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Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging
BACKGROUND: Multiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, com...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6720861/ https://www.ncbi.nlm.nih.gov/pubmed/31477013 http://dx.doi.org/10.1186/s12859-019-3055-3 |
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author | Czech, Eric Aksoy, Bulent Arman Aksoy, Pinar Hammerbacher, Jeff |
author_facet | Czech, Eric Aksoy, Bulent Arman Aksoy, Pinar Hammerbacher, Jeff |
author_sort | Czech, Eric |
collection | PubMed |
description | BACKGROUND: Multiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, comprise promising avenues for rapid advancements in the understanding of disease progression and diagnosis. As protocols for conducting such imaging experiments continue to improve, it is the intent of this study to provide and validate software for processing the large quantity of associated data in kind. RESULTS: Cytokit offers (i) an end-to-end, GPU-accelerated image processing pipeline; (ii) efficient input/output (I/O) strategies for operations specific to high dimensional microscopy; and (iii) an interactive user interface for cross filtering of spatial, graphical, expression, and morphological cell properties within the 100+ GB image datasets common to multiplexed immunofluorescence. Image processing operations supported in Cytokit are generally sourced from existing deep learning models or are at least in part adapted from open source packages to run in a single or multi-GPU environment. The efficacy of these operations is demonstrated through several imaging experiments that pair Cytokit results with those from an independent but comparable assay. A further validation also demonstrates that previously published results can be reproduced from a publicly available multiplexed image dataset. CONCLUSION: Cytokit is a collection of open source tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets that are often, but not necessarily, generated from multiplexed antibody labeling protocols over many fields of view or time periods. This project is best suited to bioinformaticians or other technical users that wish to analyze such data in a batch-oriented, high-throughput setting. All source code, documentation, and data generated for this article are available under the Apache License 2.0 at https://github.com/hammerlab/cytokit. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-3055-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6720861 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-67208612019-09-06 Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging Czech, Eric Aksoy, Bulent Arman Aksoy, Pinar Hammerbacher, Jeff BMC Bioinformatics Software BACKGROUND: Multiplexed in-situ fluorescent imaging offers several advantages over single-cell assays that do not preserve the spatial characteristics of biological samples. This spatial information, in addition to morphological properties and extensive intracellular or surface marker profiling, comprise promising avenues for rapid advancements in the understanding of disease progression and diagnosis. As protocols for conducting such imaging experiments continue to improve, it is the intent of this study to provide and validate software for processing the large quantity of associated data in kind. RESULTS: Cytokit offers (i) an end-to-end, GPU-accelerated image processing pipeline; (ii) efficient input/output (I/O) strategies for operations specific to high dimensional microscopy; and (iii) an interactive user interface for cross filtering of spatial, graphical, expression, and morphological cell properties within the 100+ GB image datasets common to multiplexed immunofluorescence. Image processing operations supported in Cytokit are generally sourced from existing deep learning models or are at least in part adapted from open source packages to run in a single or multi-GPU environment. The efficacy of these operations is demonstrated through several imaging experiments that pair Cytokit results with those from an independent but comparable assay. A further validation also demonstrates that previously published results can be reproduced from a publicly available multiplexed image dataset. CONCLUSION: Cytokit is a collection of open source tools for quantifying and analyzing properties of individual cells in large fluorescent microscopy datasets that are often, but not necessarily, generated from multiplexed antibody labeling protocols over many fields of view or time periods. This project is best suited to bioinformaticians or other technical users that wish to analyze such data in a batch-oriented, high-throughput setting. All source code, documentation, and data generated for this article are available under the Apache License 2.0 at https://github.com/hammerlab/cytokit. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-019-3055-3) contains supplementary material, which is available to authorized users. BioMed Central 2019-09-02 /pmc/articles/PMC6720861/ /pubmed/31477013 http://dx.doi.org/10.1186/s12859-019-3055-3 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Software Czech, Eric Aksoy, Bulent Arman Aksoy, Pinar Hammerbacher, Jeff Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging |
title | Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging |
title_full | Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging |
title_fullStr | Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging |
title_full_unstemmed | Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging |
title_short | Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging |
title_sort | cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6720861/ https://www.ncbi.nlm.nih.gov/pubmed/31477013 http://dx.doi.org/10.1186/s12859-019-3055-3 |
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