T cell responses in diabetes & allergies

T cell responses in diabetes & allergies

Type 1 diabetes & Allergies

At first glance, allergy and type 1 diabetes (T1D) seem like quite distinct pathologies. However, they do share several features: both develop as a complex mixture of genetic predisposition and environmental triggers, the prevalence of both is on a steep rise worldwide, and importantly, for both, pathogenesis implicates aberrant overactive T cell responses. In particular, in these two settings, CD4+ helper T cells are acknowledged as crucial players that orchestrate deleterious immune responses.

The role of T Cells in allergic responses

In allergy, CD4+ T cells responding to innocuous foreign antigens promote IgE isotype switching from antibody-producing B cells and participate to the recruitment of mast cells and eosinophils that drive anaphylactic responses (Chan and Leung, 2018; Romagnani, 2000; Wambre et al., 2012).

The role of T Cells in Type 1 Diabetes

In T1D, autoreactive CD4+ T cells responding to pancreatic self-antigens promote the activation of autoreactive B cells, leading to the production of autoantibodies during the pre-symptomatic phase of the disease, and of autoreactive CD8+ T cell responses that mediate the destruction of insulin-producing beta cells in the pancreas, eventually resulting in overt T1D where patients rely on exogenous insulin to maintain glucose homeostasis (Ahmed et al., 2019; Gomez-Tourino et al., 2016; Jacobsen et al., 2018b, 2018a; Mathieu et al., 2018).

Characterizing CD4+ T Cells

CD4+ T cells come in all kind of flavours: upon interaction with antigen-presenting cells, naïve CD4+ T cells can differentiate into a wide variety of subsets, depending on the cytokinic cues received (Appay Victor et al., 2008; Liudahl and Coussens, 2018). Each subset can be characterised by the expression of a defined set of transcription factors, phenotypic markers and cytokines. In addition, some subsets are already known for being dysregulated in various pathologies.

Characterising CD4+ T cell subsets at the polyclonal level is now fairly easy with conventional flow cytometry or gene expression assays. However, it might have less relevance to resolve disease-associated immune status. What we want to look at instead, are antigen-specific CD4+ T cells.

Identification of T Cells by Flow Cytometry, Mass Spectrometry & Transcriptomics

The expression “searching for a needle in a haystack” is often used to illustrate the challenges associated with the detection of antigen-specific CD4+ T cells: they exist at such low frequencies ex vivo (e.g: typically 1-100 cells per million peripheral CD4+ T cells) that it is a technical pickle to just be able to detect this handful of cells of interest among the millions of other ones.

Fortunately, in recent years, various technological breakthroughs in flow cytometry, mass cytometry and transcriptomics approaches have made it technically, computationally, financially and humanly possible to analyse rare subsets of antigen-specific T cells at the single-cell level (Ahmed et al., 2019; Mair et al., 2019; Sharma et al., 2018; Simoni et al., 2018; Stoeckius et al., 2017; Trzupek et al., 2019).

In the projects that I am conducting as a post-doctoral fellow at the University of Eastern Finland, we exploit these new technologies to characterize antigen-specific CD4+ T cell subsets involved in the development of allergy and T1D. More specifically, we investigate their phenotype, functionality and clonality at the single-cell level, in the hope of discovering new biomarkers for disease progression and/or new therapeutic targets.

Detecting T Cells in peripheral blood

To detect antigen-specific CD4+ T cells in peripheral blood, we rely on the CD154 enrichment assay (Bacher et al., 2013). This method is based on the upregulation of CD154 (= CD40-L) at the surface of antigen-activated CD4+ T cells shortly after TCR stimulation, and allows cells of interest to be magnetically enriched and then detected by flow cytometry, from where we can assess their phenotype and single-cell sort them for downstream transcriptomic or in vitro assays.

For the transcriptomic part of our workflow, we implement an elegant multiplexed PCR-based method developed by Han et al. to access concomitantly the expression of a defined set of transcription factors and cytokines, as well as TCR sequences (Han et al., 2014).

Importantly, during our flow cytometric single-cell sort we use an index data feature which records the coordinates of the well that each cell is sorted into (Penter et al.). As we use well-specific barcodes during the preparation of our sequencing library, we can then link the flow cytometry data and the transcriptomic data together at the single-cell level.

Distinguishing between Th17 & Th2 cells in healthy & allergic patients  

Our assessment of allergen-specific CD4+ T cells in dog-allergic patients revealed that the total frequencies of these cells is not different between patients and healthy donors (on average, 100 cells per million memory CD4+ T cells). However, multiple correspondence analysis on the integrated flow cytometric and transcriptomic data revealed that allergen-specific CD4+ T cells from healthy donors and dog-allergic patients do not cluster together, and are in fact clearly distinguishable based on their phenotype. Indeed, while allergen-specific CD4+ T cells from healthy donors display predominantly a Th17 polarization (with the expression of RORC, CCR6, IL17 and TNFα), dog-allergic patients’ are enriched in Th2 cells (with the expression of GATA3, CRTh2, IL4, IL9 and IL13). More particularly, we observed that allergen-specific CD4+ T cells from dog-allergic patients possessed a subset of CRTh2+ CD161+ cells that is completely absent in healthy donors. This subset, termed Th2A cells, was first identified by (Wambre et al., 2017) in a variety of atopic conditions and was shown to be specifically decreased in response to desensitization treatment. The Th2A subset therefore constitutes a putative therapeutic target and further assessment of its functionality and clonality could pave the way to refine immune interventions in allergic patients.

Autoreactive T cells

Concerning our assessment of autoreactive CD4+ T cells during the progression of human T1D, we observed that their overall frequency was increased in autoantibody-positive (AAb+) pre-diabetic children, but not in recent-onset diabetic children, compared to age-matched and HLA-matched healthy controls. The phenotypic assessment of autoreactive CD4+ T cells interestingly revealed an enrichment of CXCR5+ follicular T helper (Tfh) cells in AAb+ children, and particularly cells with a CXCR3+ CCR6- Th1 phenotype. Tfh cells have been put forward as a promising biomarker for T1D progression (Ahmed et al., 2019; Viisanen et al., 2017), however little is known about their epitope specificity or functional heterogeneity at the single-cell level (Gensous et al., 2018; Song and Craft, 2019). Our experimental approach might help to shed light on the involvement of these cells in the pathogenesis of human T1D and provide more insight on their potential as immunotherapeutic targets.

Future work for T Cell analysis

The two projects presented above are very much ongoing and a lot of work is still needed to complete them. Nevertheless, they illustrate how the differences between patients and healthy donors are sometimes not visible when looking at the bigger picture, but only when scrutinizing the tiny details instead.

As a side note, immunologists are just starting to appreciate how powerful and exciting the new technologies built around flow/mass cytometry and multi-omics workflows can be. We are now in an era where we can assess hundreds of different parameters at a time on a single cell and identify minute subsets of immune cells linked to disease states in autoimmunity, allergy, cancer, infectious diseases... We can only hope that the tremendous efforts implemented worldwide by the research and clinical communities will accelerate the development of new immunotherapeutic products and improve the clinical welfare of patients.

References

Ahmed, S., Cerosaletti, K., James, E., Long, S.A., Mannering, S., Speake, C., Nakayama, M., Tree, T., Roep, B.O., Herold, K.C., et al. (2019). Standardizing T-Cell Biomarkers in Type 1 Diabetes: Challenges and Recent Advances. Diabetes 68, 1366–1379.

Appay Victor, van Lier Rene A. W., Sallusto Federica, and Roederer Mario (2008). Phenotype and function of human T lymphocyte subsets: Consensus and issues. Cytometry A 73A, 975–983.

Bacher, P., Schink, C., Teutschbein, J., Kniemeyer, O., Assenmacher, M., Brakhage, A.A., and Scheffold, A. (2013). Antigen-reactive T cell enrichment for direct, high-resolution analysis of the human naive and memory Th cell repertoire. J. Immunol. Baltim. Md 1950 190, 3967–3976.

Chan, S.K., and Leung, D.Y.M. (2018). Dog and Cat Allergies: Current State of Diagnostic Approaches and Challenges. Allergy Asthma Immunol. Res. 10, 97–105.

Gensous, N., Charrier, M., Duluc, D., Contin-Bordes, C., Truchetet, M.-E., Lazaro, E., Duffau, P., Blanco, P., and Richez, C. (2018). T Follicular Helper Cells in Autoimmune Disorders. Front. Immunol. 9.

Gomez-Tourino, I., Arif, S., Eichmann, M., and Peakman, M. (2016). T cells in type 1 diabetes: Instructors, regulators and effectors: A comprehensive review. J. Autoimmun. 66, 7–16.

Han, A., Glanville, J., Hansmann, L., and Davis, M.M. (2014). Linking T-cell receptor sequence to functional phenotype at the single-cell level. Nat. Biotechnol. 32, 684–692.

Jacobsen, L.M., Haller, M.J., and Schatz, D.A. (2018a). Understanding Pre-Type 1 Diabetes: The Key to Prevention. Front. Endocrinol. 9.

Jacobsen, L.M., Newby, B.N., Perry, D.J., Posgai, A.L., Haller, M.J., and Brusko, T.M. (2018b). Immune Mechanisms and Pathways Targeted in Type 1 Diabetes. Curr. Diab. Rep. 18, 90.

Liudahl, S.M., and Coussens, L.M. (2018). Chapter 8 - To Help or To Harm: Dynamic Roles of CD4+ T Helper Cells in Solid Tumor Microenvironments. In Immunology, M.A. Hayat, ed. (Academic Press), pp. 97–116.

Mair, F., Erickson, J.R., Voillet, V., Simoni, Y., Bi, T., Tyznik, A.J., Martin, J., Gottardo, R., Newell, E.W., and Prlic, M. (2019). A targeted multi-omic analysis approach measures protein expression and low abundance transcripts on the single cell level. BioRxiv 700534.

Mathieu, C., Lahesmaa, R., Bonifacio, E., Achenbach, P., and Tree, T. (2018). Immunological biomarkers for the development and progression of type 1 diabetes. Diabetologia.

Penter, L., Dietze, K., Bullinger, L., Westermann, J., Rahn, H.-P., and Hansmann, L. FACS single cell index sorting is highly reliable and determines immune phenotypes of clonally expanded T cells. Eur. J. Immunol. 0.

Romagnani, S. (2000). The role of lymphocytes in allergic disease. J. Allergy Clin. Immunol. 105, 399–408.

Sharma, S., Gioia, L., Abe, B., Holt, M., Costanzo, A., Kain, L., Su, A., and Teyton, L. (2018). Using single cell analysis for translational studies in immune mediated diseases: Opportunities and challenges. Mol. Immunol. 103, 191–199.

Simoni, Y., Chng, M.H.Y., Li, S., Fehlings, M., and Newell, E.W. (2018). Mass cytometry: a powerful tool for dissecting the immune landscape. Curr. Opin. Immunol. 51, 187–196.

Song, W., and Craft, J. (2019). T follicular helper cell heterogeneity: Time, space, and function. Immunol. Rev. 288, 85–96.

Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P.K., Swerdlow, H., Satija, R., and Smibert, P. (2017). Large-scale simultaneous measurement of epitopes and transcriptomes in single cells. Nat. Methods 14, 865–868.

Trzupek, D., Dunstan, M., Cutler, A.J., Lee, M., Godfrey, L., Aschenbrenner, D., Uhlig, H.H., Wicker, L.S., Todd, J.A., and Ferreira, R.C. (2019). Simultaneous mRNA and protein quantification at the single-cell level delineates trajectories of CD4 + T-cell differentiation (Immunology).

Viisanen, T., Ihantola, E.-L., Näntö-Salonen, K., Hyöty, H., Nurminen, N., Selvenius, J., Juutilainen, A., Moilanen, L., Pihlajamäki, J., Veijola, R., et al. (2017). Circulating CXCR5+PD-1+ICOS+ Follicular T Helper Cells Are Increased Close to the Diagnosis of Type 1 Diabetes in Children With Multiple Autoantibodies. Diabetes 66, 437–447.

Wambre, E., James, E.A., and Kwok, W.W. (2012). Characterization of CD4+ T cell subsets in allergy. Curr. Opin. Immunol. 24, 700–706.

Wambre, E., Bajzik, V., DeLong, J.H., O’Brien, K., Nguyen, Q.-A., Speake, C., Gersuk, V.H., DeBerg, H.A., Whalen, E., Ni, C., et al. (2017). A phenotypically and functionally distinct human TH2 cell subpopulation is associated with allergic disorders. Sci. Transl. Med. 9, eaam9171.

8th Jul 2020 Celine Vandamme PhD

Recent Posts