Dynamic changes of immune cells in systemic lupus erythematosus with nephritis using longitudinal peripheral blood single-cell RNA-seq
Single-cell RNA-seq · human · 57 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
- 57 samples: 57 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.
- Paper
- Tracking clonal dynamics of CD8 T cells and immune dysregulation in progression of systemic lupus erythematosus with nephritisExperimental & molecular medicine · 2025
Read the full summary ↓
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE254176_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1068756 and SRA study SRP485810. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 57 sample titles: 57 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.
- GSM8035466 Patient 1, timepoint 1, scRNA-seq
- GSM8035467 Patient 1, timepoint 2, scRNA-seq
- GSM8035468 Patient 1, timepoint 3, scRNA-seq
- GSM8035469 Patient 2, timepoint 1, scRNA-seq
- GSM8035470 Patient 2, timepoint 2, scRNA-seq
- GSM8035471 Patient 3, timepoint 1, scRNA-seq
- GSM8035472 Patient 3, timepoint 2, scRNA-seq
- GSM8035473 Patient 3, timepoint 3, scRNA-seq
- GSM8035474 Patient 3, timepoint 4, scRNA-seq
- GSM8035475 Patient 3, timepoint 5, scRNA-seq
- GSM8035476 Patient 3, timepoint 6, scRNA-seq
- GSM8035477 Patient 4, timepoint 1, scRNA-seq
- GSM8035478 Patient 4, timepoint 2, scRNA-seq
- GSM8035479 Patient 4, timepoint 3, scRNA-seq
- GSM8035480 Patient 4, timepoint 4, scRNA-seq
- GSM8035481 Patient 4, timepoint 5, scRNA-seq
- GSM8035482 Patient 4, timepoint 6, scRNA-seq
- GSM8035483 Pateint 5, timepoint 1, scRNA-seq
- GSM8035484 Pateint 6, timepoint 1, scRNA-seq
- GSM8035485 Patient 1, timepoint 1, scTCR-seq
- GSM8035486 Patient 1, timepoint 2, scTCR-seq
- GSM8035487 Patient 1, timepoint 3, scTCR-seq
- GSM8035488 Patient 2, timepoint 1, scTCR-seq
- GSM8035489 Patient 2, timepoint 2, scTCR-seq
- GSM8035490 Patient 3, timepoint 1, scTCR-seq
- GSM8035491 Patient 3, timepoint 2, scTCR-seq
- GSM8035492 Patient 3, timepoint 3, scTCR-seq
- GSM8035493 Patient 3, timepoint 4, scTCR-seq
- GSM8035494 Patient 3, timepoint 5, scTCR-seq
- GSM8035495 Patient 3, timepoint 6, scTCR-seq
- GSM8035496 Patient 4, timepoint 1, scTCR-seq
- GSM8035497 Patient 4, timepoint 2, scTCR-seq
- GSM8035498 Patient 4, timepoint 3, scTCR-seq
- GSM8035499 Patient 4, timepoint 4, scTCR-seq
- GSM8035500 Patient 4, timepoint 5, scTCR-seq
- GSM8035501 Patient 4, timepoint 6, scTCR-seq
- GSM8035502 Pateint 5, timepoint 1, scTCR-seq
- GSM8035503 Pateint 6, timepoint 1, scTCR-seq
- GSM8035504 Patient 1, timepoint 1, scBCR-seq
- GSM8035505 Patient 1, timepoint 2, scBCR-seq
+ 17 more — browse all 57 samples with per-sample file links →
- GSE196198 Longitudinal single-cell multiomics data for different severity of COVID-19 100 samples shares longitudinal, single-cell
- GSE309593 Longitudinal Analysis of Multiple Myeloma: Therapeutic Response and Immune Microenvironment Dynamics in the Context of Tumor Heterogeneity [CITE-Seq] 91 samples shares longitudinal, dynamic
- GSE253661 Longitudinal single-cell transcriptomics of monocytes, Tfh and gd Tcells following a control human malaria infection. 16 samples shares longitudinal, single-cell
- GSE309595 Longitudinal Analysis of Multiple Myeloma: Therapeutic Response and Immune Microenvironment Dynamics in the Context of Tumor Heterogeneity [scRNA-seq] 270 samples shares longitudinal, dynamic
- GSE263733 Single-cell transcriptome analysis reveals subtype-specific clonal evolution and microenvironmental changes in liver metastasis of pancreatic adenocarcinoma and their clinical implications 218 samples shares single-cell, changes
- GSE282122 A longitudinal single-cell atlas of anti-tumour necrosis factor treatment in inflammatory bowel disease 216 samples shares longitudinal, single-cell
- GSE307215 Spatiotemporal Single-Cell Profiling Reveals T Cell Clonal Dynamics and Phenotypic Plasticity in Human Graft-versus-Host Disease 104 samples shares single-cell, dynamic
- GSE334588 Single-cell transcriptomic atlas of adult dermatomyositis, cutaneous lupus erythematosus and healthy skin 48 samples shares erythematosus, single-cell
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.