The DOI matches its Crossref record; 244 of the 244 references it cites were found in a registry or an index.
Applications of single-cell sequencing in cancer research: progress and perspectives
Open accessAbstract
Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and metabolomics sequencing, is a powerful tool to decipher the cellular and molecular landscape at a single-cell resolution, unlike bulk sequencing, which provides averaged data. The use of single-cell sequencing in cancer research has revolutionized our understanding of the biological characteristics and dynamics within cancer lesions. In this review, we summarize emerging single-cell sequencing technologies and recent cancer research progress obtained by single-cell sequencing, including information related to the landscapes of malignant cells and immune cells, tumor heterogeneity, circulating tumor cells and the underlying mechanisms of tumor biological behaviors. Overall, the prospects of single-cell sequencing in facilitating diagnosis, targeted therapy and prognostic prediction among a spectrum of tumors are bright. In the near future, advances in single-cell sequencing will undoubtedly improve our understanding of the biological characteristics of tumors and highlight potential precise therapeutic targets for patients.
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PD-1 Blockade in Tumors with Mismatch-Repair Deficiency
Le, D. T., Uram, J. N., Wang, H., Bartlett, B. R., Kemberling, H., Eyring, A. D., Skora, A. D., Luber, B. S., Azad, N. S., Laheru, D., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1500596 · Cited by 9,377
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
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
mRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969
Visualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834
Genomic analyses identify molecular subtypes of pancreatic cancer
Bailey, P., Chang, D. K., Nones, K., Johns, A. L., Patch, A.-M., Gingras, M.-C., Miller, D. K., Christ, A. N., Bruxner, T. J. C., Quinn, M. C., et al. (2016) · Nature · doi:10.1038/nature16965 · Cited by 3,621
Efremova, M., Vento-Tormo, M., Teichmann, S. A., & Vento-Tormo, R. (2020) · Nature Protocols · doi:10.1038/s41596-020-0292-x · Cited by 3,449
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
Olive, K. P., Jacobetz, M. A., Davidson, C. J., Gopinathan, A., McIntyre, D., Honess, D., Madhu, B., Goldgraben, M. A., Caldwell, M. E., Allard, D., et al. (2009) · Science · doi:10.1126/science.1171362 · Cited by 3,170
Simultaneous epitope and transcriptome measurement in single cells
Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P. (2017) · Nature Methods · doi:10.1038/nmeth.4380 · Cited by 3,098
Cited by
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Gu, Y., Hu, Y., Zhang, H., Wang, S., Xu, K., & Su, J. (2023) · Cell Proliferation · doi:10.1111/cpr.13517 · Cited by 59
Omics Technologies Improving Breast Cancer Research and Diagnostics
Orsini, A., Diquigiovanni, C., & Bonora, E. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241612690 · Cited by 52
Metadata
- Authors
- Lei, Y., Tang, R., Xu, J., Wang, W., Zhang, B., Liu, J., Yu, X., & Shi, S.
- Venue
- Journal of Hematology & Oncology
- Published
- 2021-06-09
- Volume
- 14
- Issue
- 1
- Pages
- 91-91
- ISSN
- 1756-8722 (ISSN-L)
- OpenAlex
- W3169274538
- Cited by
- 592
- References
- 244
- FWCI
- 23.28
- Type
- journal article
- Language
- en
- Open access
- gold
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- Lei, Y., Tang, R., Xu, J., Wang, W., Zhang, B., Liu, J., Yu, X., & Shi, S. (2021). Applications of single-cell sequencing in cancer research: progress and perspectives. Journal of Hematology & Oncology. https://doi.org/10.1186/s13045-021-01105-2
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
