A guide to automated apoptosis detection: How to make sense of imaging flow cytometry data.
Imaging flow cytometry is a powerful experimental technique combining the strength of microscopy and flow cytometry to enable high-throughput characterization of cell populations on a detailed microscopic scale. This approach has an increasing importance for distinguishing between different cellular...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2018-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC5955558?pdf=render |
| _version_ | 1830474473422192640 |
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| author | Dennis Pischel Jörn H Buchbinder Kai Sundmacher Inna N Lavrik Robert J Flassig |
| author_facet | Dennis Pischel Jörn H Buchbinder Kai Sundmacher Inna N Lavrik Robert J Flassig |
| author_sort | Dennis Pischel |
| collection | DOAJ |
| description | Imaging flow cytometry is a powerful experimental technique combining the strength of microscopy and flow cytometry to enable high-throughput characterization of cell populations on a detailed microscopic scale. This approach has an increasing importance for distinguishing between different cellular phenotypes such as proliferation, cell division and cell death. In the course of undergoing these different pathways, each cell is characterized by a high amount of properties. This makes it hard to filter the most relevant information for cell state discrimination. The traditional methods for cell state discrimination rely on dye based two-dimensional gating strategies ignoring information that is hidden in the high-dimensional property space. In order to make use of the information ignored by the traditional methods, we present a simple and efficient approach to distinguish biological states within a cell population based on machine learning techniques. We demonstrate the advantages and drawbacks of filter techniques combined with different classification schemes. These techniques are illustrated with two case studies of apoptosis detection in HeLa cells. Thereby we highlight (i) the aptitude of imaging flow cytometry regarding automated, label-free cell state discrimination and (ii) pitfalls that are frequently encountered. Additionally a MATLAB script is provided, which gives further insight regarding the computational work presented in this study. |
| first_indexed | 2024-12-21T15:19:25Z |
| format | Article |
| id | doaj.art-0efd45d8f6854a2eaf7d039edc74314c |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-21T15:19:25Z |
| publishDate | 2018-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-0efd45d8f6854a2eaf7d039edc74314c2022-12-21T18:59:04ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01135e019720810.1371/journal.pone.0197208A guide to automated apoptosis detection: How to make sense of imaging flow cytometry data.Dennis PischelJörn H BuchbinderKai SundmacherInna N LavrikRobert J FlassigImaging flow cytometry is a powerful experimental technique combining the strength of microscopy and flow cytometry to enable high-throughput characterization of cell populations on a detailed microscopic scale. This approach has an increasing importance for distinguishing between different cellular phenotypes such as proliferation, cell division and cell death. In the course of undergoing these different pathways, each cell is characterized by a high amount of properties. This makes it hard to filter the most relevant information for cell state discrimination. The traditional methods for cell state discrimination rely on dye based two-dimensional gating strategies ignoring information that is hidden in the high-dimensional property space. In order to make use of the information ignored by the traditional methods, we present a simple and efficient approach to distinguish biological states within a cell population based on machine learning techniques. We demonstrate the advantages and drawbacks of filter techniques combined with different classification schemes. These techniques are illustrated with two case studies of apoptosis detection in HeLa cells. Thereby we highlight (i) the aptitude of imaging flow cytometry regarding automated, label-free cell state discrimination and (ii) pitfalls that are frequently encountered. Additionally a MATLAB script is provided, which gives further insight regarding the computational work presented in this study.http://europepmc.org/articles/PMC5955558?pdf=render |
| spellingShingle | Dennis Pischel Jörn H Buchbinder Kai Sundmacher Inna N Lavrik Robert J Flassig A guide to automated apoptosis detection: How to make sense of imaging flow cytometry data. PLoS ONE |
| title | A guide to automated apoptosis detection: How to make sense of imaging flow cytometry data. |
| title_full | A guide to automated apoptosis detection: How to make sense of imaging flow cytometry data. |
| title_fullStr | A guide to automated apoptosis detection: How to make sense of imaging flow cytometry data. |
| title_full_unstemmed | A guide to automated apoptosis detection: How to make sense of imaging flow cytometry data. |
| title_short | A guide to automated apoptosis detection: How to make sense of imaging flow cytometry data. |
| title_sort | guide to automated apoptosis detection how to make sense of imaging flow cytometry data |
| url | http://europepmc.org/articles/PMC5955558?pdf=render |
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