Single-cell profiling of peripheral immune remodeling in BK polyomavirus-associated nephropathy after kidney...

BK polyomavirus-associated nephropathy (BKPyVAN) remains a major cause of allograft dysfunction after kidney transplantation. To define peripheral immune signatures associated with distinct clinical states of BKPyV infection, we performed single-cell RNA sequencing of peripheral blood mononuclear cells from kidney transplant recipients across three clinical states: stable graft function (n = 1), BKPyV DNAemia (n = 2), and biopsy-proven BKPyVAN (n = 3). We identified 17 immune cell populations and observed remodeling of both the innate and adaptive compartments. Monocyte subsets differed across clinical states, with CD16⁺ non-classical monocytes enriched in BKPyV DNAemia and inflammatory monocytes more abundant in BKPyVAN. CD8⁺ T-cell frequency was higher in BKPyVAN than in BKPyV DNAemia, while NK-cell frequency was lower in BKPyV DNAemia and BKPyVAN than in Stable, consistent with altered antiviral surveillance. A rare but transcriptionally distinct population, termed Activated Effector Cells (AECs), comprised only 0.60% of cells in BKPyVAN (104 of 17,238 cells) yet accounted for 12.15% of cluster-associated marker calls, the highest among the 17 populations, and displayed marked enrichment of cell cycle and RNA metabolic pathways. Across multiple lineages, AUC-based gene set enrichment analysis revealed convergent activation of RNA processing and intracellular transport pathways. Pseudobulk differential expression and permutation testing supported condition-associated transcriptional signatures, and leading-edge gene analysis identified candidate transcriptional readouts, including cell cycle regulators, inflammatory mediators, and RNA processing genes, with potential utility for blood-based monitoring. These findings reveal peripheral immune remodeling across the clinical states of BK polyomavirus infection and highlight the value of resolving rare, transcriptionally distinct immune populations, providing a rationale for noninvasive, transcriptomics-informed monitoring and risk stratification strategies.