Normalization of mass cytometry data with bead standards

By R. Finck, Erin F. Simonds, Astraea Jager, Smita Krishnaswamy, K. Sachs, W. Fantl, Dana Pe'er, Garry P. Nolan, Sean Bendall. Cytometry Part A, 2013

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Mass cytometry uses atomic mass spectrometry combined with isotopically pure reporter elements to currently measure as many as 40 parameters per single cell. As with any quantitative technology, there is a fundamental need for quality assurance and normalization protocols. In the case of mass cytometry, the signal variation over time due to changes in instrument performance combined with intervals between scheduled maintenance must be accounted for and then normalized. Here, samples were mixed with polystyrene beads embedded with metal lanthanides, allowing monitoring of mass cytometry instrument performance over multiple days of data acquisition. The protocol described here includes simultaneous measurements of beads and cells on the mass cytometer, subsequent extraction of the bead‐based signature, and the application of an algorithm enabling correction of both short‐ and long‐term signal fluctuations. The variation in the intensity of the beads that remains after normalization may also be used to determine data quality. Application of the algorithm to a one‐month longitudinal analysis of a human peripheral blood sample reduced the range of median signal fluctuation from 4.9‐fold to 1.3‐fold. © 2013 International Society for Advancement of Cytometry

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/08dc6d13a430cc3e56e43de228145234421a0ce4

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Citations in the corpus

High‐Dimensional Single‐Cell Mapping of Central Nervous System Immune Cells Reveals Distinct Myeloid Subsets in Health, Aging, and Disease (2018, positive): “2 Tree Star https://www.flowjo.com/ Cytobank Kotecha et al., 2010 https://www.cytobank.org/ MATLAB R2016a N/A https://www.mathworks.com/ Normalizer Finck et al., 2013 https://github.com/nolanlab/bead- normalization/releases t-SNE Van Der Maaten and Hinton, 2008 https://github.com/jkrijthe/Rtsne Flow”
Removal of batch effects using distribution‐matching residual networks (2016, methodology): “A CyTOF batch contains measurements of numerous cells from a few specimens, and each batch is affected by systematic errors (Finck et al., 2013).”
Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B cell Development (2014, methodology): “Mass Cytometry Analysis Processing of primary human bone marrow and mass cytometry analysis including data preprocessing is as previously described (Bendall et al., 2011; Fienberg et al., 2012; Finck et al., 2013; Kotecha et al., 2010; Amir et al., 2013).”
Integrated analysis of multimodal single-cell data (2020, neutral): “Prior to analysis, fcs fileswere debarcoded and bead-normalizedwith EQbeads using the Premessa package in the open-source statistical software R as previously described (Finck et al., 2013).”
Categorical Analysis of Human T Cell Heterogeneity with One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding (2016, methodology): “The signal of each parameter was normalized based on the EQ beads (Fluidigm) as previously described (23).”
Cytofkit: A Bioconductor Package for an Integrated Mass Cytometry Data Analysis Pipeline (2016, background): “Analysis of mass cytometry data has several key challenges including debarcoding [11], batch normalization [12], visualization of high-dimensional data, identification of cell subsets, inference of relatedness between cell subsets, and detection of changes in subset abundance.”