Table 1.
Quantitative tools to measure leaf epidermis features
| Tools | Category | Purpose | Description | Reference |
|---|---|---|---|---|
| Cell-type features characterisation | ||||
| MorphoLeaf | Leaf shape | Extracts leaf contour, leaf sinus, and leaf tips from multiple leaf images. Determines hierarchization of leaf teeth. Provides quantitative leaf shape parameters | A plug-in running on the Free-D software | Biot et al., 2016 |
| LeafI (Leaf Interrogator) |
Leaf shape | Quantifies leaf shape and leaf rosette from leaf image. Extracts leaf contour and leaf blade. Performs shape-space analysis and data visualisation | A GUI-based pipeline implemented in Python 3.5 with a PyQt5-based GUI | Zhang et al., 2020 |
| LIMANI (Leaf Image Analysis Interface) |
Leaf vein | Extracts the leaf vascular network by automatic image segmentation. Measures venation patterns | A web-based application with the ‘grey-scale mathematical morphology image analysis algorithm’ | Dhondt et al., 2012 |
| LAMINA (Leaf Shape Determination) |
Leaf shape | Quantifies and extracts the leaf area and leaf shape from images of diverse plant species. | A graphical application implemented in Java. | Bylesjö et al., 2008 |
| PaCeQuant | Pavement cell | Automatically segments images and quantitatively analyzes pavement cell shape characteristics | A plug-in of ImageJ | Möller et al., 2017 |
| GraVis (Visibility Graph) | Pavement cell | Describes pavement cell shape with visibility graph. Quantifies pavement cell protrusion and indentation. Analyzes characteristics of pavement cell shape | A GUI-based pipeline implemented in Python 3 | Nowak et al., 2021 |
| Stomata Counter | Stomata | Automatically identifies and counts stomata from microscopy images via deep learning | A convolutional neural network system implemented in Python 2.7 | Fetter et al., 2019 |
| LeafNet | Pavement cell and stomata | Segments and quantifies stomata and pavement cells from brightfield microscopy images with a hierarchical deep learning technique | A Python-based package that can also be used online | Li et al., 2022 |
| MorphoGraphX MGX2.0 |
Organ and tissue | Visualises and analyzes 4D biological confocal images. Extracts cell geometry data and organ/cell shape parameters | A software written in C++ and developed on GNU/Linux | de Reuille et al., 2015 Strauss et al., 2022 |
| PlantSeg | Plant organ, tissue, and cell | Provides 3D segmentation of plant tissues into cells with deep learning |
A convolutional neural network system implemented in Python | Wolny et al., 2020 |
| Cell fate regulators measurement | ||||
| POME (Polarity Measurement) |
Polarity | Quantifies the characteristics of cell polarity with a semi-automated pipeline | A pipeline composed of Fiji macro and R scripts | Gong et al., 2021a; 2021b |
| Cytrap (Cell Cycle Tracking in Plant Cell) |
Cell cycle | Monitors the cell cycle progression in Arabidopsis. | HTR2pro:CDT1a-RFP for S+G2 and CYCB1pro:CYCB1-GFP for late G2+M. | Yin et al., 2014 |
| PlaCCI (Plant Cell Cycle Indicator) |
Cell cycle | Monitors the cell cycle progression in Arabidopsis | CDT1apro:CDT1a-eCFP for G1, HTR13pro:HTR13-mCherry for entire cell cycle, and CYCB1;1pro:CYCB1;1-YFP for late G2+M | Desvoyes et al., 2020 |
| SPACE (Stomata patterning autocorrelation on epidermis) |
Peptide and stomata pattern | Quantitatively determines how signalling peptides influence stomatal patterning | A Python script with spatial autocorrelation algorithm | Zeng et al., 2020 |
| DII-Venus | Auxin response | Examines auxin response level. | Venus fluorescent protein fused with Aux/IAA auxin-interaction domain (DII). | Brunoud et al., 2012 |
| R2D2 (Ratiometric DIIs) |
Auxin response | Examines auxin responses. | Ratiometric analysis of DII-Venus and mDII-Venus | Liao et al., 2015 |
| AuxSen, Auxin Biosensor | Auxin level | Quantifies in vivo auxin levels and visualises auxin distribution. | A FRET-based biosensor containing an engineered tryptophan repressor from E. coli | Herud-Sikimić et al., 2021 |