GEO series

Peripheral blood bulk RNA-seq validation cohort for chronic antibody-mediated rejection

GSE334922 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2026/07/21 Platform GPL35109

At a glance

Single-cell RNA-seq · human · 12 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Samples: 12 samples: 12 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)

Chronic antibody-mediated rejection (CAMR) is a leading cause of late kidney allograft failure and shares features of chronic immune activation with systemic autoimmune diseases, but how circulating immune states connect to spatially organized graft injury and whether related immune features recur in autoimmune inflammation remain unclear.

Read the full summary ↓

Here, we integrated single-cell profiles from peripheral blood and kidney graft samples from patients with CAMR or stable allograft function and healthy controls with independent transplant cohorts, bulk transcriptomic datasets, spectral flow cytometry, multiplex immunofluorescence, high-resolution spatial transcriptomics, and a large blood immune reference spanning autoimmune and inflammatory diseases. CAMR was associated with reproducible myeloid expansion and enrichment of SLC8A1+ monocytes/macrophages, ANK3+CD4+ memory T cells, ZEB2hi T cells, and CD16+ NK cells. These subsets exhibited migratory and tissue-interacting programs characterized by the recurrent expression of CXCR4 and ZEB2. In graft tissue, CXCL12-expressing fibroblasts and CXCR4-expressing immune cells were increased in CAMR. High-resolution spatial transcriptomics localized this fibroblast–immune architecture to perivascular regions marked by endothelial activation and stromal remodeling, and placed CAMR-associated immune states within adjacent immune-rich neighborhoods, supporting a CXCL12-CXCR4 axis that may be associated with local immune accumulation. Comparisons with large-scale blood immune reference data and spatial transcriptomic datasets from rheumatoid arthritis and systemic lupus erythematosus suggested partial recurrence of related immune states and fibroblast–immune organization. Together, this study links circulating CAMR immune remodeling to a spatially organized CXCL12-CXCR4 fibroblast-immune niche, and suggests that elements of this niche recur across alloimmune and autoimmune inflammation.

This dataset

Open GSE334922 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 GSE334922_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1476931. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

12 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 12 sample titles: 12 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

GSM9802150 JZ26071522_CAMR1_CAMR1
GSM9802151 JZ26071523_CAMR2_CAMR2
GSM9802152 JZ26071524_CAMR3_CAMR3
GSM9802153 JZ26071525_CAMR4_CAMR4
GSM9802154 JZ26071526_SAF_1_SAF_1
GSM9802155 JZ26071527_SAF_2_SAF_2
GSM9802156 JZ26071528_SAF_3_SAF_3
GSM9802157 JZ26071529_SAF_4_SAF_4
GSM9802158 JZ26071530_HC_N1_HC_N1
GSM9802159 JZ26071531_HC_N2_HC_N2
GSM9802160 JZ26071532_HC_N3_HC_N3
GSM9802161 JZ26071533_HC_N4_HC_N4

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