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In Vitro Mesenchymal Stem Cell Differentiation Landscape

GSE241130 Homo sapiens (human) Single-cell RNA-seq 27 samples Submitted 2024/05/17 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 27 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
27 samples: 9 × Osteo-D vs 7 × Adipo-D vs 2 × Adipo-D0Groups 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
Mapping the Single-cell Differentiation Landscape of OsteosarcomabioRxiv : the preprint server for biology · 2023
Summary (written by the submitters)
We employed a tissue engineered system designed to recapitulate mesenchymal stem cell differentiation and performed single-cell profiling to identify differentiation states. We constructed the Mesenchymal Tissue Landscape (MTL) using single-cell RNA-sequencing (scRNA-seq) data from multiple mesenchymal lineages (osteogenic and adipogenic) at various differentiation stages.
Published in
Mapping the Single-cell Differentiation Landscape of Osteosarcoma
Truong DD, Weistuch C, Murgas KA et al. · bioRxiv : the preprint server for biology 2023 · PMID 37745374 · doi:10.1101/2023.09.13.555156
This dataset
Open GSE241130 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 GSE241130_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 PRJNA1006438 and SRA study SRP455700. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 37745374) ↗
Study design
9 × Osteo-D vs 7 × Adipo-D vs 2 × Adipo-D0

Supports a between-group comparison across 18 samples.

How we read this: 3 replicated groups read from 27 sample titles; they account for 18 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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