The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells
Open accessAbstract
Summary Reversing the dysfunctional T cell state that arises in cancer and chronic viral infections is the focus of therapeutic interventions; however, current therapies are effective in only some patients and some tumor types. To gain a deeper molecular understanding of the dysfunctional T cell state, we analyzed population and single-cell RNA profiles of CD8+ tumor-infiltrating lymphocytes (TILs) and used genetic perturbations to identify a distinct gene module for T cell dysfunction that can be uncoupled from T cell activation. This distinct dysfunction module is downstream of intracellular metallothioneins that regulate zinc metabolism and can be identified at single-cell resolution. We further identify Gata-3, a zinc-finger transcription factor in the dysfunctional module, as a regulator of dysfunction, and use CRISPR/Cas9 genome editing to show that it drives a dysfunctional phenotype in CD8+ TILs. Our results open novel avenues for targeting dysfunctional T cell states, while leaving activation programs intact.
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Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
(2008) · Journal of Machine Learning Research · Cited by 33,051
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Multiplex Genome Engineering Using CRISPR/Cas Systems
Cong, L., Ran, F. A., Cox, D., Lin, S., Barretto, R., Habib, N., Hsu, P. D., Wu, X., Jiang, W., Marraffini, L. A., et al. (2013) · Science · doi:10.1126/science.1231143 · Cited by 15,339
Self-citationAdjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
Genome-scale CRISPR-Cas9 knockout screening in human cells
Shalem, O., Sanjana, N. E., Hartenian, E., Shi, X., Scott, D. A., Mikkelsen, T. S., Heckl, D., Ebert, B. L., Root, D. E., Doench, J. G., et al. (2014) · Science · doi:10.1126/science.1247005 · Cited by 5,234
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-citationMolecular and cellular insights into T cell exhaustion
Wherry, E. J., & Kurachi, M. (2015) · Nature Reviews Immunology · doi:10.1038/nri3862 · Cited by 4,652
Maps of random walks on complex networks reveal community structure
Rosvall, M., & Bergstrom, C. T. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0706851105 · Cited by 4,583
Cited by
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232
Self-citationMiller, B. C., Sen, D. R., Al Abosy, R., Bi, K., Virkud, Y. V., LaFleur, M. W., Yates, K. B., Lako, A., Felt, K., Naik, G. S., et al. (2019) · Nature Immunology · doi:10.1038/s41590-019-0312-6 · Cited by 2,087
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
Siddiqui, I., Schaeuble, K., Chennupati, V., Fuertes Marraco, S. A., Calderon-Copete, S., Pais Ferreira, D., Carmona, S. J., Scarpellino, L., Gfeller, D., Pradervand, S., et al. (2019) · Immunity · doi:10.1016/j.immuni.2018.12.021 · Cited by 1,679
Lineage tracking reveals dynamic relationships of T cells in colorectal cancer
Zhang, L., Yu, X., Zheng, L., Zhang, Y., Li, Y., Fang, Q., Gao, R., Kang, B., Zhang, Q., Huang, J. Y., et al. (2018) · Nature · doi:10.1038/s41586-018-0694-x · Cited by 1,505
Metadata
- Authors
- Singer, M., Wang, C., Cong, L., Marjanovic, N. D., Kowalczyk, M. S., Zhang, H., Nyman, J., Sakuishi, K., Kurtulus, S., Gennert, D., Xia, J., Kwon, J. Y. H., Nevin, J., Herbst, R. H., Yanai, I., Rozenblatt-Rosen, O., Kuchroo, V. K., Regev, A., & Anderson, A. C.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2016-09
- Volume
- 166
- Issue
- 6
- Pages
- 1500-1511.e9
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W2510606169
- Cited by
- 420
- References
- 39
- FWCI
- 19.93
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Singer, M., Wang, C., Cong, L., Marjanovic, N. D., Kowalczyk, M. S., Zhang, H., Nyman, J., Sakuishi, K., Kurtulus, S., Gennert, D., Xia, J., Kwon, J. Y. H., Nevin, J., Herbst, R. H., Yanai, I., Rozenblatt-Rosen, O., Kuchroo, V. K., Regev, A., & Anderson, A. C. (2016). A Distinct Gene Module for Dysfunction Uncoupled from Activation in Tumor-Infiltrating T Cells. Cell. https://doi.org/10.1016/j.cell.2016.08.052
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
