Abstract

Advances in flow and mass cytometry are enabling ultra-high resolution immune profiling in mice and humans on an unprecedented scale. However, the resulting high-content datasets challenge traditional views of cytometry data, which are both limited in scope and biased by pre-existing hypotheses. Computational solutions are now emerging (e.g., Citrus, AutoGate, SPADE) that automate cell gating or enable visualization of relative subset abundance within healthy versus diseased mice or humans. Yet these tools require significant computational fluency and fail to show quantitative relationships between discrete immune phenotypes and continuous disease variables. Here we describe a simple informatics platform that uses hierarchical clustering and nearest neighbor algorithms to associate manually gated immune phenotypes with clinical or pre-clinical disease endpoints of interest in a rapid and unbiased manner. Using this approach, we identify discrete immune profiles that correspond with either weight loss or histologic colitis in a T cell transfer model of inflammatory bowel disease (IBD), and show distinct nodes of immune dysregulation in the IBDs, Crohn’s disease and ulcerative colitis. This streamlined informatics approach for cytometry data analysis leverages publicly available software, can be applied to manually or computationally gated cytometry data, is suitable for any clinical or pre-clinical setting, and embraces ultra-high content flow and mass cytometry as a discovery engine.

Highlights

  • In the current era of high content flow cytometry (FACS), and even higher content mass cytometry (CyTOF), large-scale immune profiling in mice and human patients is becoming commonplace [1,2,3]

  • Distinct immune profiles correspond to T cell transfer-induced weight loss and colitis

  • Leukocytes from spleen, mesenteric lymph nodes (MLN), and colon lamina propria were analyzed by ex vivo surface (Fig 1C) or intracellular (Fig 1D) FACS to assess the phenotypes and absolute numbers of immune cell subsets, including: CD25hiFoxp3+ T regulatory (Treg) cells, CD25loFoxp3- T conventional (Tconv) cells, CD62LloCD44hi effector/memory T cells (Teff cells), and CD62LhiCD44lo naïve T cells (Tnaive)

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Summary

Introduction

In the current era of high content flow cytometry (FACS), and even higher content mass cytometry (CyTOF), large-scale immune profiling in mice and human patients is becoming commonplace [1,2,3]. The value of using ultra-high content cytometry as a discovery tool is undermined by traditional views of FACS data, and current methods of FACS analysis. Even a single 10-parameter FACS experiment performed on human PBMC or mouse splenocytes can distinguish–assuming simple bi-modal distribution–up to 1,024 (210) distinct immune phenotypes. Informatics-Based FACS Analysis to analyze hundreds of individual immune phenotypes “one-by-one”. Cytometry data is routinely distilled down to focus on small numbers of well-characterized immune cell subsets that fit a hypothesis; this in turn generates bias, and disregards a large amount of potentially valuable information

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