Multiplexed MicroRNA biomarker detection by bridging lifetime filtering imaging and dynamic chemical labeling

Abstract

MicroRNAs (miRs) have emerged as promising biomarkers for early disease diagnosis and personalized treatment monitoring. However, their clinical utility has been hampered by technical limitations. Dynamic chemical labeling (DCL) based on capturing abasic PNA probes and reactive nucleobases, known as SMART bases, is a PCR-free approach that has proven very useful for the direct interrogation of circulating miRs. In this work, we expand the palette of tools available for the detection of DCL miR by synthesizing a new SMART nucleobase called SMART-C-Eu. This nucleobase contains a stable lanthanide cryptate. Using this SMART-C-Eu base and time-gated (TG) luminescence imaging, we successfully detect and quantify miR-122–5p in human serum samples. miR-122–5p is a well-known biomarker for drug-induced liver injury. Through a bead-counting analysis approach, statistical robustness is improved and miR-122–5p concentrations are detected in the nanomolar range. Furthermore, we extend this approach to multiplexed detection of three different miRs (miR-371a-3p, miR-451a-5p, and miR-122–5p) using spectral and temporal filtering. Importantly, we designed a user-independent multiplexed analysis using machine learning algorithms for automatic bead classification. Although the sensitivity of this technique must be further improved to detect miRs at lower concentrations, the method represents a significant advancement in miR analysis by combining ML segmentation using lifetime and intensity images. In addition, the technique offers multiplexing capabilities and the potential for automation, paving the way for more accurate and robust clinical applications in the future.