Complementing Cell Taxonomies with a Multicellular Analysis of Tissues

Area of Science:

• Systems biology
• Computational biology
• Tissue engineering

Background:

• Single-cell molecular profiling and spatial technologies reveal cellular heterogeneity in tissues.
• Understanding the link between cellular diversity and robust multicellular processes in physiology and disease remains a challenge.

Purpose of the Study:

• To present computational modeling strategies for analyzing large-scale single-cell and spatial datasets.
• To investigate the relationship between single-cell heterogeneity and multicellular organization in tissues.
• To bridge the gap between tissue structure and function in health and disease.

Main Methods:

• Utilizing emerging computational modeling strategies.
• Analyzing large-scale cross-condition single-cell and spatial datasets.
• Complementing existing cell taxonomies with spatial and dynamic information.

Main Results:

• Highlighting the potential of computational models to decipher complex cellular interactions.
• Demonstrating how to study multicellular organization using integrated datasets.
• Providing a framework to link cellular heterogeneity to tissue-level functions.

Conclusions:

• Computational modeling offers powerful tools to understand tissue organization and multicellular dynamics.
• This approach can elucidate how cellular diversity contributes to physiological and pathological processes.
• Future research can leverage these strategies to better understand tissue function and disease mechanisms.