Virus trafficking – learning from single-virus tracking

Brandenburg, Boerries; Zhuang, Xiaowei

doi:10.1038/nrmicro1615

Cited by 377 publications

(362 citation statements)

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“…However, dynamics in the physical and biological worlds are often heterogeneous. When the statistical character of the process changes intermittently with time due to stochastic switching between coexisting and often competing microscopic processes, [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16] averaging over these distinct processes may give misleading results.…”

Section: -18mentioning

confidence: 99%

“…[1][2][3][4][5][6] When datasets of this kind are analyzed, the capacity to track individual objects over a long time allows not only quantification of individual variations within populations but also complex temporal fluctuations of individual moving elements. The valuable information offered by huge datasets goes beyond what can be obtained from the classic ensemble-averaged approach, and has often provided unexpected mechanistic insights.…”

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Diagnosing Heterogeneous Dynamics in Single-Molecule/Particle Trajectories with Multiscale Wavelets

et al. 2013

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We describe a simple automated method to extract and quantify transient heterogeneous dynamical changes from large datasets generated in single molecule/particle tracking experiments. Based on wavelet transform, the method transforms raw data to locally match dynamics of interest. This is accomplished using statistically adaptive universal thresholding, whose advantage is to avoid a single arbitrary threshold that might conceal individual variability across populations. How to implement this multiscale method is described, focusing on local confined diffusion separated by transient transport periods or hopping events, with 3 specific examples: in cell biology, biotechnology, and glassy colloid dynamics. This computationallyefficient method can run routinely on hundreds of millions of data points analyzed within an hour on a desktop personal computer.Key words: single molecule imaging, dynamic heterogeneity, wavelet, active transport, electrophoresis, colloid glass 3The experimental study of dynamics has been deeply transformed during the past generation by new technologies that acquire digital images in vast quantities, allowing one to record motion of objects of interest, one-by-one in real space and time. [1][2][3][4][5][6] When datasets of this kind are analyzed, the capacity to track individual objects over a long time allows not only quantification of individual variations within populations but also complex temporal fluctuations of individual moving elements. The valuable information offered by huge datasets goes beyond what can be obtained from the classic ensemble-averaged approach, and has often provided unexpected mechanistic insights. This approach of "deep" statistical imaging has already led to significant progress in a variety of fields, from physical sciences such as diffusion 5-10 and other dynamics in condensed matter, 4,11,12 to biological sciences such as ecology 13 and cell biology. 14-16 Much important work revolves around improving experimental techniques to collect the data. 17-18Here we ask a different question: how to analyze such data for embedded information? For many problems, but not all, it is reasonable to assume random fluctuations with some probabilistic distribution around an average value. However, dynamics in the physical and biological worlds are often heterogeneous. When the statistical character of the process changes intermittently with time due to stochastic switching between coexisting and often competing microscopic processes, 1-16 averaging over these distinct processes may give misleading results.Progress is impeded by the paucity of methods to identify these distinct processes, and to quantify them, especially in the presence of noise in the data. An ideal method would be automated to handle large datasets, involve no judgment on the part of the analyst, and resolve rapid dynamic changes. Also problematical is to select a criterion by which to distinguish random fluctuations from real changes in dynamics; there is no general way to avoid judgment in selecting...

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Diagnosing Heterogeneous Dynamics in Single-Molecule/Particle Trajectories with Multiscale Wavelets

et al. 2013

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“…6), but detection of virus-endosome fusion (here defined as the release of viral content into the cytoplasm) is technically challenging (7,8). To understand the relationship between the pH trigger and virus-endosome fusion, it is essential to visualize these events in real time.…”

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Quantitative imaging of endosome acidification and single retrovirus fusion with distinct pools of early endosomes

Padilla‐Parra

Matos

Kondo

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

104

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Diverse enveloped viruses enter host cells through endocytosis and fuse with endosomal membranes upon encountering acidic pH. Currently, the pH dynamics in virus-carrying endosomes and the relationship between acidification and viral fusion are poorly characterized. Here, we examined the entry of avian retrovirus that requires two sequential stimuli-binding to a cognate receptor and low pH-to undergo fusion. A genetically encoded sensor incorporated into the viral membrane was used to measure the pH in viruscarrying endosomes. Acid-induced virus fusion was visualized as the release of a fluorescent viral content marker into the cytosol. The pH values in early acidic endosomes transporting the virus ranged from 5.6 to 6.5 but were relatively stable over time for a given vesicle. Analysis of viral motility and luminal pH showed that cells expressing the transmembrane isoform of the receptor (TVA950) preferentially sorted the virus into slowly trafficking, less acidic endosomes. In contrast, viruses internalized by cells expressing the GPI-anchored isoform (TVA800) were uniformly distributed between stationary and mobile compartments. We found that the lag times between acidification and fusion were significantly shorter and fusion pores were larger in dynamic endosomes than in more stationary compartments. Despite the same average pH within mobile compartments of cells expressing alternative receptor isoforms, TVA950 supported faster fusion than TVA800 receptor. Collectively, our results suggest that fusion steps downstream of the low-pH trigger are modulated by properties of intracellular compartments harboring the virus.confocal imaging | nano-pH-meter | single virus tracking | FRET M any enveloped viruses use endocytosis and vesicular trafficking to enter host cells, where acidification of an endosomal lumen serves as a trigger for viral fusion (1, 2). Viral fusion proteins are fine-tuned to respond to different pH values. Those that are activated at less acidic pH (∼6.0) are thought to mediate fusion with early endosomes, whereas those with a lower pH threshold (∼5.0) appear to direct the virus entry from late endosomes (1, 3). However, recent evidence implies that factors other than low pH, such as specific endosome-resident lipids, can determine the intracellular compartments from which the viral capsid is released into the cytosol (2, 4, 5). Progress in understanding the complex regulation of virus-endosome fusion has been hindered by poor accessibility of intracellular compartments and lack of direct techniques for monitoring this process in situ.Single-virus imaging is a powerful tool for gaining critical insights into virus entry (reviewed in ref. 6), but detection of virus-endosome fusion (here defined as the release of viral content into the cytoplasm) is technically challenging (7,8). To understand the relationship between the pH trigger and virus-endosome fusion, it is essential to visualize these events in real time. A ratiometric method developed by the Zhuang group (9, 10) permits monitoring endos...

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“…Ainsi, en fusionnant certaines protéines virales à des protéines fluorescentes, l'attachement, la pénétration et le transport de virus dans leurs cellules hôtes ont pu être caractérisés au cours des dernières années [1]. Cependant, la biogenèse de nouveaux virus, une étape clé de la réplication virale, n'avait pas encore été visualisée jusqu'à ce jour.…”

Section: Nolwenn Jouvenetunclassified

La biogenèse du VIH-1 dévoilée

Jouvenet

2008

Med Sci (Paris)

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Virus trafficking – learning from single-virus tracking

Cited by 377 publications

References 105 publications

Diagnosing Heterogeneous Dynamics in Single-Molecule/Particle Trajectories with Multiscale Wavelets

Diagnosing Heterogeneous Dynamics in Single-Molecule/Particle Trajectories with Multiscale Wavelets

Quantitative imaging of endosome acidification and single retrovirus fusion with distinct pools of early endosomes

La biogenèse du VIH-1 dévoilée

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