scRNA seq data from AML PDXs throughout a cytarabine treatment, and directly from AML patients at differnet stages of disease progression
Single-cell RNA-seq · human · 32 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
- 32 samples: 30 distinct titles, 2 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.
- Paper
- Identification of cells of leukemic stem cell origin with non-canonical regenerative propertiesCell reports. Medicine · 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 GSE255245_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 PRJNA1074249 and SRA study SRP488620. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 32 sample titles: 30 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.
- GSM8067260 AML1PDX_untreated_1-3pooled
- GSM8067261 AML1PDX_d7_1-3pooled
- GSM8067262 AML1PDX_d10_1-3pooled
- GSM8067263 AML1PDX_d14_1-3pooled
- GSM8067264 AML1PDX_endpoint_1-2pooled
- GSM8067265 AML2PDX_untreated1-3, GEX
- GSM8067266 AML2PDX_untreated1-3, CMO
- GSM8067267 AML2PDX_d7_1-3, GEX
- GSM8067268 AML2PDX_d7_1-3, CMO
- GSM8067269 AML2PDX_d10_1-3, GEX
- GSM8067270 AML2PDX_d10_1-3, CMO
- GSM8067271 AML2PDX_d14_1-3, GEX
- GSM8067272 AML2PDX_d14_1-3, CMO
- GSM8067273 AML2PDX_endpoint1-2, GEX
- GSM8067274 AML2PDX_endpoint1-2, CMO
- GSM8067275 AML3PDX_untreated1-2, GEX
- GSM8067276 AML3PDX_untreated1-2, CMO
- GSM8067277 AML3PDX_d10_1-3, GEX
- GSM8067278 AML3PDX_d10_1-3, CMO
- GSM8067279 AML3PDX_d14_1-3, GEX
- GSM8067280 AML3PDX_d14_1-3, CMO
- GSM8067281 AML3PDX_endpoint1-3_pooled, GEX
- GSM8067282 CB_PDX_untreated
- GSM8067283 CB_PDX_d14
- GSM8067284 CB_PDX_d17
- GSM8067285 AML1_dn
- GSM8067286 AML2_dn
- GSM8067287 AML3_dn
- GSM8067288 AML38_dn_dx
- GSM8067289 AML38_dn_refractory
- GSM8067290 BM1
- GSM8067291 BM2
- GSE289713 Superior preclinical efficacy of co-treatment with BRG1/BRM and FLT3 inhibitor against AML cells with FLT3 mutations 11 samples shares aml, treatment
- GSE256353 Upregulated cholesterol biosynthesis facilitates the survival of methylation-retaining AML cells following decitabine treatment [single_colony_TEM-Seq] 1728 samples shares aml, treatment
- GSE305389 Stromal and Endothelial Transcriptional Changes during Progression from MGUS to Myeloma and after Treatment Response [RNA-Seq] 25 samples shares progression, treatment
- GSE270296 TGF-β Signaling Pathway and Cell-Cell Interactions in the Tumor Microenvironment Drive Bladder Cancer Progression after BCG Instillation Treatment: Insights from longitudinal Single-Nucleus RNA-Seq 14 samples shares progression, treatment
- GSE297413 Genomic and transcriptomic determinants of clinical outcomes in patients with AML and DNMT3A mutations 264 samples shares aml
- GSE327386 RNA-Seq of paired diagnosis and relapse AML specimens with healthy controls 130 samples shares aml
- GSE338135 NRAS knockdown reverses monocytic cell state and resensitizes resistant FLT3-AML cells to gilteritinib 75 samples shares aml
- GSE260773 RNA-seq Analysis of Acute myeloid leukemia (AML) primary samples 69 samples shares aml
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.