Image Restoration and Analysis of Influenza Virions Binding to Membrane Receptors Reveal Adhesion-Strengthening Kinetics

Abstract

With the development of single-particle tracking (SPT) microscopy and host membrane mimics called supported lipid bilayers (SLBs), stochastic virus-membrane binding interactions can be studied in depth while maintaining control over host receptor type and concentration. However, several experimental design challenges and quantitative image analysis limitations prevent the widespread use of this approach. One main challenge of SPT studies is the low signal-to-noise ratio of SPT videos, which is sometimes inevitable due to small particle sizes, low quantum yield of fluorescent dyes, and photobleaching. These situations could render current particle tracking software to yield biased binding kinetic data caused by intermittent tracking error. Hence, we developed an effective image restoration algorithm for SPT applications called STAWASP that reveals particles with a signal-to-noise ratio of 2.2 while preserving particle features. We tested our improvements to the SPT binding assay experiment and imaging procedures by monitoring X31 influenza virus binding to α2,3 sialic acid glycolipids. Our interests lie in how slight changes to the peripheral oligosaccharide structures can affect the binding rate and residence times of viruses. We were able to detect viruses binding weakly to a glycolipid called GM3, which was undetected via assays such as surface plasmon resonance. The binding rate was around 28 folds higher when the virus bound to a different glycolipid called GD1a, which has a sialic acid group extending further away from the bilayer surface than GM3. The improved imaging allowed us to obtain binding residence time distributions that reflect an adhesion-strengthening mechanism via multivalent bonds. We empirically fitted these distributions using a time-dependent unbinding rate parameter, koff, which diverges from standard treatment of koff as a constant. We further explain how to convert these models to fit ensemble-averaged binding data obtained by assays such as surface plasmon resonance.

Parvovirus Capsid Structures Required for Infection: Mutations Controlling Receptor Recognition and Protease Cleavages

“…Characterizing the dissociation curves can also be useful for determining if adhesion-strengthening mechanisms are present, as demonstrated by related work on tracking contact times between individual parvoviruses and TfRs (43). Because the dissociation curves here did not fit the basic 1:1 binding model, we used the adhesion-strengthening model (43,57). This model assumes that the binding force can change with contact time, perhaps due to bindingtriggered conformational changes of the capsid (43).…”

Single Virion Tracking Microscopy for the Study of Virus Entry Processes in Live Cells and Biomimetic Platforms

“…It should be noted that averaged data from many single virion tracking binding/ unbinding events should match the ensemble results generated with SPR or QCM-D. However, direct imaging with single virion tracking allows collection of a richer set of data for on and off rates simultaneously because each particle trajectory is captured [37]. Furthermore, by having the signature of each individual virion's binding behavior, heterogeneities in the virus population or membrane surface can be identified, which can then be compared to infection trends to understand how population dispersity impacts infection [96].…”

“…In single virion imaging, noise is always a primary concern, regardless of the microscope configuration. To combat this, several particle detection and image restoration techniques have been developed specifically for single virion tracking [36,37]. Coordinates of the particles are obtained by scanning filtered images for areas of fluorescence intensity that exceed a certain threshold or fit a particular intensity profile [38,39].…”

“…The diffusion type can indicate the type of interactions the virion is having with the surface of the cell or extracellular environment. Tracking frame-to-frame is important in measuring binding residence times as well, and by extension, binding strength characteristics [37]. Once bound, the progression of membrane fusion can be tracked from frame-to-frame using strategies like fluorescence dequenching, where the evolution of the fluorescence signal reports on the merging of membranes, the rate of membrane mixing, and the release of viral genome.…”

“…Such studies can reveal changes in binding behavior that promote viral attachment. For example, both influenza and canine parvovirus undergo "adhesion-strengthening" where the longer a virion is bound, the more strongly it adheres to the bilayer [37,85]. Overall, these platforms are convenient for gathering insight on this critical virus entry step and how it depends on the host cell surface.…”