Mass Spectrum Reconstruction for Multiply Charged Sample Analysis
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Solution Overview
Problem
High throughput sample analysis in mass spectrometry generates large datasets that are challenging to process efficiently, especially when dealing with intact proteins or nucleic acids, which are larger and multiply charged, increasing the complexity of analysis.
Innovation Solution
A method for automatically analyzing a collection of samples by ionizing multiple samples, capturing raw mass spectra, correlating subsets of spectra to each sample, and generating reconstructed mass spectra for each sample, which can be analyzed for signal intensity and comparison to known compounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If high throughput mass spectrometry analysis is performed on large numbers of samples, then productivity is improved, but the quantity of raw data generated increases, making processing more difficult
Solution Approach 1:
The patent segments the large compiled MS dataset into individual sub-datasets, each corresponding to a specific sample. This segmentation is achieved through automated data processing that separates the continuous data stream into discrete sample groups, making the data more manageable and easier to process individually while maintaining the high throughput capability of analyzing hundreds or thousands of samples.
2Measurement precision
If intact proteins or nucleic acids are analyzed, then measurement precision is improved for large biomolecules, but device complexity increases due to multiply charged ions
Solution Approach 1:
The patent employs automated data processing with feedback mechanisms that use chronograms and correlation algorithms to automatically associate MS spectra with corresponding samples. The system provides feedback loops that refine the correlation between samples and spectra through iterative processing, using charge state information and retention time data to improve identification accuracy while reducing manual intervention requirements.
Solution Approach 2:
The patent introduces chronograms as an intermediary tool that bridges the connection between sample injection events and the resulting MS spectra. These chronograms serve as temporal maps that facilitate the correlation process, acting as a mediator that simplifies the complex task of matching multiply charged ion spectra with their source samples by providing a time-based reference framework.
3Ease of operation
If automated data processing is implemented, then ease of operation is improved, but loss of time in data processing may increase without optimized methods
Solution Approach 1:
The patent performs preliminary actions by generating chronograms and establishing correlation frameworks before the actual data analysis begins. The system pre-processes the raw MS data to create organized sub-datasets and temporal references, so that when analysis is needed, the data is already structured and ready for rapid processing. This preliminary organization significantly reduces the time required for subsequent analysis operations.
Solution Approach 2:
The patent replaces manual mechanical data processing methods with automated computational algorithms. Instead of manually correlating samples with spectra or processing large datasets through conventional methods, the system uses automated software algorithms that perform correlation analysis, spectrum reconstruction, and data association tasks computationally, dramatically reducing processing time while improving consistency 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
This method enables efficient processing of large mass spectrometry datasets, improving the accuracy of analyte identification and the confidence of analytical results, particularly for complex samples like intact proteins or nucleic acids.
Implementation Method 1
ionizing a plurality of samples
Implementation Method 2
capturing a plurality of raw mass spectra for the ionized plurality of samples
Data Source
AI summary
Methods and systems for automatically analyzing a collection of samples, the method including ionizing a plurality of samples, capturing a plurality of raw mass spectra for the ionized plurality of samples, correlating captured respective subsets of the raw mass spectra to each sample of the plurality of samples, and for each sample of the plurality of samples, generating a reconstructed mass spectrum based on the respective subset of the raw mass spectra of the sample. Methods and systems also include correlating the captured respective subsets of the raw mass spectra to each sample by generating a chronogram, and correlating a timeline of a sampling of the sample with the chronogram to correlate the captured respective subsets of the raw mass spectra to each sample. Methods and systems also include analyzing the generated reconstructed mass spectrum for each sample of the plurality of samples.