GEO series

Dissecting depression symptoms: multi-omics clustering uncovers immune-related subgroups and cell-type specific dysregulation

GSE260603 Homo sapiens (human) RNA-seq (gene expression) 273 samples Submitted 2024/03/05 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 273 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: 273 samples: 273 × total RNA-seq from sampleGroups 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.

Summary (written by the submitters)

In a subset of patients with mental disorders, such as depression, low-grade inflammation and altered immune marker concentrations are observed. However, these immune alterations are often assessed by only one data type and small markers panels. Here, we used a transdiagnostic approach and combined data from two cohorts to define subgroups of depression symptoms across the diagnostic spectrum through a large-scale multi-omics clustering approach in 237 individuals.

Read the full summary ↓

The method incorporated age, body mass index (BMI), 43 plasma immune markers and RNA-seq data from peripheral mononuclear blood cells (PBMCs). Our initial clustering revealed four clusters, including two immune-related depression symptom clusters characterized by elevated BMI, higher depression severity and elevated levels of immune markers such as interleukin-1 receptor antagonist (IL-1RA), C-reactive protein (CRP) and C-C motif chemokine 2 (CCL2 or MCP-1). In contrast, the RNA-seq data mostly differentiated a cluster with low depression severity, enriched in brain related gene sets. This cluster was also distinguished by electrocardiography data, while structural imaging data revealed differences in ventricle volumes across the clusters. Incorporating predicted cell type proportions into the clustering resulted in three clusters, with one showing elevated immune marker concentrations. The cell type proportion and genes related to cell types were most pronounced in an intermediate depression symptoms cluster, suggesting that RNA-seq and immune markers measure different aspects of immune dysregulation. Lastly, we found a dysregulation of the SERPINF1/VEGF-A pathway that was specific to dendritic cells by integrating immune marker and RNA-seq data. This shows the advantages of combining different data modalities and highlights possible markers for further stratification research of depression symptoms.

This dataset

Open GSE260603 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 GSE260603_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 PRJNA1082314 and SRA study SRP492982. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

273 × total RNA-seq from sample

Supports a between-group comparison across 273 samples.

How we read this: 1 replicated groups read from 273 sample titles; they account for 273 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

GSM8120567 total RNA-seq from sample_001 total RNA-seq from samp…
GSM8120568 total RNA-seq from sample_002 total RNA-seq from samp…
GSM8120569 total RNA-seq from sample_003 total RNA-seq from samp…
GSM8120570 total RNA-seq from sample_004 total RNA-seq from samp…
GSM8120571 total RNA-seq from sample_005 total RNA-seq from samp…
GSM8120572 total RNA-seq from sample_006 total RNA-seq from samp…
GSM8120573 total RNA-seq from sample_007 total RNA-seq from samp…
GSM8120574 total RNA-seq from sample_008 total RNA-seq from samp…
GSM8120575 total RNA-seq from sample_009 total RNA-seq from samp…
GSM8120576 total RNA-seq from sample_010 total RNA-seq from samp…
GSM8120577 total RNA-seq from sample_011 total RNA-seq from samp…
GSM8120578 total RNA-seq from sample_012 total RNA-seq from samp…
GSM8120579 total RNA-seq from sample_013 total RNA-seq from samp…
GSM8120580 total RNA-seq from sample_014 total RNA-seq from samp…
GSM8120581 total RNA-seq from sample_015 total RNA-seq from samp…
GSM8120582 total RNA-seq from sample_016 total RNA-seq from samp…
GSM8120583 total RNA-seq from sample_017 total RNA-seq from samp…
GSM8120584 total RNA-seq from sample_018 total RNA-seq from samp…
GSM8120585 total RNA-seq from sample_019 total RNA-seq from samp…
GSM8120586 total RNA-seq from sample_020 total RNA-seq from samp…
GSM8120587 total RNA-seq from sample_021 total RNA-seq from samp…
GSM8120588 total RNA-seq from sample_022 total RNA-seq from samp…
GSM8120589 total RNA-seq from sample_023 total RNA-seq from samp…
GSM8120590 total RNA-seq from sample_024 total RNA-seq from samp…
GSM8120591 total RNA-seq from sample_025 total RNA-seq from samp…
GSM8120592 total RNA-seq from sample_026 total RNA-seq from samp…
GSM8120593 total RNA-seq from sample_027 total RNA-seq from samp…
GSM8120594 total RNA-seq from sample_028 total RNA-seq from samp…
GSM8120595 total RNA-seq from sample_029 total RNA-seq from samp…
GSM8120596 total RNA-seq from sample_030 total RNA-seq from samp…
GSM8120597 total RNA-seq from sample_031 total RNA-seq from samp…
GSM8120598 total RNA-seq from sample_032 total RNA-seq from samp…
GSM8120599 total RNA-seq from sample_033 total RNA-seq from samp…
GSM8120600 total RNA-seq from sample_034 total RNA-seq from samp…
GSM8120601 total RNA-seq from sample_035 total RNA-seq from samp…
GSM8120602 total RNA-seq from sample_036 total RNA-seq from samp…
GSM8120603 total RNA-seq from sample_037 total RNA-seq from samp…
GSM8120604 total RNA-seq from sample_038 total RNA-seq from samp…
GSM8120605 total RNA-seq from sample_039 total RNA-seq from samp…
GSM8120606 total RNA-seq from sample_040 total RNA-seq from samp…

+ 233 more — browse all 273 samples with per-sample file links →

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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.