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H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry
Fluorescent cytometry refers to the quantification of cell physical properties and surface biomarkers using fluorescently-tagged antibodies. The generally preferred techniques to perform such measurements are flow cytometry, which performs rapid single cell analysis by flowing cells one-by-one throu...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6742454/ https://www.ncbi.nlm.nih.gov/pubmed/31513616 http://dx.doi.org/10.1371/journal.pone.0222265 |
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author | Pardo, Esteban González, Germán Tucker-Schwartz, Jason M. Dave, Shivang R. Malpica, Norberto |
author_facet | Pardo, Esteban González, Germán Tucker-Schwartz, Jason M. Dave, Shivang R. Malpica, Norberto |
author_sort | Pardo, Esteban |
collection | PubMed |
description | Fluorescent cytometry refers to the quantification of cell physical properties and surface biomarkers using fluorescently-tagged antibodies. The generally preferred techniques to perform such measurements are flow cytometry, which performs rapid single cell analysis by flowing cells one-by-one through a channel, and microscopy, which eliminates the complexity of the flow channel, offering multi-cell analysis at a lesser throughput. Low-magnification image-based cytometers, also called “cell astronomy” systems, hold promise of simultaneously achieving both instrumental simplicity and high throughput. In this magnification regime, a single cell is mapped to a handful of pixels in the image. While very attractive, this idea has, so far, not been proven to yield quantitative results of cell-labeling, mainly due to the poor signal-to-noise ratio present in those images and to partial volume effects. In this work we present a cell astronomy system that, when coupled with custom-developed algorithms, is able to quantify cell intensities and diameters reliably. We showcase the system using calibrated MESF beads and fluorescently stained leukocytes, achieving good population identification in both cases. The main contribution of the proposed system is in the development of a novel algorithm, H-EM, that enables inter-cluster separation at a very low magnification regime (2x). Such algorithm provides more accurate brightness estimates than DAOSTORM when compared to manual analysis, while fitting cell location, brightness, diameter, and background level concurrently. The algorithm first performs Fisher discriminant analysis to detect bright spots. From each spot an expectation-maximization algorithm is initialized over a heterogeneous mixture model (H-EM), this algorithm recovers both the cell fluorescence and diameter with sub-pixel accuracy while discriminating the background noise. Finally, a recursive splitting procedure is applied to discern individual cells in cell clusters. |
format | Online Article Text |
id | pubmed-6742454 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-67424542019-09-20 H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry Pardo, Esteban González, Germán Tucker-Schwartz, Jason M. Dave, Shivang R. Malpica, Norberto PLoS One Research Article Fluorescent cytometry refers to the quantification of cell physical properties and surface biomarkers using fluorescently-tagged antibodies. The generally preferred techniques to perform such measurements are flow cytometry, which performs rapid single cell analysis by flowing cells one-by-one through a channel, and microscopy, which eliminates the complexity of the flow channel, offering multi-cell analysis at a lesser throughput. Low-magnification image-based cytometers, also called “cell astronomy” systems, hold promise of simultaneously achieving both instrumental simplicity and high throughput. In this magnification regime, a single cell is mapped to a handful of pixels in the image. While very attractive, this idea has, so far, not been proven to yield quantitative results of cell-labeling, mainly due to the poor signal-to-noise ratio present in those images and to partial volume effects. In this work we present a cell astronomy system that, when coupled with custom-developed algorithms, is able to quantify cell intensities and diameters reliably. We showcase the system using calibrated MESF beads and fluorescently stained leukocytes, achieving good population identification in both cases. The main contribution of the proposed system is in the development of a novel algorithm, H-EM, that enables inter-cluster separation at a very low magnification regime (2x). Such algorithm provides more accurate brightness estimates than DAOSTORM when compared to manual analysis, while fitting cell location, brightness, diameter, and background level concurrently. The algorithm first performs Fisher discriminant analysis to detect bright spots. From each spot an expectation-maximization algorithm is initialized over a heterogeneous mixture model (H-EM), this algorithm recovers both the cell fluorescence and diameter with sub-pixel accuracy while discriminating the background noise. Finally, a recursive splitting procedure is applied to discern individual cells in cell clusters. Public Library of Science 2019-09-12 /pmc/articles/PMC6742454/ /pubmed/31513616 http://dx.doi.org/10.1371/journal.pone.0222265 Text en © 2019 Pardo et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://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 Pardo, Esteban González, Germán Tucker-Schwartz, Jason M. Dave, Shivang R. Malpica, Norberto H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry |
title | H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry |
title_full | H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry |
title_fullStr | H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry |
title_full_unstemmed | H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry |
title_short | H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry |
title_sort | h-em: an algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6742454/ https://www.ncbi.nlm.nih.gov/pubmed/31513616 http://dx.doi.org/10.1371/journal.pone.0222265 |
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