GEO series

Single Cell RNA-Sequencing Identifies Diverse Roles of Epithelial Cells in Idiopathic Pulmonary Fibrosis

GSE94555 Homo sapiens (human) Single-cell RNA-seq 6 samples Submitted 2017/02/06 Platform GPL16791

At a glance

Single-cell RNA-seq · human · 6 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 6 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)

Idiopathic pulmonary fibrosis (IPF) is a lethal interstitial lung disease causing alveolar remodeling, inflammation, and fibrosis. We utilized single cell RNA-sequencing (scRNA-Seq) to identify epithelial cell types and associated biological processes involved in the pathogenesis of IPF. Transcriptomic analysis of epithelial cells from normal human lung defined gene expression patterns associated with highly differentiated alveolar type 2 (AT2) cells, indicated by enrichment of RNAs critical for surfactant homeostasis.

Read the full summary ↓

In contrast, scRNA-seq of IPF cells identified three distinct subsets of epithelial cell types with characteristics of conducting airway basal and goblet cells and, an additional atypical "transitional" cell that contribute to pathological processes in IPF. Individual IPF cells frequently co-expressed alveolar AT1, AT2, and conducting airway selective markers, demonstrating "indeterminate" states of differentiation not seen in normal lung development. Pathway analysis predicted aberrant activation of canonical signaling via TGF-ß, HIPPO/YAP, P53, and AKT-PI3 Kinase. Immunofluorescence confocal microscopy identified the disruption of alveolar structure and loss of the normal proximal-peripheral differentiation of pulmonary epithelial cells. Single cell transcriptomic analyses of respiratory epithelial cells identified loss of normal epithelial cell identities and unique contributions of epithelial cells to the pathogenesis of IPF. Present scRNA-seq transcriptomic analysis of normal and IPF respiratory epithelial cells provides a rich data source to further explore lung health and disease.

This dataset

Open GSE94555 in finder → Find more human RNA-seq datasets →

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.

Series matrix ↓

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 GSE94555_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

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.

Raw reads on ENA ↗

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 PRJNA371464 and SRA study SRP098915. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 27942595) ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 6 more — browse all 6 samples with per-sample file links →

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