Quantitative Analysis of Inflammatory Uterine Lesions of Pregnant Gilts with Digital Image Analysis Following Experimental PRRSV-1 Infection

Simple Summary There is still a huge gap in the knowledge about the reproductive disorders caused by porcine reproductive and respiratory syndrome virus-1. A new, beneficial, and widely used method for the analysis of histopathological lesions in digital image analysis. Using the QuPath software, we aimed to count and classify endometrial inflammatory cells to test the method's applicability in similar experiments where inflammation needs to be objectively assessed. It proved to be effective and easy to apply, and it would be possible to gain new knowledge about the reproductive disorders caused by porcine reproductive and respiratory syndrome virus-1 by applying similar procedures in the future. Reproductive disorders caused by porcine reproductive and respiratory syndrome virus-1 are not yet fully characterized. We report QuPath-based digital image analysis to count inflammatory cells in 141 routinely, and 35 CD163 immunohistochemically stained endometrial slides of vaccinated or unvaccinated pregnant gilts inoculated with a high or low virulent PRRSV-1 strain. To illustrate the superior statistical feasibility of the numerical data determined by digital cell counting, we defined the association between the number of these cells and endometrial, placental, and fetal features. There was strong concordance between the two manual scorers. Distributions of total cell counts and endometrial and placental qPCR results differed significantly between examiner1's endometritis grades. Total counts' distribution differed significantly between groups, except for the two unvaccinated. Higher vasculitis scores were associated with higher endometritis scores, and higher total cell counts were expected with high vasculitis/endometritis scores. Cell number thresholds of endometritis grades were determined. A significant correlation between fetal weights and total counts was shown in unvaccinated groups, and a significant positive correlation was found between these counts and endometrial qPCR results. We revealed significant negative correlations between CD163+ counts and qPCR results of the unvaccinated group infected with the highly virulent strain. Digital image analysis was efficiently applied to assess endometrial inflammation objectively.

Automated summary

This study used QuPath software for digital image analysis to count and classify endometrial inflammatory cells, demonstrating the effectiveness and applicability of the method. It suggests that similar procedures can provide new knowledge about reproductive disorders caused by porcine reproductive and respiratory syndrome virus-1 in the future.