Dataset from article 'A New Horizon in Multiplexed micro-RNA Biomarker Detection: Bridging Lifetime Filtering Imaging and Dynamic Chemistry Labeling'
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AuthorPadial Jaudenes, Maria; Tabraue-Chávez, Mavys; Detassis, Simone; Ruedas Rama, María José; González García, María del Carmen; Fara, Mario Antonio; López Delgado, Francisco Javier; González Vera, Juan Antonio; Guardia Monteagudo, Juan José; Díaz Mochón, Juan José; García Fernández, Emilio; Pernagallo, Salvatore; Orte Gutiérrez, Ángel
MicroRNAmiRsBiomarkersDynamic chemistry labelingLuminescence imagingMachine learning algorithms
SponsorshipEuropean Union through the H2020 diaRNAgnosis project, under grant agreement No. 101007934; Agencia Estatal de Investigación (Spain) through grant PID2020-114256RB-I00 AEI/10.13039/501100011033; Agencia Estatal de Investigación (Spain) through grant CTQ2017-85658-R AEI/10.13039/501100011033/FEDER “Una manera de hacer Europa”.
MicroRNAs (miRs) have emerged as promising biomarkers for early disease diagnosis and personalised treatment monitoring. However, but their clinical utility has been hampered by technical limitations. Dynamic chemical labelling (DCL) based on capturing abasic PNA probes and reactive nucleobases, so-called 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 available tools for DCL miR detection methods with a newly synthesised SMART nucleobase, 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. A bead-counting analysis approach improved statistical robustness and allowed the detection of miR-122-5p concentrations 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. Consistent results were obtained using machine learning algorithms for automatic bead classification. Although this technique requires further sensitivity improvements to detect miRs at lower concentrations, it represents a significant advancement in miR analysis. It offers multiplexing capabilities and the potential for automation, paving the way for more accurate and robust clinical applications in the future.