Tracking antigen-specific T cell responses in patients with myocardial infarction

Tracking antigen-specific T cell responses in patients with myocardial infarction | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Tracking antigen-specific T cell responses in patients with myocardial infarction Anna Rizakou , Michelle Bauer , View ORCID Profile Murilo Delgobo , Alexander Hofmann , View ORCID Profile Margarete Heinrichs , Johanna Siegel , Tobias Krammer , View ORCID Profile Antoine-Emmanuel Saliba , Kenz Le Gouge , View ORCID Profile Roland Jahns , View ORCID Profile Valerie Boivin-Jahns , View ORCID Profile Viacheslav Nikolaev , Ulrich Hofmann , View ORCID Profile Encarnita Mariotti-Ferrandiz , View ORCID Profile DiyaaElDin Ashour , View ORCID Profile Stefan Frantz , View ORCID Profile Gustavo Campos Ramos doi: https://doi.org/10.1101/2025.06.05.25328514 Anna Rizakou 1 Department of Internal Medicine I, University Hospital Würzburg , Würzburg, Germany 2 Comprehensive Heart Failure Center, University Hospital Würzburg , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Michelle Bauer 1 Department of Internal Medicine I, University Hospital Würzburg , Würzburg, Germany 2 Comprehensive Heart Failure Center, University Hospital Würzburg , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Murilo Delgobo 1 Department of Internal Medicine I, University Hospital Würzburg , Würzburg, Germany 2 Comprehensive Heart Failure Center, University Hospital Würzburg , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Murilo Delgobo Alexander Hofmann 1 Department of Internal Medicine I, University Hospital Würzburg , Würzburg, Germany 2 Comprehensive Heart Failure Center, University Hospital Würzburg , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Margarete Heinrichs 1 Department of Internal Medicine I, University Hospital Würzburg , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Margarete Heinrichs Johanna Siegel 1 Department of Internal Medicine I, University Hospital Würzburg , Würzburg, Germany 2 Comprehensive Heart Failure Center, University Hospital Würzburg , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Tobias Krammer 3 Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz-Center for Infection Research (HZI) , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Antoine-Emmanuel Saliba 3 Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz-Center for Infection Research (HZI) , Würzburg, Germany 4 Institute of Molecular Infection Biology, Faculty of Medicine, University of Würzburg , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Antoine-Emmanuel Saliba Kenz Le Gouge 5 Immunoregulation-Immunopathology-Immunotherapy, UMRS959, Sorbonne Université , INSERM, Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site Roland Jahns 6 Interdisciplinary Bank of Biomaterials and Data Würzburg (IBDW), University Hospital Wurzburg , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Roland Jahns Valerie Boivin-Jahns 7 Institute of Pharmacology and Toxicology, University of Würzburg , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Valerie Boivin-Jahns Viacheslav Nikolaev 8 Institute of Experimental Cardiovascular Research, University Medical Center Hamburg-Eppendorf , Hamburg, Germany 9 DZHK (German Center for Cardiovascular Research), Partner Site Hamburg/Kiel/Lübeck , Hamburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Viacheslav Nikolaev Ulrich Hofmann 1 Department of Internal Medicine I, University Hospital Würzburg , Würzburg, Germany 2 Comprehensive Heart Failure Center, University Hospital Würzburg , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site Encarnita Mariotti-Ferrandiz 5 Immunoregulation-Immunopathology-Immunotherapy, UMRS959, Sorbonne Université , INSERM, Paris, France Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Encarnita Mariotti-Ferrandiz DiyaaElDin Ashour 1 Department of Internal Medicine I, University Hospital Würzburg , Würzburg, Germany 2 Comprehensive Heart Failure Center, University Hospital Würzburg , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for DiyaaElDin Ashour Stefan Frantz 1 Department of Internal Medicine I, University Hospital Würzburg , Würzburg, Germany 2 Comprehensive Heart Failure Center, University Hospital Würzburg , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Stefan Frantz For correspondence: Ramos_G{at}ukw.de Frantz_S{at}ukw.de Gustavo Campos Ramos 1 Department of Internal Medicine I, University Hospital Würzburg , Würzburg, Germany 2 Comprehensive Heart Failure Center, University Hospital Würzburg , Würzburg, Germany Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gustavo Campos Ramos For correspondence: Ramos_G{at}ukw.de Frantz_S{at}ukw.de Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Antigen-specific T cells can impact myocardial diseases, but appropriate model antigens and tools to track specific cells in patients are still lacking. Herein, we sought to identify and functionally characterize specific TCRs recognizing an epitope derived from the human adrenergic receptor beta 1 (ADRB1) presented on HLA-DRB1*13. Peripheral mononuclear cells obtained from HLA-DRB1*13 + subjects were stimulated in vitro with ADRB1 167-182 peptide and the expanded CD4 + T cells expressing activation induced markers were sorted for single-cell RNA / TCR-sequencing. Downstream analysis revealed clonal expansion of ADRB1 167-182 -stimulated CD4 + T cells with a distinct TCR repertoire signature. Moreover, analysis of the TCRβ complementarity determining region 3 (CDR3) led to the identification of a distinct motif enriched in ADRB1 167-182 -stimulated IFN-γ-producing cells. Selected TCRs were expressed in reporter cell lines, and their antigen specificity was further assessed by specific HLA-tetramers and antigen stimulation. Moreover, immunophenotyping of peripheral blood cells from MI patients revealed that ADRB1-specific cells preferentially exhibited a memory phenotype. Taken together, our study provides annotation and functional validation of defined TCRs specific to an antigen relevant to human cardiovascular diseases, offering new tools to track antigen-specific cells in patients with myocardial disease. Main Antigen-specific T cells impact the onset and progression of various myocardial diseases, including experimental autoimmune myocarditis 1 , 2 , 3 , post-myocardial infarction (MI) repair 4 , 5 , 6 , pressure-overload-induced heart failure (HF) 7 , 8 and myocarditis induced by immune checkpoint inhibitors 9 , 10 . These responses can either amplify tissue damage 6 , 11 or orchestrate healing 5 , requiring refined models to track and understand their complex time- and context-dependent functions. Myosin heavy chain alpha isoform (MYHCA) is a well-established cardiac antigen in defined mouse strains 12 , 13 and cardiac injury settings 14 , 9 , 4 . However, the absence of model antigens and tools to study antigen-specific T cells in patients has impeded translational progress in cardio-immunology. Herein, we modelled T cell receptor (TCR) motifs specific for an epitope in the human adrenergic receptor beta 1 (ADRB1) presented on HLA-DRB1*13 and validated new tools to track specific T helper cells in myocardial disease patients. The adult mammalian heart is characterized by low cell turnover and negligible regenerative capacity, and thus has limited tolerance to local inflammation 15 . Consequently, tissue injury caused by MI – the leading cause of death worldwide – poses unique immunological challenges. In addition to the well-established roles of myeloid cells in mediating myocardial repair, T cells can also regulate cardiac inflammation in a complex, context-dependent manner 16 . However, the absence of model cardiac antigens and defined tools to track heart-specific T cells in MI patients has hindered progress in translational cardio-immunology. In a previous study, we identified two overlapping peptide sequences derived from ADRB1 driving antigen-specific T cells responses in PBMC-derived CD4 + T cells from HLA-DRB1*13 patients with MI 17 . Furthermore, ADRB1-specific antibodies have been described in different cohorts of myocardial disease patients 18 , 19 , 20 . With mounting evidence showing ADRB1 as a relevant autoantigen in MI and HF patients 17 , 21 and mice 22 , 23 , we designed an experimental approach to expand and purify ADRB1 167-182 -specific CD4 + T cells to model specific TCR motifs, and thereby develop new tools to track specific T cells. Initially, PBMCs from HLA-DRB1*13 + subjects recruited in the frame of the “Cardiac antigens in MI” study 17 were chronically stimulated in vitro with ADRB1 167-182 peptides to expand antigen-specific T cells for downstream purification. CD4 + T cells from HLA-DRB1*13 + subjects exhibiting the strongest response to ADRB1 167-182 -stimulation were then selected for single-cell RNA-/TCR-sequencing ( Figure 1A ). After initial screening, cells from five subjects (S1-S5) were selected based on surface expression of the AIM markers, OX40 + and 4-1BB + 24 , 25 on T helper cells. Following antigen-driven clonal expansion, cells were multiplexed using 10 different hashtag antibodies and labelled with CITE-seq antibodies for CD25, CD69, CD45RA and CD45RO ( Extended Data Figure 1 ). AIM + T cells were sorted from both IL-2-control and ADRB1 167-182 -stimulated cultures and sequenced using the 5’ single-cell seq/TCR-seq 10x platform. After data filtering ( Extended Data Figure 2A-B ), 9,379 single CD4 + T cells were obtained, of which 8,315 showed a productive TCR sequence ( Extended Data Figure 1B ). Our analysis revealed 12 different clusters, including naïve T cells (expressing CD45 high , IL17R and CCR7 ), 2 Treg clusters (expressing FOXP3, IKZF2 and CD69 +/- ) and conventional T cells expressing effector/memory and cytotoxic genes ( KLRB1, GZMA and PTPN13 ) ( Figure 1B-C , Extended Data Figure 2C-D ). Additionally, we identified a cluster associated with prototypical T H 1 responses (expressing IFNG , CCL3 and GZMH ), a cluster with a T H 2 signature (expressing IL5, IL4 and GATA3 ), cells expressing the cytokines CSF2 and IL21 and a cluster of cells exhibiting high expression of Il10 with checkpoint inhibitors and exhaustion genes ( LAG3, CTLA4, PDCD1 and TOX ). Download figure Open in new tab Figure 1. Single cell RNAseq profiling of human ADRB1 antigen-specific CD4 + T cell responses. ( A ) Experiment design: PBMCs of human subjects expressing the DRB1*13 HLA allele were stimulated in vitro with ADRB1 167-182 peptide for 14 days. Donors showing the highest fold response in AIM T cells (OX40 + 4-1BB + ) were selected for scRNA/ TCR seq analysis. The ‘dotted line highlight T cell subsets selectively expanded by the antigen. ( B ) UMAP plot displays 12 clusters annotated from CD4 + T cells derived from control and ADRB1-stimulated conditions. ( C ) Violin plots of the log-normalized expression values of FOXP3, IL10, KLRB1, IFNG, IL5 and CSF2 per cluster (left and central panel) and the CITE-seq signal for CD69 and CD45RA (right panel). ( D ) Jitter plots show cell distributions for each cluster defined in 1B, split by subjects (S1-S5) and experimental conditions (IL-2 control or ADRB1 peptide). Dashed squares highlight the most expanded clusters in ADRB1-stimulated condition for each subject. Despite being maintained for two weeks in cell culture, the ADRB1 167-182 -expanded T helper cells showed a distinct transcriptome profile that was largely subject-specific. For instance, S1 and S4 had a skewed antigen specific T cell response enriched for T H 1/KLRB1-associated transcripts. In contrast, S2 and S3 displayed a response dominated by Treg/IL-10 cells, whereas S5 exhibited clonally expanded cells with T H 2/Treg response signatures ( Figure 1D ). These findings confirm that ADRB1 167-182 is an HLA-DRB1*13-restricted autoantigen and that subject-specific responses can be tailored to a pro-inflammatory, regulatory or T H 2 phenotype. To investigate if the cluster-specific responses were associated with expansion of distinct TCRs, we evaluated clonal size distribution in control (IL-2 only) and peptide-stimulated groups. Our analysis revealed that more than 50% of TCR sequences were classified as medium to largely expanded in ADRB1 167-182 -stimulated cells, in sharp contrast to the TCRs found in controls ( Figure 2A ). We further confirmed these observed ADRB1 167-182 -induced clonal expansions by querying inverted Simpson’s index, which indicated a less diverse TCR repertoire in ADRB1 167-182 -stimulated cells ( Extended Data Figure 3A ). Overall, the TCR repertoire of peptide-stimulated cells can be broadly sorted into two distinct categories: a) antigen-specific responses dominated by a few highly expanded clones, mostly with a pro-inflammatory signature (e.g., in S1, S3 and S4); or b) antigen-specific responses comprised of medium-expanded clones, as typically observed in subjects with a Treg-biased response (e.g., S2 and S5) ( Extended Data Figure 3B ). Download figure Open in new tab Figure 2. Clonal expansion, repertoire analysis of T cells and identification of CDR3β motifs in ADRB1 stimulated cells. ( A ) Graph shows the proportion of expanded clones in IL-2 control and ADRB1 peptide-stimulated groups (Large: 50<x≤400, Medium: 10<x≤50, Small: 1<x≤10, Single: 0<x≤1). ( B ) UMAP plots highlighting, per subject, the top three expanded clones. Sequences are in descending order; yellow is the top expanded and dark blue is the top three expanded. ( C ) Workflow of motif enrichment using GLIPH2 algorithm. ( D ) Network plot shows the most frequent (ball size) and with highest score (colour code) enriched motifs identified by GLIPH2. ( E ) Motifs identified by GLIPH2, and the respective subjects sharing the motif illustrated by heat map. ( F ) UMAP plots shows five TCR sequences sharing the motif 3 or the J1-4 sequence “NEKLFF”. Frequency of TRAAV26 TRBJ1.4 within the motif and global repertoire ( G ) and in control and ADRB1-stimulated cells ( H ). Next, we aligned the top three expanded clones per subject to their respective cluster in the UMAP plot to identify potential ADRB1-specific TCRs. The top expanded TCR found in S1 was present in both control and ADRB1 167-182 -stimulation conditions, and its sequence was previously annotated to recognize a cytomegalovirus antigen 26 , 27 . However, all other expanded TCR sequences identified in our dataset were exclusively found upon ADRB1 167-182 -stimulation, suggesting they might confer specificity to this antigen. In S1, the second and third most-expanded TCRs were enriched in ADRB1 167-182 -expanded T cells constituting the CSF2 and IFNG clusters ( Figure 2B and 1D ). Similarly, the top expanded clones found in S2 and S3 were mapped to Tregs/IL-10 clusters under the ADRB1 167-182 stimulation regimen, whereas the top expanded clones found in S5 were mapped to T H 2 cells ( Figure 2B ). A list of the top expanded clones in ADRB1-stimulated cells are shown in Table I and Extended Data Table I . Taken together, our data reveal that ADRB1 167-182 peptide stimulation led to clonal expansions of CD4 + T cells in HLA-DRB1*13 + subjects. View this table: View inline View popup Download powerpoint Table I Top nine TCR α/β sequences expanded in ADRB1-stimulated PBMCs. For each sequence, the main representative cluster, subject and frequency are shown. The TCR complementary-determining region 3 (CDR3) encompasses the antigen-binding sites and the rearrangement sites of TCR β variable (V) diversity (D) and joining (J) segments, and TCRα VJ segments, which account for most TCR variability. Applying a sequence-based metric, the Levenshtein distance which computes the number of amino acid substitutions, insertions or deletions between two given TCR CDR3β sequences, we estimated the sequence similarities between the top 50 CDR3β sequences found in ADRB1 167-182 -stimulated cells. As shown in Extended Data Figure 2E , while the top two expanded clones found in S5 exhibited great similarity, all remaining TCR sequences analyzed showed greater Levenshtein distances, thereby indicating that unique CDR3β TCR sequences largely characterize the ADRB1-specific TCR repertoire. Because the clonally expanded TCRs were mostly subject-specific private, we next sought to identify TCR core motifs that may be shared among individuals (public), indicative of shared antigen recognition patterns. For this purpose, 8,315 CDR3β sequences derived from control and peptide-stimulated cells were analyzed using the GLIPH2 algorithm ( Figure 2C ), which identifies conserved sequences enriched in datasets of interest against a reference repertoire of 162,165 human TCRs and represented in the network using a composite “total_score” parameter. The total_score integrates motif enrichment, HLA association, and inter-individual prevalence 28 , 29 . The top two most frequent CDR3β motifs (SPGQG*TGE; SPGL*TDT) were mainly shared within S5 and show no common CDR3α gene usage. However, the third most frequent CDR3β motif (SD*RVNEK, motif 3) showed the highest Fischer score (7.5e-08) and several additional related variants (e.g., S*DRVNEK, SDDRV*EK) were also identified ( Figure 2D-E ). Building on these observations, we interrogated the distribution and phenotype of T cells expressing this CDR3β motif. Strikingly, motif 3 was present only in ADRB1 167-182 -stimulated cell cultures and was shared between sequences from S1 and S2 subjects ( Figure 2F ). We then expanded our investigation to cells expressing “NEKLFF”, the germline sequence encoded by TRBJ1-4. With these new criteria, we found two additional sequences from S1 and S4 that were 85 and 39 times expanded, respectively ( Figure 2F ). All T cells expressing the CDR3β motif 3 also shared the exact same CDR3α TRAV26-1_TRAJ30 segment usage ( Figure 2F-G ), further confirming that this might be a bona fide ADRB1-specific TCR motif. Intriguingly, all T cells expressing this distinct TCR motif aligned within the IFNG cluster, even in S2, that showed a dominant Treg response ( Figure 2F , 1D and 2B ). Moreover, the TRAV26-1_TCBJ1-4 gene segment usage was enriched among ADRB1-stimulated cells, in sharp contrast with the global TCR repertoire ( Figure 2H ), suggesting this particular TCR αβ combination might be enriched for specificity against the HLA-DRB1*13:ADRB1 167-182 epitope. Taken together, these results demonstrate that motif 3 identifies TCR sequences conserved amongst individuals and exclusively found in ADRB1-stimlated T cells posed with a T H 1 signature. Next, to validate this identified TCR signature ( Extended Data Table I ), we selected nine TCRs ( Table I ) to be expressed on a modified Thymoma cell line (BW5147. G.1.4) and tested in vitro 30 . In brief, to avoid mismatching between the endogenous and introduced TCRs, we employed CRISPR/Cas9 mediated knockout of the endogenous TCRα constant chain. Then we retrovirally introduced the complete CD3 complex (CD3γ, CD3δ, CD3ε, and CD3ζ) 31 and human mutated CD4 (hmuCD4) 32 in order to generate a suitable cell line (BW.CD3.CD4.NFAT-ZsGreen) to express and test the transgenic TCRs identified in our scTCR datasets ( Figure 3A ). TCR activation was assessed by ZsGreen Signal reporter under the NFAT promoter. Download figure Open in new tab Figure 3: Functional validation of predicted TCR motifs. ( A ) Workflow of tgTCR cell line generation (left panel) and representative flow cytometry histograms of unmodified BWs (cyan) versus tgTCR BWs (red) (right panel). ( B ) The seven tetramer-enriched generated tgTCRs, coloured according to their corresponding cell cluster from scRNAseq ( Figure 2B ). TCR1 and TCR6 express the SD*RVNEK motif described in Figure 2 . ( C ) Staining of tetramer-enriched tgTCRs with MHCII-restricted tetramer against ADRB1 167-182 and control hCLIP peptide. Unstained tgTCRs served as FMO. (n=3 technical replicates; data are shown as mean±SEM; statistical analysis was performed with two-way ANOVA followed by Tukey’s multiple comparison test. P -values < 0.05 were considered statistically significant with: ***, P <0.001; ****, P <0.0001). ( D ) Co-culture of ADRB1 167-182 specific tgTCRs with PBMCs from matching donor (ratio 1:1) in presence of 1 µg/mL ADRB1 167-182 , 1 µg/mL MYHCA 614-629 or vehicle. (n=3 technical replicates; data are shown as mean±SEM; statistical analysis was performed with two-way ANOVA followed by Tukey’s multiple comparison test. P -values < 0.05 were considered statistically significant with: ****, P <0.0001). ( E ) Representative flow cytometry plots of tetramer-enriched TCR6 unstained (left), stained with tetramer (center) or hCLIP (right). ( F ) Representative fluorescent image of TCR6 cells stained by immunofluorescence for tetramer+ (magenta), TCRβ (cyan) and DAPI (blue). Images were acquired on a Leica Imager DMi8 equipped with a 63x objective. Scale bar: 10 μm. The nine selected TCRs of interest ( Table I , Extended Data Figure 5B ) were then retrovirally expressed in the in-house-generated cells. We obtained custom-made fluorescently labelled HLA-DRB1*13:02 MHCII tetramers loaded with ADRB1 167-182 , which allowed us to detect antigen-specific CD4 + T cells in samples from myocardial disease patients with matching HLA (kindly provided by the NIH Tetramer Core Facility). HLA-DRB1*13:02 tetramers loaded with the CLIP peptide were used as control. After establishing optimal tetramer staining conditions ( Extended Data Figure 4 ), we assessed the specific binding of all nine TCRs clonotypes to HLA-DRB1*13:02:ADRB1 167-182 tetramers. As shown in Extended Data Figure 5C , all tested TCR clones showed HLA-DRB1*13:02:ADRB1 167-182 tetramer + staining. TCR9 displayed the highest frequency of specific stained cells, followed by TCR4 and TCR6. The seven clonotypes exhibiting tetramer + stained cell frequencies greater than 8% were selected for enrichment via fluorescence-activated cell sorting (FACS) ( Extended Data Figure 5D ) and the experiment was subsequently repeated using the tetramer-enriched clonotypes ( Figure 3B-C and E). Notably, TCR6 and TCR9 again demonstrated the highest frequencies of HLA-DRB1*13:02:ADRB1 167-182 tetramer + cells. Interestingly, TCR9 and TCR6 originated from the same donor and were enriched in ADRB1-stimulated cells showing a pro-inflammatory phenotype ( Figure 3B , Table I ). The specificity of the tetramer staining was further confirmed by immunofluorescence imaging, which indicated colocalization of specific tetramer and TCRβ signal per cell base ( Figure 3F , Extended Data Figure 6 ). To further validate the expressed TCRs, the tgTCR-BW.CD3.CD4.NFAT cells were co-cultured with matching HLA-DRB1*13 + PBMCs at a 1:1 ratio and stimulated with 1 μg/mL ADRB1 167-182 or vehicle. A peptide derived from MYHCA isoform MYHCA 614-629 , irrelevant in this condition, was used as control ( Extended Data Figure 7 ). As shown in Figure 3D , cells expressing TCR6 exhibited a specific response to ADRB1 stimulation, as evidenced by ZsGreen expression. Interestingly, this TCR6 clonotype comprises the conserved CDR3β motif 3 identified in Figure 2 ( Figure 3B , Table I ). The remaining clonotypes tested did not show significant ZsGreen signal upon ADRB1-stimulation ( Figure 3D ). After identifying TCRs specific for an HLA-DRB1*13-restricted epitope enriched in the heart and validating distinct MHC-II tetramers to track specific cells, we monitored HLA-DRB1*13 frequencies in different cohorts of MI patients recruited in Würzburg 33 , 17 and compared them with available databases of German bone marrow donors 34 . As shown in Figure 4A , the combined frequency of HLA-DRB1*13:01, HLA-DRB1*13:02 and HLA-DRB1*13:03 alleles was higher in MI patients (n=87) compared to the healthy German population (n= 3,456,066), though not statistically significant. Likewise, the fraction of individuals carrying these alleles was also enriched among MI patients ( Figure 4B ). Download figure Open in new tab Figure 4: HLADRB1*13 allele as a potential risk factor for ADRB1 specific T cell responses post MI. ( A ) Donut charts depicting HLA-DRB1*13 + allele frequency in healthy population and MI patients. Statistical analysis was performed with Chi-square test followed by Yates’ correction ( P =0.1988). P -values < 0.05 were considered statistically significant. ( B ) Donut charts depicting HLADRB1*13 + carrier frequency in healthy population and MI patients. Statistical analysis was performed with Fisher’s exact test (P= 0.1327). P -values < 0.05 were considered statistically significant. ( C ) Experimental workflow (left panel) and diversity index of TCRβ chain from the bulkRNAseq data of DRB1*13 + (n=7) versus DRB1*13 − (n=19) MI patients. Both groups displayed similar Troponin levels. Data are shown as mean±SEM; statistical analysis was performed with unpaired t-test. P -values < 0.05 were considered statistically significant. ( D ) Heatmap depicting the enrichment of TCRβ sequences from Table I in the CDR3s of MI/ DRB1*13 + , ADRB1 stimulated PBMCs (from Hapke et al. ) and MI/ DRB1*13 − bulkRNAseq data, without selection for expansion. ( E ) Heatmap depicting the core motif overlapping (>50 clones, ≥1e-04) within TCRβ sequences from Table I and MI/ DRB1*13 + , ADRB1 stimulated PBMCs and MI/ DRB1*13 − . ( F ) Representative flow cytometry plot of CD4/Tetramer gated on live CD45 + /CD3 + /CD4 + cells (left), ( G ) Histogram (right panel) and quantitative analysis of 4-1BB + cells in tetramer + versus tetramer − cells for patient (n=8) (left panel). Data are shown as mean ± SEM; Statistical analysis was performed with unpaired t-test. P -values < 0.05 were considered statistically significant. ( H ) Representative flow cytometry plot of CD45RA/CCR7 gated on live CD45 + /CD3 + /CD4 + cells and ( I ) quantitative analysis of Naïve and central-memory T cells (T cm ) in tetramer + versus tetramer − cells for each patient (n=8). Data are shown as mean ± SEM; Statistical analysis was performed with unpaired t-test. P -values < 0.05 were considered statistically significant. ( J ) Representative flow cytometry plot of CD25/CD127 gated on live CD45 + /CD3 + /CD4 + cells (left panel) and ( K ) quantitative analysis of Tregs in tetramer + versus tetramer − cells for each patient (n=8). Data are shown as mean ± SEM; Statistical analysis was performed with unpaired t-test. P -values < 0.05 were considered statistically significant. To further characterize post-MI antigen-specific T cell responses in patients, we retrospectively analysed TCRseq data available from peripheral blood samples obtained from MI patients stratified by HLA genotype 35 , 33 . TCR repertoire analysis performed on samples obtained at index hospitalization revealed reduced TCRβ diversity in DRB1*13 + MI patients, suggestive of antigen-driven clonal expansions in MI patients with this HLA genotype ( Figure 4C , Extended Data Figure 8A ). A similar pattern was also observed for TCRα sequences ( Extended data Figure 8B-C ). Moreover, we found that the TCR repertoires of DRB1*13 + MI patients were enriched for CDR3β sequences annotated to be ADRB1-specific, based on our TCR modelling ( Figure 4D ) and core motif overlaps ( Figure 4E ). Using the HLA-DRB1*13:02 : ADRB1 167-182 tetramer validated in Figure 3 , we were able to track and immunophenotype antigen-specific T helper cells obtained from matching MI patients ( Figure 4F-K , Extended Data Figure 9 - 10 ). Interestingly, tetramer + CD4 + cells displayed higher surface expression levels of AIM marker 4-1BB + ( Figure 4F-G ) and increased frequency of central memory T helper (CD3 + CD4 + CCR7 hi CD45RA lo ) cells ( Figure 4H-I ), compared to patient-matched tetramer − cells. No significant differences in Treg frequency were observed between tetramer + and tetramer − cell populations ( Figure 4J-K ). These findings suggest that CD4 + T cells might be already primed against ADRB1-derived epitopes before MI onset, possibly in the context of atherosclerosis and coronary heart disease. This would explain the presence of central memory T cells in the early stages of MI, validating the higher frequency of HLA-DRB1*13 alleles and reduced TCR clonal diversity among MI patients. These results reinforce the need to understand heart-directed immune responses across the full ischemic heart disease spectrum, establishing ADRB1 as an autoantigen in coronary heart disease and MI. The previous identification of APOB1-specific CD4 + T cells and the development of specific MHC-II tetramers were game-changing advancements that enabled in-depth characterization of antigen-specific T cells in atherosclerosis 36 , 37 , 38 . However, the lack of identified cardiac antigens in humans has limited our understanding of heart-directed immune responses in patients. Our study addresses this gap by functionally validating TCR motifs specific for an antigen relevant in patients with myocardial disease and a defined HLA composition. To the best of our knowledge, this is the first annotation of cardiac-specific TCRs validated in humans. A growing number of clinical studies have characterized T cells in MI patients, 40 and the TCR resources generated in our study will allow antigen specificity to be inferred from TCR-sequencing data, both retrospectively and prospectively. Additionally, the tetramers herein described will enable future diagnostic, prognostic and mechanistic interrogation of antigen-specific T cells in heart disease patients representing a significant progress in the field of translational cardio-immunology. Data Availability The anonymised patient data can be shared under reasonable request. The RNA sequencing data will be deposited on a public repository after the peer review process is completed. Online content A detailed methodological description along with extended Figures and Tables are presented as online content. Methods Patient Cohorts In the present study, we analysed cryopreserved samples available from three clinical studies conducted in Würzburg 33 , 17 . First, we performed expansion of antigen-specific T helper cells and TCR characterization with peripheral blood mononuclear cells (PBMCs) from HLA-DRB1*13 patients previously recruited within the frame of the ‘Cardiac Antigens after MI’ (CAMI) study 17 . CAMI included patients diagnosed with first acute MI, control patients with cardiovascular risk factors but without signs of coronary artery disease who underwent elective coronary angiography (CA group) and heathy subjects (HS group). In total, PBMCs from five HLA-DRB1*13 + subjects from the CAMI cohort were selected for scRNA/TCRseq experiments. In a second series of experiments, PBMCs from 8 MI HLA-DRB1*13 + subjects were used for spectral flow cytometry investigation. We tracked ADRB1-specific T cells and immunophenotyped the tetramer + and tetramer − CD4 + populations in 6 patients from the CAMI cohort and 2 patients from the ANTIK-MI („Antikörper-Produktion durch B-Zellen nach Myokardinfarkt”) study. Finally, we retrospectively analysed a TCR sequencing dataset available from our previous study. The Etiology, Titre-Course and Survival (ETiCS) cohort included 31 patients diagnosed with first acute MI, also stratified by HLA-DRB1 genotype 35 . The characteristics of these patients are described in Extended Data Table II . Study approval The studies were approved by the ethics commission of the University of Würzburg (CAMI: 304/17-m3, ETiCS: 186/09, ANTIK-MI: 249/20-am) and met the criteria of the Declaration of Helsinki. All patients provided written informed consent. Expansion of human ADRB1 167-181 -specific CD4 + T cells Available CAMI cohort 17 PBMCs expressing one or two copies of the HLA-DRB1*13 MHC-II haplotype were thawed and resuspended in complete RPMI media supplemented with 10% of human AB serum and 10 U/mL of rhIL-2 (Peprotech). Cells were either seeded at 2×10 6 cells/mL in 48-well plate (day 0) and assigned to control (media + IL-2 only) or stimulated with 1 µg/mL of ADRB1 167-181 and 1 µg/mL of ADRB1 168-182 for 4 days at 37°C, 5% CO 2 . On day 4, cells were washed and resuspended in fresh complete RPMI media supplemented with IL-2. The washing steps were repeated on days 6, 8 and 11 and cells were always reseeded into the original plate (meaning they were in contact with autologous adherent cells throughout the experiment). On day 13, cells were re-stimulated with ADRB1 168-182 peptides as previously indicated, in complete RPMI media supplemented with IL-2, and on day 14 we proceeded with the detection and purification of antigen-specific T cells based on their surface expression of activation-induced markers, as detailed below 41 , 24 , 25 . Identification and purification of ADRB1 167-181 -specific CD4 + T cells In order to identify antigen specific T cells based on their surface expression of activation-induced markers (AIM), less than 10 6 cells were collected on days 4, 8 and 14 and processed for flow cytometry analysis. In brief, cells were washed once with PBS and stained with fixable viability dye (Zombie Aqua™ Fixable Viability Kit, BioLegend) for 15 minutes at room temperature. Next, cells were washed with FACS buffer (PBS containing 1% BSA, 0.1% Sodium Azide and 1 mM EDTA) and resuspended in 50 μL of antibody master mix, containing: anti-CD3 BV421 (clone UCHT1, 1:40, ThermoFisher), -CD4 FITC (clone SK3, 1:40, ThermoFisher), -4-1BB PE (CD137) (clone 4B4, 1:20, ThermoFisher) and - OX40 (CD134) APC (clone OX40, 1:50, Miltenyi), and Human TruStain FcX™ (1:200, BioLegend). Cells were kept for 30 minutes at 4°C and were then washed with FACS buffer. Finally, cells were resuspended in PBS and acquired using a Attune NxT FACS machine. T helper cells showing activation-induced activation markers (4-1BB + OX40 + ) on day 14 post-stimulation were defined as putative ADRB1 167-181 -specific cells, as previously reported 41 , 24 , 25 . Single cell RNAseq/TCR sequencing After expanding the ADRB1 167-181 -specific CD4 + T cells in vitro over 14 days and selecting the ones showing response to ADRB1 167-181 stimulation, cells were purified with fluorescence-activated cell sorting and then used for single cell RNA- / TCR sequencing. Cells were washed with sterile PBS, resuspended in ∼100µL and transferred to sterile U-bottom 96-well plates for staining. All buffers and centrifugation steps were conducted at 4°C. After centrifugation, cells were washed once with 200µL of PBS and then resuspended in 100µL of efluor 780 viability dye (1:1000, ThermoFisher). Cells were incubated for 15 minutes at room temperature, protected from light. Cells were then washed in sterile azide-free MACS buffer (0.5% BSA, 2mM EDTA in PBS). Afterwards, cells were resuspended in individual antibody master mix (staining volume 100µL), containing anti-CD3, -CD4, -4-1BB, -OX40, Human TruStain FcX. Immune cell populations were identified by sequencing for cellular indexing of transcriptomes and epitopes (CITE-seq), a technique that uses oligonucleotide-tagged antibodies to integrate cellular protein and transcriptome data into one single-cell readout 42 . Therefore, the following totalseqC antibodies were added to the mix: CD69 TotalSeqC (clone FN50, 1:150), CD25 TotalseqC (clone BC96, 1:150), CD45RA TotalSeqC (clone HI100, 1:150) and CD45RO TotalSeqC (clone UCHL1, 1:150). Further, to enable downstream de-multiplexing of the scRNA-/ TCRseq data from pooled samples, the following TotalSeqC hashtag antibodies were used: CO251 (S1_control), CO252 (S1_adrb1), CO253 (S2_control), CO254 (S2_adrb1), CO255 (S3_control), CO256 (S3_adrb1), CO257 (S4_control), CO258 (S4_adrb1), CO259 (S5_control) and CO260 (S5_adrb1). All hashtag antibodies belonged to the clone LNH-94/2M2 and were used at 1:100 concentration. In brief, the cells were incubated in Ab master mix for 30 minutes at 4°C. Cells were then washed by adding 150uL of pre-chilled MACS buffer, centrifuged at 400 x g and 4°C for 3 minutes and resuspended in 200µL of MACS buffer. To sort antigen-activated T cells, 4-1BB + OX40 + events were sorted from live CD3 + CD4 + T cells, with one collection tube used per donor. Collection tubes (1.5 mL biopur Eppendorf tubes) were pre-coated with 10% albumin solution (Miltenyi) for 24h and filled with 30µL of MACS buffer pre-sorting. 2/3 of sorted cells were derived from the ADRB1-stimulated condition and 1/3 from the control IL-2 condition. Finally, 20,000 cells/donor (peptide + control) were mixed in a new Eppendorf tube, (total cell number=100,000) and concentration was adjusted to 1,000 cells/µL. Single cell RNAseq and library construction Single cell RNA-sequencing Chromium controller was used to partition single cells into nanolitre-scale Gel Bead-InEmulsion (GEMs) and Chromium Next GEM Single Cell 5’ v1.1 kits for reverse transcription, cDNA amplification and library construction (10x Genomics). A SimpliAmp Thermal cycler was used for amplification and incubation steps (Applied Biosystems). Libraries were quantified by a Qubit 3.0 fluorometer (ThermoFisher) and quality was checked using a 2100 Bioanalyser with high sensitivity DNA kit (Agilent) in paired-end mode to reach at least 60000 reads per single cell for gene expression and 6000 reads for the T cell receptor repertoire, hashtags and CITE-seqs. The Cell Ranger Software suite (version 7.0.1, 10x Genomics) was used for sequence alignment, barcode processing and sample demultiplexing. scRNA-/TCRseq data analysis, TCR modelling and motif discovery The gene count matrix derived from Cell Ranger was analysed using Seurat package (version 5.0.1). The individual cell barcodes, ten different hashtag oligo sequences and the four CITE-seq antibody sequences were considered during sample de-multiplexing. In addition, cells yielding more than 50,000 unique molecular identifiers (UMI), with more than 5% of mitochondrial RNA and less than 200 features, were also excluded. During sample de-multiplexing using the HTOdemux function, 1,325 cells were considered doublets, 33 cells were considered negative and after data cleanup, 9,588 cells out of 11,200 cells remained. Cell transcriptome was log-normalized and the top 2000 most variable genes were obtained using Seurat’s default pipeline. The uniform manifold approximation and projection for dimension reduction (UMAP) was generated by the 20 principal component and a 0.5 resolution in dimensionality reduction. The analysis resulted in 14 different clusters and the function FindallMarkers in default settings was then used to estimate the differentially expressed genes in between clusters. The list of marker genes for each cluster can be found in Extended Data Table III. Two cell clusters identified as “monocytes” and “gd-Tcells” were subset-out of the main Seurat object, resulting in a “filtered” objected containing 9,379 cells. The VDJ sequences for each T cell receptor were added to the Seurat object using the combineExpression () function from scRepertoire R package (1.11.0). Cells were classified either as: single clones (1), small-expanded clones (1-10), medium expanded clones (10-50) or largely expanded (50-400 clones. The function occupiedRepertoire () was used to show the distribution of clonotype size between control and peptide-stimulated conditions. The function clonalDiversity() was used to estimate the diversity index between all samples, with clonecall parameter set as “strict” (meaning CDR3a_CDR3b) and n.boots: 8315 (total number of cells with a productive TCR chain). The inverted Simpson’s index was plotted in GraphPad Prism (version 10.2.1). The top three expanded clones per subject were manually selected and are shown in figure 2B and 2F (motif) using the highlightClonotypes() function. GLIPH2 28 was used for motif discovery, using the turboGliph package (version 0.99.2). A tab-separated table containing CDR3β, Vb, Jb, CDR3α, subject and frequency was loaded for the GLIPH2 pipeline. The gliph_combined function was used, with local_method: fisher, with clustering and scoring method coming from GLIPH2.0. The clones with the top score motifs were overlaid on the UMAP plots for analysing their phenotypic and patient-specific behaviour. This also included sequences sharing the same Jb gene 1-4 (NEKLFF) within the motif SD*RVNEK ( Figure 2F ). TCR repertoire analysis of ETiCS and CAMI cohorts Repertoire analysis of the ETiCS bulk TCR seq data 35 and computation of overlaps with other TCR datasets were performed using the immunarch R package (version 1.0.0). Briefly, the MiXCR output files of bulk TCR seq ETiCS samples were imported into R using the repLoad() function. Samples were grouped according to their HLA-DRB1 haplotyping into DRB1*13 + or DRB1*13 − . Distribution of clonotype abundances between DRB1*13 + or DRB1*13 − groups was computed using the repClonality() function. This method estimates the relative contribution of clones to the repertoire, stratifying them into four abundance-based categories: Small (1%). Overall repertoire diversity estimates were computed using the repDiversity() function based on the D50 index, which estimates the minimal number of unique clonotypes required to account for at least 50% of all sequencing reads in a given sample. Lower D50 values indicate greater clonal expansion, where fewer dominant clones occupy most of the repertoire. To evaluate TCR sharing between groups, CDR3β sequences from DRB1*13 + or DRB1*13 − groups were combined with TCRβ sequences from Table I , and ADRB1 stimulated PBMCs 17 . The function repOverlap() was used to compute pairwise Jaccard similarity based on exact CDR3β matches between groups. To account for varying repertoire sizes between bulk and single-cell datasets, a scaling factor was applied to normalize all repertoire sizes to 1 million. To compute motif overlaps of the core CDR3 region between the TCRβ sequences of the current manuscript and the different datasets, we used a modified version of repOverlap(), which computes overlaps of truncated CDR3β sequences after removing the first 3 and last 3 amino acids representing the germline-encoded sequences of the V and J genes. Generation of a T cell reporter line and expression of TCRs identified in silico BW5147 G.1.4 cells were purchased from ATTC (Manassas) and modified as previously described 30 . In brief, CRISPR/CAS9 technology was used to knock out the endogenous TCRα constant chain. Afterwards, we used murine MSCV retroviral vectors for CD3-2A_pMI-LO (Addgene plasmid # 153418; http://n2t.net/addgene:153418 ; RRID: Addgene_153418) 31 to express all CD3 chains, and a humanized CD4 construct with a NFAT-Zs-Green reporter (8xNFAT-ZsG-muhCD4, Addgene plasmid #162745; https://www.addgene.org/162745/ ; RRID: Addgene_162745) 32 . The aforementioned plasmids were purchased from Addgene and kindly deposited by Maki Nakayama. Finally, individual TCRs of interest ( Table I ) were synthesized and virally expressed in the BW-TCR-KO-mCD3 + -muhCD4 + -NFAT-ZsGreen reporter cell line (tgTCR). Characterization of cell lines expressing transgenic TCRs of interest Next, cells expressing the TCRs identified in silico were characterized. First, cells were washed and then stained with efluor 780 viability dye (ThermoFisher) for 15 min at room temperature. Next, cells were washed with FACS Buffer and resuspended in 25μL of antibody master mix containing: TCRβ BV421 (clone H57-597, 1:100, BioLegend), hCD4 PE (clone A161A1, 1:100, BioLegend), CD3ε PercpCy5.5 (clone 145-2C11, 1:100, BioLegend), TruStain FcX™ (anti-mouse CD16/32, 1:200, BioLegend). Cells were kept for 15 minutes at 4°C and were then washed with FACS buffer. Finally, cells were resuspended in FACS Buffer and acquired in Attune NxT FACS machine. The tetramer and hCLIP were kindly provided by the NIH Tetramer Core Facility. Cells were washed and plated in a 96-well plate. After centrifugation, cells were resuspended in 50 μL of RPMI complete (1%Penicillin/ Streptomycin, 1% MEM-NEAA, 1% Glutamax, 1% Sodium Pyruvate, 0.1% β-Mercaptoethanol) with 2, 6 or 18 μg/mL of Tetramer or hCLIP and kept for 30, 90 or 180 minutes at 4 or 37°C. Cells were then washed with RPMI and proceeded for surface staining. Functional validation of expressed TCRs Human PBMCs were thawed in RPMI complete medium in 10 cm petri dishes one day prior. tgTCR were co-cultured with HLA-matching PBMCs in a 1:1 ratio in U-bottom 96-well plates in the presence of 1 μg/mL ADRB1 167-182 , (QSLLTRARARGLVCTV, JPT Peptide Technologies), 1 μg/mL MYHCA 614-629 (RSLKLMATLFSTYASADR, JPT Peptide Technologies), or vehicle for 24 hours. Cells were stained for viability, CD4, CD3ε and TCRβ and analysed by flow cytometry for NFAT-ZsGreen reporter activity. Validation of tetramer staining In order to confirm tetramer specificity for ADRB1-specific T cells, we performed immunofluorescent staining to validate co-localization of tetramer (APC) with TCRβ. tgTCR cells from clone TCR6 were plated and left to rest overnight in an 8-well removable chamber plate (Ibidi) pre-coated with poly L-lysine (Sigma) to allow cell adherence, according to the manufacturer’s instructions. The following day, the medium was replaced with RPMI complete containing 2 μg/mL of Tetramer and kept for 90 minutes at 37°C. Then, cells were washed twice with PBS and the wells were removed to reveal a microscope slide. The slide was fixed in acetone for 20 minutes at -20°C, followed by three washing steps with PBS. Next, the cells were blocked in 1x Carbo-free solution (Vector Laboratories) to prevent unspecific binding of the secondary antibody. After blocking, we stained with the primary unconjugated antibody anti-APC (clone eBioAPC-6A2, 1:100, ThermoFisher) for 1 hour at room temperature in the dark. We removed primary antibody, washed with PBS and then stained with goat anti-mouse AlexaFluor 647 (1:500, ThermoFisher) for 30 minutes at room temperature in the dark. The slide was washed again and then stained with anti-TCRβ conjugated with AlexaFluor 488 (clone H57-597, 1:100, BioLegend) for 1 hour at room temperature in the dark. We performed final washing steps and mounted the slide in Fluoroshield containing DAPI (Sigma Aldrich). The slide was kept at 4°C until further analysis. Images were acquired with a Leica Imager DMi8, while image analysis was performed with ImageJ software (Fiji). Immunophenotyping and tracking of ADRB1-specific T cells in PBMCs PBMCs from DRB1*13 + patients recruited within the CAMI and ANTIK cohorts were thawed in a water bath at 37°C and washed twice with RPMI at 330 x g and room temperature for 5 minutes. Next, cells were subjected to magnetic activated cell sorting (MACS) for CD4 + T cells. In brief, cells in suspension were stained with Biotin-Antibody cocktail (biotin-conjugated antibodies against CD8a, CD14, CD15, CD16, CD19, CD36, CD56, CD123, TcRγ/δ and CD235a Miltenyi), followed by labelling with magnetic anti-biotin MicroBeads (Miltenyi). Subsequently, the cell suspension was loaded on MACS-LS columns and magnetic activated negative CD4 + T cell selection was performed according to the manufacturer’s instructions. The negatively selected CD4 + cells were resuspended in 200 μL of RPMI and plated in a U-bottom 96-well plate. After centrifugation, cells were resuspended in 50 μL of RPMI complete with 2 μg/mL of Tetramer and kept for 90 at 37°C. Then, cells were washed with RPMI and stained with efluor 780 viability dye for 15 minutes at room temperature. Next, cells were washed with FACS buffer and resuspended in 50 μL of antibody master mix, containing: anti-CD3 eFluor450 (clone UCHT1, ThermoFisher), - CD45 BUV496 (clone HI30, ThermoFisher), -CD4 Superbright 600 (clone SK3, ThermoFisher), -CCR7 (CD197) PerCP/Cy5.5 (clone G043H7, BioLegend), - CD45RA PE BUV737 (clone HI100, ThermoFisher), -CD25 FITC (clone M- A251, BioLegend), -CD127 PE-Cy7 (clone eBioRDR5, ThermoFisher), - CXCR3 BV480 (clone CXCR3-173, BD Biosciences), -CCR6 (CD196) BV650 (clone G034E3, BioLegend), -PD-1 PE-eFluor 610 (clone J105, ThermoFisher), -CD278 (ICOS) BUV395 (clone C398.4A /RUO, BD Biosciences), -CD69 PE-Fire640 (clone FN50, BioLegend), -CD134 (OX40) BV785 (clone Ber-ACT35/ACT35, BioLegend), -4-1BB PE (clone 4B4-1, ThermoFisher) and Human TruStain FcX™ (1:200, BioLegend). Cells were kept for 30 minutes at 4°C and then washed with FACS buffer. Finally, the cells were resuspended in PBS and acquired on Cytek 5-Laser Aurora. Statistical analysis Data are shown as mean ± SEM alongside the individual distribution of n datapoints. Statistical analysis was performed with an unpaired t-test, assuming normally distributed data for comparisons between two groups or One-way ANOVA with Dunnett’s multiple comparisons test for multiple groups. For comparisons within multiple groups with three subgroups each, statistical analysis used two-way ANOVA followed by Tukey’s multiple comparison test. Statistical analysis of allele frequency was performed with Chi-square test followed by Yates’ correction, and carrier frequency with Fisher’s exact test. P -values < 0.05 were considered statistically significant with: *, P <0.05; **, P < 0.01; ***, P < 0.001 and ****, P <0.0001. Statistical analysis was conducted using GraphPad Prism (version 10.2.1). Data accessibility The anonymised patient data can be shared under reasonable request. Author contributions Conceptualization: SF, GCR; Methodology: AR, MB, MDG, DA, TK, AES, KL, EMF; Investigation: AR, MB, MDG, DA, AH, MH, JS, TK, KL, EMF; Funding acquisition: SF, VN, GCR; Project administration: SF, GCR; Supervision: SF, GCR; Writing – original draft: AR, MDG, GCR; Writing – review & editing: all Funding information This work was funded by the German Research Foundation (DFG), including the Collaborative Research Centre 1525 ‘Cardio-immune interfaces’ (grant number 453989101 – project B1); and the Heisenberg Program (G.C.R. grant number 517001338). This study was also supported by the European Research Area Network—Cardiovascular Diseases (ERANET-CVD JCT2018, G.C.R. grant number 01KL1902). Competing interest none Extended Data Figures Download figure Open in new tab Extended Data Figure 1: ( A ) Gating strategy and ( B ) sorting experiment design. PBMCs from five subjects (S1-S5) were kept for 14 days in culture in the presence of either IL-2 or IL-2 combined with ADRB1 peptides (10 conditions). Each condition was stained with a different hashtag antibody along with CITE-seq for CD69, CD25, CD45RA and CD45RO. Singlets, live, CD3 + CD4 + OX40 +/- 4-1BB + events were sorted and cells from all conditions were pooled and sequenced as a single library. Following data filtration, 9,379 single cells were obtained. Download figure Open in new tab Extended Data Figure 2: scRNA/ TCR sequencing quality control and top expressed transcripts per cluster. ( A ) Number of genes, counts and percentage of mitochondrial RNA detected in single cells. ( B ) Heatmap of hashtags 1-10, showing overall cell distributions. #1, 3, 5, 7, 9 show IL-2 control-stimulated cells from S1-S5, respectively, while #2, 4, 6, 8 and 19 show the ADRB1-stimulated cells from S1-S5. ( C ) Violin plots illustrate CITE-seq signal for CD25 and CD45RO. ( D ) Heatmap showing the top 10 up-regulated transcripts per cluster. ( E ) Heatmap shows the Levenshtein distance between the top 50 expanded CDR3β sequences derived from ADRB1-stimulated cultures. The * indicates the great similarity exhibited by the top two expanded clones found in S5. Download figure Open in new tab Extended Data Figure 3: ( A ) Inverted Simpson’s index was estimated for each subject and plotted from control and peptide-stimulated group. ( B ) Frequency of top expanded clones in either ADRB1-stimulated group. Each line represents a different subject. Download figure Open in new tab Extended Data Figure 4: Establishing the experimental conditions of MHCII restricted tetramer against ADRB1 167-182 : ( A ) Representative flow cytometry plots of TCR6 cells (gated in live hCD4 + ) stained with three different concentrations (2, 6 and 18 μg/mL) of Tetramer or hCLIP for three different time points (30, 90 and 180 min) at two different temperatures (4 and 37 °C). The red square indicates the chosen experimental conditions. ( B ) Backgating of Tetramer + cells. Download figure Open in new tab Extended Data Figure 5: ( A ) Workflow of tgTCR cell line generation. ( B ) The nine generated tgTCRs coloured according to their corresponding cell cluster from scRNAseq ( Figure 2B ). TCR1 and TCR6 express the SD*RVNEK motif described in Figure 2 . ( C ) Staining of tgTCRs with MHCII restricted tetramer against ADRB1 167-182 and control hCLIP peptide. Unstained tgTCRs served as FMO (n=3 technical replicates; data are shown as mean±SEM; Statistical analysis was performed with two-way ANOVA followed by Tukey’s multiple comparison test. P -values < 0.05 were considered statistically significant with: ***, P <0.001; ****, P <0.0001). ( D ) Representative flow cytometry plots of TCR6 before sorting for tetramer + cells (left) and after sorting (right). Download figure Open in new tab Extended Data Figure 6: Representative fluorescent images of TCR6 cells stained by immunofluorescence for tetramer + (magenta), TCRβ (cyan) and DAPI (blue). Images were acquired on a Leica Imager DMi8 equipped with a 63x objective. Scale bar: 10 μm. Download figure Open in new tab Extended Data Figure 7: Gating strategy to analyse tgTCR activation after co-culture with PBMCs derived from DRB1*13 + subjects. Download figure Open in new tab Extended Data Figure 8: ( A ) Graph depicting expanded clone proportions in bulkRNAseq of DRB1*13 + (n=7) and DRB1*13 − (n=19) MI patients (Small: 0 < X ≤ 1e-04/, Medium: 1e-04 < X ≤ 0.001/, Large: 0.001 < X ≤ 0.01/, Hyperexpanded: 0.01 < X ≤ 1). Data are shown as mean±SEM; statistical analysis was performed with two-way ANOVA followed by Sidak’s multiple comparison test. P -values < 0.05 were considered statistically significant. ( B ) Diversity index of TCRα chain from the bulkRNAseq data of DRB1*13 + (n=7) versus DRB1*13 − (n=19) MI patients. Data are shown as mean±SEM; Statistical analysis was performed with unpaired t-test. P -values < 0.05 were considered statistically significant. ( C ) Graph depicting the proportion of expanded clones of α chain in bulkRNAseq of DRB1*13 + (n=7) and DRB1*13 − (n=21) MI patients (Small: 0 < X ≤ 1e-04/, Medium: 1e-04 < X ≤ 0.001/, Large: 0.001 < X ≤ 0.01/, Hyperexpanded: 0.01 < X ≤ 1). Data are shown as mean±SEM; statistical analysis was performed with two-way ANOVA followed by Sidak’s multiple comparison test. P -values < 0.05 were considered statistically significant. Download figure Open in new tab Extended Data Figure 9: ( A ) Gating strategy to analyse tetramer + and tetramer − cells originated from MI DRB1*13 + patients. ( B ) Backgating of tetramer + CD4 + T cells. ( C ), ( D ) Gating strategy to further analyse tetramer + and tetramer − cells. Download figure Open in new tab Extended Data Figure 10: ( A ) Representative histogram (left) and quantitative analysis (right) of OX40 + tetramer + versus tetramer − cells for each patient (n=8). Data are shown as mean±SEM; Statistical analysis was performed with unpaired t-test. P -values < 0.05 were considered statistically significant. ( B ) Representative histogram (left) and quantitative analysis (right) of PD-1 + tetramer + versus tetramer − cells for each patient (n=8). Data are shown as mean±SEM; Statistical analysis was performed with unpaired t-test. P -values < 0.05 were considered statistically significant. ( C ) Representative histogram (left) and quantitative analysis (right) of Th1 (CXCR3 + ) Tetramer + versus Tetramer − cells for each patient (n=8). Data are shown as mean±SEM; Statistical analysis was performed with unpaired t-test. P -values < 0.05 were considered statistically significant. ( D ) Representative histogram (left) and quantitative analysis (right) of T H 17 (CCR6 + ) tetramer + versus tetramer − cells for each patient (n=8). Data are shown as mean±SEM; Statistical analysis was performed with unpaired t-test. P -values < 0.05 were considered statistically significant. ( E ) Representative flow cytometry plot of CD45RA/CCR7 gated on live CD45 + /CD3 + /CD4 + cells and ( F ) quantitative analysis of T EMRA and T em cells in tetramer + versus tetramer − cells for each patient (n=8). Data are shown as mean±SEM; Statistical analysis was performed with unpaired t-test. P -values < 0.05 were considered statistically significant. Extended Data Tables View this table: View inline View popup Download powerpoint Extended Data Table I: Top 20 TCR αβ sequences expanded in both control and ADRB1-stimulated PBMCs. For each sequence, the main representative cluster, subject, total counts and global distribution per subject and experiment condition are shown. View this table: View inline View popup Download powerpoint Extended Data Table II: Patient characteristics. CA coronary angiography, HS healthy subject Acknowledgments We thank the NIH Tetramer Core Facility (NIH Contract 75N93020D00005 and RRID:SCR_026557) for providing the HLA-DRB1*13 tetramers used in this study. Some Figures were prepared using graphic elements freely available through the Servier Medical Art. List of non-standard abbreviations ADRB1 adrenergic receptor beta 1 AIM activation induced marker CD cluster of differentiation CDR3 complementarity-determining region 3 CITE-seq cellular indexing of transcriptomes and epitopes by sequencing FACS fluorescent activated cell sorting FOXP3 forkhead box P3 GLIPH grouping of lymphocyte interactions by paratope hotpots HF heart failure HLA human leukocyte antigen IFN-γ interferon gamma IL interleukin MHC major histocompatibility complex MI myocardial infarction MYHCA myosin heavy chain alpha isoform PBMCs peripheral blood mononuclear cells scRNA-seq single-cell RNA sequencing scTCR-seq single-cell T cell receptor sequencing TCR T cell receptor T cm T central memory cell T em T effector memory cell T EMRA effector memory cells re-expressing CD45RA T H T helper cell TRA T cell receptor alpha genes TRB T cell receptor beta genes, Treg regulatory T cell V(D)J variable diversity and joining gene segment. References ↵ Nindl , V. et al. Cooperation of Th1 and Th17 cells determines transition from autoimmune myocarditis to dilated cardiomyopathy . Eur J Immunol 42 , 2311 – 2321 ( 2012 ). doi: 10.1002/eji.201142209 OpenUrl CrossRef PubMed ↵ Gil-Cruz , C. et al. Microbiota-derived peptide mimics drive lethal inflammatory cardiomyopathy . Science 366 , 881 – 886 ( 2019 ). doi: 10.1126/science.aav3487 OpenUrl Abstract / FREE Full Text ↵ Baldeviano , G. C. et al. Interleukin-17A is dispensable for myocarditis but essential for the progression to dilated cardiomyopathy . Circ Res 106 , 1646 – 1655 ( 2010 ). doi: 10.1161/circresaha.109.213157 OpenUrl Abstract / FREE Full Text ↵ Rieckmann , M. et al. Myocardial infarction triggers cardioprotective antigen-specific T helper cell responses . J Clin Invest 129 , 4922 – 4936 ( 2019 ). doi: 10.1172/jci123859 OpenUrl CrossRef PubMed ↵ Delgobo , M. et al. Myocardial Milieu Favors Local Differentiation of Regulatory T Cells . Circ Res 132 , 565 – 582 ( 2023 ). doi: 10.1161/circresaha.122.322183 OpenUrl CrossRef PubMed ↵ Bansal , S. S. et al. Dysfunctional and Proinflammatory Regulatory T-Lymphocytes Are Essential for Adverse Cardiac Remodeling in Ischemic Cardiomyopathy . Circulation 139 , 206 – 221 ( 2019 ). doi: 10.1161/circulationaha.118.036065 OpenUrl CrossRef PubMed ↵ Kallikourdis , M. et al. T cell costimulation blockade blunts pressure overload-induced heart failure . Nat Commun 8 , 14680 ( 2017 ). doi: 10.1038/ncomms14680 OpenUrl CrossRef PubMed ↵ Nevers , T. et al. Left Ventricular T-Cell Recruitment Contributes to the Pathogenesis of Heart Failure . Circ Heart Fail 8 , 776 – 787 ( 2015 ). doi: 10.1161/circheartfailure.115.002225 OpenUrl Abstract / FREE Full Text ↵ Won , T. et al. Cardiac myosin-specific autoimmune T cells contribute to immune-checkpoint-inhibitor-associated myocarditis . Cell Rep 41 , 111611 ( 2022 ). doi: 10.1016/j.celrep.2022.111611 OpenUrl CrossRef PubMed ↵ Axelrod , M. L. et al. T cells specific for α-myosin drive immunotherapy-related myocarditis . Nature 611 , 818 – 826 ( 2022 ). doi: 10.1038/s41586-022-05432-3 OpenUrl CrossRef PubMed ↵ Myers , J. M. et al. Cardiac myosin-Th17 responses promote heart failure in human myocarditis . JCI Insight 1 ( 2016 ). doi: 10.1172/jci.insight.85851 OpenUrl CrossRef PubMed ↵ Pummerer , C. L. et al. Identification of cardiac myosin peptides capable of inducing autoimmune myocarditis in BALB/c mice . J Clin Invest 97 , 2057 – 2062 ( 1996 ). doi: 10.1172/jci118642 OpenUrl CrossRef PubMed Web of Science ↵ Massilamany , C. et al. Localization of CD8 T cell epitope within cardiac myosin heavy chain-α334-352 that induces autoimmune myocarditis in A/J mice . Int J Cardiol 202 , 311 – 321 ( 2016 ). doi: 10.1016/j.ijcard.2015.09.016 OpenUrl CrossRef PubMed ↵ Neu , N. et al. Cardiac myosin induces myocarditis in genetically predisposed mice . J Immunol 139 , 3630 – 3636 ( 1987 ). OpenUrl Abstract ↵ Medzhitov , R. , Schneider , D. S. & Soares , M. P . Disease tolerance as a defense strategy . Science 335 , 936 – 941 ( 2012 ). doi: 10.1126/science.1214935 OpenUrl Abstract / FREE Full Text ↵ Hilgendorf , I. , Frantz , S. & Frangogiannis , N. G . Repair of the Infarcted Heart: Cellular Effectors, Molecular Mechanisms and Therapeutic Opportunities . Circ Res 134 , 1718 – 1751 ( 2024 ). doi: 10.1161/circresaha.124.323658 OpenUrl CrossRef PubMed ↵ Hapke , N. et al. Identification of a novel cardiac epitope triggering T-cell responses in patients with myocardial infarction . J Mol Cell Cardiol 173 , 25 – 29 ( 2022 ). doi: 10.1016/j.yjmcc.2022.09.001 OpenUrl CrossRef PubMed ↵ Caforio , A. L. , Mahon , N. J. , Tona , F. & McKenna , W. J . Circulating cardiac autoantibodies in dilated cardiomyopathy and myocarditis: pathogenetic and clinical significance . Eur J Heart Fail 4 , 411 – 417 ( 2002 ). doi: 10.1016/s1388-9842(02)00010-7 OpenUrl CrossRef PubMed Web of Science ↵ Jahns , R. et al. Autoantibodies activating human beta1-adrenergic receptors are associated with reduced cardiac function in chronic heart failure . Circulation 99 , 649 – 654 ( 1999 ). doi: 10.1161/01.cir.99.5.649 OpenUrl Abstract / FREE Full Text ↵ Störk , S. et al. Stimulating autoantibodies directed against the cardiac beta1-adrenergic receptor predict increased mortality in idiopathic cardiomyopathy . Am Heart J 152 , 697 – 704 ( 2006 ). doi: 10.1016/j.ahj.2006.05.004 OpenUrl CrossRef PubMed Web of Science ↵ Morbach , C. et al. Prevalence of anti-beta-1 antibody 6 months after hospitalization for acute heart failure predicts adverse outcome . ESC Heart Fail 10 , 3227 – 3231 ( 2023 ). doi: 10.1002/ehf2.14509 OpenUrl CrossRef PubMed ↵ Basavalingappa , R. H. et al. β1-Adrenergic Receptor Contains Multiple IA(k) and IE(k) Binding Epitopes That Induce T Cell Responses with Varying Degrees of Autoimmune Myocarditis in A/J Mice . Front Immunol 8 , 1567 ( 2017 ). doi: 10.3389/fimmu.2017.01567 OpenUrl CrossRef PubMed ↵ Martini , E. et al. Autoimmune-Like Mechanism in Heart Failure Enables Preventive Vaccine Therapy . Circ Res 136 , 4 – 25 ( 2025 ). doi: 10.1161/circresaha.124.324999 OpenUrl CrossRef PubMed ↵ Roy , P. , Suthahar , S. S. A. , Makings , J. & Ley , K . Identification of apolipoprotein B-reactive CDR3 motifs allows tracking of atherosclerosis-related memory CD4(+)T cells in multiple donors . Front Immunol 15 , 1302031 ( 2024 ). doi: 10.3389/fimmu.2024.1302031 OpenUrl CrossRef ↵ Lemieux , A. et al. Enhanced detection of antigen-specific T cells by a multiplexed AIM assay . Cell Rep Methods 4 , 100690 ( 2024 ). doi: 10.1016/j.crmeth.2023.100690 OpenUrl CrossRef PubMed ↵ Chen , G. et al. Sequence and Structural Analyses Reveal Distinct and Highly Diverse Human CD8(+) TCR Repertoires to Immunodominant Viral Antigens . Cell Rep 19 , 569 – 583 ( 2017 ). doi: 10.1016/j.celrep.2017.03.072 OpenUrl CrossRef PubMed ↵ Tickotsky , N. , Sagiv , T. , Prilusky , J. , Shifrut , E. & Friedman , N . McPAS-TCR: a manually curated catalogue of pathology-associated T cell receptor sequences . Bioinformatics 33 , 2924 – 2929 ( 2017 ). doi: 10.1093/bioinformatics/btx286 OpenUrl CrossRef PubMed ↵ Glanville , J. et al. Identifying specificity groups in the T cell receptor repertoire . Nature 547 , 94 – 98 ( 2017 ). doi: 10.1038/nature22976 OpenUrl CrossRef PubMed ↵ Huang , H. , Wang , C. , Rubelt , F. , Scriba , T. J. & Davis , M. M . Analyzing the Mycobacterium tuberculosis immune response by T-cell receptor clustering with GLIPH2 and genome-wide antigen screening . Nat Biotechnol 38 , 1194 – 1202 ( 2020 ). doi: 10.1038/s41587-020-0505-4 OpenUrl CrossRef PubMed ↵ Richter , L. et al. Identification of a novel myosin antigen activating CD8 + T cells in C57BL/6 mice . bioRxiv , 2024.2007.2026.605251 ( 2024 ). doi: 10.1101/2024.07.26.605251 OpenUrl Abstract / FREE Full Text ↵ Mann , S. E. et al. Multiplex T Cell Stimulation Assay Utilizing a T Cell Activation Reporter-Based Detection System . Front Immunol 11 , 633 ( 2020 ). doi: 10.3389/fimmu.2020.00633 OpenUrl CrossRef PubMed ↵ Landry , L. G. et al. Proinsulin-Reactive CD4 T Cells in the Islets of Type 1 Diabetes Organ Donors . Front Endocrinol (Lausanne) 12 , 622647 ( 2021 ). doi: 10.3389/fendo.2021.622647 OpenUrl CrossRef ↵ Deubner , N. et al. Cardiac beta1-adrenoceptor autoantibodies in human heart disease: rationale and design of the Etiology, Titre-Course, and Survival (ETiCS) Study . Eur J Heart Fail 12 , 753 – 762 ( 2010 ). doi: 10.1093/eurjhf/hfq072 OpenUrl CrossRef PubMed ↵ Gonzalez-Galarza , F. F. et al. Allele frequency net database (AFND) 2020 update: gold-standard data classification, open access genotype data and new query tools . Nucleic Acids Res 48 , D783 – d788 ( 2020 ). doi: 10.1093/nar/gkz1029 OpenUrl CrossRef PubMed ↵ Le Gouge , K. et al. A distinct T cell receptor signature associates with cardiac outcome in myocardial infarction patients . medRxiv , 2023.2011.2023.23298725 ( 2023 ). doi: 10.1101/2023.11.23.23298725 OpenUrl Abstract / FREE Full Text ↵ Saigusa , R. et al. Single cell transcriptomics and TCR reconstruction reveal CD4 T cell response to MHC-II-restricted APOB epitope in human cardiovascular disease . Nat Cardiovasc Res 1 , 462 – 475 ( 2022 ). doi: 10.1038/s44161-022-00063-3 OpenUrl CrossRef PubMed ↵ Kimura , T. et al. Regulatory CD4(+) T Cells Recognize Major Histocompatibility Complex Class II Molecule-Restricted Peptide Epitopes of Apolipoprotein B . Circulation 138 , 1130 – 1143 ( 2018 ). doi: 10.1161/circulationaha.117.031420 OpenUrl CrossRef PubMed ↵ Wolf , D. et al. Pathogenic Autoimmunity in Atherosclerosis Evolves From Initially Protective Apolipoprotein B(100)-Reactive CD4(+) T-Regulatory Cells . Circulation 142 , 1279 – 1293 ( 2020 ). doi: 10.1161/circulationaha.119.042863 OpenUrl CrossRef PubMed Zhao , T. X. et al. Regulatory T-Cell Response to Low-Dose Interleukin-2 in Ischemic Heart Disease . NEJM Evidence 1 , EVIDoa2100009 ( 2022 ). doi: 10.1056/EVIDoa2100009 OpenUrl CrossRef ↵ Zhao , T. X. et al. Low-dose interleukin-2 in patients with stable ischaemic heart disease and acute coronary syndromes (LILACS): protocol and study rationale for a randomised, double-blind, placebo-controlled, phase I/II clinical trial . BMJ Open 8 , e022452 ( 2018 ). doi: 10.1136/bmjopen-2018-022452 OpenUrl Abstract / FREE Full Text References 41. ↵ Roy , P. et al. Immunodominant MHC-II (Major Histocompatibility Complex II) Restricted Epitopes in Human Apolipoprotein B . Circ Res 131 , 258 – 276 ( 2022 ). doi: 10.1161/circresaha.122.321116 OpenUrl CrossRef PubMed 42. ↵ Stoeckius , M. et al. Simultaneous epitope and transcriptome measurement in single cells . Nat Methods 14 , 865 – 868 ( 2017 ). doi: 10.1038/nmeth.4380 OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted June 06, 2025. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Tracking antigen-specific T cell responses in patients with myocardial infarction Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share Tracking antigen-specific T cell responses in patients with myocardial infarction Anna Rizakou , Michelle Bauer , Murilo Delgobo , Alexander Hofmann , Margarete Heinrichs , Johanna Siegel , Tobias Krammer , Antoine-Emmanuel Saliba , Kenz Le Gouge , Roland Jahns , Valerie Boivin-Jahns , Viacheslav Nikolaev , Ulrich Hofmann , Encarnita Mariotti-Ferrandiz , DiyaaElDin Ashour , Stefan Frantz , Gustavo Campos Ramos medRxiv 2025.06.05.25328514; doi: https://doi.org/10.1101/2025.06.05.25328514 Share This Article: Copy Citation Tools Tracking antigen-specific T cell responses in patients with myocardial infarction Anna Rizakou , Michelle Bauer , Murilo Delgobo , Alexander Hofmann , Margarete Heinrichs , Johanna Siegel , Tobias Krammer , Antoine-Emmanuel Saliba , Kenz Le Gouge , Roland Jahns , Valerie Boivin-Jahns , Viacheslav Nikolaev , Ulrich Hofmann , Encarnita Mariotti-Ferrandiz , DiyaaElDin Ashour , Stefan Frantz , Gustavo Campos Ramos medRxiv 2025.06.05.25328514; doi: https://doi.org/10.1101/2025.06.05.25328514 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Cardiovascular Medicine Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (299) Cardiovascular Medicine (4425) Dentistry and Oral Medicine (443) Dermatology (382) Emergency Medicine (607) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1507) Epidemiology (15221) Forensic Medicine (30) Gastroenterology (1123) Genetic and Genomic Medicine (6588) Geriatric Medicine (667) Health Economics (997) Health Informatics (4524) Health Policy (1368) Health Systems and Quality Improvement (1612) Hematology (540) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15910) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (145) Nephrology (667) Neurology (6588) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1143) Occupational and Environmental Health (956) Oncology (3331) Ophthalmology (970) Orthopedics (369) Otolaryngology (420) Pain Medicine (435) Palliative Medicine (129) Pathology (663) Pediatrics (1690) Pharmacology and Therapeutics (691) Primary Care Research (710) Psychiatry and Clinical Psychology (5440) Public and Global Health (9220) Radiology and Imaging (2195) Rehabilitation Medicine and Physical Therapy (1369) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (710) Sports Medicine (529) Surgery (710) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9ffd3a783c23e2c5',t:'MTc3OTQ2NzY2Nw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();