MO374: Urinary Cell Transcriptomics Provides a Non-Invasive Readout of Kidney Genes Essential to Renal Health and Disease

Abstract BACKGROUND AND AIMS Chronic kidney disease affects up to 15% of adults, current diagnostic methods for assessing kidney health and early detection of renal disease lack sensitivity and/or can be highly invasive. The kidney sheds cells into the urine and harvesting these cells and their transcriptomic profile could yield non-invasive insights into genes responsible for maintenance of kidney health and early detection of kidney disease. METHOD Cell pellets were isolated from 200 mL of fresh morning urine from 33 participants, samples were spun at 3000 g for 10 min, washed and then resuspended in PBS. Each urinary cell pellet was then profiled by standard Illumina poly-A RNA-sequencing, generating an average of 30 million paired reads per sample. These were quantified using the standard GTEx quantification pipeline and were compared against 43 different human tissues and other bodily fluids. RESULTS Detailed RNA-sequencing metric analysis revealed that urinary cell pellets can generate reliable data of comparable or better quality than saliva and cerebrospinal fluid at similar read coverage. The one hundred most highly expressed urinary cell genes show an enrichment for immunity (P = 1.2 × 10–7), glucose metabolism (P = 1.3 × 10–5) and renal mineral absorption (P = 9.8 × 10–4); themes shared with the renal transcriptome. Across all protein-coding genes, kidney cortex (r2 = 0.65) and kidney medulla (r2 = 0.64) showed the highest level of correlation with the urinary transcriptome in an analysis of 43 human tissues. The correlation between urinary cells and the kidney was particularly strong (r2 = 0.72) in an analysis restricted to highly specific kidney genes (including UMOD, KCNJ1 and SLC12A1 all known for their role in kidney health and disease). CONCLUSION Poly-A RNA-sequencing of cells harvested from human urine can yield high quality gene expression profiles that correlate with transcriptional activity of the kidney. RNA-sequencing-based profiling of urinary cells offers a completely non-invasive route to assessing the expression of kidney genes of key relevance to renal health and disease.