GEO series

Loss of Kmt2c or Kmt2d drives brain metastasis via KDM6A-dependent upregulation of MMP3 [RNA-seq]

GSE237287 Mus musculus (mouse) Single-cell RNA-seq 18 samples Submitted 2024/04/24 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 18 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Mus musculus (mouse)
Samples: 18 samples: 6 conditions, mostly in triplicateGroups 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: Loss of Kmt2c or Kmt2d drives brain metastasis via KDM6A-dependent upregulation of MMP3Nature cell biology · 2024

Summary (written by the submitters)

Histone lysine methyltransferases KMT2C and KMT2D are among the most commonly mutated genes in the highly metastatic TNBC subtype of breast cancer. However, it is not known if mutations of either of these genes similarly effect epigenomic and transcriptomic landscape or if a specific downstream target might influence metastases. Here, we generated heterogenous Kmt2c or Kmt2d KO murine TNBC cell lines side-by-side and performed in vivo metastases assay in syngeneic immunocompetent mice.

Read the full summary ↓

Deficiency for either Kmt2c or Kmt2d, both, induced brain metastases from formerly non-metastatic cells. scRNAseq showed activation of pro-inflammatory pathways but conversely also increase of immune checkpoint blocking genes. Interestingly, histone mass spectrometry revealed changes of H3K27 but not the main substrate H3K4. However, ChIPseq for both, H3K4 and H3K27 modifications showed significant changes compared to wildtype cells. Strikingly, genome occupancy of H3K27me3 was reduced while H3K27 demethylase KDM6A was enriched on genomes of KO cells. Integration with gene expression data revealed significant correlations with histone and KDM6A ChIPseq, identifying them as a main driver of Kmt2c or Kmt2d KO-specific gene regulation. Although our datasets revealed more unique than shared signatures, we found Mmp3 being a common target upon Kmt2c or Kmt2d KO. Indeed, downregulation of Mmp3 reversed induction of Kmt2c and Kmt2d KO-dependent brain metastases. Finally, we found that Kdm6a knockdown reduces Mmp3 levels, again, leading to reduction of brain metastases of Kmt2c or Kmt2d KO cells.

Published in

Loss of Kmt2c or Kmt2d drives brain metastasis via KDM6A-dependent upregulation of MMP3

Seehawer M, Li Z, Nishida J et al. · Nature cell biology 2024 · PMID 38926506 · doi:10.1038/s41556-024-01446-3

This dataset

Open GSE237287 in finder → Find more mouse RNA-seq 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 GSE237287_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: 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 PRJNA994528 and SRA study SRP449358. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38926506) ↗

Study design

6 conditions, mostly in triplicate

168_WT ×3 168_KMT2C_KO ×3 168_KMT2D_KO ×3 67_WT ×3 67_KMT2C_KO ×3 67_KMT2D_KO ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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

GSM7603313 168_WT_rep1 168_WT
GSM7603314 168_WT_rep2 168_WT
GSM7603315 168_WT_rep3 168_WT
GSM7603316 168_KMT2C_KO_rep1 168_KMT2C_KO
GSM7603318 168_KMT2C_KO_rep2 168_KMT2C_KO
GSM7603319 168_KMT2C_KO_rep3 168_KMT2C_KO
GSM7603320 168_KMT2D_KO_rep1 168_KMT2D_KO
GSM7603321 168_KMT2D_KO_rep2 168_KMT2D_KO
GSM7603322 168_KMT2D_KO_rep3 168_KMT2D_KO
GSM7603323 67_WT_rep1 67_WT
GSM7603325 67_WT_rep2 67_WT
GSM7603326 67_WT_rep3 67_WT
GSM7603327 67_KMT2C_KO_rep1 67_KMT2C_KO
GSM7603328 67_KMT2C_KO_rep2 67_KMT2C_KO
GSM7603330 67_KMT2C_KO_rep3 67_KMT2C_KO
GSM7603331 67_KMT2D_KO_rep1 67_KMT2D_KO
GSM7603332 67_KMT2D_KO_rep2 67_KMT2D_KO
GSM7603333 67_KMT2D_KO_rep3 67_KMT2D_KO

Datasets on a similar topic

GSE237289 Loss of Kmt2c or Kmt2d drives brain metastasis via KDM6A-dependent upregulation of MMP3 [scRNA-seq] 9 samples shares kmt2c, kmt2d, mmp3, kdm6a-dependent, upregulation, metastasis, rna-seq, drives
GSE237392 Loss of Kmt2c or Kmt2d drives brain metastasis via KDM6A-dependent upregulation of MMP3 81 samples shares kmt2c, kmt2d, mmp3, kdm6a-dependent, upregulation, metastasis, drives
GSE237390 Loss of Kmt2c or Kmt2d drives brain metastasis via KDM6A-dependent upregulation of MMP3 [ChIP-seq] 54 samples shares kmt2c, kmt2d, mmp3, kdm6a-dependent, upregulation, metastasis, drives
GSE325800 KMT2C and KMT2D amplify GRHL2-driven enhancer activation [RNA-Seq] 30 samples shares kmt2c, kmt2d, rna-seq
GSE316994 Epigenetic and transcriptional alterations in Kmt2c/Kmt2d-deficient gastric cancer [RNA-seq] 12 samples shares kmt2c, kmt2d, rna-seq
GSE267771 KMT2C Deficiency Drives Luminal Transdifferentiation into Double-Negative Prostate Cancer and Sensitizes to Fatty Acid Synthase Inhibition [RNA-Seq] 22 samples shares kmt2c, rna-seq, drives
GSE267773 KMT2C Deficiency Drives Luminal Transdifferentiation into Double-Negative Prostate Cancer and Sensitizes to Fatty Acid Synthase Inhibition [scRNA-Seq] 8 samples shares kmt2c, rna-seq, drives
GSE292432 Single cell RNA sequencing analysis following Kmt2c/Kmt2d and Pten knockout in mouse stomach mucosa. 10 samples shares kmt2c, kmt2d

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