Because of their key role in tissue maintenance and the inherent risk associated with their unchecked proliferative capacity, stem cell behavior is tightly regulated (Klein and Simons, 2011; He et al., 2009). This regulation is often mediated by controlling the biochemical composition of the microenvironment surrounding the stem cells, including the presence of signaling molecules and metabolic cues (Jones and Wagers, 2008; Morrison and Spradling, 2008). Additionally, physical inputs, such as mechanical cues and the architecture and topography of the extracellular matrix, also play an important role in controlling stem cell behaviour (Chacón-Martínez et al., 2018; Ahmed and Ffrench-Constant, 2016; Díaz-Torres et al., 2021). The complex and precisely constructed in vivo microenvironment of stem cells can be very challenging to mimic in the laboratory and, when possible, it is best to study stem cells in their endogenous environment. There has been significant emphasis over the last decade on the development and optimization of imaging tools and methodologies to allow live imaging of stem cells in their in vivo environment (Park et al., 2016; Rompolas et al., 2012; Martin et al., 2018; Sheng and Matunis, 2011).

Hematopoiesis, the production of the cellular components of blood, is a well-known paradigm for stem cell regulation through a niche (Martinez-Agosto et al., 2007; Huang et al., 2007; Mandal et al., 2007; Tokusumi et al., 2010). Drosophila provides a powerful and genetically tractable model to study hematopoiesis (Banerjee et al., 2019). During fly hematopoiesis, a specialized population of blood progenitors gives rise to blood cells. Drosophila blood progenitors exhibit many stem-cell-like properties, such as being controlled by a specialized population of cells that act as a hematopoietic niche, the Posterior Signaling Centre (PSC)(Krzemień et al., 2007, Mandal et al., 2007; Tokusumi et al., 2010; Cho et al., 2020). They can give rise to three highly differentiated blood cell types: plasmatocytes, lamellocytes, and crystal cells (Jung et al., 2005). However, whether they are true stem cells has not been conclusively resolved (Banerjee et al., 2019). Vertebrate hematopoietic stem cells (HSCs) have been associated with several well-defined attributes: self-renewal, the ability to differentiate into all blood lineages, and their dependence on a niche (Huang et al., 2007). Drosophila blood progenitors have been shown to exhibit most of these criteria but so far, there has been no clear evidence of either self-renewal or asymmetric cell division in the progenitors (Banerjee et al., 2019). Nevertheless, previous studies provided evidence for the existence of HSCs in the lymph gland (LG) (Cho et al., 2020; Dey et al., 2016; Minakhina and Steward, 2010). For example, a population of cells in first instar larvae were identified, which gave rise to progenitors in later larval stages and behaved in ways consistent with the idea that they were equivalent to vertebrate HSCs (Dey et al., 2016). Another study employed MARCM-based lineage tracing technique to identify distinct sub-populations of progenitors within third instar larvae, one of which exhibited characteristics such as ‘persistence’ that were consistent with the HSC fate (Minakhina and Steward, 2010).

The main site of hematopoiesis in Drosophila larvae is the primary lobe of a specialized organ known as the LG (Banerjee et al., 2019). The primary lobe contains three distinct zones: the PSC niche, the medullary zone (MZ) which houses the blood progenitors, and the cortical zone (CZ) that contains differentiated blood cells (Banerjee et al., 2019; Mandal et al., 2007). The progenitors in the MZ express markers such as the JAK-STAT receptor Domeless (Dome), while differentiated blood cells express unique markers depending on their terminal mature blood cell fate. For example, plasmatocytes express the marker P1, while crystal cells express the marker Lozenge (Lz) (Banerjee et al., 2019; Jung et al., 2005). In addition, a small population of cells has been described that lack the expression of terminal differentiation markers such as P1 but express both the progenitor marker Domeless and early differentiating blood cell markers such as Hemolectin (Hml) and Peroxidasin (Pxn) (Blanco-Obregon et al., 2020). These P1^-, Dome⁺, and Pxn⁺/Hml⁺ cells were typically found in the boundary between the MZ and CZ and were proposed to represent a separate population of cells, commonly referred to as intermediate progenitors (Blanco-Obregon et al., 2020; Sinenko et al., 2009; Spratford et al., 2021; Girard et al., 2021). Although this population is currently not well characterised, it is thought to contain cells in a transitional state as they go from a relatively quiescent multipotent state to a terminally differentiated state (Krzemien et al., 2010; Banerjee et al., 2019).

More recent data has supported the view that rather than being a homogenous population, progenitors in the MZ are heterogeneous (Blanco-Obregon et al., 2020; Cho et al., 2020; Baldeosingh et al., 2018; Girard et al., 2021). Single-cell transcriptomic analysis of the LG revealed a surprising level of heterogeneity of the developing blood cells and uncovered novel blood cell types including adipohemocytes, stem-cell like blood progenitors, and intermediate progenitors (Cho et al., 2020). Moreover, a distinct subpopulation of progenitor cells in the LG has been recently identified and termed ‘distal progenitors’ (Blanco-Obregon et al., 2020). These cells, named after their location at the distal part of the MZ near the boundary with the CZ, express some progenitor markers (Dome) but not others (Tep4) (Blanco-Obregon et al., 2020). A further subpopulation of distal progenitors, known as ‘committed progenitors’ is distinguished by its expression of the plasmatocyte marker gene eater but not the mature blood cell marker Hml (Blanco-Obregon et al., 2020). The population of progenitors that are close to the heart tube additionally exhibit distinct features in terms of regulation by Hh signalling, a key regulator of blood cell differentiation in the LG (Baldeosingh et al., 2018). These data suggest that rather than being composed of a simple and clearly defined population of progenitors, the LG contains multiple subpopulations of progenitors at various stages and states of differentiation. These observations show the limitations of using fixed tissue approaches to study blood progenitor fate which is inherently a dynamic and evolving cell state.

The main function of mature blood cells in Drosophila is to fight infection and assist in wound healing (Evans et al., 2003; Khadilkar et al., 2017; Vlisidou and Wood, 2015). It is therefore not surprising that in healthy intact flies, few mature blood cells are made in late larval stages (Banerjee et al., 2019). During early larval stages, blood progenitors undergo expansion and are typically found to be in S phase of the cell cycle (Dey et al., 2016). However, once progenitor expansion is completed in the late larval stages, they for the most part stay in the G2 phase through the action of dopamine (Sharma et al., 2019; Kapoor et al., 2022), although some intermediate progenitors remain in S phase (Sharma et al., 2019). Upon infection, there is a strong and rapid induction of mature blood cell production, which depends on the type of immune challenge and involves large scale differentiation of lamellocytes, crystal cells, or plasmatocytes (Banerjee et al., 2019; Khadilkar et al., 2017; Letourneau et al., 2016). How this induction is mediated, for example, whether it is primarily driven by changes in the cell cycle in the progenitors, predominantly by altered dynamics or patterns of differentiation, or by both factors in equal measure, is currently unclear. The key to answering these important remaining questions is to develop the ability to visualize and track fly hematopoiesis for extended periods in real time.

Here, we describe analysis of proliferation and differentiation patterns observed during long-term live imaging of intact LGs in healthy and infected larvae. This analysis utilises whole organ culture methodology and quantitative imaging tools that we developed, optimised, verified, and applied. By tracking markers for cell proliferation and division in real time as well as cell fate and differentiation, we are able to confirm that blood progenitors undergo symmetric cell divisions. Using quantitative automated image analysis of progenitors in healthy and infected flies, we elucidate the dynamics and spatiotemporal patterns of blood cell differentiation and proliferation. We describe how the modulation of key differentiation and proliferation behaviours underlies the activation of mature blood cell production following infection. These results provide a novel system-level framework for understanding how Drosophila hematopoiesis is regulated in the context of the intact whole organ in real time.

By employing organ culture, using genetically encoded markers for cell cycle, proliferation and differentiation, and implementing quantitative image analysis, we are able to study the process of fly hematopoiesis at a single cell resolution in its endogenous context. This has allowed us to observe features of hematopoiesis that only become apparent upon system- and organ-level analysis. Our observations lead us to four main conclusions. First, our results illustrate that certain populations of blood progenitors in the fly can undergo symmetric cell divisions. Second, we find that the timing of blood progenitor division is more likely to occur once cells reach a specific cell size and the division is spatially oriented with respect to the heart tube and anatomical axes. Third, we identify and characterise two distinct modes of differentiation. Fourth, we show that the induction of mature blood cell production in response to infection is not achieved by modulating progenitor proliferation or speed of differentiation but by increasing the size of a population of progenitors that expressed high levels of both differentiated and progenitor cell markers.

The ex vivo culture and imaging protocol we describe provides a powerful new way to study hematopoiesis over a prolonged time using quantitative imaging. Although such an approach holds much promise, its use calls for caution and for the consideration of certain caveats. For example, following infection, while LGs in vivo would be exposed to systemic immune signals, cultured LGs may not have access to such continuous extrinsic signals which may change their behaviour. During our work, we have looked at a number of parameters of differentiation, proliferation, and tissue homeostasis and did not see any strong differences between in vivo and ex vivo organ culture. Nonetheless such differences may exist in some circumstances. Another caveat is that due to the combination of tagged markers available for live imaging, we were only able to follow differentiation events leading to plasmatocytes and not crystal cell fate. Future work will focus on addressing this to provide a more complete view of blood cell differentiation. Finally, our observations show the existence of a large population of LG cells that expresses low levels of both dome-MESO-GFP and eater-dsRed. This would suggest that a substantial proportion of progenitors begin to differentiate but pause at this stage. However, we did not observe in our live trajectory tracking experiments this sort of paused trajectory. We can only speculate at this point that cells in the paused trajectory are produced before the early third instar larval stage, which is the stage we chose for live imaging. In our future work, we will extend our analysis to earlier larval stages, which we hope will test this hypothesis directly.

Our work supports earlier studies that described the presence of distinct subpopulations of progenitors in the LG (Cho et al., 2020; Blanco-Obregon et al., 2020). According to this emerging model, the progenitor pool is not homogenous during hematopoiesis but rather contains subpopulations at different levels of differentiation. Importantly, these studies used single-cell transcriptomic, a different approach to ours, but also suggest that multiple paths exist for blood progenitors to differentiate into plasmatocytes (Cho et al., 2020). Specifically, they identified a path that contains an intermediate mixed lineage stage of differentiation and a more direct path that does not include intermediate steps (Cho et al., 2020). Additional subpopulations that have been proposed to exist in the LG include the intermediate progenitors (Krzemien et al., 2010; Sinenko et al., 2009; Sharma et al., 2019; Cho et al., 2020; Girard et al., 2021; Spratford et al., 2021) as well as the distal progenitors (Blanco-Obregon et al., 2020), which express a mixture of both progenitor and differentiated cell markers. Intermediate progenitors express the progenitor marker Dome and early differentiation marker Pxn but not mature blood cells markers like P1 or Lz (Sharma et al., 2019; Sinenko et al., 2009; Krzemien et al., 2010). Distal progenitors also exhibit a mixed fate: expressing the progenitor marker Dome, but also hallmarks of differentiated cells such as the expression of the plasmatocyte marker eater and absence of the progenitor marker Tep4 (Blanco-Obregon et al., 2020). Other studies suggested the existence of PSC-dependent and PSC-independent progenitors (Baldeosingh et al., 2018; Mandal et al., 2007). In our live imaging experiments, we observed a substantial population of cells in the LG that simultaneously express high levels of both progenitor and differentiated cell markers, which likely includes cells belonging to one or more of these mixed lineage subpopulations (Cho et al., 2020; Blanco-Obregon et al., 2020). Our studies suggest that these mixed lineage cells play a crucial role in hematopoiesis as they represent the linear differentiation trajectory which drives increased differentiation in response to infection. Consistent with this idea, spatial analysis of where progenitors that follow the different trajectories are located shows that the linear trajectory occurs mostly in the region thought to hold intermediate progenitors.

Surprisingly, the linear differentiation trajectory is substantially slower than the sigmoid differentiation trajectory. This appears to be in contradiction to another one of our observations, that a very large proportion of the progenitors following the sigmoid trajectory are found in an intermediate state where both the progenitor and differentiation markers are expressed at low levels (see Figure 7J area #2). The accumulation of sigmoid-trajectory progenitors at the intermediate phase would suggest this is a long-lasting phase, but this is not what we saw in our direct tracking of differentiation trajectories. We propose that a possible explanation to resolve this potential contradiction is that only a small proportion of the cells that are found in the intermediate state where both the progenitor and differentiation markers are expressed at low levels go on to differentiate. Furthermore, it is unclear why the linear, slower trajectory, would be favored under conditions where we would expect a need for rapid production of immune cells. We can speculate the benefits of expanding the population of cells undergoing the linear trajectory exceed the disadvantages conferred by the slower differentiation time. We would propose, based on our observations, that the various subpopulations with mixed progenitor and differentiated cell fate have an important role during infection by acting as transit amplifying cells that allow rapid induction of the cellular immune response. Understanding the behavior of these intermediate state cells should be a focus of future investigation.

Multiple systems and approaches have been used to track HSCs and blood progenitors during hematopoiesis in their native environment in real time. Key examples include studies in zebrafish that used intravital imaging (Zhang and Liu, 2011; Frame et al., 2017), studies in mice that combined diverse approaches such as inducible lineage tracing, flow cytometry, and single-cell RNA sequencing (Upadhaya et al., 2018), as well as mouse studies based on intravital imaging of the bone marrow (Christodoulou et al., 2020). Zebrafish have proven to be particularly useful for live studies of hematopoiesis, due to the relative ease of intravital imaging, and its wealth of transgenic tools (Zhang and Liu, 2011). Zebrafish have been a powerful system for studying the embryonic development of HSCs and the hematopoietic niche as well as for drug, chemical and genetic screening (Arulmozhivarman et al., 2016). In addition, zebrafish have been proven to be very useful for modeling blood malignancies and tracking their development and disease progression (Robertson et al., 2016; Gore et al., 2018). In the mouse system, which holds many challenges for intravital imaging, alternative approaches have been used to capture the dynamics of the process of hematopoiesis (Upadhaya et al., 2018; Grinenko et al., 2018). For example, Upadhaya et al., 2018 used a drug inducible HSC labeling technique to isolate HSCs and their progeny at set time points and follow the transcriptional landscape of the progenitors as they progress along their developmental trajectory (Upadhaya et al., 2018). This type of analysis yields several intriguing insights into hematopoiesis, such as the differences in the time it takes for various blood lineages to differentiate (Upadhaya et al., 2018). Moreover, despite the technical challenges, there have been several successful attempts to image the process of hematopoiesis in vivo by using the bone marrow of the mouse skull. While initially this approach was limited to the use of isolated, labelled, and transplanted HSCs (Lo Celso et al., 2009), more recent studies used an endogenously labelled HSC line (Christodoulou et al., 2020). Although they constitute important technical breakthroughs, these studies still suffer from several challenges and allow the visualization of a relatively short time window compared to the actual time it takes progenitors to differentiate in the mouse. Consequently, these studies were limited to describing the architecture of the bone marrow niche and the location of HSCs within it (Lo Celso et al., 2009), or to general descriptions of a small subset of cell behaviors such as HSC/progenitor motility and expansion (Christodoulou et al., 2020). Our approach offers the ability to perform real time functional studies that can complement observations from these other models of hematopoiesis.

In particular, compared with these earlier studies our approach offers several key innovations. First is our ability to track multiple markers simultaneously in a quantitative way during long-term live imaging. Specifically, our approach allows us to quantitatively track, for 12 or more hours, markers of cell fate in combination with multiple other markers for proliferation, metabolism, cell signaling, and cell morphology. Moreover, the relatively short duration of the differentiation process in the fly, approximately 6–8 hr versus 1–3 weeks for various leukocyte lineages in the mouse (Upadhaya et al., 2018), allows us to observe differentiation in its entirety. Second, the ability to track a large number of progenitors and quantitate both their behavior and the expression of markers using imaging analysis tools allows the deployment of system-level approaches. This offers the capability to track hematopoiesis at a cellular and even subcellular spatial resolution and a temporal resolution of a few seconds, well beyond previous studies. Third, the ability to combine these powerful analysis tools with an infection model facilitates the ability to visualize the induction of the cellular branch of the immune response in real time in order to elucidate the underlying mechanisms. Fourth, the vast genetic toolkit and short generation time of the fly, the accessibility of the LG multi-organ co-culture system to drug (Ho et al., 2021) and organ-organ communication studies, and the detailed and extensive transcriptomic analysis of blood cell differentiation (Cho et al., 2020; Girard et al., 2021) all make it a superb system for real time analysis of hematopoiesis. Specifically, a major goal of our future work will focus on combining the analysis pipeline we describe here with markers and tools to analyze and manipulate the various cell signaling pathways that have been implicated in the regulation of hematopoiesis under homeostatic, infection, and pathogenic conditions.

All data generated or analysed during this study are included in the manuscript and supporting files. All the scripts and software generated has been deposited to the Tanentzapf lab Github (copy archived at Ho et al., 2023).

1. 1. Ahmed M
   2. Ffrench-Constant C

   (2016) Extracellular matrix regulation of stem cell behavior

   Current Stem Cell Reports 2:197–206.

   https://doi.org/10.1007/s40778-016-0056-2

   • PubMed
   • Google Scholar
2. 1. Anllo L
   2. Plasschaert LW
   3. Sui J
   4. DiNardo S

   (2019) Live imaging reveals hub cell assembly and compaction dynamics during morphogenesis of the Drosophila testis niche

   Developmental Biology 446:102–118.

   https://doi.org/10.1016/j.ydbio.2018.12.014

   • PubMed
   • Google Scholar
3. 1. Araki M
   2. Kurihara M
   3. Kinoshita S
   4. Awane R
   5. Sato T
   6. Ohkawa Y
   7. Inoue YH

   (2019) Anti-Tumour effects of antimicrobial peptides, components of the innate immune system, against haematopoietic tumours in Drosophila mxc mutants

   Disease Models & Mechanisms 12:dmm037721.

   https://doi.org/10.1242/dmm.037721

   • PubMed
   • Google Scholar
4. 1. Arulmozhivarman G
   2. Stöter M
   3. Bickle M
   4. Kräter M
   5. Wobus M
   6. Ehninger G
   7. Stölzel F
   8. Brand M
   9. Bornhäuser M
   10. Shayegi N

   (2016) In vivo chemical screen in zebrafish embryos identifies regulators of hematopoiesis using a semiautomated imaging assay

   Journal of Biomolecular Screening 21:956–964.

   https://doi.org/10.1177/1087057116644163

   • PubMed
   • Google Scholar
5. 1. Avet-Rochex A
   2. Boyer K
   3. Polesello C
   4. Gobert V
   5. Osman D
   6. Roch F
   7. Augé B
   8. Zanet J
   9. Haenlin M
   10. Waltzer L

   (2010) An in vivo RNA interference screen identifies gene networks controlling Drosophila melanogaster blood cell homeostasis

   BMC Developmental Biology 10:65.

   https://doi.org/10.1186/1471-213X-10-65

   • PubMed
   • Google Scholar
6. 1. Baldeosingh R
   2. Gao H
   3. Wu X
   4. Fossett N

   (2018) Hedgehog signaling from the posterior signaling center maintains U-shaped expression and a prohemocyte population in Drosophila

   Developmental Biology 441:132–145.

   https://doi.org/10.1016/j.ydbio.2018.06.020

   • PubMed
   • Google Scholar
7. 1. Banerjee U
   2. Girard JR
   3. Goins LM
   4. Spratford CM

   (2019) Drosophila as a genetic model for hematopoiesis

   Genetics 211:367–417.

   https://doi.org/10.1534/genetics.118.300223

   • Google Scholar
8. 1. Blanco-Obregon D
   2. Katz MJ
   3. Durrieu L
   4. Gándara L
   5. Wappner P

   (2020) Context-specific functions of notch in Drosophila blood cell progenitors

   Developmental Biology 462:101–115.

   https://doi.org/10.1016/j.ydbio.2020.03.018

   • PubMed
   • Google Scholar
9. 1. Chacón-Martínez CA
   2. Koester J
   3. Wickström SA

   (2018) Signaling in the stem cell niche: regulating cell fate, function and plasticity

   Development 145:dev165399.

   https://doi.org/10.1242/dev.165399

   • PubMed
   • Google Scholar
10. 1. Chiu H
    2. Govind S

    (2002) Natural infection of D. melanogaster by virulent parasitic wasps induces apoptotic depletion of hematopoietic precursors

    Cell Death and Differentiation 9:1379–1381.

    https://doi.org/10.1038/sj.cdd.4401134

    • PubMed
    • Google Scholar
11. 1. Cho B
    2. Yoon SH
    3. Lee D
    4. Koranteng F
    5. Tattikota SG
    6. Cha N
    7. Shin M
    8. Do H
    9. Hu Y
    10. Oh SY
    11. Lee D
    12. Vipin Menon A
    13. Moon SJ
    14. Perrimon N
    15. Nam JW
    16. Shim J

    (2020) Single-cell transcriptome maps of myeloid blood cell lineages in Drosophila

    Nature Communications 11:4483.

    https://doi.org/10.1038/s41467-020-18135-y

    • PubMed
    • Google Scholar
12. 1. Christodoulou C
    2. Spencer JA
    3. Yeh S-CA
    4. Turcotte R
    5. Kokkaliaris KD
    6. Panero R
    7. Ramos A
    8. Guo G
    9. Seyedhassantehrani N
    10. Esipova TV
    11. Vinogradov SA
    12. Rudzinskas S
    13. Zhang Y
    14. Perkins AS
    15. Orkin SH
    16. Calogero RA
    17. Schroeder T
    18. Lin CP
    19. Camargo FD

    (2020) Live-animal imaging of native haematopoietic stem and progenitor cells

    Nature 578:278–283.

    https://doi.org/10.1038/s41586-020-1971-z

    • PubMed
    • Google Scholar
13. 1. Dai W
    2. Guo X
    3. Cao Y
    4. Mondo JA
    5. Campanale JP
    6. Montell BJ
    7. Burrous H
    8. Streichan S
    9. Gov N
    10. Rappel W-J
    11. Montell DJ

    (2020) Tissue topography steers migrating Drosophila border cells

    Science 370:987–990.

    https://doi.org/10.1126/science.aaz4741

    • PubMed
    • Google Scholar
14. 1. Dey NS
    2. Ramesh P
    3. Chugh M
    4. Mandal S
    5. Mandal L

    (2016) Dpp dependent hematopoietic stem cells give rise to hh dependent blood progenitors in larval lymph gland of Drosophila

    eLife 5:e18295.

    https://doi.org/10.7554/eLife.18295

    • PubMed
    • Google Scholar
15. 1. Díaz-Torres A
    2. Rosales-Nieves AE
    3. Pearson JR
    4. Santa-Cruz Mateos C
    5. Marín-Menguiano M
    6. Marshall OJ
    7. Brand AH
    8. González-Reyes A

    (2021) Stem cell niche organization in the Drosophila ovary requires the ECM component perlecan

    Current Biology 31:1744–1753.

    https://doi.org/10.1016/j.cub.2021.01.071

    • PubMed
    • Google Scholar
16. 1. Evans CJ
    2. Hartenstein V
    3. Banerjee U

    (2003) Thicker than blood: conserved mechanisms in Drosophila and vertebrate hematopoiesis

    Developmental Cell 5:673–690.

    https://doi.org/10.1016/s1534-5807(03)00335-6

    • PubMed
    • Google Scholar
17. 1. Fairchild MJ
    2. Smendziuk CM
    3. Tanentzapf G

    (2015) A somatic permeability barrier around the germline is essential for Drosophila spermatogenesis

    Development 142:268–281.

    https://doi.org/10.1242/dev.114967

    • PubMed
    • Google Scholar
18. 1. Ferrezuelo F
    2. Colomina N
    3. Palmisano A
    4. Garí E
    5. Gallego C
    6. Csikász-Nagy A
    7. Aldea M

    (2012) The critical size is set at a single-cell level by growth rate to attain homeostasis and adaptation

    Nature Communications 3:1012.

    https://doi.org/10.1038/ncomms2015

    • PubMed
    • Google Scholar
19. 1. Frame JM
    2. Lim S-E
    3. North TE

    (2017) Hematopoietic stem cell development: using the zebrafish to identify extrinsic and intrinsic mechanisms regulating hematopoiesis

    Methods in Cell Biology 138:165–192.

    https://doi.org/10.1016/bs.mcb.2016.08.004

    • PubMed
    • Google Scholar
20. 1. Girard JR
    2. Goins LM
    3. Vuu DM
    4. Sharpley MS
    5. Spratford CM
    6. Mantri SR
    7. Banerjee U

    (2021) Paths and pathways that generate cell-type heterogeneity and developmental progression in hematopoiesis

    eLife 10:e67516.

    https://doi.org/10.7554/eLife.67516

    • PubMed
    • Google Scholar
21. 1. Gore AV
    2. Pillay LM
    3. Venero Galanternik M
    4. Weinstein BM

    (2018) The zebrafish: a fintastic model for hematopoietic development and disease

    Wiley Interdisciplinary Reviews. Developmental Biology 7:e312.

    https://doi.org/10.1002/wdev.312

    • PubMed
    • Google Scholar
22. 1. Greenspan LJ
    2. Matunis EL

    (2017) Live imaging of the Drosophila testis stem cell niche

    Methods in Molecular Biology 1463:63–74.

    https://doi.org/10.1007/978-1-4939-4017-2_4

    • PubMed
    • Google Scholar
23. 1. Grinenko T
    2. Eugster A
    3. Thielecke L
    4. Ramasz B
    5. Krüger A
    6. Dietz S
    7. Glauche I
    8. Gerbaulet A
    9. von Bonin M
    10. Basak O
    11. Clevers H
    12. Chavakis T
    13. Wielockx B

    (2018) Hematopoietic stem cells can differentiate into restricted myeloid progenitors before cell division in mice

    Nature Communications 9:1898.

    https://doi.org/10.1038/s41467-018-04188-7

    • PubMed
    • Google Scholar
24. 1. He S
    2. Nakada D
    3. Morrison SJ

    (2009) Mechanisms of stem cell self-renewal

    Annual Review of Cell and Developmental Biology 25:377–406.

    https://doi.org/10.1146/annurev.cellbio.042308.113248

    • PubMed
    • Google Scholar
25. 1. Ho KYL
    2. Khadilkar RJ
    3. Carr RL
    4. Tanentzapf G

    (2021) A gap-junction-mediated, calcium-signaling network controls blood progenitor fate decisions in hematopoiesis

    Current Biology 31:4697–4712.

    https://doi.org/10.1016/j.cub.2021.08.027

    • PubMed
    • Google Scholar
26. Software

    1. Ho KYL
    2. Carr RL
    3. Dvoskin AD
    4. Tanentzapf G

    (2023) Kinetics of blood cell differentiation during hematopoiesis revealed by quantitative long-term live imaging, version swh:1:rev:d9c8523c1660ea3fd4263074fc9262389b9105cc

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:c6b4a0686b6394180834f35330d8e7fd0b605a9a;origin=https://github.com/Tanentzapf-Lab/LiveImaging_HematopiesisKinetics_Infection_Ho_Carr;visit=swh:1:snp:3cb37be60f86ef941be41d8997fbe1c89328119d;anchor=swh:1:rev:d9c8523c1660ea3fd4263074fc9262389b9105cc
27. 1. Hombría JC-G
    2. Brown S
    3. Häder S
    4. Zeidler MP

    (2005) Characterisation of upd2, a Drosophila JAK/STAT pathway ligand

    Developmental Biology 288:420–433.

    https://doi.org/10.1016/j.ydbio.2005.09.040

    • PubMed
    • Google Scholar
28. 1. Huang X
    2. Cho S
    3. Spangrude GJ

    (2007) Hematopoietic stem cells: generation and self-renewal

    Cell Death and Differentiation 14:1851–1859.

    https://doi.org/10.1038/sj.cdd.4402225

    • PubMed
    • Google Scholar
29. 1. Icha J
    2. Weber M
    3. Waters JC
    4. Norden C

    (2017) Phototoxicity in live fluorescence microscopy, and how to avoid it

    BioEssays 39:3.

    https://doi.org/10.1002/bies.201700003

    • PubMed
    • Google Scholar
30. 1. Jones DL
    2. Wagers AJ

    (2008) No place like home: anatomy and function of the stem cell niche

    Nature Reviews. Molecular Cell Biology 9:11–21.

    https://doi.org/10.1038/nrm2319

    • PubMed
    • Google Scholar
31. 1. Jung SH
    2. Evans CJ
    3. Uemura C
    4. Banerjee U

    (2005) The Drosophila lymph gland as a developmental model of hematopoiesis

    Development 132:2521–2533.

    https://doi.org/10.1242/dev.01837

    • PubMed
    • Google Scholar
32. 1. Kakanj P
    2. Eming SA
    3. Partridge L
    4. Leptin M

    (2020) Long-Term in vivo imaging of Drosophila larvae

    Nature Protocols 15:1158–1187.

    https://doi.org/10.1038/s41596-019-0282-z

    • PubMed
    • Google Scholar
33. 1. Kapoor A
    2. Padmavathi A
    3. Madhwal S
    4. Mukherjee T

    (2022) Dual control of dopamine in Drosophila myeloid-like progenitor cell proliferation and regulation of lymph gland growth

    EMBO Reports 23:e52951.

    https://doi.org/10.15252/embr.202152951

    • PubMed
    • Google Scholar
34. 1. Khadilkar RJ
    2. Vogl W
    3. Goodwin K
    4. Tanentzapf G

    (2017) Modulation of occluding junctions alters the hematopoietic niche to trigger immune activation

    eLife 6:e28081.

    https://doi.org/10.7554/eLife.28081

    • PubMed
    • Google Scholar
35. 1. Khadilkar RJ
    2. Ho KYL
    3. Venkatesh B
    4. Tanentzapf G

    (2020) Integrins modulate extracellular matrix organization to control cell signaling during hematopoiesis

    Current Biology 30:3316–3329.

    https://doi.org/10.1016/j.cub.2020.06.027

    • PubMed
    • Google Scholar
36. 1. Kiepas A
    2. Voorand E
    3. Mubaid F
    4. Siegel PM
    5. Brown CM

    (2020) Optimizing live-cell fluorescence imaging conditions to minimize phototoxicity

    Journal of Cell Science 133:jcs242834.

    https://doi.org/10.1242/jcs.242834

    • PubMed
    • Google Scholar
37. 1. Klein AM
    2. Simons BD

    (2011) Universal patterns of stem cell fate in cycling adult tissues

    Development 138:3103–3111.

    https://doi.org/10.1242/dev.060103

    • PubMed
    • Google Scholar
38. 1. Kocks C
    2. Cho JH
    3. Nehme N
    4. Ulvila J
    5. Pearson AM
    6. Meister M
    7. Strom C
    8. Conto SL
    9. Hetru C
    10. Stuart LM
    11. Stehle T
    12. Hoffmann JA
    13. Reichhart J-M
    14. Ferrandon D
    15. Rämet M
    16. Ezekowitz RAB

    (2005) Eater, a transmembrane protein mediating phagocytosis of bacterial pathogens in Drosophila

    Cell 123:335–346.

    https://doi.org/10.1016/j.cell.2005.08.034

    • PubMed
    • Google Scholar
39. 1. Koyama LAJ
    2. Aranda-Díaz A
    3. Su Y-H
    4. Balachandra S
    5. Martin JL
    6. Ludington WB
    7. Huang KC
    8. O’Brien LE

    (2020) Bellymount enables longitudinal, intravital imaging of abdominal organs and the gut microbiota in adult Drosophila

    PLOS Biology 18:e3000567.

    https://doi.org/10.1371/journal.pbio.3000567

    • PubMed
    • Google Scholar
40. 1. Kroeger PT
    2. Tokusumi T
    3. Schulz RA

    (2012) Transcriptional regulation of eater gene expression in Drosophila blood cells

    Genesis 50:41–49.

    https://doi.org/10.1002/dvg.20787

    • PubMed
    • Google Scholar
41. 1. Krzemień J
    2. Dubois L
    3. Makki R
    4. Meister M
    5. Vincent A
    6. Crozatier M

    (2007) Control of blood cell homeostasis in Drosophila larvae by the posterior signalling centre

    Nature 446:325–328.

    https://doi.org/10.1038/nature05650

    • PubMed
    • Google Scholar
42. 1. Krzemien J
    2. Oyallon J
    3. Crozatier M
    4. Vincent A

    (2010) Hematopoietic progenitors and hemocyte lineages in the Drosophila lymph gland

    Developmental Biology 346:310–319.

    https://doi.org/10.1016/j.ydbio.2010.08.003

    • PubMed
    • Google Scholar
43. 1. Lengefeld J
    2. Cheng C-W
    3. Maretich P
    4. Blair M
    5. Hagen H
    6. McReynolds MR
    7. Sullivan E
    8. Majors K
    9. Roberts C
    10. Kang JH
    11. Steiner JD
    12. Miettinen TP
    13. Manalis SR
    14. Antebi A
    15. Morrison SJ
    16. Lees JA
    17. Boyer LA
    18. Yilmaz ÖH
    19. Amon A

    (2021) Cell size is a determinant of stem cell potential during aging

    Science Advances 7:eabk0271.

    https://doi.org/10.1126/sciadv.abk0271

    • PubMed
    • Google Scholar
44. 1. Letourneau M
    2. Lapraz F
    3. Sharma A
    4. Vanzo N
    5. Waltzer L
    6. Crozatier M

    (2016) Drosophila hematopoiesis under normal conditions and in response to immune stress

    FEBS Letters 590:4034–4051.

    https://doi.org/10.1002/1873-3468.12327

    • PubMed
    • Google Scholar
45. 1. Lo Celso C
    2. Fleming HE
    3. Wu JW
    4. Zhao CX
    5. Miake-Lye S
    6. Fujisaki J
    7. Côté D
    8. Rowe DW
    9. Lin CP
    10. Scadden DT

    (2009) Live-animal tracking of individual haematopoietic stem/progenitor cells in their niche

    Nature 457:92–96.

    https://doi.org/10.1038/nature07434

    • PubMed
    • Google Scholar
46. 1. Makhijani K
    2. Alexander B
    3. Tanaka T
    4. Rulifson E
    5. Brückner K

    (2011) The peripheral nervous system supports blood cell homing and survival in the Drosophila larva

    Development 138:5379–5391.

    https://doi.org/10.1242/dev.067322

    • PubMed
    • Google Scholar
47. 1. Mandal L
    2. Martinez-Agosto JA
    3. Evans CJ
    4. Hartenstein V
    5. Banerjee U

    (2007) A hedgehog- and antennapedia-dependent niche maintains Drosophila haematopoietic precursors

    Nature 446:320–324.

    https://doi.org/10.1038/nature05585

    • PubMed
    • Google Scholar
48. 1. Martin JL
    2. Sanders EN
    3. Moreno-Roman P
    4. Jaramillo Koyama LA
    5. Balachandra S
    6. Du X
    7. O’Brien LE

    (2018) Long-term live imaging of the Drosophila adult midgut reveals real-time dynamics of division, differentiation and loss

    eLife 7:e36248.

    https://doi.org/10.7554/eLife.36248

    • PubMed
    • Google Scholar
49. 1. Martinez-Agosto JA
    2. Mikkola HKA
    3. Hartenstein V
    4. Banerjee U

    (2007) The hematopoietic stem cell and its niche: a comparative view

    Genes & Development 21:3044–3060.

    https://doi.org/10.1101/gad.1602607

    • PubMed
    • Google Scholar
50. 1. Milton CC
    2. Grusche FA
    3. Degoutin JL
    4. Yu E
    5. Dai Q
    6. Lai EC
    7. Harvey KF

    (2014) The Hippo pathway regulates hematopoiesis in Drosophila melanogaster

    Current Biology 24:2673–2680.

    https://doi.org/10.1016/j.cub.2014.10.031

    • PubMed
    • Google Scholar
51. 1. Minakhina S
    2. Steward R

    (2010) Hematopoietic stem cells in Drosophila

    Development 137:27–31.

    https://doi.org/10.1242/dev.043943

    • PubMed
    • Google Scholar
52. 1. Mondal BC
    2. Mukherjee T
    3. Mandal L
    4. Evans CJ
    5. Sinenko SA
    6. Martinez-Agosto JA
    7. Banerjee U

    (2011) Interaction between differentiating cell- and niche-derived signals in hematopoietic progenitor maintenance

    Cell 147:1589–1600.

    https://doi.org/10.1016/j.cell.2011.11.041

    • PubMed
    • Google Scholar
53. 1. Morris LX
    2. Spradling AC

    (2011) Long-term live imaging provides new insight into stem cell regulation and germline-soma coordination in the Drosophila ovary

    Development 138:2207–2215.

    https://doi.org/10.1242/dev.065508

    • PubMed
    • Google Scholar
54. 1. Morrison SJ
    2. Spradling AC

    (2008) Stem cells and niches: mechanisms that promote stem cell maintenance throughout life

    Cell 132:598–611.

    https://doi.org/10.1016/j.cell.2008.01.038

    • PubMed
    • Google Scholar
55. 1. Oyallon J
    2. Vanzo N
    3. Krzemień J
    4. Morin-Poulard I
    5. Vincent A
    6. Crozatier M

    (2016) Two independent functions of collier/early B cell factor in the control of Drosophila blood cell homeostasis

    PLOS ONE 11:e0148978.

    https://doi.org/10.1371/journal.pone.0148978

    • PubMed
    • Google Scholar
56. 1. Park S
    2. Greco V
    3. Cockburn K

    (2016) Live imaging of stem cells: answering old questions and raising new ones

    Current Opinion in Cell Biology 43:30–37.

    https://doi.org/10.1016/j.ceb.2016.07.004

    • PubMed
    • Google Scholar
57. 1. Reilein A
    2. Cimetta E
    3. Tandon NM
    4. Kalderon D
    5. Vunjak-Novakovic G

    (2018) Live imaging of stem cells in the germarium of the Drosophila ovary using a reusable gas-permeable imaging chamber

    Nature Protocols 13:2601–2614.

    https://doi.org/10.1038/s41596-018-0054-1

    • PubMed
    • Google Scholar
58. 1. Robertson AL
    2. Avagyan S
    3. Gansner JM
    4. Zon LI

    (2016) Understanding the regulation of vertebrate hematopoiesis and blood disorders-big lessons from a small fish

    FEBS Letters 590:4016–4033.

    https://doi.org/10.1002/1873-3468.12415

    • PubMed
    • Google Scholar
59. 1. Rodrigues D
    2. Renaud Y
    3. VijayRaghavan K
    4. Waltzer L
    5. Inamdar MS

    (2021) Differential activation of JAK-STAT signaling reveals functional compartmentalization in Drosophila blood progenitors

    eLife 10:e61409.

    https://doi.org/10.7554/eLife.61409

    • PubMed
    • Google Scholar
60. 1. Rompolas P
    2. Deschene ER
    3. Zito G
    4. Gonzalez DG
    5. Saotome I
    6. Haberman AM
    7. Greco V

    (2012) Live imaging of stem cell and progeny behaviour in physiological hair-follicle regeneration

    Nature 487:496–499.

    https://doi.org/10.1038/nature11218

    • PubMed
    • Google Scholar
61. 1. Rood JE
    2. Stuart T
    3. Ghazanfar S
    4. Biancalani T
    5. Fisher E
    6. Butler A
    7. Hupalowska A
    8. Gaffney L
    9. Mauck W
    10. Eraslan G
    11. Marioni JC
    12. Regev A
    13. Satija R

    (2019) Toward a common coordinate framework for the human body

    Cell 179:1455–1467.

    https://doi.org/10.1016/j.cell.2019.11.019

    • PubMed
    • Google Scholar
62. 1. Schindelin J
    2. Arganda-Carreras I
    3. Frise E
    4. Kaynig V
    5. Longair M
    6. Pietzsch T
    7. Preibisch S
    8. Rueden C
    9. Saalfeld S
    10. Schmid B
    11. Tinevez J-Y
    12. White DJ
    13. Hartenstein V
    14. Eliceiri K
    15. Tomancak P
    16. Cardona A

    (2012) Fiji: an open-source platform for biological-image analysis

    Nature Methods 9:676–682.

    https://doi.org/10.1038/nmeth.2019

    • PubMed
    • Google Scholar
63. 1. Sharma SK
    2. Ghosh S
    3. Geetha AR
    4. Mandal S
    5. Mandal L

    (2019) Cell adhesion-mediated actomyosin assembly regulates the activity of cubitus interruptus for hematopoietic progenitor maintenance in Drosophila

    Genetics 212:1279–1300.

    https://doi.org/10.1534/genetics.119.302209

    • PubMed
    • Google Scholar
64. 1. Sheng XR
    2. Matunis E

    (2011) Live imaging of the Drosophila spermatogonial stem cell niche reveals novel mechanisms regulating germline stem cell output

    Development 138:3367–3376.

    https://doi.org/10.1242/dev.065797

    • PubMed
    • Google Scholar
65. 1. Sinenko SA
    2. Mandal L
    3. Martinez-Agosto JA
    4. Banerjee U

    (2009) Dual role of wingless signaling in stem-like hematopoietic precursor maintenance in Drosophila

    Developmental Cell 16:756–763.

    https://doi.org/10.1016/j.devcel.2009.03.003

    • PubMed
    • Google Scholar
66. 1. Siva-Jothy JA
    2. Prakash A
    3. Vasanthakrishnan RB
    4. Monteith KM
    5. Vale PF

    (2018) Oral bacterial infection and shedding in drosophila melanogaster

    Journal of Visualized Experiments 31:57676.

    https://doi.org/10.3791/57676

    • PubMed
    • Google Scholar
67. 1. Spratford CM
    2. Goins LM
    3. Chi F
    4. Girard JR
    5. Macias SN
    6. Ho VW
    7. Banerjee U

    (2021) Intermediate progenitor cells provide a transition between hematopoietic progenitors and their differentiated descendants

    Development 148:dev200216.

    https://doi.org/10.1242/dev.200216

    • PubMed
    • Google Scholar
68. 1. Stoltzfus CR
    2. Filipek J
    3. Gern BH
    4. Olin BE
    5. Leal JM
    6. Wu Y
    7. Lyons-Cohen MR
    8. Huang JY
    9. Paz-Stoltzfus CL
    10. Plumlee CR
    11. Pöschinger T
    12. Urdahl KB
    13. Perro M
    14. Gerner MY

    (2020) CytoMAP: a spatial analysis toolbox reveals features of myeloid cell organization in lymphoid tissues

    Cell Reports 31:107523.

    https://doi.org/10.1016/j.celrep.2020.107523

    • PubMed
    • Google Scholar
69. 1. Sykiotis GP
    2. Bohmann D

    (2008) Keap1/Nrf2 signaling regulates oxidative stress tolerance and lifespan in Drosophila

    Developmental Cell 14:76–85.

    https://doi.org/10.1016/j.devcel.2007.12.002

    • PubMed
    • Google Scholar
70. 1. Tinevez JY
    2. Perry N
    3. Schindelin J
    4. Hoopes GM
    5. Reynolds GD
    6. Laplantine E
    7. Bednarek SY
    8. Shorte SL
    9. Eliceiri KW

    (2017) TrackMate: an open and extensible platform for single-particle tracking

    Methods 115:80–90.

    https://doi.org/10.1016/j.ymeth.2016.09.016

    • PubMed
    • Google Scholar
71. 1. Tokusumi T
    2. Shoue DA
    3. Tokusumi Y
    4. Stoller JR
    5. Schulz RA

    (2009) New hemocyte-specific enhancer-reporter transgenes for the analysis of hematopoiesis in Drosophila

    Genesis 47:771–774.

    https://doi.org/10.1002/dvg.20561

    • PubMed
    • Google Scholar
72. 1. Tokusumi Y
    2. Tokusumi T
    3. Stoller-Conrad J
    4. Schulz RA

    (2010) Serpent, suppressor of hairless and U-shaped are crucial regulators of hedgehog niche expression and prohemocyte maintenance during Drosophila larval hematopoiesis

    Development 137:3561–3568.

    https://doi.org/10.1242/dev.053728

    • PubMed
    • Google Scholar
73. 1. Tsao CK
    2. Ku HY
    3. Lee YM
    4. Huang YF
    5. Sun YH

    (2016) Long term ex vivo culture and live imaging of Drosophila larval imaginal discs

    PLOS ONE 11:e0163744.

    https://doi.org/10.1371/journal.pone.0163744

    • PubMed
    • Google Scholar
74. 1. Upadhaya S
    2. Sawai CM
    3. Papalexi E
    4. Rashidfarrokhi A
    5. Jang G
    6. Chattopadhyay P
    7. Satija R
    8. Reizis B

    (2018) Kinetics of adult hematopoietic stem cell differentiation in vivo

    The Journal of Experimental Medicine 215:2815–2832.

    https://doi.org/10.1084/jem.20180136

    • PubMed
    • Google Scholar
75. 1. Vlisidou I
    2. Wood W

    (2015) Drosophila blood cells and their role in immune responses

    The FEBS Journal 282:1368–1382.

    https://doi.org/10.1111/febs.13235

    • PubMed
    • Google Scholar
76. 1. Ye YH
    2. McGraw EA

    (2011) Adult Drosophila melanogaster evolved for antibacterial defense invest in infection-induced expression of both humoral and cellular immunity genes

    BMC Research Notes 4:305.

    https://doi.org/10.1186/1756-0500-4-305

    • PubMed
    • Google Scholar
77. 1. Yu S
    2. Luo F
    3. Jin LH

    (2021) Rab5 and Rab11 maintain hematopoietic homeostasis by restricting multiple signaling pathways in Drosophila

    eLife 10:e60870.

    https://doi.org/10.7554/eLife.60870

    • PubMed
    • Google Scholar
78. 1. Zartman J
    2. Restrepo S
    3. Basler K

    (2013) A high-throughput template for optimizing Drosophila organ culture with response-surface methods

    Development 140:667–674.

    https://doi.org/10.1242/dev.088872

    • PubMed
    • Google Scholar
79. 1. Zhang P
    2. Liu F

    (2011) In vivo imaging of hematopoietic stem cell development in the zebrafish

    Frontiers of Medicine 5:239–247.

    https://doi.org/10.1007/s11684-011-0123-0

    • PubMed
    • Google Scholar
80. 1. Zhang S
    2. Amourda C
    3. Garfield D
    4. Saunders TE

    (2018) Selective filopodia adhesion ensures robust cell matching in the Drosophila heart

    Developmental Cell 46:189–203.

    https://doi.org/10.1016/j.devcel.2018.06.015

    • Google Scholar
81. 1. Zheng Z
    2. Lauritzen JS
    3. Perlman E
    4. Robinson CG
    5. Nichols M
    6. Milkie D
    7. Torrens O
    8. Price J
    9. Fisher CB
    10. Sharifi N
    11. Calle-Schuler SA
    12. Kmecova L
    13. Ali IJ
    14. Karsh B
    15. Trautman ET
    16. Bogovic JA
    17. Hanslovsky P
    18. Jefferis GSXE
    19. Kazhdan M
    20. Khairy K
    21. Saalfeld S
    22. Fetter RD
    23. Bock DD

    (2018) A complete electron microscopy volume of the brain of adult Drosophila melanogaster

    Cell 174:730–743.

    https://doi.org/10.1016/j.cell.2018.06.019

    • Google Scholar
82. 1. Zielke N
    2. Korzelius J
    3. van Straaten M
    4. Bender K
    5. Schuhknecht GFP
    6. Dutta D
    7. Xiang J
    8. Edgar BA

    (2014) Fly-FUCCI: a versatile tool for studying cell proliferation in complex tissues

    Cell Reports 7:588–598.

    https://doi.org/10.1016/j.celrep.2014.03.020

    • PubMed
    • Google Scholar

Author details

1. Kevin Yueh Lin Ho

   Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, Canada

   Contribution

   Conceptualization, Resources, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Contributed equally with

   Rosalyn Leigh Carr

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8083-7043

2. Rosalyn Leigh Carr

   1. Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, Canada
   2. School of Biomedical Engineering, University of British Columbia, Vancouver, Canada
   3. British Columbia Children’s Hospital, Vancouver, Canada

   Contribution

   Data curation, Software, Formal analysis, Visualization, Methodology, Writing – review and editing

   Contributed equally with

   Kevin Yueh Lin Ho

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4371-0881

3. Alexandra Dmitria Dvoskin

   Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, Canada

   Contribution

   Formal analysis, Validation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

4. Guy Tanentzapf

   Department of Cellular and Physiological Sciences, University of British Columbia, Vancouver, Canada

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   tanentz@mail.ubc.ca

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2443-233X

• 968

  views

• 87

  downloads

• 4

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.