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]
Single-cell RNA-seq · mouse · 563 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
- 563 samples: 101 × GFP-LC3 oHSC_LO vs 101 × GFP-LC3 oHSC_MID vs 100 × GFP-LC3 oHSC_HI vs 98 × GFP-LC3 yHSCGroups 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
- Autophagy counters inflammation-driven glycolytic impairment in aging hematopoietic stem cellsCell stem cell · 2024
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 GSE265909_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 PRJNA1104666 and SRA study SRP504083. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 400 samples.
How we read this: 4 replicated groups read from the first 400 of 563 sample titles; they account for 400 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.
- GSM8231288 GFP-LC3 yHSC_1 GFP-LC3 yHSC
- GSM8231289 GFP-LC3 oHSC_LO_1 GFP-LC3 oHSC_LO
- GSM8231290 GFP-LC3 oHSC_MID_1 GFP-LC3 oHSC_MID
- GSM8231291 GFP-LC3 oHSC_HI_1 GFP-LC3 oHSC_HI
- GSM8231292 GFP-LC3 yHSC_2 GFP-LC3 yHSC
- GSM8231293 GFP-LC3 oHSC_LO_2 GFP-LC3 oHSC_LO
- GSM8231294 GFP-LC3 oHSC_MID_2 GFP-LC3 oHSC_MID
- GSM8231295 GFP-LC3 oHSC_HI_2 GFP-LC3 oHSC_HI
- GSM8231296 GFP-LC3 yHSC_3 GFP-LC3 yHSC
- GSM8231297 GFP-LC3 oHSC_LO_3 GFP-LC3 oHSC_LO
- GSM8231298 GFP-LC3 oHSC_MID_3 GFP-LC3 oHSC_MID
- GSM8231299 GFP-LC3 oHSC_HI_3 GFP-LC3 oHSC_HI
- GSM8231300 GFP-LC3 yHSC_4 GFP-LC3 yHSC
- GSM8231301 GFP-LC3 oHSC_LO_4 GFP-LC3 oHSC_LO
- GSM8231302 GFP-LC3 oHSC_MID_4 GFP-LC3 oHSC_MID
- GSM8231303 GFP-LC3 oHSC_HI_4 GFP-LC3 oHSC_HI
- GSM8231304 GFP-LC3 yHSC_5 GFP-LC3 yHSC
- GSM8231305 GFP-LC3 oHSC_LO_5 GFP-LC3 oHSC_LO
- GSM8231306 GFP-LC3 oHSC_MID_5 GFP-LC3 oHSC_MID
- GSM8231307 GFP-LC3 oHSC_HI_5 GFP-LC3 oHSC_HI
- GSM8231308 GFP-LC3 yHSC_6 GFP-LC3 yHSC
- GSM8231309 GFP-LC3 oHSC_LO_6 GFP-LC3 oHSC_LO
- GSM8231310 GFP-LC3 oHSC_MID_6 GFP-LC3 oHSC_MID
- GSM8231311 GFP-LC3 oHSC_HI_6 GFP-LC3 oHSC_HI
- GSM8231312 GFP-LC3 yHSC_7 GFP-LC3 yHSC
- GSM8231313 GFP-LC3 oHSC_LO_7 GFP-LC3 oHSC_LO
- GSM8231314 GFP-LC3 oHSC_MID_7 GFP-LC3 oHSC_MID
- GSM8231315 GFP-LC3 oHSC_HI_7 GFP-LC3 oHSC_HI
- GSM8231316 GFP-LC3 yHSC_8 GFP-LC3 yHSC
- GSM8231317 GFP-LC3 oHSC_LO_8 GFP-LC3 oHSC_LO
- GSM8231318 GFP-LC3 oHSC_MID_8 GFP-LC3 oHSC_MID
- GSM8231319 GFP-LC3 oHSC_HI_8 GFP-LC3 oHSC_HI
- GSM8231320 GFP-LC3 yHSC_9 GFP-LC3 yHSC
- GSM8231321 GFP-LC3 oHSC_LO_9 GFP-LC3 oHSC_LO
- GSM8231322 GFP-LC3 oHSC_MID_9 GFP-LC3 oHSC_MID
- GSM8231323 GFP-LC3 oHSC_HI_9 GFP-LC3 oHSC_HI
- GSM8231324 GFP-LC3 yHSC_10 GFP-LC3 yHSC
- GSM8231325 GFP-LC3 oHSC_LO_10 GFP-LC3 oHSC_LO
- GSM8231326 GFP-LC3 oHSC_MID_10 GFP-LC3 oHSC_MID
- GSM8231327 GFP-LC3 oHSC_HI_10 GFP-LC3 oHSC_HI
+ 523 more — browse all 563 samples with per-sample file links →
- GSE274544 Single cell profiling reveals three endothelial to hematopoietic transitions with divergent isoform expression landscapes [scRNA-Seq] 1494 samples shares hematopoietic, scrna-seq
- GSE205593 Obesity-instructed TREM2-high macrophages identified by comparative analysis of diabetic mouse and human kidney at single-cell resolution [scRNA-seq: Baseline_mouse] 24 samples shares resolution, scrna-seq
- GSE286398 Lysine-specific demethylase 1 regulates hematopoietic stem cell expansion and myeloid cell differentiation [scRNA-seq] 13 samples shares hematopoietic, scrna-seq
- GSE327449 Heterogeneity in hematopoietic stem cell enables adaptation to the aged microenvironment [scRNA-seq] 12 samples shares hematopoietic, scrna-seq
- GSE229137 Autophagy engaging and non-engaging hematopoietic stem cells 607 samples shares hematopoietic, autophagy
- GSE277789 Spatiotemporal and genetic cell lineage tracing of endodermal organogenesis at single-cell resolution [Smart-seq3-RNA-seq-add] 830 samples shares resolution
- GSE263902 The translational landscape of mouse oocytes and preimplantation embryos revealed by high-resolution ribosome profiling 272 samples shares resolution
- GSE266496 Spatiotemporal and genetic cell lineage tracing of endodermal organogenesis at single-cell resolution [Smart-seq3-RNA-seq] 140 samples shares resolution
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.