GEO series

Integrated long-read transcriptomic profiling of peripheral blood from ankylosing spondylitis patients reveals regulatory shifts and core genes associated with programmed cell death

GSE299639 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/06/12 Platform GPL26167

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: 3 × AS_F vs 3 × AS_M vs 3 × HC_F vs 3 × HC_MGroups 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)

Ankylosing spondylitis (AS) is a chronic immune-mediated arthritis marked by persistent inflammation and progressive structural damage. Although dysregulation of programmed cell death (PCD) is increasingly recognized in AS pathogenesis, the full spectrum of transcript-level regulation remains unclear. Here, we employed Oxford Nanopore Technologies (ONT) long-read RNA sequencing to comprehensively profile peripheral blood transcriptomes from six AS patients and six matched healthy controls.

Read the full summary ↓

Our analysis identified 1,088 differentially expressed genes (DEGs) and 1,812 differentially expressed transcripts (DETs), with upregulated transcripts enriched in apoptosis, autophagy, and transcriptional regulation. We further detected 50 transcripts with significant differential usage and 304 alternative splicing events affecting immune- and PCD-related genes, including FCGR2B, TLR2, and STAT5B. Integrative multi-omic analysis revealed 26 core genes, such as NAMPT, GATA2, and DDIT3, showing consistent dysregulation at gene, isoform, and splicing levels, highlighting convergent regulatory networks underlying immune imbalance and cell death in AS. These findings provide the first isoform-resolved transcriptomic landscape of PCD regulation in AS, revealing extensive regulatory complexity and novel core genes that may represent promising targets for future mechanistic and therapeutic studies.

This dataset

Open GSE299639 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 GSE299639_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 PRJNA1275820 and SRA study SRP591763. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

3 × AS_F vs 3 × AS_M vs 3 × HC_F vs 3 × HC_M

Supports a between-group comparison across 12 samples.

How we read this: 4 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

GSM9042781 AS_F1 AS_F
GSM9042782 AS_F2 AS_F
GSM9042783 AS_F3 AS_F
GSM9042784 AS_M1 AS_M
GSM9042785 AS_M2 AS_M
GSM9042786 AS_M3 AS_M
GSM9042787 HC_F1 HC_F
GSM9042788 HC_F2 HC_F
GSM9042789 HC_F3 HC_F
GSM9042790 HC_M1 HC_M
GSM9042791 HC_M2 HC_M
GSM9042792 HC_M3 HC_M

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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.