GEO series

Divergent proteome tolerance against gain and loss of chromosome arms

GSE293644 Homo sapiens (human) RNA-seq (gene expression) 21 samples Submitted 2026/03/25 Platform GPL16791Platform GPL34284Platform GPL30882

At a glance

RNA-seq (gene expression) · human · 21 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: 21 samples: 7 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: Divergent proteome tolerance against gain and loss of chromosome armsMolecular cell · 2025

Summary (written by the submitters)

How aneuploid cells tolerate chromosome arm gains or losses remains an open question. Using an isogenic human lung cell model with either 3p loss or 3q gain, combined with quantitative mass spectrometry and isotopic labeling, we reveal distinct proteostasis mechanisms for gain- and loss-type aneuploidy.

Read the full summary ↓

Surprisingly, while compensation for 3q gain is primarily driven by increased degradation of excess protein complex subunits, 3p loss is neither counteracted by global protein degradation nor selectively reduced degradation, but rather by relatively upregulated protein synthesis to maintain protein complex stoichiometry. Additionally, proteins encoded on 3p exhibit increased thermal stability in loss-type aneuploidy, potentially via their interactions with other proteins from euploid chromosomes. Together, our findings uncover distinct proteomic buffering strategies that enable cells to tolerate either excessive or deficient single-arm aneuploidy.

Published in

Divergent proteome tolerance against gain and loss of chromosome arms

Di Y, Li W, Castellano JJ et al. · Molecular cell 2025 · PMID 41270725 · doi:10.1016/j.molcel.2025.10.023

This dataset

Open GSE293644 in finder → Find more human 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 GSE293644_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: 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 PRJNA1245409 and SRA study SRP576110. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41270725) ↗

Study design

7 conditions, mostly in triplicate

3p loss cell, mRNA, ×3 Chr3 WT cell, mRNA, ×3 3q gain cell, mRNA, ×3 3p loss cell, ribosome profiling, ×3 3p WT cell, ribosome profiling, ×3 8p loss cell, mRNA, ×3 8p WT cell, mRNA, ×3

Supports a between-group comparison across 21 samples.

How we read this: 7 replicated groups read from 21 sample titles; they account for 21 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

GSM8887651 3p loss cell, mRNA, biological replicate 1 3p loss cell, mRNA,
GSM8887652 3p loss cell, mRNA, biological replicate 2 3p loss cell, mRNA,
GSM8887653 3p loss cell, mRNA, biological replicate 3 3p loss cell, mRNA,
GSM8887654 Chr3 WT cell, mRNA, biological replicate 1 Chr3 WT cell, mRNA,
GSM8887655 Chr3 WT cell, mRNA, biological replicate 2 Chr3 WT cell, mRNA,
GSM8887656 Chr3 WT cell, mRNA, biological replicate 3 Chr3 WT cell, mRNA,
GSM8887657 3q gain cell, mRNA, biological replicate 1 3q gain cell, mRNA,
GSM8887658 3q gain cell, mRNA, biological replicate 2 3q gain cell, mRNA,
GSM8887659 3q gain cell, mRNA, biological replicate 3 3q gain cell, mRNA,
GSM8887660 3p loss cell, ribosome profiling, biological replicate 1 3p loss cell, ribosome …
GSM8887661 3p loss cell, ribosome profiling, biological replicate 2 3p loss cell, ribosome …
GSM8887662 3p loss cell, ribosome profiling, biological replicate 3 3p loss cell, ribosome …
GSM8887663 3p WT cell, ribosome profiling, biological replicate 1 3p WT cell, ribosome pr…
GSM8887664 3p WT cell, ribosome profiling, biological replicate 2 3p WT cell, ribosome pr…
GSM8887665 3p WT cell, ribosome profiling, biological replicate 3 3p WT cell, ribosome pr…
GSM8887666 8p loss cell, mRNA, biological replicate 1 8p loss cell, mRNA,
GSM8887667 8p loss cell, mRNA, biological replicate 2 8p loss cell, mRNA,
GSM8887668 8p loss cell, mRNA, biological replicate 3 8p loss cell, mRNA,
GSM8887669 8p WT cell, mRNA, biological replicate 1 8p WT cell, mRNA,
GSM8887670 8p WT cell, mRNA, biological replicate 2 8p WT cell, mRNA,
GSM8887671 8p WT cell, mRNA, biological replicate 3 8p WT cell, mRNA,

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