A Primer on the Bayesian Approach to High-Density Single-Molecule Trajectories Analysis

Beheiry, Mohamed El; Türkcan, Silvan; Richly, Maximilian U.; Triller, Antoine; Alexandrou, Antigoni; Dahan, Maxime; Masson, Jean-Baptiste

doi:10.1016/j.bpj.2016.01.018

Cited by 31 publications

(34 citation statements)

References 33 publications

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“…One can also imagine combining our approach to localization uncertainty with other types of heterogeneity in the data. Interesting examples include variability in the underlying diffusion constant or other model parameters [16], or the presence complex spatial structure [28].…”

Section: Discussionmentioning

confidence: 99%

Variational algorithms for analyzing noisy multi-state diffusion trajectories

Lindén

Elf

2018

Preprint

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Single particle tracking offers a non-invasive high-resolution probe of biomolecular reactions inside living cells. However, efficient data analysis methods that correctly account for various noise soures are needed to realize the full quantitative potential of the method. We report new algorithms for hidden Markov-based analysis of single particle tracking data, which incorporate most sources of experimental noise, including heterogeneuous localization errors and missing positions. Compared to previous implementations, the algorithms offer significant speed-ups, support for a wider range of inference methods, and a simple user interface. This will enable more advanced and exploratory quantitative analysis of single particle tracking data.

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Section: Discussionmentioning

confidence: 99%

Variational algorithms for analyzing noisy multi-state diffusion trajectories

Lindén

Elf

2018

Preprint

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“…As full trajectories were not required for VLP mapping, image to image graph matching was used to generate the most probable protein displacements (38). The live PALM data were then analysed using Bayesian inference and the modified Langevin equation to quantify the motion of individual Gag molecules (23,24,39). Using the Langevin description of the motion, the key dynamical properties were approximate as diffusion and effective energy maps, providing us with a more general understanding of the modifications of protein dynamics at the vicinity of assembling platforms.…”

Section: R a F Tmentioning

confidence: 99%

“…Then, based on the temporal changes observed in localization density maps, we showed that Gag VLP assembly in T cells requires between 5 and 7 minutes and 15min total to complete budding. Finally, by combining live PALM Bayesian inference analysis of single protein dynamic interaction maps with a diffusion and effective energy trapping model (23,24), we quantified Gag trapping energy during assembly. Moreover, we analysed the temporal correlation between changes in the density and the trapping energy, ie Gag interaction, during VLP assembly and brought evidence that the cis-packageable viral genome that encodes Gag(i)mEOS2 spatio-temporally most probably coordinates VLP assembly at the cell surface of CD4 T lymphocytes.…”

Section: Introductionmentioning

confidence: 99%

Live single molecule microscopy of HIV-1 assembly in host T cells reveals a spatio-temporal effect of the viral genome

Floderer

Masson

Boiley

et al. 2018

Preprint

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Monitoring virus assembly dynamic at the nanoscale level in host cells remains a major challenge. Human Immunodeficiency Virus type 1 (HIV-1) components are addressed to the plasma membrane where they assemble to form spherical particles of 100nm in diameter. HIV-1 Gag protein expression alone is sufficient to produce virus-like particles (VLPs) that resemble immature virus. Here, we monitored Gag assembly in host CD4 T lymphocytes using single molecule dynamics microscopy and energy mapping. A workflow allowing long time recordings of single Gag molecule localization, diffusion and effective energy maps was developed for robust quantitative analysis of HIV assembly and budding. Comparison of numerous cell plasma membrane assembling platforms in cells expressing wild type or assembly-defective Gag proteins showed that VLP formation last 15 minutes, with an assembly time of 5 minutes, and that the nucleocapsid domain is mandatory. Importantly, it reveals that the viral genome coordinates spatio-temporally HIV-1 assembly.

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“…MLE has been widely used in single molecule tracking (Mortensen et al, 2010;El Beheiry et al, 2016;Yu, 2016), microrheology (Mellnik et al, 2016), electron microscopy (Van Aert et al, 2005) and drift estimation (Kleinhans & Friedrich, 2007). If the tracks of fiducial markers contain only Gaussian noise, averaging multiple tracks is sufficient for drift estimation.…”

Section: Drift Estimation Using a Generalized Maximum Likelihood Algomentioning

confidence: 99%

Estimation of microscope drift using fluorescent nanodiamonds as fiducial markers

Colomb

Czerski

et al. 2017

Journal of Microscopy

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Fiducial markers are used to correct the microscope drift and should be photostable, be usable at multiple wavelengths and be compatible for multimodal imaging. Fiducial markers such as beads, gold nanoparticles, microfabricated patterns and organic fluorophores lack one or more of these criteria. Moreover, the localization accuracy and drift correction can be degraded by other fluorophores, instrument noise and artefacts due to image processing and tracking algorithms. Estimating mechanical drift by assuming Gaussian distributed noise is not suitable under these circumstances. Here we present a method that uses fluorescent nanodiamonds as fiducial markers and uses an improved maximum likelihood algorithm to estimate the drift with both accuracy and precision within the range 1.55-5.75 nm.

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A Primer on the Bayesian Approach to High-Density Single-Molecule Trajectories Analysis

Cited by 31 publications

References 33 publications

Variational algorithms for analyzing noisy multi-state diffusion trajectories

Variational algorithms for analyzing noisy multi-state diffusion trajectories

Live single molecule microscopy of HIV-1 assembly in host T cells reveals a spatio-temporal effect of the viral genome

Estimation of microscope drift using fluorescent nanodiamonds as fiducial markers

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