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rIDIMS: A novel tool for processing direct-infusion mass spectrometry data.

Felipe R P Mansoldo1, Iasmim Lopes de Lima2, Caroline Pais de Carvalho2

  • 1BIOINOVAR - Biotechnology Laboratories: Biocatalysis, Bioproducts and Bioenergy, Institute of Microbiology Paulo de Góes, Federal University of Rio de Janeiro (UFRJ), Rio de Janeiro, 21941-902, Brazil.

Talanta
|November 25, 2024
PubMed
Summary

A new application, rIDIMS, simplifies processing of mass spectrometry data from direct infusion (DIMS) and ambient ionization (AMS) techniques. It provides a user-friendly interface for reproducible metabolomic analysis, especially for small sample sizes.

Keywords:
Ambient ionizationDirect infusion mass spectrometryGraphical user interface for mass spectrometry data processingMasSpec penMetabolomics

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Area of Science:

  • Metabolomics
  • Analytical Chemistry
  • Bioinformatics

Background:

  • Mass spectrometry-only (MS) analysis, including direct infusion (DIMS) and ambient ionization (AMS), is popular for rapid, high-throughput metabolomics.
  • Existing tools lack user-friendly Graphical User Interfaces (GUIs) for processing DIMS/AMS data, especially for limited sample sizes and varying total ion chronograms (TICs).
  • A standardized workflow is needed to address the gap in processing specialized MS data.

Purpose of the Study:

  • To develop rIDIMS, a browser-based application with a GUI for processing DIMS and AMS data.
  • To provide a straightforward and fast workflow for scan selection, isotopologue/adduct grouping, alignment, binning, and filtering.
  • To introduce a novel, reproducible, and statistically reliable method for TIC scan selection, particularly for limited sample sizes.

Main Methods:

  • Developed rIDIMS as a browser-based application using Shiny for a user-friendly GUI.
  • Implemented functions for high-quality scan selection, isotopologue/adduct grouping, data alignment, binning, and filtering.
  • Introduced a novel TIC scan selection function for reproducible and statistically reliable results.

Main Results:

  • rIDIMS facilitates processing of AMS and DIMS data through an intuitive interface.
  • Demonstrated rIDIMS functionality in classifying coffee bean species, analyzing lipid profiles in insulin resistance syndromes, and classifying lipids for lung cancer.
  • The application generates publication-quality figures, statistical data, and tables in an HTML report.

Conclusions:

  • rIDIMS addresses the need for a user-friendly GUI for DIMS and AMS data processing.
  • The application enables reproducible and reliable metabolomic investigations, especially for studies with limited sample sizes.
  • rIDIMS contributes to the advancement of metabolomic data analysis by simplifying complex workflows.