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Real-time conversion of tissue-scale mechanical forces into an interdigitated growth pattern

Nature Plants volume 7, pages 826–841 (2021)Cite this article

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An Author Correction to this article was published on 23 June 2021

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Abstract

The leaf epidermis is a dynamic biomechanical shell that integrates growth across spatial scales to influence organ morphology. Pavement cells, the fundamental unit of this tissue, morph irreversibly into highly lobed cells that drive planar leaf expansion. Here, we define how tissue-scale cell wall tensile forces and the microtubule–cellulose synthase systems dictate the patterns of interdigitated growth in real time. A morphologically potent subset of cortical microtubules span the periclinal and anticlinal cell faces to pattern cellulose fibres that generate a patch of anisotropic wall. The subsequent local polarized growth is mechanically coupled to the adjacent cell via a pectin-rich middle lamella, and this drives lobe formation. Finite element pavement cell models revealed cell wall tensile stress as an upstream patterning element that links cell- and tissue-scale biomechanical parameters to interdigitated growth. Cell lobing in leaves is evolutionarily conserved, occurs in multiple cell types and is associated with important agronomic traits. Our general mechanistic models of lobe formation provide a foundation to analyse the cellular basis of leaf morphology and function.

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Fig. 1: Heterogeneous growth of the cell boundary in developing pavement cells.
Fig. 2: Tensile force orientation correlates with subcellular growth rates.
Fig. 3: Transfacial microtubules predict lobe initiation sites.
Fig. 4: Microtubule signals are concentrated at regions of elevated cell wall tensile stress.
Fig. 5: A system for inducible cell-autonomous lobing.
Fig. 6: Cell wall stress dictates the location and direction of furrow formation.
Fig. 7: A general model for lobe morphogenesis and interdigitated growth.

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Data availability

The Fiji macros, Python scripts and PDLP growth rate code in R used for all image analyses are available at https://github.com/yamsissamy/naturePlant_lobeInitiation. Abaqus input files for the FE models shown in the figures are available at http://tulips.unl.edu.

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Acknowledgements

We thank D. Jackson (Cold Spring Harbor Laboratory) for his generous gift of the PDLP3–GFP transgenic line and Y. Gu (Pennsylvania State University) for her generous gift of the csi1-3 mutant and csi1-3; YFP–CesA6; RFP–TUA5 transgenic line. This material is based on work supported by the National Science Foundation under grant no. 1715544 to D.B.S. and J.A.T.

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Authors and Affiliations

  1. Department of Botany and Plant Pathology, Purdue University, West Lafayette, IN, USA

    Samuel A. Belteton, Madeline I. Quinn, Matthew W. Marley & Daniel B. Szymanski

  2. Mechanical and Materials Engineering, University of Nebraska–Lincoln, Lincoln, NE, USA

    Wenlong Li, Faezeh A. Hatam & Joseph A. Turner

  3. Department of Botany, University of Wisconsin, Madison, WI, USA

    Makoto Yanagisawa

  4. Department of Biochemistry, Indiana University Bloomington, Bloomington, IN, USA

    Margaret K. Szymanski

  5. Biological Sciences, Purdue University, West Lafayette, IN, USA

    Daniel B. Szymanski

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  1. Samuel A. Belteton
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Contributions

D.B.S. and S.A.B. conceptualized the project. S.A.B. and W.L. developed the methodology. S.A.B., W.L., M.Y., M.I.Q., M.K.S., M.W.M. and F.A.H. conducted the investigation. S.A.B. and D.B.S. wrote the original draft. S.A.B., W.L., M.Y., M.I.Q., M.K.S., M.W.M., F.A.H., J.A.T. and D.B.S. reviewed and edited the draft. D.B.S. administered the project. D.B.S. and J.A.T. acquired the funding.

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Correspondence to Daniel B. Szymanski.

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Extended data

Extended Data Fig. 1 Methodology for PDLP growth analysis, their reliability as fiducial marks, and their usage in lobing segments.

Growth dependent separation of PDLP puncta pairs along an example segment. a, Using the plasma-membrane marker segments were traced at the anticlinal/outer-periclinal junction. b-c, Segment straightening followed by re-slicing produces a face-view of the anticlinal wall. d, Non-overlapping PDLPs including manually labeled 3-way-junctions were tracked over time. e, PDLP-pair distance is used to obtain sub-segmental growth rates from linear fit models over the time course. f, Sub-segmental elemental growth rates mapped onto the initial segment shape. PDLPs are stable features in the anticlinal wall and their displacement reflects localized diffuse growth. g-i, Cold-treated PDLP3:GFP; PIP2:mCherry seedlings were imaged at 4-hour intervals. j-k, Normalized signal intensity plots and overlapping peaks from PDLP3 particles of the two segments (g-i) at all 3 time-points. l, Boxplots of measured sub-segmental growth rates of cold-treated (CT) and room temperature (RT) grown seedlings. Center lines show the medians; box limits indicate the 25th and 75th percentiles as determined by R software; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles, outliers are represented by dots. n = 14, 38 sub-segments from 3 and 9 independent segments respectively. Sub-segmental growth rates from lobing segments. m-o, Plots of normalized segment length at lobe detection (gray outline) and final (black outline) time-points color coded for normalized percent growth rate per hour. Cyan and green arrowheads mark the location of newly and established lobes respectively (Top panel). Perimeter to center of mass plots highlight warping regions between lobe detection and final timepoint. Scale bar = 5 µm (a-d), 10 µm (g-i).

Extended Data Fig. 2 FE model optimization and correlation of maximum principal stress orientation with subsegment growth rate.

a, Microscopic image of the cells which were used to study the effect of the partial geometry of cells for the stress response under turgor pressure. The star shows the location of the maximum deflection of the periclinal wall for Cell 2. b, 3D finite element (FE) model of the two complete cells and the distribution of maximum principal stress (Max PS) on both anticlinal walls (AW). c, Distribution of the mean of the Max PS from the upper 1/8th of the AW for both cells for three example results of partial cells in terms of the area used in the analysis. d, Maximum of the absolute value of the percentage difference between mean Max PS distributions in the upper 1/8th of the AW in each simulation relative to using the complete geometry of cells. It indicates that the FE analysis can use a partial cell. If the area includes the maximum deflection the distribution of the Max PS does not change substantially and the magnitude is reduced by < 10%. e, Comparison of the average of Max PS from the different upper areas of materials in the AW. It turns out that the mean of Max PS of the different upper areas in the walls has the same distribution pattern but varies in magnitude. f, Optical image of the cells were used to study the correlation of the Max PS and the growth rate of the cell-cell interfaces. g, Cell 2 from the measurement is defined partially, but the maximum deflection area can be seen in the image. h,i, Comparison of maximum deflection of periclinal wall for Cell 2 in (f–g) from the simulations using different remaining areas of the cell. The results of (i) is very consistent with the optical measurement in (g). j–l, Like the study described in (f), four other segments of cells were used for the comparison, and the results show that the horizontal component of Max PS along the segment length has a strong correlation with the growth rate of the associated wall. The scale bar for all = 10 µm.

Extended Data Fig. 3 The microtubule and cellulose systems are required for lobe formation.

Mature pavement cell shapes, magenta outline, were analyzed using LobeFinder. Mutants affecting the microtubule- (b, c, i-k), cellulose- (d, e), and pectinase- (f), and secretion (g) were compared to wild-type (a, i). The temperature-sensitive mutant mor1-1 was tested at permissive (h) and restrictive temperatures (j) and compared to its wild-type counterpart (i). Effects of 0.2% pectinase (m), 1 µM oryzalin (n), and 5 nM Isoxaben (o) on cell shape from 1 to 2 DAG compared to a buffer treated control (l). A combination of 0.2% Pectinase with a reduced concentration of Oryzalin [1 µM] (p) and the combination of 0.2% Pectinase and 5 nM Isoxaben (q) both completely halted the production on new lobes (Supplementary Table 1 – for results). Cellulose synthase complex marked with YFP:CesA6 strongly colocalizes with cortical microtubules in early stage pavement cells. s, 8-min projection of 15-sec intervals, single plane time course shows how CesA tracks resemble and co-localize (t – white arrows) with cortical microtubules, mCherry:TuA5 (r). Evidence of colocalization between cellulose synthase complex marked with CesA3:GFP and cortical microtubules at the anticlinal wall. u, Top-down view of segment resliced. Dotted line shows shape and direction of segmentation. White arrows on resliced images are CesA complexes that localized to anticlinal microtubules. The colocalization of cellulose and microtubules was not affected in 8-minute projection of 15-sec intervals, single plane time-lapses, in the csi1 mutant background where CesA tracks (YFP:CesA6 - w) resemble and co-localized (x – white arrows) with cortical microtubules, RFP:TUA5 (v). Resliced views of the anticlinal wall also show evidence of cellulose and microtubule co-localization in the csi1 mutant. CesA colocalization with anticlinal microtubules is evident in the csi1 mutant background. y, Top-down view of segment resliced with dotted line showing the shape and direction of segmentation. White arrows on resliced images on the right are CesA complexes that localize to anticlinal microtubules. The anticlinal wall at the apex remains straight during lobe formation. z, Snapshots of a lobing segment before, (a′) during, (b′-c′) and after lobe formation. d′, Cross-sectional views of the anticlinal wall at the lobe apex from locations specified in (z-c′). e′, Histogram of anticlinal wall tilt showing how the vast majority of measure wall regions were completely perpendicular to the leaf surface. Scale bars = 50 µm (a-q), 25 µm (k-q), 5 µm (r-d′).

Extended Data Fig. 4 Transfacial microtubule localization and frequency analyses as a function of lobe initiation.

Analysis of microtubule turnover in time series data. Pairwise correlation results between the initial time-point and each subsequent time-point (blue line), a sliding window of paired consecutive frames over the time course (green dashed line), and each time-point to a randomly selected time-point (magenta dotted line). The random correlation results average (gray dotted line) is the background level for microtubule correlations. a-b, 15-sec interval time-lapses. c-d, 1-min interval time-lapses. e-f, 5-minute interval time-lapses. Each sampling regime had a similar pattern of correlation decay from the first time-point to background levels after ~10 min. Microtubule orientation in ROIs in opposing cells that undergo lobe initiation does not show detectable differences in alignment and coherence. g-i, Snapshots of plasma-membrane (PIP2:mCherry) of a lobing segment, yellow boxes are ROIs used to monitor microtubule behaviors in opposing cells lobe centered at the future lobe region. j-l, Snapshots of the microtubules (GFP:TUB6). Microtubules orientation and coherency results using red ovals whose tilt reflects the orientation and their aspect ratios reflect the coherency of the microtubules at the time point shown. m-n, Scatterplot of microtubules orientation (green) and microtubule coherency (purple) in cell 1 (g -top) and cell 2 (g - bottom) as a function of time before (< time-point 19) and after (≥ time-point 19) lobe detection. Distribution of manually identified transfacial microtubules in lobing segments. o-s, Top - segment shape before (blue) and after (orange) lobe detection. Yellow arrowheads are existing lobes. Cyan arrowheads are newly formed lobes. The top histogram (green) depicts transfacial microtubules at the future convex cell at the lobe initiation site. The bottom histogram (magenta) depicts transfacial microtubules of the future concave cell at the lobe initiation site. Time-lapsed analysis of the occurrence and location of transfacial microtubules in six different lobing segments. t-y, The number of transfacial microtubules in the lobing cortical domain in the future convex (green) and future concave (magenta) cells. Scale bar = 5 µm.

Extended Data Fig. 5 Cross-correlation analyses of microtubules, cell wall stress, and lobe initiation.

(a, d) Heatmap of σ-max values on the anticlinal wall for two lobing segments. (b, e) Density plots of stress alignment at the anticlinal wall for cell 1 (green) and cell 2 (magenta). (c, f) Face-view of cortical microtubules at the anticlinal wall for segment depicted on A and D respectively. g-i, Time-lapse images of the plasma-membrane, (PIP2:mCherry - magenta) and microtubules (GFP:TUB6 - green) of a lobing segment that was captured for > 4 hours before the lobe was detected. j-l, Outline of the segment shape for segment corresponding to the live-cell images above where the lobe initiation cortical domain is marked using cyan vertical lines and lobes peak locations are marked by a black vertical line. m, Density plot of manually scored transfacial microtubules where cell 1 has a peak at the future convex side of a lobe before its detection. n, The accumulated microtubule signal method can detect enriched microtubule signal (\(Enrichement = 1 - \frac{{Area\,under\,curve_{Cell1}}}{{Area\,under\,curve_{Cell2}}} \ast 100\)) in cell1 within the future convex domain. o, Summary of outer periclinal microtubule enrichment in future convex cells measured from 11 independent lobing events. Boxplots show the distribution of microtubule enrichment of future lobe convex domain prior to lobe detection. Center lines show the medians; box limits indicate the 25th and 75th percentiles as determined by R software; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles, outliers are represented by dots. The number of time-points at 10 min intervals is listed above each boxplot. Example images of two additional lobing pairs that could be converted to FE models and tested for MT and tensile stress correlations at sites of microtubule enrichment and lobe formation. (p, t) Summed microtubule signal projected images of segments before lobe detection. (q, u) Enrichment of microtubules at the future convex side of a lobe as a function of time before lobe detection. (r, v) Line plots of FE σ Max predictions along the anticlinal wall of cell 1 (green) and cell 2 (magenta), microtubule accumulated signals along the shared anticlinal wall (black dashed line). (s, w) Line plots of FE σ Max predictions along the outer periclinal wall of cell 1 (green) and cell 2 (magenta), microtubule accumulated signals outer periclinal wall of cell 1 (green dashed line) and cell 2 (magenta dashed line). Symmetry breaking events are not defined by a pre-existing segment shape. x-z, Snapshots of eight-hour time-lapse with plasma-membrane (PIP2:mCherry - magenta) and microtubule (GFP:TUB6 - green) markers of a segment that has a symmetry-breaking event against the established bulging shape. (a′ - top) Accumulated microtubule signal plot and level of enrichment within the future convex cortical region (cell 1) (a′ - bottom) Accumulated microtubule signal plot (cell 2). Scale bars = 5 µm for c, d, g–i; 10 µm for x–z.

Extended Data Fig. 6 Localized microfibril alignment can drive lobe initiation: an FE analysis.

a, Overview of a lobing pair of pavement cells. A transfacial patch of anisotropic material is located in one cell while the rest of the material in both cells is isotropic. b, The material in the connected anticlinal wall in the same cell is also defined as anisotropic. Note that no transition zone near the edges of the patch is used. c, The anisotropic patch has a width of 2μm, and the simulated MF direction is aligned with the arrows. d, Maximum principal stress map after pressurization and relaxation for the completely isotropic case; this result serves as a reference for the deformation. e, Stress map for the case which includes the anisotropic patch, which shows a stress concentration within the patch area. f,g, Comparison of the cell-cell interface geometry for the isotropic (reference – green) and anisotropic (magenta) models.

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2.

Reporting Summary

Supplementary Video 1

5.5-hour time-lapse of expanding pavement cells taken at 30-min intervals. Displacements of trackable bead pairs were used to evaluate the subsegmental growth rates using PDLPs (green) and plasma membrane (magenta).

Supplementary Video 2

Representative time-lapse where both the plasma membrane (magenta) and microtubules (green) are tracked as a function of lobe initiation.

Supplementary Video 3

Local feature detection method to identify and track segment protrusions that develop into lobes.

Supplementary Video 4

Time-lapse of a lobing segment in which views of the outer-periclinal (top panels) and anticlinal (bottom panels) cell surfaces are aligned and projected for both the initiating (left) and following (right) cells.

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Belteton, S.A., Li, W., Yanagisawa, M. et al. Real-time conversion of tissue-scale mechanical forces into an interdigitated growth pattern. Nat. Plants 7, 826–841 (2021). https://doi.org/10.1038/s41477-021-00931-z

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