Untargeted determination of cycling yeast metabolites by GCxGC-TOF MS (Lina Mikaliunaite, MDCW 2023)

Tu, 21.11.2023 | Original article from: The Multidimensional Chromatography (MDC) Workshop
A computational method for the untargeted determination of cycling yeast metabolites using a comprehensive GC×GC-TOFMS dataset was developed.
  • Photo: Untargeted determination of cycling yeast metabolites by GCxGC-TOF MS (Lina Mikaliunaite, MDCW 2023)
  • Video: LabRulez: Lina Mikaliunaite: Untargeted determination of cycling yeast metabolites by GCxGC-TOF MS (MDCW 2023)
  • 🎤 Presenter: Lina Mikaliunaite, Robert Synovec (University of Washington, Seattle, USA)

💡 Book in your calendar: 15th Multidimensional Chromatography Workshop (MDCW) January 2024

15th Multidimensional Chromatography (MDC) Workshop 202415th Multidimensional Chromatography (MDC) Workshop 2024

Abstract

A computational method for the untargeted determination of cycling yeast metabolites using a comprehensive two-dimensional gas chromatography time-of-flight mass spectrometry (GC×GC-TOFMS) dataset was developed.

The yeast metabolomic cycle for the diploid yeast strain CEN. PK with a 5 hr cycle period relative to the O₂ concentration level is comprehensively examined to determine the metabolites that exhibit cycling. Samples were collected over only two cycles (10 hr with a total of 24 time-point sampling intervals at 25 min each) as an experimental constraint. Due to the limited number of cycles expressed in the dataset, a computational method was devised to determine with statistical significance whether or not a given metabolite exhibited a temporal signal pattern that constituted cycling in the context of the 5 hr cycle period. The computational method we report compares the experimentally obtained 24 time-point metabolite signal sequences to randomly generated signal sequences coupled with statistically based confidence level LOF metrics to determine whether or not a given metabolite expresses cycling, and if so, what is the phase of the cycling.

Initially the GC×GC-TOFMS dataset was analyzed using tile-based Fisher ratio (F-ratio) analysis. Since there were 24 time-point intervals, this constituted 24 sample classes in the F-ratio calculation which produced 672 metabolite hits. Next, application of the computational method determined that there were 210 of the 672 metabolites exhibiting cycling: 55 identified metabolites and 155 unknown metabolites. Furthermore, the 210 cycling metabolites were categorized into four groups, and where applicable, a phase was determined.

The Multidimensional Chromatography Workshop
LinkedIn Logo
 

Related articles

Simulating GC×GC-TOFMS data & the robustness of chemometric software (Timothy Trinklein, MDCW 2023)
Presentation | Video

Simulating GC×GC-TOFMS data & the robustness of chemometric software (Timothy Trinklein, MDCW 2023)

We report a workflow to simulate GC×GC-TOFMS datasets with realistic run-to-run retention time shifting which are used to evaluate the robustness of chemometric software to shifting.
Tile-based fisher ratio analysis of (3D GC) GC3-TOFMS detection data (Sonia Schöneich, MDCW 2023)
Presentation | Video

Tile-based fisher ratio analysis of (3D GC) GC3-TOFMS detection data (Sonia Schöneich, MDCW 2023)

Comprehensive three-dimensional (3D) gas chromatography with TOFMS detection (GC3-TOFMS) has been developed.
Sex and person identity recognition from GC×GC analysis of scent samples (Jan Hlavsa, MDCW 2025)
Presentation | Video

Sex and person identity recognition from GC×GC analysis of scent samples (Jan Hlavsa, MDCW 2025)

Discover a fully automated GC×GC method for sex classification and identity verification of human scent samples with machine learning, achieving 85% accuracy in validation tests.
News from LabRulezGCMS Library - Week 22, 2026
Article | Application

News from LabRulezGCMS Library - Week 22, 2026

This week we bring you application note by Shimadzu, presentation by MDCW / University of Washington and poster by Waters Corporation!
 

Related articles

Simulating GC×GC-TOFMS data & the robustness of chemometric software (Timothy Trinklein, MDCW 2023)
Presentation | Video

Simulating GC×GC-TOFMS data & the robustness of chemometric software (Timothy Trinklein, MDCW 2023)

We report a workflow to simulate GC×GC-TOFMS datasets with realistic run-to-run retention time shifting which are used to evaluate the robustness of chemometric software to shifting.
Tile-based fisher ratio analysis of (3D GC) GC3-TOFMS detection data (Sonia Schöneich, MDCW 2023)
Presentation | Video

Tile-based fisher ratio analysis of (3D GC) GC3-TOFMS detection data (Sonia Schöneich, MDCW 2023)

Comprehensive three-dimensional (3D) gas chromatography with TOFMS detection (GC3-TOFMS) has been developed.
Sex and person identity recognition from GC×GC analysis of scent samples (Jan Hlavsa, MDCW 2025)
Presentation | Video

Sex and person identity recognition from GC×GC analysis of scent samples (Jan Hlavsa, MDCW 2025)

Discover a fully automated GC×GC method for sex classification and identity verification of human scent samples with machine learning, achieving 85% accuracy in validation tests.
News from LabRulezGCMS Library - Week 22, 2026
Article | Application

News from LabRulezGCMS Library - Week 22, 2026

This week we bring you application note by Shimadzu, presentation by MDCW / University of Washington and poster by Waters Corporation!
 

Related articles

Simulating GC×GC-TOFMS data & the robustness of chemometric software (Timothy Trinklein, MDCW 2023)
Presentation | Video

Simulating GC×GC-TOFMS data & the robustness of chemometric software (Timothy Trinklein, MDCW 2023)

We report a workflow to simulate GC×GC-TOFMS datasets with realistic run-to-run retention time shifting which are used to evaluate the robustness of chemometric software to shifting.
Tile-based fisher ratio analysis of (3D GC) GC3-TOFMS detection data (Sonia Schöneich, MDCW 2023)
Presentation | Video

Tile-based fisher ratio analysis of (3D GC) GC3-TOFMS detection data (Sonia Schöneich, MDCW 2023)

Comprehensive three-dimensional (3D) gas chromatography with TOFMS detection (GC3-TOFMS) has been developed.
Sex and person identity recognition from GC×GC analysis of scent samples (Jan Hlavsa, MDCW 2025)
Presentation | Video

Sex and person identity recognition from GC×GC analysis of scent samples (Jan Hlavsa, MDCW 2025)

Discover a fully automated GC×GC method for sex classification and identity verification of human scent samples with machine learning, achieving 85% accuracy in validation tests.
News from LabRulezGCMS Library - Week 22, 2026
Article | Application

News from LabRulezGCMS Library - Week 22, 2026

This week we bring you application note by Shimadzu, presentation by MDCW / University of Washington and poster by Waters Corporation!
 

Related articles

Simulating GC×GC-TOFMS data & the robustness of chemometric software (Timothy Trinklein, MDCW 2023)
Presentation | Video

Simulating GC×GC-TOFMS data & the robustness of chemometric software (Timothy Trinklein, MDCW 2023)

We report a workflow to simulate GC×GC-TOFMS datasets with realistic run-to-run retention time shifting which are used to evaluate the robustness of chemometric software to shifting.
Tile-based fisher ratio analysis of (3D GC) GC3-TOFMS detection data (Sonia Schöneich, MDCW 2023)
Presentation | Video

Tile-based fisher ratio analysis of (3D GC) GC3-TOFMS detection data (Sonia Schöneich, MDCW 2023)

Comprehensive three-dimensional (3D) gas chromatography with TOFMS detection (GC3-TOFMS) has been developed.
Sex and person identity recognition from GC×GC analysis of scent samples (Jan Hlavsa, MDCW 2025)
Presentation | Video

Sex and person identity recognition from GC×GC analysis of scent samples (Jan Hlavsa, MDCW 2025)

Discover a fully automated GC×GC method for sex classification and identity verification of human scent samples with machine learning, achieving 85% accuracy in validation tests.
News from LabRulezGCMS Library - Week 22, 2026
Article | Application

News from LabRulezGCMS Library - Week 22, 2026

This week we bring you application note by Shimadzu, presentation by MDCW / University of Washington and poster by Waters Corporation!