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Single-cell sequencing of genomic DNA resolves sub-clonal heterogeneity in a melanoma cell line

We performed shallow single-cell sequencing of genomic DNA across 1475 cells from a cell-line, COLO829, to resolve overall complexity and clonality. This melanoma tumor-line has been previously characterized by multiple technologies and is a benchmark for evaluating somatic alterations. In some of t...

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Detalles Bibliográficos
Autores principales: Velazquez-Villarreal, Enrique I., Maheshwari, Shamoni, Sorenson, Jon, Fiddes, Ian T., Kumar, Vijay, Yin, Yifeng, Webb, Michelle G., Catalanotti, Claudia, Grigorova, Mira, Edwards, Paul A., Carpten, John D., Craig, David W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7316972/
https://www.ncbi.nlm.nih.gov/pubmed/32587328
http://dx.doi.org/10.1038/s42003-020-1044-8
Descripción
Sumario:We performed shallow single-cell sequencing of genomic DNA across 1475 cells from a cell-line, COLO829, to resolve overall complexity and clonality. This melanoma tumor-line has been previously characterized by multiple technologies and is a benchmark for evaluating somatic alterations. In some of these studies, COLO829 has shown conflicting and/or indeterminate copy number and, thus, single-cell sequencing provides a tool for gaining insight. Following shallow single-cell sequencing, we first identified at least four major sub-clones by discriminant analysis of principal components of single-cell copy number data. Based on clustering, break-point and loss of heterozygosity analysis of aggregated data from sub-clones, we identified distinct hallmark events that were validated within bulk sequencing and spectral karyotyping. In summary, COLO829 exhibits a classical Dutrillaux’s monosomic/trisomic pattern of karyotype evolution with endoreduplication, where consistent sub-clones emerge from the loss/gain of abnormal chromosomes. Overall, our results demonstrate how shallow copy number profiling can uncover hidden biological insights.