Single-Molecule Detection and Characterization of Catalytic DNA Using Droplet Microfluidics

Nucleic acids, traditionally recognized for their roles in storing genetic information, have more recently been identified for their catalytic capabilities, offering a readily synthesized and chemically stable alternative to proteins. The discovery of catalytic RNA inspired the identification of single-stranded catalytic DNA molecules, known as DNAzymes. Their integration in high-sensitivity detection assays would reduce technical and logistical requirements compared to protein-based methods, revolutionizing biosensing. However, biasness of selection methods towards single-turnover catalysts has hindered comprehensive characterization of the full catalytic potential of these oligonucleotides in therapeutics and biosensing.

Here, we use flow-focusing droplet microfluidic platform to generate nano- to picolitre scale droplets, creating isolated microenvironments to enable real-time study of single enzyme kinetics. By preventing cross-contamination, this offers high-throughput characterization of catalytic oligonucleotides using fluorescence imaging. The 10-23 DNAzyme, a gold standard molecule with high catalytic activity is used as a positive control. A fluorogenic substrate cleaved by 10-23 produces a fluorescence signal, detecting its catalytic activity over time. In each experiment, over 10,000 droplets are imaged using confocal microscope allowing long-term monitoring of product formation with 3D droplet visualization, yielding rigorous datasets, to assess their folding mechanisms and kinetics.

These results will provide unique insights into the catalytic potential of nucleic acids, broadening our understanding of early life evolution. Furthermore, the study of single-enzyme kinetics will offer a comprehensive explanation on how structural folding and enzymatic activity are affected by diverse microenvironments which will further lead to the advancement of catalytic oligonucleotides in biosensing by overcoming the challenges in traditional selection methods.