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Antibody-trapping presents a widespread pitfall for microscopy and genomics in the nucleus

GSE293376 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 38 samples Submitted 2026/06/30 Platform GPL34284Platform GPL34290
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 38 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
Mus musculus (mouse)
Samples
38 samples: 19 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.
Summary (written by the submitters)
Chromatin has a complex 3D structure and diverse binding proteins that coordinate the genome's most essential functions. Many microscopy and genomics technologies that map chromatin proteins and modifications rely on the diffusion of antibodies (Abs) to target epitopes within whole nuclei. Here, we reveal a critical flaw in such methods that arises when Abs become trapped at the edge of nuclear structures and fail to reach internally-positioned epitopes.
Read the full summary ↓
This “Ab-trapping” results in artifactual peripheral signal that fundamentally distorts the apparent positions of chromatin features across the genome and nucleus. Using computational modelling and experimental validation, we demonstrate that Ab-trapping is caused by a combination of three compounding factors - high epitope abundance, high Ab affinity, and low Ab diffusion rates. Ab-trapping can thus systematically misrepresent the localization of many prevalent chromatin features like histone modifications, transcription factors, nucleolar proteins, and protein tags. We also show this artifact manifests in multiple technologies, including immunofluorescence microscopy, more recent CUT&Tag-seq, and likely any method relying on Ab diffusion. Finally, we outline readily implementable strategies to identify and mitigate Ab-trapping. Combined, our work presents a previously unrecognized yet prevalent artifact in Ab-based chromatin mapping methods and the means to resolve it.
This dataset
Open GSE293376 in finder → Find more 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 GSE293376_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 PRJNA1244296 and SRA study SRP575362. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
19 conditions, mostly in duplicate
LaminB1_ab16048_HeLa ×2 LaminB1_ab16048_Rods ×2 H3K9me2_ab1220_HeLa ×2 H3K9me2_ab1220_Rods ×2 H3K9me2_AM39239_HeLa ×2 H3K9me2_AM39239_48h_HeLa ×2 H3K9me2_AM39239_Rods ×2 H3K9me2_AM39239_mm10_Rods_48h ×2 +11 more

Supports a between-group comparison across 38 samples.

How we read this: 19 replicated groups read from 38 sample titles; they account for 38 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
Datasets on a similar topic

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