Dataset Information

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 constituent cell types. Here we describe a novel strategy to access such complex samples. Single-cell RNA-seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data were 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 themselves-all without need for known markers to classify cell types. The feasibility of the strategy was demonstrated by analyzing the transcriptomes of 85 single cells of two distinct types. We believe this strategy will enable the unbiased discovery and analysis of naturally occurring cell types during development, adult physiology, and disease.

SUBMITTER: Islam S

PROVIDER: S-EPMC3129258 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Islam Saiful S Kjällquist Una U Moliner Annalena A Zajac Pawel P Fan Jian-Bing JB Lönnerberg Peter P Linnarsson Sten S

Genome research 20110504 7

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 constituent cell types. Here we describe a novel strategy to access such complex samples. Single-cell RNA-seq expression profiles were generated, and clustered to form a two-dimensional cell map onto which expression data were projected. The resulti ...[more]

PMID: 21543516

Similar Datasets

Project description:Triple-negative breast cancer (TNBC) resistance to neoadjuvant chemotherapy (NAC) represents a major clinical challenge; therefore, delineating tumor heterogeneity can provide novel insight into resistance mechanisms and potential therapeutic targets. Herein, we identified the transcriptional landscape associated with TNBC resistance to NAC at the single-cell level by analyzing publicly available transcriptome data from more than 5,000 single cells derived from four extinction (responders) and four persistence (non-responders) patients, revealing remarkable tumor heterogeneity. Employing iterative clustering and guide-gene selection (ICGS) and uniform manifold approximation and projection (UMAP), we classified TNBC single cells into several clusters based on their distinct gene signatures. The presence of clusters indicative of immune cell activation was a hallmark of the extinction group pre-NAC, while post NAC, the extinction tissue consisted mostly of breast, omental fat, and fibroblasts. The persistent gene signatures of pre-NAC resembled the gene signature of lung epithelial, mammary, and salivary glands and acute myeloid leukemia blast cells, which were associated with enhanced cellular movement and activation of FOXM1, NOTCH1, and MYC and suppression of tumor necrosis factor (TNF) and IFNG mechanistic networks. Multivariate survival analysis identified persistence-derived three-gene signature (KIF5BhighHLA-ClowIGHG2low) predictive of relapse-free survival (hazard ratio [HR]: 2.2 [1.6-3.2, p < 0.0001]) in a second cohort of 360 TNBC patients. Mechanistically, loss of function of several upregulated genes in the persistent group (BYSL, FDPS, ENO1, MED20, MRPL9, MRPL37, NDUFB11, PMVK, MYC, and GSTP1) inhibited MDA-MB-231 and BT-549 TNBC models' colony-forming unit (CFU) potential and enhanced their sensitivity to paclitaxel. Our data unraveled the transcriptional portrait associated with NAC resistance, identified several key genes, and suggested their potential utilization as prognostic markers and therapeutic targets in TNBC.

Dataset Information

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

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets