Mass Spectrometry Imaging Data Processing for Intuitive Substance Mapping
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Solution Overview
Problem
Conventional methods for analyzing mass spectrometric imaging data are labor-intensive and require specialized knowledge, as they often involve trial-and-error peak selection and complex multivariate analyses, making it difficult to efficiently interpret the spatial distribution of substances in biological samples.
Innovation Solution
A mass analysis data processing method and apparatus that extracts the maximum intensity and corresponding mass-to-charge ratio for each micro area, creating a colored two-dimensional image and maximum-intensity spectrum, allowing for intuitive visualization of substance distribution without the need for repeated peak selection or complex multivariate analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional multivariate analysis methods (PCA, ICA, FA) are used to analyze mass spectrometric imaging data, then comprehensive substance distribution information can be obtained, but the analysis requires specialized knowledge and involves complex procedures that increase operator burden and processing time
Solution Approach 1:
The invention extracts only the maximum intensity peak from each mass spectrum, separating the essential information (maximum intensity and its spatial distribution) from the complex multivariate analysis. This extraction approach obtains substance distribution information while avoiding the complexity of PCA, ICA, or FA methods.
Solution Approach 2:
The invention creates a simplified representation (maximum intensity spectrum and its spatial distribution map) that copies the essential information from the original complex mass spectrometric imaging data, making it accessible without requiring specialized knowledge of multivariate analysis.
2Measurement precision
If trial-and-error peak selection is performed to identify substances with specific spatial distribution, then accurate substance identification can be achieved, but the process requires repeated operations that significantly increase analysis time and reduce throughput
Solution Approach 1:
The invention performs preliminary extraction of maximum intensity peaks from all mass spectra before any analysis or selection is needed. This preliminary action identifies potential substances of interest in advance, eliminating the need for repeated trial-and-error peak selection and significantly reducing analysis time while maintaining identification accuracy.
3Loss of information
If integrated mass spectrum display is used to show overall substance composition, then comprehensive chemical information is provided, but the method does not guarantee identification of substances with spatially specific distribution and requires additional operations to locate relevant peaks
Solution Approach 1:
The invention adds a spatial dimension to the mass spectrum analysis by displaying the spatial distribution map of the maximum intensity peak alongside the integrated mass spectrum. This dimensional enhancement allows operators to directly identify substances with spatially specific distribution without additional trial-and-error operations, while preserving the comprehensive chemical information from the integrated spectrum.
Data Source
AI summary
The present invention provides a method and apparatus for efficiently handling a large amount of data collected by an imaging mass analysis to present significant information for the analysis of the tissue structure of a biological sample or other objects in an intuitively understandable form for analysis operators. For each pixel 8b on a sample 8, a mass-to-charge ratio m/z(i) corresponding to the maximum intensity MI(i) in a mass spectrum is listed, and the largest value MII of the maximum intensities of all the pixels are extracted. A color scale corresponding to the intensity values within a range of 0 to MII is defined. For each pixel, the maximum intensity MI is compared with the color scale to assign a color to that pixel. A mapping image with the pixels shown in the respective colors is created and displayed. Simultaneously, a spectrum showing the relationship between MI(i) and m/z(i) of all the pixels is created in such a manner that the peak colors correspond to the pixel colors on the mapping image. The mapping image shows the tissue structure of the sample. By comparing this image with the spectrum, the m/z of a noticeable substance in the sample can be identified.