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The microglial transcriptome of age-associated deep subcortical white matter lesions suggests a neuroprotective response to blood-brain barrier dysfunction

GSE260619 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2024/05/01 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 12 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
12 samples: 4 × Control WML vs 4 × NAWM matched with the DSCL cases vs 4 × DSCL casesGroups 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
The Microglial Transcriptome of Age-Associated Deep Subcortical White Matter Lesions Suggests a Neuroprotective Response to Blood-Brain Barrier DysfunctionInternational journal of molecular sciences · 2024
Summary (written by the submitters)
Age-associated deep-subcortical white matter lesions (DSCL) are an independent risk factor for dementia, displaying high levels of CD68+ microglia. This study aimed to characterise the transcriptomic profile of microglia in DSCL and surrounding radiologically normal-appearing white matter (NAWM) compared to non-lesional control white matter.
Read the full summary ↓
CD68+ microglia were isolated from white matter groups (n=4 cases per group) from the Cognitive Function and Ageing Study neuropathology cohort by immuno-laser capture microdissection. Microarray gene expression profiling, but not RNA-sequencing, was found to be compatible with immuno-LCM-ed post-mortem material and identified significantly differentially expressed genes (DEG). Functional grouping and pathway analysis was assessed using DAVID, and immunohistochemistry was performed to validate gene expression changes at the protein level. Transcriptomic profiling of microglia in DSCL compared to non-lesional control white matter identified 181 significant DEG (93 upregulated and 88 downregulated). Functional clustering analysis revealed dysregulation of haptoglobin-hemoglobin binding (Enrichment score 2.15, p=0.017), confirmed by CD163 immunostaining, suggesting a neuroprotective microglial response to blood-brain barrier dysfunction in DSCL. In NAWM versus control white matter, microglia exhibited 347 DEGs (209 upregulated, 138 downregulated), with significant dysregulation of protein de-ubiquitination (Enrichment score 5.14, p<0.0001), implying an inability to maintain protein homeostasis in NAWM that may contribute to lesion spread. These findings enhance understanding of microglial transcriptomic changes in aging white matter pathology, highlighting a neuroprotective adaptation in DSCL microglia and a potentially lesion-promoting phenotype in NAWM microglia.
Published in
The Microglial Transcriptome of Age-Associated Deep Subcortical White Matter Lesions Suggests a Neuroprotective Response to Blood-Brain Barrier Dysfunction
Almansouri T, Waller R, Wharton SB et al. · International journal of molecular sciences 2024 · PMID 38674030 · doi:10.3390/ijms25084445
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 GSE260619_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: 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 PRJNA1082353 and SRA study SRP492666. Searching any of these in the dataset finder brings you back here.

Study design
4 × Control WML vs 4 × NAWM matched with the DSCL cases vs 4 × DSCL cases

Supports a between-group comparison across 12 samples.

How we read this: 3 replicated groups read from 12 sample titles; they account for 12 of them. 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
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