Active Control of Cell Size Generates Spatial Detail ...

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Active Control of Cell Size Generates Spatial Detail during Plant Organogenesis Graphical Abstract

Authors Antonio Serrano-Mislata, Katharina Schiessl, Robert Sablowski

Correspondence [email protected]

In Brief Different cell types have characteristic sizes, but it unclear how cell size is maintained and why it matters. SerranoMislata et al. show that plant meristem cells actively maintain a target size, which is required to generate fine detail during development, similar to the way appropriate pixel sizes are needed to render detail in digital images

Highlights d

Cell divisions are unequal and cell growth is heterogeneous in the meristem

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Simulations indicate that growth and cell cycle are coordinated in individual cells

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Meristem cell sizes are rapidly corrected after perturbation

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Abnormal cell sizes do not affect growth but perturb organ boundaries and emergence

Serrano-Mislata et al., 2015, Current Biology 25, 1–6 November 16, 2015 ª2015 The Authors http://dx.doi.org/10.1016/j.cub.2015.10.008

Please cite this article in press as: Serrano-Mislata et al., Active Control of Cell Size Generates Spatial Detail during Plant Organogenesis, Current Biology (2015), http://dx.doi.org/10.1016/j.cub.2015.10.008

Current Biology

Report Active Control of Cell Size Generates Spatial Detail during Plant Organogenesis Antonio Serrano-Mislata,1 Katharina Schiessl,1 and Robert Sablowski1,* 1Cell and Developmental Biology Department, John Innes Centre, Norwich Research Park, Norwich NR4 7UH, UK *Correspondence: [email protected] http://dx.doi.org/10.1016/j.cub.2015.10.008 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

SUMMARY

How cells regulate their dimensions is a long-standing question [1, 2]. In fission and budding yeast, cell-cycle progression depends on cell size, although it is still unclear how size is assessed [3–5]. In animals, it has been suggested that cell size is modulated primarily by the balance of external signals controlling growth and the cell cycle [1], although there is evidence of cell-autonomous control in cell cultures [6–9]. Regardless of whether regulation is external or cell autonomous, the role of cell-size control in the development of multicellular organisms remains unclear. Plants are a convenient system to study this question: the shoot meristem, which continuously provides new cells to form new organs, maintains a population of actively dividing and characteristically small cells for extended periods [10]. Here, we used live imaging and quantitative, 4D image analysis to measure the sources of cellsize variability in the meristem and then used these measurements in computer simulations to show that the uniform cell sizes seen in the meristem likely require coordinated control of cell growth and cell cycle in individual cells. A genetically induced transient increase in cell size was quickly corrected by more frequent cell division, showing that the cell cycle was adjusted to maintain cell-size homeostasis. Genetically altered cell sizes had little effect on tissue growth but perturbed the establishment of organ boundaries and the emergence of organ primordia. We conclude that meristem cells actively control their sizes to achieve the resolution required to pattern small-scale structures. RESULTS Unequal Cell Divisions and Heterogeneous Cell Growth Introduce Cell-Size Variability in the Meristem The absence of cell migration and the relatively easy access to the shoot apical meristem facilitate the analysis of how cell growth and division are coordinated during multicellular development. To track cell growth and division, we used time-lapse confocal imaging of excised Arabidopsis inflorescence apices

[11, 12] and developed a package of Python scripts and Fiji macros to landmark, segment, locate, track, and measure cells in 3D (3D_meristem_analysis, source code, and detailed description in Supplemental Information) (Figures 1A and 1B). Images were manually curated to delete cells that were incorrectly segmented or tracked; all experiments focused on cells in the two outer meristem layers (L1, L2), for which segmentation accuracy was higher. Using independent images of the same apex at two different angles, the average coefficient of variation for the volumes of matched cells was 5.4% (three apices, n = 1,902) (Figure S1). Coordination between cell growth and cell cycle not only sets the average cell size, but also constrains its variability [2]. To assess whether the uniformity of meristem cells is consistent with active control of cell sizes, we first measured the sources of size variability. Meristem cell divisions were often unequal (Figures 1D and 1F). Division ratios (defined as the volume of each sibling cell relative to their combined volume) varied between 23% and 77%, with a SD of 9.4%–11.8% (95% confidence interval, Table S1), comparable to the 9.3% reported using cell areas [14]. The coefficient of variation (CV) of mother cell volumes was significantly lower than for their daughter cells, confirming that unequal divisions increased cell-size variability during a single cell generation (Figure 1G). A key question in cell-size homeostasis is how growth rate relates to cell volume: the initial variability caused by unequal divisions could be either amplified by exponential growth (i.e., if cells have the same relative growth rate regardless of size) or reduced, if larger cells grew relatively less [15]. Furthermore, feedback between mechanical stress and local growth rates, which causes heterogeneity in the growth of neighboring cells [16], could potentially couple growth rates to cell sizes. In the meristem, relative growth rates showed a weak but significant negative correlation with cell volumes (r = 0.17, p = 8.74 e-13) (Figure 1H), rejecting the hypothesis of exponential growth, but at the same time indicating that most of the variation in growth rate was not related to cell volume. Similar results were obtained using only cells in the central region of the meristem (Table S1), suggesting that this variability is not due to regional differences in meristem growth. Visual inspection confirmed that neighboring meristem cells with similar volume often had divergent growth rates (Figures 1C–1F). In conclusion, rather than causing cell sizes to converge, local growth heterogeneity could add to the variability introduced by unequal cell divisions, while the negative correlation between growth rate and cell volume, albeit weak, might still constrain cell-size variability in the meristem. Current Biology 25, 1–6, November 16, 2015 ª2015 The Authors 1

Please cite this article in press as: Serrano-Mislata et al., Active Control of Cell Size Generates Spatial Detail during Plant Organogenesis, Current Biology (2015), http://dx.doi.org/10.1016/j.cub.2015.10.008

Figure 1. Sources of Cell-Size Variability in the Shoot Meristem (A and B) Segmented images of wild-type inflorescence apices at 0 (A) and 24 hr later (B), with matching cells in the same color; regions in white rectangles in (A) and (B) correspond to (C)–(F); IM, inflorescence meristem; FB, floral bud. (C–F) Close-up view of regions highlighted in (A) (C and D) and (B) (E and F), with cells labeled by volume (C and E) or relative growth rate over 24 hr (D and F); arrows show unequal divisions and encircled pairs of cells had similar volumes at 0 hr but different growth rates. (G) Deviation from the mean volume for cells that divided over 24 hr (red bars) and their daughter cells (blue bars); the p value is for equality of coefficients of variation (Levene’s test on relative deviations from mean) [13]. (H) Scatterplot of relative growth rates over 24 hr as a function of cell volume and corresponding linear regression (blue line), with regression function and r and p values (Pearson correlation) indicated; green and red lines show the limits of the 95% confidence interval for the slope. Scale bars, 50 (A and B) 10 mm (C–F). See also Figure S1.

Maintenance of Uniform Sizes Is Likely to Require Coordination of Cell Growth and Cell Cycle in Individual Cells We next used computer simulations to test whether the observed unequal divisions, heterogeneous local growth, and slower growth of larger cells would be sufficient to reproduce the observed distribution of meristem cell sizes, assuming that the cell proliferation rate is controlled at the population level to match the rate of tissue growth (summary in Figure 2A, detailed description and source code in Supplemental Information). As a simple approximation, growth rates were adjusted to cell volume using the linear function shown in Figure 1H, but comparable results were obtained by fitting alternative functions to the data (Figures S2C–S2K) or using a probability density function (Figures S2L–S2N). Parameter values within the 95% confidence interval of experimental measurements (Table S1) were selected to minimize divergence in cell sizes (for sensitivity to parameter values, see Figures S2A and S2B). The experimental variability introduced by imaging and image processing was subtracted (Supplemental Experimental Procedures) and variation in cell-cycle length (which could not be measured at our temporal resolution) was set to zero. Even with these conservative parameter estimates, after four cell cycles the simulated cell population had significantly diverged from the tighter distribution of cell volumes seen in real meristems (Figures 2B, 2C, 2E, S2A, and S2B). In contrast, when 2 Current Biology 25, 1–6, November 16, 2015 ª2015 The Authors

the heterogeneity in cell growth rates was compensated by adjusting individual cell-cycle lengths (simulation b in Figure 2A), the simulated cell-size distribution matched the experimental data (Figures 2B, 2D, and 2E). These simulations suggested that meristem cell sizes are not stabilized as a passive consequence of the slower growth rate of larger cells, but as a result of local coordination between cell growth and cell cycle. Meristem Cell Sizes Are Rapidly Corrected after Perturbation Coordination of cell growth and cell cycle could occur through parallel control by external signals or by a homeostatic feedback between both processes [2]. These hypotheses make different predictions about the outcome of modifying cell sizes by transiently perturbing cell-cycle progression: parallel control would perpetuate altered cell sizes, whereas feedback would correct them [4]. To test these predictions, we used localized expression of the Kip-like CDK inhibitor KRP4, which belongs to a family of key regulators of S-phase entry in plants [17]. Previous work showed that ectopic expression of the organ growth gene JAG reduces meristem cell sizes by causing meristem cells to enter S-phase at abnormally small volumes [12] and that JAG directly represses KRP4 [18]. We therefore hypothesized that KRP4 could be an endogenous regulator of meristem cell size. Accordingly, KRP4 was expressed in the inflorescence and floral meristems (Figures S3A–S3C), and the loss of function of krp4-2 mutant [18] showed a small but significant reduction in meristem cell volumes (Figures S3D–S3F). Conversely, in CLV3>>KRP4 plants, in which KRP4 was overexpressed in the

Please cite this article in press as: Serrano-Mislata et al., Active Control of Cell Size Generates Spatial Detail during Plant Organogenesis, Current Biology (2015), http://dx.doi.org/10.1016/j.cub.2015.10.008

Figure 2. Comparison between Observed and Simulated Cell-Size Distributions (A) Summary diagram of simulations; the starting cell volume v0 results from division of the mother cell volume vm with a variable ratio d, based on the measured SD of cell division ratios; the cell volume is updated at each iteration (equivalent to 1 hr) with the growth rate adjusted to volume by a linear function with coefficients a, b (Table S1); heterogeneous growth is introduced by a variability factor k, based on the measured LSDG (local SD of growth, defined as the SD of the relative growth rate for a cell and its neighbors); the term 1/24 is used to convert the growth rate from daily to hourly; in simulation a (sim_a), the number of growth iterations before cell division matches the average cell volume doubling time T for all cells; in sim b, the number of iterations before division was adjusted to individual cell growth rates by multiplying T by 1/k. (B) Boxplot of observed cell volumes in five wildtype meristems (1,746 cells). (C and D) Boxplots of simulated volumes at different iteration numbers, using sim_a and sim_b, respectively. (E) Frequency histograms of the deviation from the mean cell volume for sim_a and sim_b, compared with the experimental data (exp); simulation parameter values are listed in Table S1; for each set of values, data were pooled from five simulations (2,000 cells); the p values are for the hypothesis that simulated and observed cell volumes had the same coefficients of variation (CV) (Levene’s test on relative deviations from mean) [13]. See also Figure S2 and Supplemental Information for simulation details and source code.

inflorescence meristem using a driver derived from the CLV3 promoter [19], median cell volumes were nearly 4-fold higher in the center of the meristem (Figures 3A and 3D; Table S2). As the descendants of these large cells were displaced to the meristem periphery and floral primordia, where the driver was not expressed, cell volumes returned to normal, while cellular growth rates remained comparable (Figures 3G and 3H), and timecourse imaging showed that cell volumes were corrected because of more frequent cell divisions (Figures 3B, 3C, 3E, 3F, and S3G–S3L). A similarly transient increase in cell sizes was seen in CLV3>>KRP4 floral meristems (Figures S3M–S3P). We conclude that cell-cycle length is adjusted to maintain cellsize homeostasis in the meristem.

Organ boundaries, however, were wider and less well defined, both morphologically and based on the expression of a boundary marker (Figures 4B–4E and 4H–4K). Because growth is repressed at organ boundaries [23], wider boundaries due to larger cells might limit the number of cells available for primordium outgrowth. Accordingly, AP1>>KRP4 primordia often failed to emerge (Figures 4D and 4E) and mature AP1>>KRP4 flowers had fewer organs in the first three whorls (Figure 4L). Conversely, the krp4-2 mutant, with smaller meristem cells, formed more sepals and petals, and fewer stamens (Figure 4L). Thus, both increased and decreased cell sizes were associated with early patterning defects in the floral buds. DISCUSSION

Abnormal Cell Sizes Do Not Affect Growth but Perturb Organ Boundaries and Organ Emergence We next asked what could be the relevance of actively controlling meristem cell sizes. CLV3>>KRP4 plants had no obvious defects in meristem size, floral bud emergence (Figures 3A and 3D), and in the overall aspect of the inflorescence (Figure S4), suggesting that as observed in leaves, meristem development can accommodate considerable variation in cell size [20]. However, because cells are the minimal spatial units to establish gene expression patterns, we reasoned that cell size might affect patterning of structures a few cells across, such as organ boundaries. To test this idea, we used an AP1 driver [21] to overexpress KRP4 throughout floral buds (Figure 4A). Buds in equivalent positions around the apex had similar sizes in AP1>>KRP4 and in the wild-type, suggesting that once again organ growth accommodated the increased cell size (Figures 4B and 4C).

Together, our data show that meristem cells actively control their size. Based on the measured sources of cell-size variation in the meristem, simulations could not reproduce the distribution of meristem cell sizes assuming that cell proliferation is controlled at the population level to match the tissue growth rate. Transient inhibition of cell-cycle progression caused increased cell sizes that were rapidly corrected after the inhibition was released. Furthermore, the changes in cell size caused by loss and gain of KRP4 function implicated this gene in the control of meristem cell size, suggesting that as seen in budding yeast and mammalian cells [3, 9], the G1-S transition is a key control point to maintain cell-size homeostasis in the meristem. Tight control of cell size in the meristem could seem at odds with the view that both plant and animal growth are controlled primarily at the organ level and that altered cell size can be Current Biology 25, 1–6, November 16, 2015 ª2015 The Authors 3

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Figure 3. Recovery of Cell Volumes after Perturbation (A and D) Confocal images of inflorescence meristems with the CLV3:LhG4 driver directing Op:ERGFP [CLV3>>(GFP), A] or Op:ER-GFP combined with Op:KRP4 [CLV3>>(GFP, KRP4), D]; white rectangles in (A) and (D) enclose the regions corresponding to (B) and (E), respectively; note that, although the CLV3:LhG4 driver was expressed more widely in the inflorescence meristem than the endogenous CLV3 gene [10], it was used here only as a tool for developmentally transient expression. (B, C, E, and F) Segmented images of emerging floral buds in CLV3>>(GFP) (B and C) and CLV3>>(GFP, KRP4) (E and F) at 0 (B and E) and 24 hr later (C and F); matching cells are in the same color and asterisks mark cells in (B) and (E) that divided after 24 hr; note in (E) and (F) the high frequency of cell divisions in cells that are being displaced from the region expressing the CLV3:LhG4 driver. (G and H) Boxplots of cell volumes (G) and growth rates (H) of CLV3>>(GFP) (red) and CLV3>>(GFP, KRP4) (blue) cells with different levels of GFP expression. Scale bars, 50 mm. See also Figure S3.

compensated by changes in cell number [2, 24]. However, the compensation between cell size and cell proliferation in plants is organ specific [20], and the coordination between cell volume and cell-cycle progression changes during the transition from meristem to organ identity [12]. Based on our results, cell-size control may be especially important in the meristem and early organs because of the scale at which patterning occurs within these structures. So far, the control of cell size has been considered important primarily for cell physiology, which is affected by the volume/surface ratio [2, 24]. Our work suggests that in multicellular organisms, cell-size control can have an additional role in generating spatial detail during development. EXPERIMENTAL PROCEDURES Plant Material Plants were grown on JIC Arabidopsis Soil Mix at 16 C under continuous light (100 mE). Arabidopsis thaliana accession Landsberg-erecta (L-er) was used throughout; krp4-2, jag-2, CLV3:LhG4, Op:ER-GFP, AP1:LhG4, and pCUC1:: CUC1-GFP have been described [12, 19, 21, 22, 25]. For construction of KRP4-GFP, the KRP4 gene (AGI: At2g32710) was amplified from Arabidopsis accession Col-0 (Chr2, nucleotides 1387216013876781) and sGFP(S65T) [26] was inserted in frame at the end of the coding sequence before cloning into pPZP222 [27]. For creation of Op:KRP4, the KRP4 coding sequence (887 bp) was PCR amplified from Col-0 cDNA, re-sequenced, and subcloned downstream of the 6XOpU promoter in pOWL49 [28]. Transgenic lines were generated by floral dip transformation of L-er plants [29]; in the case of KRP4-GFP, jag-2-2 krp4-2 plants were transformed and selected for segregation as single loci and for reversion to the jag-2 phenotype [30].

4 Current Biology 25, 1–6, November 16, 2015 ª2015 The Authors

Imaging For time-lapse imaging, inflorescence apices were prepared and imaged as described [11]. Before imaging, dissected apices were left to recover in GM medium (0.1% glucose; 0.44% Murashige and Skoog medium including vitamins, 0.9% agar [pH 5.7]) for 24 hr at 16 C, continuous light. Dissected apices were imbibed for 10 min in 50 mg/ml N-(4-triethylammoniumpropyl)-4-(p-dietheylaminophenylhexatrienyl pyridium dibromide) (FM4-64, Invitrogen) and imaged with a Zeiss LSM780 confocal microscope with excitation at 488 nm, emission filters set to 572–625 nm for FM4-64 and 505-600 nm for GFP, using a 340/1.0 dipping objective. Image resolution was 0.42 3 0.42 3 0.5 mm. Image Analysis For 3D segmentation, cell measurements, matching cells at different time points and tracking cell divisions, confocal image stacks were processed using scripts written in Python 2.7.3 with functions imported from Numerical Python (http://www.numpy.org), Scientific Python (http://www.scipy.org), matplotlib (http://matplotlib.org), and SimpleITK (http://www.simpleitk.org). Fiji macros [31] were used to visualize images, select landmark points (using the 3DViewer plugin; http://fiji.sc/3D_Viewer) [32], and select cells to be removed from the analysis during manual quality control. The supplemental software file (Data S1) contains the 3D_meristem_analysis package with the annotated source code and detailed instructions on how to install and use the Python scripts and Fiji macros. The original confocal stacks, metadata, landmark coordinates, images produced at each step of processing, and cell data tables can be found at https:// open-omero.nbi.ac.uk (username ‘‘shared,’’ password ‘‘Op3n-4cc0unt’’); the folder names correspond to those listed in the raw data table (Table S2). Statistics For each treatment, measurements from three to five apices were pooled after filtering by cell layer, region of interest (meristem), GFP expression, and cell division, as specified in Table S2. The raw data were read and processed in a Python shell using the functions defined and annotated in script /3D_ meristem_analysis/python_scripts/statistical_analysis.py (Data S1). Scipy functions were used for Pearson correlation, two-tailed Mann-Whitney tests,

Please cite this article in press as: Serrano-Mislata et al., Active Control of Cell Size Generates Spatial Detail during Plant Organogenesis, Current Biology (2015), http://dx.doi.org/10.1016/j.cub.2015.10.008

Figure 4. Abnormal Cell Sizes Affect Organ Boundaries and Primordium Emergence (A) Expression of AP1>>GFP in floral buds (FB), but not in the inflorescence meristem (IM). (B and C) Larger cells in floral buds (FB) of AP1>>KRP4 (C) compared to the WT control (B). (D and E) Close-up view of WT (D) and AP1>>KRP4 (E) buds, with sepal primordia indicated by arrows; one of the lateral sepals has not emerged in E (asterisk). (F and G) Mature flowers of WT (F) and AP1>>KRP4 (G) with missing organs (Se, sepals; Pe, petals, St, stamens; Ca, carpels). (H–K) Expression of the pCUC1:CUC1-GFP [22] organ boundary marker (arrows) in WT (H and I) and AP1>>KRP4 (J and K), shown in confocal images (H and J) and in segmented images (I and K) with cells labeled by nuclear GFP expression; each panel shows transversal (top) and longitudinal (bottom) sections of the same bud, with horizontal lines marking the sectioning planes used. (L) Heatmap showing the frequency of flowers with different numbers (N) of each organ type (raw data in Table S3); p values are for the equality of median organ number compared to WT (Mann-Whitney test). Scale bars, 50 mm (A–E and H–K) and 1 mm (F and G). See also Figure S4. least-squares fitting, and for calculating the probability density function. Confidence intervals were calculated by bootstrapping (100,000 iterations, except for linear regression, for which 10,000 iterations were used). p values for the equality of coefficients of variation were calculated by applying Levene’s

test on the relative deviations from the mean as described [13]. For organ counts (Table S3), plants were grown as described above until bolting; the first three flowers were discarded, and the next 15 flowers were examined from the main inflorescence of 12 individual plants of each genotype.

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Simulations Details of the simulations are given in the Supplemental Experimental Procedures and annotated source code in Data S1; parameter values are listed in Table S1. SUPPLEMENTAL INFORMATION Supplemental Information includes Supplemental Experimental Procedures, four figures, three tables, and one data file and can be found with this article online at http://dx.doi.org/10.1016/j.cub.2015.10.008. AUTHOR CONTRIBUTIONS

13. Van Valen, L. (2005). The statistics of variation. In Variation: A Central Concept in Biology, B. Hallgrı´msson, and B.K. Hall, eds. (Academic Press), pp. 29–48. 14. Shapiro, B.E., Tobin, C., Mjolsness, E., and Meyerowitz, E.M. (2015). Analysis of cell division patterns in the Arabidopsis shoot apical meristem. Proc. Natl. Acad. Sci. USA 112, 4815–4820. 15. Mitchison, J.M. (2003). Growth during the cell cycle. Int. Rev. Cytol. 226, 165–258. 16. Uyttewaal, M., Burian, A., Alim, K., Landrein, B., Borowska-Wykre˛t, D., Dedieu, A., Peaucelle, A., Ludynia, M., Traas, J., Boudaoud, A., et al. (2012). Mechanical stress acts via katanin to amplify differences in growth rate between adjacent cells in Arabidopsis. Cell 149, 439–451.

Conceptualization, A.S.-M., K.S., and R.S.; Investigation, A.S.-M. and K.S.; Resources, K.S.; Software, Formal Analysis, and Data Curation: R.S.; Writing – Original Draft, R.S.; Writing – Review & Editing, A.S.-M., K.S., and R.S.; Funding Acquisition, R.S. and A.S.-M.; Supervision, R.S.

17. Zhao, X., Harashima, H., Dissmeyer, N., Pusch, S., Weimer, A.K., Bramsiepe, J., Bouyer, D., Rademacher, S., Nowack, M.K., Novak, B., et al. (2012). A general G1/S-phase cell-cycle control module in the flowering plant Arabidopsis thaliana. PLoS Genet. 8, e1002847.

ACKNOWLEDGMENTS

18. Schiessl, K., Muin˜o, J.M., and Sablowski, R. (2014). Arabidopsis JAGGED links floral organ patterning to tissue growth by repressing Kip-related cell cycle inhibitors. Proc. Natl. Acad. Sci. USA 111, 2830–2835.

We thank Yuval Eshed (Weizmann Institute, Israel) for the CLV3 and AP1 driver lines and Martin Howard and Richard Morris for critical comments. The work was supported by BBSRC grants BB/J007056/1 and BB/J004588/1, by a grant from the Ministerio de Educacio´n, Cultura y Deporte, Spain (EX-20100491) to A.S.M., and a EU Marie-Curie fellowship (237909) to K.S.

19. Goldshmidt, A., Alvarez, J.P., Bowman, J.L., and Eshed, Y. (2008). Signals derived from YABBY gene activities in organ primordia regulate growth and partitioning of Arabidopsis shoot apical meristems. Plant Cell 20, 1217–1230.

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Current Biology Supplemental Information

Active Control of Cell Size Generates Spatial Detail during Plant Organogenesis Antonio Serrano-Mislata, Katharina Schiessl, and Robert Sablowski

  Figure  S1  (related  to  Figure  1):  Accuracy  of  automated  segmentation  and  post-­‐segmentation   steps  used  to  select  cells  for  measurements     A:  orthogonal  views  of  a  representative  confocal  image  of  an  inflorescence  meristem   stained  with  FM4-­‐64.  B:  corresponding  image  after  watershed  segmentation.  C:  overlap   between  images  A  and  B.  D:  overlap  between  A  and  image  in  which  the  segmented  cells  were   attributed  to  cell  layers  (L1-­‐L4  in  red,  blue,  green  and  yellow,  respectively).  E:  overlap  between   A  and  image  in  which  segmented  cells  were  assigned  to  regions  of  interest  (inflorescence   meristem  in  red,  floral  bud  primordia  2,  4  and  5  in  green,  magenta  and  cyan,  respectively).  F:   Reproducibility  of  measured  cell  volumes  in  confocal  stacks  of  the  same  inflorescence  apex   imaged  independently  at  two  different  angles,  segmented  and  matched  (three  apices,  n  =  1902)  

 

Fig.  S2  (related  to  Figure  2):  Sensitivity  of  the  simulations  from  Figure  2  to  parameter  values   and  to  the  type  of  function  used  to  adjust  cell  growth  to  cell  volume.     A,  B:  sensitivity  of  simulations  (sim_a,  Fig.2A)  to  the  slope  of  the  linear  function  over  a   range  of  values  for  the  standard  deviation  of  division  ratios  (A)  and  local  standard  deviation  of   growth  rates  (LSDG)  (B);  each  element  in  the  graph  indicates  the  –log  p-­‐value  (see  colorbar)  for   the  equality  of  coefficients  of  variation  between  the  experimental  data  and  the  simulations,   calculated  by  applying  Levene’s  test  on  the  relative  deviations  from  the  mean  [32];  the  white   lines  mark  the  95%  confidence  interval  for  measured  parameters  (Table  S1).  C-­‐N:  results  of   simulations  in  which  cell  growth  rates  were  adjusted  to  cell  volumes  using  fitted  inverse  (C-­‐E),   quadratic  (F-­‐H),  logarithmic  (I-­‐K)  and  probability  density  (PDF)  functions  (L-­‐N).  C,  F,  I:  functions   were  fitted  to  the  data  shown  in  figure  1H,  using  the  least  squares  method;  function   coefficients  giving  the  best  fit  to  the  data  are  shown  in  the  top  right  of  each  panel.  L:  growth   rates  at  different  volumes  were  based  on  the  line  of  maximum  probability  across  the  PDF  (red);   above  160  µm3,  where  the  data  were  too  sparse  and  the  PDF  became  erratic,  growth  rates   were  kept  constant  (blue  line).  D,  G,  J,  M:  boxplots  of  simulated  volumes  at  different  iteration   numbers,  corresponding  to  C,  F,  I,  L;  for  simulation  parameters  other  than  function  coefficients,   the  default  values  used  in  Table  S1  were  used.  E,  H,  K,  N:  frequency  histograms  of  the  deviation   from  the  mean  cell  volume  for  simulations  (sim)  compared  with  the  experimental  data  (exp),   corresponding  to  C,  F,  I,  L;  for  each  set  of  values,  data  were  pooled  from  five  simulations  (2000   cells);  p-­‐values  are  for  the  hypothesis  that  simulated  and  observed  cell  volumes  had  the  same   coefficients  of  variance  (CV)  (Levene’s  test  on  relative  deviations  from  mean)  [32].      

 

                                             

Figure  S3  (related  to  Figure  3):  KRP4  controls  meristem  cell  size.   A,  B:  inflorescence  apices  of  jag-­‐2  krp4-­‐2  (A)  and  jag-­‐2  krp4-­‐2  transformed  with   pKRP4:KRP4-­‐GFP  (B);  the  KRP4-­‐GFP  fusion  reverted  the  partial  suppression  of  jag-­‐2  by  krp4-­‐2   (11),  resulting  in  a  plant  similar  to  the  jag-­‐2  single  mutant;  C:  3D  rendering  of  confocal  images   of  a  krp4-­‐2  pKRP4:KRP4-­‐GFP  inflorescence  apex,  showing  KRP4-­‐GFP  expression  in  the   inflorescence  meristem  (IM)  and  floral  meristem  (FM).  D,  E:  top  view  of  segmented  images  of   wild-­‐type  (D)  and  krp4-­‐2  (E)  inflorescence  apices,  with  cells  colored  by  volume  (colorbar   between  D  and  E);  IM  as  in  C,  FB  indicate  floral  buds.  F:  box  plots  of  cell  volumes  for  the  wild   type  (red,  5  apices)  and  krp4-­‐2  (blue,  4  apices)  for  all  cells  (wt  n  =  1746;  krp4-­‐2  n  =  1416)  or   mother  cells  (wt  n  =  99;  krp4-­‐2  n  =  73);  p-­‐values  are  for  equality  of  medians  (Mann-­‐Whitney   test).  G-­‐L:  additional  replicates  for  the  experiment  showing  recovery  of  cell  sizes  after   developmentally  transient  expression  of  KRP4;  segmented  images  show  cells  at  the  edge  of  the   GFP-­‐expressing  region  in  the  inflorescence  meristem  of  CLV3>>(GFP)  (G,J)  and  CLV3>>(GFP,   KRP4)  (H,K  and  I,L)  at  0h  (G,H,I)  and  24h  later  (J,K,L);  matching  cells  are  in  the  same  color  and   asterisks  mark  cells  in  G-­‐I  that  divided  after  24h.  M-­‐P:  GFP  expression  and  cell  sizes  in  the   CLV3>>(GFP)  control  (M,N)  and  in  CLV3>>(GFP,  KRP4)  floral  buds  (O,P),  shown  in  confocal   images  (M,O)  and  in  segmented  images  (N,P)  with  cells  labeled  by  GFP  expression  (relative  to   the  median  signal  of  GFP-­‐positive  cells  in  each  bud,  see  color  bars  at  the  bottom  of  each  panel);   note  the  increased  cell  sizes  in  the  central  region  the  floral  meristem  in  CLV3>>(GFP,  KRP4),  but   not  in  the  peripheral  region  of  the  meristem  and  in  organ  primordia.  Size  bars:  1  mm  (A,  B),  50   µm  (C-­‐P).          

 

    Figure  S4  (related  to  Figure  4):  Overall  appearance  of  wild-­‐type  (wt)  and  CLV3>>KRP4   inflorescences.     A,  B:  side  view  of  wt  (A)  and  CLV3>>KRP4  (B)  inflorescences  at  the  stage  when  apices  are   dissected  for  imaging;  size  bars:  10  mm.   C,  D:  top  view  of  wt  (A)  and  CLV3>>KRP4  (B);  size  bars  =  1  mm  

Table  S1  (related  to  Figure  2):  Parameter  values  for  meristem  cell  growth   Values  and  confidence  intervals  were  calculated  from  the  data  in  Table_S2.csv,  using  the   Python  script  statistical_analysis.py  (see  Materials  and  Methods  and  Supplemental_software).   Column  2  indicates  the  parameter  names  used  in  simulations  (script  cell_growth_simulation.py,   see  Materials  and  Methods  and  Supplemental_software);  default  values  used  in  the  simulations   are  underlined.       Parameter  name   Parameter  in   simulations   Mean  cell   v0   volume   Mean  relative   gr24   growth  (24h)   Standard   sigma   deviation  of  cell   division  ratio   Local  standard   -­‐   deviation  of   relative  growth   (LSDG)   Local  standard   -­‐   deviation  of   volume  ratios   (LSDR)   Corrected  LSDG   sdgr   Slope  of   regression  cell   vol  X  growth  rate   Intercept  of   regression  cell   vol  X  growth  rate   Central  zone   mean  cell   volume   Central  zone   mean  relative   growth  (24h)   Central  zone   LSDG  

Value   105.6  

95%  confidence  interval     n   n   apices   cells   103.9  to  107.5   5   1746  

1.217  

1.209  to  1.225  

5  

1746  

0.1067  

0.09419  to  0.1184  

5  

98  

0.1499  

0.1465  to  0.1532  

5  

1843  

0.06885  

0.06758  to  0.07014  

3  

1902  

0.1331  

0.1294  to  0.1369  

5  

1843  

a  

-­‐  7.593  e-­‐4  

-­‐  5.620  e-­‐4  to  -­‐  9.574  e-­‐4   5  

1746  

b  

1.297  

1.275  to  1.320  

5  

1746  

-­‐  

123.6  

117.3  to  130.2  

5  

185  

-­‐  

1.262  

1.240  to  1.284  

5  

185  

-­‐  

0.132  

0.126  to  0.138  

5  

199  

Central  zone   slope  of   regression  vol  X   growth     Central  zone   intercept  of   regression  vol  X   growth  

-­‐  

-­‐  7.183  e-­‐4  

-­‐2.951  e-­‐4  to  -­‐  11.54  e-­‐4  

5  

185  

-­‐  

1.351  

1.275  to  1.320  

5  

185  

  Table  S2  legend  (related  to  Experimental  Procedures):   Raw  values  and  summary  statistics  for  genotypes  and  treatments  analyzed  in  Figures  1-­‐3  and   S1.   Lines  1  to  4  are  used  by  the  script  statistical_analysis.py  to  read  the  data,  perform   statistical  tests  and  produce  boxplots  (see  Materials  and  Methods  and  Supplemental_software).     Lines  6  to  9  specify  the  data  source  and  where  it  was  used.  The  line  “Source  image   folders”  gives  the  names  of  image  folders  containing  the  replicates  for  each  treatment  (the   folders  can  be  found  in  https://open-­‐omero.nbi.ac.uk,  username  “shared”,  password  “Op3n-­‐ 4cc0unt”).  “Source  table  type”  specifies  the  cell  data  table  used;  _a_cell_data_checked.csv   corresponds  to  the  tables  with  matched  cell  data  after  manual  quality  control;  _cell_data.csv   corresponds  to  the  tables  with  cell  data  from  each  single  image.  “Analyzed  data  in   Figure/Table”  indicates  the  figure  or  table  where  the  data  were  used.     Lines  11  to  20  indicate  how  the  data  was  filtered.  “Variable”  corresponds  to  the  heading   of  the  column  in  the  cell  data  tables  that  contained  the  raw  values;  “Filter  n”  lines  specify  the   headings  of  columns  in  the  cell  data  tables  that  were  used  to  select  cells  with  values  between  at   least  “Filter  n  min”  and  less  than  “Filter  n  max”;  for  “ROI  number”,  1  corresponds  to  the   inflorescence  meristem.   Lines  22  to  25  provide  summary  statistics  for  each  treatment  (n,  median,  mean  and   standard  deviation).  Lines  27  to  31  give  summary  statistics  for  each  inflorescence  apex.  Raw   values  for  each  treatment  are  listed  below  line  33.    

Table  S3  legend  (related  to  Experimental  Procedures):   Floral  organ  numbers  in  wild-­‐type,  krp4-­‐2  and  AP1>>KRP4  plants.  For  each  plant,  flowers   number  4  to  18  (in  order  of  emergence)  were  dissected  and  each  type  of  floral  organ  (Se:   sepals;  Pe:  petals;  St:  stamens;  Ca:  carpels)  were  counted.     Supplemental_software  (related  to  Experimental  Procedures)     List  of  scripts  included  in  Supplemental  Software     Python  scripts:   alignment_images.py   boxplot.py   bud_analysis_lib.py   cell_data_table.py   cell_growth_simulation.py   cell_layers.py   correct_matched_cells.py   delete_or_include_cells.py   heatmap.py   match_cells.py   neighbours_growth.py   optimise_segmentation.py   rib_zone.py   score_ER.py  

select_ROIs.py   statistical_analysis.py   stats_tables.py   subtract_images.py   tiffile.py   track_cell_divisions.py   watershed_segmentation.py     Fiji  macros:   confocal_to_TIF.ijm   landmarks_3D.ijm   landmarks_alignment.ijm   match_cells.ijm   overlap_seg_signal.ijm   reset_LUT.ijm     Installing  and  using  image  analysis,  simulation  and  statistical  scripts     The  scripts  were  written  in  Python  2.7.3  using  an  Apple  computer  running  MacOS  X  10.9.4  -­‐   small  changes  may  be  needed  to  install  and  run  them  on  a  different  platform.     To  install  and  run  the  image  analysis  scripts,  expand  the  file  Supplemental_software.zip  on  the   Desktop,  open  the  file  3D_meristem_analysis/Image_processing_instructions.pdf  and  follow   the  instructions.  Fiji  [S2]  was  used  to  visualize  and  interact  with  processed  images,  using  macros   included  in  Supplemental_software.   To  run  the  statistics  and  simulation  scripts,  use  a  script  editor  to  open  the  corresponding  files   (3D_meristem_analysis/python_scripts/statistical_analysis.py  and  

3D_meristem_analysis/python_scripts/cell_growth_simulation.py)  and  follow  the  instructions   annotated  in  the  section  “Main  programme”.  Functions  need  to  be  copied  and  pasted  from  the   scripts  directly  to  a  Python  shell.     Supplemental  Experimental  Procedures     Overview  of  image  analysis   The  Supplemental_software  file  contains  the  annotated  source  code  and  detailed   instructions  on  how  to  install  and  use  the  Python  scripts  and  Fiji  macros  used  for  3D  image   analysis.  Briefly:   1.  3D  segmentation  used  the  morphological  watershed  algorithm  [S1]  implemented  in   SimpleITK,  after  Gaussian  blurring  with  given  sigma  values  for  the  x,  y  and  z  dimensions.  Cell   volumes  were  measured  by  counting  voxels  in  each  segmented  cell.     2.  To  define  regions  of  interest  (inflorescence  meristem  and  floral  buds),  four  points  per   bud  were  manually  selected  around  the  base  of  3  to  5  bud  primordia.  For  each  primordium,  a   sphere  was  drawn  around  the  center  of  mass  of  the  four  landmark  points  and  reaching  up  to   the  furthest  point.  The  region  of  interest  was  defined  as  the  section  of  this  sphere  above  the   best-­‐fitting  plane  for  the  four  points.  The  inflorescence  meristem  was  defined  in  a  similar  way,   using  as  landmarks  the  point  closest  to  the  meristem  for  each  bud.  The  main  axis  of  the   inflorescence  meristem  was  defined  as  the  vector  normal  to  the  best-­‐fitting  plane  for  the   meristem  landmarks  and  crossing  their  center  of  mass.     3.  To  define  cell  layers,  cells  touching  the  image  borders  or  facing  the  bottom  of  the   image  were  masked,  then  binary  erosion  was  used  to  identify  cells  in  the  outermost  layer;  cells   considered  abnormally  thick  for  their  layer  (more  than  1.3  times  the  median  thickness)  were   masked  and  not  attributed  to  any  layer;  cells  already  attributed  to  a  layer  were  deleted  and  the   process  was  repeated  until  all  cells  had  been  assigned  to  a  layer.     4.  To  measure  GFP,  the  signal  was  first  corrected  for  diminishing  intensity  with   increasing  depth  in  the  confocal  stack,  based  on  the  FM4-­‐64  signal  within  the  boundaries  of  

segmented  cells.  To  detect  regions  with  GFP  signal,  a  filtering  element  of  5  x  5  x  5  voxels  was   centered  on  each  voxel;  voxels  were  marked  as  positive  if  the  median  value  within  the  filtering   element  exceeded  a  selected  threshold  value,  which  was  kept  constant  in  comparisons  across   genotypes.  For  each  cell  scored  positive,  the  total  GFP  signal  within  the  positive  region  was   collected  and  normalized  to  the  median  intensity  in  all  positive  cells.  In  the  case  of  ER-­‐localised   GFP  (lines  expressing  Op:ER-­‐GFP),  the  normalised  signal  was  also  divided  by  cell  volume  to   allow  comparison  of  signal  density  in  genotypes  with  different  cell  volumes.  For  the  weak   nuclear  signal  detected  in  pCUC1::CUC1-­‐GFP  buds,    the  automatic  selection  of  positive  cells  was   checked  and  corrected  manually.   5.  To  align  cells  across  time  course  images,  four  landmarks  were  placed  manually  on   matching  cell  vertices  in  3D  renderings  of  each  confocal  stack.  These  points  were  used  for  rigid   registration,  then  for  each  cell  in  image  1,  a  sphere  of  given  radius  (5  or  8  µm)  was  drawn   around  the  cell  in  the  corresponding  position  in  image  2.  Within  this  sphere,  the  shape  of  each   cell  and  its  neighbors  was  compared  with  the  shape  of  the  initial  cell  and  its  neighbors  in  image   1  and  the  best  match  was  selected.  Cell  divisions  were  detected  when  shape  matches  were   better  for  pairs  of  fused  cells  in  image  2  than  for  each  cell  separately.  Images  of  the  matched   cells  were  used  to  manually  identify  and  delete  incorrect  matches  (2-­‐10%  of  cells  in  each  image   pair).   6.  To  calculate  the  local  standard  deviation  of  growth  (LSDG),  binary  dilation  was  used  to   find  the  immediate  neighbors  of  each  cell  that  had  been  matched  between  images  1  and  2.  For   each  cell  and  neighbor  that  had  been  successfully  matched  across  the  two  images,  relative   growth  rates  were  calculated  as  the  ratio  between  cell  volumes  in  image  2  and  image  1.  The   standard  deviation  of  the  relative  growth  rates  (defined  as  the  LSDG)  was  calculated  if  n  was  at   least  3.   Overview  of  simulations   All  simulations  used  functions  defined  in  the  Python  script   /3D_meristem_analysis/python_scripts/cell_growth_simulation.py  (Supplemental_software).  

The  functions  were  used  directly  in  a  Python  shell,  following  the  instructions  annotated  on  the   script.     The  simulations  sim_a  described  in  Fig.  2A  assumed  that  the  cell  cycle  length  T  is   constant  for  all  cells  and  coincides  with  the  number  of  hours  taken  to  double  the  tissue  volume,   based  on  the  measured  cellular  growth  rates  over  24  h.  A  typical  simulation  ran  for  336   iterations,  corresponding  to  approximately  4  cell  cycles.  Starting  populations  of  unsynchronized   cells  with  narrow  volume  distributions  were  created  by  allowing  identical  cells  to  grow   uniformly  for  a  random  number  of  iterations  between  0  and  one  cell  cycle  length,  then   normalizing  cell  volumes  to  the  experimentally  measured  average.  During  subsequent  cycles,   cell  growth  rates  were  adjusted  to  cell  volume  at  each  iteration  (based  on  the  linear  regression   between  experimentally  measured  cell  volumes  and  growth  rates)  and  were  multiplied  by  a   random  variability  factor  k  (based  on  the  experimentally  measured  standard  deviation  of   growth  rates  of  neighboring  cells),  which  was  re-­‐set  at  the  start  of  each  cell  cycle.  At  the  end  of   each  cycle,  normal  noise  in  cell  division  was  added  based  on  the  standard  deviation  of  cell   division  ratios;  if  unequal  division  resulted  in  a  daughter  cell  smaller  than  the  minimum  volume   accepted  in  the  experimental  data  (40  µm),  both  sister  cells  were  removed  from  the  simulation.   After  each  time  all  cells  were  allowed  to  grow  and  divide  for  a  full  cycle,  a  random  subset  of   cells  was  removed  to  maintain  the  population  size  around  400  (similar  to  the  number  of  cells   measured  in  one  inflorescence  apex).     Simulations  sim_b  described  in  Fig.  2A  were  as  described  above  for  sim_a,  except  that   the  number  of  cell  growth  iterations  (corresponding  to  cell  cycle  length)  of  individual  cells  was   adjusted  to  compensate  for  cell  growth  heterogeneity  by  dividing  it  by  the  growth  variability   factor  k.   For  both  sim_a  and  sim_b,  default  parameter  values  (Table  S1)  were  chosen  to  minimize   cell  variability  (lower  bounds  of  the  95%  confidence  intervals  for  the  SD  of  cell  division  ratio  and   for  the  mean  LSDG)  and  maximize  the  correction  of  cell  volumes  (most  negative  value  in  the   95%  confidence  interval  for  the  slope  of  the  regression  between  cell  volumes  and  growth   rates).  Artifactual  variability  in  growth  rates  introduced  by  imaging  and  image  processing  was   estimated  by  independently  imaging  the  same  apices  from  different  angles  at  a  single  time  

point  and  processing  the  images  as  if  they  corresponded  to  different  time  points  (Table  S2,   treatments  4-­‐7).  The  resulting  local  standard  deviation  of  volume  ratios  (LSDR,  calculated  as   described  above  for  the  LSDG)  was  used  to  calculate  the  corrected  LSDG  (Table  S1)  as  the   square  root  of  the  difference  between  the  squares  of  the  measured  mean  LSDG  and  the  mean   LSDR.   During  each  simulation,  cell  volumes  were  collected  every  six  iterations  to  produce  the   boxplots  shown  in  Fig.  2C,D  and  in  Fig.  S2  D,G,J.  To  compare  simulated  and  experimentally   measured  cell  volume  distributions  (Fig.  2B),  data  were  pooled  from  5  simulations  and  from  3-­‐5   inflorescence  apices,  and  the  p-­‐values  for  equality  of  coefficients  of  variation  were  calculated  as   described  above  (Statistics).  When  simulations  were  run  over  a  range  of  parameter  values  (Fig.   S2  A,  B),  parameters  that  were  not  varied  remained  at  the  default  values  described  above.     To  test  different  functions  for  the  relation  between  cell  volume  and  growth  rate,   inverse,  quadratic  and  logarithmic  functions  were  fitted  to  the  cell  volume  and  growth  rate  data   shown  in  Figure  1H,  using  the  least  squares  method  (source  code  in  Supplemental  Software,   script  /3D_meristem_analysis/python_scripts/statistical_analysis.py  ).  The  functions  and  best-­‐ fitting  coefficients  were  used  instead  of  the  linear  function  in  simulation  sim_a  as  described   above  to  produce  the  data  shown  in  Figure  S2  C-­‐K.  The  probability  density  function  for  growth   rate  as  a  function  of  cell  volume  was  calculated  using  Gaussian  kernel  density  (source  code  in   statistical_analysis.py).     Supplemental  References   S1.  

Soille,  P.  (2013).  Morphological  image  analysis:  principles  and  applications,  (Springer   Science  &  Business  Media).  

S2.  

Schindelin,  J.,  Arganda-­‐Carreras,  I.,  Frise,  E.,  Kaynig,  V.,  Longair,  M.,  Pietzsch,  T.,   Preibisch,  S.,  Rueden,  C.,  Saalfeld,  S.,  and  Schmid,  B.  (2012).  Fiji:  an  open-­‐source   platform  for  biological-­‐image  analysis.  Nature  Methods  9,  676-­‐682.