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

Evidence has highlighted the importance of immune cells in various gut disorders. Both the quantification and localization of these cells are essential to the understanding of the complex mechanisms implicated in these pathologies. Even if quantification can be assessed (e.g., by flow cytometry), simultaneous cell localization and quantification of whole tissues remains technically challenging. Here, we describe the use of a computer learning-based algorithm created in the Tissue Studio interface that allows for a semi-automated, robust and rapid quantitative analysis of immunofluorescence staining on whole colon sections according to their distribution in different tissue areas. Indeed, this algorithm was validated to characterize gut immune microenvironment. Its application to the preclinical colon cancer APC(Mni)(/+) mouse model is illustrated by the simultaneous counting of total leucocytes and T cell subpopulations, in the colonic mucosa, lymphoid follicles and tumors. Moreover, we quantify T cells in lymphoid follicles for which quantification is not possible with classical methods. Thus, this algorithm is a new and robust preclinical research tool, for investigating immune contexture exemplified by T cells but it is also applicable to other immune cells such as other myeloid and lymphoid populations or other cellular phenomenon along mouse gut