COEXIST: Coordinated single-cell integration of serial multiplexed tissue images

Area of Science:

• Biotechnology
• Computational Biology
• Genomics

Background:

• Multiplexed tissue imaging (MTI) relies on serial sectioning due to technological constraints.
• Integrating data from serial sections is challenging due to biological heterogeneity and spatial complexities.
• Current computational methods fail to adequately address cell population heterogeneity and spatial information.

Purpose of the Study:

• To develop a computational method for seamless single-cell level integration of serially sectioned multiplexed tissue imaging data.
• To overcome limitations in MTI panel size and enable deeper cellular profiling.
• To validate and demonstrate the performance of the novel COEXIST algorithm.

Main Methods:

• Monte-Carlo simulations were used to estimate section overlap.
• The COEXIST algorithm was developed to integrate shared molecular profiles and spatial information.
• The algorithm's performance was evaluated across multiple applications.

Main Results:

• COEXIST successfully integrates serial MTI sections at the single-cell level.
• The algorithm improves spatial single-cell profiling by combining MTI panels.
• COEXIST enables rectification of cell phenotypes and comparison of MTI platforms.

Conclusions:

• COEXIST overcomes MTI panel size limitations and enhances the analysis of intact nuclei across sections.
• The algorithm facilitates deeper profiling of cell lineages and functional states.
• COEXIST significantly advances MTI platform validation and data integration capabilities.