Deciphering the immune microenvironment of a tissue by digital imaging and cognition network

Area of Science:

• Immunology
• Computational Biology
• Gastroenterology

Background:

• Immune cells play a critical role in gut disorders.
• Quantifying and localizing immune cells is vital for understanding disease mechanisms.
• Simultaneous whole-tissue cell localization and quantification remain challenging.

Purpose of the Study:

• To develop a novel algorithm for semi-automated, quantitative analysis of immunofluorescence staining in whole colon sections.
• To enable robust and rapid assessment of immune cell distribution within different tissue areas.
• To validate the algorithm for characterizing the gut immune microenvironment.

Main Methods:

• Development of a computer learning-based algorithm within the Tissue Studio interface.
• Application of the algorithm to analyze immunofluorescence staining on whole colon sections.
• Validation using the preclinical colon cancer APCMin/+ mouse model.

Main Results:

• The algorithm allows for semi-automated, robust, and rapid quantitative analysis of immune cell distribution.
• Simultaneous counting of total leukocytes and T cell subpopulations in colonic mucosa, lymphoid follicles, and tumors was achieved.
• Accurate quantification of T cells within lymphoid follicles, previously not possible with classical methods, was demonstrated.

Conclusions:

• The developed algorithm is a new and robust preclinical research tool for investigating immune contexture.
• It is particularly useful for analyzing T cells but applicable to other immune cells and cellular phenomena in the mouse gut.
• This tool enhances the understanding of immune cell roles in gut pathologies and preclinical models.