Tumor-Associated Schwann Cell Remodeling under Metabolic Stress via Lactate Sensing Orchestrates Pancreatic Ductal Adenocarcinoma Development.
ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 44 samples
- What it is
- ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
- Organism
- Homo sapiens (human)
- Samples
- 44 samples: 44 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.
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 GSE303767_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.
Also filed as BioProject PRJNA1297641 and SRA study SRP603954. 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 44 sample titles: 44 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.
- GSM8558901 DM-T, replicate 1, scRNAseq
- GSM8558902 DM-T, replicate 2, scRNAseq
- GSM8558903 DM-T, replicate 3, scRNAseq
- GSM8558904 DM-T, replicate 4, scRNAseq
- GSM8558905 NDM-T, replicate 1, scRNAseq
- GSM8558906 NDM-T, replicate 2, scRNAseq
- GSM8558907 NDM-T, replicate 3, scRNAseq
- GSM8558908 NDM-T, replicate 4, scRNAseq
- GSM9135541 NDPC SC-1, replicate 1, MeRIP-seq
- GSM9135542 NDPC SC-2, replicate 2, MeRIP-seq
- GSM9135543 DPC SC-1, replicate 1, MeRIP-seq
- GSM9135544 DPC SC-2, replicate 2, MeRIP-seq
- GSM9135545 sgM16-1, replicate 1, MeRIP-seq
- GSM9135546 sgM16-2, replicate 2, MeRIP-seq
- GSM9135547 sgNC-1, replicate 1, MeRIP-seq
- GSM9135548 sgNC-2, replicate 2, MeRIP-seq
- GSM9135549 NDPC SC-1, replicate 1, MeRIP-seq input
- GSM9135550 NDPC SC-2, replicate 2, MeRIP-seq input
- GSM9135551 DPC SC-1, replicate 1, MeRIP-seq input
- GSM9135552 DPC SC-2, replicate 2, MeRIP-seq input
- GSM9135553 sgM16-1, replicate 1, MeRIP-seq input
- GSM9135554 sgM16-2, replicate 2, MeRIP-seq input
- GSM9135555 sgNC-1, replicate 1, MeRIP-seq input
- GSM9135556 sgNC-2, replicate 2, MeRIP-seq input
- GSM9135557 NDPC SC-1, replicate 1, RNA-seq
- GSM9135558 NDPC SC-2, replicate 2, RNA-seq
- GSM9135559 DPC SC-1, replicate 1, RNA-seq
- GSM9135560 DPC SC-2, replicate 2, RNA-seq
- GSM9135561 sgM16-1, replicate 1, RNA-seq
- GSM9135562 sgM16-2, replicate 2, RNA-seq
- GSM9135563 sgNC-1, replicate 1, RNA-seq
- GSM9135564 sgNC-2, replicate 2, RNA-seq
- GSM9135565 SC shNC-1, replicate 1, ChIP-seq
- GSM9135566 SC shNC-2, replicate 1, ChIP-seq
- GSM9135567 SC shCTCF-1, replicate 1, ChIP-seq
- GSM9135568 SC shCTCF-2, replicate 1, ChIP-seq
- GSM9135569 SC shNC-1, replicate 1, ChIP-seq input
- GSM9135570 SC shNC-2, replicate 1, ChIP-seq input
- GSM9135571 SC shCTCF-1, replicate 1, ChIP-seq input
- GSM9135572 SC shCTCF-2, replicate 1, ChIP-seq input
+ 4 more — browse all 44 samples with per-sample file links →
- GSE298580 Dual inhibition of FACT and RRM2 suppresses the progression of pancreatic ductal adenocarcinoma driven by the oncohistone H2BG53D 64 samples shares adenocarcinoma, pancreatic
- GSE217847 IL-1b+ tumor-associated macrophages fuel pathogenic inflammation in pancreatic cancer 136 samples shares tumor-associated, pancreatic
- GSE242098 Med12 ablation promotes anti-tumor function by enhancing tumor intrinsic immunogenicity in Pancreatic ductal adenocarcinoma 114 samples shares adenocarcinoma, pancreatic
- GSE115462 TP63-mediated enhancer reprogramming drives the squamous subtype of pancreatic ductal adenocarcinoma (RNA-seq) 42 samples shares adenocarcinoma, pancreatic
- GSE235246 Senescent cancer-associated fibroblasts in pancreatic adenocarcinoma impair CD8+ T cell activation and responsiveness to immunotherapy in mice 38 samples shares adenocarcinoma, pancreatic
- GSE235233 Senescent cancer-associated fibroblasts in pancreatic adenocarcinoma suppress CD8+ T cell activation and inhibit response to immune checkpoint therapy [Cultured_Human_Mouse] 16 samples shares adenocarcinoma, pancreatic
- GSE250519 A mucus production programme promotes classical pancreatic ductal adenocarcinoma 12 samples shares adenocarcinoma, pancreatic
- GSE274495 The remodeling of bivalent chromatin is essential for mouse peri-implantation embryogenesis. 421 samples shares remodeling
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.