Summary How do genes modify cellular growth to create morphological diversity? We study this problem in two related plants with differently shaped leaves: Arabidopsis thaliana (simple leaf shape) and Cardamine hirsuta (complex shape with leaflets). We use live imaging, modeling, and genetics to deconstruct these organ-level differences into their cell-level constituents: growth amount, direction, and differentiation. We show that leaf shape depends on the interplay of two growth modes: a conserved organ-wide growth mode that reflects differentiation; and a local, directional mode that involves the patterning of growth foci along the leaf edge. Shape diversity results from the distinct effects of two homeobox genes on these growth modes: SHOOTMERISTEMLESS broadens organ-wide growth relative to edge-patterning, enabling leaflet emergence, while REDUCED COMPLEXITY inhibits growth locally around emerging leaflets, accentuating shape differences created by patterning. We demonstrate the predictivity of our findings by reconstructing key features of C. hirsuta leaf morphology in A. thaliana. Video Abstract

Quantitative analysis of plant and animal morphogenesis requires accurate segmentation of individual cells in volumetric images of growing organs. In the last years, deep learning has provided robust automated algorithms that approach human performance, with applications to bio-image analysis now starting to emerge. Here, we present PlantSeg, a pipeline for volumetric segmentation of plant tissues into cells. PlantSeg employs a convolutional neural network to predict cell boundaries and graph partitioning to segment cells based on the neural network predictions. PlantSeg was trained on 1xed and live plant organs imaged with confocal and light sheet microscopes. PlantSeg delivers accurate results and generalizes well across different tissues, scales, acquisition settings even on non plant samples. We present results of PlantSeg applications in diverse developmental contexts. PlantSeg is free and open-source, with both a command line and a user-friendly graphical interface (https://github.com/hci-unihd/plant-seg).

SUMMARYThe study and eventual manipulation of leaf development in plants requires a thorough understanding of the genetic basis of leaf organogenesis. Forward genetic screens have identified hundreds of Arabidopsis mutants with altered leaf development, but the genome has not yet been saturated. To identify genes required for leaf development we are screening the Arabidopsis Salk Unimutant collection. We have identified 608 lines that exhibit a leaf phenotype with full penetrance and almost constant expressivity and 98 additional lines with segregating mutant phenotypes. To allow indexing and integration with other mutants, the mutant phenotypes were described using a custom leaf phenotype ontology. We found that the indexed mutation is present in the annotated locus for 78% of the 553 mutants genotyped, and that in half of these the annotated T-DNA is responsible for the phenotype. To quickly map non-annotated T-DNA insertions, we developed a reliable, cost-effective and easy method based on whole-genome sequencing. To enable comprehensive access to our data, we implemented a public web application named PhenoLeaf (http://genetics.umh.es/phenoleaf) that allows researchers to query the results of our screen, including text and visual phenotype information. We demonstrated how this new resource can facilitate gene function discovery by identifying and characterizing At1g77600, which we found to be required for proximal-distal cell cycle-driven leaf growth, and At3g62870, which encodes a ribosomal protein needed for cell proliferation and chloroplast function. This collection provides a valuable tool for the study of leaf development, characterization of biomass feedstocks and examination of other traits in this fundamental photosynthetic organ.

Most plant leaves exhibit bilateral symmetry, which has been hypothesized as an inevitable consequence of the existence of the proximodistal and dorsoventral axes. No gene has been described that affects leaf bilateral symmetry but not dorsoventrality in Arabidopsis thaliana. We screened for viable insertional mutations that affect leaf morphology, and out of more than 700 mutants found only one, desigual1-1 (deal1-1), that exhibited bilateral symmetry breaking but no obvious defects in dorsoventrality. We found that deal1-1 is an allele of VASCULATURE COMPLEXITY AND CONNECTIVITY (VCC). Several overlapping regulatory pathways establish the interspersed lobes and indentations along the margin of Arabidopsis thaliana leaves. These pathways involve feedback loops of auxin, the PIN-FORMED1 (PIN1) auxin efflux carrier, and the CUP-SHAPED COTYLEDON2 (CUC2) transcriptional regulator. Early vcc (deal1) leaf primordia fail to acquire bilateral symmetry and instead form ectopic lobes and sinuses. The vcc leaves show aberrant recruitment of marginal cells expressing properly polarized PIN1, resulting in misplaced auxin maxima. Normal PIN1 polarization requires CUC2 expression and CUC2 genetically interacts with VCC; VCC also affects CUC2 expression. VCC has a domain of unknown function, DUF1218, and localizes to the endoplasmic reticulum membrane. VCC acts partially redundantly with its two closest paralogs, DEAL2 and DEAL3, in early leaf margin patterning and is required for bilateral symmetry, but its loss of function does not visibly affect dorsoventrality.

Forward genetic screens have successfully identified many genes and continue to be powerful tools for dissecting biological processes in Arabidopsis and other model species. Next-generation sequencing technologies have revolutionized the time-consuming process of identifying the mutations that cause a phenotype of interest. However, due to the cost of such mapping-by-sequencing experiments, special attention should be paid to experimental design and technical decisions so that the read data allows to map the desired mutation. Here, we simulated different mapping-by-sequencing scenarios. We first evaluated which short-read technology was best suited for analyzing gene-rich genomic regions in Arabidopsis and determined the minimum sequencing depth required to confidently call single nucleotide variants. We also designed ways to discriminate mutagenesis-induced mutations from background Single Nucleotide Polymorphisms in mutants isolated in Arabidopsis non-reference lines. In addition, we simulated bulked segregant mapping populations for identifying point mutations and monitored how the size of the mapping population and the sequencing depth affect mapping precision. Finally, we provide the computational basis of a protocol that we already used to map T-DNA insertions with paired-end Illumina-like reads, using very low sequencing depths and pooling several mutants together; this approach can also be used with single-end reads as well as to map any other insertional mutagen. All these simulations proved useful for designing experiments that allowed us to map several mutations in Arabidopsis.