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Single-cell RNA sequencing in human retinal degeneration reveals distinct glial cell populations.

GSE142449 Homo sapiens (human) Single-cell RNA-seq 4 samples Submitted 2020/02/17 Platform GPL20301
At a glance

Single-cell RNA-seq · human · 4 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
4 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)
Purpose: The goals of this study were to identify cell specific expression patterns from a donor with a presumed diagnosis of autoimmune retinopathy (AIR). A particular focus of this study was to identify expression signatures of Müller glia and astrocytes in AIR. Methods: Independent libraries were prepared for foveal and peripheral retina from five human donors in two experiments.
Read the full summary ↓
Foveal and peripheral samples in donors 1-3 were acquired, analyzed, and uploaded in a previous experiment (GSE130636). In donors 4-5, new reads were acquired from a 2-mm fovea-centered punch and an 8-mm peripheral punch from the inferotemporal region in each donor. Donor 4 had no documented history of retinal disease. Donor 5 was followed for 19 years at the University of Iowa Hospitals and Clinics for progressive visual field loss and pigmentary changes and was given a presumptive diagnosis of autoimmune retinopathy. Foveal and peripheral retinal tissue were gently dissociated in papain for 1.25 hours before cryopreservation. In both experiments, libraries were sequenced on a HiSeq4000. Sequenced reads were mapped to the human genome build hg19 with CellRanger(v3.0.1) and filtered to remove cells likely to be doublets or cells with a high proportion of mitochondrial reads. Libraries from all five donors were aggregated to the same effective sequencing depth, and log-normalization of aggregated reads was performed with Seurat (v2.3.4) using a scale factor of 10,000. Results: A total of 23,429 cells were recovered after filtering with 117,885,744 corresponding reads. A total of 9,801 cells were recovered from the fovea while 13,628 cells were recovered from the periphery. A total of 7,189 cells were recovered from the donor with presumed AIR. In total, 23 clusters were identified and corresponded to all retinal cell types. The proportion of recovered cells was compared between the control (donors 1-4) and AIR (donor 5) patients. In addition, differential expression analysis identified genes enriched in each population of cells originating from the AIR donor. Particular emphasis was given to the expression profiles of glial cells (Müller cells and astrocytes) that originated from the AIR donor. Conclusions: This study provides a complementary clinical and molecular interrogation of the retina in a blinding inflammatory condition. Glial cells from the AIR donor were enriched in genes implicated in reactive gliosis, including ANXA1. Clinical, histologic, and transcriptomic evidence identify the loss of cone and rod photoreceptor cells with relative preservation of inner retinal cell types.
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 GSE142449_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 PRJNA597043 and SRA study SRP238409. Searching any of these in the dataset finder brings you back here.

Samples in this study

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