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Comparative Analysis of In vivo Endothelial Cell Translatomes Across Central Nervous System Vascular Beds (Tie2)

GSE270197 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2024/12/31 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 18 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
Mus musculus (mouse)
Samples
18 samples: 18 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
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.
Paper
Comparative analysis of In vivo endothelial cell translatomes across central nervous system vascular bedsExperimental eye research · 2024
Summary (written by the submitters)
Endothelial cells (ECs) display organ- and tissue-specific heterogeneity. In the eye, the retinal and choroidal vascular beds are distinct networks with different morphological and functional features with the former representing a tight barrier and the latter, a permeable fenestrated vasculature. Given that retinal health critically relies on the function of these vascular beds and that their dysfunction is implicated in a variety of retinal diseases, a molecular understanding of both physiological and pathophysiological characteristics of these distinct vasculatures is critical.
Read the full summary ↓
Given their interspersed anatomic distribution among parenchymal cells, the study of EC gene expression, in vivo, has been hampered by the challenge of isolating pure populations of ocular ECs in sufficient quantities for large-scale transcriptomics. To address this challenge, we present a methodological and analytical workflow to facilitate inter-tissue comparisons of the in vivo EC translatome isolated from choroid, retina, and brain using the Cre-inducible NuTRAP flox construct and two widely-used endothelial cre mouse lines, constitutive Tie2-Cre and Tamoxifen-inducible Cdh5-CreERT2, which express EGFP and mCherry tags for cell-specific isolation of nuclei and ribosomes. Within each model, inter-tissue comparison of TRAP-RNAseq enrichment (TRAP translatome vs input transcriptome) showed tissue-specific gene enrichments with differential pathway representation. For each mouse model, inter-tissue comparison of the EC translatome (choroid vs brain, choroid vs retina, and brain vs retina) showed over 50% overlap of differentially expressed genes (DEGs) between the three paired comparisons, with differential pathway representation for each tissue. Pathway analysis of DEGs in the Cdh5-NT vs Tie2-NT comparison for retina, choroid, and brain predicted inhibition of processes related to myeloid cell function and activation, consistent with more specific targeting of ECs in the Cdh5-NT than in the Tie2-NT model. While TRAP enriches for EC transcripts in both models, it also captures myeloid transcripts in the Tie2-NT model. We confirmed these observations with a cell sorting approach. We suggest experimental/analytical considerations should be taken when selecting cre-lines to target ECs.
Published in
Comparative analysis of In vivo endothelial cell translatomes across central nervous system vascular beds
Chucair-Elliott AJ, Pham K, Cleuren ACA et al. · Experimental eye research 2024 · PMID 39303842 · doi:10.1016/j.exer.2024.110101
This dataset
Open GSE270197 in finder → Find more mouse 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 GSE270197_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 PRJNA1125400 and SRA study SRP514778. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39303842) ↗
Study design
18 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 18 sample titles: 18 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.