Cargando…
Quantitative methods in microscopy to assess pollen viability in different plant taxa
High-quality pollen is a prerequisite for plant reproductive success. Pollen viability and sterility can be routinely assessed using common stains and manual microscope examination, but with low overall statistical power. Current automated methods are primarily directed towards the analysis of polle...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7648740/ https://www.ncbi.nlm.nih.gov/pubmed/33123804 http://dx.doi.org/10.1007/s00497-020-00398-6 |
_version_ | 1783607170602893312 |
---|---|
author | Ascari, Lorenzo Novara, Cristina Dusio, Virginia Oddi, Ludovica Siniscalco, Consolata |
author_facet | Ascari, Lorenzo Novara, Cristina Dusio, Virginia Oddi, Ludovica Siniscalco, Consolata |
author_sort | Ascari, Lorenzo |
collection | PubMed |
description | High-quality pollen is a prerequisite for plant reproductive success. Pollen viability and sterility can be routinely assessed using common stains and manual microscope examination, but with low overall statistical power. Current automated methods are primarily directed towards the analysis of pollen sterility, and high throughput solutions for both pollen viability and sterility evaluation are needed that will be consistent with emerging biotechnological strategies for crop improvement. Our goal is to refine established labelling procedures for pollen, based on the combination of fluorescein (FDA) and propidium iodide (PI), and to develop automated solutions for accurately assessing pollen grain images and classifying them for quality. We used open-source software programs (CellProfiler, CellProfiler Analyst, Fiji and R) for analysis of images collected from 10 pollen taxa labelled using FDA/PI. After correcting for image background noise, pollen grain images were examined for quality employing thresholding and segmentation. Supervised and unsupervised classification of per-object features was employed for the identification of viable, dead and sterile pollen. The combination of FDA and PI dyes was able to differentiate between viable, dead and sterile pollen in all the analysed taxa. Automated image analysis and classification significantly increased the statistical power of the pollen viability assay, identifying more than 75,000 pollen grains with high accuracy (R(2) = 0.99) when compared to classical manual counting. Overall, we provide a comprehensive set of methodologies as baseline for the automated assessment of pollen viability using fluorescence microscopy, which can be combined with manual and mechanized imaging systems in fundamental and applied research on plant biology. We also supply the complete set of pollen images (the FDA/PI pollen dataset) to the scientific community for future research. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00497-020-00398-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-7648740 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-76487402020-11-10 Quantitative methods in microscopy to assess pollen viability in different plant taxa Ascari, Lorenzo Novara, Cristina Dusio, Virginia Oddi, Ludovica Siniscalco, Consolata Plant Reprod Methods Paper High-quality pollen is a prerequisite for plant reproductive success. Pollen viability and sterility can be routinely assessed using common stains and manual microscope examination, but with low overall statistical power. Current automated methods are primarily directed towards the analysis of pollen sterility, and high throughput solutions for both pollen viability and sterility evaluation are needed that will be consistent with emerging biotechnological strategies for crop improvement. Our goal is to refine established labelling procedures for pollen, based on the combination of fluorescein (FDA) and propidium iodide (PI), and to develop automated solutions for accurately assessing pollen grain images and classifying them for quality. We used open-source software programs (CellProfiler, CellProfiler Analyst, Fiji and R) for analysis of images collected from 10 pollen taxa labelled using FDA/PI. After correcting for image background noise, pollen grain images were examined for quality employing thresholding and segmentation. Supervised and unsupervised classification of per-object features was employed for the identification of viable, dead and sterile pollen. The combination of FDA and PI dyes was able to differentiate between viable, dead and sterile pollen in all the analysed taxa. Automated image analysis and classification significantly increased the statistical power of the pollen viability assay, identifying more than 75,000 pollen grains with high accuracy (R(2) = 0.99) when compared to classical manual counting. Overall, we provide a comprehensive set of methodologies as baseline for the automated assessment of pollen viability using fluorescence microscopy, which can be combined with manual and mechanized imaging systems in fundamental and applied research on plant biology. We also supply the complete set of pollen images (the FDA/PI pollen dataset) to the scientific community for future research. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s00497-020-00398-6) contains supplementary material, which is available to authorized users. Springer Berlin Heidelberg 2020-10-29 2020 /pmc/articles/PMC7648740/ /pubmed/33123804 http://dx.doi.org/10.1007/s00497-020-00398-6 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Methods Paper Ascari, Lorenzo Novara, Cristina Dusio, Virginia Oddi, Ludovica Siniscalco, Consolata Quantitative methods in microscopy to assess pollen viability in different plant taxa |
title | Quantitative methods in microscopy to assess pollen viability in different plant taxa |
title_full | Quantitative methods in microscopy to assess pollen viability in different plant taxa |
title_fullStr | Quantitative methods in microscopy to assess pollen viability in different plant taxa |
title_full_unstemmed | Quantitative methods in microscopy to assess pollen viability in different plant taxa |
title_short | Quantitative methods in microscopy to assess pollen viability in different plant taxa |
title_sort | quantitative methods in microscopy to assess pollen viability in different plant taxa |
topic | Methods Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7648740/ https://www.ncbi.nlm.nih.gov/pubmed/33123804 http://dx.doi.org/10.1007/s00497-020-00398-6 |
work_keys_str_mv | AT ascarilorenzo quantitativemethodsinmicroscopytoassesspollenviabilityindifferentplanttaxa AT novaracristina quantitativemethodsinmicroscopytoassesspollenviabilityindifferentplanttaxa AT dusiovirginia quantitativemethodsinmicroscopytoassesspollenviabilityindifferentplanttaxa AT oddiludovica quantitativemethodsinmicroscopytoassesspollenviabilityindifferentplanttaxa AT siniscalcoconsolata quantitativemethodsinmicroscopytoassesspollenviabilityindifferentplanttaxa |