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Usage Information

Integrated T cell cytometry metrics for immune-monitoring applications in immunotherapy clinical trials
Dimitrios N. Sidiropoulos, … , Elana J. Fertig, Won Jin Ho
Dimitrios N. Sidiropoulos, … , Elana J. Fertig, Won Jin Ho
Published October 10, 2022
Citation Information: JCI Insight. 2022;7(19):e160398. https://doi.org/10.1172/jci.insight.160398.
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Technical Advance Immunology Oncology

Integrated T cell cytometry metrics for immune-monitoring applications in immunotherapy clinical trials

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Abstract

Mass cytometry, or cytometry by TOF (CyTOF), provides a robust means of determining protein-level measurements of more than 40 markers simultaneously. While the functional states of immune cells occur along continuous phenotypic transitions, cytometric studies surveying cell phenotypes often rely on static metrics, such as discrete cell-type abundances, based on canonical markers and/or restrictive gating strategies. To overcome this limitation, we applied single-cell trajectory inference and nonnegative matrix factorization methods to CyTOF data to trace the dynamics of T cell states. In the setting of cancer immunotherapy, we showed that patient-specific summaries of continuous phenotypic shifts in T cells could be inferred from peripheral blood–derived CyTOF mass cytometry data. We further illustrated that transfer learning enabled these T cell continuous metrics to be used to estimate patient-specific cell states in new sample cohorts from a reference patient data set. Our work establishes the utility of continuous metrics for CyTOF analysis as tools for translational discovery.

Authors

Dimitrios N. Sidiropoulos, Genevieve L. Stein-O’Brien, Ludmila Danilova, Nicole E. Gross, Soren Charmsaz, Stephanie Xavier, James Leatherman, Hao Wang, Mark Yarchoan, Elizabeth M. Jaffee, Elana J. Fertig, Won Jin Ho

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Usage data is cumulative from October 2022 through March 2023.

Usage JCI PMC
Text version 4,599 51
PDF 696 13
Figure 760 0
Supplemental data 90 0
Citation downloads 108 0
Totals 6,253 64
Total Views 6,317

Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.

Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.

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