OrbiQ Mass Spectrometer Segmentation for Proteomic Reproducibility
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Solution Overview
Problem
Current mass spectrometry techniques face challenges in achieving high reproducibility and sensitivity for protein identification and quantification, particularly in shotgun proteomics, due to stochastic data-dependent acquisition methods and limitations in selecting specific protein targets, leading to incomplete coverage of biological pathways and low overlap in replicate experiments.
Innovation Solution
The development of a quadrupole-orbitrap hybrid mass spectrometer (OrbiQ) with novel data acquisition schemes, such as real-time target confirmation (rtTC) and parallel reaction monitoring (PRM), enables targeted and reproducible monitoring of up to 1000 proteins with high sensitivity, using a 'crossover' instrument that combines the sensitivity of SRM with the high-throughput capabilities of shotgun techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If stochastic data-dependent acquisition methods are used in shotgun proteomics, then high-throughput protein identification is achieved, but reproducibility and sensitivity for specific protein targets deteriorate
Solution Approach 1:
The patent segments the proteomics workflow into two distinct modes: a discovery phase using stochastic data-dependent acquisition for high-throughput identification, and a confirmation phase using targeted parallel reaction monitoring for reproducible quantification. This segmentation allows each method to optimize for its specific purpose, resolving the contradiction between throughput and reproducibility.
Solution Approach 2:
The patent performs preliminary action by using the stochastic shotgun proteomics approach first to identify and prioritize specific protein targets of interest, then using those identified targets to guide subsequent targeted PRTM experiments. This preliminary identification step enables the second phase to focus resources on specific targets, achieving both high throughput in discovery and high reproducibility in confirmation.
2Productivity
If stochastic data-dependent acquisition methods are used in shotgun proteomics, then high-throughput protein identification is achieved, but sensitivity for specific protein targets deteriorates
Solution Approach 1:
The patent segments the proteomics workflow into two distinct modes: a discovery phase using stochastic data-dependent acquisition for high-throughput identification, and a confirmation phase using targeted parallel reaction monitoring for reproducible quantification. This segmentation allows each method to optimize for its specific purpose, resolving the contradiction between throughput and reproducibility.
Solution Approach 2:
The patent performs preliminary action by using the stochastic shotgun proteomics approach first to identify and prioritize specific protein targets of interest, then using those identified targets to guide subsequent targeted PRTM experiments. This preliminary identification step enables the second phase to focus resources on specific targets, achieving both high throughput in discovery and high reproducibility in confirmation.
3Productivity
If limited mass spectrometer time is allocated to each precursor ion, then high throughput is maintained, but incomplete coverage of biological pathways occurs
Solution Approach 1:
The patent performs preliminary action by using the stochastic shotgun proteomics approach first to identify and prioritize specific protein targets of interest, then using those identified targets to guide subsequent targeted PRTM experiments. This preliminary identification step enables the second phase to focus resources on specific targets, achieving both high throughput in discovery and high reproducibility in confirmation.
Solution Approach 2:
The patent implements feedback by using results from the discovery phase (stochastic shotgun proteomics) to inform and guide the confirmation phase (targeted PRTM). The identification of interesting targets in the first phase feeds into the design of the second phase, creating a feedback loop that optimizes both throughput and pathway coverage across the two-stage workflow.
4Reliability
If low overlap in replicate experiments occurs, then experimental variability is high, but resource consumption increases
Solution Approach 1:
The patent segments the proteomics workflow into two distinct modes: a discovery phase using stochastic data-dependent acquisition for high-throughput identification, and a confirmation phase using targeted parallel reaction monitoring for reproducible quantification. This segmentation allows each method to optimize for its specific purpose, resolving the contradiction between throughput and reproducibility.
Solution Approach 2:
The patent performs preliminary action by using the stochastic shotgun proteomics approach first to identify and prioritize specific protein targets of interest, then using those identified targets to guide subsequent targeted PRTM experiments. This preliminary identification step enables the second phase to focus resources on specific targets, achieving both high throughput in discovery and high reproducibility in confirmation.
Data Source
AI summary
Described herein are mass spectrometry systems and methods which utilize a dynamic a new data acquisition/instrument control methodology. These systems and methods employ novel artificial intelligence algorithms to greatly increase quantitative and/or identification accuracy during data acquisition. In an embodiment, the algorithms can adapt the instrument methods and systems during data acquisition to direct data acquisition resources to increase quantitative or identification accuracy of target analytes, such as proteins, peptides, and peptide fragments.