Signal Response Evaluation Applied to Untargeted Mass Spectrometry Data to Improve Data Interpretability

Area of Science:

• Analytical Chemistry
• Biochemistry
• Data Science

Background:

• Untargeted mass spectrometry (MS) data processing commonly relies on feature finding algorithms that identify chemical patterns.
• These algorithms often yield unannotated features, necessitating methods for quality assessment and improved interpretation.
• Current methods lack universality across different chemical origins and sample matrices.

Purpose of the Study:

• To develop and present a novel method for assessing the quality of individual features in untargeted MS data.
• To introduce signal response evaluation as a universally applicable approach for feature assessment, independent of chemical identity or origin.
• To provide a computational tool for enhancing the interpretation of untargeted MS data.

Main Methods:

• Developed a signal response evaluation method based on the relationship between analyte amount and MS response.
• Implemented three distinct metrics with user-defined parameters to assess feature relationships in dilution series or varying injection volumes.
• Validated the method using metabolomics data from both a uniform (NIST SRM 1950) and a variable (murine kidney tissue) biological matrix.

Main Results:

• The signal response evaluation method effectively assesses the quality of individual features in untargeted MS data.
• Demonstrated the method's applicability across different biological matrices, showcasing its robustness.
• The approach provides a quantitative measure of feature reliability, aiding in distinguishing true signals from noise.

Conclusions:

• Signal response evaluation offers a powerful, matrix-independent approach to enhance the interpretation of untargeted MS data.
• This method improves the reliability of feature identification by focusing on measurement quality.
• The provided code facilitates the implementation of this data processing technique for broader scientific use.