Evidence map›Paper›PMID 42163654›Full record

ArticleJournal of cell science2026

Radical diffusion, not lifetime, determines the range of peroxidase-based proximity labelling.

Samarpita Sen, Daniel St Johnston

Abstract read
In one paragraph

Article in Journal of cell science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Samarpita SenThe Gurdon Institute and Department of Genetics, University of Cambridge, Tennis Court Rd, Cambridge CB2 1QN, UK.ORCID 0000-0002-1026-2376
Daniel St JohnstonThe Gurdon Institute and Department of Genetics, University of Cambridge, Tennis Court Rd, Cambridge CB2 1QN, UK.ORCID 0000-0001-5582-3301

Funding

Gates Cambridge Trust OPP1144University of CambridgeWellcome Trust 224402/Z/21/Z
6 · The paper itself

Abstract

Proximity labelling offers a powerful strategy for mapping the molecular composition in the vicinity of a protein of interest. Here, we employed APEX2- and HRP-mediated biotinylation in the Drosophila follicular epithelium to analyse the apical, lateral and basal membrane proteomes, using Cadherin99c (Cad99c), Fasciclin 3 (Fas3) and Nidogen (Ndg) as baits. We unexpectedly found that standard peroxidase-based labelling conditions produced a strong basal biotinylation signal, even when the tagged cargo localized apically or laterally. This arises from the long-range diffusion of phenoxy radicals, far exceeding the presumed ∼20 nm labelling radius. The basement membrane acts as a high-capacity sink for these radicals, owing to its abundance of electron-rich amino acids. Titrating the concentration of biotin-phenol or biotin-SS-tyramide or shortening reaction times restored spatially faithful labelling at both the plasma membrane and in intracellular compartments. Our results reveal that the distance over which proteins are labelled by peroxidase-based proximity labelling is not limited by the lifespan of the biotin radicals and can extend over many micrometres when radical production exceeds the number of reactive targets. This approach therefore requires careful optimization to avoid misleading spatial signatures.

Indexed as

PeroxidaseStaining and LabelingAnimalsBasement MembraneBiotinBiotinylationCadherinsCell MembraneDiffusionDrosophila melanogasterDrosophila ProteinsBiotinCadherinsDrosophila ProteinsPeroxidaseApical–basal polarityBasement membraneDiffusionEpitheliaMembrane proteinsProximity labelling

Identifiers

PMID42163654
PMCPMC13327535

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.