Targeting Polycomb for treatment of KMT2A-rearranged AML using AML-iPSCs as a model system
RNA-seq (gene expression) · human · 56 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
- Homo sapiens (human)
- Samples
- 56 samples: 56 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
- Targeting dysregulated epigenetic and transcription factor networks in KMT2A-rearranged AML using iPSC modelsBlood neoplasia · 2026
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 GSE280428_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 PRJNA1178570 and SRA study SRP532125. 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 56 sample titles: 56 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.
- GSM8510306 control_iPSC_Rep1, RNA-seq
- GSM8510307 control_iPSC_Rep2, RNA-seq
- GSM8510308 control_iPSC_Rep3, RNA-seq
- GSM8510309 control_HSPC_DMSO_Rep1, RNA-seq
- GSM8510310 control_HSPC_DMSO_Rep2, RNA-seq
- GSM8510311 control_HSPC_DMSO_Rep3, RNA-seq
- GSM8510312 control_HSPC_UNC1999_Rep1, RNA-seq
- GSM8510313 control_HSPC_UNC1999_Rep2, RNA-seq
- GSM8510314 control_HSPC_UNC1999_Rep3, RNA-seq
- GSM8510315 control_HSPC_UNC1999_AZA_Rep1, RNA-seq
- GSM8510316 control_HSPC_UNC1999_AZA_Rep2, RNA-seq
- GSM8510317 control_HSPC_UNC1999_AZA_Rep3, RNA-seq
- GSM8510318 AML_iPSC_Rep1, RNA-seq
- GSM8510319 AML_iPSC_Rep2, RNA-seq
- GSM8510320 AML_iPSC_Rep3, RNA-seq
- GSM8510321 AML_HSPC_DMSO_Rep1, RNA-seq
- GSM8510322 AML_HSPC_DMSO_Rep2, RNA-seq
- GSM8510323 AML_HSPC_DMSO_Rep3, RNA-seq
- GSM8510324 AML_HSPC_UNC1999_Rep1, RNA-seq
- GSM8510325 AML_HSPC_UNC1999_Rep2, RNA-seq
- GSM8510326 AML_HSPC_UNC1999_Rep3, RNA-seq
- GSM8510327 AML_HSPC_UNC1999_AZA_Rep1, RNA-seq
- GSM8510328 AML_HSPC_UNC1999_AZA_Rep2, RNA-seq
- GSM8510329 AML_HSPC_UNC1999_AZA_Rep3, RNA-seq
- GSM8510375 ctrl_day0_Rep1, CAGE-seq
- GSM8510376 AML_day0_Rep1, CAGE-seq
- GSM8510377 ctrl_day8_Rep1, CAGE-seq
- GSM8510378 AML_day8_Rep1, CAGE-seq
- GSM8510379 ctrl_day10_Rep1, CAGE-seq
- GSM8510380 AML_day10_Rep1, CAGE-seq
- GSM8510381 ctrl_day12_Rep1, CAGE-seq
- GSM8510382 AML_day12_Rep1, CAGE-seq
- GSM8510383 ctrl_day0_Rep2, CAGE-seq
- GSM8510384 AML_day0_Rep2, CAGE-seq
- GSM8510385 ctrl_day8_Rep2, CAGE-seq
- GSM8510386 AML_day8_Rep2, CAGE-seq
- GSM8510387 ctrl_day10_Rep2, CAGE-seq
- GSM8510388 AML_day10_Rep2, CAGE-seq
- GSM8510389 ctrl_day12_Rep2, CAGE-seq
- GSM8510390 AML_day12_Rep2, CAGE-seq
+ 16 more — browse all 56 samples with per-sample file links →
- GSE333617 Targeting JAK1/3-STAT1 signaling attenuates cytotoxic T lymphocytes activation for the treatment in Stevens-Johnson syndrome and toxic epidermal necrolysis 26 samples shares targeting, treatment
- GSE319266 Identification of target genes and regulatory networks for bone mineral density GWAS loci through systematic targeting and inhibition [hFOB BMD STING-seq] 24 samples shares targeting, system
- GSE262166 Treatment of cholangiocarcinoma using humanized monoclonal antibodies targeting Claudin-1 77 samples shares targeting, treatment
- GSE331214 Single-cell profiling across treatment stages enables precision targeting of therapy-resistant neuroblastoma 38 samples shares targeting, treatment
- GSE319092 Integrated Multi-Omics and Interactome Analysis of CDK8 Inhibition Reveals Erythroid Differentiation Programs and BET Synergy in AML Stem-like Cells 135 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.