TrackSOM: Mapping immune response dynamics through clustering of time-course cytometry data

Givanna H Putri; Jonathan Chung; Davis N Edwards; Felix Marsh-Wakefield; Irena Koprinska; Suat Dervish; Nicholas J C King; Thomas M Ashhurst; Mark N Read

doi:10.1002/cyto.a.24668

TrackSOM: Mapping immune response dynamics through clustering of time-course cytometry data

Cytometry A. 2023 Jan;103(1):54-70. doi: 10.1002/cyto.a.24668. Epub 2022 Jul 19.

Authors

Givanna H Putri^{1

2}, Jonathan Chung^{3

4}, Davis N Edwards^{3

5}, Felix Marsh-Wakefield^{3

5

6

7}, Irena Koprinska³, Suat Dervish⁵, Nicholas J C King^{3

5

8

9}, Thomas M Ashhurst^{3

6

8

9}, Mark N Read^{2

3

4}

Affiliations

¹ School of Computer Science, The University of Sydney, Sydney, New South Wales, Australia.
² Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia.
³ The Westmead Initiative, The University of Sydney, Sydney, New South Wales, Australia.
⁴ Viral Immunopathology Laboratory, Discipline of Pathology, School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
⁵ School of Medical Sciences, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia.
⁶ Vascular Immunology Unit, Department of Pathology, The University of Sydney, Sydney, New South Wales, Australia.
⁷ Sydney Cytometry Core Research Facility, The University of Sydney and Centenary Institute, Sydney, New South Wales, Australia.
⁸ Sydney Institute for Infectious Diseases, The University of Sydney, Sydney, New South Wales, Australia.
⁹ Sydney Nano, The University of Sydney, Sydney, New South Wales, Australia.

PMID: 35758217
DOI: 10.1002/cyto.a.24668

Abstract

Mapping the dynamics of immune cell populations over time or disease-course is key to understanding immunopathogenesis and devising putative interventions. We present TrackSOM, a novel method for delineating cellular populations and tracking their development over a time- or disease-course cytometry datasets. We demonstrate TrackSOM-enabled elucidation of the immune response to West Nile Virus infection in mice, uncovering heterogeneous subpopulations of immune cells and relating their functional evolution to disease severity. TrackSOM is easy to use, encompasses few parameters, is quick to execute, and enables an integrative and dynamic overview of the immune system kinetics that underlie disease progression and/or resolution.

Keywords: West Nile virus; bioinformatics; cytometry; immune response dynamics; immunokinetics; immunology; sequential clustering; single-cell analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Animals
Cluster Analysis
Immunity
Mice
West Nile Fever* / pathology
West Nile virus* / physiology