GEO series

Myasthenia thymus reprograms class switched B cells into BAFF dependent survivors

GSE324427 Homo sapiens (human) RNA-seq (gene expression) 73 samples Submitted 2026/07/31 Platform GPL30173Platform GPL34284Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 73 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: 73 samples: 73 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.

Summary (written by the submitters)

In Myasthenia gravis (MG), autoantibody titers and thymectomy outcomes often fail to predict disease severity, suggesting that autoreactive B cells bypass normal tolerance checkpoints. Our analysis of the largest MG thymus atlas to date reveals a checkpoint swap where pathological B cells transition from T cell-dependent selection to a BAFF-driven survival program.

Read the full summary ↓

These B cells exhibit reduced antigen presentation and CD40 co-stimulation, instead upregulating TNFRSF17 to engage BAFF signals provided by specialized T follicular helper cells. This reprogramming allows polyclonal autoreactive populations to persist and seed peripheral sites independent of stringent selection, explaining longstanding clinical paradoxes. Consequently, the BAFF-BCMA axis emerges as a critical therapeutic target and a high-fidelity biomarker for monitoring MG disease activity.

This dataset

Open GSE324427 in finder → Find more human 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 GSE324427_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 PRJNA1435083 and SRA study SRP682596. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

73 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 73 sample titles: 73 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

GSM9575810 PT1,Thymoma_no_MG,CD45nEPCAMn, scRNA-seq
GSM9575811 PT1,Thymoma_no_MG,CD45nEPCAMp, scRNA-seq
GSM9575812 PT1,Thymoma_no_MG,CD45p, scRNA-seq
GSM9575813 PT1,Thymoma_no_MG,thymocytes, scRNA-seq
GSM9575814 PT10,Thymoma_no_MG, scRNA-seq
GSM9575815 PT11,Thymus_MG, scRNA-seq
GSM9575816 PT12,Thymus_MG, scRNA-seq
GSM9575817 PT12,Thymoma_MG, scRNA-seq
GSM9575818 PT13,Thymoma_MG, scRNA-seq
GSM9575819 PT14,PrimaryTumor, scRNA-seq
GSM9575820 PT14,MetastasisTumor, scRNA-seq
GSM9575821 PT15,Thymus_MG, scRNA-seq
GSM9575822 PT16,Thymus_no_MG, scRNA-seq
GSM9575823 PT16,ThymicCarcinoma, scRNA-seq
GSM9575824 PT2,Thymus_MG,CD45nEPCAMn, scRNA-seq
GSM9575825 PT2,Thymus_MG,CD45nEPCAMp, scRNA-seq
GSM9575826 PT2,Thymus_MG,CD45p, scRNA-seq
GSM9575827 PT3,Thymus_no_MG,CD45nEPCAMn, scRNA-seq
GSM9575828 PT3,Thymus_no_MG,CD45nEPCAMp, scRNA-seq
GSM9575829 PT3,Thymus_no_MG,CD45p, scRNA-seq
GSM9575830 PT4,Thymoma_no_MG,CD45nEPCAMn, scRNA-seq
GSM9575831 PT4,Thymoma_no_MG,CD45nEPCAMp, scRNA-seq
GSM9575832 PT4,Thymoma_no_MG,CD45p, scRNA-seq
GSM9575833 PT4,Thymus_no_MG,CD45nEPCAMn, scRNA-seq
GSM9575834 PT4,Thymus_no_MG,CD45nEPCAMp, scRNA-seq
GSM9575835 PT4,Thymus_no_MG,CD45p, scRNA-seq
GSM9575836 PT5,Thymus_MG, scRNA-seq
GSM9575837 PT6,Thymus_MG, scRNA-seq
GSM9575838 PT7,Thymus_M, scRNA-seq
GSM9575839 PT8,Thymus_no_MG, scRNA-seq
GSM9575840 PT9,Thymus_MG, scRNA-seq
GSM9575841 PT9,MetastasisTumor1, scRNA-seq
GSM9575842 PT9,Thymoma_MG, scRNA-seq
GSM9575843 PT9,MetastasisTumor2, scRNA-seq
GSM9575844 PT10,Thymoma_no_MG,vdj_b
GSM9575845 PT11,Thymus_MG,vdj_b
GSM9575846 PT12,Thymus_MG,vdj_b
GSM9575847 PT12,Thymoma_MG,vdj_b
GSM9575848 PT13,Thymoma_MG,vdj_b
GSM9575849 PT14,PrimaryTumor,vdj_b

+ 33 more — browse all 73 samples with per-sample file links →

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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.