CuticleTrace: A Toolkit for Capturing Cell Outlines from Leaf Cuticle with Implications for Paleoecology and Paleoclimatology

Overview

Journal Appl Plant Sci

Date 2024 Feb 19

PMID 38369978

Authors

Benjamin A Lloyd

Richard S Barclay

Regan E Dunn

Ellen D Currano

Ayuni I Mohamaad

Kymbre Skersies

Surangi W Punyasena

Affiliations

Soon will be listed here.

Abstract

Premise: Leaf epidermal cell morphology is closely tied to the evolutionary history of plants and their growth environments and is therefore of interest to many plant biologists. However, cell measurement can be time consuming and restrictive with current methods. CuticleTrace is a suite of Fiji and R-based functions that streamlines and automates the segmentation and measurement of epidermal pavement cells across a wide range of cell morphologies and image qualities.

Methods And Results: We evaluated CuticleTrace-generated measurements against those from alternate automated methods and expert and undergraduate hand tracings across a taxonomically diverse 50-image data set of variable image qualities. We observed ~93% statistical agreement between CuticleTrace and expert hand-traced measurements, outperforming alternate methods.

Conclusions: CuticleTrace is a broadly applicable, modular, and customizable tool that integrates data visualization and cell shape measurement with image segmentation, lowering the barrier to high-throughput studies of epidermal morphology by vastly decreasing the labor investment required to generate high-quality cell shape data sets.

Citing Articles

CuticleTrace: A toolkit for capturing cell outlines from leaf cuticle with implications for paleoecology and paleoclimatology.

Lloyd B, Barclay R, Dunn R, Currano E, Mohamaad A, Skersies K Appl Plant Sci. 2024; 12(1):e11566.

PMID: 38369978 PMC: 10873815. DOI: 10.1002/aps3.11566.

References

Milligan J, Flynn A, Wagner J, Kouwenberg L, Barclay R, Byars B . Quantifying the effect of shade on cuticle morphology and carbon isotopes of sycamores: present and past. Am J Bot. 2021; 108(12):2435-2451. PMC: 9306692. DOI: 10.1002/ajb2.1772. View

Fetter K, Eberhardt S, Barclay R, Wing S, Keller S . StomataCounter: a neural network for automatic stomata identification and counting. New Phytol. 2019; 223(3):1671-1681. DOI: 10.1111/nph.15892. View

Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T . Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012; 9(7):676-82. PMC: 3855844. DOI: 10.1038/nmeth.2019. View

Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S . Crop Phenomics: Current Status and Perspectives. Front Plant Sci. 2019; 10:714. PMC: 6557228. DOI: 10.3389/fpls.2019.00714. View

Moller B, Poeschl Y, Plotner R, Burstenbinder K . PaCeQuant: A Tool for High-Throughput Quantification of Pavement Cell Shape Characteristics. Plant Physiol. 2017; 175(3):998-1017. PMC: 5664455. DOI: 10.1104/pp.17.00961. View

Stringer C, Wang T, Michaelos M, Pachitariu M . Cellpose: a generalist algorithm for cellular segmentation. Nat Methods. 2020; 18(1):100-106. DOI: 10.1038/s41592-020-01018-x. View

Brown M, Jordan G . No cell is an island: characterising the leaf epidermis using epidermalmorph, a new R package. New Phytol. 2022; 237(1):354-366. PMC: 10098627. DOI: 10.1111/nph.18519. View

Li S, Li L, Fan W, Ma S, Zhang C, Kim J . LeafNet: a tool for segmenting and quantifying stomata and pavement cells. Plant Cell. 2022; 34(4):1171-1188. PMC: 8972303. DOI: 10.1093/plcell/koac021. View

Aono A, Nagai J, Dickel G, Marinho R, de Oliveira P, Papa J . A stomata classification and detection system in microscope images of maize cultivars. PLoS One. 2021; 16(10):e0258679. PMC: 8544852. DOI: 10.1371/journal.pone.0258679. View

10.

Lloyd B, Barclay R, Dunn R, Currano E, Mohamaad A, Skersies K . CuticleTrace: A toolkit for capturing cell outlines from leaf cuticle with implications for paleoecology and paleoclimatology. Appl Plant Sci. 2024; 12(1):e11566. PMC: 10873815. DOI: 10.1002/aps3.11566. View

11.

Royer D . Stomatal density and stomatal index as indicators of paleoatmospheric CO(2) concentration. Rev Palaeobot Palynol. 2001; 114(1-2):1-28. DOI: 10.1016/s0034-6667(00)00074-9. View

12.

Sai N, Bockman J, Chen H, Watson-Haigh N, Xu B, Feng X . StomaAI: an efficient and user-friendly tool for measurement of stomatal pores and density using deep computer vision. New Phytol. 2023; 238(2):904-915. DOI: 10.1111/nph.18765. View

13.

Wolny A, Cerrone L, Vijayan A, Tofanelli R, Barro A, Louveaux M . Accurate and versatile 3D segmentation of plant tissues at cellular resolution. Elife. 2020; 9. PMC: 7447435. DOI: 10.7554/eLife.57613. View

14.

Dunn R, Stromberg C, Madden R, Kohn M, Carlini A . Linked canopy, climate, and faunal change in the Cenozoic of Patagonia. Science. 2015; 347(6219):258-61. DOI: 10.1126/science.1260947. View

15.

Nowak J, Eng R, Matz T, Waack M, Persson S, Sampathkumar A . A network-based framework for shape analysis enables accurate characterization of leaf epidermal cells. Nat Commun. 2021; 12(1):458. PMC: 7815848. DOI: 10.1038/s41467-020-20730-y. View

16.

Vofely R, Gallagher J, Pisano G, Bartlett M, Braybrook S . Of puzzles and pavements: a quantitative exploration of leaf epidermal cell shape. New Phytol. 2018; 221(1):540-552. PMC: 6585845. DOI: 10.1111/nph.15461. View