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AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data
Compensating in flow cytometry is an unavoidable challenge in the data analysis of fluorescence-based flow cytometry. Even the advent of spectral cytometry cannot circumvent the spillover problem, with spectral unmixing an intrinsic part of such systems. The calculation of spillover coefficients fro...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8129071/ https://www.ncbi.nlm.nih.gov/pubmed/34001872 http://dx.doi.org/10.1038/s41467-021-23126-8 |
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author | Roca, Carlos P. Burton, Oliver T. Gergelits, Václav Prezzemolo, Teresa Whyte, Carly E. Halpert, Richard Kreft, Łukasz Collier, James Botzki, Alexander Spidlen, Josef Humblet-Baron, Stéphanie Liston, Adrian |
author_facet | Roca, Carlos P. Burton, Oliver T. Gergelits, Václav Prezzemolo, Teresa Whyte, Carly E. Halpert, Richard Kreft, Łukasz Collier, James Botzki, Alexander Spidlen, Josef Humblet-Baron, Stéphanie Liston, Adrian |
author_sort | Roca, Carlos P. |
collection | PubMed |
description | Compensating in flow cytometry is an unavoidable challenge in the data analysis of fluorescence-based flow cytometry. Even the advent of spectral cytometry cannot circumvent the spillover problem, with spectral unmixing an intrinsic part of such systems. The calculation of spillover coefficients from single-color controls has remained essentially unchanged since its inception, and is increasingly limited in its ability to deal with high-parameter flow cytometry. Here, we present AutoSpill, an alternative method for calculating spillover coefficients. The approach combines automated gating of cells, calculation of an initial spillover matrix based on robust linear regression, and iterative refinement to reduce error. Moreover, autofluorescence can be compensated out, by processing it as an endogenous dye in an unstained control. AutoSpill uses single-color controls and is compatible with common flow cytometry software. AutoSpill allows simpler and more robust workflows, while reducing the magnitude of compensation errors in high-parameter flow cytometry. |
format | Online Article Text |
id | pubmed-8129071 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-81290712021-06-01 AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data Roca, Carlos P. Burton, Oliver T. Gergelits, Václav Prezzemolo, Teresa Whyte, Carly E. Halpert, Richard Kreft, Łukasz Collier, James Botzki, Alexander Spidlen, Josef Humblet-Baron, Stéphanie Liston, Adrian Nat Commun Article Compensating in flow cytometry is an unavoidable challenge in the data analysis of fluorescence-based flow cytometry. Even the advent of spectral cytometry cannot circumvent the spillover problem, with spectral unmixing an intrinsic part of such systems. The calculation of spillover coefficients from single-color controls has remained essentially unchanged since its inception, and is increasingly limited in its ability to deal with high-parameter flow cytometry. Here, we present AutoSpill, an alternative method for calculating spillover coefficients. The approach combines automated gating of cells, calculation of an initial spillover matrix based on robust linear regression, and iterative refinement to reduce error. Moreover, autofluorescence can be compensated out, by processing it as an endogenous dye in an unstained control. AutoSpill uses single-color controls and is compatible with common flow cytometry software. AutoSpill allows simpler and more robust workflows, while reducing the magnitude of compensation errors in high-parameter flow cytometry. Nature Publishing Group UK 2021-05-17 /pmc/articles/PMC8129071/ /pubmed/34001872 http://dx.doi.org/10.1038/s41467-021-23126-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Roca, Carlos P. Burton, Oliver T. Gergelits, Václav Prezzemolo, Teresa Whyte, Carly E. Halpert, Richard Kreft, Łukasz Collier, James Botzki, Alexander Spidlen, Josef Humblet-Baron, Stéphanie Liston, Adrian AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data |
title | AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data |
title_full | AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data |
title_fullStr | AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data |
title_full_unstemmed | AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data |
title_short | AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data |
title_sort | autospill is a principled framework that simplifies the analysis of multichromatic flow cytometry data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8129071/ https://www.ncbi.nlm.nih.gov/pubmed/34001872 http://dx.doi.org/10.1038/s41467-021-23126-8 |
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