Nominated Principal Applicant
Qian (Vivian) Liu, McGill University
Principal Applicants
Keng Chou, University of British Columbia
Chen Liang, Lady Davis Institute for Medical Research, Jewish General Hospital/McGill University
Objectives
The proposed project set out to unveil the entry and transmission mechanisms of SARS-CoV-2 and its variants by probing the nanoscale organization and dynamics of the spike protein by using single-molecule imaging technology.
Impact
A key impact of this project was the development of innovative methods for analyzing three-dimensional single-molecule localization microscopy (3D SMLM) data. The researchers introduced two novel analysis methods: the Human-Vision-Inspired cluster identification method and the ordering points to identify the clustering structure (OPTICS) method. These address the limitations of existing approaches, which rely on correlation, density, or tessellation-based techniques that are not applicable for 3D SMLM data cloud and require prior user experience for parameter adjustments.
The team’s new methods overcame these challenges, providing robust platforms for 3D SMLM data analysis. This enables the broader research community to apply SMLM tools more effectively to investigate a wide range of biological structures, for example, the replication organelles formed by positive RNA viruses such as SARS-CoV-2.
Budget
CoVaRR-Net is funding this research, which was first proposed to the Canadian Institutes of Health Research’s (CIHR) Emerging COVID-19 Research Gaps and Priorities – Variants funding call, with a $203,000 cash contribution.