Bond-selective full-field optical coherence tomography.

Optical coherence tomography (OCT) is a label-free, non-invasive 3D imaging tool widely used in both biological research and clinical diagnosis. Conventional OCT modalities can only visualize specimen tomography without chemical information. Here, we report a bond-selective full-field OCT (BS-FF-OCT), in which a pulsed mid-infrared laser is used to modulate the OCT signal through the photothermal effect, achieving label-free bond-selective 3D sectioned imaging of highly scattering samples. We first demonstrate BS-FF-OCT imaging of 1 µm PMMA beads embedded in agarose gel. Next, we show 3D hyperspectral imaging of up to 75 µm of polypropylene fiber mattress from a standard surgical mask. We then demonstrate BS-FF-OCT imaging on biological samples, including cancer cell spheroids and C. elegans. Using an alternative pulse timing configuration, we finally demonstrate the capability of BS-FF-OCT on imaging a highly scattering myelinated axons region in a mouse brain tissue slice.

Single virus fingerprinting by widefield interferometric defocus-enhanced mid-infrared photothermal microscopy.

Clinical identification and fundamental study of viruses rely on the detection of viral proteins or viral nucleic acids. Yet, amplification-based and antigen-based methods are not able to provide precise compositional information of individual virions due to small particle size and low-abundance chemical contents (e.g., ~ 5000 proteins in a vesicular stomatitis virus). Here, we report a widefield interferometric defocus-enhanced mid-infrared photothermal (WIDE-MIP) microscope for high-throughput fingerprinting of single viruses. With the identification of feature absorption peaks, WIDE-MIP reveals the contents of viral proteins and nucleic acids in single DNA vaccinia viruses and RNA vesicular stomatitis viruses. Different nucleic acid signatures of thymine and uracil residue vibrations are obtained to differentiate DNA and RNA viruses. WIDE-MIP imaging further reveals an enriched ß sheet components in DNA varicella-zoster virus proteins. Together, these advances open a new avenue for compositional analysis of viral vectors and elucidating protein function in an assembled virion.

Bond-Selective Full-Field Optical Coherence Tomography.

Optical coherence tomography (OCT) is a label-free, non-invasive 3D imaging tool widely used in both biological research and clinical diagnosis. Current OCT modalities can only visualize specimen tomography without chemical information. Here, we report a bondselective full-field OCT (BS-FF-OCT), in which a pulsed mid-infrared laser is used to modulate the OCT signal through the photothermal effect, achieving label-free bond-selective 3D sectioned imaging of highly scattering samples. We first demonstrate BS-FF-OCT imaging of 1 {\mu}m PMMA beads embedded in agarose gel. Next, we then show 3D hyperspectral imaging of polypropylene fiber mattress from a standard surgical mask. We then demonstrate BS-FFOCT imaging on biological samples, including cancer cell spheroids and C. elegans. Using an alternative pulse timing configuration, we finally demonstrate the capability of BS-FF-OCT on a bulky and highly scattering 150 {\mu}m thick mouse brain slice.

Vibrational Spectroscopic Detection of a Single Virus by Mid-Infrared Photothermal Microscopy.

We report a confocal interferometric mid-infrared photothermal (MIP) microscope for ultra-sensitive and spatially resolved chemical imaging of individual viruses. The interferometric scattering principle is applied to detect the very weak photothermal signal induced by infrared absorption of chemical bonds. Spectroscopic MIP detection of single vesicular stomatitis viruses (VSVs) and poxviruses is demonstrated. The single virus spectra show high consistency within the same virus type. The dominant spectral peaks are contributed by the amide I and amide II vibrations attributed to the viral proteins. The ratio of these two peaks is significantly different between VSVs and poxviruses, highlighting the potential of using interferometric MIP microscopy for label-free differentiation of viral particles. This all-optical chemical imaging method opens a new way for spectroscopic detection of biological nanoparticles in a label-free manner and may facilitate in predicting and controlling the outbreaks of emerging virus strains.

Background-Suppressed High-Throughput Mid-Infrared Photothermal Microscopy via Pupil Engineering.

Mid-infrared photothermal (MIP) microscopy has been a promising label-free chemical imaging technique for functional characterization of specimens owing to its enhanced spatial resolution and high specificity. Recently developed wide-field MIP imaging modalities have drastically improved speed and enabled high-throughput imaging of micron-scale subjects. However, the weakly scattered signal from subwavelength particles becomes indistinguishable from the shot-noise as a consequence of the strong background light, leading to limited sensitivity. Here, we demonstrate background-suppressed chemical fingerprinting at a single nanoparticle level by selectively attenuating the reflected light through pupil engineering in the collection path. Our technique provides over 3 orders of magnitude background suppression by quasi-darkfield illumination in the epi-configuration without sacrificing lateral resolution. We demonstrate 6-fold signal-to-background noise ratio improvement, allowing for simultaneous detection and discrimination of hundreds of nanoparticles across a field of view of 70 µm × 70 µm. A comprehensive theoretical framework for photothermal image formation is provided and experimentally validated with 300 and 500 nm PMMA beads. The versatility and utility of our technique are demonstrated via hyperspectral dark-field MIP imaging of S. aureus and E. coli bacteria and MIP imaging of subcellular lipid droplets inside C. albicans and cancer cells.

Computational nanosensing from defocus in single particle interferometric reflectance microscopy.

Single particle interferometric reflectance (SPIR) microscopy has been studied as a powerful imaging platform for label-free and highly sensitive biological nanoparticle detection and characterization. SPIR's interferometric nature yields a unique 3D defocus intensity profile of the nanoparticles over a large field of view. Here, we utilize this defocus information to recover high signal-to-noise ratio nanoparticle images with a computationally and memory efficient reconstruction framework. Our direct inversion approach recovers this image from a 3D defocus intensity stack using the vectorial-optics-based forward model developed for sub-diffraction-limited dielectric nanoparticles captured on a layered substrate. We demonstrate proof-of-concept experiments on silica beads with a 50 nm nominal diameter.

High-Throughput, High-Resolution Interferometric Light Microscopy of Biological Nanoparticles.

Label-free, visible light microscopy is an indispensable tool for studying biological nanoparticles (BNPs). However, conventional imaging techniques have two major challenges: (i) weak contrast due to low-refractive-index difference with the surrounding medium and exceptionally small size and (ii) limited spatial resolution. Advances in interferometric microscopy have overcome the weak contrast limitation and enabled direct detection of BNPs, yet lateral resolution remains as a challenge in studying BNP morphology. Here, we introduce a wide-field interferometric microscopy technique augmented by computational imaging to demonstrate a 2-fold lateral resolution improvement over a large field-of-view (>100 × 100 µm2), enabling simultaneous imaging of more than 104 BNPs at a resolution of ∼150 nm without any labels or sample preparation. We present a rigorous vectorial-optics-based forward model establishing the relationship between the intensity images captured under partially coherent asymmetric illumination and the complex permittivity distribution of nanoparticles. We demonstrate high-throughput morphological visualization of a diverse population of Ebola virus-like particles and a structurally distinct Ebola vaccine candidate. Our approach offers a low-cost and robust label-free imaging platform for high-throughput and high-resolution characterization of a broad size range of BNPs.

Interferometric Reflectance Imaging Sensor (IRIS) for Molecular Kinetics with a Low-Cost, Disposable Fluidic Cartridge.

The determination of kinetic information and appropriate binding pairs is fundamental to the proper optimization and selection of ligands used in immunoassays, diagnostics, and therapeutics. However, the ability to estimate such parameters in a multiplexed and inexpensive format remains difficult and modification of the ligand is often necessary. Here, we detail the methods and materials necessary to evaluate hundreds of unlabeled ligands simultaneously using the interferometric reflectance imaging sensor (IRIS). The incorporation of a low-cost fluidic cartridge that integrates on the top of the sensor simplifies reagent handling considerably.

Resolution enhancement of wide-field interferometric microscopy by coupled deep autoencoders.

Wide-field interferometric microscopy is a highly sensitive, label-free, and low-cost biosensing imaging technique capable of visualizing individual biological nanoparticles such as viral pathogens and exosomes. However, further resolution enhancement is necessary to increase detection and classification accuracy of subdiffraction-limited nanoparticles. In this study, we propose a deep-learning approach, based on coupled deep autoencoders, to improve resolution of images of L-shaped nanostructures. During training, our method utilizes microscope image patches and their corresponding manual truth image patches in order to learn the transformation between them. Following training, the designed network reconstructs denoised and resolution-enhanced image patches for unseen input.

Nanoparticle classification in wide-field interferometric microscopy by supervised learning from model.

Interference-enhanced wide-field nanoparticle imaging is a highly sensitive technique that has found numerous applications in labeled and label-free subdiffraction-limited pathogen detection. It also provides unique opportunities for nanoparticle classification upon detection. More specifically, the nanoparticle defocus images result in a particle-specific response that can be of great utility for nanoparticle classification, particularly based on type and size. In this work, we combine a model-based supervised learning algorithm with a wide-field common-path interferometric microscopy method to achieve accurate nanoparticle classification. We verify our classification schemes experimentally by blindly detecting gold and polystyrene nanospheres, and then classifying them in terms of type and size.