Flexible multiplatform RNA profiling at the single cell level applied to enriched cancer initiating cells
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE52717 | GEO | 2014/12/31
SECONDARY ACCESSION(S): PRJNA229887
REPOSITORIES: GEO
Similar Datasets
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.
2014-12-31 | GSE52714 | GEO
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 Affymetrix U-133 Plus 2.0 data for MCF7 and MCF10A cDNA amplified from 1ng RNA and single cell samples.
2014-12-31 | GSE52712 | GEO
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 RNA-Seq data for total tumour, MCIC and RCIC samples.
2014-12-31 | GSE52715 | GEO
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 RNA-Seq data for MCF7 and MCF10A single cell samples.
2014-12-31 | GSE52716 | GEO
Project description:BACKGROUND: Although profiling of RNA in single cells has broadened our understanding of development, cancer biology and mechanisms of disease dissemination, it requires the development of reliable and flexible methods. Here we demonstrate that the EpiStem RNA-Amp™ methodology reproducibly generates microgram amounts of cDNA suitable for RNA-Seq, RT-qPCR arrays and Microarray analysis. RESULTS: Initial experiments compared amplified cDNA generated by three commercial RNA-Amplification protocols (Miltenyi ?MACS™ SuperAmp™, NuGEN Ovation® One-Direct System and EpiStem RNA-Amp™) applied to single cell equivalent levels of RNA (25-50 pg) using Affymetrix arrays. The EpiStem RNA-Amp™ kit exhibited the highest sensitivity and was therefore chosen for further testing. A comparison of Affymetrix array data from RNA-Amp™ cDNA generated from single MCF7 and MCF10A cells to reference controls of unamplified cDNA revealed a high degree of concordance. To assess the flexibility of the amplification system single cell RNA-Amp™ cDNA was also analysed using RNA-Seq and high-density qPCR, and showed strong cross-platform correlations. To exemplify the approach we used the system to analyse RNA profiles of small populations of rare cancer initiating cells (CICs) derived from a NSCLC patient-derived xenograft. RNA-Seq analysis was able to identify transcriptional differences in distinct subsets of CIC, with one group potentially enriched for metastasis formation. Pathway analysis revealed that the distinct transcriptional signatures demonstrated in the CIC subpopulations were significantly correlated with published stem-cell and epithelial-mesenchymal transition signatures. CONCLUSIONS: The combined results confirm the sensitivity and flexibility of the RNA-Amp™ method and demonstrate the suitability of the approach for identifying clinically relevant signatures in rare, biologically important cell populations.
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