The DOI matches its Crossref record; 63 of the 63 references it cites were found in a registry or an index.
TCR sequencing paired with massively parallel 3′ RNA-seq reveals clonotypic T cell signatures
Abstract
High-throughput 3' single-cell RNA-sequencing (scRNA-seq) allows cost-effective, detailed characterization of individual immune cells from tissues. Current techniques, however, are limited in their ability to elucidate essential immune cell features, including variable sequences of T cell antigen receptors (TCRs) that confer antigen specificity. Here, we present a strategy that enables simultaneous analysis of TCR sequences and corresponding full transcriptomes from 3'-barcoded scRNA-seq samples. This approach is compatible with common 3' scRNA-seq methods, and adaptable to processed samples post hoc. We applied the technique to identify transcriptional signatures associated with T cells sharing common TCRs from immunized mice and from patients with food allergy. We observed preferential phenotypes among subsets of expanded clonotypes, including type 2 helper CD4+ T cell (T H 2) states associated with food allergy. These results demonstrate the utility of our method when studying diseases in which clonotype-driven responses are critical to understanding the underlying biology.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Several methods have been published pairing gene expression with TCR sequences in single cells89–92.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [91]
Citations
Cites
The Molecular Signatures Database Hallmark Gene Set Collection
Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015) · Cell Systems · doi:10.1016/j.cels.2015.12.004 · Cited by 13,807
Molecular signatures database (MSigDB) 3.0
Liberzon, A., Subramanian, A., Pinchback, R., Thorvaldsdóttir, H., Tamayo, P., & Mesirov, J. P. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr260 · Cited by 7,111
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110
Self-citationReversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944
Self-citationcirclize implements and enhances circular visualization in R
Gu, Z., Gu, L., Eils, R., Schlesner, M., & Brors, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu393 · Cited by 4,290
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences
Raj, A., & van Oudenaarden, A. (2008) · Cell · doi:10.1016/j.cell.2008.09.050 · Cited by 2,593
Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment
Azizi, E., Carr, A. J., Plitas, G., Cornish, A. E., Konopacki, C., Prabhakaran, S., Nainys, J., Wu, K., Kiseliovas, V., Setty, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.060 · Cited by 1,976
Nanopore sequencing and assembly of a human genome with ultra-long reads
Jain, M., Koren, S., Miga, K. H., Quick, J., Rand, A. C., Sasani, T. A., Tyson, J. R., Beggs, A. D., Dilthey, A. T., Fiddes, I. T., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4060 · Cited by 1,892
Cited by
Sorted by citations
The technological landscape and applications of single-cell multi-omics
Baysoy, A., Bai, Z., Satija, R., & Fan, R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00615-w · Cited by 1,037
Moragues, T., Mendoza, D. A., Beneyton, T., Modavi, C., Simutis, K., Abate, A. R., Baret, J., deMello, A. J., Densmore, D. M., & Griffiths, A. D. (2023) · Nature Reviews Methods Primers · doi:10.1038/s43586-023-00212-3 · Cited by 355
High-throughput and single-cell T cell receptor sequencing technologies
Pai, J. A., & Satpathy, A. T. (2021) · Nature Methods · doi:10.1038/s41592-021-01201-8 · Cited by 301
Hughes, T. K., Wadsworth, M. H., Gierahn, T. M., Do, T. H., Weiss, D. I., Andrade, P. R., Ma, F., Silva, B. J. D. A., Shao, S., Tsoi, L. C., et al. (2020) · Immunity · doi:10.1016/j.immuni.2020.09.015 · Cited by 291
Self-citationVaccine-boosted CAR T crosstalk with host immunity to reject tumors with antigen heterogeneity
Ma, L., Hostetler, A., Morgan, D. M., Maiorino, L., Sulkaj, I., Whittaker, C. A., Neeser, A., Pires, I. S., Yousefpour, P., Gregory, J. R., et al. (2023) · Cell · doi:10.1016/j.cell.2023.06.002 · Cited by 232
Self-citation
Metadata
- Authors
- Tu, A. A., Gierahn, T. M., Monian, B., Morgan, D. M., Mehta, N. K., Ruiter, B., Shreffler, W. G., Shalek, A. K., & Love, J. C.
- Affiliations
- Koch Institute for Integrative Cancer Research At MIT (US)
- Harvard University (US)
- Broad Institute (US)
- Corresponding author
- Shalek, Alex K.
- Love, J. Christopher
- ORCID
- Venue
- Nature Immunology
- Published
- 2019-11-19
- Volume
- 20
- Issue
- 12
- Pages
- 1692-1699
- ISSN
- 1529-2908 (ISSN-L)
- 1529-2916
- OpenAlex
- W2991527058
- Cited by
- 128
- References
- 63
- Citations by year
- 2026: 13 · 2025: 7 · 2024: 26 · 2023: 21 · 2022: 28 · 2021: 16 · 2020: 15 · 2019: 1
- FWCI
- 4.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Nature Immunology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Broad Institute
- U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases (U19AI095261, 5P01AI039671, 5U19AI089992)
- Koch Institute - Dana-Farber/Harvard Cancer Center Bridge Project.
- U.S. Department of Health & Human Services | National Institutes of Health (1U54CA217377, 2RM1HG006193, 1R33CA202820, 1R01AI138546, 1R01DA046277, 1U2CCA23319501)
- Searle Scholars Program, Beckman Young Investigator Program,Pew-Stewart Scholars Program for Cancer Research
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
- Keywords
- T cell receptor, TCR sequencing, single-cell RNA sequencing, T cell transcriptome, Th2 cells, food allergy, immunized mice
- MeSH terms
- Animals, Cells, Cultured, Female, Humans, Immunoglobulin E, Immunization, Male, Mice, Inbred C57BL, Mice, Transgenic, Receptors, Antigen, T-Cell, alpha-beta, CD8-Positive T-Lymphocytes, Th2 Cells, Peanut Hypersensitivity, Complementarity Determining Regions, T-Cell Antigen Receptor Specificity, Papillomavirus E7 Proteins, Mice, Antigens, Plant, 2S Albumins, Plant, Single-Cell Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
