Marko Lampe

Dynamics of HIV-1 assembly and release

Assembly and release of human immunodeficiency virus (HIV) occur at the plasma membrane of infected cells and are driven by the Gag polyprotein. Previous studies analyzed viral morphogenesis using biochemical methods and static images, while dynamic and kinetic information has been lacking until very recently. Using a combination of wide-field and total internal reflection fluorescence microscopy, we have investigated the assembly and release of fluorescently labeled HIV-1 at the plasma membrane of living cells with high time resolution. Gag assembled into discrete clusters corresponding to single virions. Formation of multiple particles from the same site was rarely observed. Using a photoconvertible fluorescent protein fused to Gag, we determined that assembly was nucleated preferentially by Gag molecules that had recently attached to the plasma membrane or arrived directly from the cytosol. Both membrane-bound and cytosol derived Gag polyproteins contributed to the growing bud. After their initial appearance, assembly sites accumulated at the plasma membrane of individual cells over 1–2 hours. Assembly kinetics were rapid: the number of Gag molecules at a budding site increased, following a saturating exponential with a rate constant of ∼5×10−3 s−1, corresponding to 8–9 min for 90% completion of assembly for a single virion. Release of extracellular particles was observed at ∼1,500±700 s after the onset of assembly. The ability of the virus to recruit components of the cellular ESCRT machinery or to undergo proteolytic maturation, or the absence of Vpu did not significantly alter the assembly kinetics.

A Feedback Loop between Dynamin and Actin Recruitment during Clathrin-Mediated Endocytosis

A live-cell imaging study reveals that a positive feedback loop between dynamin and actin contributes to efficient endocytic membrane scission.

Deterministic and probabilistic approaches for tracking virus particles in time-lapse fluorescence microscopy image sequences

Modern developments in time-lapse fluorescence microscopy enable the observation of a variety of processes exhibited by viruses. The dynamic nature of these processes requires the tracking of viruses over time to explore spatial-temporal relationships. In this work, we developed deterministic and probabilistic approaches for multiple virus tracking in multi-channel fluorescence microscopy images. The deterministic approaches follow a traditional two-step paradigm comprising particle localization based on either the spot-enhancing filter or 2D Gaussian fitting, as well as motion correspondence based on a global nearest neighbor scheme. Our probabilistic approaches are based on particle filters. We describe approaches based on a mixture of particle filters and based on independent particle filters. For the latter, we have developed a penalization strategy that prevents the problem of filter coalescence (merging) in cases where objects lie in close proximity. A quantitative comparison based on synthetic image sequences is carried out to evaluate the performance of our approaches. In total, eight different tracking approaches have been evaluated. We have also applied these approaches to real microscopy images of HIV-1 particles and have compared the tracking results with ground truth obtained from manual tracking. It turns out that the probabilistic approaches based on independent particle filters are superior to the deterministic schemes as well as to the approaches based on a mixture of particle filters.

Visualizing fusion of pseudotyped HIV-1 particles in real time by live cell microscopy.

BackgroundMost retroviruses enter their host cells by fusing the viral envelope with the plasma membrane. Although the protein machinery promoting fusion has been characterized extensively, the dynamics of the process are largely unknown.ResultsWe generated human immunodeficiency virus-1 (HIV-1) particles pseudotyped with the envelope (Env) protein of ecotropic murine leukemia virus eMLV to study retrovirus entry at the plasma membrane using live-cell microscopy. This Env protein mediates highly efficient pH independent fusion at the cell surface and can be functionally tagged with a fluorescent protein. To detect fusion events, double labeled particles carrying one fluorophor in Env and the other in the matrix (MA) domain of Gag were generated and characterized. Fusion events were defined as loss of Env signal after virus-cell contact. Single particle tracking of >20,000 individual traces in two color channels recorded 28 events of color separation, where particles lost the Env protein, with the MA layer remaining stable at least for a short period. Fourty-five events were detected where both colors were lost simultaneously. Importantly, the first type of event was never observed when particles were pseudotyped with a non-fusogenic Env.ConclusionThese results reveal rapid retroviral fusion at the plasma membrane and permit studies of the immediate post-fusion events.

Dual single-scission event analysis of constitutive transferrin receptor (TfR) endocytosis and ligand-triggered β2-adrenergic receptor (β2AR) or Mu-opioid receptor (MOR) endocytosis

The endocytosis of the transferrin receptor (TfR) and β2-adrenergic receptor or Mu-opioid receptor by individual endocytic vesicles was imaged in live cells. Ligand-triggered endocytosis of GPCR has little effect on TfR endocytosis dynamics, and there is little evidence for coated-pit specialization.

Tracking multiple particles in fluorescence microscopy images via probabilistic data association

Tracking subcellular structures displayed as ‘particles’ in fluorescence microscopy images yields quantitative descriptions of the underlying dynamical processes. We have developed an approach for tracking multiple fluorescent particles. Our approach includes a localization scheme using probabilistic data association that combines a top-down strategy driven by the Kalman filter and a bottom-up strategy using standard localization algorithms for fluorescent particles. The combined scheme yields multiple positions that are incorporated to the filter via a combined innovation. To track objects in close proximity, we introduce a support map that adjusts the association probabilities. By using the combined localization scheme in conjunction with the Kalman filter we integrate localization and position estimation. The approach has been successfully applied to synthetic images as well as to real microscopy image sequences and the performance has been quantified.

Identifying Virus-Cell Fusion in Two-Channel Fluorescence Microscopy Image Sequences Based on a Layered Probabilistic Approach

The entry process of virus particles into cells is decisive for infection. In this work, we investigate fusion of virus particles with the cell membrane via time-lapse fluorescence microscopy. To automatically identify fusion for single particles based on their intensity over time, we have developed a layered probabilistic approach. The approach decomposes the action of a single particle into three abstractions: the intensity over time, the underlying temporal intensity model, as well as a high level behavior. Each abstraction corresponds to a layer and these layers are represented via stochastic hybrid systems and hidden Markov models. We use a maxbelief strategy to efficiently combine both representations. To compute estimates for the abstractions we use a hybrid particle filter and the Viterbi algorithm. Based on synthetic image sequences, we characterize the performance of the approach as a function of the image noise. We also characterize the performance as a function of the tracking error. We have also successfully applied the approach to real image sequences displaying pseudotyped HIV-1 particles in contact with host cells and compared the experimental results with ground truth obtained by manual analysis.

Probabilistic tracking of virus particles in fluorescence microscopy images

Fluorescence time-lapse microscopy is a powerful technique for observing the spatial-temporal behavior of viruses. To quantitatively analyze the exhibited dynamical relationships, tracking of viruses over time is required. We have developed probabilistic approaches based on particle filters for tracking multiple virus particles in time-lapse fluorescence microscopy images. We employ a mixture of particle filters as well as independent particle filters. For the latter, we have developed a penalization strategy to maintain the identity of the tracked objects in cases where objects are in close proximity. We have also extended the approaches for tracking in multi-channel microscopy image sequences. The approaches have been evaluated based on synthetic images and the performance has been quantified. We have also successfully applied the approaches to real microscopy images of HIV-1 particles and have compared the tracking results with ground truth from manual tracking.

Identifying fusion events in fluorescence microscopy images

We are investigating the dynamical relationships exhibited by virus particles via fluorescence time-lapse microscopy. To obtain a quantitative description of each particle over time, these objects are tracked. To derive an explicit characterization of each particle as well as to identify interesting transient behaviors, the intensity over time of each particle needs to be analyzed. We have developed an approach based on hybrid stochastic systems for identifying behaviors of interest. We employ a hybrid particle filter for estimating the behavior of individual particles. The approach has been successfully applied to particles tracked in synthetic image sequences as well as in real image sequences displaying HIV-1 particles.

TRACKING OF VIRUS PARTICLES IN TIME-LAPSE FLUORESCENCE MICROSCOPY IMAGE SEQUENCES

Modern developments in time-lapse microscopy enable the observation of a variety of processes exhibited by viruses. The dynamic nature of these processes requires the tracking of viruses over time to explore the spatio-temporal relationships. In this work, we developed deterministic and probabilistic approaches for multiple virus tracking. A quantitative comparison based on synthetic image sequences was carried out to evaluate the performance of the different algorithms. We have also applied the algorithms to real microscopy images of HIV-1 particles and have compared the tracking results with ground truth obtained from manual tracking. It turns out that the probabilistic approach outperforms the deterministic schemes