The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
Developmental Relationships of Four Exhausted CD8+ T Cell Subsets Reveals Underlying Transcriptional and Epigenetic Landscape Control Mechanisms
Open accessAbstract
SUMMARY CD8+ T cell exhaustion is a major barrier to current anti-cancer immunotherapies. Despite this, the developmental biology of exhausted CD8+ T cells (Tex) remains poorly defined, restraining improvement of strategies aimed at “re-invigorating” Tex cells. Here, we defined a four-cell-stage developmental framework for Tex cells. Two TCF1+ progenitor subsets were identified, one tissue restricted and quiescent and one more blood accessible, that gradually lost TCF1 as it divided and converted to a third intermediate Tex subset. This intermediate subset re-engaged some effector biology and increased upon PD-L1 blockade but ultimately converted into a fourth, terminally exhausted subset. By using transcriptional and epigenetic analyses, we identified the control mechanisms underlying subset transitions and defined a key interplay between TCF1, T-bet, and Tox in the process. These data reveal a four-stage developmental hierarchy for Tex cells and define the molecular, transcriptional, and epigenetic mechanisms that could provide opportunities to improve cancer immunotherapy.
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Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
The Sequence Alignment/Map format and SAMtools
Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., & 1000 Genome Project Data Processing Subgroup (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp352 · Cited by 62,041
Fast gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400
Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin, Y. C., Laslo, P., Cheng, J. X., Murre, C., Singh, H., & Glass, C. K. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.05.004 · Cited by 13,382
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422
Reversed 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
GREAT improves functional interpretation of cis-regulatory regions
McLean, C. Y., Bristor, D., Hiller, M., Clarke, S. L., Schaar, B. T., Lowe, C. B., Wenger, A. M., & Bejerano, G. (2010) · Nature Biotechnology · doi:10.1038/nbt.1630 · Cited by 4,502
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Philip, M., & Schietinger, A. (2022) · Nature Reviews Immunology · doi:10.1038/s41577-021-00574-3 · Cited by 1,052
Clinical implications of T cell exhaustion for cancer immunotherapy
Chow, A., Perica, K., Klebanoff, C. A., & Wolchok, J. D. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-022-00689-z · Cited by 1,044
Mogilenko, D. A., Shpynov, O., Andhey, P. S., Arthur, L., Swain, A., Esaulova, E., Brioschi, S., Shchukina, I., Kerndl, M., Bambouskova, M., et al. (2021) · Immunity · doi:10.1016/j.immuni.2020.11.005 · Cited by 683
Turning cold tumors hot: from molecular mechanisms to clinical applications
Zhang, J., Huang, D., Saw, P. E., & Song, E. (2022) · Trends in Immunology · doi:10.1016/j.it.2022.04.010 · Cited by 534
Liu, B., Hu, X., Feng, K., Gao, R., Xue, Z., Zhang, S., Zhang, Y., Corse, E., Hu, Y., Han, W., et al. (2021) · Nature Cancer · doi:10.1038/s43018-021-00292-8 · Cited by 473
Metadata
- Authors
- Beltra, J.-C., Manne, S., Abdel-Hakeem, M. S., Kurachi, M., Giles, J. R., Chen, Z., Casella, V., Ngiow, S. F., Khan, O., Huang, Y. J., Yan, P., Nzingha, K., Xu, W., Amaravadi, R. K., Xu, X., Karakousis, G. C., Mitchell, T. C., Schuchter, L. M., Huang, A. C., & Wherry, E. J.
- Venue
- Immunity
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 52
- Issue
- 5
- Pages
- 825-841.e8
- ISSN
- 1074-7613 (ISSN-L)
- OpenAlex
- W3020888809
- Cited by
- 1,082
- References
- 69
- FWCI
- 34.10
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Beltra, J.-C., Manne, S., Abdel-Hakeem, M. S., Kurachi, M., Giles, J. R., Chen, Z., Casella, V., Ngiow, S. F., Khan, O., Huang, Y. J., Yan, P., Nzingha, K., Xu, W., Amaravadi, R. K., Xu, X., Karakousis, G. C., Mitchell, T. C., Schuchter, L. M., Huang, A. C., & Wherry, E. J. (2020). Developmental Relationships of Four Exhausted CD8+ T Cell Subsets Reveals Underlying Transcriptional and Epigenetic Landscape Control Mechanisms. Immunity. https://doi.org/10.1016/j.immuni.2020.04.014
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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