Deconvolution of Human Urine across the Transcriptome and Metabolome

Area of Science:

• Biochemistry
• Genomics
• Urology

Background:

• Early detection of genitourinary diseases is challenging due to the lack of cellular resolution in current noninvasive assays.
• Existing urine-based tests often lack the comprehensive transcriptome data needed for accurate cell type identification.
• Messenger RNA (mRNA) in urine holds potential for early disease detection but current methods are limited to single-gene measurements.

Purpose of the Study:

• To investigate the utility of whole transcriptome analysis of urine cell-free RNA (cfRNA) and sediment RNA for noninvasive disease detection.
• To determine if urine transcriptome deconvolution can identify cell type contributions from genitourinary tract and other tissues.
• To correlate urine transcriptome data with urine metabolome profiles and plasma cfRNA.

Main Methods:

• Isolation and sequencing of cfRNA and sediment RNA from human urine samples.
• Measurement of urine metabolome in healthy controls and kidney stone patients.
• Bioinformatic analysis of urine transcriptomes for cell type deconvolution and comparison with plasma cfRNA and metabolome data.

Main Results:

• Urine transcriptome deconvolution identified cell type contributions from the genitourinary tract and other high-turnover solid tissues.
• Comparison with plasma cfRNA revealed enriched metabolic pathways and a distinct cell type spectrum in urine.
• Integration of urine transcriptomic and metabolomic data highlighted metabolic pathways involved in amino acid metabolism and proximal tubule function.

Conclusions:

• Noninvasive whole transcriptome measurements of urine cfRNA and sediment RNA provide cell type resolution for hard-to-biopsy tissues.
• Urine transcriptome analysis reflects signals enriched in metabolic pathways measurable by the urine metabolome.
• This approach offers a promising noninvasive liquid biopsy for early disease detection and monitoring.