Evidence map›Paper›PMID 35050173›Full record

ArticleMetabolites2022

Automated Sample Preparation and Data Collection Workflow for High-Throughput In Vitro Metabolomics.

Julia M Malinowska, Taina Palosaari, Jukka Sund, Donatella Carpi, Gavin R Lloyd, Ralf J M Weber, Maurice Whelan, Mark R Viant

Open access · goldAbstract read
In one paragraph

Article in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.2field-weighted citation impact, top 23% 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

4 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Article
  3. Multiplatform untargeted metabolomics.Magnetic resonance in chemistry : MRC · 2023
    Review
  4. 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

8 authors at 2 institutions in 2 countries.

Julia M MalinowskaSchool of Biosciences, University of Birmingham, Birmingham B15 2TT, UK.
Taina PalosaariJoint Research Centre (JRC), European Commission, 21027 Ispra, Italy.
Jukka SundJoint Research Centre (JRC), European Commission, 21027 Ispra, Italy.
Donatella CarpiJoint Research Centre (JRC), European Commission, 21027 Ispra, Italy.ORCID 0000-0002-2645-0582
Gavin R LloydPhenome Centre Birmingham, University of Birmingham, Birmingham B15 2TT, UK.
Ralf J M WeberSchool of Biosciences, University of Birmingham, Birmingham B15 2TT, UK.ORCID 0000-0002-8796-4771
Maurice WhelanJoint Research Centre (JRC), European Commission, 21027 Ispra, Italy.
Mark R ViantSchool of Biosciences, University of Birmingham, Birmingham B15 2TT, UK.ORCID 0000-0001-5898-4119
Joint Research Centre · ITUniversity of Birmingham · GB

Funding

Medical Research Council MR/M009157/1Natural Environment Research Council NE/P010326/1
6 · The paper itself

Abstract

Regulatory bodies have started to recognise the value of in vitro screening and metabolomics as two types of new approach methodologies (NAMs) for chemical risk assessments, yet few high-throughput in vitro toxicometabolomics studies have been reported. A significant challenge is to implement automated sample preparation of the low biomass samples typically used for in vitro screening. Building on previous work, we have developed, characterised and demonstrated an automated sample preparation and analysis workflow for in vitro metabolomics of HepaRG cells in 96-well microplates using a Biomek i7 Hybrid Workstation (Beckman Coulter) and Orbitrap Elite (Thermo Scientific) high-resolution nanoelectrospray direct infusion mass spectrometry (nESI-DIMS), across polar metabolites and lipids. The experimental conditions evaluated included the day of metabolite extraction, order of extraction of samples in 96-well microplates, position of the 96-well microplate on the instrument's deck and well location within a microplate. By using the median relative standard deviation (mRSD (%)) of spectral features, we have demonstrated good repeatability of the workflow (final mRSD < 30%) with a low percentage of features outside the threshold applied for statistical analysis. To improve the quality of the automated workflow further, small method modifications were made and then applied to a large cohort study (4860 sample infusions across three nESI-DIMS assays), which confirmed very high repeatability of the whole workflow from cell culturing to metabolite measurements, whilst providing a significant improvement in sample throughput. It is envisioned that the automated in vitro metabolomics workflow will help to advance the application of metabolomics (as a part of NAMs) in chemical safety, primarily as an approach for high throughput screening and prioritisation.

Indexed as

automationdirect infusion mass spectrometryhigh-throughput screeningin vitro metabolomicssample preparation

Identifiers

PMID35050173
PMCPMC8778710
OpenAlexW4206338160

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.