Single-Particle Diffusion Characterization by Deep Learning

Granik, Naor; Weiss, Lucien E.; Nehme, Elias; Levin, Maayan; Chein, Michael; Perlson, Eran; Roichman, Yael; Shechtman, Yoav

doi:10.1016/j.bpj.2019.06.015

Cited by 163 publications

(152 citation statements)

References 43 publications

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“…Substantial improvements in particle tracking are on the horizon, such as improved tracking methods, diffusivity estimation, and microscope and camera hardware. A recent study claims to achieve accurate diffusivity estimates and Hurst exponents from short tracks using neural network-based regression ( Granik et al. , 2019 ), which could allow for less KRE averaging and finer spatial resolution with the same quantity of tracking data.…”

Section: Discussionmentioning

confidence: 99%

Spatial heterogeneity of the cytosol revealed by machine learning-based 3D particle tracking

McLaughlin

Langdon

Crutchley

et al. 2020

MBoC

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Section: Discussionmentioning

confidence: 99%

Spatial heterogeneity of the cytosol revealed by machine learning-based 3D particle tracking

McLaughlin

Langdon

Crutchley

et al. 2020

MBoC

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“…By varying the number of molecules, type of fluorophore attached to them, localization precision, and length of the acquisition sequence, the software is able to determine the conditions for faithful detection of the biological sample. The capacity of FluoSim to mimic molecule dynamics might also be used in the near future to train the next generation of CNNs for the analysis of live imaging experiments 43 .…”

Section: Discussionmentioning

confidence: 99%

FluoSim: simulator of single molecule dynamics for fluorescence live-cell and super-resolution imaging of membrane proteins

Lagardère

Chamma

Bouilhol

et al. 2020

Preprint

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Fluorescence live-cell and super-resolution microscopy methods have considerably advanced our understanding of the dynamics and mesoscale organization of macro-molecular complexes that drive cellular functions. However, different imaging techniques can provide quite disparate information about protein motion and organization, owing to their respective experimental ranges and limitations. To address these limitations, we present here a unified computer program that allows one to model and predict membrane protein dynamics at the ensemble and single molecule level, so as to reconcile imaging paradigms and quantitatively characterize protein behavior in complex cellular environments. FluoSim is an interactive realtime simulator of protein dynamics for live-cell imaging methods including SPT, FRAP, PAF, and FCS, and super-resolution imaging techniques such as PALM, dSTORM, and uPAINT. The software, thoroughly validated against experimental data on the canonical neurexinneuroligin adhesion complex, integrates diffusion coefficients, binding rates, and fluorophore photo-physics to calculate in real time the distribution of thousands of independent molecules in 2D cellular geometries, providing simulated data of protein dynamics and localization directly comparable to actual experiments.

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“…Substantial improvements in particle tracking are on the horizon, from improved tracking methods, diffusivity estimation, and microscope and camera hardware. A recent study claims to achieve accurate diffusivity estimates and Hurst exponents from short tracks using neural-network-based regression (23), which could allow for less KDE averaging and finer spatial resolution with the same quantity of tracking data. Using light sheet microscopy would reduce photobleaching and allow for longer videos.…”

Section: Discussionmentioning

confidence: 99%

3D neural network-based particle tracking reveals spatial heterogeneity of the cytosol

McLaughlin

Langdon

Crutchley

et al. 2019

Preprint

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The spatial structure and physical properties of the cytosol are not well understood. Measurements of the material state of the cytosol are challenging due to its spatial and temporal heterogeneity, the lack of truly passive probes, and the need for probes of many sizes to accurately describe the state across scales. Recent development of genetically encoded multimeric nanoparticles (GEMs) has opened up study of the cytosol at the length scales of multiprotein complexes (20-60 nm). Using these probes to spatially resolve diffusivity of a cytoplasmic volume within a cell requires accurate and automated 3D tracking methods. We developed an image analysis pipeline for whole-cell imaging of GEMs in the context of large, multinucleate fungi where there is evidence of functional compartmentalization of the cytosol for both the nuclear division cycle and branching. We apply a neural network to track particles in 3D, generate surface meshes to project data on representations of the cell, and create dynamic visualizations of local diffusivities. Using this pipeline, we have found that there is remarkable variability in the properties of the cytosol both within a single cell and between cells. By analyzing the spatial diffusivity patterns, we saw an enrichment of low diffusivity zones at hyphal tips and near some nuclei. These results show that the physical state of the cytosol varies spatially within a single cell and exhibits significant cell-to-cell variability. Thus, molecular crowding contributes to heterogeneity within individual cells and across populations.

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Single-Particle Diffusion Characterization by Deep Learning

Cited by 163 publications

References 43 publications

Spatial heterogeneity of the cytosol revealed by machine learning-based 3D particle tracking

Spatial heterogeneity of the cytosol revealed by machine learning-based 3D particle tracking

FluoSim: simulator of single molecule dynamics for fluorescence live-cell and super-resolution imaging of membrane proteins

3D neural network-based particle tracking reveals spatial heterogeneity of the cytosol

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