Abstract
Recent advances in cytometry have radically altered the fate of single-cell proteomics by allowing a more accurate understanding of complex biological systems. Mass cytometry (CyTOF) provides simultaneous single-cell measurements that are crucial to understand cellular heterogeneity and identify novel cellular subsets. High-dimensional CyTOF data were traditionally analyzed by gating on bivariate dot plots, which are not only laborious given the quadratic increase of complexity with dimension but are also biased through manual gating. This review aims to discuss the impact of new analysis techniques for in-depths insights into the dynamics of immune regulation obtained from static snapshot data and to provide tools to immunologists to address the high dimensionality of their single-cell data.
Highlights
Reviewed by: Ju Qiu, Shanghai Institutes for Biological Sciences (CAS), China Katalin A
This review aims to discuss the impact of new analysis techniques for in-depths insights into the dynamics of immune regulation obtained from static snapshot data and to provide tools to immunologists to address the high dimensionality of their single-cell data
We propose two single-cell trajectory inference algorithms, diffusion pseudo-time (DPT) [12] to infer pseudotemporal ordering of cells and partition-based graph abstraction (PAGA) [13] for generating network topologies according to relative protein abundances from cytometry data (Table 3)
Summary
Since the invention of the first fluorescence-based flow cytometer 50 years ago, immunologists have widely adopted the technology to get a comprehensive understanding of heterogeneity among immune cells, function, cellular differentiation, signaling pathways, and biomarker discovery [1]. Analysis of high-dimensional single-cell cytometry data relies on technological advancements and novel analytical methods that can efficiently incorporate the inherent multi-parametric characteristics of such data sets. The most straightforward and traditional, albeit labor-intensive, method for cytometry data analysis is by a process known as “gating,” which uses a series of 2D plots to identify regions of interest in a bivariate scatter plot of single cells [5]. A series of gates drawn in sequence can reveal information about cellular hierarchy and identify subsets of interest from a population. This approach has several drawbacks when compared to automated strategies (Table 2). Data analysis can be handled in one of several ways as new methods
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
