Evidence map›Paper›PMID 22342946›Full record

ArticleAnalytical biochemistry2012

A benchmarked protein microarray-based platform for the identification of novel low-affinity extracellular protein interactions.

Yi Sun, Marcus Gallagher-Jones, Colin Barker, Gavin J Wright

Open access · hybridAbstract read
In one paragraph

Article in Analytical biochemistry, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing papers in PubMed
2.6field-weighted citation impact, top 10% of its field
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

36 citing papers in PubMed, 51 citations in OpenAlex.

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  20. KILchip v1.0: A NovelFrontiers in immunology · 2018
    Article
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

4 authors at 1 institution in 1 country.

Yi SunCell Surface Signalling Laboratory, Wellcome Trust Sanger Institute, Hinxton, Cambridge CB10 1HH, UK.
Marcus Gallagher-Jones
Colin Barker
Gavin J Wright
Wellcome Sanger Institute · GB

Funding

Wellcome TrustWellcome Trust 077108
6 · The paper itself

Abstract

Low-affinity extracellular protein interactions are critical for cellular recognition processes, but existing methods to detect them are limited in scale, making genome-wide interaction screens technically challenging. To address this, we report here the miniaturization of the AVEXIS (avidity-based extracellular interaction screen) assay by using protein microarray technology. To achieve this, we have developed protein tags and sample preparation methods that enable the parallel purification of hundreds of recombinant proteins expressed in mammalian cells. We benchmarked the protein microarray-based assay against a set of known quantified receptor-ligand pairs and show that it is sensitive enough to detect even very weak interactions that are typical of this class of interactions. The increase in scale enables interaction screening against a dilution series of immobilized proteins on the microarray enabling the observation of saturation binding behaviors to show interaction specificity and also the estimation of interaction affinities directly from the primary screen. These methodological improvements now permit screening for novel extracellular receptor-ligand interactions on a genome-wide scale.

Indexed as

Amino Acid SequenceAnimalsBiotinylationCost-Benefit AnalysisExtracellular SpaceHEK293 CellsHumansImmobilized ProteinsMolecular Sequence DataProtein Array AnalysisProtein BindingProtein Interaction MappingRecombinant ProteinsStreptavidinTissue Culture TechniquesZebrafishImmobilized ProteinsRecombinant ProteinsStreptavidinZebrafish Proteins

Identifiers

PMID22342946
PMCPMC3325482
OpenAlexW2003480560

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.