AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data
Carlos P. Roca (),
Oliver T. Burton,
Václav Gergelits,
Teresa Prezzemolo,
Carly E. Whyte,
Richard Halpert,
Łukasz Kreft,
James Collier,
Alexander Botzki,
Josef Spidlen,
Stéphanie Humblet-Baron and
Adrian Liston ()
Additional contact information
Carlos P. Roca: VIB Center for Brain and Disease Research
Oliver T. Burton: The Babraham Institute
Václav Gergelits: The Babraham Institute
Teresa Prezzemolo: VIB Center for Brain and Disease Research
Carly E. Whyte: The Babraham Institute
Richard Halpert: BD Life Sciences–FlowJo
Łukasz Kreft: VIB Bioinformatics Core
James Collier: VIB Bioinformatics Core
Alexander Botzki: VIB Bioinformatics Core
Josef Spidlen: BD Life Sciences–FlowJo
Stéphanie Humblet-Baron: VIB Center for Brain and Disease Research
Adrian Liston: VIB Center for Brain and Disease Research
Nature Communications, 2021, vol. 12, issue 1, 1-16
Abstract:
Abstract 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.
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-23126-8
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DOI: 10.1038/s41467-021-23126-8
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