Evidence map›Paper›PMID 31578119›Full record

ArticleSLAS discovery : advancing life sciences R & D2019

High-Content Imaging for Large-Scale Detection of Low-Affinity Extracellular Protein Interactions.

Laura Wood, Gavin J Wright

Open access · hybridAbstract read
In one paragraph

Article in SLAS discovery : advancing life sciences R & D, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.4field-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

7 citing papers in PubMed, 15 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. 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

2 authors at 1 institution in 1 country.

Laura WoodCell Surface Signalling Laboratory, Wellcome Trust Sanger Institute, Cambridge, UK.
Gavin J WrightCell Surface Signalling Laboratory, Wellcome Trust Sanger Institute, Cambridge, UK.
Wellcome Sanger Institute · GB

Funding

Wellcome TrustWellcome Trust 206194
6 · The paper itself

Abstract

Extracellular protein interactions coordinate cellular responses with their local environment and have important roles in pathogen invasion and disease. Due to technical challenges associated with studying binding events at the cell surface, the systematic and reliable identification of novel ligand-receptor pairs remains difficult. Here, we describe the development of a cell-based assay using large-scale transient transfections and high-content imaging (HCI) to detect extracellular binding events. We optimized the parameters for efficient transfection of human cells with cDNA plasmids encoding full-length cell surface receptors in 384-well plates. Using a range of well-characterized structurally diverse low-affinity cell surface interactions, we show that transfected cells probed with highly avid ligands can be used to successfully identify ligand-receptor pairs using an HCI platform and automated image analysis software. To establish the high-throughput potential of this approach, we also screened a pool of ligands against a collection of 2455 cell surface expression clones and found that known ligand-receptor interactions could be robustly and consistently detected across the library using this technology.

Indexed as

Drug DiscoveryExtracellular SpaceHigh-Throughput Screening AssaysLigandsMolecular ImagingAutomation, LaboratoryGene ExpressionGene LibraryHEK293 CellsHumansProteinsReceptors, Cell SurfaceRecombinant ProteinsLigandsProteinsReceptors, Cell SurfaceRecombinant Proteinsextracellularhigh-content imaginghigh-throughputligand–receptor interactions

Identifiers

PMID31578119
PMCPMC6873222
OpenAlexW2977967512

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.