3D-Printed high-NA catadioptric thin lens for suppression of XPM background in Stimulated Raman Scattering microscopy

Stimulated Raman Scattering (SRS) is a fast chemical imaging technique with remarkable bioscience applications. Cross Phase Modulation (XPM) is a ubiquitous nonlinear phenomenon that can create spurious background signals that render difficult a high-contrast imaging in SRS measurements. The XPM-induced signal is usually suppressed using high numerical aperture (NA) microscope objectives or condensers to collect the transmitted excitation beam. However, these high NA optics feature short working distances, hence they are not compatible with stage-top incubators, that are necessary to perform live-cell time-lapse experiments in controlled environments. Here, we show a 3D printed high NA compact catadioptric lens that fits inside stage-top incubators and allows the collection of XPM-free SRS signals. The lens delivers SRS images and spectra with a quality comparable to a signal collection with a high-NA microscope objective. We also demonstrate the compatibility of the 3D printed lens with other nonlinear microscopies usually associated with SRS in multimodal microscopes.     

Fingerprint‐to‐CH stretch continuously tunable high spectral resolution stimulated Raman scattering microscope

Stimulated Raman scattering (SRS) microscopy is a label‐free method generating images based on chemical contrast within samples, and has already shown its great potential for high‐sensitivity and fast imaging of biological specimens. The capability of SRS to collect molecular vibrational signatures in bio‐samples, coupled with the availability of powerful statistical analysis methods, allows quantitative chemical imaging of live cells with sub‐cellular resolution. This application has substantially driven the development of new SRS microscopy platforms. Indeed, in recent years, there has been a constant effort on devising configurations able to rapidly collect Raman spectra from samples over a wide vibrational spectral range, as needed for quantitative analysis by using chemometric methods. In this paper, an SRS microscope which exploits spectral shaping by a narrowband and rapidly tunable acousto‐optical tunable filter (AOTF) is presented. This microscope enables spectral scanning from the Raman fingerprint region to the Carbon‐Hydrogen (CH)‐stretch region without any modification of the optical setup. Moreover, it features also a high enough spectral resolution to allow resolving Raman peaks in the crowded fingerprint region. Finally, application of the developed SRS microscope to broadband hyperspectral imaging of biological samples over a large spectral range from 800 to 3600 cm^?1, is demonstrated.     

Histological classification of atherosclerotic arteries using high-speed confocal Raman microscopy with machine learning

Confocal Raman microscopy is a useful tool to observe composition and constitution of label-free samples at high spatial resolution. However, accurate characterization of microstructure of tissue and its application in diagnostic imaging are challenging due to weak Raman scattering signal and complex chemical composition of tissue. We have developed a method to improve imaging speed, diffraction efficiency, and spectral resolution of confocal Raman microscopy. In addition to the novel imaging technique, the machine learning method enables confocal Raman microscopy to visualize accurate histology of tissue sections. Here, we have demonstrated the performance of the proposed method by measuring histological classification of atherosclerotic arteries and compared the histological confocal Raman images with the conventional staining method. Our new confocal Raman microscopy enables us to comprehend the structure and biochemical composition of tissue and diagnose the buildup of atherosclerotic plaques in the arterial wall without labeling.     

Blood plasma resonance Raman spectroscopy combined with multivariate analysis for esophageal cancer detection

We herein report a novel, reliable and inexpensive method for detecting esophageal cancer using blood plasma resonance Raman spectroscopy combined with multivariate analysis methods. The blood plasma samples were divided into late stage cancer group (n = 164), early stage cancer group (n = 35) and normal group (n = 135) based on clinical pathological diagnosis. Using a specially designed quartz capillary tube as sample holder, we obtained higher quality resonance Raman spectra of blood plasma than existing method. The study demonstrated that the carotenoids levels in blood plasma were reduced in esophageal cancer patients. The area under the receiver operating characteristic curve (and 95% confidence interval) calculated by wavenumber selection and principal component analysis combined with linear discriminant analysis (PC-LDA) algorithm were 0.894 (0.858-0.929), 0.901 (0.841-0.960) and 0.871 (0.799-0.942) for differentiating late cancer from normal, late cancer from early cancer, and early cancer from normal respectively. The contribution from the two carotenoids wavenumber regions of 1155 and 1515 cm⁻¹ were more than 84.2%. The results show that the plasma carotenoids could be a potential biomarker for screening esophageal cancer using resonance Raman spectroscopy combined with wavenumber selection and PC-LDA algorithms.      

Comparison between high definition FT‐IR,Raman and AFM‐IR for subcellular chemical imaging of cholesteryl esters in prostate cancer cells

The family of vibrational spectroscopic imaging techniques grows every few years and there is a need to compare and contrast new modalities with the better understood ones, especially in the case of demanding biological samples. Three vibrational spectroscopy techniques (high definition Fourier‐transform infrared [FT‐IR], Raman and atomic force microscopy infrared [AFM‐IR]) were applied for subcellular chemical imaging of cholesteryl esters in PC‐3 prostate cancer cells. The techniques were compared and contrasted in terms of image quality, spectral pattern and chemical information. All tested techniques were found to be useful in chemical imaging of cholesterol derivatives in cancer cells. The results obtained from FT‐IR and Raman imaging showed to be comparable, whereas those achieved from AFM‐IR study exhibited higher spectral heterogeneity. It confirms AFM‐IR method as a powerful tool in local chemical imaging of cells at the nanoscale level. Furthermore, due to polarization effect, p‐polarized AFM‐IR spectra showed strong enhancement of lipid bands when compared to FT‐IR.     

Shedding new light on lipid functions with CARS and SRS microscopy

Modern optical microscopy has granted biomedical scientists unprecedented access to the inner workings of a cell, and revolutionized our understanding of the molecular mechanisms underlying physiological and disease states. In spite of these advances, however, visualization of certain classes of molecules (e.g. lipids) at the sub-cellular level has remained elusive. Recently developed chemical imaging modalities – Coherent Anti-Stokes Raman Scattering (CARS) microscopy and Stimulated Raman Scattering (SRS) microscopy – have helped bridge this gap. By selectively imaging the vibration of a specific chemical group, these non-invasive techniques allow high-resolution imaging of individual molecules in vivo, and circumvent the need for potentially perturbative extrinsic labels. These tools have already been applied to the study of fat metabolism, helping uncover novel regulators of lipid storage. Here we review the underlying principle of CARS and SRS microscopy, and discuss the advantages and caveats of each technique. We also review recent applications of these tools in the study of lipids as well as other biomolecules, and conclude with a brief guide for interested researchers to build and use CARS/SRS systems for their own research. This article is part of a Special Issue entitled Tools to study lipid functions.     

Vibrational two-photon microscopy for tissue imaging: Short-wave infrared surface-enhanced resonance hyper-Raman scattering

Multiphoton microscopy using short-wave infrared (SWIR) radiation offers nondestructive and high-resolution imaging through tissue. Two-photon fluorescence (TPF), for example, is commonly employed to increase the penetration depth and spatial resolution of SWIR imaging, but the broad spectral peaks limit its multiplexing capabilities. Hyper-Raman scattering, the vibrational analog of TPF, yields spectral features on the order of 20 cm^?1 and reporter-functionalized noble metal nanoparticles (NPs) provide a platform for both hyper-Raman signal enhancement and selective targeting in biological media. Herein we report the first tissue imaging study employing surface-enhanced resonance hyper-Raman scattering (SERHRS), the two-photon analog of surface-enhanced resonance Raman scattering. Specifically, we employ multicore gold-silica NPs (Au@SiO₂ NPs) functionalized with a near infrared-resonant cyanine dye, 3,3′-diethylthiatricarbocyanine iodide as a SERHRS reporter. SWIR SERHRS spectra are efficiently acquired from mouse spleen tissue. SWIR SERHRS combines two-photon imaging advantages with narrow vibrational peak widths, presenting future applications of multitargeted bioimaging.     

In vivo sensing of cutaneous edema: A comparative study of diffuse reflectance,Raman spectroscopy and multispectral imaging

Quantitative noninvasive assessment of water content in tissues is important for biomedicine. Optical spectroscopy is potentially capable of solving this problem; however, its applicability for clinical diagnostics remains questionable. The presented study compares diffuse reflectance spectroscopy, Raman spectroscopy and multispectral imaging in the characterization of cutaneous edema. The source-detector geometries for each method are selected based on Monte Carlo simulations results to detect the signal from the dermis. Then, the kinetics of the edema development is studied for two models. All methods demonstrate synchronous trends for histamine-induced edema: The water content reaches a maximum of 1 hour after histamine application and then gradually decreases. For the venous occlusion, a 51% increase in water content is observed with Raman spectroscopy. The differences in water content estimation by three methods are explained based on the light propagation model. The obtained results are essential for introducing quantitative optical water measurement technology to the clinics.     

Rational design of thiolated polyenes as trifunctional Raman reporter molecules in surface‐enhanced Raman scattering nanotags for cytokine detection in a lateral flow assay

The characteristic vibrational spectroscopic fingerprint of Raman reporter molecules adsorbed on noble metal nanoparticles is employed for the identification of target proteins by the corresponding surface‐enhanced Raman scattering (SERS) nanotag‐labeled antibodies. Here, we present the modular synthesis of thiolated polyenes with two to five C═C double bonds introduced via stepwise Wittig reactions. The experimental characterization of their electronic and vibrational properties is complemented by density functional theory calculations. Highly SERS‐active nanotags are generated by using the thiolated polyenes as Raman reporter molecules in Au/Au core/satellite supraparticles with multiple hot spots. The cytokines IL‐1β and IFN‐γ are detected in a duplex SERS‐based lateral flow assay on a nitrocellulose test strip by Raman microscopy. The thiolated polyenes are suitable for use in immuno‐SERS applications such as point‐of‐care testing as well as cellular and tissue imaging.     

Deuterium Raman imaging for lipid analysis

Raman microscopy has been used to deduce information about the distributions of endogenous biomolecules without exogenous labeling. Several functional groups, such as alkynes (CC), nitriles (CN), and carbon-deuterium (C–D) bonds, have been employed in recent years as Raman tags to detect target molecules in cells. In this article, we review some recent advances in applications using deuterated fatty acids for lipid analysis, such as investigation of tumor-selective cytotoxicity of γ-linolenic acid (GLA), simultaneous two-color imaging of stearate and oleate using deuterated and protonated alkynes, Raman hyperspectral imaging, and analyses of the physical properties of lipids through spectral unmixing of the C–D vibrational frequencies. In addition, we review some advanced methods for observing intracellular metabolic activities, such as de novo lipogenesis from deuterium-labeled precursors.     

Supervised learning methods for the recognition of melanoma cell lines through the analysis of their Raman spectra

Malignant melanoma is an aggressive form of skin cancer, which develops from the genetic mutations of melanocytes – the most frequent involving BRAF and NRAS genes. The choice and the effectiveness of the therapeutic approach depend on tumour mutation; therefore, its assessment is of paramount importance. Current methods for mutation analysis are destructive and take a long time; instead, Raman spectroscopy could provide a fast, label-free and non-destructive alternative. In this study, confocal Raman microscopy has been used for examining three in vitro melanoma cell lines, harbouring different molecular profiles and, in particular, specific BRAF and NRAS driver mutations. The molecular information obtained from Raman spectra has served for developing two alternative classification algorithms based on linear discriminant analysis and artificial neural network. Both methods provide high accuracy (≥90%) in discriminating all cell types, suggesting that Raman spectroscopy may be an effective tool for detecting molecular differences between melanoma mutations.     

Label‐free stimulated Raman scattering imaging reveals silicone breast implant material in tissue

Millions of women worldwide have silicone breast implants. It has been reported that implant failure occurs in approximately a tenth of patients within 10 years, and the consequences of dissemination of silicone debris are poorly understood. Currently, silicone detection in histopathological slides is based on morphological features as no specific immunohistochemical technique is available. Here, we show the feasibility and sensitivity of stimulated Raman scattering (SRS) imaging to specifically detect silicone material in stained histopathological slides, without additional sample treatment. Histology slides of four periprosthetic capsules from different implant types were obtained after explantation, as well as an enlarged axillary lymph node from a patient with a ruptured implant. SRS images coregistered with bright‐field images revealed the distribution and quantity of silicone material in the tissue. Fast and high‐resolution imaging of histology slides with molecular specificity using SRS provides an opportunity to investigate the role of silicone debris in the pathophysiology of implant‐linked diseases.     

Intracellular dynamics of topoisomerase I inhibitor,CPT-11, by slit-scanning confocal Raman microscopy

Most molecular imaging technologies require exogenous probes and may have some influence on the intracellular dynamics of target molecules. In contrast, Raman scattering light measurement can identify biomolecules in their innate state without application of staining methods. Our aim was to analyze intracellular dynamics of topoisomerase I inhibitor, CPT-11, by using slit-scanning confocal Raman microscopy, which can take Raman images with high temporal and spatial resolution. We could acquire images of the intracellular distribution of CPT-11 and its metabolite SN-38 within several minutes without use of any exogenous tags. Change of subcellular drug localization after treatment could be assessed by Raman imaging. We also showed intracellular conversion from CPT-11 to SN-38 using Raman spectra. The study shows the feasibility of using slit-scanning confocal Raman microscopy for the non-labeling evaluation of the intracellular dynamics of CPT-11 with high temporal and spatial resolution. We conclude that Raman spectromicroscopic imaging is useful for pharmacokinetic studies of anticancer drugs in living cells. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Imaging the invisible—Bioorthogonal Raman probes for imaging of cells and tissues

A revolutionary avenue for vibrational imaging with super‐multiplexing capability can be seen in the recent development of Raman‐active bioortogonal tags or labels. These tags and isotopic labels represent groups of chemically inert and small modifications, which can be introduced to any biomolecule of interest and then supplied to single cells or entire organisms. Recent developments in the field of spontaneous Raman spectroscopy and stimulated Raman spectroscopy in combination with targeted imaging of biomolecules within living systems are the main focus of this review. After having introduced common strategies for bioorthogonal labeling, we present applications thereof for profiling of resistance patterns in bacterial cells, investigations of pharmaceutical drug‐cell interactions in eukaryotic cells and cancer diagnosis in whole tissue samples. Ultimately, this approach proves to be a flexible and robust tool for in vivo imaging on several length scales and provides comparable information as fluorescence‐based imaging without the need of bulky fluorescent tags.     

Multimodal Raman spectroscopy and optical coherence tomography for biomedical analysis

Optical techniques hold great potential to detect and monitor disease states as they are a fast, non-invasive toolkit. Raman spectroscopy (RS) in particular is a powerful label-free method capable of quantifying the biomolecular content of tissues. Still, spontaneous Raman scattering lacks information about tissue morphology due to its inability to rapidly assess a large field of view. Optical Coherence Tomography (OCT) is an interferometric optical method capable of fast, depth-resolved imaging of tissue morphology, but lacks detailed molecular contrast. In many cases, pairing label-free techniques into multimodal systems allows for a more diverse field of applications. Integrating RS and OCT into a single instrument allows for both structural imaging and biochemical interrogation of tissues and therefore offers a more comprehensive means for clinical diagnosis. This review summarizes the efforts made to date toward combining spontaneous RS-OCT instrumentation for biomedical analysis, including insights into primary design considerations and data interpretation.     

In vivo coherent anti-Stokes Raman scattering microscopy reveals vitamin A distribution in the liver

Here we present a microscope setup for coherent anti-Stokes Raman scattering (CARS) imaging, devised to specifically address the challenges of in vivo experiments. We exemplify its capabilities by demonstrating how CARS microscopy can be used to identify vitamin A (VA) accumulations in the liver of a living mouse, marking the positions of hepatic stellate cells (HSCs). HSCs are the main source of extracellular matrix protein after hepatic injury and are therefore the main target of novel nanomedical strategies in the development of a treatment for liver fibrosis. Their role in the VA metabolism makes them an ideal target for a CARS-based approach as they store most of the body's VA, a class of compounds sharing a retinyl group as a structural motive, a moiety that is well known for its exceptionally high Raman cross section of the C═C stretching vibration of the conjugated backbone.     

Organelle specific simultaneous Raman/green fluorescence protein microspectroscopy for living cell physicochemical studies

We demonstrate a novel bio‐spectroscopic technique, “simultaneous Raman/GFP microspectroscopy”. It enables organelle specific Raman microspectroscopy of living cells. Fission yeast, Schizosaccharomyces pombe, whose mitochondria are green fluorescence protein (GFP) labeled, is used as a test model system. Raman excitation laser and GFP excitation light irradiate the sample yeast cells simultaneously. GFP signal is monitored in the anti‐Stokes region where interference from Raman scattering is negligibly small. Of note, 13 568 Raman spectra measured from different points of 19 living yeast cells are categorized according to their GFP fluorescence intensities, with the use of a two‐component multivariate curve resolution with alternate least squares (MCR‐ALS) analysis in the anti‐Stokes region. This categorization allows us to know whether or not Raman spectra are taken from mitochondria. Raman spectra specific to mitochondria are obtained by an MCR‐ALS analysis in the Stokes region of 1389 strongly GFP positive spectra. Two mitochondria specific Raman spectra have been obtained. The first one is dominated by protein Raman bands and the second by lipid Raman bands, being consistent with the known molecular composition of mitochondria. In addition, the second spectrum shows a strong band of ergosterol at 1602 cm^?1, previously reported as “Raman spectroscopic signature of life of yeast.”     

N2NSR-OCT: Simultaneous denoising and super-resolution in optical coherence tomography images using semisupervised deep learning

Optical coherence tomography (OCT) imaging shows a significant potential in clinical routines due to its noninvasive property. However, the quality of OCT images is generally limited by inherent speckle noise of OCT imaging and low sampling rate. To obtain high signal-to-noise ratio (SNR) and high-resolution (HR) OCT images within a short scanning time, we presented a learning-based method to recover high-quality OCT images from noisy and low-resolution OCT images. We proposed a semisupervised learning approach named N2NSR-OCT, to generate denoised and super-resolved OCT images simultaneously using up- and down-sampling networks (U-Net (Semi) and DBPN (Semi)). Additionally, two different super-resolution and denoising models with different upscale factors (2× and 4× ) were trained to recover the high-quality OCT image of the corresponding down-sampling rates. The new semisupervised learning approach is able to achieve results comparable with those of supervised learning using up- and down-sampling networks, and can produce better performance than other related state-of-the-art methods in the aspects of maintaining subtle fine retinal structures.     

Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods. The kNN-G has also been the basis for imputation methods using, e.g., neighbor averaging and graph diffusion. However, due to the lack of an agreed-upon optimal objective function for choosing hyperparameters, these methods tend to oversmooth data, thereby resulting in a loss of information with regard to cell identity and the specific gene-to-gene patterns underlying regulatory mechanisms. In this paper, we investigate the tuning of kNN- and diffusion-based denoising methods with a novel non-stochastic method for optimally preserving biologically relevant informative variance in single-cell data. The framework, Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision (DEWÄKSS), uses a self-supervised technique to tune its parameters. We demonstrate that denoising with optimal parameters selected by our objective function (i) is robust to preprocessing methods using data from established benchmarks, (ii) disentangles cellular identity and maintains robust clusters over dimension-reduction methods, (iii) maintains variance along several expression dimensions, unlike previous heuristic-based methods that tend to oversmooth data variance, and (iv) rarely involves diffusion but rather uses a fixed weighted kNN graph for denoising. Together, these findings provide a new understanding of kNN- and diffusion-based denoising methods. Code and example data for DEWÄKSS is available at https://gitlab.com/Xparx/dewakss/-/tree/Tjarnberg2020branch.     

Therapeutic prognosis of prostate cancer using surface-enhanced Raman scattering of patient urine and multivariate statistical analysis

Surface-enhanced Raman scattering (SERS) is highly sensitive and label-free analytical technique based on Raman spectroscopy aided by field-multiplying plasmonic nanostructures. We report the use of SERS measurements of patient urine in conjunction with biostatistical algorithms to assess the treatment response of prostate cancer (PCa) in 12 recurrent (Re) and 63 nonrecurrent (NRe) patient cohorts. Multiple Raman spectra are collected from each urine sample using monodisperse silver nanoparticles (AgNPs) for Raman signal enhancement. Genetic algorithms-partial least squares-linear discriminant analysis (GA-PLS-LDA) was employed to analyze the Raman spectra. Comprehensive GA-PLS-LDA analyses of these Raman spectral features (p = 3.50 × 10⁻¹⁶ ) yield an accuracy of 86.6%, sensitivity of 86.0%, and specificity 87.1% in differentiating the Re and NRe cohorts. Our study suggests that SERS combined with multivariate GA-PLS-LDA algorithm can potentially be used to detect and monitor the risk of PCa relapse and to aid with decision-making for optimal intermediate secondary therapy to recurred patients.