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Transcriptional analysis of direct NSD2 target genes in t(4;14) multiple myeloma reveals H3K36me2-dependent regulation and H3K27me3 antagonism [CUT&TAG]

GSE294275 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 40 samples Submitted 2026/07/01 Platform GPL34281
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 40 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
40 samples: 20 conditions, mostly in duplicateGroups 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
Study of NSD2 using a dTAG system reveals its molecular mechanism and oncogenic implications in t(4;14) multiple myelomaBlood · 2026
Summary (written by the submitters)
This study aimed to identify the direct transcriptional targets of the H3K36me2 methyltransferase NSD2 in t(4;14) multiple myeloma (MM), where its aberrant expression drives oncogenesis, and to elucidate the molecular mechanisms underlying its regulatory role. To achieve this with precise temporal control and minimize cellular adaptation artifacts, we established an inducible dTAG-mediated acute NSD2 degradation system in the KMS-11 t(4;14) MM cell line.
Read the full summary ↓
Using this model, we performed time-resolved SLAM-seq to measure newly synthesized mRNA levels at 7, 14, and 21 days post-NSD2 degradation, enabling the identification of primary transcriptional targets. Further SLAM-seq datasets were generated under conditions of NSD2 depletion followed by reconstitution with either wild-type NSD2 (NSD2WT) or a catalytically inactive mutant (NSD2Y1179A) to evaluate the requirement for SET domain activity. The contribution of H3K27me3 antagonism to NSD2-mediated gene regulation was investigated by performing SLAM-seq on NSD2-depleted cells treated with distinct PRC2 inhibitors (EPZ-6438 and MAK-658). Complementing the transcriptomic data, we performed CUTTAG to map the genomic distribution of the H3K27me3 histone mark following NSD2 depletion relative to control conditions. Analysis of these datasets revealed 308 primary NSD2 transcriptional targets, predominantly downregulated upon NSD2 loss and enriched for functionally relevant transcription factors. Reconstitution experiments demonstrated that NSD2's transcriptional effects are almost entirely dependent on its SET domain catalytic activity. Furthermore, PRC2 inhibition partially rescued the downregulation of approximately half of the NSD2 target genes, highlighting a significant role for H3K27me3 antagonism. Importantly, CUTTAG analysis showed that the compensatory increase in H3K27me3 upon NSD2 loss occurred primarily across genome-wide intergenic regions, rather than directly at the promoters or gene bodies of NSD2 target genes. Collectively, these deposited transcriptomic (SLAM-seq) and epigenomic (H3K27me3 CUTTAG) data support a model where NSD2 regulates target gene transcription via H3K36me2 deposition, which, in part, functions by antagonizing H3K27me3 deposition at distal regulatory elements to foster a permissive chromatin environment, providing a valuable resource for understanding NSD2 function in t(4;14) MM and Wolf-Hirschhorn syndrome.
Published in
Study of NSD2 using a dTAG system reveals its molecular mechanism and oncogenic implications in t(4;14) multiple myeloma
Wang Y, Liu S, Ghamlouch H et al. · Blood 2026 · PMID 41758961 · doi:10.1182/blood.2025031663
This dataset
Open GSE294275 in finder → Find more human ChIP / ATAC / CUT&Tag datasets →
Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE294275_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA1248927 and SRA study SRP577979. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41758961) ↗
Study design
20 conditions, mostly in duplicate
CUTTAG_IgG_KMS11_dTAGparental_dTAGv1_0d ×2 CUTTAG_H3K27m3_KMS11_dTAGparental_dTAGv1_0d ×2 CUTTAG_H3K27m3_KMS11_dTAGparental_dTAGv1_7d ×2 CUTTAG_H3K27m3_KMS11_dTAGparental_dTAGv1_14d ×2 CUTTAG_H3K27m3_KMS11_dTAGparental_dTAGv1_21d ×2 CUTTAG_H3K27m3_KMS11_NSD2dTAG#8_dTAGv1_0d ×2 CUTTAG_H3K27m3_KMS11_NSD2dTAG#8_dTAGv1_7d ×2 CUTTAG_H3K27m3_KMS11_NSD2dTAG#8_dTAGv1_14d ×2 +12 more

Supports a between-group comparison across 40 samples.

How we read this: 20 replicated groups read from 40 sample titles; they account for 40 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.

Samples in this study
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