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Optimizing sparse sequencing of single cells for highly multiplex copy number profiling.

ABSTRACT: Genome-wide analysis at the level of single cells has recently emerged as a powerful tool to dissect genome heterogeneity in cancer, neurobiology, and development. To be truly transformative, single-cell approaches must affordably accommodate large numbers of single cells. This is feasible in the case of copy number variation (CNV), because CNV determination requires only sparse sequence coverage. We have used a combination of bioinformatic and molecular approaches to optimize single-cell DNA amplification and library preparation for highly multiplexed sequencing, yielding a method that can produce genome-wide CNV profiles of up to a hundred individual cells on a single lane of an Illumina HiSeq instrument. We apply the method to human cancer cell lines and biopsied cancer tissue, thereby illustrating its efficiency, reproducibility, and power to reveal underlying genetic heterogeneity and clonal phylogeny. The capacity of the method to facilitate the rapid profiling of hundreds to thousands of single-cell genomes represents a key step in making single-cell profiling an easily accessible tool for studying cell lineage.

SUBMITTER: Baslan T

PROVIDER: S-EPMC4417119 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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Optimizing sparse sequencing of single cells for highly multiplex copy number profiling.

Baslan Timour T Kendall Jude J Ward Brian B Cox Hilary H Leotta Anthony A Rodgers Linda L Riggs Michael M D'Italia Sean S Sun Guoli G Yong Mao M Miskimen Kristy K Gilmore Hannah H Saborowski Michael M Dimitrova Nevenka N Krasnitz Alexander A Harris Lyndsay L Wigler Michael M Hicks James J

Genome research 20150409 5

Genome-wide analysis at the level of single cells has recently emerged as a powerful tool to dissect genome heterogeneity in cancer, neurobiology, and development. To be truly transformative, single-cell approaches must affordably accommodate large numbers of single cells. This is feasible in the case of copy number variation (CNV), because CNV determination requires only sparse sequence coverage. We have used a combination of bioinformatic and molecular approaches to optimize single-cell DNA am ...[more]

PMID: 25858951

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Project description:BackgroundWith functionally heterogeneous cells, tumors comprise a complex ecosystem to promote tumor adaptability and evolution under strong selective pressure from the given microenvironment. Diversifying tumor cells or intra-tumor heterogeneity is essential for tumor growth, invasion, and immune evasion. However, no reliable method to classify tumor cell subtypes is yet available. In this study, we introduced the single-cell sequencing combined with copy number characteristics to identify the types of tumor cells in microsatellite stable (MSS) colorectal cancer (CRC).MethodsTo characterize the somatic copy number alteration (SCNA) of MSS CRC in a single cell profile, we analyzed 26 tissue samples from 19 Korean patients (GSE132465, the Samsung Medical Center [SMC] dataset) and then verified our findings with 15 tissue samples from five Belgian patients (GSE144735, the Katholieke Universiteit Leuven 3 [KUL3] dataset). The Cancer Genome Atlas (TCGA) cohort, GSE39582 cohort, and National Cancer Center (NCC) cohort (24 MSS CRC patients were enrolled in this study between March 2017 and October 2017) were used to validate the clinical features of prognostic signatures.ResultsWe employed single cell RNA-sequencing data to identify three types of tumor cells in MSS CRC by their SCNA characteristics. Among these three types of tumor cells, C1 and C3 had a higher SCNA burden; C1 had significant chromosome 13 and 20 amplification, whereas C3 was the polar opposite of C1, which exhibited deletion in chromosome 13 and 20. The three types of tumor cells exhibited various functions in the tumor microenvironment and harbored different mutations. C1 and C2 were linked to the immune response and hypoxia, respectively, while C3 was critical for cell adhesion activity and tumor angiogenesis. Additionally, one gene ( OLFM4 ) was identified as epithelium-specific biomarker of better prognosis of CRC (TCGA cohort: P = 0.0110; GSE39582 cohort: P = 0.0098; NCC cohort: P = 0.0360).ConclusionsOn the basis of copy number characteristics, we illustrated tumor heterogeneity in MSS CRC and identified three types of tumor cells with distinct roles in tumor microenvironment. By understanding heterogeneity in the intricate tumor microenvironment, we gained an insight into the mechanisms of tumor evolution, which may support the development of therapeutic strategies.

Dataset Information

Optimizing sparse sequencing of single cells for highly multiplex copy number profiling.

Publications

Optimizing sparse sequencing of single cells for highly multiplex copy number profiling.

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