Compressed sensing expands the multiplexity of imaging mass cytometry

Area of Science:

• Biomedical Imaging
• Computational Biology
• Immunohistochemistry

Background:

• Current antibody-based imaging methods face limitations in simultaneous reporter detection, restricting multiplexity.
• Compressed sensing offers a way to reconstruct high-dimensional data from limited measurements.
• Previous work applied compressed sensing to transcriptomic data using the CISI framework.

Purpose of the Study:

• To extend the Composite In Situ Imaging (CISI) framework to protein expression data obtained via Imaging Mass Cytometry (IMC).
• To enable higher multiplexity protein imaging for enhanced spatial biology insights.

Main Methods:

• The study adapted the CISI framework for protein expression analysis using Imaging Mass Cytometry (IMC) data.
• Developed a method (CISI-IMC) to reconstruct spatial protein expression from composite imaging channels.
• Trained the CISI-IMC framework on diverse human tissue data for universal applicability.

Main Results:

• CISI-IMC accurately recovered spatial expression of 16 immune and stromal marker proteins from 8 composite channels.
• Achieved an average Pearson's correlation of 0.8 across proteins, demonstrating high accuracy.
• The trained framework showed universal decompression capabilities across various tumor and healthy tissue types.

Conclusions:

• CISI-IMC significantly advances protein imaging, allowing for higher multiplexity and detailed spatial analysis.
• The developed expression dictionary and barcoding matrix are valuable for cell type classification.
• This work establishes a foundation for future high-plex protein imaging applications.