Revealing the Assembly of Filamentous Proteins with Scanning Transmission Electron Microscopy

Martinez-Torres, Cristina; Burla, Federica; Alkemade, Celine; Koenderink, Gijsje H.

doi:10.1101/697649

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…Edges are tracked with a modified Canny edge detection algorithm with the wavelet transform (WT) as outlined in ref. 95 By using the first derivative of a Gaussian in the WT, the 2D smoothed gradient of the image is computed. 96 In addition, for each point in the image the amplitude of the gradient (the WT modulus) and its direction (the WT argument) are obtained.…”

Section: Refined Membrane Detectionmentioning

confidence: 99%

DisGUVery: a versatile open-source software for high-throughput image analysis of Giant Unilamellar Vesicles

Buren

Koenderink

Martinez-Torres

2022

Preprint

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Giant Unilamellar Vesicles (GUVs) are cell-sized aqueous compartments enclosed by a phospholipid bilayer. Due to their cell-mimicking properties, GUVs have become a widespread experimental tool in synthetic biology to study membrane properties and cellular processes. In stark contrast to the experimental progress, quantitative analysis of GUV microscopy images has received much less attention. Currently, most analysis is performed either manually or with custom-made scripts, which makes analysis time-consuming and results difficult to compare across studies. To make quantitative GUV analysis accessible and fast, we present DisGUVery, an open-source, versatile software that encapsulates multiple algorithms for automated detection and analysis of GUVs in microscopy images. With a performance analysis, we demonstrate that DisGUVery's three vesicle detection modules successfully identify GUVs in images obtained with a wide range of imaging sources, in various typical GUV experiments. Multiple pre-defined analysis modules allow the user to extract properties such as membrane fluorescence, vesicle shape and internal fluorescence from large populations. A new membrane segmentation algorithm facilitates spatial fluorescence analysis of non-spherical vesicles. Altogether, DisGUVery provides an accessible tool to enable high-throughput automated analysis of GUVs, and thereby to promote quantitative data analysis in GUV research.

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Section: Refined Membrane Detectionmentioning

confidence: 99%

DisGUVery: a versatile open-source software for high-throughput image analysis of Giant Unilamellar Vesicles

Buren

Koenderink

Martinez-Torres

2022

Preprint

Self Cite

View full text Add to dashboard Cite

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Stochastic Modeling and Simulation of Filament Aggregation in Alzheimer’s Disease

Ojha,

Yarahmadian,

2024

Processes

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Alzheimer’s disease has been a serious problem for humankind, one without a promising cure for a long time now, and researchers around the world have been working to better understand this disease mathematically, biologically and computationally so that a better cure can be developed and finally humanity can get some relief from this disease. In this study, we try to understand the progression of Alzheimer’s disease by modeling the progression of amyloid-beta aggregation, leading to the formation of filaments using the stochastic method. In a noble approach, we treat the progression of filaments as a random chemical reaction process and apply the Monte Carlo simulation of the kinetics to simulate the progression of filaments of lengths up to 8. By modeling the progression of disease as a progression of filaments and treating this process as a stochastic process, we aim to understand the inherent randomness and complex spatial–temporal features and the convergence of filament propagation process. We also analyze different reaction events and observe the events such as primary as well as secondary elongation, aggregations and fragmentation using different propensities for different possible reactions. We also introduce the random switching of the propensity at random time, which further changes the convergence of the overall dynamics. Our findings show that the stochastic modeling can be utilized to understand the progression of amyloid-beta aggregation, which eventually leads to larger plaques and the development of Alzheimer disease in the patients. This method can be generalized for protein aggregation in any disease, which includes both the primary and secondary aggregation and fragmentation of proteins.

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Revealing the Assembly of Filamentous Proteins with Scanning Transmission Electron Microscopy

Cited by 2 publications

References 47 publications

DisGUVery: a versatile open-source software for high-throughput image analysis of Giant Unilamellar Vesicles

DisGUVery: a versatile open-source software for high-throughput image analysis of Giant Unilamellar Vesicles

Stochastic Modeling and Simulation of Filament Aggregation in Alzheimer’s Disease

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