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Efficient Chemical Reprogramming of Human T Cells into Functional Megakaryocytes and Platelets

GSE310060 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2025/11/27 Platform GPL34284Platform GPL28038
At a glance

Single-cell RNA-seq · human · 24 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
24 samples: 8 conditions, mostly in triplicateGroups 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)
The generation of megakaryocytes (MKs) from human somatic cells through chemical reprogramming represents a promising strategy for developing alternative platelet sources. Building on our prior chemical reprogramming protocol for converting erythroblasts to MKs, we established a robust method that successfully generated induced MKs (iMKs) from human cord blood-derived CD3⁺ T cells, which is a more abundant source.
Read the full summary ↓
This method utilized a five- small-molecule (5M) cocktail containing a reprogramming booster, AZD4205, to promote erasure of T cell identity and facilitate fate transition towards MKs. T cell-derived iMKs exhibited characteristic MK cellular and molecular signatures, demonstrating the capacity to produce proplatelets and release functional platelets in vitro and in vivo. ScRNA-sequencing further revealed that iMKs were heterogeneous with distinct functional profiles, including cycling, immune, and thrombopoiesis-biased MKs. Our findings highlight an optimized chemical reprogramming pathway that enables efficient conversion of T cells to MKs, providing a practical and convenient approach to generate clinically relevant MKs and platelets.
This dataset
Open GSE310060 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 GSE310060_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 PRJNA1365086 and SRA study SRP644989. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in triplicate
T_iMK_D0 ×3 T_iMK_D7 ×3 T_iMK_D14 ×3 Con ×3 AZD ×3 SM ×3 SM_AZD ×3 scRNA-seq_TiMK-D ×2 +1 more

Supports a between-group comparison across 23 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 23 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
Datasets on a similar topic

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