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GSE305370 Homo sapiens (human) Single-cell RNA-seq 80 samples Submitted 2025/10/23 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 80 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
80 samples: 16 conditions, mostly with about 5 replicates eachGroups 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
Active learning framework leveraging transcriptomics identifies modulators of disease phenotypesScience (New York, N.Y.) · 2025
Summary (written by the submitters)
In the past decade, the advent of single-cell genomics has enabled the measurement of DNA, RNA, and proteins in single cells. These technologies allow the study of biology at an unprecedented scale and resolution. Among the outcomes have been detailed maps of early human embryonic development, the discovery of new disease-associated cell types, and cell-targeted therapeutic interventions.
Read the full summary ↓
Moreover, with recent advances in experimental techniques it is now possible to measure multiple genomic modalities in the same cell. While multimodal single-cell data is increasingly available, data analysis methods are still scarce. Due to the small volume of a single cell, measurements are sparse and noisy. Differences in molecular sampling depths between cells (sequencing depth) and technical effects from handling cells in batches (batch effects) can often overwhelm biological differences. When analyzing multimodal data, one must account for different feature spaces, as well as shared and unique variation between modalities and between batches. Furthermore, current pipelines for single-cell data analysis treat cells as static snapshots, even when there is an underlying dynamical biological process. Accounting for temporal dynamics alongside state changes over time is an open challenge in single-cell data science. Generally, genetic information flows from DNA to RNA to proteins. DNA must be accessible (ATAC data) to produce RNA (GEX data), and RNA in turn is used as a template to produce protein (ADT data). These processes are regulated by feedback: for example, a protein may bind DNA to prevent the production of more RNA. This genetic regulation is the foundation for dynamic cellular processes that allow organisms to develop and adapt to changing environments. In single-cell data science, dynamic processes have been modeled by so-called pseudotime algorithms that capture the progression of the biological process. Yet, generalizing these algorithms to account for both pseudotime and real time is still an open problem.
Published in
Active learning framework leveraging transcriptomics identifies modulators of disease phenotypes
DeMeo B, Nesbitt C, Miller SA et al. · Science (New York, N.Y.) 2025 · PMID 41129612 · doi:10.1126/science.adi8577
This dataset
Open GSE305370 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 GSE305370_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 PRJNA1300406 and SRA study SRP605909. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41129612) ↗
Study design
16 conditions, mostly with about 5 replicates each
CITE-seq GEX library from mobilized peripher… ×5 CITE-seq GEX library from mobilized peripher… ×5 CITE-seq GEX library from mobilized peripher… ×5 CITE-seq GEX library from mobilized peripher… ×5 CITE-seq ADT library from mobilized peripher… ×5 CITE-seq ADT library from mobilized peripher… ×5 CITE-seq ADT library from mobilized peripher… ×5 CITE-seq ADT library from mobilized peripher… ×5 +8 more

Supports a between-group comparison across 80 samples.

How we read this: 16 replicated groups read from 80 sample titles; they account for 80 of them. 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

+ 40 more — browse all 80 samples with per-sample file links →

Datasets on a similar topic

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.