Learning to Count with Quantitative Single-Molecule Imaging

*To request access to the videoconference, email dsivak@sfu.ca

Synopsis

In recent years, a variety of single-molecule imaging methods have been developed that can resolve sub-diffraction limited, nanometer scale cellular features. Perhaps the most powerful among these are the family of techniques that are collectively referred to as single-molecule localization microscopy (SMLM).  In addition to generating super-resolved images, SMLM datasets contain the necessary information for counting single molecules. If interpreted correctly, these datasets would reveal quantitative properties of macromolecular complexes such as their abundance and stoichiometry or oligomerization state. And these properties could be inferred for proteins expressed at much higher densities, or for complexes within a much closer spatial proximity to one another, than is possible with alternative approaches.  For several years now, my lab has been actively developing statistical methods for extracting molecular counts from single-molecule images.  We have recently developed a classical learning algorithm that is able to accurately estimate the protomeric fractions of molecular complexes without the need for the many calibrations that often plague these techniques, making quantitative SMLM vastly more applicable within a variety of biological systems.