Plant Microtechnique with Resin: Towards Plant Histolomics

Plant microtechnique involves a sequence of skill-intensive histological procedures that often yield poorly reproducible images and limited quantitative information. Nevertheless, it provides an essential cellular and tissue context needed to understand biological functions. In this work, we present an optimized resin-based microtechnique that replaces paraffin embedding, incorporates a chemically activated adhesive treatment for glass slides, and develop a trichrome stain for resin sections. All these improvements enhanced section stability and image reproducibility, enabled a broader color palette with sharp contrast of tissues, cells and organelles, and selected ultrastructural features using light microscopy. Based on these preparations, a quantitative micrograph analysis workflow was developed based on image segmentation and feature extraction using MATLAB (R2024a) and Adobe Photoshop (CS6). This approach enables the measurement of a wide range of morphometric and compositional features, generating structured histological datasets that we refer to as plant histolomes. As an illustrative application, this workflow was applied to leaves from several model plants species and integrated multiple anatomical traits into a composite feature, the "C<sub>4</sub> Kranz-anatomy level", enabling quantitative comparison along the C<sub>3</sub>-C<sub>4</sub> anatomical transition. The resin-based microtechnique and the histolomics framework developed in this work provide a robust and reproducible basis for the quantitative plant histology, bridging classical microscopy with a data-driven tissue analysis.

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Publication Details

Journal
Plants
Published
2026-02-19
DOI
https://doi.org/10.3390/plants15040643
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Plant Microtechnique with Resin: Towards Plant Histolomics

Araceli Patrón-Soberano, Ivan Takeshi Cerritos-Castro, Ana Paulina Barba-de la Barba-de la Rosa
Plants
Cell Image Analysis Techniques
article

Plant Microtechnique with Resin: Towards Plant Histolomics

Araceli Patrón-Soberano, Ivan Takeshi Cerritos-Castro, Ana Paulina Barba-de la Barba-de la Rosa
article en

Abstract

Plant microtechnique involves a sequence of skill-intensive histological procedures that often yield poorly reproducible images and limited quantitative information. Nevertheless, it provides an essential cellular and tissue context needed to understand biological functions. In this work, we present an optimized resin-based microtechnique that replaces paraffin embedding, incorporates a chemically activated adhesive treatment for glass slides, and develop a trichrome stain for resin sections. All these improvements enhanced section stability and image reproducibility, enabled a broader color palette with sharp contrast of tissues, cells and organelles, and selected ultrastructural features using light microscopy. Based on these preparations, a quantitative micrograph analysis workflow was developed based on image segmentation and feature extraction using MATLAB (R2024a) and Adobe Photoshop (CS6). This approach enables the measurement of a wide range of morphometric and compositional features, generating structured histological datasets that we refer to as plant histolomes. As an illustrative application, this workflow was applied to leaves from several model plants species and integrated multiple anatomical traits into a composite feature, the "C<sub>4</sub> Kranz-anatomy level", enabling quantitative comparison along the C<sub>3</sub>-C<sub>4</sub> anatomical transition. The resin-based microtechnique and the histolomics framework developed in this work provide a robust and reproducible basis for the quantitative plant histology, bridging classical microscopy with a data-driven tissue analysis.

PlantsVol. 15(4)
Institute for Scientific and Technological Research (MX)
Consejo Nacional de Ciencia y Tecnología, Instituto Potosino de Investigación Científica y Tecnológica
Openalex Percentile: Top 57%
Cell Image Analysis Techniques
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