A multiscale growth atlas of Arabidopsis: linking cell dynamics to organ development

Summary

• Plant development depends on coordinated growth at cellular and organ scales, yet comparative analyses are hindered by inconsistent reporting of growth across studies. We conducted a meta‐analysis of Arabidopsis thaliana growth dynamics, integrating data from 176 studies to create the first multiscale atlas of plant growth.

• We developed a unified mathematical framework to harmonise growth data from diverse organs (shoot apical meristem, root, hypocotyl, and leaf), methodologies, and experimental setups, allowing the conversion and direct comparison of expansion rates at cellular and organ levels.

• Analyses revealed both organ‐specific and general growth strategies linked to size control. In the meristem, a conserved offset in cell expansion between central and peripheral zones was observed. Root elongation was driven mainly by cell expansion and differentiation in the elongation zone, rather than meristem activity. Hypocotyl and leaf growth showed unexpected parallels: early exponential elongation resembled primary morphogenesis, while later linear growth matched secondary morphogenesis. Comparing dark‐ vs light‐grown hypocotyls and juvenile vs transition leaves showed that organ size was modulated by a trade‐off between growth rate and duration of the scaling phase. Cellular‐scale growth during early development was shown to influence final organ size, underscoring the need for early‐stage measurements.

• This growth atlas provides benchmark values and a reference framework for interpreting mutant phenotypes, guiding experimental design, and advancing our understanding of growth regulation across plant organs.

Introduction

Plants compete with each other and adapt to their environment by modulating organ growth, which is shaped by genetic programmes, physiology, and external cues (Johnson & Lenhard, 2011; Weraduwage et al., 2015; de Wit et al., 2018). Quantifying growth dynamics is therefore key to understanding development and has broad applications in agriculture, ecology, and modelling (Van Lijsebettens & Van Montagu, 2005; Louarn & Song, 2020; Hilty et al., 2021). Resource allocation is central: mature leaves act as carbon sources, while developing organs such as young leaves, roots, and hypocotyls serve as sinks (Walter et al., 2009; Johnson & Lenhard, 2011; Weraduwage et al., 2015; White et al., 2016). As Arabidopsis transitions from vegetative to reproductive phases, each organ follows a growth programme defined by its identity, stage, and position in the life cycle or heteroblasty (Zotz et al., 2011; Poethig, 2013).

Organs development falls into two categories: determinate (e.g. leaves, flowers, and fruits) and indeterminate (e.g. roots and shoot apical meristem (SAM); Barthélémy & Caraglio, 2007). Indeterminate organs grow while maintaining their structure; determinate organs reach a fixed size and shape (Kwiatkowska, 2003; Petricka et al., 2012). Determinate growth proceeds through primary morphogenesis – characterised by rapid proliferation and patterning – followed by secondary morphogenesis, marked by reduced division and continued expansion (Bar & Ori, 2014; Rodriguez et al., 2014).

Despite extensive research, fundamental questions are unresolved due to inconsistent or incomparable growth measurements across studies: How do development dynamics vary across organs and cell types? Which developmental stages or cell behaviours most influence final size? Are growth rates controlled by shared or distinct mechanisms? To address these gaps, we conducted a large‐scale meta‐analysis of growth data from 176 studies on Arabidopsis thaliana and other model species. We extracted dynamic parameters at organ, tissue, and cellular levels, focusing on both indeterminate (SAM, root) and determinate (leaf and hypocotyl) organs. While previous meta‐analyses focused on the relations between cell size and cell number during development (Gázquez & Beemster, 2017; Ma et al., 2025), we aim at linking expansion rates at the cell and organ scales. We analysed the effects of experimental variables – temperature, light, sugars, and growth medium – within their standard range, and included less studied structures, such as floral organs, pollen tubes, and root hairs to compare growth across organs and cell types.

This work presents the first coherent atlas of Arabidopsis growth dynamics, linking cell expansion to organ development across stages and variations in standard environmental conditions. It reveals fundamental differences in cellular growth timescales and key principles of size control. Designed as a resource, the atlas offers reference values for experimental design, parameter sets for modelling, and a unified framework for interpreting quantitative growth data. Ultimately, our goal was to support phenotype analysis, targeted genetic screens, and integration of gene expression data with developmental dynamics.

Materials and Methods

Growth data collection

Growth data from organs of Arabidopsis thaliana (L.) Heynh. were compiled by searching for published studies using a combination of the following keywords: “Arabidopsis thaliana,” “growth,” “expansion,” “growth rate,” “absolute growth rate,” “relative growth rate,” “shoot apical meristem,” “root,” “hypocotyl,” “leaf,” “pollen tube,” and “root hair.” SAM data for Solanum lycopersicum L. and Anagallis arvensis L. were collected using the same keywords (limited to SAM). Collected papers are shown in Table 1, details of each study in Supporting InformationTables S1–S6.

Table 1.

Studies used for data collection and analysis.

Organ	References
SAM	Laufs et al. (1998); Jacqmard et al. (1999, 2003); Dumais & Kwiatkowska (2002); Kwiatkowska (2003, 2004, 2006); Grandjean et al. (2004); Reddy et al. (2004); Reddy & Meyerowitz (2005); Müller et al. (2006); Kwiatkowska & Routier‐Kierzkowska (2009); Yadav et al. (2010); Kierzkowski et al. (2012); Uyttewaal et al. (2012); Burian et al. (2013, 2016); Barbier de Reuille et al. (2015); Serrano‐Mislata et al. (2015); Louveaux et al. (2016); Yang et al. (2016); Jones et al. (2017); Jackson et al. (2019); D'Ario et al. (2021); Matz et al. (2022); Shi et al. (2024)
Root	Baskin et al. (1992, 1995, 2004); Beemster & Baskin (1998, 2000); Mullen et al. (1998); Freixes et al. (2002); Wiedemeier et al. (2002); Ma et al. (2003); Nelissen et al. (2003); Sabatini et al. (2003); van der Weele et al. (2003); West et al. (2004); Swarup et al. (2005, 2007); Dello Ioio et al. (2007); Rahman et al. (2007); Anderson et al. (2010); Ballif et al. (2011); Hernández‐Barrera et al. (2011); Zheng et al. (2011); Jun et al. (2013); Liu et al. (2013); López‐Bucio et al. (2014); Pacheco‐Escobedo et al. (2016); Yang et al. (2017); Reyes‐Hernández et al. (2019); Goldy et al. (2021)
Hypocotyl	Yanovsky et al. (1995); Desnos et al. (1996); Gendreau et al. (1997); Gray et al. (1998); Cowling & Harberd (1999); Dowson‐Day & Millar (1999); Collett et al. (2000); Shinomura et al. (2000); Le et al. (2005); Derbyshire et al. (2007b); de Lucas et al. (2008); Gu et al. (2010); Zhang et al. (2010b); Miedes et al. (2013); Narukawa et al. (2015); Xiao et al. (2016); Bou Daher et al. (2018); Yang et al. (2018); Galstyan & Nemhauser (2019); Xin et al. (2020); Zdanio et al. (2020); Dhiman et al. (2023)
Leaf	De Veylder et al. (2001); Autran et al. (2002); Granier et al. (2002); Boudolf et al. (2004); Beemster et al. (2005); Cookson et al. (2005, 2006); Cookson & Granier (2006); Fleury et al. (2007); Ramirez‐Parra & Gutierrez (2007); Takahashi et al. (2008); Lee et al. (2009); Dhondt et al. (2010); Gonzalez et al. (2010, 2015); Skirycz et al. (2010, 2011); Kheibarshekan Asl et al. (2011); Massonnet et al. (2011); Baerenfaller et al. (2012, 2015); Kuchen et al. (2012); Remmler & Rolland‐Lagan (2012); Wuyts et al. (2012); Debernardi et al. (2014); Kalve et al. (2014b); Apelt et al. (2015); Kumar et al. (2015); Baute et al. (2017); Saini et al. (2017); Wang et al. (2017); Beltramino et al. (2018); Fox et al. (2018); Kim et al. (2018); Challa et al. (2019); Hoshino et al. (2019); Sizani et al. (2019); Arribas‐Hernández et al. (2020); Zhang et al. (2020); Harline et al. (2022); Le Gloanec et al. (2022, 2024); Tang et al. (2023); Ezaki et al. (2024); Li et al. (2024)
Root hair	Bates & Lynch (1996); Galway et al. (1997); Wymer et al. (1997); Bibikova et al. (1999); Ketelaar et al. (2002); Schoenaers et al. (2017); Herburger et al. (2022); Kang et al. (2023)
Pollen tube	Tian et al. (2006); Boavida & McCormick (2007); Szumlanski & Nielsen (2009); Zhang et al. (2010a, 2016); Su et al. (2012); Boisson‐Dernier et al. (2013); Lassig et al. (2014); Yang et al. (2014); Chang & Huang (2015); Damineli et al. (2017); Luo et al. (2017); Synek et al. (2017); Fabrice et al. (2018); Lan et al. (2018); Rottmann et al. (2018); Sede et al. (2018); Dias et al. (2019); Reimann et al. (2020); Zhou et al. (2020)
Floral organs	Huang & Irish (2015); Hervieux et al. (2016); McKim et al. (2017); Le Gloanec et al. (2022); Silveira et al. (2022); Gómez‐Felipe et al. (2024)

Fiji (Schindelin et al., 2012) was used to measure length, width, and area from published images and to extract graph values using the Picture Calibration plugin (Frederic V. Hessman, http://www.astro.physik.uni‐goettingen.de/~hessman/ImageJ/Figure_Calibration). Measurement error was estimated based on graph point size. For growth heatmaps, the Color Summarizer tool (Martin Krzywinski, http://mkweb.bcgsc.ca/colorsummarizer) was used to analyse colour distribution (six to eight clusters), with each colour matched to a growth value from the scale. Pixel counts per cluster were used to calculate average growth in the region of interest.

Division data collection

Division data were collected only for the SAM epidermis. For studies with time‐lapse images, cells were counted and division rates calculated; raw meshes were used when available. Mitotic indices were converted to rates following (Smith & Dendy, 1962); see Methods S1 for details.

Plastochron data collection

Plastochron values were calculated, for the vegetative phase, by dividing the number of leaves produced by the time until bolting, and for the reproductive phase, by dividing the number of flowers over a certain time. Where possible, we specifically collected the number of organs initiated over time, which is shown in Fig. 1(m) (Groot & Meicenheimer, 2000; Wang et al., 2008; Guenot et al., 2012; Landrein et al., 2015; Serrano‐Mislata et al., 2017; Olas et al., 2019). Two articles presented a plastochron calculated by the evolution of primordia in the SAM over time (Zhao & Traas, 2021; Burian et al., 2022).

Fig. 1.

Division and expansion rates in the shoot apical meristem (SAM). (a) Example of Mitotic Index analysis in a fixed SAM of Arabidopsis thaliana from Laufs et al. (1998). Arrowheads indicate mitotic cells. (b) Example of cell division analysis from lineage tracking in A. thaliana, by Matz et al. (2022). Cells dividing in the 24 h interval are coloured. Inset shows the initial timepoint. (c) Example of cell expansion analysis from lineage tracking in Solanum lycopersicum, from Barbier de Reuille et al. (2015). (d) Average cell size is maintained over time in the meristem. Median (line) and interquartile range (shadow) cell volume over time in four A. thaliana inflorescence meristems from D'Ario et al. (2021). (e) The Mitotic Index is proportional to the ratio of mitosis duration over the length of the whole cell cycle. (f) Lineage analysis shows how many divisions happened over a period of time by linking daughter cells to their predecessors, allowing us to calculate cells' relative division rate (RDR). (g) Cell expansion time‐lapses allow us to calculate a relative growth rate (RGR) over time. (h) In dividing cells, the length of the cell cycle is equivalent to the time it takes to double the number of cells (Fiorani & Beemster, 2006). Since the average size of cells is constant over time (d), the time needed to double the number of cells (T _cycle) and to double the size of a cell (T _{size × 2}) are the same, allowing us to compare RDR and RGR in the same period. (i–k) Comparison of cell dynamics in the inflorescence (IM) and vegetative meristem (VM). (i) Mitotic Index values collected from A. thaliana's SAM articles (% of dividing cells/total number of cells). (j) RDR in % of new cells per 24 h, calculated from the Mitotic Index. Dots show values obtained by assuming a mitosis duration of 1.5 h, with handles showing the variation obtained with 1 h (upper panel) and 2 h (lower panel). Longer mitosis duration implies longer cell cycles, and thus a smaller relative number of new cells produced over a period of time. (k) RDR in % per 24 h from lineage analysis of A. thaliana. (l) RGR of A. thaliana's SAM. (m) Plastochron of A. thaliana's leaves (green boxplots) and flowers, in hours between two organs. Vegetative (VM) and inflorescence (IM) plastochron on SD conditions. Values were grouped in bins of 12 leaves. Boxes contain points within the interquartile range (IQR), line shows median. Whiskers extend to 1.5 IQRs of the lower and upper quartiles. (n–p) Heat maps of median RGR of CZ and PZ for A. thaliana (n), Anagallis arvensis (o), S. lycopersicum (p). (q) Correlation between RGR or RDR in PZ vs CZ inflorescence (pink) and vegetative meristem (green). Bars: (a, b) 25 μm; (c) 50 μm. In (j–m), the diagonal lines link the central zone (CZ), and peripheral zone (PZ) of the same study. Studies without a PZ/CZ separation are shown as SAM.

Growth models

Growth during secondary morphogenesis of hypocotyls and leaves was fitted using a logistic (sigmoid) function: $S\left(t\right)=\frac{S_{\max }}{1+e^{-k\left(t-t_{\mathrm{mid}}\right)}}+d$, via scipy.optimize.curve_fit in Python. For leaves with early‐stage data only, an exponential fit $S\left(t\right)=S_0{e}^{\mathit{kt}}$ was used. Absolute growth rate (AGR) was derived from the derivative of the fit, and relative growth rate (RGR) from the ratio of AGR : size. RGR values were standardised across timescales using an exponential model (see Box 1; Methods S1). Fit quality was assessed using a chi‐squared test (95% confidence level), and error propagation details are provided in the Methods S1.

Box 1. Definitions of growth parameters and formulas.

Models and applications	Parameters	Definition and meaning	Formula	Units
Exponential cell proliferation Used for shoot apical meristem (SAM) cell division	Cell count increase	Assuming the rate of cell division is maintained, the number of cells increases exponentially over time. Cell cycle duration ($T_{\text{cycle}}$) is the time needed for a newly divided daughter cell to undergo division itself. At $t=T_{\text{cycle}}$, all cells have divided, and the total number of cells has doubled: $N_{T_{\text{cycle}}}=2N_0$.	$N\left(t\right)=N_0{2}^{t/T_{\text{cycle}}}$	No unit (cell count)
	Relative division rate (RDR)	Change in cell count, relative to the initial number of cells, over a given time interval. We chose to express the RDR over 24 h. Conversion to another interval (e.g. 12 h) requires non‐trivial calculations (see Methods S1). The RDR is measured from time‐lapse experiments and can be used to compute $T_{\text{cycle}}$ directly. RDR and RGR are equivalent in meristematic cells.	$\mathrm{RDR}=100\left(\frac{N_{24\mathrm{h}}-N_{0\mathrm{h}}}{N_{0\mathrm{h}}}\right)$ $\mathrm{RDR}=100\left(2^{24/T_{\text{cycle}}}-1\right)$	% d−1
	Mitotic index (MI)	Number of cells in mitosis relative to the total number of cells, counted from fixed samples. To deduce $T_{\text{cycle}}$ from the MI, the duration of mitosis ($\mathrm{\tau }$) must be assumed.	$\mathrm{MI}=100\frac{N_{\text{mitosis}}}{N_{\text{total}}}$ $\mathrm{MI}=2^{\mathrm{\tau }/T_{\text{cycle}}}-1$	%
Exponential cell size increase Used for SAM cell area, leaf 1ary morphogenesis, root cell elongation	Cell size increase	Exponential model of organ or cell size increase, useful when cells or organs maintain a constant expansion rate ($r$) for some time.	$S_{\exp }\left(t\right)=S_0{e}^{\mathit{rt}}$	mm2, μm2 (area); mm, μm (length)
	Absolute growth rate (AGR)	Absolute change in size over time, for example addition of cell or organ length (or area) per unit time. The AGR value depends on the time interval chosen (see Methods S1; Fig. S1).	$\mathrm{AGR}=\frac{S\left(t_2\right)-S\left(t_1\right)}{t_2-t_1}$	mm2 h−1 or mm2 d−1 (areal growth); mm h−1 or mm d−1 (elongation)
	Relative growth rate (RGR)	Relative change in size over time, for example ratio of added length vs original length (or area) per time interval. If $r$ is constant over the time interval (e.g. in the SAM), RGR is also given by the second formula. This formula is used for RGR conversion between time scales (e.g. % h−1 to % d−1).	$\mathrm{RGR}=\frac{100}{t_2-t_1}\left(\frac{S\left(t_2\right)-S\left(t_1\right)}{S\left(t_1\right)}\right)$ $\mathrm{RGR}=\frac{100}{t_2-t_1}\left(e^{r\left(t_2-t_1\right)}-1\right)$	% h−1 or % d−1
Logistic organ size increase Used for hypocotyl elongation, leaf 2ary morphogenesis	Size over time	Logistic model of organ size ($S$). Originally used for population growth, this equation is useful for modelling determinate growth in some organs. $S_{\max }$ is the maximum value of the size with $d$ being an offset, $t_{\mathrm{mid}}$ is the time value at the midpoint, and $k$ is the midpoint gradient.	$S_{\mathrm{sig}}\left(t\right)=\frac{S_{\max }}{1+e^{-k\left(t-t_{\mathrm{mid}}\right)}}+d$	mm2, μm2 (area); mm, μm (length)
	AGR	If the size is fitted to a logistic model, the AGR can be computed using a time derivative.	$\mathrm{AGR}=S_{\mathrm{sig}}^{\prime }\left(t\right)=\frac{S_{\max }k{e}^{-k\left(t-t_{\mathrm{mid}}\right)}}{{\left(e^{-k\left(t-t_{\mathrm{mid}}\right)}+1\right)}^2}$	mm2 h−1 or mm2 d−1 (areal growth); mm h−1 or mm d−1 (elongation)
	Max AGR	Maximal value of AGR from logistic model. Due to the shape of the logistic function, Max AGR occurs when the size is equal to half of its final value.	$S_{\mathrm{sig}}^{\prime }\left(t_{\mathrm{mid}}\right)=\frac{k{S}_{\max }}{4}$	mm2 h−1 or mm2 d−1 (areal growth); mm h−1 or mm d−1 (elongation)
	RGR	Assuming a logistic model of size, RGR at any given timepoint is given by dividing the AGR by size (second formula).	$\mathrm{RGR}=\frac{100}{t_2-t_1}\left(\frac{S\left(t_2\right)-S\left(t_1\right)}{S\left(t_1\right)}\right)$ $\mathrm{RGR}=100\left(\frac{\mathrm{AGR}\left(t\right)}{S_{\mathrm{sig}}\left(t\right)}\right)$	% h−1 or % d−1
	Duration of linear growth phase ΔT	Duration of the linear growth phase of the logistic model, calculated by the difference of the extreme roots of the 4th derivative of the model.	$\mathrm{\Delta }T=\frac{\mathrm{lo}{\mathrm{g}}_{\mathrm{e}}\left(49+20\sqrt{6}\right)}{k}$	Hours or days
Root growth along length Used for root tip velocity, cell elongation, definition of zones	Cell velocity ($V_{\text{cell}})$	Due to root elongation, each cell moves relative to the substrate, at a speed which depends both on the cell position along the root (s) and the RGR of all the other cells before it (with position s = 0 in the mature zone). $V_{\text{cell}}$ can also be expressed as a velocity relative to root tip, the position s is then measured starting from the tip.	$V_{\text{cell}}={\int }_{s=0\left(\text{mature zone}\right)}^s\frac{\mathrm{RGR}}{100}\mathrm{d}s$ or $V_{\text{cell}}={\int }_{s=0\left(\text{root}\mathrm{tip}\right)}^s\frac{\mathrm{RGR}}{100}\mathrm{d}s$	mm h−1 or μm min−1
	Tip velocity (TVcell)	The speed of root elongation is equal to the velocity of a cell at the root tip (TVcell), given by the integral of RGR over the root length. In this case, choosing the position reference at the root tip or in the mature zone gives the same result.	$\mathrm{T}{\mathrm{V}}_{\text{cell}}={\int }_{s=0\left(\text{mature zone}\right)}^{s=\mathrm{end}\left(\text{root}\mathrm{tip}\right)}\frac{\mathrm{RGR}}{100}\mathrm{d}s$ or $\mathrm{T}{\mathrm{V}}_{\text{cell}}={\int }_{s=0\left(\text{root}\mathrm{tip}\right)}^{s=\mathrm{end}\left(\text{mature zone}\right)}\frac{\mathrm{RGR}}{100}\mathrm{d}s$	mm h−1 or μm min−1
	RGR	At any position ($s$) along the root, the relative growth rate is given by the spatial derivative of the cell displacement velocity ($V_{\text{cell}}$) at this position.	$\mathrm{RGR}=100\frac{\mathrm{d}{V}_{\text{cell}}}{\mathrm{d}s}$	% h−1 or % min−1

Statistical tests

Statistical analyses were performed in Python. Differences between two distributions were assessed using the Kolmogorov–Smirnov (KS) test from the scipy.stats package. Significance levels were classified as very strong (P < 0.001), strong (P < 0.01), or moderate (P < 0.05). Correlations were evaluated using a weighted least squares model from statsmodels.api, yielding regression lines with associated P‐values and coefficients of determination (R ²). For simple linear regression, R ² is related to the Pearson correlation coefficient by $\left|r\right|=\sqrt{R^2}$ (Asuero et al., 2006). Correlations were considered significant for P < 0.05; those with 0.05 < P < 0.10 were noted as potential trends for future investigation. Significant R ² values were interpreted according to thresholds in Table 2 (Evans, 1996).

Table 2.

Interpretation of R ² values (Evans, 1996).

R 2	Absolute value of r	Correlation
0	0	None
0.01	0.1	Very weak
0.04	0.2	Weak
0.09	0.3	Weak
0.16	0.4	Moderate
0.25	0.5	Moderate
0.36	0.6	Strong
0.49	0.7	Strong
0.64	0.8	Very strong
0.81	0.9	Very strong
1	1	Perfect

Figure construction

Data were plotted using Python with matplotlib.pyplot and seaborn packages. Collected data and scripts to reproduce analyses are included in Dataset S1. Figures were constructed using Adobe Illustrator.

Results

A unifying framework for analysing growth data

Several challenges arise when comparing growth data between studies, which explains why this has not been attempted before. First, the raw data are often not presented, and the results of growth analysis are often published as graphs or colourmaps. We used existing tools for extracting values from graphs and created a new protocol to obtain quantitative data from colourmaps, based on colour extraction and classification (see the Materials and Methods section). To validate our approach, we compared results obtained with our protocol with raw growth values for 20 colourmap zones from five different studies (Kierzkowski et al., 2012, 2019; Barbier de Reuille et al., 2015; Le Gloanec et al., 2022; Gómez‐Felipe et al., 2024), giving a mean relative error of 5.65% (±5.61%) (Table S7). The discrepancy between data extracted from published colourmaps and actual growth values was higher on curved organs, or when the quantification was performed on saturated colourmaps.

While the same vocabulary is often used across studies, the mathematical definitions used are extremely variable, leading to completely different meanings of the same words. For example, ‘growth rate’ can represent either how fast the organ tip is displaced (Baskin et al., 1992), or changes in cell area over time (Le Gloanec et al., 2022). Differences between absolute and relative rates are often overlooked. We established a coherent mathematical framework based on the commonly used models for plant growth, defined in Box 1 (Reed & Holland, 1919; Pearl et al., 1928; Fiorani & Beemster, 2006; Peters & Baskin, 2006).

Organ size trajectories are often modelled using logistic functions (Winsor, 1932; Hunt, 1982; Paine et al., 2012; Kawano et al., 2020; Hilty et al., 2021), which we used for the determinate growth of leaves and hypocotyls. The function describes an S‐shaped curve beginning with an early exponential phase, followed by a linear phase and a plateau as the organ reaches its final size. The primary morphogenesis of the leaf lies in the early part of this S‐curve; therefore, we assumed an exponential model to fit data in this developmental stage. Growth in the SAM is indeterminate as cells continuously grow and divide; therefore, we chose an exponential relation between cell size and time (Green, 1976). For the root data, we computed cell displacement velocity by integrating RGRs over the root length (Silk et al., 1989). Finally, we converted parameters into compatible units to compare growth dynamics between different organs or cells. Such a conversion is not trivial due to the exponential nature of growth. For example, if we assume that an organ maintains the same RGR over time, its expansion over 24 h would not be simply two times higher than its expansion measured over 12 h (Fig. S1).

Linking cell division with expansion reveals SAM growth variability

The SAM maintains a stable pool of undifferentiated cells in its central zone (Steeves & Sussex, 1989) while continuously initiating lateral organs (leaves, stems, and flowers) from the peripheral zone in a phyllotactic pattern (Kuhlemeier, 2017). As cells move outward from the centre toward the periphery and eventually exit the meristem, the meristematic cell population is maintained via balancing rates of division and organ initiation (Dumais & Kwiatkowska, 2002; Kwiatkowska, 2003). Disruption of this balance can lead to meristem termination (Müller et al., 2006).

Despite extensive research on SAM cell dynamics, systematic comparisons have been lacking due to methodological diversity (Fig. 1a–c). We re‐analysed 24 studies (Tables S8–S10) spanning A. thaliana, A. arvensis, and S. lycopersicum to address key questions: How do cell dynamics differ between central and peripheral regions? Are vegetative or reproductive meristems more active? How do conditions affect growth? We developed standardised metrics for comparing cell division and expansion rates across studies (Box 1), establishing a benchmark for SAM growth (Fig. 1; Table 3).

Table 3.

Shoot apical meristem (SAM) relative increase in cell size relative growth rate (RGR_cell) for three different species.

Species	Phase	Zone	RGR (% per 24 h), mean ± SD	n studies	n samples	n heatmaps
Arabidopsis thaliana	IM	CZ	19.0 ± 15.3	10	18	19
		PZ	34.2 ± 17.4	9	14	15
Anagallis arvensis	IM and VM	CZ	12.4 ± 2.32	3	5	12
		PZ	29.2 ± 7.81	3	5	12
Solanum lycopersicum	VM	CZ	53.9 ± 10.8	2	4	7
		PZ	73.4 ± 29.3	2	4	7

Data were collected on inflorescence meristems (IM) or on vegetative meristems (VM; Tables S10, S12). n _studies and n _samples indicate respectively the number of publications and the total number of apices used for the analysis of each organ zone. n _heatmaps indicates the number of heatmaps we re‐analysed, either extracting information from published figures or from raw data. CZ, central zone; PZ, peripheral zone.

Cell division has been first quantified from fixed samples, measuring the Mitotic Index as the fraction of dividing cells. This can be converted into cell cycle duration, provided the mitosis duration is known (Fig. 1a,e). By contrast, lineage tracking from live time‐lapse data yields a relative division rate (RDR; Fig. 1b,f). We compiled Mitotic Index data for Arabidopsis and converted it into RDRs using mitosis durations from 1 to 2 h (Beck et al., 2011; Yin et al., 2014; Fung‐Uceda et al., 2018; D'Ario et al., 2021), resulting in a broad range of values (Fig. 1j). Live imaging produced a narrower range, suggesting that it is more reliable (Fig. 1k). Comparing both methods indicated that mitosis likely lasts closer to 2 h.

Cell expansion has been measured by tracking cell size over time using 3D confocal imaging or electron microscopy of organ replica (Fig. 1c,h; Dumais & Kwiatkowska, 2002; Barbier de Reuille et al., 2015). In the SAM's outer layer, cell expansion occurs mostly in‐plane, making surface area a good proxy for volume growth (Methods S1; Jones et al., 2017). We extracted parameters from colormaps and converted them into RGR (% per 24 h) for central and peripheral regions (Fig. 1l; Table 3).

Next, we tested the consistency between cell division and cell expansion data in the SAM. Meristematic cells typically maintain a stable average size by balancing expansion and division – dividing once their size has doubled (Fig. 1d; Laufs et al., 1998; Jones et al., 2017; D'Ario et al., 2021). This occurs when the cell cycle duration matches the time needed to double in size, meaning RDR and RGR values should align (Fig. 1d; Box 1). Indeed, Mitotic Index, lineage, and expansion studies yielded broadly consistent values (Fig. 1j–l; Tables S8–S10). However, we found wide variation in SAM growth dynamics even under standard conditions. While division rate variation may reflect stochastic events, it cannot fully explain the fivefold range observed in RGRs (Fig. 1l).

We investigated whether growth conditions explain SAM growth variability. Factors like light intensity, day length, temperature, and ecotype showed limited or inconsistent effects (Figs S3, S4b), possibly due to sparse or conflicting data. We also found no clear differences between confocal and replica imaging (Fig. S5c), nor between time‐lapse durations or interval frequencies (Fig. S5a,b). Surprisingly, growth did not slow in more intensive imaging setups, suggesting experienced researchers can maintain healthy SAMs despite longer or more frequent imaging sessions.

The variability in SAM growth rates may reflect changes over its lifespan, from early vegetative to late inflorescence phases. Yet current data are insufficient to test basic differences in SAM growth between the reproductive and vegetative phases, as the latter is under‐studied (Fig. 1j–l). As an indirect proxy, plastochron duration – the interval between successive organ initiations – has been used to infer meristem activity (Wang et al., 2008). Plastochron is generally longer in vegetative SAMs (30 ± 12 h) than in inflorescences (9.6 ± 3.0 h) and shortens over time before flowering (Figs 1m, S2; Table S11). However, plastochron and cell dynamics may be uncoupled, as changes in SAM size during development can accelerate organ initiation without increasing proliferation (Vidaurre et al., 2007; Kwiatkowska, 2008; Landrein et al., 2015; Kinoshita et al., 2020). Organ‐counting methods also often overestimate plastochron compared with live imaging, which shows much shorter intervals (8–9.6 h; Zhao & Traas, 2021; Burian et al., 2022). To better understand SAM dynamics and growth variability, it will be essential to measure cell expansion across successive developmental stages.

Cell dynamics within SAM zones are linked across species

Cell dynamics have been studied in species beyond A. thaliana, including the scarlet pimpernel (A. arvensis) and tomato (S. lycopersicum), but their cell expansion rates have not been compared. We standardised all cell data into RGRs, in % per 24 h (Figs 1n–p, S4a; Table 3). Growth rates were similar in Arabidopsis and Anagallis (KS test for peripheral zone: P = 0.96). Tomato vegetative meristems grew twice as fast on average, with substantially larger cells – median area of 88 μm² in tomato vs 24 μm² in A. thaliana (Kierzkowski et al., 2012; Jones et al., 2017) – and therefore easier to image precisely. Altogether, tomato is an excellent, yet underexplored, system for studying meristem dynamics.

In all species, the central region of the SAM consistently grew more slowly than the peripheral region, in line with observed cell division patterns (Fig. 1j–l; Tables 3, S10). Growth rates in the central and peripheral regions were strongly correlated (R ² = 0.83, n = 50; Fig. 1q), with a consistent absolute offset of c. 12% per 24 h, despite the wide range of growth values observed (central region RGR: 2.5–61% per 24 h). This offset may be important for preserving the shape of the shoot apex as cells grow and move outward. Investigating how growth in both regions responds to experimental conditions – and whether this absolute difference can be disrupted – could provide new insights into SAM growth regulation.

Cell dynamics at the root of organ‐scale movement

The root's ability to penetrate soil is critical for water and nutrient uptake; therefore, most studies focus on how fast the root tip moves through its substrate (Benfey et al., 2010; Fendrych et al., 2018; Ötvös et al., 2021). To link cellular processes to this macroscopic behaviour, we analysed how cell growth relates to root tip speed and how experimental conditions influence wild‐type (WT) root dynamics. We chose the term ‘tip velocity’ (μm h⁻¹), as this type of measurement is often named, somewhat ambiguously, ‘root growth’ or ‘elongation rate’. We re‐analysed data from 28 studies in A. thaliana covering both cellular and whole‐root scales.

Tip velocity varied widely across Arabidopsis ecotypes and even within Columbia‐0 (Col‐0) (mean 384 ± 119 μm h⁻¹, range 87–626 μm h⁻¹ across 18 studies; Table S13; Fig. 2a). Differences in age reporting (days after germination (DAG) vs stratification) partly explain the variability, but aligning datasets still revealed divergent growth profiles (Fig. S6a). In maize and several dicots, root tip velocity peaks early then declines (Muller et al., 1998; Chapman et al., 2002), whereas most Arabidopsis studies report only an increase, possibly due to shorter observation windows. Root velocity was reported to increase over time on Hoagland medium (Baskin et al., 1992, 1995; Beemster & Baskin, 1998), while remaining stable on Murashige & Skoog medium (Beemster et al., 2002). However, the meta‐analysis showed no consistent link between velocity profiles and medium composition (Fig. S6b).

Fig. 2.

Arabidopsis thaliana root growth parameters and dynamics. (a) Root tip displacement velocity over time. Inset shows a hypothetical curve. (b) Cell displacement does not always indicate cell expansion; only green cells expanded from T ₀ to T ₁, while the grey cell did not, but was displaced nonetheless. (c) Absolute growth depends on initial cell size, while relative growth does not. All cells grew 50%, but the larger cell had a greater absolute growth. (d) Root zones defined by cellular profiles: meristem zone (MZ) by threshold of cell length (MZ₁) or root growth rate (RGR) (MZ₂); elongation zone (EZ) by linear velocity transition (EZ_s); transition zone (TZ) between MZ and EZ, and differentiation zone (DZ) by 95% of maximum velocity (EZ_e). QC, quiescent center. (e) Cellular analysis of a 30‐min time lapse of 5‐d‐old A. thaliana root. Two length scales emphasise different root regions: Scale A highlights the EZ's start but not its end, while Scale B details small cells in the MZ. Absolute growth rate (AGR) identifies EZ boundaries but masks MZ differences, while RGR defines MZ₂, TZ, and EZ. Cell velocity heat map shows a peak near the root base, not the tip. (f) Root tip velocity does not have a significant correlation with MZ elongation rate (MZ length × MZ max RGR). (g) Root tip velocity correlates very strongly with EZ elongation rate (EZ length × Max RGR). (h, i) Tip velocity correlates strongly with EZ length (i), but not with max RGR (h). (j, k) Root tip cellular velocity correlates strongly with age (j), which can be linked to an increase in length of the EZ (k, in orange) by displacing the EZ/DZ boundary, while the MZ size (blue) remains constant and the TZ (yellow) elongates slightly. Raw data used in (e) were published in Goldy et al. (2023). EZ_e, EZ end; EZ_s, EZ start.

To understand early acceleration in root tip velocity, we re‐analysed 17 studies measuring cell expansion. Methods and terminology varied between papers; therefore, we established consistent definitions (Table S14). Cell velocity describes landmark displacement, for example nuclei (Hernández‐Herrera et al., 2022), cell walls (Baskin et al., 1995), or external markers (Baskin et al., 1995; Beemster & Baskin, 1998; van der Weele et al., 2003). While often called ‘cell growth’ in the literature, cell velocity does not measure cell expansion, as it depends on cell position along the root (Fig. 2b). Absolute growth rate (AGR_cell) reflects the addition of cell length (or area, volume), while relative growth rate (RGR_cell) measures proportional size change over time (Fig. 2c). Measuring RGR_cell is especially relevant in roots, where neighbouring cells elongate at similar relative rates despite differing lengths.

Roots are divided into regions, starting from the tip: meristem (MZ), elongation (EZ), and differentiation zones (DZ), with some studies, including a transition zone (TZ) between MZ and EZ. However, inconsistent criteria hinder cross‐study comparisons (Ivanov & Dubrovsky, 2013). We compared quantitative definitions on collected datasets (Figs 2d, S7a). Zones are illustrated on re‐analysed time‐lapse data (Goldy et al., 2023; Figs 2e, S7b).

The meristem zone near the quiescent centre contains small, dividing cells. Its boundary is often identified visually, based on cell length, but the gradual increase in size could lead to ambiguities (Fig. 2e). We used two boundary definitions: MZ₁, based on a twofold increase in cortex cell length (Baskin et al., 1995; González‐García et al., 2011; Liu et al., 2013; Goldy et al., 2023), and MZ₂, based on reaching 30% of the maximal elongation rate (Ishikawa & Evans, 1993; Mullen et al., 1998; Figs 2d, S7a). Both methods produced similar results (Fig. S7c; R ² = 0.64, P = 0.002). However, the MZ₂ definition proved more robust, since it does not depend on cell sizes that vary strongly due to stochastic division events (Schmidt et al., 2014; Goh et al., 2023).

The elongation zone begins where cells stop dividing and elongate rapidly, followed by the differentiation zone, where cells reach final size and produce root hairs (Ivanov & Dubrovsky, 2013; Zluhan‐Martínez et al., 2021). The start of the EZ is often detected qualitatively, noting the ‘first rapidly elongating’ cell (Casamitjana‐Martínez et al., 2003; Dello Ioio et al., 2007; Wendrich et al., 2017). We quantitatively defined the beginning of the zone (EZ_start) based on velocity profile changes (Peters & Baskin, 2006) and its end (EZ_end) where velocity exceeds 95% of the maximum (Yang et al., 2017; Figs 2d,e, S7a). The transition zone, between MZ and EZ, marks the shift from cell division to elongation. However, there is no consensus on defining the zone boundary or whether cells still divide within it (Fig. S7d; Table S15; Ishikawa & Evans, 1997; Baluska et al., 2010; Ivanov & Dubrovsky, 2013). We delimited the transition zone using the previously defined MZ₂ and EZ_start definitions. Across datasets, the elongation zone was nearly three times longer than the meristem, while the transition zone was about half the meristem length. The transition zone length was the most variable, suggesting it could be more responsive to developmental or environmental cues (Table 4).

Table 4.

Mean values and SD of Arabidopsis thaliana's root zone lengths.

Zone	Start	End	Length (μm), mean ± SD
Meristem zone (MZ)	Root tip	MZ2	333 ± 60.2
Transition zone (TZ)	MZ2	EZstart	137 ± 63.5
Elongation zone (EZ)	EZstart	EZend	962 ± 321

n = 21 data points, 17 publications, data from roots at different ages were considered separately.

Cell expansion and differentiation timing controls root tip velocity

After defining consistent root zones, we examined how cell dynamics contribute to organ‐level movement. Most of the 17 studies on cellular dynamics did not report root tip velocity; therefore, we computed the maximal value of cell velocity as a proxy (Figs 2f–j, S8). Root tip displacement depends on both cell production and elongation (Baskin et al., 1995; Beemster & Baskin, 1998; West et al., 2004). Therefore, we investigated how the meristem and elongation zone contributed to organ‐scale movements.

Although previous studies linked smaller meristems to slower root growth (Beemster & Baskin, 1998), we found no significant correlation between tip velocity and meristem length or its absolute elongation rate (Figs 2f, S9b). By contrast, the rate at which the elongation zone extends showed a strong positive correlation with tip velocity (Fig. 2g; R ² = 0.888, P < 0.001), highlighting its central role in root displacement. The elongation zone is both larger in size and faster growing than the meristem, consistent with its key role in pushing the root tip forward. We next asked which feature of the elongation zone was more influential. While the maximum cell relative growth rate (RGR_cell) in the elongation zone did not correlate with tip velocity (Fig. 2h), its total length did (Fig. 2i; R ² = 0.518, P < 0.001). This suggests that EZ size, more than cell growth rate, governs root movement. A partial trade‐off between RGR_cell and EZ length (Fig. S10) hints that roots may adjust cell elongation to fine‐tune speed for a given size of elongation zone.

We then explored environmental effects. Higher temperatures increased RGR_cell but reduced EZ length, resulting in no net change in tip velocity (Fig. S11). Higher sucrose levels were linked to longer EZ but potentially slower cell elongation (Fig. S12). Differences in growth media also seemed to influence EZ size, although it could be due to age differences between samples (Fig. S13). As most studies varied multiple conditions together, future work should isolate environmental variables while controlling for age.

Finally, we investigated the cellular causes of root tip velocity increase over time (Fig. 2a). In agreement with organ‐scale measurements, we found a strong positive correlation (R ² = 0.384; P = 0.006) between the tip velocity estimated from cellular data and root age (Fig. 2j). While the length of the elongation zone increased with age (R ² = 0.346, P = 0.01), the cell elongation rate did not (Fig. S14), confirming earlier findings (Baskin et al., 1995; Beemster & Baskin, 1998). Tracking zone boundaries over time showed that the elongation zone expands by delaying differentiation, shifting its distal limit away from the root tip, while the meristem boundary remains fixed (Figs 2k, S14e). This contrasts with prior suggestions that tip velocity acceleration is driven by meristem growth, which may reflect differing zone definitions. The transition zone also lengthened slightly with age, but this factor could not explain changes in tip velocity (Figs S9c, S14f).

In summary, elongation zone expansion is, by far, the best predictor of how fast the root tip moves. The size of the EZ, associated with the timing of cell differentiation, plays a predominant role in accelerating root tip displacement over time. While temperature and sucrose concentration did not affect root tip velocity, there is a potential effect on cell elongation rate, compensated by a change in EZ size. Yet, many studies on the regulation of root growth focus on the cellular dynamics of the meristem zone. Future works on the elongation zone could fully elucidate how cell dynamics controls, or maintains via homeostasis, the speed of root movement.

Early growth variation is magnified to determine hypocotyl size

Hypocotyl elongation is essential for seedling emergence after germination. In darkness, cells elongate rapidly to push the seedling through the soil, while under light, elongation halts in favour of cotyledon and leaf development (Nemhauser & Chory, 2002). As elongation occurs mainly without cell division, the hypocotyl serves as a model for studying cell expansion under genetic, hormonal, and environmental influences (Vandenbussche et al., 2005; Boron & Vissenberg, 2014).

Most studies focus on final hypocotyl length, overlooking growth dynamics. To investigate hypocotyl development in light and dark growth conditions, we analysed data from 22 studies, including four with cellular resolution (Table S3). We aimed to address the following questions: Which growth parameters influence final size? How are growth dynamics affected by environmental conditions? How do growth patterns differ between light and dark conditions?

We first analysed data at the organ scale. The hypocotyl length over time followed a logistic curve with three phases – exponential, linear, and plateau – allowing us to define the duration of linear growth (ΔT) and extract the organ‐scale growth rates (Figs 3a, S15; Methods S1). The absolute growth rate (AGR_org, mm d⁻¹), commonly referred to as ‘growth rate’, reflects how quickly the hypocotyl tip moves upward as the organ elongates. It corresponds to the slope (derivative) of the logistic curve. The relative growth rate (RGR_org, % d⁻¹) expresses the percentage increase in length relative to the organ's current size. An analogy can be drawn to a savings account: hypocotyl length is like the account balance, RGR_org is like the interest rate, while AGR_org is the daily monetary gain. As with compound interest, AGR_org increases with the total length of the hypocotyl, reaching its maximal value halfway in the linear growth phase. Past this point, however, the strong decline in RGR_org results in a decrease in AGR_org, much like a falling interest rate would affect monetary gain.

Fig. 3.

Arabidopsis thaliana's hypocotyl grows diffusely, and the region of fast growth changes over time. (a) Logistic curve fitted to hypocotyl growth data (crosses). Red dots mark maximal AGR_org and the transition to the plateau phase. The linear growth period (ΔT) was calculated as twice the time difference between these points. Final length, Max AGR_org, Length at Max AGR_org, and RGR_org at Max AGR_org were extracted from the fitted curves. (b) Fitted length, AGR_org, and RGR_org curves for dark (blue) and light (orange) conditions. (c) Correlation between final length and Max AGR_org × ΔT, with marker sizes representing sample numbers per study. (d) Distribution of Max AGR_org, ΔT, RGR_org at Max AGR_org, and Length at Max AGR_org for light and dark conditions. Using a Kolmogorov–Smirnov (KS) test, significant differences are observed for Max AGR_org and hypocotyl length at Max AGR_org. Boxes contain points within the interquartile range (IQR), line shows median. Whiskers extend to 1.5 IQRs of the lower and upper quartiles. (e) Cellular‐level length curves (thick lines), dark‐grown in blue (Le et al., 2005; Bou Daher et al., 2018; Xin et al., 2020) and light‐grown in orange (Gendreau et al., 1997; Le et al., 2005), overlaid on macroscopic growth curves (thin lines) from Columbia‐0 (solid line) and Landsberg erecta (dashed) ecotypes. (f) RGR_cell heat maps show cell expansion along the hypocotyl length. Grey box indicates final size for dark‐grown samples; dotted white box extrapolates the first time point. (g) Time‐lapse cellular growth data with RGR_cell (% h⁻¹) for dark‐grown at 6‐h intervals (Bou Daher et al., 2018) and light‐grown at 24‐h intervals (Gendreau et al., 1997) hypocotyls. AGR, absolute growth rate; RGR, relative growth rate.

Dark‐grown hypocotyls were on average five times longer than those grown in light (Fig. 3b; Table 5). To estimate hypocotyl elongation during the linear phase, we multiplied its duration (ΔT) by the peak absolute growth rate (maximal AGR_org) occurring mid‐phase. This product correlated strongly with final hypocotyl length, indicating that organ size is mainly built up during the linear growth phase (Fig. 3c). The difference between dark‐ and light‐grown hypocotyls was associated with a fivefold higher AGR_org during the linear phase, while its duration (ΔT) is comparable (Fig. 3d; Table 5). Within each condition, dark‐grown hypocotyls showed size variation mainly driven by ΔT, while light‐grown variation was possibly linked to AGR_org (Fig. S16). This points to different mechanisms controlling final length depending on light availability.

Table 5.

Organ‐scale measurements of Arabidopsis thaliana's hypocotyl dynamics.

Parameter	Light	Dark	Statistically different?
Final length (mm)	3.28 ± 1.68	17.1 ± 3.70	Yes*** (P = 3.77e‐07)
Length at Max AGRorg (mm)	2.08 ± 1.42	8.39 ± 1.78	Yes*** (P = 4.14e‐06)
Max AGRorg (mm d−1)	1.11 ± 0.754	5.96 ± 1.53	Yes*** (P = 3.77e‐07)
Time of Max AGRorg (d)	3.70 ± 1.33	3.25 ± 0.717	No (P = 0.651)
Duration of linear phase ΔT (d)	3.25 ± 1.62	3.55 ± 1.55	No (P = 0.400)
RGRorg at Max AGRorg (% d−1)	65.4 ± 32.1	97.0 ± 35.5	Yes* (P = 0.0147)

Mean final length, Max AGR_org and RGR_org at Max AGR_org of light and dark growth hypocotyls ± SD. Ten datasets for dark conditions and 16 for light conditions, extracted from 19 publications. Evidence to reject the null hypothesis with a KS test was classified into ***, very strong; **, strong; *, moderate based on P‐value.

Light parameters – intensity, wavelength, and duration of exposure – affect the hormonal regulation of growth via distinct pathways, some related to sugar metabolism (Derbyshire et al., 2007a; Pelletier et al., 2010; de Wit et al., 2016; Sinclair et al., 2017). In light‐grown hypocotyls, intensity and photoperiod negatively correlated with final length, with saturation above 100 μmol m⁻² s⁻¹. In the dark, sucrose had no effect on final size but extended ΔT and reduced AGR_org, suggesting slower, prolonged growth (Fig. S17; Table S16).

During early development, hypocotyl length cannot be precisely measured at the macroscopic level, leading to very large uncertainties associated with RGR_org (Fig. 3b). Therefore, we used cellular‐scale data to investigate the exponential phase of development. In agreement with organ‐scale findings, dark‐grown hypocotyls were already c. 3× longer than light‐grown at 2 DAG (Figs 3e, S18), due to higher elongation rates at the base (200–300% d⁻¹ vs < 100%; Figs 3f, S19). In both conditions, the growth gradient reversed its direction from base to tip over c. 24–30 h (Fig. 3g). After the gradient reversal, light‐grown hypocotyls showed uniform, slow growth (c. 1% h⁻¹), while cell dynamics in dark‐grown seedlings beyond Day 4 remain unreported.

In summary, the fivefold difference in final size between dark‐ and light‐grown hypocotyls is mainly driven by higher absolute elongation rates during the linear growth phase, as this phase lasts a similar duration in both conditions. In light‐grown seedlings, size variation appears linked to differences in growth rate, whereas in darkness, exceptionally long hypocotyls often result from a prolonged linear phase. While most elongation occurs during the linear phase, early exponential growth during the first 2 d postgermination likely sets the foundation for final size, suggesting the linear phase amplifies initial differences. Cellular analyses under both light and dark conditions are essential for revealing whether growth gradients persist beyond 3 DAG and contribute to overall elongation.

Regulation of leaf size during secondary morphogenesis

Leaf development follows distinct phases: primordium initiation at the SAM, primary morphogenesis (when shape is established through cell division and expansion) and secondary morphogenesis, where final size is achieved mainly through cell expansion (Gonzalez et al., 2012; Tsukaya, 2013; Bar & Ori, 2014; Kalve et al., 2014a; Rodriguez et al., 2014; Vanhaeren et al., 2015). Many species, including A. thaliana, exhibit heteroblasty (the sequential formation of leaves with distinct sizes) regulated by genes recently linked to leaf growth (Zotz et al., 2011; Tang et al., 2023; Li et al., 2024). Yet, fundamental questions remain: At which developmental stage is final leaf size determined? Which growth parameters are most influential? How do these parameters vary with heteroblasty or respond to environmental factors?

In this section, we focused on secondary morphogenesis – the ‘scaling phase’ – when leaves visibly enlarge with minimal shape change and compiled 36 datasets tracking A. thaliana leaf area during this stage. Leaves 1 and 2 were grouped as ‘juvenile’ due to their similarity, while we referred to the larger Leaf 6 as a ‘transition’ leaf in the heteroblastic sequence (Yu et al., 2013; Rodriguez et al., 2014). Despite ecotype variation in leaf number, the first six leaves develop similarly across genotypes (Passardi et al., 2007; Massonnet et al., 2010). Most datasets used fixed leaves collected at intervals; only two followed individual leaves over time.

Leaf age was inconsistently reported across studies – referenced as days after sowing, stratification, germination, initiation, or measurement start (Fig. 4a inset). To harmonise timelines, we converted all data to DAG, using germination durations estimated from sowing or stratification (Groot & Meicenheimer, 2000; Boyes et al., 2001). However, inconsistencies in initiation timing, early growth, and methodology resulted in up to 10‐d discrepancies in reported leaf 1 initiation times (Fig. 4a). Therefore, we used a model of leaf growth to define a common temporal reference.

Fig. 4.

Growth parameters in Arabidopsis thaliana's secondary morphogenesis leaf development. (a) Logistic fit of leaf area over time in days after germination (DAG) for A. thaliana Leaves 1–6. Inset: comparative timeline of development with different measure starting points and day of each leaf initiation. (b) Curves shown in (a) aligned at T ₀ (defined as in the inset/Box 1). Inset: example of logistic fit to raw data point (x) and main parameters extracted for analysis (see Box 1 for definitions). The silhouettes of leaves in the right show the range of sizes from different studies than those shown by the curves. (c) AGR_org curves derived from the logistic fits, aligned according to (b). Inset: schematic showing positions of Max AGR and ΔT on a curve of AGR over time. (d) RGR_org curves calculated from (c, a). Inset: schematic showing positions of RGR at Max AGR and ΔT on a curve of RGR over time. Shaded zones in (c, d) indicate propagated error from calculations. (e) Correlation between final size and total linear expansion (Max AGR_org ⨯ ΔT). (f) Hypotheses about the relation between Max AGR_org and ΔT. Organ size could be affected by each parameter independently. (g) Correlations of final size and total linear expansion, Max AGR_org, and ΔT separated for Leaves 1, 2, and 6. (h) Distributions of final size, Max AGR_org and ΔT of Leaves 1, 2, and 6, split into groups according to size (darker shade for bigger leaves, lighter for smaller). The asterisk marks distributions with a P < 0.05 with a KS test. Violins show kernel density estimation (KDE) limited to data range. Boxes contain points within the (interquartile range (IQR)), white point shows median. Whiskers extend to 1.5 IQRs of the lower and upper quartiles. (i, j) Correlation of Max AGR_org (i) and ΔT (j) with cumulative light. (k) Relation between ΔT and Max AGR for the different groups shown in (h). The dotted line shows the time compensation as Max AGR_org increases in order to reach the median final size of the group. (l) Same plot as (k) with the marker shade showing cumulative light. AGR, absolute growth rate; RGR, relative growth rate.

Leaf size dynamics fit well with a logistic model, which is made up of an initial exponential phase, a linear growth period, and a plateau (Cookson et al., 2005, 2006; Cookson & Granier, 2006; Massonnet et al., 2010; Baerenfaller et al., 2012, 2015; Kuchen et al., 2012). To validate our datasets, we compared plateau values of our logistic fits to leaf sizes reported in other studies (Table S17), confirming marked differences between juvenile and mature leaves (Fig. 4b). We defined the transition between exponential and linear phases (T ₀) based on derivatives of the logistic function (Fig. 4b inset; Box 1; Methods S1). Aligning curves on T ₀ revealed distinct growth dynamics between juvenile and transition leaves (Fig. 4b–d).

We calculated the absolute growth rate (AGR_org, mm² d⁻¹) as the first derivative of the logistic fit (Fig. 4c), which peaks midway through the linear phase when the leaf reaches about half of its final area. The relative growth rate (RGR_org, % d⁻¹) declines steadily throughout development (Fig. 4d). High uncertainty in RGR_org estimates during early stages, due to small organ size and high residuals, limits accuracy near T ₀ (Figs 4d, S20), requiring alternative methods for early‐phase estimation of RGR.

Total area gained during the linear phase (Max AGR_org × ΔT) strongly correlated with final leaf size, highlighting this phase as a key determinant of organ size (Fig. 4e). Differences in size between juvenile and transition leaves could result from variation in either AGR_org or ΔT, while a compensation between the two parameters would lead to a similar size (Fig. 4f). Leaf 6 exhibited significantly higher Max AGR_org than juvenile leaves, but similar ΔT (Tables 6, S18; Fig. S21), suggesting that the AGR primarily drives heteroblastic size differences, but not growth duration.

Table 6.

Organ‐scale measurements of Arabidopsis thaliana's leaf dynamics.

Parameter	Leaves 1 and 2 (juvenile)	Leaf 6 (transition)	Statistically different?
Final size (mm2)	34.5 ± 22.7	109 ± 30.8	Yes*** (P = 8.15e‐08)
Max AGRorg (mm2 d−1)	4.20 ± 2.04	11.9 ± 6.06	Yes*** (P = 6.18e‐06)
ΔT (d)	10.1 ± 3.72	12.0 ± 3.62	No (P = 0.109)
RGRorg at Max AGRorg (% d−1)	29.2 ± 9.38	23.5 ± 7.42	No (P = 0.0981)

Mean final size, Max AGR, ΔT, and RGR at Max AGR of Leaves 1 and 2 and 6 ± SD. Twenty‐seven datasets for Leaves 1 and 2 and 15 for Leaf 6, extracted from 31 publications (curves without a plateau in final size were excluded). Evidence to reject the null hypothesis with a KS test was classified into ***, very strong; **, strong; *, moderate based on P‐value.

Analysing juvenile and transition leaves separately revealed additional complexity. In the juvenile and transition groups, Max AGR_org and final size were strongly correlated. However, within juvenile leaves, ΔT also showed a moderate positive correlation with final size (Fig. 4g). To further explore differences in juvenile and transition leaves, we subdivided them into ‘larger’ and ‘smaller’ subgroups (Fig. 4h). Larger juvenile leaves grew both faster and slightly longer than smaller ones (KS test: Max AGR_org P = 0.0036; ΔT P = 0.025). Larger transition leaves grew faster than smaller ones, over a similar duration (KS test: Max AGR_org P = 0.033; ΔT P = 0.141). The size distributions of ‘larger juvenile’ and ‘smaller transition’ leaves overlapped. Similar AGR_org and ΔT distributions between these groups suggest that juvenile and transition leaves can reach comparable sizes via similar dynamics.

We next assessed the impact of environmental factors – temperature, sucrose, light intensity, and photoperiod. While higher temperatures are known to enhance RGR_org (Granier et al., 2002), the narrow temperature range (20–23°C) among studies precluded strong correlations. In juvenile leaves, higher temperatures moderately correlated with smaller leaf size (R ² = 0.17, P = 0.029), but not in Leaf 6 (Fig. S22). Sucrose and ecotype showed no effect on size, nor did light intensity or photoperiod, aside from a potential positive effect of photoperiod on Leaf 6 area (Fig. S23). To explore potential interactions between light intensity and photoperiod, we used the cumulative light index (CLI, mol m⁻² d⁻¹), calculated as light intensity multiplied by daylight duration (Granier et al., 2002). In juvenile leaves, we observed no effect within the narrow range of CLI values. In transition leaves, while the cumulative light did not affect final size (Fig. S23a), it significantly increased Max AGR_org (Fig. 4i) and reduced ΔT (Fig. 4j), suggesting a compensatory mechanism. Within each of the subgroups of smaller/larger juvenile/transition leaves, the final size results from a balance between AGR and duration of linear growth phase (Fig. 4h,k). In transition leaves, datasets with the highest levels of cumulative light clearly differed from the others, tipping the balance with the fastest AGR_org values and the shortest ΔT recorded (Fig. 4l). Remarkably, these were associated either with short‐day/high‐intensity or long‐day/low‐intensity combinations (Fig. S23d,e).

Overall, our findings point to a continuum of growth regulation between juvenile and transition leaves. While the smallest juvenile leaves result from slower and shorter growth phases, all other leaf sizes appear primarily determined by AGR, with a conserved duration of the linear growth phase. Since juvenile leaves depend on cotyledon‐derived sugars, future studies should explore whether shorter growth duration correlates with smaller seed size. In transition leaves, cumulative light influences the balance between growth rate and phase duration, while other, yet unidentified, factors likely contribute to final size determination.

Bridging leaf primary to secondary morphogenesis

While leaf shape is largely established during primary morphogenesis, the link between growth at early stages and final organ size is unclear (Vuolo et al., 2018; Kierzkowski et al., 2019; Zhang et al., 2020; Le Gloanec et al., 2022, 2024; Li et al., 2024). We used organ‐scale measurements to investigate size regulation during secondary morphogenesis; however, they do not offer a sufficient resolution for quantifying growth during primary morphogenesis (Fig. 4d). To address this, we analysed high‐resolution data from nine studies tracking early development of juvenile leaves, either following individual cells over time or inferring dynamics from fixed samples across days (seven and two studies, respectively; Table S19).

Over the first days of leaf development, the organ area increased exponentially, consistent with the assumption that primary morphogenesis corresponds to the first phase of a logistic growth model (Fig. S24). We converted leaf age into DAG for all studies; however, the curves did not align well, since during exponential growth, even small time shifts between individuals result in large size differences (Figs 5a, S25). In addition, high‐resolution studies track organ growth only until reaching c. 10% of final size, making it difficult to link the exponential growth phase with secondary morphogenesis and size control.

Fig. 5.

Growth dynamics of Arabidopsis thaliana's juveniles leaves during primary morphogenesis. (a) Exponential fit of leaf area over time during early development, aligned at days after germination (DAG). Data were extracted from graphs (orange), or from measuring areas of heatmaps (purple). Solid lines show time‐lapses, dashed lines are from pseudo time lapses (fixed leaves from plants at different stages). Grey lines in the background are the logistic fits from Fig. 4(a). (b) Relative growth rate (RGR) obtained from early leaf studies. Blue lines show cellular RGR obtained from graphs, pink lines from heatmaps. Orange and purple dots are the exponents of the fits used in (a), shown at the median day of their respective time lapse. Grey lines show the RGR curves from Fig. 4(d), starting from T ₀. The x‐axis shows timelines on DAG for data on early development and DAT₀ for late development, with T ₀ defined according to Fig. 4. (c) Timeline of early studies compared with the logistic curves aligned at T ₀ (Fig. 4b), according to the aligned averages of exponential and logistic fits (Supporting Information Fig. S26). Primordium phase is defined from the initiation to the appearance of a gradient of growth (Le Gloanec et al., 2022, 2024), followed by a morphogenesis phase until T ₀, when scaling starts. The silhouettes show a representative RGR at each phase. (d) RGR curves of different regions of first leaves from five studies with heatmaps of the morphogenesis phase, showing higher growth in the basal part of the blade (pink lines), while the apical region (green line) and the midrib (blue line) show similar RGR. (e) Schematic heatmap of average RGR_cell in the apical, basal, and midrib regions of the leaf during morphogenesis.

To establish a unified timeframe spanning primary and secondary morphogenesis, we first synchronised our high‐resolution datasets (Fig. 5a) with organ‐scale timelines (Fig. 4a). By using the organ‐scale leaf area at T ₀ (2.58 mm² ± 1.98) as a reference, we identified the matching high‐resolution measurement at 8.5 DAG (2.64 mm² ± 2.28). This correspondence enabled us to align both timelines and reconstruct a continuous growth profile of juvenile leaves (Figs 5b,c, S26). The relative growth rate (RGR_org) declines rapidly as the leaf expands. At c. 2 DAG, the leaf primordium is c. 50 μm long (c. 2000 μm² in area), growing by 300% d⁻¹ (Le Gloanec et al., 2022). Around 4 DAG, lamina initiation marks the start of primary morphogenesis as the leaf expands by 150% d⁻¹. Growth becomes more heterogeneous, with slower expansion in the apex and midrib, and fastest at the base (Fig. 5d,e). These gradients establish leaf shape, which stabilises by 8.5 DAG (T ₀), marking the transition to secondary morphogenesis. During the ‘scaling phase’, RGR_org drops from c. 97% d⁻¹ at T ₀ to c. 6% d⁻¹ at 10 d after T ₀.

We found that during secondary morphogenesis, final leaf size is largely determined by the AGR and the duration of the linear growth phase (ΔT), while RGR has little influence (Fig. 4e–h). This reflects the sharp decline in RGR over time, a prerequisite for establishing linear growth in late leaf development. By contrast, the exponential growth and high RGR during primary morphogenesis point to the key role of RGR in initiating leaf expansion. A recent comparison between juvenile and adult leaves further revealed a trade‐off between RGR and the duration of primary morphogenesis (Li et al., 2024). Fully understanding leaf size control and the transition between growth phases will require further studies combining high‐resolution cellular time‐lapse imaging with long‐term organ‐scale tracking (Remmler & Rolland‐Lagan, 2012; Le Gloanec et al., 2024).

Mapping growth reveals potential mechanisms

Gene expression database maps (e.g. https://bar.utoronto.ca/eplant/; http://plants.ensembl.org/) are instrumental in identifying genes active in specific organs and developmental stages. However, no equivalent resource exists for growth data, making it difficult to compare developmental dynamics with gene expression. Using our framework, we extracted and standardised growth data from multiple studies into a common format (Figs 1, 2, 3, 4, 5). We also incorporated data from floral organs (Fig. 6; Table S20; Huang & Irish, 2015; Hervieux et al., 2016; McKim et al., 2017; Le Gloanec et al., 2022; Silveira et al., 2022; Gómez‐Felipe et al., 2024). To compare diffuse and tip growth dynamics, we assembled data from 20 studies on pollen tubes and 9 on root hairs (Fig. S27; Tables S5, S6).

Fig. 6.

Heatmap summary of Max AGR and RGR of Arabidopsis organs. (a) AGR estimates for different Arabidopsis thaliana organs. The silhouette of an A. thaliana plant shows the relevant organs in dark grey (shoot apical meristem (SAM) and pollen tube not shown due to the scale). Graph shows values equivalent to the heatmaps. (b) RGR estimates for A. thaliana organs according to adequate time frames for measuring (heatmaps) or on the same time (graph). Silhouettes are not to scale. AGR, absolute growth rate; DAG, days after germination; DAI, days after initiation; RGR, relative growth rate.

AGR (in unit area per time), which reflects the amount of biomass added per unit time, serves as an indicator of the resources – sugars, water, and nutrients – allocated to a growing organ (Fig. 6a). The AGR depends not only on how fast an organ expands (RGR, in % per time), but also on its size. In leaves, despite the rapid decline of RGR during leaf secondary morphogenesis, the AGR increases as the leaf becomes larger, peaking c. 5 d after T ₀ when leaves are about half their final size (Fig. 4b). Transition leaves reach a much higher maximal AGR than juveniles (about threefold), likely reflecting different resource availability: early leaves rely mainly on cotyledon starch, while later leaves draw on photosynthetic output from earlier‐formed foliage to fuel growth. Small organs such as the hypocotyl or early primary root show low AGR values, likely due to limited resource availability during early development. Although adult roots can reach considerable lengths, their actively growing zones remain narrow (< 1 mm in length, c. 100 μm in width), minimising overall resource demand for root elongation. Similarly, SAM regeneration involves the expansion of a small region, resulting in a low AGR and modest material requirements.

The RGR, which measures the percentage increase in area over time, provides a different perspective on organ development (Fig. 6b). RGR is tightly linked to cell wall deformation and growth mechanics. Small structures such as the SAM or root elongation zone can exhibit high RGR despite low AGR, indicating rapid cell wall expansion and remodelling with minimal biomass input. Leaves increase in size visibly to the naked eye during secondary development due to their large AGR, leading to the impression that cells expand rapidly, which is refuted by the low RGR during this phase.

Given the wide range of RGR values across organs, we present them on three timescales – per day, hour, and minute – from slower to faster expansion. On the per day scale, the highest RGRs occur in early shoot primordia and dark‐grown hypocotyls, where rapid cell expansion dominates. Stamen filaments, which elongate at a similar rate to light‐grown hypocotyls, emerge as a promising model for studying anisotropic growth. Surprisingly, inflorescence stems, despite their lignified cell walls, show localised elongation rates in the same order of magnitude as light‐grown hypocotyls (c. 100% d⁻¹ vs 200% d⁻¹; Hall & Ellis, 2012; Phyo et al., 2017).

Comparing growth on the hour scale shows that the root elongation zone expands over four times faster than the dark‐grown hypocotyl, despite similar AGRs. We propose that roots can maintain the fastest cellular deformation observed in any organ owing to constantly replacing cells in the elongation zone. On the minute scale, root hairs and pollen tubes show extremely high RGRs due to their tip‐localised growth, exceeding by several orders of magnitude those of the fastest diffuse‐growing tissues such as the root elongation zone (Fig. S27; Tables S5, S6; Rounds & Bezanilla, 2013). This likely reflects differences in cell wall composition, such as reduced cellulose content or shorter fibres at the tip (Chebli et al., 2012; Mravec et al., 2017; Schoenaers et al., 2017; Bidhendi et al., 2020), and potentially distinct wall loosening mechanisms.

Comparative growth maps offer a valuable framework for interpreting phenotypes and designing genetic screens. Processes that influence photosynthesis, carbon allocation, or the transport of water and nutrients are likely to impact developmental stages with high AGRs, such as leaf secondary morphogenesis (Fig. 6a). By contrast, perturbations affecting cell wall expansion and remodelling are expected to influence stages with high RGRs, including root elongation, dark‐grown hypocotyl growth, leaf initiation, and primary morphogenesis (Fig. 6b). Additionally, the timing of cell differentiation may play a critical role by altering the size of the fast‐growing zone or the duration of primary morphogenesis. Systematically compiling growth data from mutants would provide insights into the genetic control of growth dynamics across different organs.

Discussion

Our meta‐analysis reveals shared principles of size control across organs with determinate development. In both leaves and hypocotyls, final size is largely determined during secondary morphogenesis or the linear ‘scaling’ phase, characterised by its duration (ΔT) and absolute growth rate (AGR_org). Large size differences, such as those between light‐ and dark‐grown hypocotyls or between early and later leaves, were mainly due to differences in growth rate, while ΔT remained similar, in agreement with previous analyses (Gázquez & Beemster, 2017; Ma et al., 2025). This suggests that ΔT may be optimised for differentiation, regardless of organ size. We speculate that AGR_org, reflecting the rate of biomass accumulation, is limited by nutrient availability and transport. As leaves develop and start photosynthesis, they transition from carbon sinks to sources, supporting their own growth and that of other organs (Dethloff et al., 2017). The higher AGR_org observed in Leaves 5 and 6 compared with earlier leaves may result from increased sugar availability in the rosette. In hypocotyls, growth depends on sugars from cotyledon starch degradation. Mutants defective in starch breakdown (sex1) or sugar transport (suc2, sweet11) show reduced elongation, which can be rescued by exogenous sugars (de Wit et al., 2018). Measuring AGR_org in these mutants could clarify how sugar availability and transport shape growth dynamics during the scaling phase.

While organ size remains small during early development, the exponential growth of this phase is crucial for plant survival. Hypocotyl elongation enables seed coat rupture during germination (Sliwinska et al., 2009), and leaves establish their basic shape during primary morphogenesis (Echevin et al., 2019). During early development, the organ absolute growth rate (AGR_org) is low, reflecting modest nutrient demands; yet RGRs at both organ and cell levels (RGR_org and RGR_cell) peak, indicating rapid cell wall expansion. The exponential growth phase is likely the most sensitive to changes in cell wall extensibility, whether due to mutations in wall‐related genes or signalling pathways that regulate them. Sugars, in addition to their metabolic role discussed above, may contribute as signalling molecules in early development. For instance, during germination, glucose can counteract ABA‐induced inhibition of hypocotyl growth (Xue et al., 2021). We found that differences in RGRs during early development affect size at the onset of the linear phase. In leaves, early size differences could be either amplified or buffered during further development, depending on the coupling between growth dynamics in primary and secondary morphogenesis, with possibly similar phases in hypocotyls. However, the growth phase transition remains unexplored in hypocotyls, and its investigation has only begun in leaves (Tang et al., 2023; Le Gloanec et al., 2024).

In roots and SAMs – classic models of indeterminate growth – our analysis uncovered surprising knowledge gaps and new directions. While root studies often focus on tip displacement speed, we show that understanding root extension requires focusing on elongation zone dynamics, specifically measuring the zone size and its cell RGRs. Although cell production in the meristem correlates with root tip velocity (Beemster & Baskin, 1998; Muller et al., 1998), its link to cell elongation – and thus root movement – remains unclear.

In the SAM, we found a consistent offset in cell RGR between central and peripheral zones across species. According to the Lockhart model, cell growth rate can increase via changes in either the wall yield threshold (additive effect) or wall extensibility (multiplicative effect) (Lockhart, 1965). Our findings suggest that, as cells drift from the central to the peripheral zone, their increased expansion rate may reflect a change in wall yield threshold rather than extensibility.

In this study, we aimed to establish benchmark growth parameters for WT Arabidopsis under standard conditions. Most data were from Col‐0, although other ecotypes, differing in leaf heteroblasty and meristem morphology (Massonnet et al., 2010; Landrein et al., 2015), were included. These differences may explain some variation, especially in juvenile leaves. Still, the broad range of growth rates and organ sizes was unexpected.

Most studies used temperatures within the optimal range for Arabidopsis growth, 21–25°C (Granier et al., 2002; Rivero‐Lepinckas et al., 2006; Stavang et al., 2009; Yang et al., 2017; Casal & Balasubramanian, 2019; Chung et al., 2020), as stress responses are triggered and growth is reduced beyond this range (Vasseur et al., 2011; de Jonge et al., 2016; Savvides et al., 2016). Our analysis revealed organ‐specific temperature responses: in roots, higher temperatures increased cell elongation rates (RGR_cell) but reduced elongation zone size, resulting in similar tip velocity. In juvenile leaves, elevated temperatures were linked to smaller final size, likely due to reduced organ‐scale growth rate (AGR_org). Data for other organs were insufficient. Comparing hypocotyl dynamics under high temperature and red‐shifted light would be valuable, given shared signalling pathways in heat and shade responses (Casal & Balasubramanian, 2019; Legris, 2023).

Light intensity directly regulates sugar production, limiting growth at both low and very high intensities (Poorter et al., 2019, 2022). We found that cumulative light – combining light intensity and duration – influenced the dynamics of secondary morphogenesis in both juvenile and transition leaves, though in distinct ways. Increased light exposure shortened the period of linear growth in both leaf types; however, this was compensated by an accelerated organ‐scale growth rate (AGR_org) only in transition leaves. This difference may stem from the fact that older leaves can draw on the sugar resources produced by the rosette to fuel their growth, while juvenile leaves cannot. In roots, which act as carbohydrate sinks, sucrose content in the substrate appeared to have no impact on root tip velocity. Yet, our analysis indicated a positive effect on elongation zone length, potentially offset by a reduction in cell elongation rates.

Some effects might have been obscured in our study due to the co‐variation of environmental or developmental conditions. For instance, while the choice of medium appears to affect root growth, we could not exclude the effect observed as being due to root age. Interactions between variables might be complex, as we found that temperature and sucrose could exert opposing effects on the homeostatic regulation of root growth.

We found a general lack of data on growth dynamics across developmental stages and the plant life cycle, reflecting the difficulty of publishing descriptive work. While SAM morphology is known to change between vegetative and inflorescence phases (Kinoshita et al., 2020), corresponding shifts in growth dynamics and their impact on organ initiation remain unstudied. SAM behaviour may also evolve within the vegetative phase, potentially altering the plastochron (Fouracre & Poethig, 2020). Similarly, detailed growth dynamics in juvenile vs adult leaves are needed to understand heteroblasty, while in mature roots (beyond 15 d) growth remains largely unexplored in Arabidopsis.

Analysing mature plant morphology, such as in genetic screens, does not reveal its developmental origins. For example, reduced organ size may stem from a shorter growth period, slower linear expansion during secondary morphogenesis, or even a slight reduction in early exponential growth. Our growth maps offer a first step toward a developmental atlas, aiming to support targeted screens, refine experiments and modelling, by providing benchmark values, tools, and biological interpretations of growth parameters. These maps could be expanded online and integrated into large‐scale efforts like the Plant Cell Atlas (https://www.plantcellatlas.org/). While projects such as the Human Cell Atlas are already underway (Rood et al., 2025), the accessibility of plant systems makes them ideal for linking spatial growth dynamics to molecular regulation – an essential step toward understanding how development is shaped by genetic, environmental, and pathogenic factors.

Competing interests

None declared.

Author contributions

VA, EB and A‐LR‐K conceived the project and drafted the manuscript. VA and EB collected and collated the data and developed the analytical approach. VA carried out the analyses. EB designed the graphics and figures. A‐LR‐K supervised and funded the project. VA and EB share first authorship.

Disclaimer

The New Phytologist Foundation remains neutral with regard to jurisdictional claims in maps and in any institutional affiliations.

Supporting information

Dataset S1 Raw collected data and scripts to reproduce models, plots, and statistical tests.

Fig. S1 Consequence of cell exponential growth for time unit conversion.

Fig. S2 Plastochron of Arabidopsis thaliana inflorescence.

Fig. S3 Analysis of SAM growth along environmental factors.

Fig. S4 Analysis of SAM growth along plant ecotype and developmental phase.

Fig. S5 Analysis of SAM growth for different imaging techniques.

Fig. S6 Direct measures of root tip velocity according to root age.

Fig. S7 Definition of root zones.

Fig. S8 Root cellular tip velocity and RGR curves along the root longitudinal profile and according to root age.

Fig. S9 Root tip velocity does not correlate with cell growth rate in the MZ, nor with the size of MZ and TZ.

Fig. S10 Compensation between the maximal value of RGR_cell and the length of EZ according to different conditions.

Fig. S11 Correlations between different root growth parameters and temperature.

Fig. S12 Correlations between different root growth parameters and sugar in the medium.

Fig. S13 Correlations between different root growth parameters and type of medium.

Fig. S14 Correlations between different root growth parameters and root age.

Fig. S15 Residuals of the hypocotyl fit.

Fig. S16 Correlations between size and growth parameters within light‐grown or dark‐grown hypocotyls.

Fig. S17 Effects of environmental conditions on hypocotyl final size and growth parameters.

Fig. S18 Comparative timeline of hypocotyl RGR_org and RGR_cell.

Fig. S19 Cellular growth dynamics of hypocotyls.

Fig. S20 Residuals from leaves fit.

Fig. S21 Distributions of growth parameters between juvenile and transition leaves.

Fig. S22 Temperature, ecotype and sucrose concentration in the medium do not correlate with leaf growth parameters.

Fig. S23 Leaf final size does not correlate with cumulative light, photoperiod nor light intensity.

Fig. S24 Early leaf area exponential fit.

Fig. S25 Effect of time shifts alignment on organ size.

Fig. S26 Early and late leaf area timeline alignment.

Fig. S27 Tip growth: pollen tube and root hair.

Methods S1 Parameters, definitions and equations of models, zones, conversions and error propagation.

Table S1 SAM articles and conditions.

Table S2 Root articles and conditions.

Table S3 Hypocotyl articles and conditions.

Table S4 Leaf articles and conditions.

Table S5 Root hair articles and values.

Table S6 Pollen tubes articles and values.

Table S7 Color Summarizer vs original data from MGX comparison.

Table S8 SAM mitotic index values.

Table S9 SAM cell lineage values.

Table S10 SAM cell expansion values.

Table S11 SAM plastochron values.

Table S12 SAM cell expansion raw values.

Table S13 Root organ scale tip velocity studies.

Table S14 Root cellular‐scale studies conditions.

Table S15 Root cyclin marker.

Table S16 Hypocotyl conditions (simplified).

Table S17 Leaf final size.

Table S18 Leaf secondary morphogenesis growth parameters.

Table S19 Leaf primary morphogenesis studies details.

Table S20 Final figure values.

Please note: Wiley is not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.

Data availability

The data that support the findings of this study are available in the Supporting Information of this article (Dataset S1).