Automated Nanoparticle Analysis in Surface Plasmon Resonance Microscopy

Abstract

The unique capability of surface plasmon resonance microscopy (SPRM) in single nanoparticle analysis has found use in various chemical and biological applications. While SPRM offers exceptional sensitivity, the statistical analysis of numerous nanoparticles has been extremely laborious and time-consuming. Herein, we presented an image processing software package for nanoparticle analysis in SPRM, which is empowered by a deep learning algorithm. This package enabled fully automated nanoparticle identification, digital counting, three-dimensional tracking of particle locations, and quantification of dwell time and Brownian motion properties. With a built-in image filtering process to improve the contrast, robust identification and analysis have been achieved from SPRM images of low refractive index nanoparticles. This software tool would largely promote the translation of SPRM technology into the digital sensing platform for high throughput sample screening.

Dynamic single-molecule sensing by actively tuning binding kinetics for ultrasensitive biomarker detection

“…Images were acquired by a scientific complementary-metal-oxide-semiconductor (sCMOS) camera (Photometrics Prime 95B) with a full field of view of 102.4 × 102.4 μm 2 at a frame rate of 16.7 fps. The image sequences were processed during acquisition by a home-developed software package ( 40 ).…”

Label-free imaging and biomarker analysis of exosomes with plasmonic scattering microscopy

“…The image contrast is determined by the interference between the surface plasmonic wave and the plasmonic wave scattered by the analyte 36 . The surface plasmonic wave has ~10 µm propagation length with the incident wavelength of 660 nm, leading to a parabolic tail following the spot at the location of the analyte in the SPR image, which is hard to be processed with regular software and can only be automatically analysed with specifically designed image processing algorithms 23,34,35 . The PSM is constructed differently from SPR microscopy by employing one objective to observe the plasmonic wave scattered by analytes on the top of the gold-coated glass slide (Fig.…”

“…The SPR microscopy constructed by oil immersion objective with a high numerical aperture (NA) allows the single exosome analysis, but the high NA objective usually has a large magnification, resulting in a small field of view and, therefore, the limited throughput [29][30][31][32] . Second, the surface plasmonic waves have long decaying length along the surface, and thus the SPR microscopy has a parabolic tail-shaped point spread function, resulting in low spatial resolution, and making it challenging to process the images automatically with regular image processing tool [33][34][35] .…”

“…29–32 Second, the surface plasmonic waves have long decaying length along the surface, and thus, the SPR microscopy has a parabolic tail-shaped point spread function, resulting in low spatial resolution, and making it challenging to process the images automatically with regular image processing tool. 33–35…”

Computer Vision Analysis on Material Characterization Images

“…Moreover, DL is also used to quantify Brownian motion characteristics of nanoparticles in surface plasma resonance microscopy images, [72] track small and dense particles, [73] track a single particle in liquid-cell transmission electron microscopy (LCTEM), [74] and predict particle motion. [75,76]…”