Reproducible single cell annotation of programs underlying T-cell subsets, activation states, and functions Article Swipe
Dylan Kotliar
,
Michelle Curtis
,
Ryan Agnew
,
Kathryn Weinand
,
Aparna Nathan
,
Yuriy Baglaenko
,
Yu Zhao
,
Pardis C. Sabeti
,
Deepak A. Rao
,
Soumya Raychaudhuri
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.05.03.592310
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.05.03.592310
T-cells recognize antigens and induce specialized gene expression programs (GEPs) enabling functions including proliferation, cytotoxicity, and cytokine production. Traditionally, different classes of helper T-cells express mutually exclusive responses – for example, Th1, Th2, and Th17 programs. However, new single-cell RNA sequencing (scRNA-Seq) experiments have revealed a continuum of T-cell states without discrete clusters corresponding to these subsets, implying the need for new analytical frameworks. Here, we advance the characterization of T-cells with T-CellAnnoTator (TCAT), a pipeline that simultaneously quantifies pre-defined GEPs capturing activation states and cellular subsets. From 1,700,000 T-cells from 700 individuals across 38 tissues and five diverse disease contexts, we discover 46 reproducible GEPs reflecting the known core functions of T-cells including proliferation, cytotoxicity, exhaustion, and T helper effector states. We experimentally characterize several novel activation programs and apply TCAT to describe T-cell activation and exhaustion in Covid-19 and cancer, providing insight into T-cell function in these diseases.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.05.03.592310
- https://www.biorxiv.org/content/biorxiv/early/2024/05/05/2024.05.03.592310.full.pdf
- OA Status
- green
- Cited By
- 17
- References
- 72
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396675814
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- DOI
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https://doi.org/10.1101/2024.05.03.592310Digital Object Identifier
- Title
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Reproducible single cell annotation of programs underlying T-cell subsets, activation states, and functionsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-05Full publication date if available
- Authors
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Dylan Kotliar, Michelle Curtis, Ryan Agnew, Kathryn Weinand, Aparna Nathan, Yuriy Baglaenko, Yu Zhao, Pardis C. Sabeti, Deepak A. Rao, Soumya RaychaudhuriList of authors in order
- Landing page
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https://doi.org/10.1101/2024.05.03.592310Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2024/05/05/2024.05.03.592310.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2024/05/05/2024.05.03.592310.full.pdfDirect OA link when available
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Annotation, Cell, Computational biology, Computer science, Cell biology, Chemistry, Biology, Artificial intelligence, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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17Total citation count in OpenAlex
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2025: 14, 2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.quantifies | 79 |
| abstract_inverted_index.reflecting | 107 |
| abstract_inverted_index.sequencing | 41 |
| abstract_inverted_index.(scRNA-Seq) | 42 |
| abstract_inverted_index.exhaustion, | 117 |
| abstract_inverted_index.experiments | 43 |
| abstract_inverted_index.frameworks. | 64 |
| abstract_inverted_index.individuals | 93 |
| abstract_inverted_index.pre-defined | 80 |
| abstract_inverted_index.production. | 18 |
| abstract_inverted_index.single-cell | 39 |
| abstract_inverted_index.specialized | 6 |
| abstract_inverted_index.characterize | 125 |
| abstract_inverted_index.reproducible | 105 |
| abstract_inverted_index.corresponding | 54 |
| abstract_inverted_index.cytotoxicity, | 15, 116 |
| abstract_inverted_index.Traditionally, | 19 |
| abstract_inverted_index.experimentally | 124 |
| abstract_inverted_index.proliferation, | 14, 115 |
| abstract_inverted_index.simultaneously | 78 |
| abstract_inverted_index.T-CellAnnoTator | 73 |
| abstract_inverted_index.characterization | 69 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5081489856 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I107606265, https://openalex.org/I1283280774, https://openalex.org/I136199984 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.44999998807907104 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.96543043 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |