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Global cell-state and gene-program representations reveal conserved and context-specific perturbation responses of cells

GSE295214 Homo sapiens (human) Single-cell RNA-seq 85 samples Submitted 2026/05/05 Platform GPL34762
At a glance

Single-cell RNA-seq · human · 85 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
85 samples: 85 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)
Mammalian organisms comprise numerous types of cells with distinct gene-expression states, which arise from single zygotes differentiating on a complex landscape of cell states. The surge of single-cell transcriptomic data from human and other animals offer the possibility of constructing a global cell-state map, which will be foundational for understanding the molecular basis of normal tissue functions and dysfunction in diseases.
Read the full summary ↓
However, this challenging task requires computationally efficient integration of many large datasets and accurate removal of artifactual batch effects while preserving true biological variations. Here, we developed a contrastive learning model that combines the strong batch-correction power of pairwise integration with the computational scalability of deep learning and used this model to construct a global cell-state map from human and mouse single-cell transcriptomic data. This model outperforms other state-of-the-art foundation models for batch correction and dataset integration. The resulting global cell-state manifold enables projection and annotation of cell states, generation of cell-state trajectories, and prediction of causal genes driving cell-state transitions. To further illustrate its power for understanding the molecular basis of cell-state transitions, we performed genome-scale perturb-seq experiments on human embryonic stem cells and systematically mapped the cell states induced by genetic perturbations to the global cell-state manifold. We identified gene programs that are differentially regulated both under normal development and upon genetic perturbations and distinct classes of perturbation-induced cell-state transitions that are aligned with or orthogonal to normal cell differentiation. The global cell-state manifold and related computational tools provide a powerful resource for understanding cell states and transitions in health and disease.
This dataset
Open GSE295214 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 GSE295214_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 PRJNA1252779. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
85 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 85 sample titles: 85 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

+ 45 more — browse all 85 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.