Adaptive Precursor Isolation for Mass Spectrometry Quantitation
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Solution Overview
Problem
Current mass spectrometry methods for protein quantitation face challenges due to interference from isobarically tagged peptides with similar mass-to-charge ratios, leading to inaccurate relative abundance measurements and limited dynamic range, especially when co-isolation and co-dissociation occur, affecting the precision and accuracy of peptide quantitation.
Innovation Solution
The implementation of a method called 'QuantMode' that analyzes precursor ion isolation windows to determine and minimize interference, adjusting the range of m/z units to reduce interference, and selectively fragmenting ions based on interference levels to generate accurate product ion mass spectrometry data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a standard precursor isolation window is used for tandem mass spectrometry, then the throughput and coverage of protein quantitation is maintained, but interference from co-isolated species with similar mass-to-charge ratios compromises the accuracy and precision of quantitation
Solution Approach 1:
The system performs preliminary analysis of the precursor ion population before isolation, using machine learning trained on spectral data to predict interference levels. This pre-screening allows the system to identify precursors likely to cause or suffer from interference, enabling selective application of narrower isolation windows only when necessary, thus maintaining throughput while improving accuracy for problematic cases
Solution Approach 2:
The system dynamically adjusts the precursor isolation window width based on predicted interference levels. For precursors with high predicted interference, the isolation window is narrowed to reduce co-isolation of interfering species. For precursors with low predicted interference, the standard window width is maintained to preserve throughput. This adaptive parameter adjustment resolves the contradiction between accuracy and throughput
2Measurement precision
If the precursor isolation window is narrowed to reduce interference, then the accuracy of quantitation improves, but the number of precursors that can be analyzed decreases, reducing throughput
Solution Approach 1:
The system applies different isolation window widths to different precursors based on their local interference characteristics. Rather than using a uniform narrow window for all precursors, the machine learning model predicts interference levels for each precursor individually, and the isolation window is adjusted locally for each case. This allows maximum throughput to be maintained while achieving high precision for precursors that require it
Solution Approach 2:
The system applies interference reduction measures (narrower isolation windows) only partially - specifically to precursors predicted to have high interference levels - rather than applying them excessively to all precursors. This selective application maintains high quantitation precision for problematic cases while preserving throughput for the majority of precursors that do not require intervention
3Reliability
If interfering species are co-isolated and co-dissociated, then the dynamic range of quantitation is limited, but reducing the isolation window increases the risk of missing low-abundance precursors
Solution Approach 1:
The system uses machine learning models trained on spectral data to predict interference levels for each precursor. This feedback mechanism allows the system to identify precursors that are likely to suffer from interference effects, and apply targeted interference reduction strategies only to those cases. The model continuously learns from spectral patterns to improve its predictions, ensuring that dynamic range is maintained while preserving detection sensitivity for low-abundance precursors
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
This approach enhances the accuracy and precision of protein quantitation by reducing interference, maintaining high throughput, and increasing the number of quantifiable spectra and proteins, thereby improving the dynamic range and reliability of protein quantification results.
Implementation Method 1
generating a distribution of precursor ions from the analyte; analyzing the mass-to-charge ratios of at least a portion of the distribution of precursor ions
Implementation Method 2
fragmenting ions corresponding to a preselected range of m/z units about the precursor peak, thereby generating fragment ions
Data Source
AI summary
Described herein are methods and systems which enable a unique platform for analyte quantitation. The methods and systems relate to determining the amount of interference in a precursor ion isolation window resulting from an impurity. Once the level of impurity is determined, several methods can be employed to reduce the amount of interference in a subsequent MS/MS spectrum. The methods and systems described herein enable increased quantitation accuracy while maintaining high levels of throughput.