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Flexible multiplatform RNA profiling at the single cell level applied to enriched cancer initiating cells: RNA-Seq CIC data

ABSTRACT: Accurate profiling of RNA expression of single cells is a valuable approach for broadening our understanding of cancer biology and mechanisms of dissemination, but requires the development of reliable methods for their molecular characterization. Here we evaluate a single cell methodology which generates microgram amounts of cDNA suitable for next generation sequencing (RNA-Seq), high throughput RT-qPCR and Affymetrix array analysis. The approach was tested by comparing expression profiles of amplified single MCF7 and MCF10A cells to profiles generated from unamplified RNA. The expression profiles were compared by Affymetrix-U133 arrays, RNA-Seq and high-density qPCR. There were strong cross-platform correlations of >80% and concordance between single cell and unamplified material of >70%. We exemplify the approach through analysis of rare sorted cancer initiating cells (CICs) derived from a NSCLC patient-derived xenograft. Populations of 10 cells from total tumour and two distinct subsets of CIC, putatively involved in primary tumor maintenance or metastasis formation were FACS sorted then directly amplified. CIC expression profiles strongly correlated with published stem-cell and epithelial-mesenchymal transition (EMT) signatures. Our results confirm the utility of the amplification system and our methodology to detect and distinguish RNA profiles in rare cell populations that inform on EMT and stem-cell characteristics. This GEO dataset comprises the RNA-Seq data for total tumour, MCIC and RCIC samples.

ORGANISM(S): Homo sapiens

PROVIDER: GSE52715 | GEO | 2014/12/31

SECONDARY ACCESSION(S): PRJNA230351

REPOSITORIES: GEO

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Flexible multiplatform RNA profiling at the single cell level applied to enriched cancer initiating cells: RT-PCR

Project description:Accurate profiling of RNA expression of single cells is a valuable approach for broadening our understanding of cancer biology and mechanisms of dissemination, but requires the development of reliable methods for their molecular characterization. Here we evaluate a single cell methodology which generates microgram amounts of cDNA suitable for next generation sequencing (RNA-Seq), high throughput RT-qPCR and Affymetrix array analysis. The approach was tested by comparing expression profiles of amplified single MCF7 and MCF10A cells to profiles generated from unamplified RNA. The expression profiles were compared by Affymetrix-U133 arrays, RNA-Seq and high-density qPCR. There were strong cross-platform correlations of >80% and concordance between single cell and unamplified material of >70%. We exemplify the approach through analysis of rare sorted cancer initiating cells (CICs) derived from a NSCLC patient-derived xenograft. Populations of 10 cells from total tumour and two distinct subsets of CIC, putatively involved in primary tumor maintenance or metastasis formation were FACS sorted then directly amplified. CIC expression profiles strongly correlated with published stem-cell and epithelial-mesenchymal transition (EMT) signatures. Our results confirm the utility of the amplification system and our methodology to detect and distinguish RNA profiles in rare cell populations that inform on EMT and stem-cell characteristics. This GEO dataset comprises the RT-qPCR data for MCF7 and MCF10A unamplified cDNA from 1μg RNA, RNA-AMP cDNA from 1ng RNA and RNA-AMP cDNA from single cell samples.

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