Localization of unlabeled bepirovirsen antisense oligonucleotide in murine tissues using in situ hybridization and CARS imaging.

Current methods for detecting unlabeled antisense oligonucleotide (ASO) drugs rely on immunohistochemistry (IHC) and/or conjugated molecules, which lack sufficient sensitivity, specificity, and resolution to fully investigate their biodistribution. Our aim was to demonstrate the qualitative and quantitative distribution of unlabeled bepirovirsen, a clinical stage ASO, in livers and kidneys of dosed mice using novel staining and imaging technologies at subcellular resolution. ASOs were detected in formalin-fixed paraffin-embedded (FFPE) and frozen tissues using an automated chromogenic in situ hybridization (ISH) assay: miRNAscope. This was then combined with immunohistochemical detection of cell lineage markers. ASO distribution in hepatocytes versus nonparenchymal cell lineages was quantified using HALO AI image analysis. To complement this, hyperspectral coherent anti-Stokes Raman scattering (HS-CARS) imaging microscopy was used to specifically detect the unique cellular Raman spectral signatures following ASO treatment. Bepirovirsen was localized primarily in nonparenchymal liver cells and proximal renal tubules. Codetection of ASO with distinct cell lineage markers of liver and kidney populations aided target cell identity facilitating quantification. Positive liver signal was quantified using HALO AI, with 12.9% of the ASO localized to the hepatocytes and 87.1% in nonparenchymal cells. HS-CARS imaging specifically detected ASO fingerprints based on the unique vibrational signatures following unlabeled ASO treatment in a totally nonperturbative manner at subcellular resolution. Together, these novel detection and imaging modalities represent a significant increase in our ability to detect unlabeled ASOs in tissues, demonstrating improved levels of specificity and resolution. These methods help us understand their underlying mechanisms of action and ultimately improve the therapeutic potential of these important drugs for treating globally significant human diseases.

Temporally optimized and spectrally shaped hyperspectral coherent anti-Stokes Raman scattering microscopy.

Coherent anti-Stokes Raman scattering (CARS) microscopy offers label-free chemical contrasts based on molecular vibrations. Hyperspectral CARS (HS-CARS) microscopy enables comprehensive microscale chemical characterization of biological samples. Various HS-CARS methods have been developed with individual advantages and disadvantages. We present what we believe to be a new temporally optimized and spectrally shaped (TOSS) HS-CARS method to overcome the limitations of existing techniques by providing precise control of the spatial and temporal profiles of the excitation beams for efficient and accurate measurements. This method uniquely uses Fourier transform pulse shaping based on a two-dimensional spatial light modulator to control the phase and amplitude of the excitation beams. TOSS-HS-CARS achieves fast, stable, and flexible acquisition, minimizes photodamage, and is highly adaptable to a multimodal multiphoton imaging system.

Programmable hyperspectral coherent anti-Stokes Raman scattering microscopy.

Hyperspectral coherent Raman scattering microscopy provides a significant improvement in acquisition time compared to spontaneous Raman scattering yet still suffers from the time required to sweep through individual wavenumbers. To address this, we present the use of a pulse shaper with a 2D spatial light modulator for phase- and amplitude-based shaping of the Stokes beam to create programmable spectrally tailored excitation envelopes. This enables collection of useful spectral information in a more rapid and efficient manner.

Weakly Supervised Identification and Localization of Drug Fingerprints Based on Label-Free Hyperspectral CARS Microscopy.

Understanding drug fingerprints in complex biological samples is essential for the development of a drug. Hyperspectral coherent anti-Stokes Raman scattering (HS-CARS) microscopy, a label-free nondestructive chemical imaging technique, can profile biological samples based on their endogenous vibrational contrast. Here, we propose a deep learning-assisted HS-CARS imaging approach for the investigation of drug fingerprints and their localization at single-cell resolution. To identify and localize drug fingerprints in complex biological systems, an attention-based deep neural network, hyperspectral attention net (HAN), was developed. By formulating the task to a multiple instance learning problem, HAN highlights informative regions through the attention mechanism when being trained on whole-image labels. Using the proposed technique, we investigated the drug fingerprints of a hepatitis B virus therapy in murine liver tissues. With the increase in drug dosage, higher classification accuracy was observed, with an average area under the curve (AUC) of 0.942 for the high-dose group. Besides, highly informative tissue structures predicted by HAN demonstrated a high degree of similarity with the drug localization shown by the in situ hybridization staining results. These results demonstrate the potential of the proposed deep learning-assisted optical imaging technique for the label-free profiling, identification, and localization of drug fingerprints in biological samples, which can be extended to nonperturbative investigations of complex biological systems under various biological conditions.

Nonlinear Imaging Histopathology: A Pipeline to Correlate Gold-Standard Hematoxylin and Eosin Staining With Modern Nonlinear Microscopy.

Hematoxylin and eosin (H&E) staining, the century-old technique, has been the gold standard tool for pathologists to detect anomalies in tissues and diseases such as cancer. H&E staining is a cumbersome, time-consuming process that delays and wastes precious minutes during an intraoperative diagnosis. However, even in the modern era, real-time label-free imaging techniques such as simultaneous label-free autofluorescence multiharmonic (SLAM) microscopy have delivered several more layers of information to characterize a tissue with high precision. Still, they have yet to translate to the clinic. The slow translation rate can be attributed to the lack of direct comparisons between the old and new techniques. Our approach to solving this problem is to: 1) reduce dimensions by pre-sectioning the tissue in 500 µm slices, and 2) produce fiducial laser markings which appear in both SLAM and histological imaging. High peak-power femtosecond laser pulses enable ablation in a controlled and contained manner. We perform laser marking on a grid of points encompassing the SLAM region of interest. We optimize laser power, numerical aperture, and timing to produce axially extended marking, hence multilayered fiducial markers, with minimal damage to the surrounding tissues. We performed this co-registration over an area of 3 × 3 mm2 of freshly excised mouse kidney and intestine, followed by standard H&E staining. Reduced dimensionality and the use of laser markings provided a comparison of the old and new techniques, giving a wealth of correlative information and elevating the potential of translating nonlinear microscopy to the clinic for rapid pathological assessment.

Effects of phosphate and silicate on stiffness and viscoelasticity of mature biofilms developed with simulated drinking water.

Biofilms, a porous matrix of cells aggregated with extracellular polymeric substances under the influence of chemical constituents in the feed water, can develop a viscoelastic response to mechanical stresses. In this study, the roles of phosphate and silicate, common additives in corrosion control and meat processing, on the stiffness, viscoelasticity, porous structure networks, and chemical properties of biofilm were investigated. Three-year biofilms on PVC coupons were grown from sand-filtered groundwater with or without one of the non-nutrient (silicate) or nutrient additives (phosphate or phosphate blends). Compared with non-nutrient additives, the phosphate and phosphate-blend additives led to a biofilm with the lowest stiffness, most viscoelastic, and more porous structure, including more connecting throats with greater equivalent radii. The phosphate-based additives also led to more organic species in the biofilm matrix than the silicate additive did. This work demonstrated that nutrient additives could promote biomass accumulation but also reduce mechanical stability.

Label-free visualization and characterization of extracellular vesicles in breast cancer.

Despite extensive interest, extracellular vesicle (EV) research remains technically challenging. One of the unexplored gaps in EV research has been the inability to characterize the spatially and functionally heterogeneous populations of EVs based on their metabolic profile. In this paper, we utilize the intrinsic optical metabolic and structural contrast of EVs and demonstrate in vivo/in situ characterization of EVs in a variety of unprocessed (pre)clinical samples. With a pixel-level segmentation mask provided by the deep neural network, individual EVs can be analyzed in terms of their optical signature in the context of their spatial distribution. Quantitative analysis of living tumor-bearing animals and fresh excised human breast tissue revealed abundance of NAD(P)H-rich EVs within the tumor, near the tumor boundary, and around vessel structures. Furthermore, the percentage of NAD(P)H-rich EVs is highly correlated with human breast cancer diagnosis, which emphasizes the important role of metabolic imaging for EV characterization as well as its potential for clinical applications. In addition to the characterization of EV properties, we also demonstrate label-free monitoring of EV dynamics (uptake, release, and movement) in live cells and animals. The in situ metabolic profiling capacity of the proposed method together with the finding of increasing NAD(P)H-rich EV subpopulations in breast cancer have the potential for empowering applications in basic science and enhancing our understanding of the active metabolic roles that EVs play in cancer progression.

Single-photon peak event detection (SPEED): a computational method for fast photon counting in fluorescence lifetime imaging microscopy.

Fluorescence lifetime imaging microscopy (FLIM) characterizes samples by examining the temporal properties of fluorescence emission, providing useful contrast within samples based on the local physical and biochemical environment of fluorophores. Despite this, FLIM applications have been limited in scope by either poor accuracy or long acquisition times. Here, we present a method for computational single-photon counting of directly sampled time-domain FLIM data that is capable of accurate fluorescence lifetime and intensity measurements while acquiring over 160 Mega-counts-per-second with sub-nanosecond time resolution between consecutive photon counts. We demonstrate that our novel method of Single-photon PEak Event Detection (SPEED) is more accurate than direct pulse sampling and faster than established photon counting FLIM methods. We further show that SPEED can be implemented for imaging and quantifying samples that benefit from higher -throughput and -dynamic range imaging with real-time GPU-accelerated processing and use this capability to examine the NAD(P)H-related metabolic dynamics of apoptosis in human breast cancer cells. Computational methods for photon counting such as SPEED open up more opportunities for fast and accurate FLIM imaging and additionally provide a basis for future innovation into alternative FLIM techniques.

Computational adaptive optics for polarization-sensitive optical coherence tomography.

Defocus aberration in optical systems, including optical coherence tomography (OCT) systems employing Gaussian illumination, gives rise to the well-known compromise between transverse resolution and depth-of-field. This results in blurry images when out-of-focus, whilst other low-order aberrations (e.g., astigmatism, coma, etc.) present in both the OCT system and biological samples further reduce image resolution and contrast. Computational adaptive optics (CAO) is a computed optical interferometric imaging technique that modifies the phase of the OCT data in the spatial frequency domain to correct optical aberrations and provide improvement of the image quality throughout the three-dimensional (3D) volume. In this Letter, we report the first implementation of CAO for polarization-sensitive OCT to correct defocus and other low-order aberrations, providing enhanced polarization-sensitive imaging contrast (i.e., intensity and phase retardation) on a 3D OCT phantom, molded plastics, ex vivo chicken breast tissue, and ex vivo human breast cancer tissue.

Intraoperative Label-Free Multimodal Nonlinear Optical Imaging for Point-of-Procedure Cancer Diagnostics.

Intraoperative imaging in surgical oncology can provide information about the tumor microenvironment as well as information about the tumor margin. Visualizing microstructural features and molecular and functional dynamics may provide important diagnostic and prognostic information, especially when obtained in real-time at the point-of-procedure. A majority of current intraoperative optical techniques are based on the use of the labels, such as fluorescent dyes. However, these exogenous agents disrupt the natural microenvironment, perturb biological processes, and alter the endogenous optical signatures that cells and the microenvironment can provide. Portable nonlinear imaging systems have enabled intraoperative imaging for real-time detection and diagnosis of tissue. We review the development of a label-free multimodal nonlinear optical imaging technique that was adapted into a portable imaging system for intraoperative optical assessment of resected human breast tissue. New developments have applied this technology to assessing needle-biopsy specimens. Needle-biopsy procedures most always precede surgical resection and serve as the first sampling of suspicious masses for diagnosis. We demonstrate the diagnostic feasibility of imaging core needle-biopsy specimens during veterinary cancer surgeries. This intraoperative label-free multimodal nonlinear optical imaging technique can potentially provide a powerful tool to assist in cancer diagnosis at the point-of-procedure.

Compressive sensing for polarization sensitive optical coherence tomography.

In this report, we report on the implementation of compressive sensing (CS) and sparse sampling in polarization sensitive optical coherence tomography (PS-OCT) to reduce the number of B-scans (frames consisting of an array of A-scans, where each represents a single depth profile of reflections) required for effective volumetric (3D dataset composed of an array of B-scans) PS-OCT measurements (i.e. OCT intensity, and phase retardation) reconstruction. Sparse sampling of PS-OCT is achieved through randomization of step sizes along the slow-axis of PS-OCT imaging, covering the same spatial ranges as those with equal slow-axis step sizes, but with a reduced number of B-scans. Tested on missing B-scan rates of 25%, 50% and 75%, we found CS could reconstruct reasonably good (as evidenced by a correlation coefficient >0.6) PS-OCT measurements with a maximum reduced B-scan rate of 50%, thereby accelerating and doubling the rate of volumetric PS-OCT measurements.

Label-Free Multimodal Multiphoton Intravital Imaging.

Label-free intravital optical imaging is an emergent visualization tool that is not only useful for basic biological research, but also for preclinical research with potential translational clinical applications. The complete absence of exogenous labeling or genetic alterations avoids plausible harmful perturbation to biological processes and the pristine physiological environment, as the endogenous biomolecules enable intrinsic imaging contrasts to interrogate various live multicellular organisms of interest. This tool has evolved from single-modality, single-photon imaging into multimodal multiphoton imaging, in order to gain different contrasts simultaneously during imaging sessions, and permit long-term time-lapse studies that have begun to spawn more diverse applications.

Diagnostic accuracy of optical coherence tomography for assessing surgical margins of canine soft tissue sarcomas in observers of different specialties.

OBJECTIVE: To determine the diagnostic accuracy of optical coherence tomography (OCT) to assess surgical margins of canine soft tissue sarcoma (STS) and determine the influence of observer specialty and training. STUDY DESIGN: Blinded clinical prospective study. ANIMALS: Twenty-five dogs undergoing surgical excision of STS. METHODS: In vivo and ex vivo surgical margins were imaged with OCT after tumor resection. Representative images and videos were used to generate a training presentation and data sets. These were completed by 16 observers of four specialties (surgery, radiology, pathology, and OCT researchers). Images and videos from data sets were classified as cancerous or noncancerous. RESULTS: The overall sensitivity and specificity were 88.2% and 92.8%, respectively, for in vivo tissues and 82.5% and 93.3%, respectively, for ex vivo specimens. The overall accurate classification for all specimens was 91.4% in vivo and 89.5% ex vivo. There was no difference in accuracy of interpretation of OCT imaging by observers of different specialties or experience levels. CONCLUSION: Use of OCT to accurately assess surgical margins after STS excision was associated with a high sensitivity and specificity among various specialties. Personnel of all specialties and experience levels could effectively be trained to interpret OCT imaging. CLINICAL SIGNIFICANCE: Optical coherence tomography can be used by personnel of different specialty experience levels and from various specialties to accurately identify canine STS in vivo and ex vivo after a short training session. These encouraging results provide evidence to justify further research to assess the ability of OCT to provide real-time assessments of surgical margins and its applicability to other neoplasms.

Dynamic Signatures of Lipid Droplets as New Markers to Quantify Cellular Metabolic Changes.

The metabolic properties of live cells are very susceptible to intra- or extracellular perturbations, making their measurements challenging tasks. We show that the dynamics of lipid droplets (LDs) carry information to measure the lipid metabolism of live cells. Coherent anti-Stokes Raman scattering microscopy was used to statistically quantify LD dynamics in living cells in a label-free manner. We introduce dynamic signatures of cells derived from the LD displacement, speed, travel length, and directionality, which allows for the detection of cellular changes induced by stimuli such as fluorescent labeling, temperature change, starvation, and chemical treatment. Histogram fittings of the dynamic signatures using log-normal distribution functions provide quantification of changes in cellular metabolic states. The LD dynamics also enable separation of subpopulations of LDs correlated with different functions. We demonstrate that LD dynamics measured by chemical imaging are new markers to quantify the metabolic changes in live cells.

Automated fast computational adaptive optics for optical coherence tomography based on a stochastic parallel gradient descent algorithm.

The transverse resolution of optical coherence tomography is decreased by aberrations introduced from optical components and the tested samples. In this paper, an automated fast computational aberration correction method based on a stochastic parallel gradient descent (SPGD) algorithm is proposed for aberration-corrected imaging without adopting extra adaptive optics hardware components. A virtual phase filter constructed through combination of Zernike polynomials is adopted to eliminate the wavefront aberration, and their coefficients are stochastically estimated in parallel through the optimization of the image metrics. The feasibility of the proposed method is validated by a simulated resolution target image, in which the introduced aberration wavefront is estimated accurately and with fast convergence. The computation time for the aberration correction of a 512 × 512 pixel image from 7 terms to 12 terms requires little change, from 2.13 s to 2.35 s. The proposed method is then applied for samples with different scattering properties including a particle-based phantom, ex-vivo rabbit adipose tissue, and in-vivo human retina photoreceptors, respectively. Results indicate that diffraction-limited optical performance is recovered, and the maximum intensity increased nearly 3-fold for out-of-focus plane in particle-based tissue phantom. The SPGD algorithm shows great potential for aberration correction and improved run-time performance compared to our previous Resilient backpropagation (Rprop) algorithm when correcting for complex wavefront distortions. The fast computational aberration correction suggests that after further optimization our method can be integrated for future applications in real-time clinical imaging.

Single-shot two-dimensional spectroscopic magnetomotive optical coherence elastography with graphics processing unit acceleration.

Biomechanical contrast within tissues can be assessed based on the resonant frequency probed by spectroscopic magnetomotive optical coherence elastography (MM-OCE). However, to date, in vivo MM-OCE imaging has not been achieved, mainly due to the constraints on imaging speed. Previously, spatially-resolved spectroscopic contrast was achieved in a "multiple-excitation, multiple-acquisition" manner, where seconds of coil cooling time set between consecutive imaging frames lead to total acquisition times of tens of minutes. Here, we demonstrate an improved data acquisition speed by providing a single chirped force excitation prior to magnetomotion imaging with a BM-scan configuration. In addition, elastogram reconstruction was accelerated by exploiting the parallel computing capability of a graphics processing unit (GPU). The accelerated MM-OCE platform achieved data acquisition in 2.9 s and post-processing in 0.6 s for a 2048-frame BM-mode stack. In addition, the elasticity sensing functionality was validated on tissue-mimicking phantoms with high spatial resolution. For the first time, to the best of our knowledge, MM-OCE images were acquired from the skin of a living mouse, demonstrating its feasibility for in vivo imaging.

K-means clustering of coherent Raman spectra from extracellular vesicles visualized by label-free multiphoton imaging.

Extracellular vesicles (EVs) have emerged as potential biomarkers in cancer research and for clinical diagnosis. Little is known, however, about their spatial distributions in tissue and the different subpopulations that may exist. Here we report the use of label-free nonlinear optical imaging techniques to provide spatially resolved chemical information of EVs within untreated tissues. A multimodal nonlinear optical imaging system incorporating multiphoton autofluorescence and hyperspectral coherent anti-Stokes Raman scattering (CARS) imaging was built to visualize the spatial tissue distribution and probe the spectra of EVs. K-means clustering is performed on the CARS spectra from EVs in rat mammary tissues and human breast tumor tissue to reveal both the spatial distribution of EV clusters and their different chemical signatures. Correlations are identified between EV clusters and metabolic information.

Two-photon microscope using a fiber-based approach for supercontinuum generation and light delivery to a small-footprint optical head.

In this Letter, we report a low-cost, portable, two-photon excitation fluorescence microscopy imager that uses a fiber-based approach for both femtosecond supercontinuum (SC) generation and light delivery to the optical head. The SC generation is based on a tapered polarization-maintaining photonic crystal fiber that uses pre-chirped femtosecond narrowband pulses to generate a coherent SC spectrum with a bandwidth of approximately 300 nm. Using this approach, high-power, near-transform-limited, wavelength-selectable SC pulses are generated and directly delivered to the imaging optical head. Preliminary testing of this imager on brain slices is presented, demonstrating a high signal-to-noise ratio and sub-cellular imaging capabilities to a depth of approximately 200 µm. These results demonstrate the suitability of the technology for ex vivo and potentially in vivo cellular-level biomedical imaging applications.

In vivo characterization of minipig skin as a model for dermatological research using multiphoton microscopy.

Minipig skin is one of the most widely used non-rodent animal skin models for dermatological research. A thorough characterization of minipig skin is essential for gaining deeper understanding of its structural and functional similarities with human skin. In this study, three-dimensional (3-D) in vivo images of minipig skin was obtained non-invasively using a multimodal optical imaging system capable of acquiring two-photon excited fluorescence (TPEF) and fluorescence lifetime imaging microscopy (FLIM) images simultaneously. The images of the structural features of different layers of the minipig skin were qualitatively and quantitatively compared with those of human skin. Label-free imaging of skin was possible due to the endogenous fluorescence and optical properties of various components in the skin such as keratin, nicotinamide adenine dinucleotide phosphate (NAD(P)H), melanin, elastin, and collagen. This study demonstrates the capability of optical biopsy techniques, such as TPEF and FLIM, for in vivo non-invasive characterization of cellular and functional features of minipig skin, and the optical image-based similarities of this commonly utilized model of human skin. These optical imaging techniques have the potential to become promising tools in dermatological research for developing a better understanding of animal skin models, and for aiding in translational pre-clinical to clinical studies.

Effect of Nonphosphorus Corrosion Inhibitors on Biofilm Pore Structure and Mechanical Properties.

Understanding the effects of biofilm structural and mechanical properties, which can influence biofilm cohesiveness and detachment under physical stress, is critical for biofilm and biofilm-associated pathogen control. In this study, we used optical coherence tomography (OCT) and nanoindentation to determine the role of silicate and tin (two experimental nonphosphate corrosion inhibitors) on the porous structure and stiffness of three types of multispecies biofilms. These biofilms were grown from groundwater (a drinking water source), and this groundwater was amended with either tin or silicate corrosion inhibitor (0.5 mg/L as Sn and 20 mg/L as SiO2). Based on the elastic moduli of these biofilms, tin biofilms and groundwater biofilms were the stiffest, followed by silicate biofilms. The thickness normalized by the growth time for silicate biofilms was highest at 38 ± 7.1 µm/month, compared to 21 ± 3.2 and 11 ± 2.4 µm/month for tin biofilms and groundwater biofilms, respectively. The silicate biofilms had the greatest overall porosities and were thickest among the three biofilms. Based on the pore network modeling (PNM) of OCT images, larger pores and connections were found in the silicate biofilms compared to those in tin and groundwater biofilms. Our analysis showed that the thicker and more porous biofilms (silicate biofilms) were potentially less resistant to deformation than the thinner and denser biofilms (tin and groundwater biofilms).