GEO series

Multi-omics sequencing reveals the signature of the genome, cell composition and transcriptome of nodal RDD [scRNA-seq]

GSE263859 Homo sapiens (human) Single-cell RNA-seq 1 samples Submitted 2025/04/16 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 1 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: 1 samples
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)

RDD is a poorly understood disease with low incidence. Here, we performed whole genome sequencing, single cell RNA-seq and spatial transcriptome sequencing of a nodal RDD, describing its cell composition, spatial arrangement and molecular characteristics from a multi-omics perspective. By integrating single cell RNA-seq data, we found that compared with lymph node and LCH, RDD harbors completely different cell composition and molecular feature.

Read the full summary ↓

Compared with lymph node, a subset of ion-responded macrophage appeared in RDD. Compared with LCH, macrophages in RDD are enriched for RHO GTPase pathway. And RHO GTPase high macrophages have unique cell-cell interactions and are the most actively proliferating cells in RDD. Our results provide a valuable insight for understanding the molecular basis of nodal RDD.

This dataset

Open GSE263859 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 GSE263859_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 PRJNA1099628 and SRA study SRP501457. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Samples in this study

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