scRNAseq and CITEseq of aged HSPCs derived from FlkSwitch mouse model
Single-cell RNA-seq · mouse · 24 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Mus musculus (mouse)
- Samples
- 24 samples: 24 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
- A Rare Hematopoietic Stem Cell-Derived Megakaryocyte Progenitor Accumulates via Enhanced Survival and Contributes to Exacerbated Thrombopoiesis Upon AgingAging cell · 2025
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.
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.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE280510_RAW.tar (it can run to tens of gigabytes).
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.
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 PRJNA1178736 and SRA study SRP488052. Searching any of these in the dataset finder brings you back here.
Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.
How we read this: Read from 24 sample titles: 24 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.
- GSM8062651 Sample1, replicate 1, scRNAseq [A_GEL_1_S1]
- GSM8062652 Sample2, replicate 1, scRNAseq [B_GEL_1_S2]
- GSM8062653 Sample3, replicate 1, scRNAseq [C_GEL_1_S3]
- GSM8062654 Sample4, replicate 1, scRNAseq [D_GEL_1_S4]
- GSM8062655 Sample1, replicate 1, scRNAseq [A_CMO_1_S9]
- GSM8062656 Sample2, replicate 1, scRNAseq [B_CMO_1_S10]
- GSM8062657 Sample3, replicate 1, scRNAseq [C_CMO_1_S11]
- GSM8062658 Sample4, replicate 1, scRNAseq [D_CMO_1_S12]
- GSM8062659 Sample1, replicate 2, scRNAseq [A_GEL_1_S12]
- GSM8062660 Sample2, replicate 2, scRNAseq [B_GEL_1_S1]
- GSM8062661 Sample3, replicate 2, scRNAseq [C_GEL_1_S2]
- GSM8062662 Sample4, replicate 2, scRNAseq [D_GEL_1_S3]
- GSM8062663 Sample1, replicate 2, scRNAseq [A_CMO_1_S8]
- GSM8062664 Sample2, replicate 2, scRNAseq [B_CMO_1_S9]
- GSM8062665 Sample3, replicate 2, scRNAseq [C_CMO_1_S10]
- GSM8062666 Sample4, replicate 2, scRNAseq [D_CMO_1_S11]
- GSM8595332 Sample1, replicate 1, CITE-Seq
- GSM8595333 Sample2, replicate 1, CITE-Seq
- GSM8595334 Sample3, replicate 1, CITE-Seq
- GSM8595335 Sample4, replicate 1, CITE-Seq
- GSM8595336 Sample1, replicate 2, CITE-Seq
- GSM8595337 Sample2, replicate 2, CITE-Seq
- GSM8595338 Sample3, replicate 2, CITE-Seq
- GSM8595339 Sample4, replicate 2, CITE-Seq
- GSE271578 Kinetical scRNAseq Analysis Reveals Immune Mechanisms Underlying Hindered Lung Recovery following Influenza Virus Infection in Aged Hosts 12 samples shares scrnaseq, aged
- GSE272755 Transcriptional effects of lysosome stress on mouse bone marrow-derived macrophages and primary microglia 263 samples shares derived
- GSE319684 Timecourse of FACS sorted Ly6C+ monocytes, tissue resident alveolar macrophages (trAMs), monocyte derived alveolar macrophages (bmAMs) and epithelial cells from SARS-CoV-2 (maVie16) vs. IAV (PR8) infected mouse lungs 176 samples shares derived
- GSE275514 Astrocyte-Derived Clusterin Disrupts Glial Physiology to Obstruct Remyelination in Mouse Models of Demyelinating Diseases 18 samples shares derived
- GSE220548 Human iPSC-Derived Mononuclear Phagocytes Improve Cognition and Neural Health across Multiple Mouse Models of Aging and Alzheimer's Disease 15 samples shares derived
- GSE265909 yHSC and oHSC scRNAseq with oHSC AThi ATlo Index Sorting. Assessing the transcriptional changes associated with autophagy engagement in old hematopoietic stem cells at single cell resolution. [scRNA-Seq] 563 samples shares scrnaseq
- GSE311155 Age- and Sex- Driven Transcriptional and Metabolic Diversity in Myeloid-Derived Suppressor Cells After Mouse Sepsis 57 samples shares derived
- GSE294562 Characterization of a panel of mouse primary cell lines from Myc-driven mammary gland tumours and lung metastasis derived from them 45 samples shares derived
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.