Mass cytometry has revolutionized the study of cellular and phenotypic diversity, significantly expanding the number of phenotypic and functional characteristics that can be measured at the single-cell level. This high-dimensional analysis platform has necessitated the development of new data analysis approaches. Many of these algorithms circumvent traditional approaches used in flow cytometric analysis, fundamentally changing the way these data are analyzed and interpreted. For the beginner, however, the large number of algorithms that have been developed, as well as the lack of consensus on best practices for analyzing these data, raise multiple questions: Which algorithm is the best for analyzing a dataset? How do different algorithms compare? How can one move beyond data visualization to gain new biological insights? In this article, we describe our experiences as recent adopters of mass cytometry. By analyzing a single dataset using five cytometry by time-of-flight analysis platforms (viSNE, SPADE, X-shift, PhenoGraph, and Citrus), we identify important considerations and challenges that users should be aware of when using these different methods and common and unique insights that can be revealed by these different methods. By providing annotated workflow and figures, these analyses present a practical guide for investigators analyzing high-dimensional datasets. In total, these analyses emphasize the benefits of integrating multiple cytometry by time-of-flight analysis algorithms to gain complementary insights into these high-dimensional datasets.
Abbreviations used in this article:
- cytometry by time-of-flight
- gammaherpesvirus 68
- IL-10 knockout
- Lewis lung carcinoma
- t-distributed stochastic neighbor embedding.
This work was supported by National Institutes of Health Grants R01 AI121300 and R01 CA168558 (to L.F.v.D.) and R01 CA162226 and P50 CA058187 (to R.A.N.), an American Heart Association National Scientist Development grant, the Crohn’s and Colitis Foundation of America, and a Career Enhancement Award from the University of Colorado Lung Cancer Specialized Program of Research Excellence (all to E.T.C.). The Flow Cytometry Shared Resource receives direct funding support from the National Cancer Institute through Cancer Center Support Grant P30CA046934.
The online version of this article contains supplemental material.
- Received October 30, 2017.
- Accepted October 30, 2017.
- Copyright © 2017 by The American Association of Immunologists, Inc.