spatialstein: An Open-Source Workflow for Annotation, Deconvolution, and Spatially Aware Segmentation of Mass...

Area of Science:

• Computational mass spectrometry
• Bioinformatics
• Analytical chemistry

Background:

• Mass Spectrometry Imaging (MSI) data presents unique challenges distinct from optical imaging.
• Standard analysis algorithms often fail to account for MSI data intricacies like signal intensity variability and isobaric interference.
• These issues compromise the accuracy of MSI segmentation and data interpretation.

Purpose of the Study:

• To demonstrate how accounting for the complex structure of MSI data enhances analysis accuracy.
• To introduce a novel computational approach for separating overlapping isotopic envelopes and mitigating pixel-to-pixel signal variability.
• To present spatialstein, an open-source workflow for MSI data analysis.

Main Methods:

• Leveraging advancements in computational mass spectrometry to resolve overlapping isotopic patterns.
• Implementing algorithms to correct for signal intensity variations across pixels.
• Developing a modular, open-source workflow (spatialstein) for MSI data processing.

Main Results:

• The spatialstein workflow successfully separates overlapping isotopic envelopes.
• Pixel-to-pixel signal intensity variability is effectively mitigated.
• The workflow provides tentative molecular formula annotation, deconvolved ion images, and segmented images, improving MSI analysis accuracy.

Conclusions:

• Accounting for MSI data's intricate structure significantly increases analysis accuracy.
• spatialstein offers a robust and adaptable solution for MSI data processing and interpretation.
• The open-source nature of spatialstein promotes its wider application in scientific research.