In renal histopathology, the routine clinical use of several histological stains presents challenges for the direct application of stain-specific deep learning-based analysis tools to whole-slide images. We present an approach to the in silico histological staining of kidney tissue where samples stained with hematoxylin and eosin (H&E) are virtually restained with periodic acid-Schiff (PAS). Our approach is underpinned by cycle-consistent generative adversarial neural networks trained on the National Unified Renal Translational Research Enterprise data set & horbar;the first UK-wide Biobank for chronic kidney disease & horbar;which features diverse data from 16 nephrology centers. Our work is divided into the following 4 main components: (1) we developed a virtual staining model, which infers PAS staining from H & E; (2) 2 board-certified pathologists assessed the virtual staining by attempting to distinguish it from real examples; (3) we trained a glomerular segmentation model using 3 independent renal segmentation data sets (Kidney Precision Medicine Project, Human BioMolecular Atlas Program [Kidney], and data by Jayapandian et al); and (4) we demonstrated the utility of virtual staining by inferring PAS staining from previously unseen H&E test images and applying our PAS-specific glomerular segmentation model. Each pathologist was able to identify 52.5% and 75.8% of the virtually stained images, respectively, showing an overlap in the variability of the authentic and synthetic staining. We discussed the utility of virtual staining in digital pathology, the need for pathology-specific testing with respect to chronic damage, and minimal changes and steps for incorporating more stains. Furthermore, alongside this article, we included complete glomerular annotations for 20 Kidney Precision Medicine Project H&E-stained slides.