In vivo simultaneous nonlinear absorption Raman and fluorescence (SNARF) imaging of mouse brain cortical structures.

Label-free multiphoton microscopy is a powerful platform for biomedical imaging. Recent advancements have demonstrated the capabilities of transient absorption microscopy (TAM) for label-free quantification of hemoglobin and stimulated Raman scattering (SRS) microscopy for pathological assessment of label-free virtual histochemical staining. We propose the combination of TAM and SRS with two-photon excited fluorescence (TPEF) to characterize, quantify, and compare hemodynamics, vessel structure, cell density, and cell identity in vivo between age groups. In this study, we construct a simultaneous nonlinear absorption, Raman, and fluorescence (SNARF) microscope with the highest reported in vivo imaging depth for SRS and TAM at 250-280 µm to enable these multimodal measurements. Using machine learning, we predict capillary-lining cell identities with 90% accuracy based on nuclear morphology and capillary relationship. The microscope and methodology outlined herein provides an exciting route to study several research topics, including neurovascular coupling, blood-brain barrier, and neurodegenerative diseases.

Direct Quantification of Single Red Blood Cell Hemoglobin Concentration with Multiphoton Microscopy.

Blood disorders, diseases, and infections often affect the shape, number, and content of red blood cells (RBCs) dramatically. To combat these pathologies, many therapies target RBCs and their contents directly. Mean corpuscular hemoglobin concentration (MCHC) is an important pathological metric in both identification and treatment. However, current methods for RBC analysis and MCHC quantification rely on bulk measurements. Single RBC measurements could provide necessary insight into the heterogeneity of RBC health and improve therapeutic efficacy. In this study, we present a novel multimodal multiphoton approach for quantifying hemoglobin concentration at single RBC resolution. We achieve this by collecting two images simultaneously that allows us to excite water with stimulated Raman scattering and hemoglobin with transient absorption. This multimodal imaging is enabled by a newly designed orthogonal modulation theme for dual-channel lock-in detection. By leveraging water as an internal standard, we quantify MCHC of healthy RBCs and RBCs infected with Plasmodium yoelii, a commonly studied rodent parasite model.

The effect of chloride, sulfate and dissolved inorganic carbon on iron release from cast iron.

Iron corrosion in drinking water distribution systems causes water discoloration, water quality deterioration, hydraulic loss, and even pipe failures, which are usually influenced by pipe scale structure, water hydraulics, water chemistry, and other factors. This work evaluated the effects of chloride, sulfate, and dissolved inorganic carbon (DIC) on iron release from a 90-year-old cast iron pipe section at water pH 8.0 under stagnant conditions. Experimental results showed that the addition of 150 mg/L sulfate to water significantly increased the mean total iron concentrations to 1.13-2.68 mg/L, relative to 0.54-0.79 mg/L for the baseline water with only 10 mg C/L DIC. Similar results were observed under conditions when chloride was added, and when sulfate and chloride were added together. In contrast, the mean total iron concentrations were significantly reduced by 53-80% in waters with higher DIC of 50 mg C/L, as compared to similar waters with lower DIC of 10 mg C/L. The Larson Ratio could be a good indicator for iron release depending on the circumstances. Iron release was predicted by molecular radial diffusion modelling that accounted for water quality, scale characteristics, hydraulics, and other condition-related information. The results provided insightful information for water systems that have cast iron pipes and galvanized iron pipes and that might encounter changes in water treatment and water sources. More studies are needed to better understand the cast iron corrosion mechanisms under the examined water chemistries.

Frequency Modulation Stimulated Raman Scattering Microscopy through Polarization Encoding.

Stimulated Raman scattering (SRS) microscopy is a powerful method for imaging molecular distributions based on their intrinsic vibrational contrast. However, despite a growing list of biological applications, SRS is frequently hindered by a parasitic background signal which both overpowers the signal in low-signal applications and makes the extraction of quantitative information from images challenging. Frequency modulation (FM) has been used to suppress this parasitic background. However, many FM-SRS methods require either the acquisition of multiple images or the addition of multiple optomechanical components and an extensive realignment procedure. Herein, we report a new procedure for alignment-free FM-SRS utilizing polarization encoding. We demonstrate the efficacy of this approach, along with parabolic amplification of the Stokes pulse, at removing parasitic background signals in SRS microscopy applications. We further highlight how this technique can be used to suppress Raman signals from major molecular species to unveil spectral signatures from nucleic acids in both murine brain tissue and whole blood. Due to its ease of use and demonstrated experimental capabilities, we expect this technique to see broad use in the SRS microscopy community.

Denoising of stimulated Raman scattering microscopy images via deep learning.

Stimulated Raman scattering (SRS) microscopy is a label-free quantitative chemical imaging technique that has demonstrated great utility in biomedical imaging applications ranging from real-time stain-free histopathology to live animal imaging. However, similar to many other nonlinear optical imaging techniques, SRS images often suffer from low signal to noise ratio (SNR) due to absorption and scattering of light in tissue as well as the limitation in applicable power to minimize photodamage. We present the use of a deep learning algorithm to significantly improve the SNR of SRS images. Our algorithm is based on a U-Net convolutional neural network (CNN) and significantly outperforms existing denoising algorithms. More importantly, we demonstrate that the trained denoising algorithm is applicable to images acquired at different zoom, imaging power, imaging depth, and imaging geometries that are not included in the training. Our results identify deep learning as a powerful denoising tool for biomedical imaging at large, with potential towards in vivo applications, where imaging parameters are often variable and ground-truth images are not available to create a fully supervised learning training set.

In Vitro Quantification of Single Red Blood Cell Oxygen Saturation by Femtosecond Transient Absorption Microscopy.

Hemoglobin, the oxygen carrying protein, ferries nearly all bodily oxygen from the lungs to cells and tissues in need. Blood oxygen saturation (sO2) thus plays an important role in maintaining energy homeostasis throughout the body. Clinical and research tools have been developed to monitor sO2 at a wide range of temporal and spatial scales. However, real-time quantification of sO2 at single red blood cell (RBC) resolution remains challenging. Such capability is critically important to study energy metabolism in heterogeneous tissues including brain and tumor tissue. In this work, we develop a ratiometric transient absorption microscopy technique to image hemoglobin sO2. By exploiting differences in transient lifetime kinetics between  oxyhemoglobin and deoxyhemoglobin, we directly quantified the sO2 of single RBCs in real-time without the need for injection of exogenous agents. This simple and high-speed nonlinear optical imaging technique is well suited for in vitro and in vivo quantification of sO2.

Intraoperative assessment of skull base tumors using stimulated Raman scattering microscopy.

Intraoperative consultations, used to guide tumor resection, can present histopathological findings that are challenging to interpret due to artefacts from tissue cryosectioning and conventional staining. Stimulated Raman histology (SRH), a label-free imaging technique for unprocessed biospecimens, has demonstrated promise in a limited subset of tumors. Here, we target unexplored skull base tumors using a fast simultaneous two-channel stimulated Raman scattering (SRS) imaging technique and a new pseudo-hematoxylin and eosin (H&E) recoloring methodology. To quantitatively evaluate the efficacy of our approach, we use modularized assessment of diagnostic accuracy beyond cancer/non-cancer determination and neuropathologist confidence for SRH images contrasted to H&E-stained frozen and formalin-fixed paraffin-embedded (FFPE) tissue sections. Our results reveal that SRH is effective for establishing a diagnosis using fresh tissue in most cases with 87% accuracy relative to H&E-stained FFPE sections. Further analysis of discrepant case interpretation suggests that pseudo-H&E recoloring underutilizes the rich chemical information offered by SRS imaging, and an improved diagnosis can be achieved if full SRS information is used. In summary, our findings show that pseudo-H&E recolored SRS images in combination with lipid and protein chemical information can maximize the use of SRS during intraoperative pathologic consultation with implications for tissue preservation and augmented diagnostic utility.

In Situ Stimulated Raman Scattering (SRS) Microscopy Study of the Dissolution of Sustained-Release Implant Formulation.

Localized drug delivery systems (DDSs) provide therapeutic levels of drug agent while mitigating side effects of systemic delivery. These systems offer controlled release over extended periods of time making them attractive therapies. Monitoring drug dissolution is vital for developing safe and effective means of drug delivery. Currently, dissolution characterization methods are limited to bulk analysis and cannot provide dissolution kinetics at high spatial resolution. However, dissolution rates of drug particles can be heterogeneous with influences from many factors. Insights into finer spatiotemporal dynamics of single particle dissolution could potentially improve pharmacokinetic modeling of dissolution for future drug development. In this work, we demonstrate high-resolution chemical mapping of entecavir, a hepatitis B antiviral drug, embedded in a slow release poly(d,l-lactic acid) formulation with stimulated Raman scattering (SRS) microscopy. By tracking the volume change of individual micron-sized drug particles within the polymer matrix, we establish an analytical protocol for quantitatively profiling dissolution of single crystalline particles in implant formulations in an in situ manner.