Chan Cao’s primary research interests lie in single-molecule analysis and nanobiotechnology. Her research paves the way for leveraging this bio-inspired knowledge to address critical global challenges, including precision medicine, data storage, and the energy crisis.
She originally discovered the use of wild-type aerolysin nanopore for the discrimination of different lengths of oligonucleotides with sensitivity far exceeding other established nanopore sensors, thus advancing the development of next-generation sequencing technology. She also explored the molecular sensing mechanisms of aerolysin nanopore sensing and significantly improved the reading accuracy of nanopore sequencing, enabling single-nucleobase resolution by eliminating the influence of neighboring bases, which addressed the limitation of low accuracy in base calling in nanopore technology.
Chan further applied engineered aerolysin nanopores to real-world scenarios. In molecular data storage and decoding, engineered aerolysin nanopores can accurately read digital information encoded in tailored polymers without compromising information density, opening up possibilities to develop writing-reading technologies to process digital data using a biological-inspired platform. In biomarker detection, she explored the potential of aerolysin pore variants to detect post-translational modifications on proteins involved in known neurodegenerative diseases, which is expected to be used for early diagnosis of such diseases.
To tackle persistent issues in protein sequencing, such as low sensitivity and low abundance, Chan is developing methods and platforms for protein analysis based on biological nanopores and has made significant breakthroughs. Through novel nanopore engineering and condition optimization, Chan and her team successfully obtained smaller-sized nanopores, which can generate a strong electroosmotic flow that enables an efficient capture and translocation of nature proteins, resulting in distinct fingerprints from single-protein translocations. With further development, fingerprint-predictions could infer de novo protein sequence information from single–molecule data, offering a powerful tool for proteomics.
