miCAT: A toolbox for analysis of cell phenotypes and interactions in multiplex image cytometry data

Single-cell, spatially resolved ‘omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational multiplex image cytometry analysis toolbox (miCAT) to enable interactive, quantitative, and comprehensive exploration of indivi...

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Autores principales: Schapiro, Denis, Jackson, Hartland W, Raghuraman, Swetha, Fischer, Jana R, Zanotelli, Vito R. T., Schulz, Daniel, Giesen, Charlotte, Catena, Raúl, Varga, Zsuzsanna, Bodenmiller, Bernd
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5617107/
https://www.ncbi.nlm.nih.gov/pubmed/28783155
http://dx.doi.org/10.1038/nmeth.4391
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author Schapiro, Denis
Jackson, Hartland W
Raghuraman, Swetha
Fischer, Jana R
Zanotelli, Vito R. T.
Schulz, Daniel
Giesen, Charlotte
Catena, Raúl
Varga, Zsuzsanna
Bodenmiller, Bernd
author_facet Schapiro, Denis
Jackson, Hartland W
Raghuraman, Swetha
Fischer, Jana R
Zanotelli, Vito R. T.
Schulz, Daniel
Giesen, Charlotte
Catena, Raúl
Varga, Zsuzsanna
Bodenmiller, Bernd
author_sort Schapiro, Denis
collection PubMed
description Single-cell, spatially resolved ‘omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational multiplex image cytometry analysis toolbox (miCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell-to-cell interactions, microenvironments, and morphological structures within intact tissues. We highlight the unique abilities of miCAT by analysis of highly multiplexed mass cytometry images of human breast cancer tissues.
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spelling pubmed-56171072018-02-07 miCAT: A toolbox for analysis of cell phenotypes and interactions in multiplex image cytometry data Schapiro, Denis Jackson, Hartland W Raghuraman, Swetha Fischer, Jana R Zanotelli, Vito R. T. Schulz, Daniel Giesen, Charlotte Catena, Raúl Varga, Zsuzsanna Bodenmiller, Bernd Nat Methods Article Single-cell, spatially resolved ‘omics analysis of tissues is poised to transform biomedical research and clinical practice. We have developed an open-source, computational multiplex image cytometry analysis toolbox (miCAT) to enable interactive, quantitative, and comprehensive exploration of individual cell phenotypes, cell-to-cell interactions, microenvironments, and morphological structures within intact tissues. We highlight the unique abilities of miCAT by analysis of highly multiplexed mass cytometry images of human breast cancer tissues. 2017-08-07 2017-09 /pmc/articles/PMC5617107/ /pubmed/28783155 http://dx.doi.org/10.1038/nmeth.4391 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Schapiro, Denis
Jackson, Hartland W
Raghuraman, Swetha
Fischer, Jana R
Zanotelli, Vito R. T.
Schulz, Daniel
Giesen, Charlotte
Catena, Raúl
Varga, Zsuzsanna
Bodenmiller, Bernd
miCAT: A toolbox for analysis of cell phenotypes and interactions in multiplex image cytometry data
title miCAT: A toolbox for analysis of cell phenotypes and interactions in multiplex image cytometry data
title_full miCAT: A toolbox for analysis of cell phenotypes and interactions in multiplex image cytometry data
title_fullStr miCAT: A toolbox for analysis of cell phenotypes and interactions in multiplex image cytometry data
title_full_unstemmed miCAT: A toolbox for analysis of cell phenotypes and interactions in multiplex image cytometry data
title_short miCAT: A toolbox for analysis of cell phenotypes and interactions in multiplex image cytometry data
title_sort micat: a toolbox for analysis of cell phenotypes and interactions in multiplex image cytometry data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5617107/
https://www.ncbi.nlm.nih.gov/pubmed/28783155
http://dx.doi.org/10.1038/nmeth.4391
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