Go to JCI Insight
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Alerts
  • Advertising/recruitment
  • Subscribe
  • Contact
  • Current Issue
  • Past Issues
  • By specialty
    • COVID-19
    • Cardiology
    • Gastroenterology
    • Immunology
    • Metabolism
    • Nephrology
    • Neuroscience
    • Oncology
    • Pulmonology
    • Vascular biology
    • All ...
  • Videos
    • Conversations with Giants in Medicine
    • Author's Takes
  • Reviews
    • View all reviews ...
    • 100th Anniversary of Insulin's Discovery (Jan 2021)
    • Hypoxia-inducible factors in disease pathophysiology and therapeutics (Oct 2020)
    • Latency in Infectious Disease (Jul 2020)
    • Immunotherapy in Hematological Cancers (Apr 2020)
    • Big Data's Future in Medicine (Feb 2020)
    • Mechanisms Underlying the Metabolic Syndrome (Oct 2019)
    • Reparative Immunology (Jul 2019)
    • View all review series ...
  • Viewpoint
  • Collections
    • Recently published
    • In-Press Preview
    • Commentaries
    • Concise Communication
    • Editorials
    • Viewpoint
    • Top read articles
  • Clinical Medicine
  • JCI This Month
    • Current issue
    • Past issues

  • Current issue
  • Past issues
  • Specialties
  • Reviews
  • Review series
  • Conversations with Giants in Medicine
  • Author's Takes
  • Recently published
  • In-Press Preview
  • Commentaries
  • Concise Communication
  • Editorials
  • Viewpoint
  • Top read articles
  • About
  • Editors
  • Consulting Editors
  • For authors
  • Alerts
  • Advertising/recruitment
  • Subscribe
  • Contact
Autoreactive CD8+ T cell exhaustion distinguishes subjects with slow type 1 diabetes progression
Alice E. Wiedeman, … , Peter S. Linsley, S. Alice Long
Alice E. Wiedeman, … , Peter S. Linsley, S. Alice Long
Published December 9, 2019
Citation Information: J Clin Invest. 2020;130(1):480-490. https://doi.org/10.1172/JCI126595.
View: Text | PDF
Research Article Autoimmunity Immunology

Autoreactive CD8+ T cell exhaustion distinguishes subjects with slow type 1 diabetes progression

  • Text
  • PDF
Abstract

Although most patients with type 1 diabetes (T1D) retain some functional insulin-producing islet β cells at the time of diagnosis, the rate of further β cell loss varies across individuals. It is not clear what drives this differential progression rate. CD8+ T cells have been implicated in the autoimmune destruction of β cells. Here, we addressed whether the phenotype and function of autoreactive CD8+ T cells influence disease progression. We identified islet-specific CD8+ T cells using high-content, single-cell mass cytometry in combination with peptide-loaded MHC tetramer staining. We applied a new analytical method, DISCOV-R, to characterize these rare subsets. Autoreactive T cells were phenotypically heterogeneous, and their phenotype differed by rate of disease progression. Activated islet-specific CD8+ memory T cells were prevalent in subjects with T1D who experienced rapid loss of C-peptide; in contrast, slow disease progression was associated with an exhaustion-like profile, with expression of multiple inhibitory receptors, limited cytokine production, and reduced proliferative capacity. This relationship between properties of autoreactive CD8+ T cells and the rate of T1D disease progression after onset make these phenotypes attractive putative biomarkers of disease trajectory and treatment response and reveal potential targets for therapeutic intervention.

Authors

Alice E. Wiedeman, Virginia S. Muir, Mario G. Rosasco, Hannah A. DeBerg, Scott Presnell, Bertrand Haas, Matthew J. Dufort, Cate Speake, Carla J. Greenbaum, Elisavet Serti, Gerald T. Nepom, Gabriele Blahnik, Anna M. Kus, Eddie A. James, Peter S. Linsley, S. Alice Long

×

Figure 1

Islet-specific CD8+ T cells are dominated by three CXCR3+ memory phenotypes across subjects with T1D.

Options: View larger image (or click on image) Download as PowerPoint
Islet-specific CD8+ T cells are dominated by three CXCR3+ memory phenoty...
The DISCOV-R analysis method was applied to total CD8+ and islet-specific T cells from subjects with T1D (n = 46); the T cells had been assayed with the Tmr-CyTOF panel. (A) Schematic of the DISCOV-R method (see Methods and Supplemental Figure 3 for details) for 1 individual. (B and C) Distribution of islet-specific cells across the 12 aligned clusters for subjects with at least 5 Tmr+ cells (n = 39). (B) Data are displayed as a stacked bar graph for each subject, colored by cluster. The 3 clusters that are most dominant among islet-specific cells across subjects (clusters 1, 11, and 12) have heavy outlining and are stacked at the bottom. (C) Clusters containing more than 20% islet-specific cells for an individual are indicated in black. Arrows indicate clusters predominant in at least 25% of the samples; the detached bottom row indicates the mean frequency of cells within a cluster for all individuals on a scale from 0% (white) to 20% or higher (black). (D) Heatmap of Z scores using arcsinh-transformed expression of 22 consistent markers (rows) for all individual clusters (columns) from all T1D subjects (n = 46), grouped into 12 aligned clusters (annotated with numbers and colors). Negative Z scores (aqua) represent underexpression, and positive Z scores (yellow) represent overexpression of markers in an individual cluster compared with the mean of expression intensity on total CD8+ T cells within a subject. Frequency of islet-specific (Tmr+) cells within an individual cluster is annotated above (white = 0%, black = 20%+). (E) Heatmap of the mean absolute arcsinh-transformed expression of 24 markers for the 3 islet-specific clusters and total CD8+ T cells. Expression intensity ranges from 0 (dark purple) to 4+ (yellow).
Follow JCI:
Copyright © 2021 American Society for Clinical Investigation
ISSN: 0021-9738 (print), 1558-8238 (online)

Sign up for email alerts