Stimulated Raman Scattering Microscopy Reveals Aberrant Triglyceride Accumulation in Lymphatic Metastasis of Papillary Thyroid Carcinoma.

Lipid metabolic alterations are known to play a crucial role in cancer metastasis. As a key hub in lipid metabolism, intracellular neutral lipid accumulation in lipid droplets (LDs) has become a signature of aggressive human cancers. Nevertheless, it remains unclear whether lipid accumulation displays distinctive features in metastatic lesions compared to the primary ones. Here, we integrated multicolor stimulated Raman scattering (SRS) imaging with confocal Raman spectroscopy on the same platform to quantitatively analyze the amount and composition of LDs in intact human thyroid tissues in situ without any processing or labeling. Inspiringly, we found aberrant accumulation of triglycerides (TGs) in lymphatic metastases but not in normal thyroid, primary papillary thyroid carcinoma (PTC), or normal lymph node. In addition, the unsaturation degree of unsaturated TGs was significantly higher in the lymphatic metastases from patients diagnosed with late-stage (T3/T4) PTC compared to those of patients diagnosed with early-stage (T1/T2) PTC. Furthermore, both public sequencing data analysis and our RNA-seq transcriptomic experiment showed significantly higher expression of alcohol dehydrogenase-1B (ADH1B), which is critical to lipid uptake and transport, in lymphatic metastases relative to the primary ones. In summary, these findings unravel the lipid accumulation as a novel marker and therapeutic target for PTC lymphatic metastasis that has a poor response to the regular radioactive iodine therapy.

Unveiling the Spatiotemporal and Dose Responses within a Single Live Cancer Cell to Photoswitchable Upconversion Nanoparticle Therapeutics Using Hybrid Hyperspectral Stimulated Raman Scattering and Transient Absorption Microscopy.

Photodynamic therapy (PDT) provides an alternative approach to targeted cancer treatment, but the therapeutic mechanism of advanced nanodrugs applied to live cells and tissue is still not well understood. Herein, we employ the hybrid hyperspectral stimulated Raman scattering (SRS) and transient absorption (TA) microscopy developed for real-time in vivo visualization of the dynamic interplay between the unique photoswichable lanthanide-doped upconversion nanoparticle-conjugated rose bengal and triphenylphosphonium (LD-UCNP@CS-Rb-TPP) probe synthesized and live cancer cells. The Langmuir pharmacokinetic model associated with SRS/TA imaging is built to quantitatively track the uptakes and pharmacokinetics of LD-UCNP@CS-Rb-TPP within cancer cells. Rapid SRS/TA imaging quantifies the endocytic internalization rates of the LD-UCNP@CS-Rb-TPP probe in individual HeLa cells, and the translocation of LD-UCNP@CS-Rb-TPP from mitochondria to cell nuclei monitored during PDT can be associated with mitochondria fragmentations and the increased nuclear membrane permeability, cascading the dual organelle ablations in cancer cells. The real-time SRS spectral changes of cellular components (e.g., proteins, lipids, and DNA) observed reflect the PDT-induced oxidative damage and the dose-dependent death pattern within a single live cancer cell, thereby facilitating the real-time screening of optimal light dose and illumination duration controls in PDT. This study provides new insights into the further understanding of drug delivery and therapeutic mechanisms of photoswitchable LD-UCNP nanomedicine in live cancer cells, which are critical in the optimization of nanodrug formulations and development of precision cancer treatment in PDT.

Visualization of Modified Bisarylbutadiyne-Tagged Small Molecules in Live-Cell Nuclei by Stimulated Raman Scattering Microscopy.

Visualizing the distribution of small-molecule drugs in living cells is an important strategy for developing specific, effective, and minimally toxic drugs. As an alternative to fluorescence imaging using bulky fluorophores or cell fixation, stimulated Raman scattering (SRS) imaging combined with bisarylbutadiyne (BADY) tagging enables the observation of small molecules closer to their native intracellular state. However, there is evidence that the physicochemical properties of BADY-tagged analogues of small-molecule drugs differ significantly from those of their parent drugs, potentially affecting their intracellular distribution. Herein, we developed a modified BADY to reduce deviations in physicochemical properties (in particular, lipophilicity and membrane permeability) between tagged and parent drugs, while maintaining high Raman activity in live-cell SRS imaging. We highlight the practical application of this approach by revealing the nuclear distribution of a modified BADY-tagged analogue of JQ1, a bromodomain and extra-terminal motif inhibitor with applications in targeted cancer therapy, in living HeLa cells. The modified BADY, methoxypyridazyl pyrimidyl butadiyne (MPDY), revealed intranuclear JQ1, while BADY-tagged JQ1 did not show a clear nuclear signal. We anticipate that the present approach combining MPDY tagging with live-cell SRS imaging provides important insight into the behavior of intracellular drugs and represents a promising avenue for improving drug development.

On-the-fly Raman microscopy guaranteeing the accuracy of discrimination.

Accelerating the measurement for discrimination of samples, such as classification of cell phenotype, is crucial when faced with significant time and cost constraints. Spontaneous Raman microscopy offers label-free, rich chemical information but suffers from long acquisition time due to extremely small scattering cross-sections. One possible approach to accelerate the measurement is by measuring necessary parts with a suitable number of illumination points. However, how to design these points during measurement remains a challenge. To address this, we developed an imaging technique based on a reinforcement learning in machine learning (ML). This ML approach adaptively feeds back "optimal" illumination pattern during the measurement to detect the existence of specific characteristics of interest, allowing faster measurements while guaranteeing discrimination accuracy. Using a set of Raman images of human follicular thyroid and follicular thyroid carcinoma cells, we showed that our technique requires 3,333 to 31,683 times smaller number of illuminations for discriminating the phenotypes than raster scanning. To quantitatively evaluate the number of illuminations depending on the requisite discrimination accuracy, we prepared a set of polymer bead mixture samples to model anomalous and normal tissues. We then applied a home-built programmable-illumination microscope equipped with our algorithm, and confirmed that the system can discriminate the sample conditions with 104 to 4,350 times smaller number of illuminations compared to standard point illumination Raman microscopy. The proposed algorithm can be applied to other types of microscopy that can control measurement condition on the fly, offering an approach for the acceleration of accurate measurements in various applications including medical diagnosis.

Stimulated Raman Scattering Microscopy Reveals Bioaccumulation of Small Microplastics in Protozoa from Natural Waters.

Microplastics (MPs) are pollutants of global concern, and bioaccumulation determines their biological effects. Although microorganisms form a large fraction of our ecosystem's biomass and are important in biogeochemical cycling, their accumulation of MPs has never been confirmed in natural waters because current tools for field biological samples can detect only MPs > 10 µm. Here, we show that stimulated Raman scattering microscopy (SRS) can image and quantify the bioaccumulation of small MPs (<10 µm) in protozoa. Our label-free method, which differentiates MPs by their SRS spectra, detects individual and mixtures of different MPs (e.g., polyethylene, polypropylene, polyvinyl chloride, polyethylene terephthalate, polystyrene, and poly(methyl methacrylate)) in protozoa. The ability of SRS to quantify cellular MP accumulation is similar to that of flow cytometry, a fluorescence-based method commonly used to determine cellular MP accumulation. Moreover, we discovered that protozoa in water samples from Yangtze River, Xianlin Wastewater Treatment Plant, Lake Taihu and the Pearl River Estuary accumulated MPs < 10 µm, but the proportion of MP-containing cells was low (∼2-5%). Our findings suggest that small MPs could potentially enter the food chain and transfer to organisms at higher trophic levels, posing environmental and health risks that deserve closer scrutiny.

Spectral fingerprinting of cellular lipid droplets using stimulated Raman scattering microscopy and chemometric analysis.

Hyperspectral stimulated Raman scattering (SRS) microscopy is a powerful method for direct visualisation and compositional analysis of cellular lipid droplets. Here we report the application of spectral phasor analysis as a convenient method for the segmentation of lipid droplets using the hyperspectral SRS spectrum in the high wavenumber and fingerprint region of the spectrum. Spectral phasor analysis was shown to discriminate six fatty acids based on vibrational spectroscopic features in solution. The methodology was then applied to studying fatty acid metabolism and storage in a mammalian cancer cell model and during drug-induced steatosis in a hepatocellular carcinoma cell model. The accumulation of fatty acids into cellular lipid droplets was shown to vary as a function of the degree of unsaturation, whilst in a model of drug-induced steatosis, the detection of increased saturated fatty acid esters was observed. Taking advantage of the fingerprint and high wavenumber regions of the SRS spectrum has yielded a greater insight into lipid droplet composition in a cellular context. This approach will find application in the label-free profiling of intracellular lipids in complex disease models.

Investigating ionizing radiation-induced changes in breast cancer cells using stimulated Raman scattering microscopy.

Significance: Altered lipid metabolism of cancer cells has been implicated in increased radiation resistance. A better understanding of this phenomenon may lead to improved radiation treatment planning. Stimulated Raman scattering (SRS) microscopy enables label-free and quantitative imaging of cellular lipids but has never been applied in this domain. Aim: We sought to investigate the radiobiological response in human breast cancer MCF7 cells using SRS microscopy, focusing on how radiation affects lipid droplet (LD) distribution and cellular morphology. Approach: MCF7 breast cancer cells were exposed to either 0 or 30 Gy (X-ray) ionizing radiation and imaged using a spectrally focused SRS microscope every 24 hrs over a 72-hr time period. Images were analyzed to quantify changes in LD area per cell, lipid and protein content per cell, and cellular morphology. Cell viability and confluency were measured using a live cell imaging system while radiation-induced lipid peroxidation was assessed using BODIPY C11 staining and flow cytometry. Results: The LD area per cell and total lipid and protein intensities per cell were found to increase significantly for irradiated cells compared to control cells from 48 to 72 hrs post irradiation. Increased cell size, vacuole formation, and multinucleation were observed as well. No significant cell death was observed due to irradiation, but lipid peroxidation was found to be greater in the irradiated cells than control cells at 72 hrs. Conclusions: This pilot study demonstrates the potential of SRS imaging for investigating ionizing radiation-induced changes in cancer cells without the use of fluorescent labels.

Label-Free Identification of Carbonaceous Particles Using Nonlinear Optical Microscopy.

The adverse health effects of ambient carbonaceous particles (CPs) such as carbon black (CB), black carbon (BC), and brown carbon (BrC) are becoming more evident and depend on their composition and emission source. Therefore, identifying and quantifying these particles in biological samples are important to better understand their toxicity. Here, we report the development of a nonlinear optical approach for the identification of CPs such as CB and BrC using imaging conditions compatible with biomedical samples. The unique visible light fingerprint of CB and BrC nanoparticles (NPs) upon illumination with a femtosecond (fs) pulsed laser at 1300 nm excitation wavelength is an effective approach for their identification in their biological context. The emission from spectral features of these CPs was investigated with time-domain fluorescence lifetime imaging (FLIM) to further support their identification. This study is performed for different types of CPs embedded in agarose gel as well as in in vitro mammalian cells. The unique nonlinear emissive behavior of CP NPs used for their label-free identification is further complementary with fluorophores typically used for specific staining of biological samples thus providing the relevant bio-context.

Full-Spectrum CARS Microscopy of Cells and Tissues with Ultrashort White-Light Continuum Pulses.

Coherent anti-Stokes Raman scattering (CARS) microscopy is an emerging nonlinear vibrational imaging technique that delivers label-free chemical maps of cells and tissues. In narrowband CARS, two spatiotemporally superimposed picosecond pulses, pump and Stokes, illuminate the sample to interrogate a single vibrational mode. Broadband CARS (BCARS) combines narrowband pump pulses with broadband Stokes pulses to record broad vibrational spectra. Despite recent technological advancements, BCARS microscopes still struggle to image biological samples over the entire Raman-active region (400-3100 cm-1). Here, we demonstrate a robust BCARS platform that answers this need. Our system is based on a femtosecond ytterbium laser at a 1035 nm wavelength and a 2 MHz repetition rate, which delivers high-energy pulses used to produce broadband Stokes pulses by white-light continuum generation in a bulk YAG crystal. Combining such pulses, pre-compressed to sub-20 fs duration, with narrowband pump pulses, we generate a CARS signal with a high (<9 cm-1) spectral resolution in the whole Raman-active window, exploiting both the two-color and three-color excitation mechanisms. Aided by an innovative post-processing pipeline, our microscope allows us to perform high-speed (≈1 ms pixel dwell time) imaging over a large field of view, identifying the main chemical compounds in cancer cells and discriminating tumorous from healthy regions in liver slices of mouse models, paving the way for applications in histopathological settings.

Histological diagnosis of unprocessed breast core-needle biopsy via stimulated Raman scattering microscopy and multi-instance learning.

Core-needle biopsy (CNB) plays a vital role in the initial diagnosis of breast cancer. However, the complex tissue processing and global shortage of pathologists have hindered traditional histopathology from timely diagnosis on fresh biopsies. In this work, we developed a full digital platform by integrating label-free stimulated Raman scattering (SRS) microscopy with weakly-supervised learning for rapid and automated cancer diagnosis on un-labelled breast CNB. Methods: We first compared the results of SRS imaging with standard hematoxylin and eosin (H&E) staining on adjacent frozen tissue sections. Then fresh unprocessed biopsy tissues were imaged by SRS to reveal diagnostic histoarchitectures. Next, weakly-supervised learning, i.e., the multi-instance learning (MIL) model was conducted to evaluate the ability to differentiate between benign and malignant cases, and compared with the performance of supervised learning model. Finally, gradient-weighted class activation mapping (Grad-CAM) and semantic segmentation were performed to spatially resolve benign/malignant areas with high efficiency. Results: We verified the ability of SRS in revealing essential histological hallmarks of breast cancer in both thin frozen sections and fresh unprocessed biopsy, generating histoarchitectures well correlated with H&E staining. Moreover, we demonstrated that weakly-supervised MIL model could achieve superior classification performance to supervised learnings, reaching diagnostic accuracy of 95% on 61 biopsy specimens. Furthermore, Grad-CAM allowed the trained MIL model to visualize the histological heterogeneity within the CNB. Conclusion: Our results indicate that MIL-assisted SRS microscopy provides rapid and accurate diagnosis on histologically heterogeneous breast CNB, and could potentially help the subsequent management of patients.

Direct Counting and Imaging Chain Lengths of Lipids by Stimulated Raman Scattering Microscopy.

Direct counting and mapping the chain lengths of fatty acids on a microscopic scale are of particular importance but remain an unsolvable challenge. Although the current hyperspectral stimulated Raman scattering (SRS) microscopy has gained exceptional capability in chemical imaging of the degree of desaturation, the complete lipid characterization, including the carbon chain length quantification, is awaiting a major breakthrough. Here, we pushed the spectral resolution limit of hyperspectral SRS microscopy to 5.4 cm-1 by employing a highly efficient spectral compressor, which achieved spectral narrowing of the fs laser without much energy loss. The SRS imaging with such high spectral resolution enabled us to differ eight types of saturated lipids with carbon chain lengths from C8:0 to C22:0 by interrogating their subtly red-shifting Raman bands of alkyl C-C gauche stretches between 1070 and 1110 cm-1. The SRS microscopy with superior spectral resolution will pave the way for comprehensive lipid characterization and contribute to uncovering the abnormal pathways of lipid metabolism in cancer.

Stimulated Raman Scattering Microscopy Enables Gleason Scoring of Prostate Core Needle Biopsy by a Convolutional Neural Network.

Focal therapy (FT) has been proposed as an approach to eradicate clinically significant prostate cancer while preserving the normal surrounding tissues to minimize treatment-related toxicity. Rapid histology of core needle biopsies is essential to ensure the precise FT for localized lesions and to determine tumor grades. However, it is difficult to achieve both high accuracy and speed with currently available histopathology methods. Here, we demonstrated that stimulated Raman scattering (SRS) microscopy could reveal the largely heterogeneous histologic features of fresh prostatic biopsy tissues in a label-free and near real-time manner. A diagnostic convolutional neural network (CNN) built based on images from 61 patients could classify Gleason patterns of prostate cancer with an accuracy of 85.7%. An additional 22 independent cases introduced as external test dataset validated the CNN performance with 84.4% accuracy. Gleason scores of core needle biopsies from 21 cases were calculated using the deep learning SRS system and showed a 71% diagnostic consistency with grading from three pathologists. This study demonstrates the potential of a deep learning-assisted SRS platform in evaluating the tumor grade of prostate cancer, which could help simplify the diagnostic workflow and provide timely histopathology compatible with FT treatment. SIGNIFICANCE: A platform combining stimulated Raman scattering microscopy and a convolutional neural network provides rapid histopathology and automated Gleason scoring on fresh prostate core needle biopsies without complex tissue processing.

Quantitative Imaging of Intracellular Density with Ratiometric Stimulated Raman Scattering Microscopy.

Cell size and density are tightly controlled in mammalian cells. They impact a wide range of physiological functions, including osmoregulation, tissue homeostasis, and growth regulation. Compared to size, density variation for a given cell type is typically much smaller, implying that cell-type-specific density plays an important role in cell function. However, little is known about how cell density affects cell function or how it is regulated. Current tools for intracellular cell density measurements are limited to either suspended cells or cells grown on 2D substrates, neither of which recapitulate the physiology of single cells in intact tissue. While optical measurements have the potential to noninvasively measure cell density in situ, light scattering in multicellular systems prevents direct quantification. Here, we introduce an intracellular density imaging technique based on ratiometric stimulated Raman scattering microscopy (rSRS). It uses intrinsic vibrational information from intracellular macromolecules to quantify dry mass density. Moreover, water is used as an internal standard to correct for aberration and light scattering effects. We demonstrate real-time measurement of intracellular density and show that density is tightly regulated across different cell types and can be used to differentiate cell types as well as cell states. We further demonstrate dynamic imaging of density change in response to osmotic challenge as well as intracellular density imaging of a 3D tumor spheroid. Our technique has the potential for imaging intracellular density in intact tissue and understanding density regulation and its role in tissue homeostasis.

Multiple Parallel Fusion Network for Predicting Protein Subcellular Localization from Stimulated Raman Scattering (SRS) Microscopy Images in Living Cells.

Stimulated Raman Scattering Microscopy (SRS) is a powerful tool for label-free detailed recognition and investigation of the cellular and subcellular structures of living cells. Determining subcellular protein localization from the cell level of SRS images is one of the basic goals of cell biology, which can not only provide useful clues for their functions and biological processes but also help to determine the priority and select the appropriate target for drug development. However, the bottleneck in predicting subcellular protein locations of SRS cell imaging lies in modeling complicated relationships concealed beneath the original cell imaging data owing to the spectral overlap information from different protein molecules. In this work, a multiple parallel fusion network, MPFnetwork, is proposed to study the subcellular locations from SRS images. This model used a multiple parallel fusion model to construct feature representations and combined multiple nonlinear decomposing algorithms as the automated subcellular detection method. Our experimental results showed that the MPFnetwork could achieve over 0.93 dice correlation between estimated and true fractions on SRS lung cancer cell datasets. In addition, we applied the MPFnetwork method to cell images for label-free prediction of several different subcellular components simultaneously, rather than using several fluorescent labels. These results open up a new method for the time-resolved study of subcellular components in different cells, especially cancer cells.

Novel rapid intraoperative qualitative tumor detection by a residual convolutional neural network using label-free stimulated Raman scattering microscopy.

Determining the presence of tumor in biopsies and the decision-making during resections is often dependent on intraoperative rapid frozen-section histopathology. Recently, stimulated Raman scattering microscopy has been introduced to rapidly generate digital hematoxylin-and-eosin-stained-like images (stimulated Raman histology) for intraoperative analysis. To enable intraoperative prediction of tumor presence, we aimed to develop a new deep residual convolutional neural network in an automated pipeline and tested its validity. In a monocentric prospective clinical study with 94 patients undergoing biopsy, brain or spinal tumor resection, Stimulated Raman histology images of intraoperative tissue samples were obtained using a fiber-laser-based stimulated Raman scattering microscope. A residual network was established and trained in ResNetV50 to predict three classes for each image: (1) tumor, (2) non-tumor, and (3) low-quality. The residual network was validated on images obtained in three small random areas within the tissue samples and were blindly independently reviewed by a neuropathologist as ground truth. 402 images derived from 132 tissue samples were analyzed representing the entire spectrum of neurooncological surgery. The automated workflow took in a mean of 240 s per case, and the residual network correctly classified tumor (305/326), non-tumorous tissue (49/67), and low-quality (6/9) images with an inter-rater agreement of 89.6% (κ = 0.671). An excellent internal consistency was found among the random areas with 90.2% (Cα = 0.942) accuracy. In conclusion, the novel stimulated Raman histology-based residual network can reliably detect the microscopic presence of tumor and differentiate from non-tumorous brain tissue in resection and biopsy samples within 4 min and may pave a promising way for an alternative rapid intraoperative histopathological decision-making tool.

Stimulated Raman scattering microscopy in chemistry and life science - Development, innovation, perspectives.

In this review, we present a summary of the basics of the Stimulated Raman Scattering (SRS) phenomenon, methods of detecting the signal, and collection of the SRS images. We demonstrate the advantages of SRS imaging, and recent developments, but also the limitations, especially in image capture speeds and spatial resolution. We also compare the use of SRS microscopy in biological system studies with other techniques such as fluorescence microscopy, second-harmonic generation (SHG)-based microscopy, coherent anti-Stokes Raman scattering (CARS), and spontaneous Raman, and we show the compatibility of SRS-based systems with other discussed methods. The review is also focused on indicating innovations in SRS microscopy, on the background of which we present the layout and performance of our homemade setup built from commercially available elements enabling for imaging of the molecular structure of single cells over the spectral range of 800-3600 cm-1. Methods of image analysis are discussed, including machine learning methods for obtaining images of the distribution of selected molecules and for the detection of pathological lesions in tissues or malignant cells in the context of clinical diagnosis of a wide range of diseases with the use of SRS microscopy. Finally, perspectives for the development of SRS microscopy are proposed.

Label-Free Imaging of Lipid Droplets in Prostate Cells Using Stimulated Raman Scattering Microscopy and Multivariate Analysis.

Hyperspectral stimulated Raman scattering (SRS) microscopy is a powerful imaging modality for the analysis of biological systems. Here, we report the application of k-means cluster analysis (KMCA) of multi-wavelength SRS images in the high-wavenumber region of the Raman spectrum as a robust and reliable method for the segmentation of cellular organelles based on the intrinsic SRS spectrum. KMCA has been applied to the study of the endogenous lipid biochemistry of prostate cancer and prostate healthy cell models, while the corresponding SRS spectrum of the lipid droplet (LD) cluster enabled direct comparison of their composition. The application of KMCA in visualizing the LD content of prostate cell models following the inhibition of de novo lipid synthesis (DNL) using the acetyl-coA carboxylase inhibitor, 5-(tetradecyloxy)-2-furoic acid (TOFA), is demonstrated. This method identified a reliance of prostate cancer cell models upon DNL for metabolic requirements, with a significant reduction in the cellular LD content after treatment with TOFA, which was not observed in normal prostate cell models. SRS imaging combined with KMCA is a robust method for investigating drug-cell interactions in a label-free manner.

Biomolecular Imaging of Cellular Uptake of Nanoparticles using Multimodal Nonlinear Optical Microscopy.

Probing gold nanoparticles (AuNPs) in living systems is essential to reveal the interaction between AuNPs and biological tissues. Moreover, by integrating nonlinear optical signals such as stimulated Raman scattering (SRS), two-photon excited fluorescence (TPEF), and transient absorption (TA) into an imaging platform, it can be used to reveal biomolecular contrast of cellular structures and AuNPs in a multimodal manner. This article presents a multimodal nonlinear optical microscopy and applies it to perform chemically specific imaging of AuNPs in cancer cells. This imaging platform provides a novel approach for developing more efficient functionalized AuNPs and determining whether they are within vasculatures surrounding the tumor, pericellular, or cellular spaces.

Label-Free Histology and Evaluation of Human Pancreatic Cancer with Coherent Nonlinear Optical Microscopy.

Surgeries achieving maximal tumor resection remain the major effective treatment of pancreatic cancer. Rapid and precise intraoperative diagnosis of pancreatic tissues is critical for optimum surgical outcomes but is challenging for the current staining-based histological methods. We demonstrated that label-free coherent nonlinear optical microscopy with combined stimulated Raman scattering (SRS) and second harmonic generation (SHG) could reveal key diagnostic features of both normal and cancerous human pancreatic tissues. Adjacent pairs of tissue sections from resection margins of 37 patients were imaged by SRS and hematoxylin and eosin staining for direct comparison, demonstrating high diagnostic concordance (Cohen's kappa, κ > 0.97) between them. Fresh unprocessed tissues showed well-preserved histoarchitectures including pancreatic ducts, islets, acini, and nerves. Moreover, the area ratios of collagen fibers were analyzed and found to correlate with the drainage pancreatic amylase level (odds ratio = 28.0, p = 0.0017). Our results indicated that SRS/SHG histology provides potential for rapid intraoperative diagnosis of pancreatic cancer as well as a predictive value of postoperative pancreatic fistula.

Probing Orientations and Conformations of Peptides and Proteins at Buried Interfaces.

Molecular structures of peptides/proteins at interfaces determine their interfacial properties, which play important roles in many applications. It is difficult to probe interfacial peptide/protein structures because of the lack of appropriate tools. Sum frequency generation (SFG) vibrational spectroscopy has been developed into a powerful technique to elucidate molecular structures of peptides/proteins at buried solid/liquid and liquid/liquid interfaces. SFG has been successfully applied to study molecular interactions between model cell membranes and antimicrobial peptides/membrane proteins, surface-immobilized peptides/enzymes, and physically adsorbed peptides/proteins on polymers and 2D materials. A variety of other analytical techniques and computational simulations provide supporting information to SFG studies, leading to more complete understanding of structure-function relationships of interfacial peptides/proteins. With the advance of SFG techniques and data analysis methods, along with newly developed supplemental tools and simulation methodology, SFG research on interfacial peptides/proteins will further impact research in fields like chemistry, biology, biophysics, engineering, and beyond.