AutoSpill is a principled framework that simplifies the analysis of multichromatic flow cytometry data

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.

Astrocyte-targeted gene delivery of interleukin 2 specifically increases brain-resident regulatory T cell numbers and protects against pathological neuroinflammation

“…For brain panels, the entire brain sample was acquired. Data were compensated using AutoSpill 57 . Examples for mouse cells were always presented as concatenated biological replicates.…”

Unlocking autofluorescence in the era of full spectrum analysis: Implications for immunophenotype discovery projects

“…In recent years, with improvements in the quality of optical and electronics design and the availability of spectral cytometers (Cytek Aurora and Sony SP6800) ( 13 , 30 , 31 ), the development of strategies to capture and extract AF signatures from polychromatic panel measurements without compromising data or panel size has become a widely shared goal. Although the addition of AF as a new reference during unmixing calculations adds complexity to pre‐defined panels due to unanticipated spectral overlap with fluorochromes tagging critical rare markers, novel post‐acquisition algorithms to mathematically resolve fluorochromes in polychromatic assays ( 31 , 32 , 33 , 34 , 35 ) when applied to fine measurements of spectral signatures, enhance the quality of the processed data. With their paradigm shift from emission maxima and dye brightness to full spectrum signatures, leading to the improved management of spectral similarity‐driven spreading ( 36 , 37 , 38 ), full spectral unmixing algorithms increase the chances of success by extracting subset‐specific AF from each measurement, improving the definition of rare markers and cellular phenotypes.…”

Context-dependent effects of IL-2 rewire immunity into distinct cellular circuits

“…Flow cytometry samples were acquired on a Yeti/ZE5 (Propel Labs/Bio-Rad), Symphony (BD Biosciences), Fortessa (BD Biosciences), or Aurora (Cytek) spectral flow cytometer. Data was compensated via AutoSpill (Roca et al, 2021a).…”