Single-cell RNA-seq variant analysis for exploration of genetic heterogeneity in cancer

Inter- and intra-tumour heterogeneity is caused by genetic and non-genetic factors, leading to severe clinical implications. High-throughput sequencing technologies provide unprecedented tools to analyse DNA and RNA in single cells and explore both genetic heterogeneity and phenotypic variation betw...

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Bibliographic Details
Published in:Scientific reports 2019-07, Vol.9 (1), p.9524-11, Article 9524
Main Authors: Fasterius, Erik, Uhlén, Mathias, Al-Khalili Szigyarto, Cristina
Format: Article
Language:English
Subjects:
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Brain cancer
Cluster Analysis
Deoxyribonucleic acid
DNA
DNA sequencing
Gene expression
Genetic analysis
Genetic diversity
Genetic factors
Genetic Heterogeneity
Glioblastoma
Glioblastoma - genetics
Glioblastoma - pathology
Heterogeneity
High-Throughput Nucleotide Sequencing
Humanities and Social Sciences
Humans
Information processing
Lymph nodes
Metastases
multidisciplinary
Neoplasms - genetics
Neoplasms - pathology
Next-generation sequencing
Phenotypic variations
Polymorphism, Single Nucleotide
Prognosis
Ribonucleic acid
RNA
RNA, Small Cytoplasmic - metabolism
RNA-Seq
Science
Science (multidisciplinary)
Single-Cell Analysis - methods
Subpopulations
Tumors
Zebrafish
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Summary:Inter- and intra-tumour heterogeneity is caused by genetic and non-genetic factors, leading to severe clinical implications. High-throughput sequencing technologies provide unprecedented tools to analyse DNA and RNA in single cells and explore both genetic heterogeneity and phenotypic variation between cells in tissues and tumours. Simultaneous analysis of both DNA and RNA in the same cell is, however, still in its infancy. We have thus developed a method to extract and analyse information regarding genetic heterogeneity that affects cellular biology from single-cell RNA-seq data. The method enables both comparisons and clustering of cells based on genetic variation in single nucleotide variants, revealing cellular subpopulations corroborated by gene expression-based methods. Furthermore, the results show that lymph node metastases have lower levels of genetic heterogeneity compared to their original tumours with respect to variants affecting protein function. The analysis also revealed three previously unknown variants common across cancer cells in glioblastoma patients. These results demonstrate the power and versatility of scRNA-seq variant analysis and highlight it as a useful complement to already existing methods, enabling simultaneous investigations of both gene expression and genetic variation.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-45934-1