Abstract
Single-cell mass cytometry significantly increases the dimensionality of cytometry analysis as compared to fluorescence flow cytometry, providing unprecedented resolution of cellular diversity in tissues. However, analysis and interpretation of these high-dimensional data poses a significant technical challenge. Here, we present cytofkit, a new Bioconductor package, which integrates both state-of-the-art bioinformatics methods and in-house novel algorithms to offer a comprehensive toolset for mass cytometry data analysis. Cytofkit provides functions for data pre-processing, data visualization through linear or non-linear dimensionality reduction, automatic identification of cell subsets, and inference of the relatedness between cell subsets. This pipeline also provides a graphical user interface (GUI) for ease of use, as well as a shiny application (APP) for interactive visualization of cell subpopulations and progression profiles of key markers. Applied to a CD14−CD19− PBMCs dataset, cytofkit accurately identified different subsets of lymphocytes; applied to a human CD4+ T cell dataset, cytofkit uncovered multiple subtypes of TFH cells spanning blood and tonsils. Cytofkit is implemented in R, licensed under the Artistic license 2.0, and freely available from the Bioconductor website, https://bioconductor.org/packages/cytofkit/. Cytofkit is also applicable for flow cytometry data analysis.
Highlights
Mass cytometry, or cytometry by time-of-flight (CyTOF), uniquely combines metal-labeling of antibodies with mass spectrometry to enable high-dimensional measurement of the characteristics of individual cells [1,2]
In order to assess the accuracy of cytofkit, we manually gated populations of CD4+, CD8+, γδT, CD3+CD56+ NKT and CD3−CD56+ NK cells from the CD14−CD19− PBMCs dataset
Cytofkit was implemented using R and has been published on Bioconductor. It is available on github
Summary
Cytometry by time-of-flight (CyTOF), uniquely combines metal-labeling of antibodies with mass spectrometry to enable high-dimensional measurement of the characteristics of individual cells [1,2]. September 23, 2016 cytofkit limitations of spectral overlap in flow cytometry, and allow for simultaneous analysis of more than 40 markers per cell [3,4]. This technology has been successfully applied in a number of areas including mapping phenotypic heterogeneity of leukemia [5], inferring cellular progression and hierarchies [6], assessing drug effects on immune cells [7,8] and uncovering mechanisms of cellular reprogramming [9]. Traditional manual gating, the gold-standard method for flow cytometry data analysis, is not practical for mass cytometry due to its high dimensionality. Most automated methods designed for flow cytometry data do not perform well for mass cytometry data [10]
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