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Deciphering engraftment of bone marrow human single cells after allogeneic stem cell transplantation

GSE249722 Homo sapiens (human) Single-cell RNA-seq 28 samples Submitted 2024/10/01 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 28 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
28 samples: 28 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
Deciphering bone marrow engraftment after allogeneic stem cell transplantation in humans using single-cell analysesThe Journal of clinical investigation · 2024
Summary (written by the submitters)
Engraftment of donor cells is a prerequisite to successful allogeneic hematopoietic stem cell transplantation. However, it has been poorly characterized in detail both in experimental models and in human. Bone marrow examination by mass cytometry in 26 patients with myeloid malignancies and 20 healthy donors provides evidence that the proportions of myeloid and B-cell progenitors profoundly differed as compared to healthy controls with a shift toward terminal myeloid differentiation and sharp decrease of myeloid and B-cell progenitors.
Read the full summary ↓
Principal component analysis clearly separated in 2 recipient groups (R1 and R2) driven by previous GVHD (in R2 patients). We then used single cell CITEseq to decipher donor cell engraftment in 10/26 patients (compared with 4 healthy donors). Forty-one clusters were resolved encompassing all bone marrow cellular components. Projection of patients and R1 and R2 showed drastic segregation of the 2 patient groups which were highly significant different repartition in a compositional analysis. In R2 patients sustained engraftment relied on only 10 clusters that included only committed myeloid progenitors, one stem-cell memory T-cell cluster and activated NK-cell clusters. Gene expression hallmarks disclosed repeatedly similar pathway in transplant recipients, namely, TNFα signaling via NFĸ, interferonƔ response, and allograft rejection. Thus, the cellular composition of the engrafted bone marrow in human is strongly influenced by T-cell mediated processes and inflammation leading to an emergency hematopoiesis.
Published in
Deciphering bone marrow engraftment after allogeneic stem cell transplantation in humans using single-cell analyses
Bordenave J, Gajda D, Michonneau D et al. · The Journal of clinical investigation 2024 · PMID 39207851 · doi:10.1172/JCI180331
This dataset
Open GSE249722 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 GSE249722_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 PRJNA1050242 and SRA study SRP476683. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39207851) ↗
Study design
28 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 28 sample titles: 28 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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