# Negative-control-anchored urinary microbiome profiling with absolute 16S quantification: a pilot study in newly diagnosed, treatment-naive bladder cancer and healthy individuals

## TL;DR

This study introduces a new method to analyze urine microbiomes by using bacterial load measurements to improve accuracy, comparing healthy individuals and bladder cancer patients.

## Contribution

The novel contribution is a negative-control-anchored workflow integrating absolute 16S quantification to enhance reliability in low-biomass urine microbiome studies.

## Key findings

- Bacterial load was lower in bladder cancer patients compared to healthy controls, though not statistically significant.
- A threshold of 1000 reads was established to exclude low-quality samples based on negative controls.
- Biomass-aware quality control reduced background-driven over-interpretation in low-biomass urine datasets.

## Abstract

Recent studies utilizing 16S rRNA amplicon sequencing have challenged the notion of urine sterility, yet urine is a low-biomass specimen in which apparent community profiles can be strongly influenced by background signal from reagents and processing. To address this interpretability gap, we integrate culture-independent absolute 16S rRNA gene quantification with urinary 16S amplicon sequencing in a negative-control-anchored workflow. Bacterial load provides a biomass-aware quality control gate that defines interpretable low-biomass thresholds and objective exclusion criteria. As a pilot application, we compared midstream urine collected prior to instrumentation from healthy volunteers and newly diagnosed bladder cancer (BC) patients, quality filtering retained 29 controls and 5 BC cases. Samples > 106 copies/ml typically produced > 10 000 reads; near 105 copies/ml read counts dropped sharply yet remained distinguishable from background. Thirteen negative controls (V3–V4 PCR and stabilization buffer; median 90, mean 124 reads) supported excluding samples with < 1000 reads. Median bacterial load was lower in BC than in controls (7.0  × 103 vs 1.07 × 106 copies/ml), although not significant in this underpowered cohort (P = 0.07). This cohort-size-independent framework enables load-based triage for sequencing, reduces background-driven over-interpretation in low-biomass urine datasets, and supports modeling bacterial load as a covariate or stratifier in future studies of the bladder cancer microbiome.

We developed a negative-control anchored urine microbiome workflow, using absolute bacterial load to improve low-biomass data reliability and explore differences between healthy individuals and bladder cancer patients.