An Adaptive Real-Time 3D Single Particle Tracking Method for Monitoring Viral First Contacts

Abstract

Here, an adaptive real-time 3D single particle tracking method is proposed, which is capable of capturing heterogeneous dynamics. Using a real-time measurement of a rapidly diffusing particle's positional variance, the 3D precision adaptive real-time tracking (3D-PART) microscope adjusts active-feedback parameters to trade tracking speed for precision on demand. This technique is demonstrated first on immobilized fluorescent nanoparticles, with a greater than twofold increase in the lateral localization precision (≈25 to ≈11 nm at 1 ms sampling) as well as a smaller increase in the axial localization precision (≈ 68 to ≈45 nm). 3D-PART also shows a marked increase in the precision when tracking freely diffusing particles, with lateral precision increasing from ≈100 to ≈70 nm for particles diffusing at 4 µm² s^-1 , although with a sacrifice in the axial precision (≈250 to ≈350 nm). This adaptive microscope is then applied to monitoring the viral first contacts of virus-like particles to the surface of live cells, allowing direct and continuous measurement of the viral particle at initial contact with the cell surface.

Keywords: 3D microscopy; adaptive methods; real-time single particle tracking; virus tracking.

Figure 1.

Schematic of PART. At high diffusive speeds, the variance of the particle’s position within the tracking volume is large. At these high diffusive speeds, the scan size is made large to ensure tracking robustness (top panel). At slow diffusive speeds, the particle’s positional variance is small. The scan size is then decreased in size to effectively increase the tracking precision (bottom panel).

Figure 2.

Tracking precision comparison. The 3D-PART mechanism was switched on at t=5 sec. (a-c) Tracking precision as a function of time for X, Y and Z respectively. The particle position is measured using the readouts from the piezoelectric stage.

Figure 3.

(a) Collected emission intensity from a fixed fluorescent nanoparticle using 3D-DyPLoT (t < 5 sec) and 3D-PART (t > 5 sec). (b) Lateral (XY) tracking precision comparison between 3D-DyPLoT (red circles) and 3D-PART (green circles) at varying count rates. (c) Axial (Z) tracking precision comparison between 3D-DyPLoT (red circles) and 3D-PART (green circles) at varying count rates. In both cases, the axial scan range (~ 2 μm) is the same. The improvement in the lateral precision increases due to the (1) the finer sampling in the XY plane and (2) the increased photon count rate. Since the axial sampling is not changed, the axial precision is only increased indirectly by the increase in photon count rates. Bin size: (a) 1 ms; (b, c) 100 ms. Background level for all experiments: ~ 200 Hz.

Figure 4.

Tracking Brownian motion of 110 nm beads in water with precision modulation switching on at time=7.4 s. (a) 3D moving trajectory of beads. (b) Fluorescence intensity as a function of time. At the time indicated by the arrow the tracking was switched from 3D-DyPLoT to 3D-PART. (c) MSD of the trajectory in (a). The blue line is the measured MSD while the dotted red line is best fit line from linear regression. (d-f) x, y and z real-time tracking precision as a function of time. Bin size: 1 ms.

Figure 5.

3D-PART enables tracking of heterogeneous dynamics. (a) 3D trajectory of 110 nm green fluorescent bead freely diffusing and intermittently binding to the coverslip. The inset is a zoom in on the part of the trajectory where the particle is bound to the coverslip. The precision of the XYZ stage readouts are labeled as σ_x,σ_y, σ_z, respectively, showing good agreement with the fixed particle data shown in Figure 2. (b) Intensity trace of the particle shown in (a). The spike in intensity (from ~100 kHz to ~200 kHz, indicated by arrow) results from the shrunken scan area dictated by 3D-PART. (c-e) The X, Y, and Z position of particle as a function of time as measured by the piezoelectric stage position. The σ_x,σ_y, σ_z on top of the plots are the standard deviations for the X, Y, and Z stage readout while the particle is bound. (f-h) Deviation of particle from center of tracking volume during diffusion and landing events as measured by the real-time position estimation algorithm. A drastic reduction in the variance of the position estimates can be seen coinciding with the binding of the particle to the coverslip and the spike in intensity (indicated by arrow).” The values σ_xk,σ_yk, and σ_zk demonstrate the change in the particle position uncertainty as the particle transitions from free diffusion to bound on the coverslip. Bin size: (b-h) 1 ms.

Figure 6.

PART measurement of a single virus landing on the membrane of a live HuH7 cell. (a) 3D trajectory of single VSV-G-YFP virus-like particle. (inset) Bright field image of HuH7 cell overlaid with top-down view of the viral trajectory in the XY plane. Scale bar: 10 μm. (b-d) The x, y and z position of particle as a function of time, color-coded for the different diffusive states extracted by the changepoint algorithm. The VLP starts in a freely diffusive state (green) with a diffusion coefficient of about 1.6 μm²/s, transitioning to a bound state (dark blue) with a diffusion coefficient of 0.58 μm²/s. The arrow indicates an increase in XY motion just prior to a jump to a higher diffusive state (yellow), presumably the VLP unbinding from the cell surface (See Figure S13).

Figure 7.

3D-PART measurement of a single virus landing on the membrane of a HuH7 cell. The transparent ellipse is a guide to the eye and is generated by fitting an ellipse to the 2D optical image of the cell and extending in Z to the virus-like particle binding event. The different colors represent elapsed time, as indicated by the colorbar, with the binding event occurring at the end of the trajectory (red). The virus diffuses in 3D with a curved excluded volume, indicating close approach to the cell surface before the actual binding event.