Comparison between a deep-learning and a pixel-based approach for the automated quantification of HIV target cells in foreskin tissue.

The availability of target cells expressing the HIV receptors CD4 and CCR5 in genital tissue is a critical determinant of HIV susceptibility during sexual transmission. Quantification of immune cells in genital tissue is therefore an important outcome for studies on HIV susceptibility and prevention. Immunofluorescence microscopy allows for precise visualization of immune cells in mucosal tissues; however, this technique is limited in clinical studies by the lack of an accurate, unbiased, high-throughput image analysis method. Current pixel-based thresholding methods for cell counting struggle in tissue regions with high cell density and autofluorescence, both of which are common features in genital tissue. We describe a deep-learning approach using the publicly available StarDist method to count cells in immunofluorescence microscopy images of foreskin stained for nuclei, CD3, CD4, and CCR5. The accuracy of the model was comparable to manual counting (gold standard) and surpassed the capability of a previously described pixel-based cell counting method. We show that the performance of our deep-learning model is robust in tissue regions with high cell density and high autofluorescence. Moreover, we show that this deep-learning analysis method is both easy to implement and to adapt for the identification of other cell types in genital mucosal tissue.

Soluble E-cadherin: A marker of genital epithelial disruption.

PROBLEM: The genital epithelial barrier is a crucial first line of defence against HIV, and epithelial disruption may enhance HIV susceptibility. Assessment of genital epithelial integrity requires biopsies, but their collection is not practical in many research settings. A validated biomarker of genital epithelial barrier integrity would therefore be useful. The purpose of this study was to evaluate soluble E-cadherin (sE-cad) as a marker of genital epithelial disruption. METHOD OF STUDY: Using in vitro models of endocervical and foreskin epithelial cells, we assessed changes in sE-cad, IL-6, IL-1ß, and IL-1α levels following mechanical disruption. We also assessed changes in sE-cad levels in vivo in cervicovaginal secretions after epithelial disruption by endocervical cytobrush sampling in Canadian women, and assessed the relationship between levels of sE-cad in coronal sulcus swabs to membrane-bound E-cadherin in the overlying foreskin tissue in Ugandan men. RESULTS: sE-cad levels immediately increased after in vitro epithelial physical disruption with the degree of elevation dependent on the extent of disruption, as did levels of IL-1ß and IL-1α; this was followed by a delayed increase in IL-6 levels. In vivo results confirmed that sE-cad levels in cervicovaginal secretions were elevated 6 h after cytobrush sampling when compared to baseline. Furthermore, levels of sE-cad in the prepuce were inversely correlated with the amount of membrane-bound E-cadherin of overlying tissue. CONCLUSION: Our results validate the use of sE-cad as a marker of epithelial disruption and demonstrate that the processes of physical disruption and inflammation in the genital tract are strongly intertwined.

Quantitative Immunofluorescent Imaging of Immune Cells in Mucosal Tissues.

Understanding the interplay between commensals, pathogens, and immune cells in the skin and mucosal tissues is critical to improve prevention and treatment of a myriad of diseases. While high-parameter flow cytometry is the current gold standard for immune cell characterization in blood, it is less suitable for mucosal tissues, where structural and spatial information is lost during tissue disaggregation. Immunofluorescence overcomes this limitation, serving as an excellent alternative for studying immune cells in mucosal tissues. However, the use of immunofluorescent microscopy for analyzing clinical samples is hampered by a lack of high-throughput quantitative analysis techniques. In this chapter, we describe methods for sectioning, staining, and imaging whole sections of human foreskin tissue. We also describe methods to automate immune cell quantification from immunofluorescent images, including image preprocessing and methods to quantify both circular and irregularly shaped immune cells using open-source software.