Minimizing biases associated with tracking analysis of submicron particles in heterogeneous biological fluids

Wang, Xiaocong; Nunn, Kenetta L.; Harit, Dimple; McKinley, Scott A.; Lai, Samuel K.

doi:10.1016/j.jconrel.2015.10.021

Cited by 19 publications

(21 citation statements)

References 43 publications

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“…Videos (512x512, 16-bit image depth) were captured with MetaMorph imaging software (Molecular Devices, Sunnyvale, CA) at a temporal resolution of 66.7 ms and spatial resolution of 10 nm (nominal pixel resolution 0.156 mm per pixel) for 20 s. Subpixel tracking resolution was obtained by determining the precise location of the particle centroid by light-intensity-weighted averaging of neighboring pixels. Trajectories were analyzed using "frame-by-frame" weighting [49] in which mean squared displacements (MSD) and effective diffusivities (Deff) are first calculated for individual particle traces. Averages and distributions are then calculated at each frame based on only the particles present in that frame before averaging across all frames in the movie.…”

Section: Methodsmentioning

confidence: 99%

Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D

Newby

Schaefer

Lee

et al. 2018

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

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168

120

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Particle tracking is a powerful biophysical tool that requires conversion of large video files into position time series, i.e. traces of the species of interest for data analysis. Current tracking methods, based on a limited set of input parameters to identify bright objects, are ill-equipped to handle the spectrum of spatiotemporal heterogeneity and poor signal-to-noise ratios typically presented by submicron species in complex biological environments. Extensive user involvement is frequently necessary to optimize and execute tracking methods, which is not only inefficient but introduces user bias. To develop a fully automated tracking method, we developed a convolutional neural network for particle localization from image data, comprised of over 6,000 parameters, and employed machine learning techniques to train the network on a diverse portfolio of video conditions. The neural network tracker provides unprecedented automation and accuracy, with exceptionally low false positive and false negative rates on both 2D and 3D simulated videos and 2D experimental videos of difficult-to-track species. Significance StatementThe increasing availability of powerful light microscopes capable of collecting terabytes of high-resolution 2D and 3D videos in a single day has created a great demand for automated image analysis tools. Tracking the movement of nanometer scale particles (e.g., virus, proteins, synthetic drug particles) is critical for understanding how pathogens breach mucosal barriers and for the design of new drug therapies. Our advancement is to use an artificial neural network that provides, first and foremost, substantially improved automation. Additionally, our method improves accuracy compared to current methods and reproducibility across users and labs.

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Section: Methodsmentioning

confidence: 99%

Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D

Newby

Schaefer

Lee

et al. 2018

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

Self Cite

168

120

View full text Add to dashboard Cite

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“…Subpixel tracking resolution was obtained by determining the precise location of the particle centroid by light-intensity-weighted averaging of neighboring pixels. Trajectories were analyzed using “frame-by-frame” weighting 51 in which mean squared displacements (MSD) and effective diffusivities (D eff ) are first calculated for individual particle traces. Averages and distributions are then calculated at each frame based on only the particles present in that frame before averaging across all frames in the movie.…”

Section: Methodsmentioning

confidence: 99%

Herpes simplex virus-binding IgG traps HSV in human cervicovaginal mucus across the menstrual cycle and diverse vaginal microbial composition

et al. 2018

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IgG possesses an important yet little recognized effector function in mucus. IgG bound to viral surface can immobilize otherwise readily diffusive viruses to the mucin matrix, excluding them from contacting target cells and facilitating their elimination by natural mucus clearance mechanisms. Cervicovaginal mucus (CVM) is populated by a microbial community, and its viscoelastic and barrier properties can vary substantially not only across the menstrual cycle, but also in women with distinct microbiota. How these variations impact the "muco-trapping" effector function of IgGs remains poorly understood. Here we obtained multiple fresh, undiluted CVM specimens (n = 82 unique specimens) from six women over time, and employed high-resolution multiple particle tracking to quantify the mobility of fluorescent Herpes Simplex Viruses (HSV-1) in CVM treated with different HSV-1-binding IgG. The IgG trapping potency was then correlated to the menstrual cycle, and the vaginal microbial composition was determined by 16 s rRNA. In the specimens studied, both polyclonal and monoclonal HSV-1-binding IgG appeared to consistently and effectively trap HSV-1 in CVM obtained at different times of the menstrual cycle and containing a diverse spectrum of commensals, including G. vaginalis-dominant microbiota. Our findings underscore the potential broad utility of this "muco-trapping" effector function of IgG to reinforce the vaginal mucosal defense, and motivates further investigation of passive immunization of the vagina as a strategy to protect against vaginally transmitted infections.

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“…The tracking resolution was determined by tracking the displacements of particles immobilized with a strong adhesive, following a previously described method 19 . Particle trajectories were analyzed using MATLAB software as described previously 20 . Sub-pixel tracking resolution was achieved by determining the precise location of the particle centroid by light-intensity-weighted averaging of neighboring pixels.…”

Section: Methodsmentioning

confidence: 99%

“…Particle trajectories were analyzed using a MATLAB version of open-source particle-tracking code, originally developed in IDL by Crocker and Hoffman 29 . We have adapted this code to analyze particle trajectories on a “frame-by-frame” basis, as described previously 20 .…”

Section: Methodsmentioning

confidence: 99%

A blueprint for robust crosslinking of mobile species in biogels with weakly adhesive molecular anchors

et al. 2017

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Biopolymeric matrices can impede transport of nanoparticulates and pathogens by entropic or direct adhesive interactions, or by harnessing “third-party” molecular anchors to crosslink nanoparticulates to matrix constituents. The trapping potency of anchors is dictated by association rates and affinities to both nanoparticulates and matrix; the popular dogma is that long-lived, high-affinity bonds to both species facilitate optimal trapping. Here we present a contrasting paradigm combining experimental evidence (using IgG antibodies and Matrigel®), a theoretical framework (based on multiple timescale analysis), and computational modeling. Anchors that bind and unbind rapidly from matrix accumulate on nanoparticulates much more quickly than anchors that form high-affinity, long-lived bonds with matrix, leading to markedly greater trapping potency of multiple invading species without saturating matrix trapping capacity. Our results provide a blueprint for engineering molecular anchors with finely tuned affinities to effectively enhance the barrier properties of biogels against diverse nanoparticulate species.

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Minimizing biases associated with tracking analysis of submicron particles in heterogeneous biological fluids

Cited by 19 publications

References 43 publications

Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D

Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D

Herpes simplex virus-binding IgG traps HSV in human cervicovaginal mucus across the menstrual cycle and diverse vaginal microbial composition

A blueprint for robust crosslinking of mobile species in biogels with weakly adhesive molecular anchors

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