Fluorescent sensor array constructed by functionalized carbon nanodots for qualitative and quantitative analysis of urinary organic acids biomarkers

Adequate assessment of the level of organic acids (OA) in human urine is significant for non-invasive diagnosis and monitoring of OA-related diseases. However, the common lock-and-key sampling method for specific OA analysis is dramatically perturbed by the complicated chemical components in urine. A comprehensive qualitative and quantitative analysis of OAs in urine samples remains a significant challenge. Herein, a cross-reactive fluorescent sensor array based on amino acid-functionalized carbon nanodots (CDs) was designed for simultaneous qualitative and quantitative analysis of seven kinds of OAs, including homovanillic acid (HVA), vanillylmandelic acid (VMA), 3-hydroxyisovaleric acid (3-HA), lactic acid (LA), pyruvic acid (PA), glutaric acid (GA), and methylmalonic acid (MMA). The array system can specifically recognize multiple OAs in urine samples over a wide concentrations range (0.1-1000 mu M) with 100% accuracy. The wide concentration range and high accuracy of the CD array system for OA analysis depend on a distinct fluorescence response from the differential binding affinity of various OAs to the CDs. The excellent recognition ability and selectivity of the sensing system were further confirmed by the double-blind and interference experiments. The sensor array achieved satisfactory distinguishing effects of separate OAs and binary OA samples (HVA/VMA) in urine samples. In particular, the quantitative analysis of the one-OA system or two-OA system (HVA/VMA) can be realized by the array system. These results confirm that the sensor array based on the CDs is an efficient platform for OA analysis and a convenient tool for the non-invasive diagnosis of OA-related diseases.

Automated summary

The carbon nanodot sensor array can accurately identify and quantitatively analyze multiple organic acids in urine samples with high selectivity and sensitivity. It can achieve the quantitative analysis of single organic acid systems or dual organic acid systems, making it an effective method for OA analysis.