Cancer is one of the leading causes of death worldwide, with around 19.3 million new cases reported every year. The rate of cancer survival increases when identified at early stages due to treatment being more effective. However, earlier stages of cancer are not usually characterized by the onset of physical symptoms, which accounts for some of the difficulty in catching the disease early. There is a medical need for highly sensitive and rapid detection of biomarkers for early cancer detection. Nucleic acids are a type of cancer-related biomarker found in low concentrations in cancer patients but have significantly different levels in healthy patients. Various miRNAs have been studied and their up- or downregulation in blood, plasma, serum or tissue samples have been linked to different types of cancer. The most notable challenge in cancer biomarker detection is the low-concentrations of these alterations in liquid biopsies. Single-molecule methodologies are rarely used in cancer screening, despite their capacity for absolute detection. This limitation arises because the inherent complexity of biological samples often generates high aberrant signals, necessitating extensive sample preparation. In this study, we apply single-molecule Förster resonance energy transfer (smFRET) and fluorescence cross-correlation spectroscopy (FCCS) to complex liquid matrices (e.g., blood serum) for biomarker detection. Our approach minimizes sample preparation while improving signal-to-noise performance. The present focuses on optimizing a smFRET and FCCS assay using fluorescently labelled oligonucleotides. This has enabled determination of conditions for limits of detection, oligonucleotide annealing, and sample preparation to perform measurements within blood-serum. Once the optimized smFRET and FCCS assay parameters have been determined using fluorescently labelled control oligonucleotides, they will be applied to miRNA detection. This will validate the assays nucleic acid detection feasibility before being applied to patient samples for heterogeneity and direct biomarker detection.
Alexandra Moreno (Sun,) studied this question.