{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,20]],"date-time":"2025-09-20T04:47:04Z","timestamp":1758343624133,"version":"3.44.0"},"reference-count":38,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T00:00:00Z","timestamp":1758240000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012652","name":"ETH Z\u00c3\u00bcrich Foundation","doi-asserted-by":"publisher","award":["ETH-39 14-2"],"award-info":[{"award-number":["ETH-39 14-2"]}],"id":[{"id":"10.13039\/501100012652","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004784","name":"Novartis Stiftung\u00c2\u00a0f\u00c3\u00bcr\u00c2\u00a0Medizinisch-Biologische Forschung","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004784","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["CL 792\/1-1"],"award-info":[{"award-number":["CL 792\/1-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zentrum f\u00c3\u00bcr Personalisierte Medizin"},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["EXC 2180"],"award-info":[{"award-number":["EXC 2180"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Trajectory inference refers to the task of reconstructing state sequences of dynamic processes from single-cell RNA sequencing (scRNAseq) data. This task frequently results in ambiguous results due to the noisiness of the data. While this issue has been alleviated by the incorporation of directional information from RNA velocity analyses, it remains difficult to resolve complex differentiation topologies, such as convergent trajectories.<\/jats:p>\n<jats:p>We introduce exploratory trajectory inference to address this challenge. This approach considers unsupervised clustering analysis of trajectory ensembles derived from simulation-based trajectory inference to deduce differentiation lineages in a data-driven fashion. We assess this approach to resolve the convergent differentiation trajectories in CD8 T-cell differentiation in chronic infections. We utilize an original scRNAseq time-series dataset of CD8 T cells collected during the time course of a chronic LCMV infection. Simulation-based trajectory inference identified a branch region early during chronic infection where cells separate into an exhausted and a memory-like lineage. Exploratory trajectory inference further allowed us to identify a convergent differentiation trajectory traversing memory-like states and ending in the exhausted population. Adoptive transfer experiments showed CD8 T cells with predicted memory-like fate differentiating into both memory-like and exhaustion states, confirming the convergent differentiation topology.<\/jats:p>\n<jats:p>We expect exploratory trajectory inference to be applicable in other scRNAseq-based studies aiming at comprehensive characterization of differentiation trajectories with bifurcating and convergent topologies.<\/jats:p>","DOI":"10.1371\/journal.pone.0332406","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T21:27:21Z","timestamp":1758317241000},"page":"e0332406","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploratory trajectory inference reveals convergent lineages for CD8 T cells in chronic LCMV infection"],"prefix":"10.1371","volume":"20","author":[{"given":"Jan 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