Miso: an R package for multiple isotope labeling assisted metabolomics data analysis.

Area of Science:

• Metabolomics
• Computational Biology
• Biochemistry

Background:

• Stable isotope labeling is crucial for metabolomics structure elucidation.
• Existing computational tools lack support for multiple-precursor labeling studies.

Purpose of the Study:

• To develop Miso, an R package for automated analysis of multiple-precursor stable isotope labeling experiments.
• To provide an efficient workflow for detecting labeled molecules in metabolomics.

Main Methods:

• Development of the Miso R package.
• Application of Miso to analyze liquid chromatography-mass spectrometry (LC-MS) data.
• Utilized duckweed plants fed with unlabeled and labeled tyrosine (tyrosine-2H4, tyrosine-13C915N1).

Main Results:

• Miso successfully detects the complete repertoire of labeled molecules from multiple precursors.
• The generated data matrix includes retention time, m/z values, and the number of labeled atoms for identified ions.
• Demonstrated capability using LC-MS data from duckweed plants with varied tyrosine labeling.

Conclusions:

• Miso offers an automated and efficient solution for analyzing complex labeling patterns in metabolomics.
• The package facilitates structure elucidation and database querying in biological studies.
• Miso is publicly available on CRAN with supporting tutorials on GitHub.