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A mass cytometry method pairing T cell receptor and differentiation state analysis

Nature Immunology volume 25, pages 1754–1763 (2024)Cite this article

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Abstract

T cell antigen receptor (TCR) recognition followed by clonal expansion is a fundamental feature of adaptive immune responses. Here, we present a mass cytometric (CyTOF) approach to track T cell responses by combining antibodies for specific TCR Vα and Vβ chains with antibodies against T cell activation and differentiation proteins in mice. This strategy identifies expansions of CD8+ and CD4+ T cells expressing specific Vβ and Vα chains with varying differentiation states in response to Listeria monocytogenes, tumors and respiratory influenza infection. Expanded T cell populations expressing Vβ chains could be directly linked to the recognition of specific antigens from Listeria, tumor cells or influenza. In the setting of influenza infection, we found that common therapeutic approaches of intramuscular vaccination or convalescent serum transfer altered the TCR diversity and differentiation state of responding T cells. Thus, we present a method to monitor broad changes in TCR use paired with T cell phenotyping during adaptive immune responses.

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Fig. 1: Tracking the phenotype of T cell responses against attenuated Listeria with CyTOF.
Fig. 2: OVA-expanded and LADD-expanded CD8+ and CD4+ T cells detect LADD-encoded antigens.
Fig. 3: An activated Vβ14+ CD8+ T cell population expands in an OVA-expressing tumor model.
Fig. 4: Tracking the expansion and recognition of CD8+ T cells specific for influenza antigens.
Fig. 5: Vβ8.3+CD8+ T cells have different phenotypes and frequencies in response to influenza immunization.
Fig. 6: Early convalescent therapy restricts the CD8+ T cell repertoire during influenza infection.

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Data availability

Preprocessed and debarcoded CyTOF data of gated populations used in this study have been uploaded to Mendeley Data under https://doi.org/10.17632/dpm5rm7cxj.1. Variable chain usage information derived from TCR-seq data used in this study have been uploaded to Mendeley Data under https://doi.org/10.17632/dpm5rm7cxj.1.

Code availability

Automated Vα-Vβ assignments script is available at GitHub via https://github.com/SpitzerLab/Semi-supervised-Clonotyping-Assignments/.

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Acknowledgements

We acknowledge the UCSF Parnassus Flow Cytometry CoLab (RRID: SCR_018206) for assisting in generating mass cytometry data and the UC Berkeley Cancer Research Laboratory Flow Cytometry Facility. We also thank D. Portnoy (University of California, Berkeley) for providing attenuated Listeria strains for this study. Research reported here was supported in part by grants from the NIH 1DP2CA247830-01, P30 DK063720, DP5 OD023056, R01 DE032033, DoD award BC220499, the Parker Institute for Cancer Immunotherapy, and the NIH S10 Instrumentation Grant S10 1S10OD018040-01. J.G.C. is an HHMI Gilliam Fellow. M.D. is a Pew-Stewart Scholar and a St. Baldrick’s Scholar with generous support from Hope with Hazel.

Author information

Author notes

  1. These authors contributed equally: Jesse Garcia Castillo, Rachel DeBarge.

Authors and Affiliations

  1. Division of Immunology and Molecular Medicine, Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA

    Jesse Garcia Castillo, Abigail Mende & Michel DuPage

  2. Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco, San Francisco, CA, USA

    Rachel DeBarge, Iliana Tenvooren, Diana M. Marquez & Matthew H. Spitzer

  3. Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA

    Rachel DeBarge, Iliana Tenvooren, Diana M. Marquez & Matthew H. Spitzer

  4. Institute for Medical Microbiology, Immunology and Hygiene, Technische Universität München (TUM), Munich, Germany

    Adrian Straub

  5. Institute for Medical Microbiology, Immunology and Hygiene, Technische Universität München (TUM), Munich, Germany; Partner site Munich, German Center for Infection Research (DZIF), Munich, Germany

    Dirk H. Busch

  6. Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA

    Matthew H. Spitzer

  7. Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA

    Matthew H. Spitzer

  8. Chan Zuckerberg Biohub San Francisco, San Francisco, CA, USA

    Matthew H. Spitzer

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Contributions

J.G.C., R.D., M.H.S. and M.D. conceived, designed and directed the study. J.G.C., R.D., A.M., I.T. and D.M.M. performed experiments and generated data. R.D., A.S. and M.H.S generated computational methodology for single-cell data analysis. Writing, review and editing of primary manuscript were done by J.G.C., R.D., A.S., D.H.B., M.H.S. and M.D. Funding acquisition for study was done by M.H.S. and M.D.

Corresponding authors

Correspondence to Matthew H. Spitzer or Michel DuPage.

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Competing interests

R.D. is a shareholder in BioNTech. M.H.S. is founder and shareholder of Teiko.bio and Prox Biosciences, has received a speaking honorarium from Fluidigm, Arsenal and Kumquat, has been a paid consultant for Five Prime, Ono, January, Earli, Astellas and Indaptus, and has received research funding from Roche/Genentech, Pfizer, Valitor and Bristol Myers Squibb. The other authors declare no competing interests.

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Nature Immunology thanks Sean Bendall, Alexandre Harari and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Nick Bernard, in collaboration with the Nature Immunology team.

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Extended data

Extended Data Fig. 1 Tracking a Listeria-specific CD8+ T cell response against LADD by TCR use and phenotyping with CyTOF.

(a) Gating strategy for all CyTOF analyses preceding input into semi-supervised Vα-Vβ assignment script. (b) Representative flow plots and quantification of roliferating CD8+ T cells from spleens 5 days after LADD, LADD-OVA, or PBS injection IV. P values (from top to bottom) 0.0286, 0.0159. (c) Bar plot of Vα2, Vβ14, and Vα2_Vβ14 percentages in each cluster for conditions LAAD and LAAD-OVA. (d) UMAP visualization of pooled CD8+ T cells colored by the expression of Vβ12+ in LADD, LADD-OVA, or PBS infected mice. Results from n=5 for PBS, n=4 for LADD, n=4 for LADD-OVA, P values were calculated by unpaired two-tailed student’s T-test, mean ± s.e.m.

Extended Data Fig. 2 Tracking Listeria-specific CD4+ T cells against LADD by TCR use and phenotyping with CyTOF.

(a) Representative flow plots and quantification of proliferating (Ki-67+) CD4+ T cells from spleen 5 days after LADD, LADD-OVA, or PBS injection. P values (from top to bottom) 0.0159, 0.0286. (b) UMAP visualization of CD4+ T conventional (non-Treg) cell clusters based on expression of non-TCR proteins. (c) Heatmap of non-TCR protein expression annotated by cluster and fraction of cells falling into each cluster. (d) UMAP visualization of CD4+ T cells colored by the expression Ki-67 in LADD-infected mice. (e) Frequency of Ki-67+ and Ki-67- CD4+ T cells using specific TCR Vβ (left) or Vα chains (right) in response to PBS, LADD, or LADD-OVA. (f-h) Frequency of Vβ13+ (f), Vβ14+ (g), or Vβ13+Vα2+ among Ki-67+(h) versus Ki-67- CD4+ T cells in LADD or LADD-OVA infected mice. P values (from left to right) 0.0286, 0.0079, 0.0286, 0.0159, 0.0159, 0.0286, 0.0079, 0.0159, 0.0286. (i-k) UMAP visualization of pooled CD4+ T cells colored by the expression of Vβ13+ (i), Vβ13+Vα2+ (j), and Vβ14 (k) in LADD, LADD-OVA, or PBS injected mice. (m) Bar plot of Vβ13+, Vβ13+Vα2+, and Vβ14+ percentages in Teff clusters. Results from n=5 for PBS, n=4 for LADD, n=4 for LADD-OVA, P values were calculated by unpaired two-tailed Student’s t-test, mean ± s.e.m.

Extended Data Fig. 3 Comparative analysis of Vβ and Vα frequencies and Vβ14+Vα2+ CD8 expansion captured by CyTOF versus TCR sequencing during LADD-OVA infection.

(a) Schematic of experimental approach to compare CyTOF to mRNA-based TCR-seq (iRepertoire) from spleens of LADD-OVA-infected mice. (b) Two-tailed spearman correlation analysis of Vβ and Vα frequencies in CD8+ T cells (left) and CD4+ T cells (right). (c-d) Comparative quantification of the TRBV/Vβ and TRAV/Vα chains captured by CyTOF of all CD8+ T (c) and CD4+ T cells (d). Note: Anti-Vα3.2 RR3- 16 clone binds specifically to a Vα3.2 allele preferentially expressed in CD8+ T cells over CD4+ T cells59. (e) Quantification of the TRBV/Vβ chain (top) and TRAV/Vα chain (bottom) frequencies in SIINFEKL-loaded H-2Kbtetramer+ CD8+ T cells sorted from spleens. (f) Fold change calculation of TRBV/Vβ chain and TRAV/Vα chain frequencies of SIINFEKL-loaded H-2Kb tetramer+ CD8+ T cells over frequencies of bulk CD8+ T cells. (g) Schematic comparison of approach used in this study compared to Straub et al.37. (h) Re-annotated clusters identified by Straub et al. plotted in UMAP space. (i) Scaled gene expression within the scRNA-seq dataset of genes encoding the proteins measured in Fig. 1, stratified by cluster. (j) UMAP visualization of all clustered cells colored by Ki67 or PD-1 gene expression. (k) UMAP visualization of all clustered cells colored by TRBV31 assignment or TRAV14D-2 assignment (left), and UMAP visualization of TRBV31 assigned cells colored by TRAV14D-2 assignment (right). (l) UMAP visualization of all clustered cells colored by identified specificity. (m) Quantification of TRBV gene usage among SIINFEKL-reactive cells (right) or of cells of unidentified reactivity (left). (n) Quantification of TRBV gene usage among SIINFEKLreactive cells expressing TRAV14D-2. (o) Quantification of TRBV/Vβ chain assignment of all CD8 T cells as measured by CyTOF or scRNA-seq. (p) Quantification of the TRBV/Vβ chains measured in the CyTOF assay of all CD8 T cells that were assigned a TRBV/Vβ chain by either CyTOF or scRNA-seq. (q) Two-tailed spearman correlation analysis of data shown in (j). Results from n=3 LADD-OVA infected mice for bulk TCRseq (a-f), data are presented as mean values ± s.e.m.

Extended Data Fig. 4 Expansion and activation of T cell subsets during influenza infection across multiple tissues.

(a) Gating strategy for all CyTOF analyses preceding input into semi-supervised Vα-Vβ assignment script. (b) Representative flow plots and quantification of proliferating (PD-1+Ki67+) CD8+ T cells. P values (from top to bottom) <0.0001, <0.0001, 0.0008. P values (from top to bottom) <0.0001, <0.0001, 0.0008. (c) Representative flow plots and quantification of proliferating (Ki67+) Foxp3- CD4+ Teff cells. P values (from top to bottom) <0.0001, <0.0001, 0.0008. P values (from top to bottom) <0.0001, <0.0001, 0.0004. (d) Representative flow plots and quantification of proliferating (Ki67+) Foxp3+ Tregs. P values (from top to bottom) <0.0001, <0.0001, 0.0008. P values (from top to bottom) <0.0001. Results from n=7 for PBS, n=7 for PR8, P values were calculated by unpaired two-tailed Student’s t-test, mean ± s.e.m.

Extended Data Fig. 5 Identification of influenza NP-specific Vβ14+ CD4+ T cells and Vβ9+ eTregs after influenza infection.

(a) UMAP visualization of CD4+ T conventional (non- Treg) cell clusters based on expression of non-TCR proteins. (b) Heatmap of non-TCR protein expression annotated by cluster and fraction of cells falling into each cluster. (c) UMAP visualization of CD4+ T cells colored by the expression Ki-67 in influenza-infected mice. (d) Frequencies of Vβ14+ CD4 T cells in non-proliferating (Ki-67-) vs. proliferating (Ki-67+) cells across indicated tissues. P values (from left to right) 0.0003, 0.0002, 0.0002. (e) UMAP visualization of pooled CD4+ T cells colored by Vβ14 expression. (f) Quantification of fold-enrichment in Vβ-chain usage by CD4+ T cells stimulated with the PR8 NP peptide (LILRGSVAHKSCLPACV). P value is <0.0001. (g) Schematic summarizing that Vβ14+CD4+ T cells are enriched to recognize PR8 NP antigen from influenza. (h) UMAP of Treg populations based on expression of non-TCR proteins. (i) Heatmap of non-TCR protein expression annotated by cluster and fraction of cells falling into each cluster. (j) UMAP visualization of Tregs colored by the expression Ki-67 in influenza-infected mice. (k) Frequencies of Vβ9+ Tregs in non-proliferating (Ki-67-) vs. proliferating (Ki-67+) cells across indicated tissues. P values (from left to right) 0.0207, 0.0002. (l) UMAP visualization of pooled Tregs from PBS or influenza-infected mice colored by Vβ9 expression. Results from n=7 for PBS, n=7 for PR8, P values were calculated by unpaired two-tailed Student’s t-test (d,k) or two-way ANOVA (f), mean ± s.e.m.

Extended Data Fig. 6 Influenza-specific conventional CD4+ T cell differentiation and Vβ9+ Treg expansion is altered by vaccination.

(a) UMAP visualization of CD4+ T conventional (non-Treg) cell clusters based on expression of non-TCR proteins. (b) Heatmap of non-TCR protein expression annotated by cluster and fraction of cells falling into each cluster. (c) UMAP visualization of CD4+ T cells colored by the expression Ki-67 from all cohorts of mice. (d) Frequencies of Vβ14+ CD4 T cells in nonproliferating (Ki-67-) vs. proliferating (Ki-67+) cells in lungs of primary, vaccinated, or rechallenged mice. P values (from left to right) 0.0387, 0.0037, 0.0145. (e) UMAP visualization of pooled CD4+ T cells from primary or rechallenged mice colored by Vβ14 expression. (f) UMAP of Treg populations based on the expression of non-TCR proteins. (g) Heatmap of non-TCR protein expression annotated by cluster and fraction of cells falling into each cluster. (h) UMAP visualization of Tregs colored by the expression Ki-67 from all cohorts of mice. (i) Frequencies of Vβ9+ Tregs in nonproliferating (Ki-67-) vs. proliferating (Ki-67+) cells in lungs of primary, vaccinated, or rechallenged mice. P values (from left to right) <0.0001, <0.0001, <0.0001. (j) UMAP visualization of pooled Tregs from primary or rechallenged mice colored by Vβ9 expression. Results from n=7 for PBS, n=7 for PR8, P values were calculated by oneway ANOVA, mean ± s.e.m.

Extended Data Fig. 7 Convalescent therapy has a limited effect on the CD4+ Teff and Treg repertoire during influenza infection.

(a) UMAP visualization of CD4+ T conventional (non-Treg) cell clusters based on expression of non-TCR proteins. (b) Heatmap of non-TCR protein expression annotated by cluster and fraction of cells falling into each cluster. (c) UMAP visualization of CD4+ T cells colored by the expression Ki-67 from all cohorts of mice. (d) Frequencies of Vβ14+ and Vβ10b+ CD4 T cells in non-proliferating (Ki-67-) vs. proliferating (Ki-67+) cells from the lungs of influenza infected mice treated with naive or convalescent serum at specified times after infection. (e) UMAP visualization of pooled CD4+ T cells from early vs. late convalescent serum treated mice colored by Vβ14 expression. (f) UMAP of Tregs based on the expression of non-TCR proteins. (g) Heatmap of non-TCR protein expression annotated by cluster and fraction of cells falling into each cluster. (h) UMAP visualization of Tregs colored by the expression Ki-67 from all cohorts of mice. (i) Frequencies of Vβ9+ Tregs in nonproliferating (Ki-67-) vs. proliferating (Ki-67+) cells from the lungs of infected mice treated with naive or convalescent serum at specified times after infection. P values (from top to bottom) 0.0299, 0.0087. (j) UMAP visualization of pooled Tregs from early vs. late convalescent serum treated mice colored by Vβ9 expression. Results from n=7 for PBS, n=14 for naïve serum-treated infected mice, n=7 for convalescent serum treated infected mice at 4 hours, n=7 for convalescent serum-treated infected mice at 4 days, P values were calculated by one-way ANOVA, mean ± s.e.m.

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Garcia Castillo, J., DeBarge, R., Mende, A. et al. A mass cytometry method pairing T cell receptor and differentiation state analysis. Nat Immunol 25, 1754–1763 (2024). https://doi.org/10.1038/s41590-024-01937-3

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