{"doi":"10.1101/2023.09.27.559702","title":"T cell receptor-centric perspective to multimodal single-cell data analysis","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>The T-cell receptor (TCR) carries critical information regarding T-cell functionality. The TCR, despite its importance, is underutilized in single cell transcriptomics, with gene expression (GEx) features solely driving current analysis strategies. Here, we argue for a switch to a TCR-first approach, which would uncover unprecedented insights into T cell and TCR repertoire mechanics. To this end, we curated a large T-cell atlas from 12 prominent human studies, containing in total 500,000 T cells spanning multiple diseases, including melanoma, head-and-neck cancer, T-cell cancer, and lung transplantation. Herein, we identified severe limitations in cell-type annotation using unsupervised approaches and propose a more robust standard using a semi-supervised method or the TCR arrangement. We then showcase the utility of a TCR-first approach through application of the novel STEGO.R tool for the successful identification of hyperexpanded clones to reveal treatment-specific changes. Additionally, a meta-analysis based on neighbor enrichment revealed previously unknown public T-cell clusters with potential antigen-specific properties as well as highlighting additional common TCR arrangements. Therefore, this paradigm shift to a TCR-first with STEGO.R highlights T-cell features often overlooked by conventional GEx-focused methods, and enabled identification of T cell features that have the potential for improvements in immunotherapy and diagnostics.</jats:p>\n                <jats:sec>\n                  <jats:title>One Sentence Summary</jats:title>\n                  <jats:p>Revamping the interrogation strategies for single-cell data to be centered on T cell receptor (TCR) rather than the generic gene expression improved the capacity to find relevant disease specific TCR.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Key Points</jats:title>\n                  <jats:list list-type=\"bullet\">\n                    <jats:list-item>\n                      <jats:p>The TCR-first approach captures dynamic T cell features, even within a clonal population.</jats:p>\n                    </jats:list-item>\n                    <jats:list-item>\n                      <jats:p>A novel ∼500,000 T-cell atlas to enhance single cell analysis, especially for restricted populations.</jats:p>\n                    </jats:list-item>\n                    <jats:list-item>\n                      <jats:p>Novel STEGO.R program and pipeline allows for consistent and reproducible interrogating of scTCR-seq with GEx.</jats:p>\n                    </jats:list-item>\n                  </jats:list>\n                </jats:sec>","journal":null,"year":null,"id":617373,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":1,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1592156,"name":"My Ha","orcid":"0000-0002-6262-341X","position":1,"is_corresponding":false},{"id":1455655,"name":"Sebastiaan Valkiers","orcid":"0000-0002-4940-1310","position":2,"is_corresponding":false},{"id":1592158,"name":"Nicky de Vrij","orcid":"0000-0002-7962-745X","position":3,"is_corresponding":false},{"id":954072,"name":"Benson Ogunjimi","orcid":"0000-0002-0831-2063","position":4,"is_corresponding":false},{"id":696408,"name":"Kris Laukens","orcid":"0000-0002-8217-2564","position":5,"is_corresponding":false},{"id":1282411,"name":"Pieter Meysman","orcid":"0000-0001-5903-633X","position":6,"is_corresponding":false},{"id":1282402,"name":"Kerry A. Mullan","orcid":"0000-0003-4400-1198","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"T cell receptor-centric perspective to multimodal single-cell data analysis","abstract":"<jats:title>Abstract</jats:title>\n                <jats:p>The T-cell receptor (TCR) carries critical information regarding T-cell functionality. The TCR, despite its importance, is underutilized in single cell transcriptomics, with gene expression (GEx) features solely driving current analysis strategies. Here, we argue for a switch to a TCR-first approach, which would uncover unprecedented insights into T cell and TCR repertoire mechanics. To this end, we curated a large T-cell atlas from 12 prominent human studies, containing in total 500,000 T cells spanning multiple diseases, including melanoma, head-and-neck cancer, T-cell cancer, and lung transplantation. Herein, we identified severe limitations in cell-type annotation using unsupervised approaches and propose a more robust standard using a semi-supervised method or the TCR arrangement. We then showcase the utility of a TCR-first approach through application of the novel STEGO.R tool for the successful identification of hyperexpanded clones to reveal treatment-specific changes. Additionally, a meta-analysis based on neighbor enrichment revealed previously unknown public T-cell clusters with potential antigen-specific properties as well as highlighting additional common TCR arrangements. Therefore, this paradigm shift to a TCR-first with STEGO.R highlights T-cell features often overlooked by conventional GEx-focused methods, and enabled identification of T cell features that have the potential for improvements in immunotherapy and diagnostics.</jats:p>\n                <jats:sec>\n                  <jats:title>One Sentence Summary</jats:title>\n                  <jats:p>Revamping the interrogation strategies for single-cell data to be centered on T cell receptor (TCR) rather than the generic gene expression improved the capacity to find relevant disease specific TCR.</jats:p>\n                </jats:sec>\n                <jats:sec>\n                  <jats:title>Key Points</jats:title>\n                  <jats:list list-type=\"bullet\">\n                    <jats:list-item>\n                      <jats:p>The TCR-first approach captures dynamic T cell features, even within a clonal population.</jats:p>\n                    </jats:list-item>\n                    <jats:list-item>\n                      <jats:p>A novel ∼500,000 T-cell atlas to enhance single cell analysis, especially for restricted populations.</jats:p>\n                    </jats:list-item>\n                    <jats:list-item>\n                      <jats:p>Novel STEGO.R program and pipeline allows for consistent and reproducible interrogating of scTCR-seq with GEx.</jats:p>\n                    </jats:list-item>\n                  </jats:list>\n                </jats:sec>","is_dataset_classified":null,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W4387160044","authors":[],"funders":[],"total_grants":0,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[{"year":2023,"count":1},{"year":2025,"count":2}],"oa_status":"green","license":"cc-by","oa_locations":[{"url":"https://www.biorxiv.org/content/biorxiv/early/2023/09/29/2023.09.27.559702.full.pdf","host_type":"repository"},{"url":"https://www.biorxiv.org/content/biorxiv/early/2023/09/29/2023.09.27.559702.full.pdf","host_type":"repository"},{"url":"https://syndication.highwire.org/content/doi/10.1101/2023.09.27.559702","host_type":"publisher"},{"url":"https://doi.org/10.1101/2023.09.27.559702","host_type":"repository"},{"url":"https://hdl.handle.net/10067/1999660151162165141","host_type":"repository"}],"fields_of_study":["Single-cell and spatial transcriptomics","T-cell and B-cell Immunology","Immune Cell Function and Interaction"],"mesh_terms":[],"keywords":["T-cell receptor","Computational biology","T cell","Biology","Cell","Computer science","Genetics","Immune system"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-03T01:34:00.120528Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}