How organs attain their species-specific size and shape in a reproducible manner is an important question in biology. Tissue morphogenesis constitutes a multi-scale process that occurs in three dimensions (3D). Thus, quantitative cell and developmental biology must address not only molecular processes but also cellular and tissue-level properties. It necessitates the quantitative 3D analysis of cell size, cell shape, and cellular topology, an approach that has received much less attention (Boutros et al., 2015; Jackson et al., 2019).

Morphogenesis involves the coordination of cellular behavior between cells or complex populations of cells, leading to emergent properties of the tissue that are not directly encoded in the genome (Coen et al., 2017; Coen and Rebocho, 2016; Gibson et al., 2011; Jackson et al., 2019). For example, plant cells are linked through their cell walls. The physical coupling of plant cells can cause mechanical stresses that may control tissue shape by influencing growth patterns and gene expression (Bassel et al., 2014; Hamant et al., 2008; Hervieux et al., 2016; Kierzkowski et al., 2012; Landrein et al., 2015; Louveaux et al., 2016; Sampathkumar et al., 2014; Sapala et al., 2018; Sassi et al., 2014; Uyttewaal et al., 2012). Concepts involving the minimization of mechanical stresses caused by differential growth within a tissue have been employed to explain morphogenesis of different plant organs with curved shapes (Lee et al., 2019; Liang and Mahadevan, 2011; Rebocho et al., 2017).

Developmental changes in the appearance of organs, such as leaves and sepals, are often assessed by focusing on the organ surface (Hervieux et al., 2016; Hong et al., 2016; Kierzkowski et al., 2019). This strategy, however, neglects internal cellular growth patterns. Cellular patterns in deeper tissue layers have classically been studied using 2D sectioning techniques with modern variations, for example in the study of the hypocotyl, relying on automated quantitative histology (Sankar et al., 2014). However, 2D analysis of cellular patterns can also result in misconceptions as was noticed for the early Arabidopsis embryo (Yoshida et al., 2014).

For a more complete understanding of morphogenesis, a 3D cellular level description and quantification of the entire tissue under study is essential (Hong et al., 2018; Kierzkowski and Routier-Kierzkowska, 2019; Sapala et al., 2019). Digital 3D organs with cellular resolution are a desired goal, however, it remains a challenge to generate such accurate digital representations. Substantial efforts in animal developmental biology often still lack single-cell resolution (Anderson et al., 2019; Asadulina et al., 2012; Dreyer et al., 2010; Lein et al., 2007; Rein et al., 2002). Notable exceptions are Caenorhabditis elegans (Long et al., 2009) and the early embryo of the ascidian Phallusia mammillata (Guignard et al., 2020; Sladitschek et al., 2020). Model plants, such as Arabidopsis thaliana, are uniquely suited for this task. Plants feature a relatively small number of different cell types. Moreover, plant cells are immobile. As a consequence, one can often observe characteristic cell division patterns associated with the formation and organization of tissues and organs. A cellular level 3D digital organ atlas has been obtained for the early stages of the Arabidopsis embryo (Yoshida et al., 2014) which has very few cells. Mostly, complete atlases have been made for larger organs with simple layered structures like roots and hypocotyls (Bassel et al., 2014; Montenegro-Johnson et al., 2015; Pasternak et al., 2017; Schmidt et al., 2014; Yoshida et al., 2014). The applied approach, however, was incompatible with fluorescent stains. Full 3D processing of live imaged data sets has also been possible in some systems, such as the shoot apex but is limited by light penetration to outer layers (Montenegro-Johnson et al., 2019; Refahi et al., 2020; Willis et al., 2016).

The ovule is the female reproductive organ of higher plants. It harbors the embryo sac with the egg cell that is protected by two integuments, lateral determinate tissues that develop into the seed coat following fertilization. The Arabidopsis ovule has been established as a model to study several important aspects of tissue morphogenesis including primordium formation, the establishment of the female germ line, and integument formation (Chaudhary et al., 2018; Gasser and Skinner, 2019; Nakajima, 2018; Schmidt et al., 2015). The ovule exhibits an elaborate tissue architecture exemplified by its multiple cell types and extreme curvature visible in the highly asymmetric growth of the two integuments. This property makes it ideal for addressing the complexity of morphogenetic processes. Qualitative descriptions of ovule development exist (Christensen et al., 1997; Robinson-Beers et al., 1992; Schneitz et al., 1995) but a quantitative cellular characterization is only available for the tissue that forms the germ line (Lora et al., 2017; Hernandez-Lagana et al., 2020).

To make the next step in the study of ovule morphogenesis therefore requires 3D digital ovules with cellular resolution over all developmental stages. Here, we constructed a canonical 3D digital atlas of Arabidopsis ovule development with cellular resolution. The atlas covers all stages from early primordium outgrowth to the mature pre-fertilization ovule and provides quantitative information about various cellular parameters. It also provides a proof-of-concept analysis of spatial gene expression in 3D with cellular resolution. Our quantitative phenotypic analysis revealed a range of novel aspects of ovule morphogenesis and a new function for the regulatory gene INNER NO OUTER.

Arabidopsis ovules become apparent as finger-like protrusions that emanate from the placental tissue of the carpel (Robinson-Beers et al., 1992; Schneitz et al., 1995; Figure 1A). The ovule is a composite of three clonally distinct radial layers (Jenik and Irish, 2000; Schneitz et al., 1995). Thus, its organization into L1 (epidermis), L2 (first subepidermal layer), and L3 (innermost layer) follows a general principle of plant organ architecture (Satina et al., 1940). Following primordium formation, three proximal-distal (PD) pattern elements can be recognized: the distal nucellus, central chalaza, and proximal funiculus, respectively (Figure 1A,B). The nucellus produces the megaspore mother cell (MMC), a large L2-derived cell that eventually undergoes meiosis. Only one of the meiotic products, the functional megaspore, survives and continues development. It develops into the eight-nuclear, seven-celled haploid embryo sac. The embryo sac, or female gametophyte, carries the egg cell. The chalaza is characterized by two integuments that initiate at its flanks. The two sheet-like integuments are determinate lateral organs of epidermal origin that undergo planar or laminar growth (Jenik and Irish, 2000; Schneitz et al., 1995; Truernit and Haseloff, 2008). The integuments grow around the nucellus in an asymmetric fashion eventually forming a hood-like structure and contributing to the curved shape (anatropy) of the mature ovule. Each of the two integuments initially forms a bilayered structure of regularly arranged cells. Eventually, the inner integument forms a third layer. The outer integument consists of two cell layers throughout its development. The two integuments leave open a small cleft, the micropyle, through which a pollen tube can reach the interior of the ovule (Figure 1A,C). The funiculus represents a stalk-like structure that carries the vasculature and connects the ovule to the placenta. Ovules eventually orient along the apical-basal or long axis of the gynoecium (pistil) with the micropyle facing toward the stigma (anterior half of ovule oriented gynapically), whereas the opposite side of the ovule faces the bottom of the gynoecium (posterior half, oriented gynbasally) (Simon et al., 2012; Figure 1B, see below).

Figure 1

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Includes information of the available wild-type dataset of ovules at different developmental stages and their respective IDs.

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Cell-type labeling of 3D digital ovules with cellular resolution

Following the generation of 3D cell meshes, we added specific labels to individual cells, thereby describing tissue types, such as radial cell layers (L1, L2, L3), nucellus, internal tissue of the chalaza, inner or outer integument, or the funiculus (see Supplementary file 1 for cell types). Staining with TO-PRO-3 also allowed the identification of cells undergoing mitosis (Figure 1D). Available computational pipelines for near-automatic, geometry-based cell type identification (Montenegro-Johnson et al., 2015; Montenegro-Johnson et al., 2019; Schmidt et al., 2014) failed to provide reasonably good and consistent results. This was likely due to the ovule exhibiting a more complex tissue architecture. We therefore performed cell type labeling by a combination of semi-automated and manual cell type labeling. The entire procedure, from imaging to the final segmented and labeled digital ovule, takes about 45 min per z-stack. For younger ovules, the procedure takes even less time.

In summary, the combined efforts resulted in a high-quality reference set of 158 hand-curated 3D digital ovules of the wild-type Col-0 accession (≥10 samples per stage). They feature cellular resolution, cover all stages, and include annotated cell types and cellular features (Figure 1F; Video 1).

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Overall assessment of ovule development

The different stages of Arabidopsis ovule development were defined previously (Schneitz et al., 1995). Throughout this work, we used a more precise definition of the subdivision of stage 1 into stage 1-I and 1-II. The subdivision was based on the first appearance of the signal of a reporter for WUSCHEL expression that became robustly apparent when ovule primordia consisted of 50 cells (see below). We first determined the average number of cells per ovule and stage (Figure 2A,B) and found an incremental increase in cell number for every consecutive stage of development until ovules at stage 3-VI exhibited an average of about 1900 cells (1897 ± 179.9 (mean ± SD)) (Table 1). We also assessed the mean volume per ovule for each stage by summing up the cell volumes of all cells in a given ovule (Figure 2A,C; Table 1). We measured a mean total volume for ovules at stage 3-VI of about 5 × 10⁵ μm³ (4.9 × 10⁵ ± 0.7 × 10⁵).

Figure 2

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Includes the list of ovule IDs, stage, total number of cells and total volume of the available wild-type dataset.

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Table 1

Stage*	N cells	Volume (x104 μm3)	N mitotic cells	% mitotic cells
1-I	39.6 ± 5.3	0.5 ± 0.09	1.0 ± 0.0	0.7 ± 1.2
1-II	74.0 ± 17.1	1.0 ± 0.2	1.3 ± 0.5	0.7 ± 0.9
2-I	176.9 ± 31.5	2.5 ± 0.4	3.1 ± 2.1	1.8 ± 1.2
2-II	220.6 ± 24.9	2.7 ± 0.6	2.7 ± 1.6	1.1 ± 0.7
2-III	324.1 ± 32.9	4.1 ± 0.7	3.6 ± 1.7	1.0 ± 0.7
2-IV	447.1 ± 30.7	5.9 ± 0.6	4.1 ± 1.7	0.9 ± 0.4
2-V	648.7 ± 81.5	9.7 ± 1.6	7.3 ± 3.0	1.1 ± 0.5
3-I	948.1 ± 92.5	18.1 ± 2.7	6.4 ± 3.0	0.7 ± 0.3
3-II	1178.0 ± 58.0	27.0 ± 2.5	10.4 ± 4.4	0.9 ± 0.4
3-III	1276.0 ± 97.7	28.9 ± 3.8	10.7 ± 2.8	0.9 ± 0.2
3-IV	1387 ± 111.9	34.0 ± 3.3	5.36 ± 1.8	0.4 ± 0.1
3-V	1580.0 ± 150.7	39.4 ± 3.5	7.9 ± 5.3	0.5 ± 0.3
3-VI	1897.0 ± 179.9	49.4 ± 7.2	11.1 ± 2.7	0.6 ± 0.2

1. * Number of 3D digital ovules scored: 10 (stages 2-II- 3-II, 3-IV, 3-VI), 11 (stages 2-I, 3-III, 3-V), 13 (stage 2-II), 14 (stage 1-I), 28 (stage 1-II).

   Values represent mean ± SD.

Relative tissue growth patterns and dynamics during ovule development

To get a first insight into potential tissue-specific growth patterns, we performed a stage-specific, quantitative cellular analysis of different tissues following primordium formation (Table 2). We observed little if any growth of the nucellus (excluding the embryo sac) except at the end of stage 3, with the funiculus halting growth at stage 3-II, while the cell numbers and tissue volume of the chalaza and integuments continuously increased during stages 2 and 3 (for a detailed description see Appendix1 with Appendix 1—figure 1 and Appendix 1—figure 2).

Table 2

	Tissue
Stage*	Nucellus		Central region		Inner integument		Outer integument		Funiculus
	N cells	Volume (x104 μm3)	N cells	Volume (x104 μm3)	N cells	Volume (x104 μm3)	N cells	Volume (x104 μm3)	N cells	Volume (x104 μm3)
2-III	58.1 ± 8.1	0.6 ± 0.1	35.0 ± 5.7	0.4 ± 0.08	23.5 ± 5.5	0.30 ± 0.07	53.8 ± 12.1	0.8 ± 0.2	153.7 ± 28.5	2.0 ± 0.5
2-IV	64.6 ± 5.9	0.7 ± 0.1	57.6 ± 9.7	0.7 ± 0.01	48.1 ± 5.9	0.62 ± 0.08	66.6 ± 10.6	1.4 ± 0.3	210.2 ± 23.6	2.5 ± 0.3
2-V	64.5 ± 10.5	0.7 ± 0.1	85.0 ± 12.2	1.2 ± 0.2	79.3 ± 8.4	1.0 ± 0.1	124.0 ± 20.2	3.4 ± 0.6	295.9 ± 50.0	3.4 ± 0.8
3-I	66.9 ± 13.4	0.7 ± 0.1	118.9 ± 25.4	1.9 ± 0.4	132.6 ± 18.2	2.04 ± 0.4	235.9 ± 37.6	8.8 ± 1.7	392.4 ± 36.4	4.5 ± 0.6
3-II	56.0 ± 14.4	0.7 ± 0.1	158.1 ± 16.6	2.9 ± 0.3	194.7 ± 22.9	4.0 ± 0.6	289.1 ± 12.6	13.2 ± 1.3	477.1 ± 54.2	6.0 ± 0.8
3-III	64.4 ± 16.6	0.8 ± 0.1	172.2 ± 23.4	3.1 ± 0.5	216.8 ± 25.4	4.7 ± 0.8	324.5 ± 21.4	13.9 ± 1.8	496.2 ± 50.6	6.0 ± 1.0
3-IV	77.7 ± 12.2	1.0 ± 0.2	191.3 ± 20.1	3.6 ± 0.5	255.8 ± 28.4	5.8 ± 0.6	349.6 ± 26.6	15.9 ± 1.3	511.1 ± 75.8	7.3 ± 1.7
3-V	77.4 ± 20.4	1.2 ± 0.3	209.2 ± 31.7	3.9 ± 0.7	343.5 ± 48.3	7.6 ± 1.1	439.8 ± 57.0	18.6 ± 1.3	506.8 ± 40.1	7.1 ± 0.8
3-VI	85.9 ± 17.2	1.3 ± 0.2	268.5 ± 31.4	5.3 ± 0.8	453.9 ± 69.8	10.6 ± 1.9	551.6 ± 62.7	22.7 ± 3.7	533.0 ± 68.6	7.7 ± 1.1

1. *Number of 3D digital ovules scored: 10 (stages 2-II- 3-II, 3-IV, 3-VI), 11 (stages 3-III, 3-V).

   Values represent mean ± SD.

To gain more insight into the growth dynamics during ovule development, we assessed its temporal progression. To this end, we established a two-step procedure. First, we investigated pistil growth for the covered developmental period by measuring pistil length of individual live pistils attached to the plant at different times spanning floral stages 8–12 (stages according to Smyth et al., 1990; Figure 3A). We then generated a pistil growth curve by fitting a curve to a plot of pistil length over time (Figure 3B) (Supplementary file 2). Next, we dissected ovules from pistils of various lengths, determined the stage of the ovules for each pistil, and used the pistil growth curve for an estimate of the duration of the different ovule stages (see Materials and methods). It was previously noticed that early stage ovules do not develop synchronously within a pistil (Schneitz et al., 1995; Wolny et al., 2020; Yu et al., 2020). In line with these results, we observed ovules of different stages within a given pistil (Supplementary file 3). Asynchrony was obvious for all stages and ovules within a pistil were usually found to be distributed across two to four consecutive stages. We determined the number of ovules per stage for a given pistil and defined the stage with the largest number of ovules as reference. Stages 3-II and 3-III were grouped together as pistils usually contained about equal numbers of both stages. From the pistil growth curve, we then derived the time interval between the minimal and maximal pistil lengths covering a given stage. This time interval provided an estimate of the duration of a given ovule stage (Figure 3C).

Figure 3

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Includes the information on pistil lengths measured for each stage and the calculated duration in hours of ovule stages.

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Under our growth conditions, we observed that ovules emerge from placental tissue in pistils with a length of about 0.33 mm and that by the time of anthesis pistils had reached a length of about 2.3 mm. Ovules developed from early stage 1 to the end of stage 3 within a period of 146.2 hr or 6.1 days. The three main stages differed in their durations. Stage 1 required about 31.7 hr (1.32 days), while stage 2 was 58.9 hr (2.45 days) and stage 3 was 55.6 hr (2.32 days), roughly 1.9 or 1.8 times longer, respectively. These results provide a refined timeline for ovule development when compared to previous estimates (Schneitz et al., 1995). Using the mean ovule volume per stage (Table 1), we estimated average growth rates for certain time intervals. For up to the end of stage 1-II, we estimated an average growth rate of 0.3 × 10³ µm³/hr, of 1.5 × 10³ µm³/hr for the interval including stages 2-I to , and of 7.2 × 10³ µm³/hr for the interval including stages 3-I to 3-IV. The growth rate dropped to 2.9 × 10³ µm³/hr for stage 3-V.

We then investigated relative growth (Figure 4). In a first step, we normalized global growth and cell proliferation rates during two consecutive stages by the respective rate of the stage 1-I/1-II interval (Figure 4A). Both relative growth rate curves showed a dynamic behavior across ovule development and for several stage intervals we determined similar relative growth rates. Interestingly, however, we noticed that from stages 2-I to 2-II the relative cell proliferation rate was higher than the relative growth rate suggesting growth accompanied by cell proliferation. By contrast, the opposite was observed for stages 2-IV to 3-IV indicating that ovule growth during these stages was mainly due to cell expansion. In a second step, we investigated the relative growth of the ovule by plotting the ratio between the mean total volume or mean total number of cells for two consecutive stages (Figure 4B). The curves in Figure 4A and B qualitatively resemble each other. This is presumably due to the relatively minor variations in the duration of the individual ovule stages.

Figure 4

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Includes the relative growth and proliferation, the relative growth and proliferation rate and the tissues relative growth per each ovule developmental stage.

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Next, we investigated the relative growth of the major tissues (Figure 4C–J). We took the ratio between the mean total volume or mean total cell number of a tissue for two consecutive stages and divided it by the corresponding relative ovule growth. The ratio relates the tissue growth rate to overall ovule growth. Values above one indicate that the tissue is growing at a higher rate than overall ovule growth rate and values below one indicate the opposite. We observed that the L3 showed the most relative growth and the L1 the least during stage 1-I (Figure 4C–F). During stages 1-II to 2-II, all three layers contributed similarly to the overall growth. From stages 2-III to 3-VI, the nucellus, chalaza, integuments, and eventually the embryo sac showed dynamic changes during development (Figure 4G–J). For example, up to stage 3-I the outer integument exhibited more relative growth than the inner integument, while from stage 3-I on the relative growth pattern was reversed.

In summary, the results reveal ovule growth to be dynamic both in terms of overall growth and the respective relative contributions of individual tissues during development.

Scattered buildup of WUSCHEL expression in the nucellus

3D digital ovules allow a detailed investigation of spatial gene expression patterns and with cellular resolution. As proof-of-concept, we analyzed the expression of WUSCHEL (WUS, AT2G17950) in the young ovule. WUS controls stem cell development in the shoot apical meristem (Gaillochet et al., 2015; Mayer et al., 1998). WUS is also active during ovule development and promotes early pattern formation in the chalaza and integument initiation as wus mutants show misorganized gene expression in the chalaza and lack integuments (Gross-Hardt et al., 2002; Sieber et al., 2004). Moreover, WUS is also required for MMC formation (Lieber et al., 2011) but its expression must be excluded from the MMC to allow proper progress through meiosis (Zhao et al., 2017). A combination of in situ hybridization experiments, reporter gene analyses, and immunodetection indicated that WUS is expressed in the epidermis of the nucellus. Thus, it is currently thought that WUS performs its functions in a non-cell-autonomous fashion (Gross-Hardt et al., 2002; Lieber et al., 2011; Sieber et al., 2004; Zhao et al., 2017). However, it remains unclear when WUS expression first appears and thus at what stage it may become functionally relevant, whether its expression is always restricted to the nucellus, and whether WUS expression is limited to the nucellar epidermis or is present in L2 cells (and possibly the MMC) as well. To address these issues, we took advantage of a Col reporter line carrying a reporter for WUS promoter activity (pWUS::2xVENUS:NLS::tWUS (pWUS)) (Zhao et al., 2017). We generated a total of 67 3D digital ovules from the pWUS reporter line covering stages 1-I to 2-III (9 ≤ n ≤ 21 per stage).

We first asked when the pWUS signal became detectable during ovule development. We found that with two exceptions pWUS signals could robustly be observed starting with ovules carrying 50 or more cells (Figure 5A,C). Of the 38 digital ovules spanning stage 1 only two cases with fewer than 50 cells showed either one (1048_A, 32 cells) or two (805_E, 48 cells) cells expressing pWUS signal, while all ovules with 50 or more cells showed pWUS signal. The morphological distinction between stages 1-I and 1-II had previously not been clearly defined (Schneitz et al., 1995). Thus, we used pWUS expression as a convenient marker to more precisely discriminate between the end of stage 1-I (ovules with fewer than 50 cells) and the start of stage 1-II (ovules with 50 or more cells).

Figure 5

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Includes the list of ovule IDs, stage, total number of cells, number of cells per L1, L2, L3 layer, total number of WUS expressing cells and number of WUS expressing cells per L1, L2, L3 layer of the available pWUS dataset.

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Next, we explored the elaboration of the spatial pWUS expression pattern during early primordium development (Figure 5C–F). Reporter signal was initially detected in the epidermis of the distal tip of the primordium in individual cells or in small irregular patches of cells exhibiting reporter expression. The patchiness of the signal continued through stage 2-II. By stage 2-III, however, most of the epidermal cells of the nucellus exhibited pWUS signal. A pWUS reporter signal in the nucellar epidermis at early stage 2 is consistent with previous reports (Tucker et al., 2012; Zhao et al., 2017). However, we noticed that starting with stage 2-I about half of the ovules also exhibited reporter signal in a few cells of the first subepidermal layer (L2) (Figure 5B,D–F). In those instances between one to seven L2 cells were found to express the reporter. All the pWUS-expressing L2 cells were neighboring the MMC and resided next to the bottom of the MMC. We never observed reporter signal in the MMC itself. Finally, we never observed reporter signals in the central region or the integuments.

In summary, our expression study of the pWUS reporter line provided new insight into the temporal and spatial establishment of WUS expression in the ovule. The data suggest strong temporal control of the initiation of WUS expression at early stage 1-II indicating that WUS is not involved in ovule formation prior to the 50-cell primordium. They further suggest that WUS expression is mostly, though not entirely, restricted to the nucellar epidermis. Spatial regulation of WUS within the nucellus appears less strict as indicated by the variable and spotty expression of pWUS in the nucellar epidermis during stages 1-II to 2-I and the occasional signal in L2 cells of the nucellus from stage 2-I onwards. Our data also provide additional support for the current notion that WUS affects MMC formation, pattern formation in the chalaza, and integument initiation in a non-cell-autonomous fashion.

Even growth of ovule primordia

To gain more detailed insight into specific aspects of ovule development, we explored a range of stage and tissue-specific cellular properties of the 3D digital ovules. For example, it is not known if primordium outgrowth occurs evenly or in distinct growth phases. The results in Figure 4A indicate that the growth rate increases from the youngest detectable primordia to stage 2-I primordia but they do not reveal whether primordium outgrowth undergoes temporal fluctuations. To address this question, we investigated early primordium development up to stage 2-I. We generated a dataset comprising 138 digital primordia of stage 2-I or younger. This dataset included ovules from the high-quality dataset described above but also encompassed ovule primordia that did not make the cut for the high-quality dataset as they possessed slightly higher percentages of undersegmented cells (more than 4% but less than 9% of missegmented cells per primordium). This approach increased sample size and still allowed error-free determination of cell numbers (by including information from the TO-PRO-3 channel). Moreover, we could determine the total volume of the primordium, by summing up the volumes of its constituting cells, as undersegmenation errors minimally influence this parameter. This dataset was also used to determine the PD extension of the primordia.

We ordered the individual digital primordia according to increasing PD extension (primordium height), primordium volume, or cell number (Figure 6A–D). We did not detect distinct subclasses of primordia but observed steady and continuous rises in primordium volume and cell number. Taken together, the data support the notion that from the youngest detectable primordia to late stage 2-I primordia outgrowth does not undergo major fluctuations.

Figure 6

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Includes the list of ovule IDs, total number of cells, total volume and the PD extension of early stage wild-type ovule.

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Stage 2-I is defined by the emergence of the large L2-derived MMC at the distal tip of the primordium (Schneitz et al., 1995). We addressed the question whether ovule primordia reach a certain size before they develop the MMC or undergo a gradual transition between stage 1-II and stage 2-I. To this end, we determined which stage 1 ovules exhibited maximum values for the three parameters mentioned above and which stage 2-I ovules featured the smallest values (Figure 6B–D). Taking these considerations into account, ovule primordia grow to a volume of about 1.8 × 10⁴ μm³, or a cell number range of approximately 125–135 total cells, and to a height range of about 41– 43 μm when they enter stage 2-I. For each of the three parameters, we noticed a small number of ovules that fell into the range of overlap: five for the PD extension and seven each for total volume of primordium and total cell number per primordium. These numbers account for 3.6% and 5.1% of the total of 138 scored ovules, respectively. The results indicate that ovule primordia reach a size threshold before they transition from stage 1-II to stage 2-I.

Assessment of the cell volume of the L2 cells in our datasets of stage 1-II and stage 2-I digital ovules combined with visual inspection of the digital ovule primordia revealed that the MMC can easily be distinguished based on its presence in the L2 and its comparably large cell volume (Figure 5D–F). At stage 2-I, we measured an average MMC volume of 543 μm³ (543.3 ± 120.6). The smallest MMC at stage 2-I possessed a volume of 339.2 μm³, well within the range observed previously (Lora et al., 2017). The largest L2 cell at stage 1-II had a volume of 297.6 μm³. We observed a mean cell volume of stage 1-II L2 cells of 153.5 μm³ ± 48.3 (n = 335) and the volume of this large cell was well beyond the 75% percentile (187.7 μm³). However, visual inspection revealed that this cell did not reside at the tip of the primordium while all the L2 cells located at the tip of the different stage 1-II primordia showed a cell volume that was close to the mean value. While we cannot exclude that MMC development starts already earlier than stage 2-I, we propose that by definition a stage 2-I MMC has a minimal cell volume of 335 μm³.

First morphological manifestation of polarity in the young ovule

The final gynapical-gynbasal orientation of the ovule relates to its alignment with respect to the apical-basal axis of the gynoecium (Simon et al., 2012). Here, we addressed the question when and how this orientation is set up. Upon inspecting the 3D meshes of gynoecium fragments, we noticed that many ovule primordia were positioned at a hitherto undescribed slant relative to the placenta surface (Figure 7A). We quantified the slant by measuring the PD distances of the shorter and longer sides of primordia of different stages (Figure 7—figure supplement 1). We observed that the slant was barely noticeable at stage 1-I, became more tangible during stage 1-II, and was prominent by stage 2-I (Figure 7B,C). Moreover, we found that the slant was oriented at a right angle to the apical-basal axis of the gynoecium with the small angle of the slant facing the developing septum. Since this axis showed a different spatial arrangement to the future gynapical-gynbasal orientation (see below), we defined the small angle half of the primordium as the posterior and the opposite half as the anterior sides of the primordium. Slant formation also coincides with anterior expression of PHABULOSA (PHB) during stage 1 (Sieber et al., 2004). Thus, slanting represents the first morphological manifestation of polarity in the young ovule primordium. We then investigated when and how the gynapical-gynbasal orientation of the ovule is established by looking at 40 stage 2-III and 2-IV 3D digital ovules still attached to the placenta (Figure 7D). We always observed initiation of the outer integument to occur at the posterior region. In ovules exhibiting slightly more advanced outer integuments we also detected a turn of the funicular PD axis (Figure 7D). Ovules with a noticeable turn were oriented along the apical-basal axis of the gynoecium with the anterior side pointing apically and the posterior side facing basally (Figure 7D,E). Thus, we propose that the final gynapical-gynbasal orientation of the ovule is the result of a multi-step morphogenetic process involving the early establishment of an anterior-posterior axis oriented normally to the long axis of the gynoecium followed by outer integument initiation and a funicular turn.

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Includes the list of ovule IDs, the length along the anterior surface, length along the posterior surface and the length difference used for quantifying the slant of early stage wild-type ovule.

The additional excel file indicates the ovule IDs, stage and the orientation along the anterior-posterior axis of early stage wild-type ovule.

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Anterior-posterior polarity within the chalaza

Slanting and the posterior initiation of the outer integument provide anatomically recognizable signs of anterior-posterior polarity in the ovule. Slanting also predicts the curvature of the prospective funiculus (Figure 1A). Moreover, the shifted position of the phloem within the funiculus (Müller et al., 2015; Figure 7—figure supplement 2) and the arrangement of the nuclei in the four-nuclear embryo sac (Figure 7—figure supplement 3A–B) indicate a similar polarity in proximal and distal interior tissues. Thus, we investigated whether an anterior-posterior axis is also discernible within the interior central region. Previous genetic results as well as evolutionary considerations implied that the central region or chalaza can be subdivided into distal and proximal tiers flanked by the inner and outer integuments, respectively (Baker et al., 1997; Endress, 2011; Gasser and Skinner, 2019; Sieber et al., 2004). We focussed on the subepidermal cells of the proximal chalaza that constitute the majority of the central region.

Both integuments are initiated in the epidermis (Jenik and Irish, 2000; Schneitz et al., 1995). We noticed that beginning with stages 2-IV/2-V subepidermal cells restricted to the anterior side of the proximal chalaza contribute to the outer integument as well (Figure 8A,B). By contrast, we never found interior cells positioned more posteriorly to be part of the outer integument. In addition, cells of the abaxial or outer layer of the outer integument (oi2) extend more proximally than cells of the adaxial or inner layer of the outer integument (oi1) (Figure 8A,B). This interpretation of the morphology is supported by clonal analysis (see Figure 4R in Jenik and Irish, 2000). We defined the anterior region delineated anteriorly and medio-laterally by the oi1 and oi2 layers of the outer integument as anterior chalaza. The posterior chalaza was defined as bounded medio-laterally and posteriorly by the proximal oi2 layer (Figure 8A,B). We found the two tissues to be composed of different numbers of cells. Moreover, the size and shape of the cells differed noticeably (Figure 8F–I; Table 3). We found that the anterior chalaza was always characterized by fewer but bigger cells in comparison to the posterior chalaza for which we observed more but smaller cells. Initially, the respective tissue volumes differed but became very similar by stage 3-I. Thus, the increases in the volumes of the anterior and posterior chalaza appear to be largely driven by increases in cell size and cell proliferation, respectively.

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Includes the list of ovule IDs, stage, number of cells and tissue volume of posterior and anterior chalaza of wild-type ovule.

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Table 3

	Tissue
Stage*	Anterior proximal chalaza			Posterior proximal chalaza
	N cells	Cell volume (μm3)	Tissue volume (x104 μm3)	N cells	Cell volume (μm3)	Tissue volume (x104 μm3)
2-III	-	-	-	35 ± 5	113 ± 41.2	0.4 ± 0.08
2-IV	9 ± 2	160 ± 59.6	0.14 ± 0.03	48 ± 9	109 ± 45.6	0.52 ± 0.1
2-V	13 ± 6	226.4 ± 77.3	0.31 ± 0.1	71.2 ± 10	120 ± 52.7	0.85 ± 0.1
3-I	32 ± 8	270.4 ± 121.3	0.87 ± 0.2	86 ± 20	120 ± 59.5	1 ± 0.2
3-II	50 ± 8	298 ± 156	1.5 ± 0.2	108 ± 19	127 ± 70	1.3 ± 0.2
3-III	51 ± 13	318.1 ± 192	1.6 ± 0.35	121 ± 32	124 ± 68	1.5 ± 0.3
3-IV	52 ± 11	347.5 ± 198.2	1.8 ± 0.36	138 ± 18	128 ± 73	1.7 ± 0.2
3-V	60 ± 14	336.8 ± 227.4	2 ± 0.4	148 ± 27	126 ± 70.5	1.8 ± 0.4
3-VI	59 ± 6	428.7 ± 284.4	2.5 ± 0.4	209 ± 31	132 ± 90.7	2.7 ± 0.4

1. * Number of 3D digital ovules scored: 10 (stages 2-II- 3-II, 3-IV, 3-VI), 11 (stages 3-III, 3-V).

   Values represent mean ± SD.

We noticed that despite reaching similar sizes, the two tissues exhibited differences in shape. Development of the anterior chalaza eventually resulted in a prominent anterior bulging of the outer integument likely due to the enlargement of the anterior chalazal cells. Moreover, the anterior chalaza showed two ‘wing-like’ lateral extensions (Figure 8D,E). We assume that the two ‘wings’ of the anterior chalaza further contribute to the bulging of the outer integument and the hood-like structure of the ovule. By contrast, the posterior chalaza acquired a more radially symmetric, rod-like shape. Starting with stages 3-I/3-II bulging of the anterior chalaza led to the appearance of the nucellus and inner integument sitting ‘on top’ of the posterior chalaza (Figure 8A).

In summary, morphological inspection of the interior chalaza of 3D digital ovules indicated that the central region is of complex composition and exhibits a recognizable anterior-posterior polarity that noticeably contributes to the shape of the ovule.

A few scattered asymmetric cell divisions initiate a parenchymatic inner integument layer

A new cell layer is produced by the inner integument shortly before fertilization (Debeaujon et al., 2003; Schneitz et al., 1995). The ii1 or endothelial layer originates this additional cell layer (ii1’) by periclinally oriented asymmetric cell divisions (Figure 9A). Cells of the ii1’ layer are immediately distinguishable from ii1 cells by their altered shape and staining characteristics (Schneitz et al., 1995). Moreover, they do not express a transcriptional reporter for the epidermis-specific ARABIDOPSIS THALIANA MERISTEM L1 (ATML1) gene (Debeaujon et al., 2003). Finally, during early seed development, ii1 cells will produce tannins while ii1’ cells will contribute to parenchymatic ground tissue. Thus, cambium-like activity of the ii1 layer results in asymmetric cell divisions, thickening of the inner integument, and formation of distinct tissues with separate functions.

Figure 9

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Includes the list of ovule IDs, stage, number of cells and tissue volume of parenchymatic inner integument of wild-type ovule.

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The cellular basis and 3D architecture of ii1’ layer formation is poorly understood. We followed its formation through all of stage 3 with the help of our 3D digital ovules (Figure 9B–G). To aid the analysis, we removed the outer integument and layer ii2 in MGX. We could observe the ii1’ layer at various stages of development in 36 digital ovules. In contrast to what was previously described we already observed first signs of ii1’ formation at stage 3-II (Figure 9B,C). Two out of the 10 digital ovules showed one cell of this layer. At stage 3-III 4/11 and at stage 3-VI 9/10, digital ovules showed at least one ii1’ layer cell, respectively. By stages 3-V and 3-VI, all digital ovules exhibited this layer. The number of ii1’ layer cells increased to 60.8 ± 19.5 at stage 3-VI. Thus, at this stage the average ii1' layer consisted of 13.4% of the total cells of the inner integument and with a volume of 1.53 × 10⁴ μm³±0.5×10⁴ μm³ contributed 14.4% to its total volume.

With respect to the spatial organization of the ii1’ layer, we observed single cells or patches of cells that were located on both lateral sides of the posterior inner integument at stages 3-II and 3-III (Figure 9C). Due to further divisions a patch of connected cells became visible at later stages. Proximal-distal and lateral extension of the ii1’ layer occurred until it showed a ring-like structure covering the proximal half of the inner integument at stage 3-VI (Figure 9D–F). The boundary of the ii1’ layer was not smooth but exhibited an irregular appearance.

We then asked if the ii1’ layer is generated entirely through periclinal cell divisions of ii1 cells or if anticlinal cell divisions in existing ii1’ cells contribute to the formation of this layer. To this end, we scored 978 cells of the ii1’ layers across the 36 ovules exhibiting a ii1’ layer and identified 14 cells in mitosis as indicated by the presence of mitotic figures in the TO-PRO-3 channel (Figure 9G). Only one of the 14 cells showed a periclinal cell division that generated a cell of the ii1’ layer. Interestingly, however, the other 13 mitotic cells were experiencing anticlinal cell divisions.

Taken together, the results suggest that initiation of the ii1’ layer does not involve simultaneous asymmetric cell divisions in many ii1 cells resulting in the formation of a ring or large patch of ii1’ tissue. Rather, asymmetric periclinal cell divisions occur only in a few spatially scattered founder cells distributed within the ii1 cell layer. Further anticlinal cell divisions in the ii1’ daughters of the founder cells then result in the formation of a continuous ii1’ cell layer with irregular edges.

INNER NO OUTER affects development of the nucellus

The YABBY transcription factor gene INNER NO OUTER (INO, AT1G23420) is an essential regulator of early pattern formation in the ovule (Baker et al., 1997; Balasubramanian and Schneitz, 2002; Balasubramanian and Schneitz, 2000; Meister et al., 2002; Schneitz et al., 1997; Sieber et al., 2004; Villanueva et al., 1999). The ovule phenotype of plants with a defect in INO has been extensively characterized. Loss-of-function ino mutants carry ovules that fail to form an outer integument. Moreover, development of the female gametophyte is usually blocked at the mono-nuclear embryo sac stage. However, it remains unclear if the inner integument and the nucellus, apart from the defect in embryo sac development, are affected in ino mutants. To address these and other issues (see below), we generated ino-5, a putative null allele of INO in Col-0 that was induced by a CRISPR/Cas9-based approach (see Materials and methods) and performed a quantitative phenotypic analysis of ino-5 ovules using a dataset of 120 3D digital ino-5 ovules covering stages 1-II to 3-VI (3 ≤ n ≤ 20 per stage) (Figure 10A). Ovules lacking INO activity are difficult to stage as they lack many of the distinct criteria that define the different stages of wild-type ovule development. To circumvent this problem, we staged ino-5 ovules by comparing the total number of cells and the total volume of ino-5 3D digital ovules to the corresponding stage-specific values of wild-type 3D digital ovules for which the outer integument had been removed (Supplementary file 4).

Figure 10

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Includes the list of ovule IDs, stage, total number of cells and total volume of early stage ino-5 ovules.

The additional sheet includes the list of ovule IDs, stage, number of cells and tissue volume of nucellus and funiculus wild-type and ino-5 ovules.

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We first analyzed the total cell number per primordium and the total volume per primordium for ino-5 ovules of stages 1-II to 2-II. We did not find robust differences between ino-5 and wild type for the two parameters (Figure 10B,C). This was to be expected as INO expression became first detectable around stage 2-II/III (Balasubramanian and Schneitz, 2000; Meister et al., 2004; Meister et al., 2002; Villanueva et al., 1999). We then asked if INO affects the development of the nucellus and the inner integument. To this end, we analyzed cellular features of the two tissues of ino-5 for stages 2-III to 3-VI. We first investigated nucellar development. As previously described we observed that embryo sac development did not extend beyond the mono-nuclear embryo sac stage, although up to four-nuclear embryo sacs could be detected as well (Figure 10D). We then assessed cell number and tissue volume of the nucellus of ino-5 (excluding the embryo sac). We observed an elevated number of nucellar cells in ino-5 ovules starting from late stage 2-V/3-I (Figure 10E; Tables 2 and 4). The nucellar volume of ino-5 was also increased relative to wild type (Figure 10F; Tables 2 and 4). The results indicate that INO not only affects ontogenesis of the embryo sac but also restricts cell number and nucellus volume during nucellus development.

Table 4

	Tissue
Stage*	Nucellus		Inner integument		Funiculus		Central region
	N cells	Volume (x104 μm3)	N cells	Volume (x104 μm3)	N cells	Volume (x104 μm3)	N cells	Volume (x104 μm3)
2-III	49 ± 7.6	0.5 ± 0.08	31.3 ± 3.2	0.4 ± 0.03	189.0 ± 29	2.4 ± 0.4	35.8 ± 37.1	0.3 ± 0.3
2-IV	69.7 ± 7.5	0.7 ± 0.1	41.6 ± 8.3	0.5 ± 0.1	202.7 ± 25	2.2 ± 0.4	68.3 ± 6.6	0.6 ± 0.2
2-V	71.4 ± 8.1	0.8 ± 0.1	70.7 ± 17.7	0.9 ± 0.2	286.4 ± 35.2	3.4 ± 0.5	108.1 ± 19.3	1.2 ± 0.3
3-I	81.4 ± 9.5	0.9 ± 0.1	122.0 ± 27.3	1.8 ± 0.5	379.0 ± 29.7	4.7 ± 0.5	134.0 ± 23.1	1.8 ± 0.4
3-II	92.6 ± 15.0	1.1 ± 0.2	185.1 ± 21.9	3.9 ± 0.7	402.9 ± 32.6	5.1 ± 0.7	176.9 ± 24.6	2.6 ± 0.4
3-III	93.0 ± 12.0	1.1 ± 0.1	208.4 ± 14.6	4.8 ± 0.5	451.6 ± 32.7	5.9 ± 0.7	202.9 ± 24.1	3.2 ± 0.5
3-IV	102.7 ± 12.8	1.5 ± 0.2	296.9 ± 43.5	7.3 ± 1.3	481.3 ± 45.7	6.7 ± 0.5	215.4 ± 30.7	3.6 ± 0.7
3-V	116.3 ± 12.5	1.8 ± 0.2	360.8 ± 35.2	9.6 ± 1.0	522.6 ± 47.4	7.2 ± 1.1	239.6 ± 31.2	4.2 ± 0.7
3-VI	121.1 ± 15.2	1.9 ± 0.3	406.1 ± 61.3	10.0 ± 1.6	599.6 ± 40.6	8.8 ± 0.8	254.4 ± 48.5	4.5 ± 0.9

1. * Number of 3D digital ovules scored: 3 (stage 2-IV), 6 (stage 2-III), 7 (stages 3-III, 3-VI), 9 (stage 3-II), 10 (stage 3-V), 14 (stages 3-I, 3-IV), 20 (stage 2-V ). Values represent mean ± SD.

We then investigated cell number and tissue volume for the inner integument of ino-5 (Figure 10G,H; Tables 2 and 4). We found that the two parameters did not deviate noticeably from wild type except for stages 3-IV and 3-V where we observed a small but perceivable increase in cell number and tissue volume. However, this increase appeared to be transient as both parameters were normal in ino-5 ovules of stage 3-VI. These findings indicate that INO does not exert a major influence on the monitored cellular characteristics of the inner integument.

In summary, the data suggest that INO exerts a hitherto unknown function in nucellus development but does not affect morphogenesis of the inner integument.

Ovule curvature represents a multi-step process

The curvature of the Arabidopsis ovule constitutes an interesting and unique morphogenetic process. It raises the question if curvature can be subdivided into distinct, genetically controlled steps. During stages 2-III to 2-V of wild-type ovule development the central region of the ovule primordium forms a prominent kink resulting in the nucellus pointing toward the anterior side of the ovule (Figures 1A and A). Following kink formation differential growth results in the bending of the integuments and the nucellus resulting in the curved shape of the ovule (Figure 1A,F). INO is required for regular kink formation as this feature was absent in stage 2-V ino-5 ovules (Figure 11A). In later stage ino-5 ovules, we found aberrant growth on the anterior side of the chalaza and the nucellus pointing toward the posterior side of the ovule (Figure 11A). Apart from the absence of the regularly oriented kink in ino ovules, there is an obvious effect on the curvature of the ovule since the nucellus and inner integument develop into straight rather than curved structures. These observations indicated that INO-dependent differential growth patterns in the central region underlie kink formation.

Figure 11

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Includes the list of ovule IDs, genotype, stage, number of different division planes scored and the volume of the pair of asymmetric enlarged oblique divided cells.

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To understand the cellular basis of early kink formation and how INO controls this aspect, we investigated the cellular architecture in the subepidermal central region of wild type and ino-5. At stage 2-III, we counted 6.7 ± 2.3 periclinal cell division planes in the subepidermal proximal chalaza (Figure 11B,C). This number increased to 12.6 ± 3.3 by stage 2-IV. We also observed a small number of division planes oriented in a longitudinal-anticlinal fashion (3.2. ± 2.2 at stage 2-III and 1.8. ± 2.1 at stage 2-IV) (Figure 11B,D). These division planes were distributed throughout the subepidermal proximal chalaza. Interestingly, we also found a few oblique division planes (1.3 ± 1.5 at stage 2-III and 1.4 ± 0.9 at stage 2-IV) (Figure 11B,E). They were preferentially found at the posterior chalaza just underlying the initiating outer integument. Moreover, we observed that the progeny of the posterior oblique cell divisions had undergone asymmetric enlargement with the noticeably bigger cell being located directly adjacent to the large epidermal cells associated with the outer integument initiation (Figure 11B). We note that by stage 2-IV, a few oblique division planes were also observed at the anterior chalaza.

In ino-5 ovules, we noticed fewer periclinal division planes at stage 2-III/IV (Figure 11B,C). Furthermore, we did not observe oblique division planes (Figure 11B,E). Interestingly, however, the number of longitudinal-anticlinal division planes was enhanced (Figure 11B,D). Thus, in the absence of INO function, fewer periclinal cell divisions take place. Moreover, symmetric longitudinal-anticlinal cell divisions occur in place of the asymmetric oblique cell divisions in the subepidermal proximal chalaza leading to the absence of the enlarged cells abutting the posterior epidermis and a failure of kink formation.

In summary, our data indicate that differential cellular growth patterns in the proximal chalaza underly kink formation. We propose that the stage 2-III oblique cell divisions accompanied by asymmetric cell enlargement that are preferentially located in the posterior chalaza contribute to early kink formation. We further propose that the periclinal and longitudinal anticlinal divisions result in a widening of the chalaza. The control of these cellular patterns requires INO activity. Available evidence indicates that the spatial distribution of INO transcripts as well as INO protein is restricted to the epidermis and eventually the oi2 layer of the outer integument (Balasubramanian and Schneitz, 2000; Meister et al., 2002; Villanueva et al., 1999). This notion will require further exploration using 3D digital ovules. With this caveat in mind, however, it appears that INO regulates kink formation in a non-cell-autonomous fashion.

3D digital organs represent central tools for the study of tissue morphogenesis. In the past, it was extremely time-consuming and difficult to obtain 3D digital tissue that allowed the analysis of cellular architecture and gene and protein expression data with accurate cellular resolution including interior tissues. Here, we provide proof of concept for the power and versatility of combining the recently established segmentation pipeline PlantSeg (Wolny et al., 2020) with ClearSee-based staining of fixed tissue (Kurihara et al., 2015; Tofanelli et al., 2019). PlantSeg was successfully applied to diverse plant organs and even to animal tissue. Tissue clearing with ClearSee is compatible with several fluorescent proteins and stains and the optimized method employed here can be applied to a range of plant tissues as well (Kurihara et al., 2015; Tofanelli et al., 2019; Ursache et al., 2018). Using this approach, 3D digital ovules could be obtained with unprecedented speed and accuracy. It will be straightforward to apply this strategy to other plant tissues. Since PlantSeg provided excellent 3D cell segmentation of the Drosophila wing disc (Wolny et al., 2020) we suggest that combining PlantSeg with modern clearing methods, such as CLARITY (Tomer et al., 2014), may also provide a promising new strategy for the generation of 3D digital animal tissues.

This work demonstrates the unparalleled analytical power inherent in the examination of 3D digital organs with full cellular resolution. Investigating 3D digital ovules yielded new insights that could only be obtained when studying the morphogenesis of an organ with such a complex architecture in 3D and with cellular resolution throughout all tissues. For example, the analysis enabled the global assessment of a diverse set of 3D cellular features of the ovule, revealed new insights into the dynamic growth patterns underlying its development, and provided new information regarding the temporal and spatial control of WUS expression and its role in ovule development.

The results provide a new understanding of the establishment of ovule polarity. It was previously observed that ovules are oriented relative to the long axis of the gynoecium (Simon et al., 2012). The gynapical (micropylar) side is pointing to the stigma and the gynbasal (chalazal) side toward the base of the gynoecium. It remained unclear, however, when this polarity became established and whether it could be recognized throughout the internal tissues of the ovule. Our results indicate that the final orientation of the ovule does not correspond to how the polarity is established. The emergence of a slant in the ovule primordium represents the earliest morphological manifestation of polarity in the ovule. Importantly, this early anterior-posterior-axis was initiated at a right angle to the long axis of the gynoecium. Our anatomical and marker gene expression data further suggest that the anterior-posterior polarity is maintained during subsequent development and throughout all major tissues along the PD axis of the ovule. This is indicated by the polar distribution of the distal two nuclei of the four-nuclear embryo sac, the morphological differences between the anterior and posterior chalaza in the proximal central region, and the posterior placement of the phloem within the funiculus. The gynbasal-gynapical orientation of the ovule is eventually achieved by a turn in the funiculus. Thus, the alignment of the ovule with the apical-basal axis of the gynoecium does not directly correspond to the establishment of anterior-posterior polarity in the primordium but rather depends on a subsequent morphogenetic process in the funiculus. A basic molecular framework underlying primordium outgrowth and integument formation has been established (Chaudhary et al., 2018; Cucinotta et al., 2014; Gasser and Skinner, 2019). However, it is not known what regulates anterior-posterior polarity, slanting, and funiculus twisting. The mechanism may involve PHB and cues from the replum-septum boundary that is positioned in the medial plane of the gynoecium (Reyes-Olalde and de Folter, 2019; Roeder and Yanofsky, 2006).

Our data broadened the repertoire of modes of how a new cell layer can be formed in plants. Cambium-like activity of the innermost layer of the inner integument (endothelium or ii1 layer) results in the formation of the ii1’ layer (Debeaujon et al., 2003; Schneitz et al., 1995). Early morphological analysis in 2D led to the hypothesis that all cells located in the roughly proximal half of the ii1 layer undergo periclinal divisions resulting in the formation of the ii1’ cell layer. This model conformed to the broadly supported notion that periclinal asymmetric cell divisions underlie the formation of new cell layers in plants. For example, in the eight-cell Arabidopsis embryo, all cells undergo asymmetric cell divisions and thus contribute to the formation of the epidermis (Jürgens and Mayer, 1994; Yoshida et al., 2014). Similarly, periclinal asymmetric cell divisions in all the radially arranged cortex/endodermis initials are thought to contribute to the formation of the cortex and endodermis cell layers of the main root (Di Laurenzio et al., 1996; Dolan et al., 1993). By contrast, our investigation of 3D digital ovules revealed a distinct mode for the formation of the ii1’ cell layer. The analysis indicated that only a small number of ii1 cells undergo asymmetric cell divisions and produce the first ii1’ cells. Much of subsequent ii1’ layer formation appears to rely on symmetric cell divisions that originate in the daughters of those few ii1’ founder cells. Thus, formation of the ii1’ layer largely depends on lateral extension starting from a few scattered ii1’ pioneer cells, a process that may be regarded as layer invasion. It will be interesting to investigate the molecular control of this new type of cell layer formation in future studies.

3D digital organs drastically improve the power of a comparative phenotypic analysis and thus may enable new insight into gene function. As proof of concept we investigated 3D digital ino-5 ovules. The anatomical aspects of the ino phenotype were thought to be reasonably well understood as several labs had carefully studied the ino phenotype by traditional means, such as tissue sections of paraffin-embedded specimens, optical sections through whole-mount ovules, and scanning electron microscopy (Baker et al., 1997; Balasubramanian and Schneitz, 2002; Balasubramanian and Schneitz, 2000; Meister et al., 2002; Schneitz et al., 1997; Sieber et al., 2004; Villanueva et al., 1999). This study, however, uncovered that INO plays an even more important role in the organization of the early ovule than previously appreciated. Indeed, we have found that ino mutants not only show a missing outer integument and defective embryo sac development but also exhibit previously unrecognized cellular aberrations in the nucellus and chalaza. It has long been recognized that ontogenesis of the embryo sac correlates with proper integument development although the molecular mechanism remains unclear (Gasser et al., 1998; Grossniklaus and Schneitz, 1998). Our new data allow an alternative interpretation involving hormonal signaling from the chalaza. Cytokinin is known to affect ovule patterning through the control of the expression of the auxin efflux carrier gene PIN-FORMED 1 (PIN1) and a chalaza-localized cytokinin signal is required for early gametophyte development (Bencivenga et al., 2012; Ceccato et al., 2013; Cheng et al., 2013). Thus, the defects in the development of the nucellus and embryo sac of ino-5 may relate to its cellular mis-organization of the chalaza and thus altered cytokinin and in turn auxin signaling. It will be interesting to explore this notion in future studies.

Finally, we provide new insight into the genetic and cellular processes regulating ovule curvature. Previous studies hypothesized that ovule curvature is a multi-step process with a major involvement by the proximal chalaza and the integuments (Baker et al., 1997; Schneitz et al., 1997). Here, a detailed comparison between wild-type and ino-5 3D digital ovules provided evidence for two distinct processes that contribute to ovule curvature: kink formation in the ovule primordium and later bending of the developing integuments. Both processes require INO function as neither kink formation nor integument bending take place in ino-5. The data support the hypothesis that a small number of asymmetric oblique cell divisions in the stage 2-III posterior chalaza lead to the characteristic early kink of the ovule primordium. Regarding integument bending it is noteworthy that despite the straight growth of the inner integument of ino-5 its general cellular parameters appear largely unaffected. In addition, integument bending is prominent in mutants lacking an embryo sac (Schneitz et al., 1997). These observations indicate that the outer integument imposes bending onto the inner integument and nucellus. Future studies will address in more detail the interplay between genetics, cellular behavior, and tissue mechanics that underlies ovule curvature.

The entire 3D digital ovule datasets can be downloaded from the BioStudies data repository (https://www.ebi.ac.uk/biostudies/). Each dataset contains raw cell boundaries, cell boundaries, predictions from PlantSeg, nuclei images, segmented cells as well as the annotated 3D cell meshes, and the associated attribute files in csv format. The 3D mesh files can be opened in MorphoGraphX. Accession S-BSST475: the wild-type high-quality dataset and the additional dataset with more segmentation errors. Accession S-BSST498: pWUS::2xVenus:NLS. Accession S-BSST497: ino-5. Accession S-BSST513: Long-format Excel-files listing all the scored attributes of all the cells from the analyzed ovules.

1. 1. Vijayan A
   2. Tofanelli R
   3. Strauss S
   4. Cerrone L
   5. Wolny A
   6. Strohmeier J
   7. Kreshuk A
   8. Hamprecht FA
   9. Smith RS
   10. Schneitz K

   (2020) BioStudies

   ID S-BSST475. WT Arabidopsis ovule atlas: A 3D digital cell atlas of wild-type Arabidopsis ovule development with cellular and tissue resolution.

   https://www.ebi.ac.uk/biostudies/studies/S-BSST475
2. 1. Vijayan A
   2. Tofanelli R
   3. Strauss S
   4. Cerrone L
   5. Wolny A
   6. Strohmeier J
   7. Kreshuk A
   8. Hamprecht FA
   9. Smith RS
   10. Schneitz K

   (2020) BioStudies

   ID S-BSST498. 3D gene expression atlas of WUSCHEL in Arabidopsis ovules with cellular, nuclear and tissue resolution.

   https://www.ebi.ac.uk/biostudies/studies/S-BSST498
3. 1. Vijayan A
   2. Tofanelli R
   3. Strauss S
   4. Cerrone L
   5. Wolny A
   6. Strohmeier J
   7. Kreshuk A
   8. Hamprecht FA
   9. Smith RS
   10. Schneitz K

   (2020) BioStudies

   ID S-BSST497. ino Arabidopsis ovule atlas: A 3D digital cell atlas of ino ovules in Arabidopsis with cellular and tissue resolution.

   https://www.ebi.ac.uk/biostudies/studies/S-BSST497
4. 1. Vijayan A
   2. Tofanelli R
   3. Strauss S
   4. Cerrone L
   5. Wolny A
   6. Strohmeier J
   7. Kreshuk A
   8. Hamprecht FA
   9. Smith RS
   10. Schneitz K

   (2020) BioStudies

   ID S-BSST513. 3D qualitative attribute dataset of Arabidopsis ovule atlas.

   https://www.ebi.ac.uk/biostudies/studies/S-BSST513

1. 1. Anderson C
   2. Hill B
   3. Lu HC
   4. Moverley A
   5. Yang Y
   6. Oliveira NMM
   7. Baldock RA
   8. Stern CD

   (2019) A 3D molecular atlas of the chick embryonic heart

   Developmental Biology 456:40–46.

   https://doi.org/10.1016/j.ydbio.2019.07.003

   • PubMed
   • Google Scholar
2. 1. Asadulina A
   2. Panzera A
   3. Verasztó C
   4. Liebig C
   5. Jékely G

   (2012) Whole-body gene expression pattern registration in Platynereis larvae

   EvoDevo 3:27.

   https://doi.org/10.1186/2041-9139-3-27

   • Google Scholar
3. Preprint

   1. Bailoni A
   2. Pape C
   3. Wolf S
   4. Beier T
   5. Kreshuk A
   6. Hamprecht FA

   (2019) A generalized framework for agglomerative clustering of signed graphs applied to instance segmentation

   arXiv.

   https://arxiv.org/abs/1906.11713

   • Google Scholar
4. 1. Baker SC
   2. Robinson-Beers K
   3. Villanueva JM
   4. Gaiser JC
   5. Gasser CS

   (1997)

   Interactions among genes regulating ovule development in Arabidopsis thaliana

   Genetics 145:1109–1124.

   • PubMed
   • Google Scholar
5. 1. Balasubramanian S
   2. Schneitz K

   (2000)

   NOZZLE regulates proximal-distal pattern formation cell proliferation and early sporogenesis during ovule development in Arabidopsis thaliana

   Development 127:4227–4238.

   • PubMed
   • Google Scholar
6. 1. Balasubramanian S
   2. Schneitz K

   (2002)

   NOZZLE links proximal-distal and adaxial-abaxial pattern formation during ovule development in Arabidopsis thaliana

   Development 129:4291–4300.

   • PubMed
   • Google Scholar
7. 1. Barbier de Reuille P
   2. Routier-Kierzkowska AL
   3. Kierzkowski D
   4. Bassel GW
   5. Schüpbach T
   6. Tauriello G
   7. Bajpai N
   8. Strauss S
   9. Weber A
   10. Kiss A
   11. Burian A
   12. Hofhuis H
   13. Sapala A
   14. Lipowczan M
   15. Heimlicher MB
   16. Robinson S
   17. Bayer EM
   18. Basler K
   19. Koumoutsakos P
   20. Roeder AH
   21. Aegerter-Wilmsen T
   22. Nakayama N
   23. Tsiantis M
   24. Hay A
   25. Kwiatkowska D
   26. Xenarios I
   27. Kuhlemeier C
   28. Smith RS

   (2015) MorphoGraphX: a platform for quantifying morphogenesis in 4D

   eLife 4:05864.

   https://doi.org/10.7554/eLife.05864

   • PubMed
   • Google Scholar
8. 1. Bassel GW
   2. Stamm P
   3. Mosca G
   4. Barbier de Reuille P
   5. Gibbs DJ
   6. Winter R
   7. Janka A
   8. Holdsworth MJ
   9. Smith RS

   (2014) Mechanical constraints imposed by 3D cellular geometry and arrangement modulate growth patterns in the Arabidopsis embryo

   PNAS 111:8685–8690.

   https://doi.org/10.1073/pnas.1404616111

   • PubMed
   • Google Scholar
9. 1. Bauby H
   2. Divol F
   3. Truernit E
   4. Grandjean O
   5. Palauqui JC

   (2007) Protophloem differentiation in early Arabidopsis thaliana development

   Plant and Cell Physiology 48:97–109.

   https://doi.org/10.1093/pcp/pcl045

   • PubMed
   • Google Scholar
10. 1. Beeckman T
    2. De Rycke R
    3. Viane R
    4. Inzé D

    (2000) Histological study of seed coat development in Arabidopsis thaliana

    Journal of Plant Research 113:139–148.

    https://doi.org/10.1007/PL00013924

    • Google Scholar
11. 1. Bencivenga S
    2. Simonini S
    3. Benková E
    4. Colombo L

    (2012) The transcription factors BEL1 and SPL are required for cytokinin and auxin signaling during ovule development in Arabidopsis

    The Plant Cell 24:2886–2897.

    https://doi.org/10.1105/tpc.112.100164

    • PubMed
    • Google Scholar
12. 1. Bink K
    2. Walch A
    3. Feuchtinger A
    4. Eisenmann H
    5. Hutzler P
    6. Höfler H
    7. Werner M

    (2001) TO-PRO-3 is an optimal fluorescent dye for nuclear counterstaining in dual-colour FISH on paraffin sections

    Histochemistry and Cell Biology 115:293–299.

    https://doi.org/10.1007/s004180100254

    • PubMed
    • Google Scholar
13. 1. Boutros M
    2. Heigwer F
    3. Laufer C

    (2015) Microscopy-based high-content screening

    Cell 163:1314–1325.

    https://doi.org/10.1016/j.cell.2015.11.007

    • PubMed
    • Google Scholar
14. 1. Ceccato L
    2. Masiero S
    3. Sinha Roy D
    4. Bencivenga S
    5. Roig-Villanova I
    6. Ditengou FA
    7. Palme K
    8. Simon R
    9. Colombo L

    (2013) Maternal control of PIN1 is required for female gametophyte development in Arabidopsis

    PLOS ONE 8:e66148.

    https://doi.org/10.1371/journal.pone.0066148

    • PubMed
    • Google Scholar
15. Book

    1. Chaudhary A
    2. Gao J
    3. Schneitz K

    (2018) The genetic control of ovule development, In: Reference Module in Life Sciences

    Elsevier.

    https://doi.org/10.1016/B978-0-12-809633-8.20737-1

    • Google Scholar
16. 1. Cheng CY
    2. Mathews DE
    3. Schaller GE
    4. Kieber JJ

    (2013) Cytokinin-dependent specification of the functional megaspore in the Arabidopsis female gametophyte

    The Plant Journal 73:929–940.

    https://doi.org/10.1111/tpj.12084

    • PubMed
    • Google Scholar
17. 1. Christensen CA
    2. King EJ
    3. Jordan JR
    4. Drews GN

    (1997) Megagametogenesis in Arabidopsis wild type and the gf mutant

    Sexual Plant Reproduction 10:49–64.

    https://doi.org/10.1007/s004970050067

    • Google Scholar
18. 1. Clough SJ
    2. Bent AF

    (1998) Floral dip: a simplified method for Agrobacterium-mediated transformation of Arabidopsis thaliana

    The Plant Journal 16:735–743.

    https://doi.org/10.1046/j.1365-313x.1998.00343.x

    • PubMed
    • Google Scholar
19. 1. Coen E
    2. Kennaway R
    3. Whitewoods C

    (2017) On genes and form

    Development 144:4203–4213.

    https://doi.org/10.1242/dev.151910

    • PubMed
    • Google Scholar
20. 1. Coen E
    2. Rebocho AB

    (2016) Resolving conflicts: modeling genetic control of plant morphogenesis

    Developmental Cell 38:579–583.

    https://doi.org/10.1016/j.devcel.2016.09.006

    • PubMed
    • Google Scholar
21. 1. Cucinotta M
    2. Colombo L
    3. Roig-Villanova I

    (2014) Ovule development, a new model for lateral organ formation

    Frontiers in Plant Science 5:117.

    https://doi.org/10.3389/fpls.2014.00117

    • PubMed
    • Google Scholar
22. 1. Debeaujon I
    2. Nesi N
    3. Perez P
    4. Devic M
    5. Grandjean O
    6. Caboche M
    7. Lepiniec L

    (2003) Proanthocyanidin-accumulating cells in Arabidopsis testa: regulation of differentiation and role in seed development

    The Plant Cell 15:2514–2531.

    https://doi.org/10.1105/tpc.014043

    • PubMed
    • Google Scholar
23. 1. Di Laurenzio L
    2. Wysocka-Diller J
    3. Malamy JE
    4. Pysh L
    5. Helariutta Y
    6. Freshour G
    7. Hahn MG
    8. Feldmann KA
    9. Benfey PN

    (1996) The SCARECROW gene regulates an asymmetric cell division that is essential for generating the radial organization of the Arabidopsis root

    Cell 86:423–433.

    https://doi.org/10.1016/S0092-8674(00)80115-4

    • PubMed
    • Google Scholar
24. 1. Dolan L
    2. Janmaat K
    3. Willemsen V
    4. Linstead P
    5. Poethig S
    6. Roberts K
    7. Scheres B

    (1993)

    Cellular organisation of the Arabidopsis thaliana root

    Development 119:71–84.

    • PubMed
    • Google Scholar
25. 1. Dreyer D
    2. Vitt H
    3. Dippel S
    4. Goetz B
    5. El Jundi B
    6. Kollmann M
    7. Huetteroth W
    8. Schachtner J

    (2010) 3d standard brain of the red flour beetle Tribolium castaneum: a tool to study metamorphic development and adult plasticity

    Frontiers in Systems Neuroscience 4:3.

    https://doi.org/10.3389/neuro.06.003.2010

    • PubMed
    • Google Scholar
26. 1. Endress PK

    (2011) Angiosperm ovules: diversity, development, evolution

    Annals of Botany 107:1465–1489.

    https://doi.org/10.1093/aob/mcr120

    • PubMed
    • Google Scholar
27. 1. Florijn RJ
    2. Slats J
    3. Tanke HJ
    4. Raap AK

    (1995) Analysis of antifading reagents for fluorescence microscopy

    Cytometry 19:177–182.

    https://doi.org/10.1002/cyto.990190213

    • PubMed
    • Google Scholar
28. 1. Fulton L
    2. Batoux M
    3. Vaddepalli P
    4. Yadav RK
    5. Busch W
    6. Andersen SU
    7. Jeong S
    8. Lohmann JU
    9. Schneitz K

    (2009) DETORQUEO, QUIRKY, and ZERZAUST represent novel components involved in organ development mediated by the receptor-like kinase STRUBBELIG in Arabidopsis thaliana

    PLOS Genetics 5:e1000355.

    https://doi.org/10.1371/journal.pgen.1000355

    • PubMed
    • Google Scholar
29. 1. Gaillochet C
    2. Daum G
    3. Lohmann JU

    (2015) O cell, where art thou? the mechanisms of shoot meristem patterning

    Current Opinion in Plant Biology 23:91–97.

    https://doi.org/10.1016/j.pbi.2014.11.002

    • PubMed
    • Google Scholar
30. 1. Gasser CS
    2. Broadhvest J
    3. Hauser BA

    (1998) Genetic analysis of ovule development

    Annual Review of Plant Physiology and Plant Molecular Biology 49:1–24.

    https://doi.org/10.1146/annurev.arplant.49.1.1

    • PubMed
    • Google Scholar
31. 1. Gasser CS
    2. Skinner DJ

    (2019) Development and evolution of the unique ovules of flowering plants

    Current Topics in Developmental Biology 131:373–399.

    https://doi.org/10.1016/bs.ctdb.2018.10.007

    • PubMed
    • Google Scholar
32. 1. Gibson WT
    2. Veldhuis JH
    3. Rubinstein B
    4. Cartwright HN
    5. Perrimon N
    6. Brodland GW
    7. Nagpal R
    8. Gibson MC

    (2011) Control of the mitotic cleavage plane by local epithelial topology

    Cell 144:427–438.

    https://doi.org/10.1016/j.cell.2010.12.035

    • PubMed
    • Google Scholar
33. 1. Gross-Hardt R
    2. Lenhard M
    3. Laux T

    (2002) WUSCHEL signaling functions in interregional communication during Arabidopsis ovule development

    Genes & Development 16:1129–1138.

    https://doi.org/10.1101/gad.225202

    • PubMed
    • Google Scholar
34. 1. Grossniklaus U
    2. Schneitz K

    (1998) The molecular and genetic basis of ovule and megagametophyte development

    Seminars in Cell & Developmental Biology 9:227–238.

    https://doi.org/10.1006/scdb.1997.0214

    • PubMed
    • Google Scholar
35. 1. Guignard L
    2. Fiúza UM
    3. Leggio B
    4. Laussu J
    5. Faure E
    6. Michelin G
    7. Biasuz K
    8. Hufnagel L
    9. Malandain G
    10. Godin C
    11. Lemaire P

    (2020) Contact area-dependent cell communication and the morphological invariance of ascidian embryogenesis

    Science 369:eaar5663.

    https://doi.org/10.1126/science.aar5663

    • PubMed
    • Google Scholar
36. 1. Hamant O
    2. Heisler MG
    3. Jönsson H
    4. Krupinski P
    5. Uyttewaal M
    6. Bokov P
    7. Corson F
    8. Sahlin P
    9. Boudaoud A
    10. Meyerowitz EM
    11. Couder Y
    12. Traas J

    (2008) Developmental patterning by mechanical signals in Arabidopsis

    Science 322:1650–1655.

    https://doi.org/10.1126/science.1165594

    • PubMed
    • Google Scholar
37. Preprint

    1. Hernandez-Lagana E
    2. Mosca G
    3. Sato EM
    4. Pires N
    5. Frey A
    6. Giraldo-Fonseca A
    7. Grossniklaus U
    8. Hamant O
    9. Godin C
    10. Boudaoud A
    11. Grimanelli D
    12. Autran D
    13. Baroux C

    (2020) Developmental constraints modulate reproductive fate and plasticity within the Arabidopsis ovule primordium

    bioRxiv.

    https://doi.org/10.1101/2020.07.30.226670

    • Google Scholar
38. 1. Hervieux N
    2. Dumond M
    3. Sapala A
    4. Routier-Kierzkowska A-L
    5. Kierzkowski D
    6. Roeder AHK
    7. Smith RS
    8. Boudaoud A
    9. Hamant O

    (2016) A mechanical feedback restricts sepal growth and shape in Arabidopsis

    Current Biology 26:1019–1028.

    https://doi.org/10.1016/j.cub.2016.03.004

    • Google Scholar
39. 1. Hong L
    2. Dumond M
    3. Tsugawa S
    4. Sapala A
    5. Routier-Kierzkowska AL
    6. Zhou Y
    7. Chen C
    8. Kiss A
    9. Zhu M
    10. Hamant O
    11. Smith RS
    12. Komatsuzaki T
    13. Li CB
    14. Boudaoud A
    15. Roeder AH

    (2016) Variable cell growth yields reproducible organ development through spatiotemporal averaging

    Developmental Cell 38:15–32.

    https://doi.org/10.1016/j.devcel.2016.06.016

    • PubMed
    • Google Scholar
40. 1. Hong L
    2. Dumond M
    3. Zhu M
    4. Tsugawa S
    5. Li C-B
    6. Boudaoud A
    7. Hamant O
    8. Roeder AHK

    (2018) Heterogeneity and robustness in plant morphogenesis: from cells to organs

    Annual Review of Plant Biology 69:469–495.

    https://doi.org/10.1146/annurev-arplant-042817-040517

    • Google Scholar
41. 1. Jackson MDB
    2. Duran-Nebreda S
    3. Kierzkowski D
    4. Strauss S
    5. Xu H
    6. Landrein B
    7. Hamant O
    8. Smith RS
    9. Johnston IG
    10. Bassel GW

    (2019) Global topological order emerges through local mechanical control of cell divisions in the Arabidopsis shoot apical meristem

    Cell Systems 8:53–65.

    https://doi.org/10.1016/j.cels.2018.12.009

    • PubMed
    • Google Scholar
42. 1. Jenik PD
    2. Irish VF

    (2000)

    Regulation of cell proliferation patterns by homeotic genes during Arabidopsis floral development

    Development 127:1267–1276.

    • PubMed
    • Google Scholar
43. Book

    1. Jürgens G
    2. Mayer U

    (1994)

    Arabidopsis

    In: Bard J, editors. EMBRYOS, Colour Atlas of Developing Embryos. London: Wolfe Publishing. pp. 7–21.

    • Google Scholar
44. 1. Kierzkowski D
    2. Nakayama N
    3. Routier-Kierzkowska AL
    4. Weber A
    5. Bayer E
    6. Schorderet M
    7. Reinhardt D
    8. Kuhlemeier C
    9. Smith RS

    (2012) Elastic domains regulate growth and organogenesis in the plant shoot apical meristem

    Science 335:1096–1099.

    https://doi.org/10.1126/science.1213100

    • PubMed
    • Google Scholar
45. 1. Kierzkowski D
    2. Runions A
    3. Vuolo F
    4. Strauss S
    5. Lymbouridou R
    6. Routier-Kierzkowska AL
    7. Wilson-Sánchez D
    8. Jenke H
    9. Galinha C
    10. Mosca G
    11. Zhang Z
    12. Canales C
    13. Dello Ioio R
    14. Huijser P
    15. Smith RS
    16. Tsiantis M

    (2019) A growth-based framework for leaf shape development and diversity

    Cell 177:1405–1418.

    https://doi.org/10.1016/j.cell.2019.05.011

    • PubMed
    • Google Scholar
46. 1. Kierzkowski D
    2. Routier-Kierzkowska AL

    (2019) Cellular basis of growth in plants: geometry matters

    Current Opinion in Plant Biology 47:56–63.

    https://doi.org/10.1016/j.pbi.2018.09.008

    • PubMed
    • Google Scholar
47. 1. Koncz C
    2. Schell J

    (1986) The promoter of TL-DNA gene 5 controls the tissue-specific expression of chimaeric genes carried by a novel type of Agrobacterium binary vector

    Molecular and General Genetics MGG 204:383–396.

    https://doi.org/10.1007/BF00331014

    • Google Scholar
48. 1. Kurihara D
    2. Mizuta Y
    3. Sato Y
    4. Higashiyama T

    (2015) ClearSee: a rapid optical clearing reagent for whole-plant fluorescence imaging

    Development 142:4168–4179.

    https://doi.org/10.1242/dev.127613

    • PubMed
    • Google Scholar
49. 1. Landrein B
    2. Kiss A
    3. Sassi M
    4. Chauvet A
    5. Das P
    6. Cortizo M
    7. Laufs P
    8. Takeda S
    9. Aida M
    10. Traas J
    11. Vernoux T
    12. Boudaoud A
    13. Hamant O

    (2015) Mechanical stress contributes to the expression of the STM homeobox gene in Arabidopsis shoot meristems

    eLife 4:e07811.

    https://doi.org/10.7554/eLife.07811

    • Google Scholar
50. 1. Lee KJI
    2. Bushell C
    3. Koide Y
    4. Fozard JA
    5. Piao C
    6. Yu M
    7. Newman J
    8. Whitewoods C
    9. Avondo J
    10. Kennaway R
    11. Marée AFM
    12. Cui M
    13. Coen E

    (2019) Shaping of a three-dimensional carnivorous trap through modulation of a planar growth mechanism

    PLOS Biology 17:e3000427.

    https://doi.org/10.1371/journal.pbio.3000427

    • Google Scholar
51. 1. Lein ES
    2. Hawrylycz MJ
    3. Ao N
    4. Ayres M
    5. Bensinger A
    6. Bernard A
    7. Boe AF
    8. Boguski MS
    9. Brockway KS
    10. Byrnes EJ
    11. Chen L
    12. Chen L
    13. Chen TM
    14. Chin MC
    15. Chong J
    16. Crook BE
    17. Czaplinska A
    18. Dang CN
    19. Datta S
    20. Dee NR
    21. Desaki AL
    22. Desta T
    23. Diep E
    24. Dolbeare TA
    25. Donelan MJ
    26. Dong HW
    27. Dougherty JG
    28. Duncan BJ
    29. Ebbert AJ
    30. Eichele G
    31. Estin LK
    32. Faber C
    33. Facer BA
    34. Fields R
    35. Fischer SR
    36. Fliss TP
    37. Frensley C
    38. Gates SN
    39. Glattfelder KJ
    40. Halverson KR
    41. Hart MR
    42. Hohmann JG
    43. Howell MP
    44. Jeung DP
    45. Johnson RA
    46. Karr PT
    47. Kawal R
    48. Kidney JM
    49. Knapik RH
    50. Kuan CL
    51. Lake JH
    52. Laramee AR
    53. Larsen KD
    54. Lau C
    55. Lemon TA
    56. Liang AJ
    57. Liu Y
    58. Luong LT
    59. Michaels J
    60. Morgan JJ
    61. Morgan RJ
    62. Mortrud MT
    63. Mosqueda NF
    64. Ng LL
    65. Ng R
    66. Orta GJ
    67. Overly CC
    68. Pak TH
    69. Parry SE
    70. Pathak SD
    71. Pearson OC
    72. Puchalski RB
    73. Riley ZL
    74. Rockett HR
    75. Rowland SA
    76. Royall JJ
    77. Ruiz MJ
    78. Sarno NR
    79. Schaffnit K
    80. Shapovalova NV
    81. Sivisay T
    82. Slaughterbeck CR
    83. Smith SC
    84. Smith KA
    85. Smith BI
    86. Sodt AJ
    87. Stewart NN
    88. Stumpf KR
    89. Sunkin SM
    90. Sutram M
    91. Tam A
    92. Teemer CD
    93. Thaller C
    94. Thompson CL
    95. Varnam LR
    96. Visel A
    97. Whitlock RM
    98. Wohnoutka PE
    99. Wolkey CK
    100. Wong VY
    101. Wood M
    102. Yaylaoglu MB
    103. Young RC
    104. Youngstrom BL
    105. Yuan XF
    106. Zhang B
    107. Zwingman TA
    108. Jones AR

    (2007) Genome-wide atlas of gene expression in the adult mouse brain

    Nature 445:168–176.

    https://doi.org/10.1038/nature05453

    • PubMed
    • Google Scholar
52. 1. Liang H
    2. Mahadevan L

    (2011) Growth, geometry, and mechanics of a blooming lily

    PNAS 108:5516–5521.

    https://doi.org/10.1073/pnas.1007808108

    • PubMed
    • Google Scholar
53. 1. Lieber D
    2. Lora J
    3. Schrempp S
    4. Lenhard M
    5. Laux T

    (2011) Arabidopsis WIH1 and WIH2 genes act in the transition from somatic to reproductive cell fate

    Current Biology 21:1009–1017.

    https://doi.org/10.1016/j.cub.2011.05.015

    • PubMed
    • Google Scholar
54. 1. Long F
    2. Peng H
    3. Liu X
    4. Kim SK
    5. Myers E

    (2009) A 3D digital atlas of C. elegans and its application to single-cell analyses

    Nature Methods 6:667–672.

    https://doi.org/10.1038/nmeth.1366

    • PubMed
    • Google Scholar
55. 1. Lora J
    2. Herrero M
    3. Tucker MR
    4. Hormaza JI

    (2017) The transition from somatic to germline identity shows conserved and specialized features during angiosperm evolution

    New Phytologist 216:495–509.

    https://doi.org/10.1111/nph.14330

    • Google Scholar
56. 1. Louveaux M
    2. Julien JD
    3. Mirabet V
    4. Boudaoud A
    5. Hamant O

    (2016) Cell division plane orientation based on tensile stress in Arabidopsis thaliana

    PNAS 113:E4294–E4303.

    https://doi.org/10.1073/pnas.1600677113

    • PubMed
    • Google Scholar
57. 1. Mansfield SG
    2. Briarty LG
    3. Erni S

    (1991) Early embryogenesis in Arabidopsis thaliana . I. the mature embryo sac

    Canadian Journal of Botany 69:447–460.

    https://doi.org/10.1139/b91-062

    • Google Scholar
58. 1. Mayer KF
    2. Schoof H
    3. Haecker A
    4. Lenhard M
    5. Jürgens G
    6. Laux T

    (1998) Role of WUSCHEL in regulating stem cell fate in the Arabidopsis shoot meristem

    Cell 95:805–815.

    https://doi.org/10.1016/S0092-8674(00)81703-1

    • PubMed
    • Google Scholar
59. 1. Meister RJ
    2. Kotow LM
    3. Gasser CS

    (2002)

    SUPERMAN attenuates positive INNER NO OUTER autoregulation to maintain polar development of Arabidopsis ovule outer integuments

    Development 129:4281–4289.

    • PubMed
    • Google Scholar
60. 1. Meister RJ
    2. Williams LA
    3. Monfared MM
    4. Gallagher TL
    5. Kraft EA
    6. Nelson CG
    7. Gasser CS

    (2004) Definition and interactions of a positive regulatory element of the Arabidopsis INNER NO OUTER promoter

    The Plant Journal 37:426–438.

    https://doi.org/10.1046/j.1365-313X.2003.01971.x

    • PubMed
    • Google Scholar
61. 1. Montenegro-Johnson TD
    2. Stamm P
    3. Strauss S
    4. Topham AT
    5. Tsagris M
    6. Wood AT
    7. Smith RS
    8. Bassel GW

    (2015) Digital single-cell analysis of plant organ development using 3DCellAtlas

    The Plant Cell 27:1018–1033.

    https://doi.org/10.1105/tpc.15.00175

    • PubMed
    • Google Scholar
62. 1. Montenegro-Johnson T
    2. Strauss S
    3. Jackson MDB
    4. Walker L
    5. Smith RS
    6. Bassel GW

    (2019) 3DCellAtlas Meristem: a tool for the global cellular annotation of shoot apical meristems

    Plant Methods 15:33.

    https://doi.org/10.1186/s13007-019-0413-0

    • PubMed
    • Google Scholar
63. 1. Müller B
    2. Fastner A
    3. Karmann J
    4. Mansch V
    5. Hoffmann T
    6. Schwab W
    7. Suter-Grotemeyer M
    8. Rentsch D
    9. Truernit E
    10. Ladwig F
    11. Bleckmann A
    12. Dresselhaus T
    13. Hammes UZ

    (2015) Amino acid export in developing Arabidopsis seeds depends on UmamiT facilitators

    Current Biology 25:3126–3131.

    https://doi.org/10.1016/j.cub.2015.10.038

    • PubMed
    • Google Scholar
64. 1. Musielak TJ
    2. Schenkel L
    3. Kolb M
    4. Henschen A
    5. Bayer M

    (2015) A simple and versatile cell wall staining protocol to study plant reproduction

    Plant Reproduction 28:161–169.

    https://doi.org/10.1007/s00497-015-0267-1

    • Google Scholar
65. 1. Nakajima K

    (2018) Be my baby: patterning toward plant germ cells

    Current Opinion in Plant Biology 41:110–115.

    https://doi.org/10.1016/j.pbi.2017.11.002

    • Google Scholar
66. 1. Oliphant TE

    (2007) Python for scientific computing

    Computing in Science & Engineering 9:10–20.

    https://doi.org/10.1109/MCSE.2007.58

    • Google Scholar
67. 1. Pasternak T
    2. Haser T
    3. Falk T
    4. Ronneberger O
    5. Palme K
    6. Otten L

    (2017) A 3D digital atlas of the Nicotiana tabacum root tip and its use to investigate changes in the root apical meristem induced by the Agrobacterium 6b oncogene

    The Plant Journal 92:31–42.

    https://doi.org/10.1111/tpj.13631

    • Google Scholar
68. Software

    1. R Development Core Team

    (2019) R: A language and environment for statistical computing

    R Foundation for Statistical Computing, Vienna, Austria.

    http://www.r-project.org
69. Software

    1. R Studio Team

    (2019) RStudio: Integrated Development for R

    RStudio, Inc.

    https://rstudio.com/products/rstudio/
70. 1. Rebocho AB
    2. Southam P
    3. Kennaway JR
    4. Bangham JA
    5. Coen E

    (2017) Generation of shape complexity through tissue conflict resolution

    eLife 6:e20156.

    https://doi.org/10.7554/eLife.20156

    • PubMed
    • Google Scholar
71. Preprint

    1. Refahi Y
    2. Zardilis A
    3. Michelin G
    4. Wightman R
    5. Leggio B
    6. Legrand J
    7. Faure E
    8. Vachez L
    9. Armezzani A
    10. Risson A-E
    11. Zhao F
    12. Das P
    13. Prunet N
    14. Meyerowitz E
    15. Godin C
    16. Malandain G
    17. Jönsson H
    18. Traas J

    (2020) A multiscale analysis of early flower development in Arabidopsis provides an integrated view of molecular regulation and growth control

    bioRxiv.

    https://doi.org/10.1101/2020.09.25.313312

    • Google Scholar
72. 1. Rein K
    2. Zöckler M
    3. Mader MT
    4. Grübel C
    5. Heisenberg M

    (2002) The Drosophila standard brain

    Current Biology 12:227–231.

    https://doi.org/10.1016/S0960-9822(02)00656-5

    • PubMed
    • Google Scholar
73. 1. Reyes-Olalde JI
    2. de Folter S

    (2019) Control of stem cell activity in the carpel margin meristem (CMM) in Arabidopsis

    Plant Reproduction 32:123–136.

    https://doi.org/10.1007/s00497-018-00359-0

    • Google Scholar
74. 1. Robinson-Beers K
    2. Pruitt RE
    3. Gasser CS

    (1992) Ovule development in wild-type Arabidopsis and two female-sterile mutants

    The Plant Cell 4:1237–1249.

    https://doi.org/10.2307/3869410

    • PubMed
    • Google Scholar
75. 1. Roeder AH
    2. Yanofsky MF

    (2006) Fruit development in Arabidopsis

    The Arabidopsis Book 4:e0075.

    https://doi.org/10.1199/tab.0075

    • PubMed
    • Google Scholar
76. Book

    1. Ronneberger O
    2. Fischer P
    3. Brox T

    (2015) U-Net: Convolutional networks for biomedical image segmentation

    In: Navab N, Hornegger J, Wells W. M, Frangi A. F, editors. Medical Image Computing and Computer-Assisted Intervention (MICCAI), Lecture Notes in Computer Science. Cham: Springer International Publishing. pp. 234–241.

    https://doi.org/10.1007/978-3-030-59713-9

    • Google Scholar
77. 1. Sampathkumar A
    2. Krupinski P
    3. Wightman R
    4. Milani P
    5. Berquand A
    6. Boudaoud A
    7. Hamant O
    8. Jönsson H
    9. Meyerowitz EM

    (2014) Subcellular and supracellular mechanical stress prescribes cytoskeleton behavior in Arabidopsis cotyledon pavement cells

    eLife 3:e01967.

    https://doi.org/10.7554/eLife.01967

    • PubMed
    • Google Scholar
78. 1. Sankar M
    2. Nieminen K
    3. Ragni L
    4. Xenarios I
    5. Hardtke CS

    (2014) Automated quantitative histology reveals vascular morphodynamics during Arabidopsis hypocotyl secondary growth

    eLife 3:e01567.

    https://doi.org/10.7554/eLife.01567

    • PubMed
    • Google Scholar
79. 1. Sapala A
    2. Runions A
    3. Routier-Kierzkowska AL
    4. Das Gupta M
    5. Hong L
    6. Hofhuis H
    7. Verger S
    8. Mosca G
    9. Li CB
    10. Hay A
    11. Hamant O
    12. Roeder AH
    13. Tsiantis M
    14. Prusinkiewicz P
    15. Smith RS

    (2018) Why plants make puzzle cells, and how their shape emerges

    eLife 7:e32794.

    https://doi.org/10.7554/eLife.32794

    • PubMed
    • Google Scholar
80. 1. Sapala A
    2. Runions A
    3. Smith RS

    (2019) Mechanics, geometry and genetics of epidermal cell shape regulation: different pieces of the same puzzle

    Current Opinion in Plant Biology 47:1–8.

    https://doi.org/10.1016/j.pbi.2018.07.017

    • PubMed
    • Google Scholar
81. 1. Sarkans U
    2. Gostev M
    3. Athar A
    4. Behrangi E
    5. Melnichuk O
    6. Ali A
    7. Minguet J
    8. Rada JC
    9. Snow C
    10. Tikhonov A
    11. Brazma A
    12. McEntyre J

    (2018) The BioStudies database—one stop shop for all data supporting a life sciences study

    Nucleic Acids Research 46:D1266–D1270.

    https://doi.org/10.1093/nar/gkx965

    • Google Scholar
82. 1. Sassi M
    2. Ali O
    3. Boudon F
    4. Cloarec G
    5. Abad U
    6. Cellier C
    7. Chen X
    8. Gilles B
    9. Milani P
    10. Friml J
    11. Vernoux T
    12. Godin C
    13. Hamant O
    14. Traas J

    (2014) An auxin-mediated shift toward growth isotropy promotes organ formation at the shoot meristem in Arabidopsis

    Current Biology 24:2335–2342.

    https://doi.org/10.1016/j.cub.2014.08.036

    • PubMed
    • Google Scholar
83. 1. Satina S
    2. Blakeslee AF
    3. Avery AG

    (1940) Demonstration of the three germ layers in the shoot apex of Datura by means of induced polyploidy in periclinal chimeras

    American Journal of Botany 27:895–905.

    https://doi.org/10.1002/j.1537-2197.1940.tb13952.x

    • Google Scholar
84. 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

    • Google Scholar
85. 1. Schmidt T
    2. Pasternak T
    3. Liu K
    4. Blein T
    5. Aubry-Hivet D
    6. Dovzhenko A
    7. Duerr J
    8. Teale W
    9. Ditengou FA
    10. Burkhardt H
    11. Ronneberger O
    12. Palme K

    (2014) The iRoCS toolbox - 3D analysis of the plant root apical meristem at cellular resolution

    The Plant Journal 77:806–814.

    https://doi.org/10.1111/tpj.12429

    • Google Scholar
86. 1. Schmidt A
    2. Schmid MW
    3. Grossniklaus U

    (2015) Plant germline formation: common concepts and developmental flexibility in sexual and asexual reproduction

    Development 142:229–241.

    https://doi.org/10.1242/dev.102103

    • Google Scholar
87. 1. Schneitz K
    2. Hülskamp M
    3. Pruitt RE

    (1995) Wild-type ovule development in Arabidopsis thaliana: a light microscope study of cleared whole-mount tissue

    The Plant Journal 7:731–749.

    https://doi.org/10.1046/j.1365-313X.1995.07050731.x

    • Google Scholar
88. 1. Schneitz K
    2. Hülskamp M
    3. Kopczak SD
    4. Pruitt RE

    (1997)

    Dissection of sexual organ ontogenesis: a genetic analysis of ovule development in Arabidopsis thaliana

    Development 124:1367–1376.

    • Google Scholar
89. 1. Sieber P
    2. Gheyselinck J
    3. Gross-Hardt R
    4. Laux T
    5. Grossniklaus U
    6. Schneitz K

    (2004) Pattern formation during early ovule development in Arabidopsis thaliana

    Developmental Biology 273:321–334.

    https://doi.org/10.1016/j.ydbio.2004.05.037

    • Google Scholar
90. 1. Simon MK
    2. Williams LA
    3. Brady-Passerini K
    4. Brown RH
    5. Gasser CS

    (2012) Positive- and negative-acting regulatory elements contribute to the tissue-specific expression of INNER NO OUTER, a YABBY-type transcription factor gene in Arabidopsis

    BMC Plant Biology 12:214.

    https://doi.org/10.1186/1471-2229-12-214

    • Google Scholar
91. 1. Sladitschek HL
    2. Fiuza UM
    3. Pavlinic D
    4. Benes V
    5. Hufnagel L
    6. Neveu PA

    (2020) MorphoSeq: full single-cell transcriptome dynamics up to gastrulation in a chordate

    Cell 181:922–935.

    https://doi.org/10.1016/j.cell.2020.03.055

    • PubMed
    • Google Scholar
92. 1. Smyth DR
    2. Bowman JL
    3. Meyerowitz EM

    (1990) Early flower development in Arabidopsis

    The Plant Cell 2:755–767.

    https://doi.org/10.1105/tpc.2.8.755

    • PubMed
    • Google Scholar
93. Conference

    1. Speth M
    2. Andres B
    3. Reinelt G
    4. Schnörr C

    (2011)

    Globally optimal image partitioning by multicuts

    Presented at the International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition. pp. 31–44.

    • Google Scholar
94. 1. Strauss S
    2. Sapala A
    3. Kierzkowski D
    4. Smith RS

    (2019) Quantifying plant growth and cell proliferation with MorphoGraphX

    Methods in Molecular Biology 1992:269–290.

    https://doi.org/10.1007/978-1-4939-9469-4_18

    • PubMed
    • Google Scholar
95. 1. Tofanelli R
    2. Vijayan A
    3. Scholz S
    4. Schneitz K

    (2019) Protocol for rapid clearing and staining of fixed Arabidopsis ovules for improved imaging by confocal laser scanning microscopy

    Plant Methods 15:120.

    https://doi.org/10.1186/s13007-019-0505-x

    • PubMed
    • Google Scholar
96. 1. Tomer R
    2. Ye L
    3. Hsueh B
    4. Deisseroth K

    (2014) Advanced CLARITY for rapid and high-resolution imaging of intact tissues

    Nature Protocols 9:1682–1697.

    https://doi.org/10.1038/nprot.2014.123

    • PubMed
    • Google Scholar
97. 1. Truernit E
    2. Haseloff J

    (2008) Arabidopsis thaliana outer ovule integument morphogenesis: Ectopic expression of KNAT1 reveals a compensation mechanism

    BMC Plant Biology 8:35.

    https://doi.org/10.1186/1471-2229-8-35

    • Google Scholar
98. 1. Tucker MR
    2. Okada T
    3. Hu Y
    4. Scholefield A
    5. Taylor JM
    6. Koltunow AMG

    (2012) Somatic small RNA pathways promote the mitotic events of megagametogenesis during female reproductive development in Arabidopsis

    Development 139:1399–1404.

    https://doi.org/10.1242/dev.075390

    • Google Scholar
99. 1. Ursache R
    2. Andersen TG
    3. Marhavý P
    4. Geldner N

    (2018) A protocol for combining fluorescent proteins with histological stains for diverse cell wall components

    The Plant Journal 93:399–412.

    https://doi.org/10.1111/tpj.13784

    • Google Scholar
100. 1. Uyttewaal M
     2. Burian A
     3. Alim K
     4. Landrein B
     5. Borowska-Wykręt D
     6. Dedieu A
     7. Peaucelle A
     8. Ludynia M
     9. Traas J
     10. Boudaoud A
     11. Kwiatkowska D
     12. Hamant O

     (2012) Mechanical stress acts via katanin to amplify differences in growth rate between adjacent cells in Arabidopsis

     Cell 149:439–451.

     https://doi.org/10.1016/j.cell.2012.02.048

     • PubMed
     • Google Scholar
101. 1. Valuchova S
     2. Mikulkova P
     3. Pecinkova J
     4. Klimova J
     5. Krumnikl M
     6. Bainar P
     7. Heckmann S
     8. Tomancak P
     9. Riha K

     (2020) Imaging plant germline differentiation within Arabidopsis flowers by light sheet microscopy

     eLife 9:e52546.

     https://doi.org/10.7554/eLife.52546

     • PubMed
     • Google Scholar
102. 1. Van Hooijdonk CA
     2. Glade CP
     3. Van Erp PE

     (1994) TO-PRO-3 iodide: a novel HeNe laser-excitable DNA stain as an alternative for propidium iodide in multiparameter flow cytometry

     Cytometry 17:185–189.

     https://doi.org/10.1002/cyto.990170212

     • PubMed
     • Google Scholar
103. 1. Villanueva JM
     2. Broadhvest J
     3. Hauser BA
     4. Meister RJ
     5. Schneitz K
     6. Gasser CS

     (1999) INNER NO OUTER regulates abaxial- adaxial patterning in Arabidopsis ovules

     Genes & Development 13:3160–3169.

     https://doi.org/10.1101/gad.13.23.3160

     • PubMed
     • Google Scholar
104. 1. Wang Z-P
     2. Xing H-L
     3. Dong L
     4. Zhang H-Y
     5. Han C-Y
     6. Wang X-C
     7. Chen Q-J

     (2015) Egg cell-specific promoter-controlled CRISPR/Cas9 efficiently generates homozygous mutants for multiple target genes in Arabidopsis in a single generation

     Genome Biology 16:144.

     https://doi.org/10.1186/s13059-015-0715-0

     • Google Scholar
105. 1. Willis L
     2. Refahi Y
     3. Wightman R
     4. Landrein B
     5. Teles J
     6. Huang KC
     7. Meyerowitz EM
     8. Jönsson H

     (2016) Cell size and growth regulation in the Arabidopsis thaliana apical stem cell niche

     PNAS 113:E8238–E8246.

     https://doi.org/10.1073/pnas.1616768113

     • PubMed
     • Google Scholar
106. 1. Wolny A
     2. Cerrone L
     3. Vijayan A
     4. Tofanelli R
     5. Barro AV
     6. Louveaux M
     7. Wenzl C
     8. Strauss S
     9. Wilson-Sánchez D
     10. Lymbouridou R
     11. Steigleder SS
     12. Pape C
     13. Bailoni A
     14. Duran-Nebreda S
     15. Bassel GW
     16. Lohmann JU
     17. Tsiantis M
     18. Hamprecht FA
     19. Schneitz K
     20. Maizel A
     21. Kreshuk A

     (2020) Accurate and versatile 3D segmentation of plant tissues at cellular resolution

     eLife 9:e57613.

     https://doi.org/10.7554/eLife.57613

     • PubMed
     • Google Scholar
107. 1. Xie K
     2. Zhang J
     3. Yang Y

     (2014) Genome-wide prediction of highly specific guide RNA spacers for CRISPR-Cas9-mediated genome editing in model plants and major crops

     Molecular Plant 7:923–926.

     https://doi.org/10.1093/mp/ssu009

     • PubMed
     • Google Scholar
108. 1. Yoshida S
     2. Barbier de Reuille P
     3. Lane B
     4. Bassel GW
     5. Prusinkiewicz P
     6. Smith RS
     7. Weijers D

     (2014) Genetic control of plant development by overriding a geometric division rule

     Developmental Cell 29:75–87.

     https://doi.org/10.1016/j.devcel.2014.02.002

     • PubMed
     • Google Scholar
109. 1. Yu SX
     2. Zhou LW
     3. Hu LQ
     4. Jiang YT
     5. Zhang YJ
     6. Feng SL
     7. Jiao Y
     8. Xu L
     9. Lin WH

     (2020) Asynchrony of ovule primordia initiation in Arabidopsis

     Development 1:dev.196618.

     https://doi.org/10.1242/dev.196618

     • Google Scholar
110. 1. Zhao X
     2. Bramsiepe J
     3. Van Durme M
     4. Komaki S
     5. Prusicki MA
     6. Maruyama D
     7. Forner J
     8. Medzihradszky A
     9. Wijnker E
     10. Harashima H
     11. Lu Y
     12. Schmidt A
     13. Guthörl D
     14. Logroño RS
     15. Guan Y
     16. Pochon G
     17. Grossniklaus U
     18. Laux T
     19. Higashiyama T
     20. Lohmann JU
     21. Nowack MK
     22. Schnittger A

     (2017) RETINOBLASTOMA RELATED1 mediates germline entry in Arabidopsis

     Science 356:eaaf6532.

     https://doi.org/10.1126/science.aaf6532

     • PubMed
     • Google Scholar

Author details

1. Athul Vijayan

   Plant Developmental Biology, School of Life Sciences, Technical University of Munich, Freising, Germany

   Contribution

   Conceptualization, Formal analysis, Investigation, Visualization, Methodology, Writing - review and editing

   Contributed equally with

   Rachele Tofanelli

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1837-6359

2. Rachele Tofanelli

   Plant Developmental Biology, School of Life Sciences, Technical University of Munich, Freising, Germany

   Contribution

   Conceptualization, Formal analysis, Investigation, Visualization, Methodology, Writing - review and editing

   Contributed equally with

   Athul Vijayan

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5196-1122

3. Sören Strauss

   Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany

   Contribution

   Software, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Lorenzo Cerrone

   Heidelberg Collaboratory for Image Processing, Dept. of Physics and Astronomy, Heidelberg University, Heidelberg, Germany

   Contribution

   Software, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Adrian Wolny

   1. Heidelberg Collaboratory for Image Processing, Dept. of Physics and Astronomy, Heidelberg University, Heidelberg, Germany
   2. European Molecular Biology Laboratory, Heidelberg, Germany

   Contribution

   Software, Investigation, Methodology

   Competing interests

   No competing interests declared

6. Joanna Strohmeier

   Plant Developmental Biology, School of Life Sciences, Technical University of Munich, Freising, Germany

   Contribution

   Investigation

   Competing interests

   No competing interests declared

7. Anna Kreshuk

   European Molecular Biology Laboratory, Heidelberg, Germany

   Contribution

   Conceptualization, Software, Funding acquisition, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1334-6388

8. Fred A Hamprecht

   Heidelberg Collaboratory for Image Processing, Dept. of Physics and Astronomy, Heidelberg University, Heidelberg, Germany

   Contribution

   Conceptualization, Software, Funding acquisition, Methodology

   Competing interests

   No competing interests declared

9. Richard S Smith

   Department of Comparative Development and Genetics, Max Planck Institute for Plant Breeding Research, Cologne, Germany

   Present address

   The John Innes Centre, Norwich Research Park, Norwich, United Kingdom

   Contribution

   Conceptualization, Software, Supervision, Funding acquisition, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9220-0787

10. Kay Schneitz

    Plant Developmental Biology, School of Life Sciences, Technical University of Munich, Freising, Germany

    Contribution

    Conceptualization, Formal analysis, Supervision, Funding acquisition, Visualization, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    kay.schneitz@tum.de

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6688-0539

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