Evidence map›Paper›PMID 36980190›Full record

ArticleCells2023

High-Throughput Quantitative Screening of Glucose-Stimulated Insulin Secretion and Insulin Content Using Automated MALDI-TOF Mass Spectrometry.

Clément Philippe Delannoy, Egon Heuson, Adrien Herledan, Frederik Oger, Bryan Thiroux, Mickaël Chevalier, Xavier Gromada, Laure Rolland, Philippe Froguel, Benoit Deprez and 2 more

Open access · goldFull text read
In one paragraph

Article in Cells, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.9field-weighted citation impact, top 15% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
  3. 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

12 authors at 3 institutions in 1 country.

Clément Philippe DelannoyUniv. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, CNRS, U1283-UMR 8199-EGID, F-59000 Lille, France.ORCID 0000-0002-2070-4000
Egon HeusonUniv. Lille, CNRS, Centrale Lille, Univ. Artois, UMR 8181-UCCS-Unité de Catalyse et de Chimie du Solide, F-59000 Lille, France.ORCID 0000-0001-6904-9214
Adrien HerledanUniv. Lille, Inserm, Institut Pasteur de Lille, U1177-Drugs and Molecule for Living Systems, F-59000 Lille, France.
Frederik OgerUniv. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, CNRS, U1283-UMR 8199-EGID, F-59000 Lille, France.ORCID 0000-0003-0006-5835
Bryan ThirouxUniv. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, CNRS, U1283-UMR 8199-EGID, F-59000 Lille, France.
Mickaël ChevalierUniv. Lille, UMRt BioEcoAgro 1158-INRAE, Équipe Métabolites Secondaires D'origine Microbienne, Institut Charles Viollette, F-59000 Lille, France.
Xavier GromadaUniv. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, CNRS, U1283-UMR 8199-EGID, F-59000 Lille, France.
Laure RollandUniv. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de Risque et Determinants Moléculaires des Maladies liées au Vieillissement, F-59000 Lille, France.
Philippe FroguelUniv. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, CNRS, U1283-UMR 8199-EGID, F-59000 Lille, France.
Benoit DeprezUniv. Lille, Inserm, Institut Pasteur de Lille, U1177-Drugs and Molecule for Living Systems, F-59000 Lille, France.ORCID 0000-0002-2777-4538
Sébastien PaulUniv. Lille, CNRS, Centrale Lille, Univ. Artois, UMR 8181-UCCS-Unité de Catalyse et de Chimie du Solide, F-59000 Lille, France.ORCID 0000-0001-9877-9902
Jean-Sébastien AnnicotteUniv. Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167-RID-AGE-Facteurs de Risque et Determinants Moléculaires des Maladies liées au Vieillissement, F-59000 Lille, France.ORCID 0000-0002-2109-4849
Centre National de la Recherche Scientifique · FRInserm · FRUniversité de Lille · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Type 2 diabetes (T2D) is a metabolic disorder characterized by loss of pancreatic β-cell function, decreased insulin secretion and increased insulin resistance, that affects more than 537 million people worldwide. Although several treatments are proposed to patients suffering from T2D, long-term control of glycemia remains a challenge. Therefore, identifying new potential drugs and targets that positively affect β-cell function and insulin secretion remains crucial. Here, we developed an automated approach to allow the identification of new compounds or genes potentially involved in β-cell function in a 384-well plate format, using the murine β-cell model Min6. By using MALDI-TOF mass spectrometry, we implemented a high-throughput screening (HTS) strategy based on the automation of a cellular assay allowing the detection of insulin secretion in response to glucose, i.e., the quantitative detection of insulin, in a miniaturized system. As a proof of concept, we screened siRNA targeting well-know β-cell genes and 1600 chemical compounds and identified several molecules as potential regulators of insulin secretion and/or synthesis, demonstrating that our approach allows HTS of insulin secretion in vitro.

Indexed as

Diabetes Mellitus, Type 2InsulinAnimalsGlucoseHigh-Throughput Screening AssaysHumansInsulin, Regular, HumanInsulin SecretionMiceSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationGlucoseInsulinInsulin, Regular, Humanhigh-throughput screeninginsulin secretionMALDI-TOF mass spectrometrypancreatic beta celltype 2 diabetes

Identifiers

PMID36980190
PMCPMC10047017
OpenAlexW4323667134

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read58
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.