GEO series

Single-cell RNA-seq atlas of peripheral blood and kidney graft immune cells in chronic antibody-mediated rejection

GSE334923 Homo sapiens (human) Single-cell RNA-seq 48 samples Submitted 2026/07/21 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 48 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Homo sapiens (human)
Samples: 48 samples: 48 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 GSE334923 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 GSE334923_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 PRJNA1476928. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

48 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 48 sample titles: 48 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

GSM9802162 CAMR12_T
GSM9802163 CAMR14_T
GSM9802164 CAMR16_T
GSM9802165 CAMR19_T
GSM9802166 CAMR33_T
GSM9802167 CAMR34_T
GSM9802168 CAMR36_T
GSM9802169 CAMRC1P_P
GSM9802170 CAMRC3P_P
GSM9802171 CAMRT1P_P
GSM9802172 CAMRT2P_P
GSM9802173 CAR01_P
GSM9802174 CAR02_P
GSM9802175 CAR04_P
GSM9802176 CAR12_P
GSM9802177 CAR13_P
GSM9802178 CAR14_P
GSM9802179 CAR16_P
GSM9802180 CAR18_P
GSM9802181 CAR19_P
GSM9802182 CAR21_P
GSM9802183 CAR26_P
GSM9802184 CAR30_P
GSM9802185 CAR33_P
GSM9802186 CAR34_P
GSM9802187 CAR35_P
GSM9802188 CAR36_P
GSM9802189 CAR37_P
GSM9802190 CAR38_P
GSM9802191 HC01_P
GSM9802192 HC02_P
GSM9802193 HC03_P
GSM9802194 HC04_P
GSM9802195 HC05_P
GSM9802196 HC-N1LLM_P
GSM9802197 HC-N2DXH_P
GSM9802198 HC-N3FY_P
GSM9802199 HC-N4TQL_P
GSM9802200 SAF01_P
GSM9802201 SAF03_P

+ 8 more — browse all 48 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.