Evidence map›Paper›PMID 38310603›Full record

ArticleJournal of the American Society for Mass Spectrometry2024

AutonoMS: Automated Ion Mobility Metabolomic Fingerprinting.

Gabriel K Reder, Erik Y Bjurström, Daniel Brunnsåker, Filip Kronström, Praphapan Lasin, Ievgeniia Tiukova, Otto I Savolainen, James N Dodds, Jody C May, John P Wikswo and 2 more

Abstract read
In one paragraph

Article in Journal of the American Society for Mass Spectrometry, 2024. 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
–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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

12 authors.

Gabriel K RederDepartment of Computer Science and Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden.ORCID 0000-0001-8918-0789
Erik Y BjurströmDepartment of Life Sciences, Chalmers University of Technology, Gothenburg 412 96, Sweden.
Daniel BrunnsåkerDepartment of Computer Science and Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden.
Filip KronströmDepartment of Computer Science and Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden.
Praphapan LasinDepartment of Life Sciences, Chalmers University of Technology, Gothenburg 412 96, Sweden.
Ievgeniia TiukovaDepartment of Life Sciences, Chalmers University of Technology, Gothenburg 412 96, Sweden.
Otto I SavolainenDepartment of Life Sciences, Chalmers University of Technology, Gothenburg 412 96, Sweden.
James N DoddsChemistry Department, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States.ORCID 0000-0002-9702-2294
Jody C MayDepartment of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.ORCID 0000-0003-4871-5024
John P WikswoVanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, Tennessee 37235, United States.
John A McLeanDepartment of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.ORCID 0000-0001-8918-6419
Ross D KingDepartment of Computer Science and Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden.

Funding

Single cell, multi-parametric high throughput platform to classify endocrine disruptor potential of mixturesP42ES027704 · NIEHS · TEXAS A&M UNIVERSITY · PI Efstratios Pistikopoulos · 2017 to 2026
$21.2M
Increasing the Coverage, Sensitivity and Specificity of Rapid Lipidomic MeasurementsR01GM141277 · NIGMS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BAKER, ERIN S · 2022 to 2025
$1.2M
NIEHS NIH HHS P42 ES027704NIGMS NIH HHS R01 GM141277
6 · The paper itself

Abstract

Automation is dramatically changing the nature of laboratory life science. Robotic lab hardware that can perform manual operations with greater speed, endurance, and reproducibility opens an avenue for faster scientific discovery with less time spent on laborious repetitive tasks. A major bottleneck remains in integrating cutting-edge laboratory equipment into automated workflows, notably specialized analytical equipment, which is designed for human usage. Here we present AutonoMS, a platform for automatically running, processing, and analyzing high-throughput mass spectrometry experiments. AutonoMS is currently written around an ion mobility mass spectrometry (IM-MS) platform and can be adapted to additional analytical instruments and data processing flows. AutonoMS enables automated software agent-controlled end-to-end measurement and analysis runs from experimental specification files that can be produced by human users or upstream software processes. We demonstrate the use and abilities of AutonoMS in a high-throughput flow-injection ion mobility configuration with 5 s sample analysis time, processing robotically prepared chemical standards and cultured yeast samples in targeted and untargeted metabolomics applications. The platform exhibited consistency, reliability, and ease of use while eliminating the need for human intervention in the process of sample injection, data processing, and analysis. The platform paves the way toward a more fully automated mass spectrometry analysis and ultimately closed-loop laboratory workflows involving automated experimentation and analysis coupled to AI-driven experimentation utilizing cutting-edge analytical instrumentation. AutonoMS documentation is available at https://autonoms.readthedocs.io.

Indexed as

MetabolomicsSoftwareAutomationHumansMass SpectrometryReproducibility of Results

Identifiers

PMID38310603
PMCPMC10921458

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.