Deep learning-based size prediction for optical trapped nanoparticles and extracellular vesicles from limited bandwidth camera detection.

Due to its ability to record position, intensity, and intensity distribution information, camera-based monitoring of nanoparticles in optical traps can enable multi-parametric morpho-optical characterization at the single-particle level. However, blurring due to the relatively long (10s of microsecond) integration times and aliasing from the resulting limited temporal bandwidth affect the detected particle position when considering nanoparticles in traps with strong stiffness, leading to inaccurate size predictions. Here, we propose a ResNet-based method for accurate size characterization of trapped nanoparticles, which is trained by considering only simulated time series data of nanoparticles' constrained Brownian motion. Experiments prove the method outperforms state-of-art sizing algorithms such as adjusted Lorentzian fitting or CNN-based networks on both standard nanoparticles and extracellular vesicles (EVs), as well as maintains good accuracy even when measurement times are relatively short (<1s per particle). On samples of clinical EVs, our network demonstrates a well-generalized ability to accurately determine the EV size distribution, as confirmed by comparison with gold-standard nanoparticle tracking analysis (NTA). Furthermore, by combining the sizing network with still frame images from high-speed video, the camera-based optical tweezers have the unique capacity to quantify both the size and refractive index of bio-nanoparticles at the single-particle level. These experiments prove the proposed sizing network as an ideal path for predicting the morphological heterogeneity of bio-nanoparticles in optical potential trapping-related measurements.