Noise Reduction Method for Quantifying Nanoparticle Light Scattering in Low Magnification Dark-Field Microscope Far-Field Images

Abstract

Nanoparticles have become a powerful tool for cell imaging, biomolecule and cell and protein interaction studies, but are difficult to rapidly and accurately measure in most assays. Dark-field microscope (DFM) image analysis approaches used to quantify nanoparticles require high-magnification near-field (HN) images that are labor intensive due to a requirement for manual image selection and focal adjustments needed when identifying and capturing new regions of interest. Low-magnification far-field (LF) DFM imagery is technically simpler to perform but cannot be used as an alternate to HN-DFM quantification, since it is highly sensitive to surface artifacts and debris that can easily mask nanoparticle signal. We now describe a new noise reduction approach that markedly reduces LF-DFM image artifacts to allow sensitive and accurate nanoparticle signal quantification from LF-DFM images. We have used this approach to develop a “Dark Scatter Master” (DSM) algorithm for the popular NIH image analysis program ImageJ, which can be readily adapted for use with automated high-throughput assay analyses. This method demonstrated robust performance quantifying nanoparticles in different assay formats, including a novel method that quantified extracellular vesicles in patient blood sample to detect pancreatic cancer cases. Based on these results, we believe our LF-DFM quantification method can markedly decrease the analysis time of most nanoparticle-based assays to impact both basic research and clinical analyses.

A low cost mobile phone dark-field microscope for nanoparticle-based quantitative studies

“…Nanoparticle-based quantitative bioassays are not in common usage, but exhibit certain advantages over conventional enzyme-based assays, since the nanoparticle probes are less labile and produce stable endpoint results. We therefore also analyzed MDFM performance with a variant of a novel assay that employs nanoparticle-based DFM signal to achieve results that are more rapid, sensitive and specific than can be achieved by conventional assays(Liang et al, 2017; Sun et al, 2016). We have previously used this approach to detect and monitor pancreatic cancer-derived exosomes in patient blood samples, while our current study applies a modification of this assay to distinguish patients with pediatric tuberculosis (TB) from their healthy controls (Figure S3).…”

“…Lighting induced artifacts observed with the current MFDM prototype prevent its use for high-quality DFM imagery, but do not decrease its utility for nanoparticle-based quantitation assays once images are processed to correct for artifacts commonly associated with low-magnification far-field DFM images, including uneven lighting and other signal artifacts using our previously published DFM image processing approach. (Sun et al, 2016)…”

“…Table S1 lists the components of these systems. All images were processed and quantified using our previously reported “DarkScatterMaster” (DSM) DFM algorithm(Sun et al, 2016) using the following software input parameters: contour threshold (Ct) = 253.020, center scale (S) = 0.8, type = Red, Low (Lt)/High (Ht) quantification limit: 0/62. Motorola Moto G2 (XT1068) images were captured with a 1/15 s exposure time with Open Camera (Version 1.32.1)(Harman, 2017) using an ISO 5000 configuration and allowing autofocus and 4× digital zoom.…”

Ultra-Sensitive Automated Profiling of EpCAM Expression on Tumor-Derived Extracellular Vesicles

“…FF-DF images were captured using an Eclipse Ti-S inverted microscope (Nikon Instruments Inc.) equipped with a motorized stage, a 4× objective lens (NA = 0.13), and a dark-field condenser (1.20 < NA < 1.43) illuminated by a 100 W halogen lamp. FF-DF images of nanoparticle-scattered light were automatically captured using a DS-Ri2 color camera (Nikon Instruments Inc.) and automatically processed on Image J image analysis software (NIH) using the Dark Scatter Master (DSM) plugin (Sun et al, 2016) to avoid operator bias.…”

The video-rate imaging of sub-10 nm plasmonic nanoparticles in a cellular medium free of background scattering

“…Even some background scavenging reagents, background smoothing algorithms, etc., have been tried to reduce the intracellular scattering background, however, such problem has not been fully solved. [25][26][27][28] To detect small plasmonic nanoparticles, several types of photothermal imaging methods were developed based on plasmonic resonance absorption instead of scattering, since the absorption cross section of nanoparticles scales down with three power of particle size (slower than scattering). Laser scanning photothermal imaging is able to detect nanoparticles down to a few nanometers, and enable high resolution imaging of cellular structure.…”