Label-free urinary protein detection through machine learning analysis of single droplet evaporation patterns

Abstract

Background

Chronic kidney disease (CKD) is a major global public health issue, with a steadily increasing incidence. Urinary protein detection serves as a crucial indicator for the diagnosis, monitoring and management of CKD. However, current methods for urinary protein measurement, such as urine dipstick tests, colorimetric assays, and 24-h total urine protein analysis, have certain limitations that restrict their routine application in CKD screening and follow-up. Therefore, there is an urgent need for a simple, convenient, and rapid diagnostic approach for kidney function assessment.

Results

In this study, we have developed and validated a novel method for urine protein quantification based on dried droplet morphology analysis. Our approach demonstrates robust performance across a wide range of protein concentrations and is resilient to common interfering substances and variations in sample processing. It's worth noting that while our method shows excellent agreement with the colorimetric assay, it offers several potential advantages. These include reduced sample volume requirements, simplified sample preparation, and rapid analysis time. These factors could make our method particularly suitable for point-of-care testing or resource-limited settings where traditional laboratory infrastructure may be unavailable.

Significance

The novel method for protein quantification in urine based on the morphology of a dried droplet uses only one drop of urine specimen. Combined with machine learning models, by identifying protein content in urine droplet drying patterns without the need for staining or antibody binding, may provide a more convenient alternative to current techniques for the assessment of proteinuria. Simple, low-cost, and fast, the system can be used as a powerful tool for CKD surveillance at the point of care.