Using single-cell multiple omics approaches to resolve tumor heterogeneity
Abstract It has become increasingly clear that both normal and cancer tissues are composed of heterogeneous populations. Genetic variation can be attributed to the downstream effects of inherited mutations, environmental factors, or inaccurately resolved errors in transcription and replication. When...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-12-01
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| Series: | Clinical and Translational Medicine |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40169-017-0177-y |
| _version_ | 1818191134501896192 |
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| author | Michael A. Ortega Olivier Poirion Xun Zhu Sijia Huang Thomas K. Wolfgruber Robert Sebra Lana X. Garmire |
| author_facet | Michael A. Ortega Olivier Poirion Xun Zhu Sijia Huang Thomas K. Wolfgruber Robert Sebra Lana X. Garmire |
| author_sort | Michael A. Ortega |
| collection | DOAJ |
| description | Abstract It has become increasingly clear that both normal and cancer tissues are composed of heterogeneous populations. Genetic variation can be attributed to the downstream effects of inherited mutations, environmental factors, or inaccurately resolved errors in transcription and replication. When lesions occur in regions that confer a proliferative advantage, it can support clonal expansion, subclonal variation, and neoplastic progression. In this manner, the complex heterogeneous microenvironment of a tumour promotes the likelihood of angiogenesis and metastasis. Recent advances in next-generation sequencing and computational biology have utilized single-cell applications to build deep profiles of individual cells that are otherwise masked in bulk profiling. In addition, the development of new techniques for combining single-cell multi-omic strategies is providing a more precise understanding of factors contributing to cellular identity, function, and growth. Continuing advancements in single-cell technology and computational deconvolution of data will be critical for reconstructing patient specific intra-tumour features and developing more personalized cancer treatments. |
| first_indexed | 2024-12-12T00:09:47Z |
| format | Article |
| id | doaj.art-cc9bcefb2be74ba88aca49d8719b023d |
| institution | Directory Open Access Journal |
| issn | 2001-1326 |
| language | English |
| last_indexed | 2024-12-12T00:09:47Z |
| publishDate | 2017-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Clinical and Translational Medicine |
| spelling | doaj.art-cc9bcefb2be74ba88aca49d8719b023d2022-12-22T00:45:01ZengWileyClinical and Translational Medicine2001-13262017-12-016111610.1186/s40169-017-0177-yUsing single-cell multiple omics approaches to resolve tumor heterogeneityMichael A. Ortega0Olivier Poirion1Xun Zhu2Sijia Huang3Thomas K. Wolfgruber4Robert Sebra5Lana X. Garmire6Cancer Epidemiology Program, University of Hawaii Cancer CenterCancer Epidemiology Program, University of Hawaii Cancer CenterCancer Epidemiology Program, University of Hawaii Cancer CenterCancer Epidemiology Program, University of Hawaii Cancer CenterCancer Epidemiology Program, University of Hawaii Cancer CenterIcahn Institute and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount SinaiCancer Epidemiology Program, University of Hawaii Cancer CenterAbstract It has become increasingly clear that both normal and cancer tissues are composed of heterogeneous populations. Genetic variation can be attributed to the downstream effects of inherited mutations, environmental factors, or inaccurately resolved errors in transcription and replication. When lesions occur in regions that confer a proliferative advantage, it can support clonal expansion, subclonal variation, and neoplastic progression. In this manner, the complex heterogeneous microenvironment of a tumour promotes the likelihood of angiogenesis and metastasis. Recent advances in next-generation sequencing and computational biology have utilized single-cell applications to build deep profiles of individual cells that are otherwise masked in bulk profiling. In addition, the development of new techniques for combining single-cell multi-omic strategies is providing a more precise understanding of factors contributing to cellular identity, function, and growth. Continuing advancements in single-cell technology and computational deconvolution of data will be critical for reconstructing patient specific intra-tumour features and developing more personalized cancer treatments.http://link.springer.com/article/10.1186/s40169-017-0177-ySingle-cell sequencingCancerMutationGene expressionMethylationHeterogeneity |
| spellingShingle | Michael A. Ortega Olivier Poirion Xun Zhu Sijia Huang Thomas K. Wolfgruber Robert Sebra Lana X. Garmire Using single-cell multiple omics approaches to resolve tumor heterogeneity Clinical and Translational Medicine Single-cell sequencing Cancer Mutation Gene expression Methylation Heterogeneity |
| title | Using single-cell multiple omics approaches to resolve tumor heterogeneity |
| title_full | Using single-cell multiple omics approaches to resolve tumor heterogeneity |
| title_fullStr | Using single-cell multiple omics approaches to resolve tumor heterogeneity |
| title_full_unstemmed | Using single-cell multiple omics approaches to resolve tumor heterogeneity |
| title_short | Using single-cell multiple omics approaches to resolve tumor heterogeneity |
| title_sort | using single cell multiple omics approaches to resolve tumor heterogeneity |
| topic | Single-cell sequencing Cancer Mutation Gene expression Methylation Heterogeneity |
| url | http://link.springer.com/article/10.1186/s40169-017-0177-y |
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