A Bcl11a-driven mechanism maintaining hematopoietic stem cell functionality gives rise to dysfunction during aging [RNA-Seq]
RNA-seq (gene expression) · mouse · 52 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Mus musculus (mouse)
- Samples
- 52 samples: 47 distinct titles, 3 of them repeatedGroups 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.
Read the full summary ↓
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 GSE266509_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1107481 and SRA study SRP505623. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 52 sample titles: 47 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.
- GSM8248618 3m_iMOS_Bcl11aMUT_T1_rep1 [RNA-Seq]
- GSM8248619 3m_iMOS_Bcl11aMUT_T1_rep2 [RNA-Seq]
- GSM8248620 3m_iMOS_Bcl11aMUT_T2_rep1 [RNA-Seq]
- GSM8248621 3m_iMOS_Bcl11aMUT_T2_rep2 [RNA-Seq]
- GSM8248622 3m_iMOS_Bcl11aWT_T1_rep1 [RNA-Seq]
- GSM8248623 3m_iMOS_Bcl11aWT_T1_rep2 [RNA-Seq]
- GSM8248624 3m_iMOS_Bcl11aWT_T2_rep1 [RNA-Seq]
- GSM8248625 3m_iMOS_Bcl11aWT_T2_rep2 [RNA-Seq]
- GSM8248626 12m_iMOS_Bcl11aMUT_T1_rep1 [RNA-Seq]
- GSM8248627 12m_iMOS_Bcl11aMUT_T1_rep2 [RNA-Seq]
- GSM8248628 12m_iMOS_Bcl11aMUT_T2_rep1 [RNA-Seq]
- GSM8248629 12m_iMOS_Bcl11aMUT_T2_rep2 [RNA-Seq]
- GSM8248630 12m_iMOS_Bcl11aWT_T1_rep1 [RNA-Seq]
- GSM8248631 12m_iMOS_Bcl11aWT_T1_rep2 [RNA-Seq]
- GSM8248632 12m_iMOS_Bcl11aWT_T2_rep1 [RNA-Seq]
- GSM8248633 12m_iMOS_Bcl11aWT_T2_rep2 [RNA-Seq]
- GSM8248634 5FU_iMOS_Bcl11aMUT_T1_rep1 [RNA-Seq]
- GSM8248635 5FU_iMOS_Bcl11aMUT_T1_rep2 [RNA-Seq]
- GSM8248636 5FU_iMOS_Bcl11aMUT_T2_rep1 [RNA-Seq]
- GSM8248637 5FU_iMOS_Bcl11aMUT_T2_rep2 [RNA-Seq]
- GSM8248638 5FU_iMOS_Bcl11aWT_T1_rep1 [RNA-Seq]
- GSM8248639 5FU_iMOS_Bcl11aWT_T1_rep2 [RNA-Seq]
- GSM8248640 5FU_iMOS_Bcl11aWT_T2_rep1 [RNA-Seq]
- GSM8248641 5FU_iMOS_Bcl11aWT_T2_rep2 [RNA-Seq]
- GSM8248642 Fcer1gKO_iMOS_Bcl11aMUT_T2_rep1 [RNA-Seq]
- GSM8248643 Fcer1gKO_iMOS_Bcl11aMUT_T2_rep2 [RNA-Seq]
- GSM8248644 Fcer1gKO_iMOS_Bcl11aMUT_T2_rep3 [RNA-Seq]
- GSM8248645 Fcer1gKO_iMOS_Bcl11aWT_T2_rep1 [RNA-Seq]
- GSM8248646 Fcer1gKO_iMOS_Bcl11aWT_T2_rep2 [RNA-Seq]
- GSM8248647 Fcer1gKO_iMOS_Bcl11aWT_T2_rep3 [RNA-Seq]
- GSM8248648 Bcl11aKO_rep1 [RNA-Seq]
- GSM8248649 Bcl11aKO_rep2 [RNA-Seq]
- GSM8248650 Bcl11aKO_rep3 [RNA-Seq]
- GSM8248651 C57BL6_rep1 [RNA-Seq]
- GSM8248652 C57BL6_rep2 [RNA-Seq]
- GSM8248653 C57BL6_IC_rep1 [RNA-Seq]
- GSM8248654 C57BL6_IC_rep2 [RNA-Seq]
- GSM8248655 C57BL6_IC_rep3 [RNA-Seq]
- GSM8248656 C57BL6_OVA_rep1 [RNA-Seq]
- GSM8248657 C57BL6_OVA_rep2 [RNA-Seq]
+ 12 more — browse all 52 samples with per-sample file links →
- GSE286961 Deep learning identifies Pbx1 as a network hub of hematopoietic stem cell aging [Pbx1 RNA-seq] 12 samples shares hematopoietic, rna-seq, aging
- GSE286957 Deep learning identifies Pbx1 as a network hub of hematopoietic stem cell aging [26hr RNA-seq] 8 samples shares hematopoietic, rna-seq, aging
- GSE274544 Single cell profiling reveals three endothelial to hematopoietic transitions with divergent isoform expression landscapes [scRNA-Seq] 1494 samples shares hematopoietic, rna-seq
- 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 hematopoietic, rna-seq
- GSE309071 Single cell profiling reveals three endothelial to hematopoietic transitions with divergent isoform expression landscapes [RNA-seq] 221 samples shares hematopoietic, rna-seq
- GSE274033 RNA-seq of sorted murine long-term hematopoietic stem cells segmented on CUX1 levels with and without CUX1 knockdown 35 samples shares hematopoietic, rna-seq
- GSE233056 A gene regulatory network of the endothelial-to-hematopoietic transition [RNA-seq] 28 samples shares hematopoietic, rna-seq
- GSE276137 The ac4C acetyltransferase Nat10 promotes RNA stability to support hematopoietic stem cell fate [RNA-seq-32D] 24 samples shares hematopoietic, rna-seq
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.