classical model for microtubule dynamics which is described by a catastrophe rate, a growth speed, and a shrinkage speed. The growth speed is force-dependent with a characteristic growing force. For simplicity, we ignored rescue events. The catastrophe rate was set as kcat = vg/LMT, in which the mean microtubule length LMT was 5 µm. To further restrict the microtubule length, we set a maximum of 7.5 µm. This limitation was necessary to avoid that long microtubules were pushing the minus ends to the periphery.

PCM complexes: We described a PCM complex as a bead with a radius of 50 nm. We randomly placed one microtubule nucleation site and one dynein on the bead. To effectively account for an unspecific adhesive interaction between PCM complexes, we introduced two molecules that can bind to each other. One was implemented as a 10 nm Cytosim fiber and the binding partner as a Cytosim hand with a binding range of 100 nm, binding rate of 10 s–1, force-free unbinding rate of 0.01 s–1, and characteristic unbinding force of 3 pN. We randomly placed one of each molecule on a PCM complex. In the simulations with strong adhesive interactions, we increased the number of adhesive binding molecules on the beads and kept all the other parameters the same. We defined five random attachment points on the beads and placed to each point five molecules. In this setup each PCM complex was covered with 25 Cytosim binding filaments and 25 Cytosim binding hands. Therefore, when two PCM complexes were close to each they formed multiple bonds between each other.

CAMSAP-kin14 complexes: We described a complex consisting of a CAMSAP-stabilized microtubule end with kinesin 14 motors attached as a Cytosim bead with a radius of 50 nm. We attached five kin14 motors and one microtubule nucleation site randomly on the bead. When we implemented adhesive interaction between CAMSAP-kin14 complexes, we used exactly the same binding molecules and arrangements as used for the PCM complexes.

Steric interactions: In all simulations, we considered steric interaction between the beads which either describe the PCM complex or the CAMSAP-kin14 complexes. All other steric interactions, except with the cell boundary were ignored.

Simulations and data analysis: We set the time step for the simulation to 0.01 s and simulated for a total of 30 min, after which a definite steady state was reached. For each configuration, we run 10 simulations and analyzed them afterward to obtain statistics on the emerging structures. From the last frame of the simulation, we obtained all positions of the complexes and calculated the mean position, which defines the center of mass. We subtracted the center of mass from all positions and derived all distances of the complexes to the center of mass. For a few examples, we determined the empirical cumulative distribution of the distances to the center of mass. We used the standard NumPy functions to determine the mean and standard deviation of the distances to the center of mass for each simulation.

Statistical analysis

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All statistical analyses were performed using GraphPad Prism 9. Statistical details for each experiment can be found in the corresponding figure legends and supporting files.

Data and software availability

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All mentioned ImageJ plugins have source code available and are licensed under open-source GNU GPL v3 license. The source data for the original Western blots are available within the paper.

Data availability

The configuration file for Cytosim, the software used for simulations, is included as Supplementary file 1. All numerical data and all raw Western blot data are included as Source Data files.

References

  1. Book
    1. Klumpp S
    2. Keller C
    3. Berger F
    4. Lipowsky R
    (2015)
    Molecular Motors: Cooperative Phenomena of Multiple Molecular Motors
    In: De S, Hwang W, Kuhl E, editors. In Multiscale Modeling in Biomechanics and Mechanobiology. London: Springer. pp. 27–61.
    1. Lee S
    2. Rhee K
    (2010)
    CEP215 is involved in the dynein-dependent accumulation of pericentriolar matrix proteins for spindle pole formation
    Cell Cycle 9:774–783.
    1. Rios RM
    (2014) The centrosome-Golgi apparatus nexus
    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 369:20130462.
    https://doi.org/10.1098/rstb.2013.0462

Decision letter

  1. Jens Lüders
    Reviewing Editor; Institute for Research in Biomedicine, Spain
  2. Suzanne R Pfeffer
    Senior Editor; Stanford University School of Medicine, United States
  3. Laurence Pelletier
    Reviewer; Lunenfeld-Tanenbaum Research Institute, Canada

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Decision letter after peer review:

[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]

Thank you for submitting the paper "Centriole-independent centrosome assembly in interphase mammalian cells" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Laurence Pelletier (Reviewer #3).

We are sorry to say that, after consultation with the reviewers, we have decided that this work will not be considered further for publication by eLife in its current form.

While the reviewers agree that the data presented are abundant and of high quality, there were general concerns that this paper did not provide a significant advance either conceptually or mechanistically beyond prior work in either understanding the roles of the proteins presented or how interphase microtubule arrays are physiologically organized. However, the reviewers felt that this work could be reconsidered as a Research Advance if additional data is added to bolster the impact of the findings and the manuscript is rewritten to make the premise clearer, as described below.

Reviewer #1 (Recommendations for the authors):

Here, Chen et al. report the presence of acentriolar PCM in interphase cells following removal of the centrioles, the PCM and Golgi microtubule scaffolding protein AKAP450, and the microtubule minus end protein CAMSAP2. This PCM is able to form a single cylindrical centralized MTOC and microtubule array, the establishment and maintenance of which is dependent on pericentrin, g-tubulin, CDK5RAP2, and ninein. Dynein mediated microtubule-based transport establishes and maintains this singular array, bringing and keeping together small PCM foci. Intriguingly, the presence of CAMSAP2 works against the establishment of this single centralized microtubule array as microtubule minus ends are distributed throughout the cytoplasm. However, microtubule minus ends can be forced into a centralized localization by tethering CAMSAP to a rapalog-inducible minus end directed motor. Overall, this paper contains abundant data that are well presented and the experiments are well controlled and documented. I think this study is important as we can learn a lot about how the centrosome functions by divorcing its many parts and asking what they can do on their own. That said, this paper is a tour de force or what cells can do rather than what they actually do. The authors should consider putting these findings into the context of what they teach us about the normal processes they represent.

1. As mentioned above, I think some of the main findings of this paper are too buried and can be easily missed. Conceptually, it is exciting to have a platform to test how PCM can organize microtubules divorced from the normal complication of studying the centrosome. There are many helpful cartoons that help the reader interpret the figures and similar attention should be paid to explaining the rationale behind each experiment, again considering what they might teach us about the way in which PCM is able to normally function.

2. I find the vocabulary in the paper off putting. First, the word centrosome should mean something, i.e. centrioles surrounded by PCM, or at least as a centriole bearing entity. This word has been often misused in the literature, for example in the early mouse embryo where cells do not have centrioles, yet the MTOCs in these cells have been mislabeled as centrosomes. Centriole-less or acentriolar PCM would be a better term as has been recently used in other systems (Garbrecht et al., 2021; Magescas et al., 2021). Although it is true that PCM is not the perfect term either as pericentriolar should be in relation to a centriole, this term on its own has meaning as evidenced by its usage throughout the text. Second, the term acentrosomal MTOC (aMTOC) or noncentrosomal MTOC (ncMTOC) has often been used to describe MTOCs that are not derived from the centrosome such as in many types of differentiated cells. Thus, the use of this term to describe the PCM here is similarly problematic. I recommended referring to this structure as PCM for consistency.

3. A key interesting point in this paper is that CAMSAP antagonizes radial MTOC formation by pericentrin. I wish this concept had been further explored either experimentally or conceptually. The authors allude to its significance in the discussion, but instead focus on how PCM might be "redeployed" in differentiated cells, which does not seem to be the normal mode of microtubule organization seen in organismal studies. Instead, non-centrosomal proteins like CAMSAP and other proteins function in building these decentralized arrays concomitant with the centrosome attenuating its microtubule organizing capacity.

4. Figure 7 is a nice experiment as it indicates that, although not surprising, driving microtubule minus ends together creates a radial microtubule array, albeit as a ring-like structure rather than a single condensed point. Addition of pericentrin helps to focus the microtubule array into a condensed point. Under other conditions presented elsewhere in the paper, pericentrin is not able to counteract the presence of CAMSAP2 which drives microtubules into a decentralized array. Thus, this experiment suggests that pericentrin and the focusing of minus ends have additive effects. A more appropriate control for this experiment however is to show the results with and without the addition of Rapalog to control for any effects of the FKBP, FRB, or Rapalog itself.

5. The microtubule orientations shown in Figure 4G and Figure 4S1B are nice, however, it is important to show the associated motor localization from which the psuedocoloring was drawn.

6. I am confused by the timeline of siRNA treatments in Figure 5 and associated supplement relative to nocodazole treatment and washout. The text and cartoons depict that siRNA knockdown of targets was achieved just prior to nocodazole washout, which seems unlikely. Can you please clarify the timeline. In addition, the phenotypes siRNA depletions during steady state and after nocodazole washout are not the same. Is this true or just the images shown? Is this teaching us something interesting about establishment vs. maintenance of PCM structures?

Reviewer #2 (Recommendations for the authors):

Centrosomes are the major microtubule-organizing centers that support formation of a radial microtubule array in interphase and catalyse spindle formation in mitosis. Centrosomes are built on a centriolar core that accumulates a stable matrix called the pericentriolar material; microtubule-nucleating and anchoring activities are concentrated in the pericentriolar material. When centrioles are removed from interphase cells, weaker MTOCs form at the Golgi in an AKAP450-dependent manner. In this manuscript, Chen et al. characterize MTOCs in the absence of centrioles and AKAP450; they build on prior work they had published showing that, while microtubules are disorganized in the absence of centrioles and AKAP450, additional inactivation of the microtubule minus end-binding protein CAMSAP2 leads to the spatial concentration of centrosomal components like pericentrin and CDK5RAP2, which leads to the formation of a more organized microtubule network. They refer to these structures as "centriole-independent centrosomes" and provide evidence for dynein-mediated transport as being important for their formation. Some of the statements, including in the title and abstract, are not well justified. Overall, the work does not yet lead to a significant advance in understanding of how interphase microtubule arrays are organized.

There are a number of issues with this manuscript that make it difficult to provide a clear set of recommendations. The term "centriole-independent centrosomes" is incorrect. Centrosomes are defined structures, which contain a pair of centrioles that promoted localized assembly of a stable pericentriolar material matrix independently of microtubules. This stable structure, in turn, serves as the nucleation site for microtubules. This is clearly not the case for the accumulations described which form by dynein-driven forces bringing components together and are microtubule-dependent. More troubling is that the manuscript lacks a clear purpose. Why do the authors study a highly artificial system in which PLK4 is inhibited and AKAP450 and CAMPSAP2 are knocked out? What is the relevance of this state? The fact that they can tether dynein to CAMSAP2 and drive organization is unsurprising, given the significant prior work (starting from Verde et al. 1991) on dynein-based organization of microtubules into organized arrays. There are potentially interesting mechanistic questions like how CAMSAP2 competes with potential dynein recruiters in the pericentriolar material but no analysis of depth is conducted here. Overall, the effort comes across as not being sufficiently clearly motivated with a direct line of experimentation that has yielded new insights into microtubule organization in interphase cells.

Reviewer #3 (Recommendations for the authors):

In this manuscript, Chen et al. investigated the prospect of forming a microtubule organizing centre (MTOC) without centrioles, the catalysts of centrosomal MTOC formation. Upon disabling non-centrosomal MTOC pathways and depleting centrioles from cells, the authors describe the formation of a single centrally located acentrosomal MTOC (aMTOC). The authors go on to describe that the self-assembly of this structure is primarily driven by dynein driven aggregation of pericentriolar material (PCM). Particularly, the authors describe key PCM components that are necessary in sustaining the aMTOC that include: PCNT, γ-tubulin, CDK5RAP2 and ninein. The findings of this paper could yield further insight into the PCM redistribution and microtubule reorganization that occurs in differentiated cells such as myotubes and neurons, where centrosomal MTOCs are abandoned for more efficient non-centrosomal microtubule arrays.

The conclusions of this paper are well supported by the data. These include elaborate but well controlled experiments using complexly engineered cellular models, treatment schemes, and convincing imaging. However, some aspects of the manuscript could be clarified and extended to provide more robust support for their findings.

1. The authors clearly showed the process of PCM assembly in AKAP450/CAMSAP2-deleted acentriolar cells and their characteristics. However, it was not clear whether microtubules were nucleated from the assembled PCM in Figure 5B-E as microtubules could be nucleated in the cytoplasm. Can the authors perform microtubule regrowth assays using two different conditions (cold treatment and nocodazole) and stain them with antibodies against a-Tub and PCNT?

2. The authors suggest that an aMTOC can be formed in the absence of microtubule nucleation activity from the centrosome and Golgi apparatus. Can the authors show the same phenotypes in the presence of both Centrinone B and Brefeldin A (to disassemble the Golgi apparatus) in wild type hTERT-RPE1 and CAMSAP2 KO cell lines?

3. Removal of centrosomes typically triggers a p53-dependent G1 arrest however the authors acknowledge this and seem to have created RPE-1 models with AKAP450, CAMPSAP2, and p53 KO (Line 156-160, Figure 2E). While this paper is focused on aMTOC formation in interphase, I am curious as to whether the cells are still cycling after the cylindrical aMTOC formation. If so, what does progression through mitosis look like?

4. The authors describe an interesting phenomenon in this manuscript; however, the significance of this finding is not well emphasized in the abstract or in the ending paragraph of the introduction. In the last paragraph of the discussion the authors describe the implications of their findings in the re-organization of microtubules during cellular differentiation, however it would have been nice to delve deeper into how their findings could apply to these different models besides the identification of the redistribution of common players. Though they do concede that much is left to be discovered when it comes to these differentiated models.

[Editors’ note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Self-assembly of pericentriolar material in interphase cells lacking centrioles." for further consideration by eLife. Your revised article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Suzanne Pfeffer as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Laurence Pelletier (Reviewer #1).

The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:

This new manuscript has been significantly improved based on the reviewers' input from a previous submission. Before we can accept the manuscript, we would like the authors to address two remaining points that we believe will further improve the story.

Essential revisions:

1) Considering the highly manipulated model used by the authors to analyse PCM protein assembly and function, the introduction and discussion could be further improved to contextualise this work and explain its relevance in a more physiological setting. e.g during differentiation and in different cell types. See also comments by reviewer 2 and 3 for further details.

2) The impact of the simulations should be improved. As conducted/presented they merely confirm the already existing experimental data. However, the full potential of simulations, e.g. testing different outcomes by varying parameters and manipulating the system in different ways, has not been explored.

– What is the rationale behind the choice of the parameters defined by the authors (e.g. absolute and relative number of MTs, number of PCM complexes, number of MTs nucleated per PCM complex?)

– What is the range of these parameters that result in the outcome observed in cells?

– What happens if the orientation of CAMSAP-bound MTs would be biased (as may be the case during cell differentiation).

Exploring these types of questions will add significant additional value to the simulations that go beyond what is already demonstrated in cells.

Reviewer #2 (Recommendations for the authors):

Here Chen et al. submit a revised manuscript characterizing the self-assembly of PCM in cells that lack centrosomes, Golgi derived microtubules, and CAMSAP. As in the original version of the paper, the data are abundant, but well-presented and rigorously executed. While my concerns still remain about the relevance of these studies in this highly manipulated in vitro cell context to what cells do in vivo, the new data and framing do alleviate some of my original concerns. In particular, the authors underscored the importance of the fact that the structure that forms when CAMSAP microtubules are driven together with dynein can be refined by the presence of PCNT1. This point was bolstered in the text and through the addition of a computer simulation. While the ability of motors to generate a radial array of microtubules on their own has been previously demonstrated in many contexts over several decades, the demonstration that PCM can refine this interaction does extend this concept. The question does still remain whether this highly manipulated cell culture system with a nuanced relationship to in vivo contexts will appeal to the broad readership of eLife.

1) While I like the new simulations added as Figure 8 to experimentally bolster the point that CAMSAP microtubules steal PCM from the caMTOC, I do wish there was a way to directly test the sufficiency of PCM cohesion to counteract this process (i.e. experimentally reinforcing the point in Figure 8D) perhaps with phosphomimetics or other mutations that might create more stable interactions between PCM molecules. Such mutations might not be available and so could be beyond the scope of this work.

2) I still take issue with the way in which these findings are related to actual in vivo contexts, especially their relation to how MTOCs might form in differentiated cells. There are really three ways in which this work might relate to MTOCs in differentiated cells:

1) It might have a direct bearing on structures that are clearly derived from PCM in vivo: These include at the base of cilia in C. elegans sensory neurons or PCM packets (C. elegans, Magescas et al., 2019), flares (Drosophila, Megraw, 2002), or fragments (Rusan and Wadsworth, 2005) seen at mitotic exit, some of which persist in interphase.

2) This work might relate to non-centrosomal/acentrosomal MTOCs seen in interphase cells, but there is little evidence that any of these structures are directly derived from PCM rather than sharing the same components as the PCM in some cases. Here we get into a bit of semantics again, but I think PCM should mean material surrounding centrioles (or that used to surround centrioles as is the case in the cases cited above). If PCM proteins assemble in different locations in the cell, these structures would no longer be called PCM. I like the idea that the CAMSAP-associated (or other non-centrosomal) microtubules could deploy a tug of war with the PCM proteins, but this idea is currently just speculative and should be deemed as such. If the non-centrosomal microtubules had a way to be biased asymmetrically (as in differentiated cells), the clustering of these PCM proteins would create a way to make positive feedback to further reinforce a non-centrosomal network.

3) This work might also give a mechanism for the way in which PCM is stripped from the centrosome following mitosis, a common occurrence across cell types and organisms upon mitotic exit. In this case, non-centrosomal microtubules could strip PCM from the centrosome once the matrix was crippled following the inactivation of mitotic kinases.

Some of these ideas are explored in the Discussion, but the Introduction is still imprecise in discussion of the potential relationship of the work to actual in vivo contexts. I would encourage the authors to be more precise with the potential implications in the Introduction and explore some of these concepts further in the Discussion.

For example:

Line 57-58: "These properties…are relevant because in most differentiated cell types…PCM forms acentrosomal MTOCs.": Please see point 2 above, but there is little evidence that PCM (meaning structures that derive from the centrosome) rather than PCM proteins forms acentrosomal MTOCs.

Reviewer #3 (Recommendations for the authors):

In this revised manuscript Chen et al. investigate the mechanisms underlying the self-assembly of PCM proteins into tight clusters that are able to function as MTOCs in interphase RPE1 cells, under conditions where these cells lack their two main MTOCs at centrioles and at the Golgi. These conditions, achieved by centrinone treatment to eliminate centrioles, and AKAP450 KO to eliminate the Golgi-associated MTOC, allow to study the MTOC formation properties of various proteins without interference by centrosomal or Golgi-associated MTOC activity. In this revised version the authors complemented an already comprehensive set of data with modelling studies, computationally confirming the observations made in cell-based experiments. Also, the relevance of the observations, made in an artificial situation of complete absence of centrosomal and Golgi MTOCs, has been addressed in the text.

Overall the study contains an impressive amount of information and useful insight regarding the ability of PCM proteins to self-assemble into structures that provide microtubule nucleation and anchoring sites, to control the shape of the cellular microtubule network. Some of this information would be difficult to obtain in the presence of the dominant centrosome and Golgi-associated microtubule arrays. On the other hand, an important criticism refers to the fact that the experimental setup is based on a highly artificial situation, raising concerns about how relevant the observations are in a physiological setting. Although improved, the additional data and rewriting still does not fully address this point. Also, limitations in the computational simulations need to be addressed.

Specific points:

1) Pericentrin and CDK5RAP2, which both are important for caMTOC formation and function, are not very important for the interphase centrosomal MTOC. This has been shown by Gavilan et al., 2018 using multiple knockout approaches similar to those in the current manuscript. This suggests that despite the importance of pericentrin and CDK5RAP2 for caMTOCs, they are not very important at the major physiological MTOC that they localize to, at least during interphase. This should be discussed and the above study cited (it was cited but not in this context). Do the authors envision a specific scenario in which their findings would help understanding MTOC assembly?

2) The Gavilan et al. study above also described cytoplasmic clusters of PCM proteins that form in centriole-lacking AKAP450 KO cells and that nucleate and organize microtubules (referred to a cytoplasmic or 'cMTOCs'). They probe these with a panel of antibodies and reveal their composition including the absence of CEP192 (similar to the analysis of caMTOCs in the current manuscript). This should be discussed/cited.

3) I wonder why the authors refer to some of the caMTOCs as 'cylindrical' – what is the evidence for this? It would imply some kind of geometry, but to my eye they rather appear to be clusters arranged in a roughly linear fashion. In the absence of data supporting a cylindrical shape, I would suggest changing this description.

4) As presented, the computer simulation data does not add much to the manuscript. It mainly confirms the experimental data, so what is the point of it, if one already has cell-based experimental evidence? It would be useful to make predictions and then design experiments in cells to test these, but this has not been done. At the very least, it would be useful to use the modelling to define parameter ranges within which the observed effects are true. This would give the modelling more meaning, since this may not be feasible to do in cells. For example, the authors make several assumptions regarding specific parameters included in the simulations such as the numbers of microtubules, PCM complexes, and CAMSAP-kin14 complexes, but there is no indication of how they came up with these numbers – are they related in any way to estimates in cells? In some simulations this does not seem to be the case. For example, to model the effect of PCM cluster dispersion, the authors assume 300 randomly oriented, CAMSAP-associated MTs, and only 50 PCM clusters each with one associated MT, but in cells this ratio seems to be the opposite – only 25% of all MTs have minus-end-out orientation (not cluster-associated).

Also, what is the outcome of simulations, if any of these parameters were to be gradually increased or decreased? In the absence of such data, it seems as if the authors have picked numbers that produce the desired outcome observed in cells. Indeed, it may be informative to ask under what conditions this outcome is not observed.

Regarding the more general simulation input parameters in Table 1, the authors have included references only for some of them. In the text they state "The numerical values for the parameters of our simulations have been taken from literature or reasonably chosen otherwise". What does 'reasonably chosen otherwise' mean? I understand that there may not be a reference for every parameter, but in these cases there should be at least a brief explanation describing how it was chosen/estimated.

Line 605: as far as I know, NEDD1 is not a gTuRC activator.

https://doi.org/10.7554/eLife.77892.sa1

Author response

[Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]

While the reviewers agree that the data presented are abundant and of high quality, there were general concerns that this paper did not provide a significant advance either conceptually or mechanistically beyond prior work in either understanding the roles of the proteins presented or how interphase microtubule arrays are physiologically organized. However, the reviewers felt that this work could be reconsidered as a Research Advance if additional data is added to bolster the impact of the findings and the manuscript is rewritten to make the premise clearer, as described below.

We thank the reviewers for the thoughtful and critical comments. We have profoundly revised our paper by adding new data and thoroughly re-writing the manuscript, including the Title, Abstract, Introduction and Discussion, to make the premise and the novelty of our study clearer. In short, the goal of our work was to investigate how centriole-independent processes – self-association of pericentriolar material (PCM) and dynein-mediated PCM transport – contribute to the assembly of interphase microtubule-organising centers (MTOCs). We also aimed to determine which molecular players participate in this process and how PCM assembly is affected by the presence of noncentrosomal microtubules. To conceptualize our experimental findings, we have now added a completely new Results section, where we used Cytosim simulations to model the distribution and self-assembly of PCM in different conditions, and we showed that our major experimental findings can be recapitulated in silico using simple assumptions. Thus, our experimental results complemented by our mechanistic simulations provide important new insights into the properties and functions of interphase regulators of microtubule minus-end organization.

As explained in detail below and in the revised manuscript, an important conclusion from our work is that interphase PCM can self-assemble in the absence of centrioles in a manner that requires a specific subset of PCM proteins. The presence of immobile randomly distributed non-centrosomal microtubules is sufficient to disrupt PCM self-assembly, a conclusion that we have developed further using simulations. We also explored whether a compact MTOC can be formed simply by minus-enddirected transport of stabilized microtubule minus ends and found that this is not the case – both simulations and experiments indicate that in this situation, microtubule minus ends form a more loose ring-like structure rather than a single compact MTOC. However, a compact MTOC does form when self-clustering PCM components are also present in the system. We think that these non-trivial findings are relevant for understanding MTOC assembly in interphase cells and MTOC organization during cell differentiation, when the centrosome is inactivated and PCM components are used to build non-centrosomal microtubule systems.

Concerning potential resubmission as a Research Advance, one reviewer wrote: “The eLife paper I was thinking of was Wang et al., 2015, which revealed that a ninein and g-tubulin pathway function in parallel to a patronin pathway in organizing non-centrosomal microtubule arrays in worms." That detail should have been included in the review as that paper is not from your team; I think that a rewriting to make the premise clear in relation to prior work could make a revised submission either a RESEARCH ADVANCE related to that story or if you prefer, an independent submission, but taking to heart, the comments of the reviewers.

Our paper is indeed related to the very nice study by Wang et al., 2015, which we cited, but the relationship is much more distant with respect to the model system, content and conclusions than what is normally expected from an eLife Research Advance. Therefore, we think that an independent submission would be more appropriate in this case.

Reviewer #1 (Recommendations for the authors):

Here, Chen et al. report the presence of acentriolar PCM in interphase cells following removal of the centrioles, the PCM and Golgi microtubule scaffolding protein AKAP450, and the microtubule minus end protein CAMSAP2. This PCM is able to form a single cylindrical centralized MTOC and microtubule array, the establishment and maintenance of which is dependent on pericentrin, g-tubulin, CDK5RAP2, and ninein. Dynein mediated microtubule-based transport establishes and maintains this singular array, bringing and keeping together small PCM foci. Intriguingly, the presence of CAMSAP2 works against the establishment of this single centralized microtubule array as microtubule minus ends are distributed throughout the cytoplasm. However, microtubule minus ends can be forced into a centralized localization by tethering CAMSAP to a rapalog-inducible minus end directed motor. Overall, this paper contains abundant data that are well presented and the experiments are well controlled and documented. I think this study is important as we can learn a lot about how the centrosome functions by divorcing its many parts and asking what they can do on their own. That said, this paper is a tour de force or what cells can do rather than what they actually do. The authors should consider putting these findings into the context of what they teach us about the normal processes they represent.

We fully agree that we should have explained better what our findings teach us about the normal processes of MTOC assembly and microtubule organization. In the revised version of the paper, we outlined more clearly that the purpose of our experiments was to understand how two centriole-independent PCM properties – the propensity to self-associate (and potentially even form condensates, as proposed by the Hyman and other labs for mitotic cells) and dynein-dependent transport can contribute to the formation of an interphase MTOC. We also studied how the presence of CAMSAP-stabilized non-centrosomal microtubules affects PCM self-assembly, because such an effect is relevant for understanding how PCM is organized in differentiated cells, where noncentrosomal pathways of microtubule minus-end stabilization play a major role. Finally, we investigated whether minus-end-directed transport of stable microtubule minus-ends is by itself sufficient to form a single compact MTOC. In the revised version of the paper, we used simulations (new Figure 8) to conceptualise our experimental findings and showed that they could be recapitulated using simple assumptions, highlighting basic principles for self-organization.

In brief, we found that PCM clustering and dynein-mediated transport, acting together, can indeed generate a single compact acentriolar MTOC, and that this process requires a subset of PCM components, including pericentrin, ninein and CDK5RAP2, but not CEP192 or NEDD1 (Figures 1 and 2). These PCM components have different functions: pericentrin is required for PCM clustering and dynein transport, CDK5RAP2 for efficient γ-TURC mediated microtubule nucleation, and ninein likely contributes to PCM clustering and as well as microtubule minus-end anchoring but has a weaker impact on nucleation (Figure 5). PCM clustering increases microtubule density (Figure 3), suggesting that for efficient microtubule nucleation and minus-end stabilization, the proximity of multiple PCM components is an advantage. Strikingly, a similar set of PCM components is also present in noncentrosomal, PCM-based MTOCs such as those found in the nuclear envelope and the Golgi membranes in muscle cells where they are targeted by AKAP450 (Vergarajauregui et al., eLife 2020).

Importantly, although we did observe pericentrin-driven PCM clustering in our system, we saw no evidence that interphase PCM is subject to phase separation, because PCM clusters show no evidence of rapid protein exchange with the cytoplasmic pool (Figure 1H,I). Moreover, PCM clusters are sensitive to microtubule organization, and their formation is disrupted by the presence of CAMSAPstabilized minus-ends (Figures 4 and 6A-C, Videos 4-6). This again helps to explain how interphase PCM can be organized in differentiating cells when centrosome function is attenuated and CAMSAPstabilized minus ends become more abundant. In cells with inactivated centrosomes, targeting of CAMSAP-stabilized minus ends to a certain structure may be sufficient to organise the PCM, which in turn can strengthen MTOC properties of this structure. This notion matches the data on noncentrosomal microtubule organization at the Golgi membranes and the apical surface of epithelial cells, where CAMSAPs and PCM components cooperate.

When CAMSAP-stabilized minus ends were brought together by a minus-end-directed motor, a single compact MTOC containing PCM could form (Figure 6E,F). This result might seem trivial, but interestingly and in line with our newly added simulations, minus-end-directed transport of microtubule minus ends in the absence of attractive interactions between these ends is insufficient to form a compact MTOC (Figures 7 and 8). The presence of pericentrin (a factor inducing PCM clustering) is needed for the formation of a compact MTOC, as shown both by our experiments and simulations (Figures 7 and 8). Compact MTOC formation in the absence of centrioles thus requires that all components are subject to minus-end-directed transport. This underscores an important function of centrioles in bringing together proteins, some of which do and some of which don’t associate with dynein in order to generate a highly focused radial microtubule system, such as the one observed in immune cells.

All these points are now outlined better in the revised manuscript.

1. As mentioned above, I think some of the main findings of this paper are too buried and can be easily missed. Conceptually, it is exciting to have a platform to test how PCM can organize microtubules divorced from the normal complication of studying the centrosome. There are many helpful cartoons that help the reader interpret the figures and similar attention should be paid to explaining the rationale behind each experiment, again considering what they might teach us about the way in which PCM is able to normally function.

We agree with this comment, and in the revised version of the paper, we have explained better the rationale of each experiment.

2. I find the vocabulary in the paper off putting. First, the word centrosome should mean something, i.e. centrioles surrounded by PCM, or at least as a centriole bearing entity. This word has been often misused in the literature, for example in the early mouse embryo where cells do not have centrioles, yet the MTOCs in these cells have been mislabeled as centrosomes. Centriole-less or acentriolar PCM would be a better term as has been recently used in other systems (Garbrecht et al., 2021; Magescas et al., 2021). Although it is true that PCM is not the perfect term either as pericentriolar should be in relation to a centriole, this term on its own has meaning as evidenced by its usage throughout the text. Second, the term acentrosomal MTOC (aMTOC) or noncentrosomal MTOC (ncMTOC) has often been used to describe MTOCs that are not derived from the centrosome such as in many types of differentiated cells. Thus, the use of this term to describe the PCM here is similarly problematic. I recommended referring to this structure as PCM for consistency.

We fully agree that proper terminology and following the conventions in the field is important, though different researchers often use (and feel comfortable with) different terms. As correctly pointed out by the reviewer, acentriolar microtubule-organising structures have been called centrosomes previously. For example, the reviewer suggests that we should adopt the nomenclature from the recently published paper by Garbrecht et al., 2021, but the Galbrecht paper is in fact entitled “An acentriolar centrosome at the C. elegans ciliary base”, and the terms “acentriolar centrosome” and “acentriolar PCM” are used equally frequently in this manuscript, while the term “centriole-less PCM” is not used at all. This latter term was indeed introduced by Magescas et al. 2021, but we note that the terminology is not even uniform in the two manuscripts describing exactly the same acentriolar structure in C. elegans, making the choice difficult. Moreover, in our manuscript, we also describe CAMSAP2-dependent microtubule organization, and CAMSAP2 is not a PCM component.

So how should we call the structure we study? Importantly, in the previous version of the manuscript, we did not call this structure “acentriolar centrosome”, though we did discuss the process we investigated as “centrosome assembly in acentriolar cells”. Taking into account the opinions of Reviewers #1 and #2, who think that the term “centrosome” must be reserved for a centriole-bearing entity, we changed the title of the paper to “Self-assembly of pericentriolar material in interphase cells lacking centrioles”. Further, we think that the term acentriolar MTOC, which was very consistently used throughout the paper, accurately described the structure we are studying, because it is located centrally, lacks centrioles and potently organizes microtubules. However, we fully agree that this term can be confused with the term acentrosomal MTOC, which has been used to denote a variety of microtubule-nucleating and anchoring structures, containing or lacking PCM and located either centrally or peripherally. In the revised version of the manuscript, we, therefore, switched to the term compact acentriolar MTOC (caMTOC), because it is distinct from the previously used terms aMTOC and ncMTOC. This term identifies the major features of the studied structure – compact organization, absence of centrioles, the ability to organize a focused microtubule array, and most importantly, it can be applied to both a PCM-based structure and the structure induced by the minus-end-directed transport of CAMSAP2-decorated minus ends, even though CAMSAP2 is not a PCM component.

3. A key interesting point in this paper is that CAMSAP antagonizes radial MTOC formation by pericentrin. I wish this concept had been further explored either experimentally or conceptually. The authors allude to its significance in the discussion, but instead focus on how PCM might be "redeployed" in differentiated cells, which does not seem to be the normal mode of microtubule organization seen in organismal studies. Instead, non-centrosomal proteins like CAMSAP and other proteins function in building these decentralized arrays concomitant with the centrosome attenuating its microtubule organizing capacity.

In the revised version of the paper, we have explored how CAMSAP antagonizes radial MTOC formation by pericentrin in much more detail by adding new imaging data (new Video 4) and simulations (new Figure 8). We show that CAMSAP-decorated microtubule minus ends in AKAP450knockout cells are distributed throughout the cytoplasm and display very limited mobility on the timescale of hours (new Video 4), and our simulations demonstrate that random distribution of microtubules with immobile stable minus ends is sufficient to perturb self-assembly of PCM into a single MTOC (new Figure 8). We also describe better the significance of our findings for understanding MTOC organization in differentiated cells, where the same PCM components as identified in our work (pericentrin, CDK5RAP2, γ-tubulin and ninein) are used to nucleate and anchor microtubules at locations other than the centrosome.

4. Figure 7 is a nice experiment as it indicates that, although not surprising, driving microtubule minus ends together creates a radial microtubule array, albeit as a ring-like structure rather than a single condensed point. Addition of pericentrin helps to focus the microtubule array into a condensed point. Under other conditions presented elsewhere in the paper, pericentrin is not able to counteract the presence of CAMSAP2 which drives microtubules into a decentralized array. Thus, this experiment suggests that pericentrin and the focusing of minus ends have additive effects. A more appropriate control for this experiment however is to show the results with and without the addition of Rapalog to control for any effects of the FKBP, FRB, or Rapalog itself.

We agree that this is indeed an important point, which we have now strengthened by simulations in Cytosim that show that minus-end-directed transport of microtubule minus ends that do not associate with each other generates a ring rather than a compact MTOC (new Figure 8). Our simulations further showed that the addition of self-associating PCM in the system induces MTOC compaction.

We note that controls with and without rapalog were already included in the manuscript (see the original Figure 6F top panel and Figure 7A top panel), and we have now added additional images showing single and double transfections with the used FKBP and FRB fusions in AKAP450/CAMSAP2/p53/PCNT KO cells before and after rapalog treatment (new Figure 7 —figure supplement 1).

5. The microtubule orientations shown in Figure 4G and Figure 4S1B are nice, however, it is important to show the associated motor localization from which the psuedocoloring was drawn.

These images are now included in the Figure 4 —figure supplement 1B.

6. I am confused by the timeline of siRNA treatments in Figure 5 and associated supplement relative to nocodazole treatment and washout. The text and cartoons depict that siRNA knockdown of targets was achieved just prior to nocodazole washout, which seems unlikely. Can you please clarify the timeline. In addition, the phenotypes siRNA depletions during steady state and after nocodazole washout are not the same. Is this true or just the images shown? Is this teaching us something interesting about establishment vs. maintenance of PCM structures?

The timeline is now indicated more clearly in Figure 5B. Please note that nocodazole treatments and washouts are much shorter than siRNA treatment. Furthermore, it is indeed entirely correct that the morphology of the acentriolar MTOC before and after nocodazole treatment is not the same, because the very compact cylindrical structure forms slowly. We have now investigated this point in more detail by using cold treatment (new Figure 4I and Figure 4 —figure supplement 1C) and found that the cylindrical structure is formed by association with microtubules: cold treatment revealed the presence of short cold-stable microtubules associated with the cylindrical MTOC and partial deformation of the PCM cluster upon complete microtubule disassembly (Figure 4I and Figure 4 —figure supplement 1C).

Reviewer #2 (Recommendations for the authors):

Centrosomes are the major microtubule-organizing centers that support formation of a radial microtubule array in interphase and catalyse spindle formation in mitosis. Centrosomes are built on a centriolar core that accumulates a stable matrix called the pericentriolar material; microtubule-nucleating and anchoring activities are concentrated in the pericentriolar material. When centrioles are removed from interphase cells, weaker MTOCs form at the Golgi in an AKAP450-dependent manner. In this manuscript, Chen et al. characterize MTOCs in the absence of centrioles and AKAP450; they build on prior work they had published showing that, while microtubules are disorganized in the absence of centrioles and AKAP450, additional inactivation of the microtubule minus end-binding protein CAMSAP2 leads to the spatial concentration of centrosomal components like pericentrin and CDK5RAP2, which leads to the formation of a more organized microtubule network. They refer to these structures as "centriole-independent centrosomes" and provide evidence for dynein-mediated transport as being important for their formation. Some of the statements, including in the title and abstract, are not well justified. Overall, the work does not yet lead to a significant advance in understanding of how interphase microtubule arrays are organized.

We agree with the reviewer that the conceptual contribution of our findings was not explained sufficiently well. We have rectified this in the revised manuscript both by improving the writing, and by including a set of simulations to conceptualise different properties of PCM and microtubule organization that either lead to or preclude PCM self-assembly into an MTOC.

To put it shortly, our paper provides new insights into the centriole-independent properties of PCM and the effect of non-centrosomal microtubules on PCM self-assembly.

1. We showed that clustering of PCM components and their dynein-mediated transport are both necessary and sufficient to form a single compact MTOC, provided that no other MTOCs or stable microtubules are present in the cell. This indicates that centrioles are not essential for generating a single compact interphase MTOC, but make PCM assembly more robust, resistant to microtubule perturbations and reorganization and keep PCM together by preventing its motility on minus-end-out microtubules.

2. We identified PCM components which are collectively able to generate a centrioleindependent MTOC, characterised their functions and showed that PCM clustering increases microtubule density.

3. We showed that the presence of a stable randomly organized microtubule network stabilized by CAMSAP is sufficient to disrupt PCM self-organization. This means that interphase PCM self-association and transport are tuned in a way that prevents strong PCM condensation. This finding is relevant and timely in light of recent work showing that mitotic centrosomes may form by phase separation of PCM components and is also important for understanding PCM organization in differentiating cells, where centrosomes are inactivated.

4. We showed that transport of CAMSAP-stabilized microtubule minus ends with a minus-enddirected motor is by itself not sufficient to form a compact MTOC, but cooperation with selfclustering PCM can help to achieve compaction, providing clues on how CAMSAP and PCM work together to organize different non-centrosomal MTOCs.

There are a number of issues with this manuscript that make it difficult to provide a clear set of recommendations. The term "centriole-independent centrosomes" is incorrect. Centrosomes are defined structures, which contain a pair of centrioles that promoted localized assembly of a stable pericentriolar material matrix independently of microtubules. This stable structure, in turn, serves as the nucleation site for microtubules. This is clearly not the case for the accumulations described which form by dynein-driven forces bringing components together and are microtubule-dependent. More troubling is that the manuscript lacks a clear purpose. Why do the authors study a highly artificial system in which PLK4 is inhibited and AKAP450 and CAMPSAP2 are knocked out? What is the relevance of this state? The fact that they can tether dynein to CAMSAP2 and drive organization is unsurprising, given the significant prior work (starting from Verde et al. 1991) on dynein-based organization of microtubules into organized arrays. There are potentially interesting mechanistic questions like how CAMSAP2 competes with potential dynein recruiters in the pericentriolar material but no analysis of depth is conducted here. Overall, the effort comes across as not being sufficiently clearly motivated with a direct line of experimentation that has yielded new insights into microtubule organization in interphase cells.

Our paper was indeed entitled “Centriole-independent centrosome assembly in interphase mammalian cells”, putting the emphasis on the centriole-independent pathways of centrosome assembly studied in this manuscript, but the structure that we studied was indeed consistently termed throughout the whole paper an “acentriolar MTOC” and not a “centrioleindependent centrosome”. As discussed in the response to Reviewer #1, opinions differ on whether or not a compact acentriolar MTOC can be called a centrosome. For example, a recent Current Biology paper by Galbrecht et al. (2021) describing an acentriolar MTOC was entitled “An acentriolar centrosome at the C. elegans ciliary base”. Furthermore, even if centrosomes are defined as strictly centriole-dependent structures, different pathways of their assembly may be both centrioledependent and independent (e.g. through phase separation of PCM, as proposed for mitotic centrosomes by the Hyman lab, Woodruff et al., Cell 2017). However, as discussed above, we respect the opinions of Reviewers #1 and #2 and changed the title of the paper to “Self-assembly of pericentriolar material in interphase cells lacking centrioles” and also renamed acentriolar MTOC into compact acentriolar MTOC (caMTOC) to avoid confusion with the term “acentrosomal MTOC”.

Notably, this reviewer also suggests that the term MTOC may be inappropriate to describe the structure we study, because it falls apart during nocodazole treatment and is thus sensitive to the state of the microtubule array, and only a microtubule-independent structure should be called an MTOC. We respectfully disagree for the following reasons:

1. Some MTOCs are microtubule-sensitive, with the Golgi apparatus providing the best-studied example of a microtubule- and dynein-dependent structure which nucleates and anchors microtubules. The Golgi complex forms an MTOC which tethers ~50% of all microtubules in wild type RPE1 cells at steady state, when cells are not treated with any inhibitors (Efimov et al., Dev Cell 2007).

The importance of this MTOC is supported by a large body of work in different systems. Yet the Golgi rapidly falls apart if cells are treated with nocodazole at 37°C.

2. In the revised version of the paper, we show that the compact acentriolar MTOC that we study is not fully dependent on microtubules – if microtubules are disassembled at 4°C, the PCM cluster stays together, even at 37°C (new Figure 4I) and can nucleate microtubules similar to the centrosome, when nocodazole is removed (new Figure 5 —figure supplement 1A). As explained in the paper, the fact that caMTOC is driven apart when cells are treated with nocodazole at 37°C, is due to the fact that the cells pass through a transient stage where the more stable minus-end-out microtubules become relatively abundant and serve as rails for dynein-mediated PCM transport out of the cluster. These experiments teach us two important things: 1/ the interactions between a subset of PCM components can keep them together, so that they form a compact microtubule-nucleating and anchoring structures even in the absence of a centriole template or microtubules; 2/ these interactions are not sufficiently tight to prevent motor-dependent movement of PCM clusters, and this makes PCM sensitive to the position of stable minus ends. This property is likely important for understanding how PCM components are organized in differentiated cells lacking centrosomes.

To conclude, we think that the term compact acentriolar MTOC (caMTOC), is appropriate, because it is distinct from the previously used terms aMTOC or ncMTOC. This term identifies the major features of the studied structure – compact organization, absence of centrioles and the capacity organized a focused microtubule array, and it can be applied to both a PCM-based structure and the structure induced by the minus-end-directed transport of CAMSAP2-decorated minus ends, even though CAMSAP2 is not a PCM component.

Purpose of this study and novel conclusions: our goal was to explore the mechanisms driving selfassembly of PCM into MTOCs. Three fundamentally different processes play a role in centrosome formation: 1/specific binding of PCM to the centriole wall; 2/self-association of PCM components or even their phase separation, whereby centrioles may serve as catalysts of condensate formation; 3/dynein-mediated transport of PCM and microtubule minus ends. In principle, one could argue that each of these processes alone or their combinations could be sufficient to form an MTOC, and in this paper, we generate a system where we can study the contributions and molecular components of the second and the third process in detail, independently of the first one. To achieve this, we inhibit PLK4 to remove centrioles, and we also remove AKAP450 so that we can study MTOC formation independently of PCM binding to Golgi membranes, to simplify the analysis. In this system, we then remove additional factors to study their impact on PCM self-assembly. We show that the presence of CAMSAP2-stabilized microtubules is sufficient to disrupt PCM self-assembly, and in the revised version of the paper, we analyse this phenomenon in more detail. Our simulations show that an immobile randomly organized microtubule network is sufficient to perturb PCM clustering due to the dyneindriven cluster motility, and one does not need to invoke any assumptions on specific competition of CAMSAP2 with dynein recruiters to explain the observed phenotype. Furthermore, the results on minus-end-directed motor tethering to CAMSAP2-stabilized minus ends are less trivial than one could have thought – both experiments and simulations show that by itself, such tethering is insufficient for microtubule minus-end focusing, which requires cooperation with self-clustering microtubule nucleators. The synergy between CAMSAPs and PCM components is a recurring theme in organizing microtubule arrays in differentiated cells, and our findings help to understand its basis. All these aspects of our work are motivated and emphasized better in the revised manuscript.

Reviewer #3 (Recommendations for the authors):

In this manuscript, Chen et al. investigated the prospect of forming a microtubule organizing centre (MTOC) without centrioles, the catalysts of centrosomal MTOC formation. Upon disabling non-centrosomal MTOC pathways and depleting centrioles from cells, the authors describe the formation of a single centrally located acentrosomal MTOC (aMTOC). The authors go on to describe that the self-assembly of this structure is primarily driven by dynein driven aggregation of pericentriolar material (PCM). Particularly, the authors describe key PCM components that are necessary in sustaining the aMTOC that include: PCNT, γ-tubulin, CDK5RAP2 and ninein. The findings of this paper could yield further insight into the PCM redistribution and microtubule reorganization that occurs in differentiated cells such as myotubes and neurons, where centrosomal MTOCs are abandoned for more efficient non-centrosomal microtubule arrays.

The conclusions of this paper are well supported by the data. These include elaborate but well controlled experiments using complexly engineered cellular models, treatment schemes, and convincing imaging. However, some aspects of the manuscript could be clarified and extended to provide more robust support for their findings.

1. The authors clearly showed the process of PCM assembly in AKAP450/CAMSAP2-deleted acentriolar cells and their characteristics. However, it was not clear whether microtubules were nucleated from the assembled PCM in Figure 5B-E as microtubules could be nucleated in the cytoplasm. Can the authors perform microtubule regrowth assays using two different conditions (cold treatment and nocodazole) and stain them with antibodies against a-Tub and PCNT?

This is an excellent suggestion which we have followed. Using cold treatment with and without nocodazole, we show that the PCM cluster indeed represents the major microtubulenucleating structure in the cell (new Figure 5 —figure supplement 1A). We note that at early stages of microtubule re-growth, EB1 is a better marker of nascent microtubules than α-tubulin, because EB1 stains all microtubules but does not associate with the pool of free tubulin, abundant in such cells.

2. The authors suggest that an aMTOC can be formed in the absence of microtubule nucleation activity from the centrosome and Golgi apparatus. Can the authors show the same phenotypes in the presence of both Centrinone B and Brefeldin A (to disassemble the Golgi apparatus) in wild type hTERT-RPE1 and CAMSAP2 KO cell lines?

We have generated the requested data (new Figure 1 —figure supplement 1C-F). Interestingly, the disruption of the Golgi apparatus with Brefeldin A combined with centrinone B treatment could indeed trigger formation of compact acentriolar MTOCs in CAMSAP2 KO but not in wild type cells. The efficiency of this process was much lower than in AKAP450/CAMSAP2 KO cells, possibly because the treatment was relatively short (2 hr, due to the toxicity of Brefeldin A), whereas compact acentriolar MTOCs form slowly. Furthermore, AKAP450 attached to the remnants of the Golgi matrix might still perturb PCM organization and compaction in Brefeldin A-treated cells.

3. Removal of centrosomes typically triggers a p53-dependent G1 arrest however the authors acknowledge this and seem to have created RPE-1 models with AKAP450, CAMPSAP2, and p53 KO (Line 156-160, Figure 2E). While this paper is focused on aMTOC formation in interphase, I am curious as to whether the cells are still cycling after the cylindrical aMTOC formation. If so, what does progression through mitosis look like?

To address the reviewer’s suggestion, we have added a new Supplemental figure (Figure 2 —figure supplement 3 of the revised paper), where we show that AKAP450, CAMPSAP2 and p53 KO cells indeed divide also in presence of centrinone B and form bipolar spindles with unfocused poles. The duration of mitosis is variable, which is not surprising given previous extensive work showing that the presence of centrosomes makes cell division more robust. The fact that these cells keep cycling in spite of prolongated mitosis is also not surprising as it fits with previous work showing that elimination of p53 prevents cell cycle arrest in such conditions. Furthermore, we note that our paper is focused on interphase cells, and a more detailed analysis of mitosis and its abnormalities in the knockout cell models we have generated goes beyond its scope.

4. The authors describe an interesting phenomenon in this manuscript; however, the significance of this finding is not well emphasized in the abstract or in the ending paragraph of the introduction. In the last paragraph of the discussion the authors describe the implications of their findings in the re-organization of microtubules during cellular differentiation, however it would have been nice to delve deeper into how their findings could apply to these different models besides the identification of the redistribution of common players. Though they do concede that much is left to be discovered when it comes to these differentiated models.

We fully agree that the significance of our findings should have been explained better, and we have done our best to rectify this in the revised manuscript, as is also explained in responses to the comments of Reviewers #1 and #2.

[Editors’ note: what follows is the authors’ response to the second round of review.]

Essential revisions:

1) Considering the highly manipulated model used by the authors to analyse PCM protein assembly and function, the introduction and discussion could be further improved to contextualise this work and explain its relevance in a more physiological setting. e.g during differentiation and in different cell types. See also comments by reviewer 2 and 3 for further details.

We have edited the Introduction and Discussion to better contextualize our work and explain its physiological relevance. The changes are highlighted in blue.

We further note that conceptually, there is no fundamental difference between knocking out one, two, three or more proteins to explore cellular mechanisms (all these experiments can be regarded as artificial, and the impact of different knockouts depends on the relative importance of the studied proteins). Generation of multiple simultaneous gene knockouts is an important way to move forward in characterizing molecular mechanisms in the mammalian system, which, unlike flies or worms, is hallmarked by a high degree of functional redundancy.

2) The impact of the simulations should be improved. As conducted/presented they merely confirm the already existing experimental data. However, the full potential of simulations, e.g. testing different outcomes by varying parameters and manipulating the system in different ways, has not been explored.

– What is the rationale behind the choice of the parameters defined by the authors (e.g. absolute and relative number of MTs, number of PCM complexes, number of MTs nucleated per PCM complex?)

– What is the range of these parameters that result in the outcome observed in cells?

We have improved the impact of simulations by adding experimental data confirming two specific predictions of the modelling: that increasing PCM-self-interaction will allow PCM protein clustering even in the presence of CAMSAP2-stabilized microtubule minus ends, and that biases in the distribution of CAMSAP2-stabilized minus ends can lead to biases in the distribution of PCM complexes. These data are included in the new Figure 8 —figure supplement 2.

Furthermore, in the previous version of the manuscript, we have made choices based on our estimates of these parameters. In the revised version of the manuscript, we have systematically varied the number of components in the system, explored their effect on microtubule and PCM organisation and obtained parameter ranges which match those observed in cells. These new simulations are shown in Figure 8 —figure supplement 1 and discussed in the Results and Discussion. We also improved the Supplemental Table S1, where the other parameters used for simulations are listed and explained.

– What happens if the orientation of CAMSAP-bound MTs would be biased (as may be the case during cell differentiation).

We have included a new set of simulations (Figure 8 —figure supplement 1E), where we biased the distribution of stabilized and immotile microtubule minus ends to the cell margin and showed that this causes a similar bias in the distribution of PCM complexes. This mimics the situation found in epithelial cells, where CAMSAP/Patronin-stabilized microtubule minus ends are attached to the cell cortex (for example, due to binding to the cortical actin cytoskeleton through spectraplakin), and where γ-TuRC-containing protein complexes are also located at the cortex through mechanisms that are not yet clear. Furthermore, we added a new Supplemental Figure (Figure 8 —figure supplement 2B) where we show images of AKAP450 knockout cells expressing mCherry-CAMSAP2, where we observed that a partially biased distribution of CAMSAP2-stabilized minus-ends triggers a bias in the distribution of the PCM component pericentrin.

Exploring these types of questions will add significant additional value to the simulations that go beyond what is already demonstrated in cells.

We fully agree, and we hope that the reviewers find the new additions useful.

Reviewer #2 (Recommendations for the authors):

Here Chen et al. submit a revised manuscript characterizing the self-assembly of PCM in cells that lack centrosomes, Golgi derived microtubules, and CAMSAP. As in the original version of the paper, the data are abundant, but well-presented and rigorously executed. While my concerns still remain about the relevance of these studies in this highly manipulated in vitro cell context to what cells do in vivo, the new data and framing do alleviate some of my original concerns. In particular, the authors underscored the importance of the fact that the structure that forms when CAMSAP microtubules are driven together with dynein can be refined by the presence of PCNT1. This point was bolstered in the text and through the addition of a computer simulation. While the ability of motors to generate a radial array of microtubules on their own has been previously demonstrated in many contexts over several decades, the demonstration that PCM can refine this interaction does extend this concept. The question does still remain whether this highly manipulated cell culture system with a nuanced relationship to in vivo contexts will appeal to the broad readership of eLife.

1) While I like the new simulations added as Figure 8 to experimentally bolster the point that CAMSAP microtubules steal PCM from the caMTOC, I do wish there was a way to directly test the sufficiency of PCM cohesion to counteract this process (i.e. experimentally reinforcing the point in Figure 8D) perhaps with phosphomimetics or other mutations that might create more stable interactions between PCM molecules. Such mutations might not be available and so could be beyond the scope of this work.

To experimentally bolster the point that the dispersion of PCM proteins in CAMSAP2-expressing cells can be counteracted by their increased self-association, we have now attached inducible homodimerization FKBP domains to pericentrin. We showed that this fusion protein is dispersed in AKAP450 knockout cells treated with centrinone but forms a cluster upon the addition of the compound triggering FKBP homodimerization, while CAMSAP2-stabilized microtubule minus ends remain dispersed (new Figure 8 —figure supplement 2A). This outcome was predicted by our modelling.

2) I still take issue with the way in which these findings are related to actual in vivo contexts, especially their relation to how MTOCs might form in differentiated cells. There are really three ways in which this work might relate to MTOCs in differentiated cells:

1) It might have a direct bearing on structures that are clearly derived from PCM in vivo: These include at the base of cilia in C. elegans sensory neurons or PCM packets (C. elegans, Magescas et al., 2019), flares (Drosophila, Megraw, 2002), or fragments (Rusan and Wadsworth, 2005) seen at mitotic exit, some of which persist in interphase.

2) This work might relate to non-centrosomal/acentrosomal MTOCs seen in interphase cells, but there is little evidence that any of these structures are directly derived from PCM rather than sharing the same components as the PCM in some cases. Here we get into a bit of semantics again, but I think PCM should mean material surrounding centrioles (or that used to surround centrioles as is the case in the cases cited above). If PCM proteins assemble in different locations in the cell, these structures would no longer be called PCM. I like the idea that the CAMSAP-associated (or other non-centrosomal) microtubules could deploy a tug of war with the PCM proteins, but this idea is currently just speculative and should be deemed as such. If the non-centrosomal microtubules had a way to be biased asymmetrically (as in differentiated cells), the clustering of these PCM proteins would create a way to make positive feedback to further reinforce a non-centrosomal network.

3) This work might also give a mechanism for the way in which PCM is stripped from the centrosome following mitosis, a common occurrence across cell types and organisms upon mitotic exit. In this case, non-centrosomal microtubules could strip PCM from the centrosome once the matrix was crippled following the inactivation of mitotic kinases.

Some of these ideas are explored in the Discussion, but the Introduction is still imprecise in discussion of the potential relationship of the work to actual in vivo contexts. I would encourage the authors to be more precise with the potential implications in the Introduction and explore some of these concepts further in the Discussion.

For example:

Line 57-58: "These properties…are relevant because in most differentiated cell types…PCM forms acentrosomal MTOCs.": Please see point 2 above, but there is little evidence that PCM (meaning structures that derive from the centrosome) rather than PCM proteins forms acentrosomal MTOCs.

We understand the semantic point of the reviewer, though we note that there is no consensus in the literature on the terminology describing centrosomal and non-centrosomal MTOCs and their components. For example, in the previous round of review, Reviewer #1, who is an expert in the field, has encouraged us to term the acentriolar structure that we study “acentriolar PCM”. We made a different choice, for reasons outlined in our previous rebuttal, but this clearly illustrates that the perception of terminology in this field varies greatly even among the few experts directly involved in these studies. To address the point of this reviewer, we edited the paper in some places to indicate more clearly that we are investigating the behavior of “PCM components” or “PCM proteins” when discussing acentriolar structures. However, using the term “PCM” seems to be by far the most practical way of collectively describing the molecules studied in this paper, especially when they collectively form a compact MTOC. Importantly, in the revised version of the manuscript, we took care to avoid the use of the term “PCM” and instead talk about PCM components or proteins when discussing naturally occurring acentrosomal MTOCs (e.g. line 57).

Furthermore, we have included more explicitly in the Introduction (lines 58-63) and Discussion (lines 648-654 and 680-685) the points and the papers suggested by the reviewer to better relate our results to the physiological situation. We further note that we did show in the paper that PCM components follow CAMSAP organisation (when CAMSAP-decorated ends are fully dispersed, PCM proteins are dispersed, and when minus ends are clustered, PCM proteins accumulate at the same site). In the revised version of the paper, we extended this conclusion by showing that in simulations, peripheral enrichment of stable minus ends relocalizes PCM proteins to the cell margin (Figure 8 —figure supplement 1E). Furthermore, we included experimental data showing that biases in the distribution of CAMSAP-stabilized microtubule minus ends cause a similar bias in the distribution of pericentrin clusters (new Figure 8 —figure supplement 2B).

Reviewer #3 (Recommendations for the authors):

In this revised manuscript Chen et al. investigate the mechanisms underlying the self-assembly of PCM proteins into tight clusters that are able to function as MTOCs in interphase RPE1 cells, under conditions where these cells lack their two main MTOCs at centrioles and at the Golgi. These conditions, achieved by centrinone treatment to eliminate centrioles, and AKAP450 KO to eliminate the Golgi-associated MTOC, allow to study the MTOC formation properties of various proteins without interference by centrosomal or Golgi-associated MTOC activity. In this revised version the authors complemented an already comprehensive set of data with modelling studies, computationally confirming the observations made in cell-based experiments. Also, the relevance of the observations, made in an artificial situation of complete absence of centrosomal and Golgi MTOCs, has been addressed in the text.

Overall the study contains an impressive amount of information and useful insight regarding the ability of PCM proteins to self-assemble into structures that provide microtubule nucleation and anchoring sites, to control the shape of the cellular microtubule network. Some of this information would be difficult to obtain in the presence of the dominant centrosome and Golgi-associated microtubule arrays. On the other hand, an important criticism refers to the fact that the experimental setup is based on a highly artificial situation, raising concerns about how relevant the observations are in a physiological setting. Although improved, the additional data and rewriting still does not fully address this point. Also, limitations in the computational simulations need to be addressed.

Specific points:

1) Pericentrin and CDK5RAP2, which both are important for caMTOC formation and function, are not very important for the interphase centrosomal MTOC. This has been shown by Gavilan et al., 2018 using multiple knockout approaches similar to those in the current manuscript. This suggests that despite the importance of pericentrin and CDK5RAP2 for caMTOCs, they are not very important at the major physiological MTOC that they localize to, at least during interphase. This should be discussed and the above study cited (it was cited but not in this context). Do the authors envision a specific scenario in which their findings would help understanding MTOC assembly?

We have improved the discussion of this point and cited the Gavilan et al., 2018 study in this context (lines 624-626 of the revised paper). Our work adds to a body of data indicating that the centrosome, one type of a physiological MTOC in interphase cells, relies for its function on several redundant pathways, one which depends on pericentrin and CDK5RAP2 and another one on CEP192 and NEDD1. The second pathway is highly important in mitosis (and therefore hard to eliminate using constitutive knockouts) but does not seem to contribute much to centriole-independent microtubule organization in any interphase setting. Precise measurements of the relative importance of the two pathways at interphase centrosomes requires inducible knockout studies combined with quantification of steady-state centrosomal microtubules, centrosome-dependent microtubule nucleation and anchoring, preferably in the absence of the confounding contribution of the AKAP450 and CAMSAP-dependent pathways (which generate a major part of microtubule density). Such experiments would extend the work by Gavilan et al., who performed counting of EB1 comets emerging from the centrosome with/without nocodazole treatment and would require extensive analyses that go beyond the scope of the current study.

2) The Gavilan et al. study above also described cytoplasmic clusters of PCM proteins that form in centriole-lacking AKAP450 KO cells and that nucleate and organize microtubules (referred to a cytoplasmic or 'cMTOCs'). They probe these with a panel of antibodies and reveal their composition including the absence of CEP192 (similar to the analysis of caMTOCs in the current manuscript). This should be discussed/cited.

We now cite these findings in the Discussion (lines 613-615).

3) I wonder why the authors refer to some of the caMTOCs as 'cylindrical' – what is the evidence for this? It would imply some kind of geometry, but to my eye they rather appear to be clusters arranged in a roughly linear fashion. In the absence of data supporting a cylindrical shape, I would suggest changing this description.

This is an interesting semantic point. Initially, we also called caMTOCs “linear”, but then a biophysicist pointed out to us that since these structures exist in a three-dimensional cellular space, one should assume that they represent a cylinder rather than a line (i.e. a one- or two-dimensional structure).

4) As presented, the computer simulation data does not add much to the manuscript. It mainly confirms the experimental data, so what is the point of it, if one already has cell-based experimental evidence? It would be useful to make predictions and then design experiments in cells to test these, but this has not been done. At the very least, it would be useful to use the modelling to define parameter ranges within which the observed effects are true. This would give the modelling more meaning, since this may not be feasible to do in cells. For example, the authors make several assumptions regarding specific parameters included in the simulations such as the numbers of microtubules, PCM complexes, and CAMSAP-kin14 complexes, but there is no indication of how they came up with these numbers – are they related in any way to estimates in cells? In some simulations this does not seem to be the case. For example, to model the effect of PCM cluster dispersion, the authors assume 300 randomly oriented, CAMSAP-associated MTs, and only 50 PCM clusters each with one associated MT, but in cells this ratio seems to be the opposite – only 25% of all MTs have minus-end-out orientation (not cluster-associated).

Also, what is the outcome of simulations, if any of these parameters were to be gradually increased or decreased? In the absence of such data, it seems as if the authors have picked numbers that produce the desired outcome observed in cells. Indeed, it may be informative to ask under what conditions this outcome is not observed.

The most important point of our simulations is to show that simple assumptions about the minus-end-directed motility and self-association of PCM components are sufficient to explain why a caMTOC forms in the absence, but not in presence of CAMSAP-stabilized microtubule minus ends. Another important conclusion is the demonstration that the same assumptions are sufficient to explain how PCM and CAMSAP-mediated minus end stabilization can cooperate during MTOC formation.

In the revised version of the paper, we have experimentally tested two specific predictions:

1. Our simulations predicted that if self-association of pericentrin would be increased, it would be able to form a single cluster in acentriolar AKAP450 knockout cells expressing CAMSAP2 (cells with dispersed stable minus ends). We have now tested this prediction by adding an inducible homodimerization domain to pericentrin and showed that in the presence, but not the absence of a homodimerizer compound, this modified pericentrin protein strongly clusters (new Figure 8 —figure supplement 2A), exactly as our simulations predict (Figure 8D).

2. We added a new set of simulations (new Figure 8 —figure supplement 1E), where we show that a bias in the distribution of stable microtubule minus ends leads to a similar bias in the distribution of PCM complexes. To test this prediction, we mildly overexpressed CAMSAP2 in acentriolar AKAP450 knockout cells. While endogenous CAMSAP2-stabilized minus ends in such cells are randomly distributed, CAMSAP2 overexpression causes partial minus end bundling in some cells, resulting in their enrichment in certain cell areas. We observed that also endogenous pericentrin clusters tend to be enriched in the same areas (Figure 8 —figure supplement 2B)

We agree that varying specific parameters of the simulations can be very informative because it allows exploring conditions that cannot be achieved in cells. We have therefore strongly extended the simulations and their description in Figure 8 —figure supplement 1 and the Results. We also added a paragraph to the Discussion.

From a systematic variation of the number of components in the simulations, we were able to draw very general conclusions about the self-organized structures that we observe. We see this behaviour in all of our additional experiments: if the number of components is large enough, the global organization emerges. For smaller numbers of components, the system becomes more noisy, components interact only sporadically and no global organization emerges. In conclusion, if the concentration or density of the components is not large enough, we are not able to recapitulate the experimental findings in our simulations. In general, all the interactions that we defined were reversible, and therefore the observed structures were dynamic. Therefore, the emergence of these dynamic structures not only depend on the specific interactions, but also on the dynamics and the crowdedness of the environment. Because of this non-trivial interplay of components, it is difficult to systematically relate all parameters to each other. Nonetheless with our additional systematic simulations, we identified conditions in which the experimentally observed structures can be recapitulated in our simulations. Specifically, we found that when two types of stabilized microtubule minus ends are present – minus ends attached to motile complexes that can self-associate and minus ends that cannot self-associate and can be either immobile or move on other microtubules, the ratio between different types of ends is important for the final outcome. This helps to understand how global patterns of microtubule organization can be controlled by relatively simple parameters, such as the abundance of CAMSAP proteins during cell differentiation or PCM self-clustering during mitotic onset.

Regarding the more general simulation input parameters in Table 1, the authors have included references only for some of them. In the text they state "The numerical values for the parameters of our simulations have been taken from literature or reasonably chosen otherwise". What does 'reasonably chosen otherwise' mean? I understand that there may not be a reference for every parameter, but in these cases there should be at least a brief explanation describing how it was chosen/estimated.

We have amended Supplemental Table S1 to better explain the choice of simulation parameters.

Line 605: as far as I know, NEDD1 is not a gTuRC activator.

We agree that this point remains unclear and amended the text to state that NEDD1 is a γ-TuRC binding protein (line 697 of the revised manuscript).

https://doi.org/10.7554/eLife.77892.sa2

Article and author information

Author details

  1. Fangrui Chen

    Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands
    Contribution
    Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Resources, Validation, Visualization, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2830-7760
  2. Jingchao Wu

    Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands
    Contribution
    Investigation, Methodology, Resources
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2958-3751
  3. Malina K Iwanski

    Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands
    Contribution
    Investigation, Methodology, Visualization, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4903-9796
  4. Daphne Jurriens

    Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands
    Contribution
    Investigation, Methodology
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5123-3099
  5. Arianna Sandron

    Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands
    Contribution
    Investigation, Resources
    Competing interests
    No competing interests declared
  6. Milena Pasolli

    Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6079-4808
  7. Gianmarco Puma

    Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  8. Jannes Z Kromhout

    Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands
    Contribution
    Investigation
    Competing interests
    No competing interests declared
  9. Chao Yang

    Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands
    Contribution
    Resources
    Competing interests
    No competing interests declared
  10. Wilco Nijenhuis

    1. Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands
    2. Center for Living Technologies, Eindhoven‐Wageningen‐Utrecht Alliance, Utrecht, Netherlands
    Contribution
    Resources, Writing - original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7095-0955
  11. Lukas C Kapitein

    1. Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands
    2. Center for Living Technologies, Eindhoven‐Wageningen‐Utrecht Alliance, Utrecht, Netherlands
    Contribution
    Supervision, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9418-6739
  12. Florian Berger

    Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands
    Contribution
    Investigation, Visualization, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3355-4336
  13. Anna Akhmanova

    Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Utrecht, Netherlands
    Contribution
    Conceptualization, Funding acquisition, Project administration, Supervision, Writing - original draft, Writing - review and editing
    For correspondence
    a.akhmanova@uu.nl
    Competing interests
    Senior editor, eLife
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9048-8614

Funding

China Scholarship Council (Scholraship)

  • Fangrui Chen

China Scholarship Council (Scholarship)

  • Jingchao Wu

China Scholarship Council (Scholaship)

  • Chao Yang

Nederlandse Organisatie voor Wetenschappelijk Onderzoek (Spinoza prize)

  • Anna Akhmanova

European Research Council (Consolidator Grant 819219)

  • Lukas C Kapitein

Eindhoven-Wageningen-Utrecht Alliance (Support for Center for Living Technologies)

  • Lukas C Kapitein

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Lynne Cassimeris (Lehigh University, USA), Pierre Gönczy and Didier Trono (EPFL, Switzerland), Dr. Duane Compton (Geisel School of Medicine at Dartmouth, USA) and Dr. Laurence Pelletier (Lunenfeld-Tanenbaum Research Institute, Canada) for the gift of materials and Ilya Grigoriev and Eugene Katrukha (Biology Imaging Center, Utrecht University) for the help with imaging and image analysis. This work was supported by China Scholarship Council scholarships to Fangrui Chen, Jingchao Wu and Chao Yang, the Netherlands Organization for Scientific Research Spinoza prize to AA, as well as the European Research Council Consolidator Grant 819,219 to LCK and the Eindhoven-Wageningen-Utrecht Alliance (https://www.ewuu.nl) that supports the Center for Living Technologies.

Senior Editor

  1. Suzanne R Pfeffer, Stanford University School of Medicine, United States

Reviewing Editor

  1. Jens Lüders, Institute for Research in Biomedicine, Spain

Reviewer

  1. Laurence Pelletier, Lunenfeld-Tanenbaum Research Institute, Canada

Publication history

  1. Preprint posted: August 23, 2021 (view preprint)
  2. Received: February 14, 2022
  3. Accepted: July 4, 2022
  4. Accepted Manuscript published: July 5, 2022 (version 1)
  5. Version of Record published: July 18, 2022 (version 2)

Copyright

© 2022, Chen et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Fangrui Chen
  2. Jingchao Wu
  3. Malina K Iwanski
  4. Daphne Jurriens
  5. Arianna Sandron
  6. Milena Pasolli
  7. Gianmarco Puma
  8. Jannes Z Kromhout
  9. Chao Yang
  10. Wilco Nijenhuis
  11. Lukas C Kapitein
  12. Florian Berger
  13. Anna Akhmanova
(2022)
Self-assembly of pericentriolar material in interphase cells lacking centrioles
eLife 11:e77892.
https://doi.org/10.7554/eLife.77892
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