Quantitative Immunofluorescent Imaging of Immune Cells in Mucosal Tissues

HIV-associated penile anaerobes disrupt epithelial barrier integrity

Specific anaerobic taxa within the penile microbiome-the Bacteria Associated with Seroconversion, Inflammation and Immune Cells (BASIC) species-enhance HIV-1 susceptibility, in part by recruiting susceptible cells to the inner foreskin. However, their effect on epithelial barrier integrity has not been described. Using foreskin tissues and penile swabs from 116 males undergoing voluntary medical male circumcision, we assessed the relationship between BASIC species and foreskin epithelial thickness, junction protein expression, and cellular proliferation. The absolute abundance of BASIC species was associated with reduced tissue expression of the epithelial junction proteins claudin-1 and E-cadherin, and with elevated soluble E-cadherin in penile secretions, suggesting proteolytic cleavage. These effects were not seen in participants with a high abundance of control taxa without high levels of BASIC species. The BASIC species Prevotella bivia, but not Peptostreptococcus anaerobius or Dialister micraerophilus, was shown to directly degrade recombinant human E-cadherin and to increase the release of soluble E-cadherin from foreskin epithelial cells in vitro. In vivo BASIC species absolute abundance was also linked to a thicker nucleated epithelium and increased keratinocyte proliferation, with no change in stratum corneum thickness. Therefore, BASIC species may enhance penile HIV susceptibility by directly disrupting epithelial integrity, in addition to previously described target cell recruitment.

Impact of antimicrobials on penile HIV susceptibility and immunology in uncircumcised men: A randomized phase 1/2 clinical trial

Within the penile microbiome, bacteria associated with seroconversion, immunology, and cells (BASIC species) enhance HIV susceptibility in heterosexual uncircumcised men by inducing foreskin inflammation and HIV target cell recruitment. This phase 1/2 clinical trial randomizes HIV-uninfected Ugandan men (n = 125) to either oral tinidazole, topical metronidazole, topical clindamycin, or topical hydrogen peroxide to define impact on ex vivo foreskin HIV susceptibility, penile immunology, and BASIC species density. Antimicrobials are well tolerated, and 116 (93%) participants complete the protocol. Topical metronidazole and oral tinidazole reduce the inner foreskin tissue density of HIV-susceptible CD4+ T cells (predefined primary endpoint). Antimicrobials also have varying but substantial effects on reducing prepuce inflammation and BASIC species density, reducing density of foreskin T cell subsets, and increasing foreskin epithelial integrity. Immune alterations correlate strongly with changes in the abundance of BASIC species. Clinical interventions targeting the penile microbiota, particularly topical metronidazole, may reduce HIV susceptibility in uncircumcised men.

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.