Collision Cross-Section Fragment Ion Analysis for Chemical Class Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for structural elucidation of compounds in complex samples, such as biofluids and tissues, face challenges due to the large number of unknown metabolites and limited knowledge of fragmentation patterns, making it difficult to rapidly identify coregulated chemical classes for drug discovery, metabolomics, and biomarker discovery.
Innovation Solution
The use of collision cross-section (CCS) values of fragment ions to determine chemical class composition through methods involving fragmentation, ion-mobility separation, and correlation with pre-determined values, along with calculating composite values of precursor and product ions to increase measurement selectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual mass spectra data interpretation is used, then identification accuracy can be maintained, but analysis throughput and productivity are severely limited
Solution Approach 1:
The patent creates a reference database of collision cross-section (CCS) values and fragmentation patterns from known chemical classes. This database serves as a copy of expected patterns that can be rapidly compared against experimental data, eliminating the need for manual interpretation while maintaining identification accuracy through systematic pattern matching
Solution Approach 2:
The patent replaces the manual mechanical process of spectral interpretation with automated computational methods. By using algorithms to compare experimental CCS and fragmentation data against the reference database, the system achieves high-throughput analysis without sacrificing the accuracy that comes from expert knowledge
2Quantity of substance
If comprehensive metabolite profiling is pursued to identify all chemical classes, then coverage is improved, but the complexity of data interpretation and analysis increases significantly
Solution Approach 1:
The patent segments the complex task of comprehensive metabolite profiling into manageable components by categorizing compounds into distinct chemical classes, each with characteristic CCS and fragmentation patterns. This segmentation allows the system to handle large numbers of compounds systematically by matching them against predefined class profiles rather than attempting to analyze each compound individually
Solution Approach 2:
The patent introduces CCS values as an additional parameter alongside traditional mass spectrometry data. This parameter change enables more efficient discrimination between chemical classes and simplifies data interpretation by providing an independent dimension for classification that reduces the complexity of analyzing comprehensive metabolite profiles
3Loss of time
If limited fragmentation pattern knowledge is used, then analysis time is reduced, but identification accuracy and reliability deteriorate
Solution Approach 1:
The patent performs preliminary action by pre-characterizing fragmentation patterns and CCS values for multiple chemical classes before analysis. This preparatory work creates a reference database that enables rapid, reliable identification during actual experiments without requiring extensive fragmentation pattern knowledge to be available in real-time, thus maintaining both speed and accuracy
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables high-throughput and robust identification of chemical classes in complex samples, improving the analysis of large datasets and facilitating drug discovery and biomarker identification by providing a rapid and accurate method for determining chemical class composition.
Implementation Method 1
separating the product ions using ion-mobility
Data Source
AI summary
The present disclosure generally relates to methods for determining chemical class compositions present in a sample using collision cross-section fragment ion values.