{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T22:34:10Z","timestamp":1781822050537,"version":"3.54.5"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1011300","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T00:00:00Z","timestamp":1689811200000}}],"reference-count":23,"publisher":"Public Library of Science (PLoS)","issue":"7","license":[{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000051","name":"National Human Genome Research Institute","doi-asserted-by":"publisher","award":["R21 HG012482"],"award-info":[{"award-number":["R21 HG012482"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000049","name":"National Institute on Aging","doi-asserted-by":"publisher","award":["U54 AG075931"],"award-info":[{"award-number":["U54 AG075931"]}],"id":[{"id":"10.13039\/100000049","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R01 GM122078"],"award-info":[{"award-number":["R01 GM122078"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R01 GM131399"],"award-info":[{"award-number":["R01 GM131399"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000026","name":"National Institute on Drug Abuse","doi-asserted-by":"publisher","award":["U01 DA045300"],"award-info":[{"award-number":["U01 DA045300"]}],"id":[{"id":"10.13039\/100000026","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["NSF1945971"],"award-info":[{"award-number":["NSF1945971"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Pelotonia Institute of Immuno-Oncology"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Single-cell RNA sequencing (scRNA-seq) data has been widely used for cell trajectory inference, with the assumption that cells with similar expression profiles share the same differentiation state. However, the inferred trajectory may not reveal clonal differentiation heterogeneity among T cell clones. Single-cell T cell receptor sequencing (scTCR-seq) data provides invaluable insights into the clonal relationship among cells, yet it lacks functional characteristics. Therefore, scRNA-seq and scTCR-seq data complement each other in improving trajectory inference, where a reliable computational tool is still missing. We developed LRT, a computational framework for the integrative analysis of scTCR-seq and scRNA-seq data to explore clonal differentiation trajectory heterogeneity. Specifically, LRT uses the transcriptomics information from scRNA-seq data to construct overall cell trajectories and then utilizes both the TCR sequence information and phenotype information to identify clonotype clusters with distinct differentiation biasedness. LRT provides a comprehensive analysis workflow, including preprocessing, cell trajectory inference, clonotype clustering, trajectory biasedness evaluation, and clonotype cluster characterization. We illustrated its utility using scRNA-seq and scTCR-seq data of CD8\n                    <jats:sup>+<\/jats:sup>\n                    T cells and CD4\n                    <jats:sup>+<\/jats:sup>\n                    T cells with acute lymphocytic choriomeningitis virus infection. These analyses identified several clonotype clusters with distinct skewed distribution along the differentiation path, which cannot be revealed solely based on scRNA-seq data. Clones from different clonotype clusters exhibited diverse expansion capability, V-J gene usage pattern and CDR3 motifs. The LRT framework was implemented as an R package \u2018LRT\u2019, and it is now publicly accessible at\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/JuanXie19\/LRT\" xlink:type=\"simple\">https:\/\/github.com\/JuanXie19\/LRT<\/jats:ext-link>\n                    . In addition, it provides two Shiny apps \u2018shinyClone\u2019 and \u2018shinyClust\u2019 that allow users to interactively explore distributions of clonotypes, conduct repertoire analysis, implement clustering of clonotypes, trajectory biasedness evaluation, and clonotype cluster characterization.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1011300","type":"journal-article","created":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T14:01:13Z","timestamp":1688997673000},"page":"e1011300","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":9,"title":["LRT: Integrative analysis of scRNA-seq and scTCR-seq data to investigate clonal differentiation heterogeneity"],"prefix":"10.1371","volume":"19","author":[{"given":"Juan","family":"Xie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyeongseon","family":"Jeon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Xin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3264-8392","authenticated-orcid":true,"given":"Qin","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8072-5671","authenticated-orcid":true,"given":"Dongjun","family":"Chung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,7,10]]},"reference":[{"issue":"7","key":"pcbi.1011300.ref001","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1038\/s41576-020-0223-2","article-title":"Lineage tracing meets single-cell omics: opportunities and challenges","volume":"21","author":"DE Wagner","year":"2020","journal-title":"Nature Reviews Genetics"},{"issue":"4","key":"pcbi.1011300.ref002","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1038\/nbt.2859","article-title":"The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells","volume":"32","author":"C Trapnell","year":"2014","journal-title":"Nature biotechnology"},{"issue":"1","key":"pcbi.1011300.ref003","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12864-018-4772-0","article-title":"Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics","volume":"19","author":"K Street","year":"2018","journal-title":"BMC genomics"},{"issue":"5","key":"pcbi.1011300.ref004","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/s41587-019-0071-9","article-title":"A comparison of single-cell trajectory inference methods","volume":"37","author":"W Saelens","year":"2019","journal-title":"Nature biotechnology"},{"issue":"1","key":"pcbi.1011300.ref005","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-020-16821-5","article-title":"Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data","volume":"11","author":"H Zafar","year":"2020","journal-title":"Nature communications"},{"issue":"4","key":"pcbi.1011300.ref006","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1038\/nmeth.3800","article-title":"T cell fate and clonality inference from single-cell transcriptomes","volume":"13","author":"MJ Stubbington","year":"2016","journal-title":"Nature methods"},{"issue":"6","key":"pcbi.1011300.ref007","doi-asserted-by":"crossref","first-page":"e20201329","DOI":"10.1084\/jem.20201329","article-title":"STARTRAC analyses of scRNAseq data from tumor models reveal T cell dynamics and therapeutic targets","volume":"218","author":"D Bhatt","year":"2021","journal-title":"Journal of Experimental Medicine"},{"issue":"1","key":"pcbi.1011300.ref008","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1038\/s41591-018-0266-5","article-title":"Low and variable tumor reactivity of the intratumoral TCR repertoire in human cancers","volume":"25","author":"W Scheper","year":"2019","journal-title":"Nature medicine"},{"issue":"2","key":"pcbi.1011300.ref009","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.ygeno.2020.12.036","article-title":"Comprehensive analysis of TCR repertoire in COVID-19 using single cell sequencing","volume":"113","author":"P Wang","year":"2021","journal-title":"Genomics"},{"key":"pcbi.1011300.ref010","first-page":"5281","article-title":"immunarch: an R package for painless bioinformatics analysis of T-cell and B-cell immune repertoires","author":"I. 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