Unknown,Transcriptomics,Genomics,Proteomics

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Characterization of the single-cell transcriptional landscape by highly multiplex RNA-Seq

ABSTRACT: Our understanding of the development and maintenance of tissues has been greatly aided by large-scale gene expression analysis. However, tissues are invariably complex, and expression analysis of a tissue confounds the true expression patterns of its constitutent cell types. Here we propose a novel strategy to access such complex samples. Hundreds of single-cell RNA-Seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data was projected. The resulting cell map integrates three levels of organization: the whole population of cells, the functionally distinct subpopulations it contains, and the single cells themselvesM-bM-^@M-^Tall without need for known markers to classify cell types. The feasibility of the strategy is demonstrated by analyzing the complete transcriptomes of 436 single cells of three distinct types. We believe this strategy will enable the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology and disease. Comparison of single-cell mRNA expression in 436 single cells from three cell types

ORGANISM(S): Mus musculus

SUBMITTER: Sten Linnarsson

PROVIDER: E-GEOD-21180 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Technological advances in transcriptional profiling and in the availability of the single-cell sampling from rare and specialised cell types have come together to allow us to explore molecular processes in the development of tumours and in solid tumour biology. None of the existing gene expression profiling platforms has the sensitivity to directly deal with the small mRNA population from single-cells. Therefore, it is essential to amplify the mRNA from a single cell without distorting the relative transcript abundance and studying the products of this amplification with the available transcriptome profiling techniques. We used such an exponential amplification approach to study the transcriptional heterogeneity and the representation of cancer-associated pathways in single cells isolated from the clonal progenies of a murine colon cancer cell line (CT26). With the methods developed and single-cell microarray analysis (Affymetrix GeneChipM-. Mouse Genome 430 2.0 3M-^R arrays), we were able to demonstrate considerable, intra- as well as inter-clonal, transcriptional heterogeneity. Detailed ontological classification indicated that most of the transcriptional variations existed towards the genes involved in DNA replication, nucleic acid synthesis and cellular machinery biogenesis. Finally, with the help of the IngenuityTM pathway elucidation software, 51 genes commonly expressed between single-cells and previously shown to be associated with cancers of different histological origins were used to create a pathway of genes acting potentially of direct or indirect importance in promoting such cancers of this specific histological origin. This study demonstrated the utility of single-cell micro-genomic studies to understand the molecular mechanisms at the single cancer cell level and thereby to understand the early transcriptional reprogramming in tumorigenesis. It also provided further evidence of the transcriptomic variations between each cell of a tumour and the rapid diversity of cloned (single) cells, thereby reinforcing the major task faced by researchers to define potent treatments to eradicate cancer.

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Characterization of the single-cell transcriptional landscape by highly multiplex RNA-Seq

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