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Single-Cell mRNA Sequencing Reveals Rare Intestinal Cell Types

GSE62270 Mus musculus (mouse) Single-cell RNA-seq 35 samples Submitted 2015/08/19 Platform GPL19057Platform GPL17021
At a glance

Single-cell RNA-seq · mouse · 35 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Mus musculus (mouse)
Samples
35 samples
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Summary (written by the submitters)
Understanding the development and function of an organ requires the characterization of all of its cell types. Traditional methods for visualizing and isolating sub-populations of cells are based on mRNA or protein expression of only few known marker genes. The unequivocal identification of a specific marker gene, however, poses a major challenge, particularly if this cell type is rare.
Read the full summary ↓
Identifying rare cell types, such as stem cells, short-lived progenitors, cancer stem cells, or circulating tumor cells is crucial to acquire a better understanding of normal or diseased tissue biology. To address this challenge we sequenced the transcriptome of hundreds of randomly selected cells from mouse intestinal organoids, cultured self-organizing epithelial structures that contain all cell lineages of the mammalian intestine. Organoid buds, like intestinal crypts, harbor stem cells that continuously differentiate into a variety of cell types, occurring at widely different abundances. Since available computational methods can only resolve more abundant cell types, we developed RaceID, an algorithm for rare cell type identification in complex populations of single cells. We demonstrate that this algorithm can resolve cell types represented by only a single cell in a population of randomly sampled organoid cells. We use this algorithm to identify Reg4 as a novel marker for enteroendocrine cells, a rare population of hormone producing intestinal cells. Next, we use Reg4 expression to enrich for these rare cells and investigate the heterogeneity within this population. Reassuringly, RaceID confirmed the existence of known enteroendocrine lineages, and moreover, discovered novel subtypes, which we subsequently validated in vivo. Having validated RaceID by this proof-of-principle experiment we then apply the algorithm to ex vivo isolated LGR5 positive cells and their direct progeny and demonstrate homogeneity of the stem cell pool. We envision broad applicability of our method for discovering rare cell types and the corresponding marker genes in healthy and diseased organs.
This dataset
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE62270_RAW.tar (it can run to tens of gigabytes).

3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA263622 and SRA study SRP048838. Searching any of these in the dataset finder brings you back here.

Samples in this study

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