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Transcriptional Characterisation of Proliferative Vitreoretinopathy

GSE179603 Homo sapiens (human) RNA-seq (gene expression) 27 samples Submitted 2022/05/25 Platform GPL15433
At a glance

RNA-seq (gene expression) · human · 27 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
27 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 development of vitreoretinal scars remains an unsolved challenge in clinical practice and often leads to repeated revision surgery and blindness due to lack of efficient therapy. The aim of this study was to characterize the cellular and molecular environment in vitreoretinal scar tissue from patients with proliferative vitreoretinopathy (PVR) in comparison to membranes of the vitreoretinal junction, in order to subsequently identify potential drug treatment options using bioinformatics techniques.
Read the full summary ↓
Methods: A total of 23 patients undergoing vitrectomy for retinal detachment due to proliferative vitreoretinopathy (PVR, n = 15), idiopathic macular hole (MH, n = 10) or idiopathic macular pucker (MP, n = 8), were included in this study. Vitreoretinal samples were analysed by RNA sequencing, MS-CyTOF proteomic and by conventional immunohistochemistry. The cellular microenvironment was assessed by bioinformatics cell type enrichment analysis. Drug target screening was performed by using a transcriptome-based drug-repurposing approach using drug-exposure transcriptome data from public available databases. Results: RNA sequencing of vitreoretinal tissue samples showed distinct transcriptional differences of PVR, ERM and ILM samples allowing an accurate clustering according to the tissue types. The cellular microenvironment of PVR membranes was characterized by the enrichment of melanocytes, astrocytes and various immune cell types, such as M2 macrophages. Differential gene expression (DEG) analysis revealed XX up- and XX downregulated genes in PVR compared to ILM. Among them, FN1 (fibronectin1), SPARC (osteonectin) and various collagens were among the most upregulated factors in PVR, contributing to biological processes such as the organization of extracellular structures, the regulation of cell adhesion and the morphogenesis of blood vessels. Finally, we identified 13 drugs that induce a gene expression profile complementary to the PVR signature and thus represent potential therapeutic options for PVR, including aminocaproic acid and various topoisomerase 2A inhibitors such as doxorubicin, etoposide and mitoxantrone. Conclusion: The present study reveals significant differences in the transcriptional signature of vitreoretinal scar tissue including PVR, MP and ILM tissue. Among a plethora of differentially expressed genes, FN1 and SPARC appeared to be central mediators underlying PVR development. Based on the transcriptome analyses, aminocaproic acid and topoisomerase-2 inhibitors, such as doxorubicin, etoposide and mitoxantrone, were identified as compatible drugs for the treatment of PVR.
This dataset
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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 GSE179603_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA744251 and SRA study SRP327213. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 35579905) ↗
Samples in this study

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+ 27 more — browse all 27 samples with per-sample file links →

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