Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Massively parallel single-cell RNA-Seq for dissecting cell type and cell state compositions

ABSTRACT: In multi-cellular organisms, biological function emerges when cells of heterogeneous types and states are combined into complex tissues. Nevertheless unbiased dissection of tissues into coherent cell subpopulations is currently lacking. We introduce an automated, massively parallel single cell RNA sequencing method for intuitively analyzing in-vivo transcriptional states in thousands of single cells. Combined with unsupervised classification algorithms, it facilitates ab initio and marker-free characterization of classical hematopoietic cell types from splenic tissues. Importantly, modeling single cells transcriptional states in dendritic cells subpopulations, where a cell type hierarchy is difficult to define with marker-based approaches, uncovers complex combinatorial activity of multiple gene modules and capture cell-to-cell variability in steady state conditions and following pathogen activation. Massively parallel single cell RNA-seq thereby emerges as an effective tool for unbiased dissection of complex tissues. CD11c+ enriched splenocyte mRNA profiles from single cells were generated by deep sequencing of thousands of single cells, sequenced in several batches in an Illumina Hiseq 2000 The 'umitab.txt' processed data file contains the mRNA counts (post-filtering RMT counts) of a gene per each well (columns) The 'experimental_design.txt' contains a detailed information regarding each well. The 'readme0421.txt' was provided with details about each supplementary file.

ORGANISM(S): Mus musculus

SUBMITTER: Ido Amit

PROVIDER: E-GEOD-54006 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types.

Jaitin Diego Adhemar DA Kenigsberg Ephraim E Keren-Shaul Hadas H Elefant Naama N Paul Franziska F Zaretsky Irina I Mildner Alexander A Cohen Nadav N Jung Steffen S Tanay Amos A Amit Ido I

Science (New York, N.Y.) 20140201 6172

In multicellular organisms, biological function emerges when heterogeneous cell types form complex organs. Nevertheless, dissection of tissues into mixtures of cellular subpopulations is currently challenging. We introduce an automated massively parallel single-cell RNA sequencing (RNA-seq) approach for analyzing in vivo transcriptional states in thousands of single cells. Combined with unsupervised classification algorithms, this facilitates ab initio cell-type characterization of splenic tissu ...[more]

PMID: 24531970

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Project description:We used microfluidic single cell RNA-seq on 198 individual mouse lung epithelial cells at 4 different stages throughout development to measure the transcriptional states which define the developmental and cellular hierarchy of the distal mouse lung epithelium. We classified 80 cells comprising the distal lung epithelium at E18.5 into distinct populations using an unbiased genome-wide approach that did not require a priori knowledge of the underlying cell types or prior purification of cell types. This M-bM-^@M-^\reverse tissue engineeringM-bM-^@M-^] approach confirmed the basic outlines of the conventional model of cell type diversity in the distal lung and led to the discovery of a large number of novel transcriptional regulators and cell type markers that discriminate between the different populations. Moreover, we reconstructed the steps during maturation of bipotential progenitors into both alveolar lineages based on the presence of undifferentiated, differentiated as well as differentiation intermediate cells at the single time point E18.5. Finally, we followed Sftpc-positive cells throughout their lifecycle (E14.5, E16.5, E18.5, adult) and identified 7 gene sets that differentiate between the multipotential, bipotential, mature, as well as intermediate states of the AT2 lineage. 198 single-cell transcriptomes from mouse lung epithelium were analyzed in total, two 200-cell bulk control samples as well as one no-cell control; All single cell and control samples contain 92 external RNA spike-ins; For time point E18.5, three individual experiments were performed using 3 different pregnant mice (3 biological replicates): Replicate 1 (pooled sibling lungs) yielded 20 single cell transcriptomes, replicate 2 (one single embryonic lung) yielded 34 single cell transcriptomes and replicate 3 (pooled siibling lungs) yielded 26 single cell transcriptomes; In addition, a 200-cell bulk control sample was prepared for E18.5 replicate 1 and E18.5 replicate 3 experiments; A no-cell control sample was generated for the E18.5 replicate 1 experiment; For time point E14.5, one experiment (one pregnant mouse, pooled sibling lungs) was performed yielding 45 single cell transcriptomes; For time point E16.5, one experiment (one pregnant mouse, pooled sibling lungs) was performed yielding 27 single cell transcriptomes; For the adult time point, one 107 day old mouse was used and transcriptomes of 46 single cells were obtained; All single cell samples were processed on the microfluidic platform, 200-cell-bulk and no-cell control samples were processed in microliter volumes in PCR tubes.

Dataset Information

Massively parallel single-cell RNA-Seq for dissecting cell type and cell state compositions

Publications

Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types.

Similar Datasets

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