High-Speed Lens-Free Holographic Microscopy for Quantitative Large-Area Binding Sensors

Abstract

The ongoing COVID-19 pandemic reminds us of the importance of biosensors. Accurate measurements of protein biomarker levels are important for disease diagnostics. Cost-effective, easy-to-use, and sensitive point-of-care sensors could provide faster, cheaper, and/or more accurate measurements than widely-used methods like enzyme-linked immunosorbent assays (ELISAs) or lateral flow assays. The motivation of this dissertation is to develop a biosensor that is sensitive, cost-effective, high-throughput, and field-portable. This biosensor should be applicable to current sensing tasks and be exceptionally accessible for low-resource communities. Lens-free holographic microscopy (LFHM) offers submicron resolution over an ultra-large eld-of-view >20 mm^2 with simple hardware, making it a great candidate for biosensing applications. However, there are a few challenges in applying LFHM to current sensing tasks. Firstly, various pixel super-resolution techniques have been shown to enhance resolution and boost signal-to-noise ratio (SNR) by combining multiple partially-redundant low-resolution frames. However, it has been unclear which technique performs best for small-target sensing in LFHM. Here, we quantitatively compare SNR and resolution in experiments using no regularization, cardinal-neighbor regularization, and a novel implementation of sparsity-promoting regularization that uses analytically-calculated gradients from Bayer-pattern image sensors. We find that sparsity-promoting regularization enhances the SNR by ~8 dB compared to the other methods when imaging micron-scale beads with surface coverages up to ~4%. In short, we implemented a computational algorithm optimized for sensing of small targets in LFHM. Secondly, LFHM has not been applied to protein molecule sensing in solution yet since many LFHM have been limited to imaging dried (static) samples. Here, we implement a high-speed LFHM to image microsphere beads binding in solution so that protein molecule sensing is possible using biological sensing elements functionalized microspheres. We designed and built a custom high-speed high-power LED array, which provides for the first time, pixel super-resolved imaging of >10^4 2-μm beads in solution undergoing Brownian motion. We analyze the tradeoff between image brightness and motion blur. In this dissertation, we sense interferon-gamma (an immune system biomarker) and NeutrAvidin molecules in solution by combining LFHM with a one-step bead-based agglutination assay, enabled by our high-speed LFHM and automated image processing routines. We call this a quantitative large-area binding (QLAB) sensor. The automated image processing routines enable the counting of individual beads and clusters, providing a quantitative sensor readout that depends on both bead and analyte concentration. Fits to chemical binding theory are provided. For NeutrAvidin, we find a limit of detection (LOD) of <27 ng/mL (450 pM) and a dynamic range of 2-4 orders of magnitude. For mouse interferon-gamma, the LOD is <3 ng/mL (200 pM) and the dynamic range is at least 4 orders of magnitude. The QLAB sensor holds promise for point-of-care applications in low-resource communities and where protocol simplicity is important.