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Circadian rhythmicity and biopsychosocial characteristics influence opioid use in chronic low back pain

GSE295758 Homo sapiens (human) RNA-seq (gene expression) 120 samples Submitted 2025/07/16 Platform GPL28038
At a glance

RNA-seq (gene expression) · human · 120 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
120 samples: 120 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
Circadian rhythmicity and biopsychosocial characteristics influence opioid use in chronic low back painThe Journal of clinical investigation · 2025
Summary (written by the submitters)
Background. Inter- and intra-individual fluctuations in pain intensity pose a major challenge to treatment efficacy, with a majority perceiving their pain relief as inadequate. Recent preclinical studies have identified circadian rhythmicity as a potential contributor to these fluctuations and therapeutic target. Methods.
Read the full summary ↓
We therefore sought to determine the impact of these rhythms in people with chronic low back pain (CLBP) through a detailed characterization, including questionnaires to evaluate biopsychosocial characteristics, ecological momentary assessment (7-day e-diaries at 8:00/14:00/20:00) to assess pain fluctuations, and intra-day blood transcriptomics (8:00/20:00) to identify genes/pathways of interest. Results. While most individuals displayed constant or variable/mixed pain phenotypes, a distinct subset had daily fluctuations of increasing pain scores (>30% change in intensity over 12-hours in ≥4/7 days). This population had no opioid users, better biopsychosocial profiles, and differentially expressed transcripts relative to other pain phenotypes. The circadian-governed neutrophil degranulation pathway was particularly enriched among arhythmic individuals; the link between neutrophil degranulation and opioid use was further confirmed in a separate CLBP cohort. Conclusion. Our findings identify pain rhythmicity and the circadian expression of neutrophil degranulation pathways as indicators of CLBP outcomes, which may help provide a personalized approach to phenotyping biopsychosocial characteristics and medication use. This highlights the need to better understand the impact of circadian rhythmicity across chronic pain conditions.
Published in
Circadian rhythmicity and biopsychosocial characteristics influence opioid use in chronic low back pain
Taccardi D, Zacharias AM, Gowdy HG et al. · The Journal of clinical investigation 2025 · PMID 41031896 · doi:10.1172/JCI188620
This dataset
Open GSE295758 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 GSE295758_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 PRJNA1255758 and SRA study SRP581427. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41031896) ↗
Study design
120 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 120 sample titles: 120 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

+ 80 more — browse all 120 samples with per-sample file links →

Datasets on a similar topic

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