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Perturbational single-cell RNA-seq time course of CD34+ HSPC megakaryocyte differentiation with CMO multiplexing (Days 0–7, 1 uM compounds)

GSE305979 Homo sapiens (human) Single-cell RNA-seq 34 samples Submitted 2025/10/23 Platform GPL21697Platform GPL24676
At a glance

Single-cell RNA-seq · human · 34 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
34 samples: 34 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
Active learning framework leveraging transcriptomics identifies modulators of disease phenotypesScience (New York, N.Y.) · 2025
Summary (written by the submitters)
We profiled transcriptomic responses of a panel of small molecules during in vitro megakaryocyte (Mk) differentiation of CD34+ hematopoietic progenitors from a single donor. Cells were cultured in SFEM with TPO and CC100; an untreated Day 0 sample (unhashed) was collected, and additional samples on Days 1, 2, 5, and 7 were multiplexed with Cell Multiplexing Oligos (CMOs) and pooled 12 samples per library (~100k cells per well contributed), with duplicates for each compound distributed across libraries and plate-level positive/negative controls.
Read the full summary ↓
Libraries were prepared with 10x Genomics Single Cell 3’ v3.1 and sequenced on Illumina NovaSeq 6000. FASTQs were generated with bcl2fastq and processed with Cell Ranger (STAR/GRCh38). Hashed data were QC-filtered, demultiplexed by Gaussian mixture modeling of CLR-normalized CMO counts, and further filtered to singlets; counts were then normalized to 10,000 per cell and log-transformed. For consistent annotation, the dataset was mapped to a hematopoiesis reference using harmony-corrected PCs and SymphonyPy KNN label transfer, with robustness assessed by distance/similarity metrics and marker expression. This submission provides Day 0 raw counts, Days 1–7 raw counts (pre-demultiplexing), demultiplexed singlet counts with well/treatment assignments, and normalized counts with transferred cell-type labels.
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 GSE305979 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 GSE305979_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 PRJNA1305497 and SRA study SRP608758. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41129612) ↗
Study design
34 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 34 sample titles: 34 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
Datasets on a similar topic

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