ImmunoCluster provides a computational framework for the non-specialist to profile high- dimensional cytometry data

TY - JOUR

T1 - ImmunoCluster provides a computational framework for the non-specialist to profile high- dimensional cytometry data

AU - Opzoomer, James W

AU - Timms, Jessica A

AU - Blighe, Kevin

AU - Mourikis, Thanos P

AU - Chapuis, Nicolas

AU - Bekoe, Richard

AU - Kareemaghay, Sedigeh

AU - Nocerino, Paola

AU - Apollonio, Benedetta

AU - Ramsay, Alan G

AU - Tavassoli, Mahvash

AU - Harrison, Claire

AU - Ciccarelli, Francesca D

AU - Parker, Peter

AU - Fontenay, Michaela

AU - Barber, Paul R

AU - Arnold, James N

AU - Kordasti, Shahram

N1 - Funding Information: We would like to thank the following funding bodies for their support: Guy?s and St Thomas Hospital Charity: grant number: G170701, Jessica A Timms, Shahram Kordasti. Cancer Research UK (CRUK), King?s Health Partners Centre: Jessica A Timms, Shahram Kordasti C604/A25135. Aplastic Anemia and MDS International Foundation (AAMDSIF): Shahram Kordasti. Blood Cancer UK: Shahram Kordasti. European Research Council (ERC): James N Arnold Start up grant 335326. Medical Research Council (MRC): James W Opzoomer MR/N013700/1. Medical Research Council (MRC): James W Opzoomer Doctoral Training Partnership in Biomedical Sciences. Rosetrees Trust (Rosetrees): Sedigeh Kareemaghay, Mahvash Tavassoli M117-F2. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. The research was supported by the Cancer Research UK King?s Health Partners Centre and Experimental Cancer Medicine Centre at King?s College London, and the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy?s and St Thomas? NHS Foundation Trust and King?s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. LifeArc 1118406 Jessica Timms Shahram Kordasti. Funding Information: We would like to thank the following funding bodies for their support: Guy’s and St Thomas Hospital Charity: grant number: G170701, Jessica A Timms, Shahram Kordasti. Cancer Research UK (CRUK), King’s Health Partners Centre : Jessica A Timms, Shahram Kordasti C604/A25135. Aplastic Anemia and MDS International Foundation (AAMDSIF): Shahram Kordasti. Blood Cancer UK: Shahram Kordasti. European Research Council (ERC): James N Arnold Start up grant 335326. Medical Research Council (MRC): James W Opzoomer MR/N013700/1. Medical Research Council (MRC): James W Opzoomer Doctoral Training Partnership in Biomedical Sciences. Rosetrees Trust (Rosetrees): Sedigeh Kareemaghay, Mahvash Tavassoli M117-F2. The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication. The research was supported by the Cancer Research UK King’s Health Partners Centre and Experimental Cancer Medicine Centre at King’s College London, and the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. Publisher Copyright: © Opzoomer et al. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/4

Y1 - 2021/4

N2 - High-dimensional cytometry is an innovative tool for immune monitoring in health and disease, and it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of large multiparametric datasets usually requires specialist computational knowledge. Here, we describe ImmunoCluster (https://github.com/ kordastilab/ImmunoCluster), an R package for immune profiling cellular heterogeneity in highdimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a nonspecialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users’ needs. The protocol consists of three core computational stages: (1) data import and quality control; (2) dimensionality reduction and unsupervised clustering; and (3) annotation and differential testing, all contained within an R-based open-source framework.

AB - High-dimensional cytometry is an innovative tool for immune monitoring in health and disease, and it has provided novel insight into the underlying biology as well as biomarkers for a variety of diseases. However, the analysis of large multiparametric datasets usually requires specialist computational knowledge. Here, we describe ImmunoCluster (https://github.com/ kordastilab/ImmunoCluster), an R package for immune profiling cellular heterogeneity in highdimensional liquid and imaging mass cytometry, and flow cytometry data, designed to facilitate computational analysis by a nonspecialist. The analysis framework implemented within ImmunoCluster is readily scalable to millions of cells and provides a variety of visualization and analytical approaches, as well as a rich array of plotting tools that can be tailored to users’ needs. The protocol consists of three core computational stages: (1) data import and quality control; (2) dimensionality reduction and unsupervised clustering; and (3) annotation and differential testing, all contained within an R-based open-source framework.

UR - http://www.scopus.com/inward/record.url?scp=85105757875&partnerID=8YFLogxK

U2 - 10.7554/eLife.62915

DO - 10.7554/eLife.62915

M3 - Article

C2 - 33929322

SN - 2050-084X

VL - 10

JO - eLife

JF - eLife

M1 - e62915

ER -