Precision single-particle localization using radial variance transform

Kashkanova, Anna; Shkarin, Alexey; Mahmoodabadi, Reza Gholami; Blessing, Martin; Tuna, Yazgan; Gemeinhardt, André; Sandoghdar, Vahid

doi:10.1364/oe.420670

Cited by 15 publications

(16 citation statements)

References 31 publications

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“…They were analyzed by applying median background correction and radial variance transform. 16 Particles were tracked using trackpy python package. 17 The particle size was extracted from its diffusion constant.…”

Section: Methodsmentioning

confidence: 99%

Interferometric nanoparticle tracking analysis enables label-free discrimination of extracellular vesicles from large lipoproteins

Kashkanova

Blessing

Reischke

et al. 2022

Preprint

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Extracellular vesicles (EVs) are increasingly gaining interest as biomarkers and therapeutics. Accurate sizing and quantification of EVs remain problematic, given their nanometer size range and small scattering cross-sections. This is compounded by the fact that common EV isolation methods result in co-isolation of particles with comparable features. Especially in blood plasma, similarly-sized lipoproteins outnumber EVs to a great extent. Recently, interferometric nanoparticle tracking analysis (iNTA) was introduced as a particle analysis method that enables determining the size and refractive index of nanoparticles with high sensitivity and precision. In this work, we apply iNTA to differentiate between EVs and lipoproteins, and compare its performance to conventional nanoparticle tracking analysis (NTA). We show that iNTA can accurately quantify EVs in artificial EV-lipoprotein mixtures and in plasma-derived EV samples of varying complexity. Conventional NTA could not report on EV numbers, as it was not able to distinguish EVs from lipoproteins. iNTA has the potential to become a new standard for label-free EV characterization in suspension.

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Section: Methodsmentioning

confidence: 99%

Interferometric nanoparticle tracking analysis enables label-free discrimination of extracellular vesicles from large lipoproteins

Kashkanova

Blessing

Reischke

et al. 2022

Preprint

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“…The majority of the detected particles exhibits a contrast of |C max | ' 20%. By keeping the focal plane at a constant position, we recorded a time series for this region and used a radial variance transform (RVT) to localize the nanoparticles 49 .…”

Section: D Tracking Of Clathrin-coated Pitsmentioning

confidence: 99%

“…While PSFs in fluorescence microscopy can be approximated by 2D Gaussian profiles, the richer structure of iPSFs in iSCAT render this approach less robust. To address this issue, we recently introduced a method based on the radial variance transform (RVT) to localize nanoparticles in W-iSCAT 49 . In our current work, we applied RVT also to C-iSCAT videos.…”

Section: Single-particle Trackingmentioning

confidence: 99%

Confocal interferometric scattering microscopy reveals 3D nanoscopic structure and dynamics in live cells

et al. 2023

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Bright-field light microscopy and related phase-sensitive techniques play an important role in life sciences because they provide facile and label-free insights into biological specimens. However, lack of three-dimensional imaging and low sensitivity to nanoscopic features hamper their application in many high-end quantitative studies. Here, we demonstrate that interferometric scattering (iSCAT) microscopy operated in the confocal mode provides unique label-free solutions for live-cell studies. We reveal the nanometric topography of the nuclear envelope, quantify the dynamics of the endoplasmic reticulum, detect single microtubules, and map nanoscopic diffusion of clathrin-coated pits undergoing endocytosis. Furthermore, we introduce the combination of confocal and wide-field iSCAT modalities for simultaneous imaging of cellular structures and high-speed tracking of nanoscopic entities such as single SARS-CoV-2 virions. We benchmark our findings against simultaneously acquired fluorescence images. Confocal iSCAT can be readily implemented as an additional contrast mechanism in existing laser scanning microscopes. The method is ideally suited for live studies on primary cells that face labeling challenges and for very long measurements beyond photobleaching times.

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“…This is a challenging image analysis problem because the number of features typically is not known a priori, each feature can cover a large area with alternating bright and dark fringes, and neighboring particles' fringes can interfere with each other. Circular Hough transforms, 27,32,33 voting algorithms 32 and symmetrybased transforms 27,34 leverage a feature's radial symmetry to coalesce its concentric rings into a simple peak that can be detected with standard particle-tracking algorithms. 35 Image noise and interference artifacts can violate the assumptions underlying these algorithms, leading to poor localization and an undesirable rate of false-positive and false-negative detections.…”

Section: Feature Detection and Localizationmentioning

confidence: 99%

Machine learning enables precise holographic characterization of colloidal materials in real time

Altman

Grier

2023

Soft Matter

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Precision single-particle localization using radial variance transform

Cited by 15 publications

References 31 publications

Interferometric nanoparticle tracking analysis enables label-free discrimination of extracellular vesicles from large lipoproteins

Interferometric nanoparticle tracking analysis enables label-free discrimination of extracellular vesicles from large lipoproteins

Confocal interferometric scattering microscopy reveals 3D nanoscopic structure and dynamics in live cells

Machine learning enables precise holographic characterization of colloidal materials in real time

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