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CSF immune cell alterations in women with neuropsychiatric Long COVID

GSE297499 Homo sapiens (human) RNA-seq (gene expression) 46 samples Submitted 2026/01/21 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 46 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
46 samples: 46 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
Cerebrospinal Fluid Immune Cell Alterations in Women With Neuropsychiatric Long COVIDThe Journal of infectious diseases · 2026
Summary (written by the submitters)
Background: Women are disproportionately affected by neuropsychiatric symptoms following recovery from acute COVID-19, commonly termed neuropsychiatric long COVID (NP-long COVID). However, whether there are central nervous system-specific changes in gene expression in women with NP-long COVID remains unknown. Methods: Twenty-two women with and ten women without NP-long COVID were enrolled from New Haven, CT, and the surrounding region and consented to a blood draw and large volume lumbar puncture.
Read the full summary ↓
Total RNA was extracted from cerebrospinal fluid (CSF) cells and peripheral blood mononuclear cells (PBMC). Polyadenylated RNA was then sequenced, and differential expression analyses were performed using DESeq2. Results: Both CSF and PBMC samples showed differential gene expression associated with long COVID status. While some pathways were dysregulated in both the CSF and PBMC of long COVID compared to controls, including those related to androgen response, MTORC1 signaling, and lipid metabolism, these are likely driven by different suites of genes. Moreover, there were also CSF-specific differentially expressed genes in people with long COVID, indicating compartment-specific immune responses. Notably, oxidative stress, reactive oxygen species, and P53 response were all enriched in the CSF of women with long COVID but not PBMC. Conclusions: Women with NP-long COVID show distinct, compartment-specific, transcriptional profiles in the CSF.
Published in
Cerebrospinal Fluid Immune Cell Alterations in Women With Neuropsychiatric Long COVID
Orlinick B, Mehta S, McAlpine L et al. · The Journal of infectious diseases 2026 · PMID 40920586 · doi:10.1093/infdis/jiaf468
This dataset
Open GSE297499 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 GSE297499_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 PRJNA1265093 and SRA study SRP586486. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40920586) ↗
Study design
46 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 46 sample titles: 46 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

+ 6 more — browse all 46 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.