Single-cell transcriptomics in cancer: computational challenges and opportunities
Cancer: analyzing the RNA of single cells By analyzing gene expression patterns in individual tumor cells, researchers can gain patient-specific insights that might inform more effective cancer treatment. Tumors are highly dynamic and heterogeneous collections of cells. Single-cell transcriptomics t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2020-09-01
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| Series: | Experimental and Molecular Medicine |
| Online Access: | https://doi.org/10.1038/s12276-020-0422-0 |
| Summary: | Cancer: analyzing the RNA of single cells By analyzing gene expression patterns in individual tumor cells, researchers can gain patient-specific insights that might inform more effective cancer treatment. Tumors are highly dynamic and heterogeneous collections of cells. Single-cell transcriptomics techniques can offer a valuable window into that complexity but only if the appropriate computational tools are used to analyze the data. Jean Fan of Harvard University, Cambridge, USA, and colleagues have reviewed some of these computational strategies and how they can be employed in cancer research. Single-cell analysis algorithms, for example, can reveal characteristics that distinguish healthy cells from cancerous cells, or indicate how the cells within the tumor may be communicating with each other to promote malignant growth. These are still new technologies, however, and the authors highlight the limitations of the conclusions that can currently be drawn from such analyses. |
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| ISSN: | 1226-3613 2092-6413 |