Urinary Cell-Free DNA detection platform for bacterial cystitis diagnosis via spot-based machine learning

Bacterial cystitis is a prevalent infectious condition that is primarily caused by uropathogenic Escherichia coli (E. coli). The rapid and precise identification of this pathogen is crucial for guiding timely and effective clinical intervention. Urinary cell-free DNA (cfDNA) has emerged as a promising biomarker for cystitis, yet its complex origins from both inflammatory and infectious processes pose challenges in distinguishing bacterial from non-bacterial etiologies.

Here, we developed an innovative image-based, machine learning-assisted fluorescent detection system for simultaneous quantitative and qualitative analysis of urinary cfDNA to diagnose bacterial cystitis.

The detection system utilizes polyethyleneimine (PEI)-functionalized glass slides for efficient cfDNA enrichment, followed by fluorescence imaging with propidium iodide labeling. The performance of the detection system was rigorously validated using a bacterial cystitis mouse model and human urine specimens.

This platform enables rapid quantification of cfDNA within 20 min, with a calibration range of 31.25-4000 ng mL^-1 and a detection limit of 20.29 ng mL^-1. Integration with the YOLOv5 object detection algorithm facilitates automated analysis of cfDNA fluorescence spot patterns, revealing distinct size distributions that differentiate bacterial (77.12 % smaller spots) from non-bacterial cystitis (13.87 % smaller spots). Applied to 148 clinical urine samples, this approach achieved a diagnostic accuracy of 94.43 %, with 94.45 % sensitivity and 94.35 % specificity for bacterial cystitis, markedly improving upon conventional methods (65.96 % specificity, 78.22 % sensitivity).

Requiring no urine pretreatment, this rapid, on-site platform represents a transformative advance in point-of-care testing (POCT) for bacterial cystitis, with potential applications for digital biomarker detection in diverse inflammatory and infectious diseases.