Miso

Miso analyzes LC-MS metabolomics data from multiple-precursor-based stable isotope labeling experiments to detect and catalog unlabeled and labeled ions and support structural elucidation.

Key Features:

• Automated data analysis workflow: Provides an automated workflow to process complex datasets from multiple-precursor-based stable isotope labeling experiments.
• Comprehensive detection of labeled molecules: Identifies and catalogs unlabeled and labeled ions with information on retention time, m/z, and the number of labeled atoms.
• Integration with LC-MS data: Processes LC-MS datasets, exemplified by application to duckweed samples fed with unlabeled, tyrosine-2H4, and tyrosine-13C915N1 tracers.
• Structured output for database queries: Generates a data matrix structured for direct use in database queries and downstream analyses.

Scientific Applications:

• Structural elucidation in metabolomics: Supports identification of labeled atoms to aid structural elucidation of metabolites in stable isotope labeling studies.
• Analysis of multiple-precursor labeling experiments: Enables analysis of datasets derived from experiments using multiple isotopically labeled precursors.
• Plant tracer experiments: Applicable to plant feeding experiments such as duckweed fed with tyrosine-2H4 and tyrosine-13C915N1 for tracer-based metabolite discovery.
• Integration with metabolic databases: Produces outputs that facilitate integration with existing databases for pathway and interaction studies.

Methodology:

Processes LC-MS data to detect unlabeled and labeled ions and produces a matrix containing retention time, m/z values, labeling information, and the number of labeled atoms.