Rapid and non-invasive renal injury diagnosis unlocked by a glimpse into urinary protein particle size and charge

Urinary protein, an important marker for early detection of kidney injury, would change in type and content dynamically with the degree of kidney injury due to the particle size and charge selectivity of the glomerular filtration system, making it significantly valuable for accurate classification and early diagnosis. In this study, we developed a fluorescence sensor (Ami-AuNP/DNAs) based on charge interaction to rapidly identify the progression of kidney injury. When the positively charged Ami-AuNP combines with negatively charged DNAs, fluorescence quenching occurs, and urine proteins that appear compete with the DNAs, leading to fluorescence recovery. Based on these signal changes, PCA and PSO-BP neural network analysis were used to successfully identified kidney injury progression in 197 animal kidney injury and 62 clinical chronic kidney disease urine samples through a simple urine sample drop. Additionally, the sensor could also evaluate the effect of Huangkui capsule on kidney injury in adriamycin nephropathy model mice. Accordingly, this method transforms complex biological signals in vivo into macroscopic visual optical signals, amplifying differences of urinary protein, making up for the deficiency of the traditional method in hysteresis and low accuracy, and promoting urinary protein as the potential noninvasive biomarker for evaluating kidney injury.