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Automated identification and tracking of cells in Cytometry of Reaction Rate Constant (CRRC)
Cytometry of Reaction Rate Constant (CRRC) is a method for studying cell-population heterogeneity using time-lapse fluorescence microscopy, which allows one to follow reaction kinetics in individual cells. The current and only CRRC workflow utilizes a single fluorescence image to manually identify c...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10317225/ https://www.ncbi.nlm.nih.gov/pubmed/37399195 http://dx.doi.org/10.1371/journal.pone.0282990 |
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author | Nebbioso, Giammarco Yosief, Robel Koshkin, Vasilij Qiu, Yumin Peng, Chun Elisseev, Vadim Krylov, Sergey N. |
author_facet | Nebbioso, Giammarco Yosief, Robel Koshkin, Vasilij Qiu, Yumin Peng, Chun Elisseev, Vadim Krylov, Sergey N. |
author_sort | Nebbioso, Giammarco |
collection | PubMed |
description | Cytometry of Reaction Rate Constant (CRRC) is a method for studying cell-population heterogeneity using time-lapse fluorescence microscopy, which allows one to follow reaction kinetics in individual cells. The current and only CRRC workflow utilizes a single fluorescence image to manually identify cell contours which are then used to determine fluorescence intensity of individual cells in the entire time-stack of images. This workflow is only reliable if cells maintain their positions during the time-lapse measurements. If the cells move, the original cell contours become unsuitable for evaluating intracellular fluorescence and the CRRC experiment will be inaccurate. The requirement of invariant cell positions during a prolonged imaging is impossible to satisfy for motile cells. Here we report a CRRC workflow developed to be applicable to motile cells. The new workflow combines fluorescence microscopy with transmitted-light microscopy and utilizes a new automated tool for cell identification and tracking. A transmitted-light image is taken right before every fluorescence image to determine cell contours, and cell contours are tracked through the time-stack of transmitted-light images to account for cell movement. Each unique contour is used to determine fluorescence intensity of cells in the associated fluorescence image. Next, time dependencies of the intracellular fluorescence intensities are used to determine each cell’s rate constant and construct a kinetic histogram “number of cells vs rate constant.” The new workflow’s robustness to cell movement was confirmed experimentally by conducting a CRRC study of cross-membrane transport in motile cells. The new workflow makes CRRC applicable to a wide range of cell types and eliminates the influence of cell motility on the accuracy of results. Additionally, the workflow could potentially monitor kinetics of varying biological processes at the single-cell level for sizable cell populations. Although our workflow was designed ad hoc for CRRC, this cell-segmentation/cell-tracking strategy also represents an entry-level, user-friendly option for a variety of biological assays (i.e., migration, proliferation assays, etc.). Importantly, no prior knowledge of informatics (i.e., training a model for deep learning) is required. |
format | Online Article Text |
id | pubmed-10317225 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-103172252023-07-04 Automated identification and tracking of cells in Cytometry of Reaction Rate Constant (CRRC) Nebbioso, Giammarco Yosief, Robel Koshkin, Vasilij Qiu, Yumin Peng, Chun Elisseev, Vadim Krylov, Sergey N. PLoS One Research Article Cytometry of Reaction Rate Constant (CRRC) is a method for studying cell-population heterogeneity using time-lapse fluorescence microscopy, which allows one to follow reaction kinetics in individual cells. The current and only CRRC workflow utilizes a single fluorescence image to manually identify cell contours which are then used to determine fluorescence intensity of individual cells in the entire time-stack of images. This workflow is only reliable if cells maintain their positions during the time-lapse measurements. If the cells move, the original cell contours become unsuitable for evaluating intracellular fluorescence and the CRRC experiment will be inaccurate. The requirement of invariant cell positions during a prolonged imaging is impossible to satisfy for motile cells. Here we report a CRRC workflow developed to be applicable to motile cells. The new workflow combines fluorescence microscopy with transmitted-light microscopy and utilizes a new automated tool for cell identification and tracking. A transmitted-light image is taken right before every fluorescence image to determine cell contours, and cell contours are tracked through the time-stack of transmitted-light images to account for cell movement. Each unique contour is used to determine fluorescence intensity of cells in the associated fluorescence image. Next, time dependencies of the intracellular fluorescence intensities are used to determine each cell’s rate constant and construct a kinetic histogram “number of cells vs rate constant.” The new workflow’s robustness to cell movement was confirmed experimentally by conducting a CRRC study of cross-membrane transport in motile cells. The new workflow makes CRRC applicable to a wide range of cell types and eliminates the influence of cell motility on the accuracy of results. Additionally, the workflow could potentially monitor kinetics of varying biological processes at the single-cell level for sizable cell populations. Although our workflow was designed ad hoc for CRRC, this cell-segmentation/cell-tracking strategy also represents an entry-level, user-friendly option for a variety of biological assays (i.e., migration, proliferation assays, etc.). Importantly, no prior knowledge of informatics (i.e., training a model for deep learning) is required. Public Library of Science 2023-07-03 /pmc/articles/PMC10317225/ /pubmed/37399195 http://dx.doi.org/10.1371/journal.pone.0282990 Text en © 2023 Nebbioso et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Nebbioso, Giammarco Yosief, Robel Koshkin, Vasilij Qiu, Yumin Peng, Chun Elisseev, Vadim Krylov, Sergey N. Automated identification and tracking of cells in Cytometry of Reaction Rate Constant (CRRC) |
title | Automated identification and tracking of cells in Cytometry of Reaction Rate Constant (CRRC) |
title_full | Automated identification and tracking of cells in Cytometry of Reaction Rate Constant (CRRC) |
title_fullStr | Automated identification and tracking of cells in Cytometry of Reaction Rate Constant (CRRC) |
title_full_unstemmed | Automated identification and tracking of cells in Cytometry of Reaction Rate Constant (CRRC) |
title_short | Automated identification and tracking of cells in Cytometry of Reaction Rate Constant (CRRC) |
title_sort | automated identification and tracking of cells in cytometry of reaction rate constant (crrc) |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10317225/ https://www.ncbi.nlm.nih.gov/pubmed/37399195 http://dx.doi.org/10.1371/journal.pone.0282990 |
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