Fast denoising and lossless spectrum extraction in stimulated Raman scattering microscopy

Stimulated Raman scattering (SRS) microscopy is a nonlinear optical imaging method for visualizing chemical content based on molecular vibrational bonds. However, the imaging speed and sensitivity are currently limited by the noise of the light beam probing the Raman process. In this paper, we present a fast non-average denoising and high-precision Raman shift extraction method, based on a self-reinforcing signal-to-noise ratio (SNR) enhancement algorithm, for SRS spectroscopy and microscopy. We compare the results of this method with the filtering methods and the reported experimental methods to demonstrate its high efficiency and high precision in spectral denoising, Raman peak extraction and image quality improvement. We demonstrate a maximum SNR enhancement of 10.3 dB in fixed tissue imaging and 11.9 dB in vivo imaging. This method reduces the cost and complexity of the SRS system and allows for high-quality SRS imaging without use of special laser, complicated system design and Raman tags.     

Identification of blood species based on surface-enhanced Raman scattering spectroscopy and convolutional neural network

The identification of blood species is of great significance in many aspects such as forensic science, wildlife protection, and customs security and quarantine. Conventional Raman spectroscopy combined with chemometrics is an established method for identification of blood species. However, the Raman spectrum of trace amount of blood could hardly be obtained due to the very small cross-section of Raman scattering. In order to overcome this limitation, surface-enhanced Raman scattering (SERS) was adopted to analyze trace amount of blood. The 785 nm laser was selected as the optimal laser to acquire the SERS spectra, and the blood SERS spectra of 19 species were measured. The convolutional neural network (CNN) was used to distinguish the blood of 19 species including human. The recognition accuracy of the blood species was obtained with 98.79%. Our study provides an effective and reliable method for identification and classification of trace amount of blood.     

Optimization of spatial resolution and scattering effects for biomedical fluorescence imaging by using sub-regions of the shortwave infrared spectrum

We evaluated the impact of light-scattering effects on spatial resolution in different shortwave infrared (SWIR) sub-regions by analyzing two SWIR emissive phantoms made of polydimethylsiloxane (PDMS)-gold nanoclusters (Au NCs) composite covered with mice skin, or capillary tubes filled with Au NCs or IRDye 800CW at different depth in intralipids and finally, after administration of the Au NCs intravenously in mice. Our findings highlighted the benefit of working at the highest tested spectral range of the SWIR region with a 50% enhancement of spatial resolution measured in artificial model when moving from NIR-II (1000-1300 nm) to NIR-IIa (1300-1450 nm) region, and a 25% reduction of the scattering from the skin determined by point spread function analysis from the NIR-II to NIR-IIb region (1500-1700 nm). We also confirmed that a series of Monte Carlo restoration of images significantly improved the spatial resolution in vivo in mice in deep tissues both in the NIR-II and NIR-IIa spectral windows.     

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.     

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.     

Deep learning: step forward to high-resolution in vivo shortwave infrared imaging

Shortwave infrared window (SWIR: 1000–1700 nm) represents a major improvement compared to the NIR-I region (700–900 nm) in terms of temporal and spatial resolutions in depths down to 4 mm. SWIR is a fast and cheap alternative to more precise methods such as X-ray and opto-acoustic imaging. Main obstacles in SWIR imaging are the noise and scattering from tissues and skin that reduce the precision of the method. We demonstrate that the combination of SWIR in vivo imaging in the NIR-IIb region (1500–1700 nm) with advanced deep learning image analysis allows to overcome these obstacles and making a large step forward to high resolution imaging: it allows to precisely segment vessels from tissues and noise, provides morphological structure of the vessels network, with learned pseudo-3D shape, their relative position, dynamic information of blood vascularization in depth in small animals and distinguish the vessels types: artieries and veins. For demonstration we use neural network IterNet that exploits structural redundancy of the blood vessels, which provides a useful analysis tool for raw SWIR images.     

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.     

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.     

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.     

A representation learning approach for recovering scatter-corrected spectra from Fourier-transform infrared spectra of tissue samples

Infrared spectra obtained from cell or tissue specimen have commonly been observed to involve a significant degree of scattering effects, often Mie scattering, which probably overshadows biochemically relevant spectral information by a nonlinear, nonadditive spectral component in Fourier transform infrared (FTIR) spectroscopic measurements. Correspondingly, many successful machine learning approaches for FTIR spectra have relied on preprocessing procedures that computationally remove the scattering components from an infrared spectrum. We propose an approach to approximate this complex preprocessing function using deep neural networks. As we demonstrate, the resulting model is not just several orders of magnitudes faster, which is important for real-time clinical applications, but also generalizes strongly across different tissue types. Using Bayesian machine learning approaches, our approach unveils model uncertainty that coincides with a band shift in the amide I region that occurs when scattering is removed computationally based on an established physical model. Furthermore, our proposed method overcomes the trade-off between computation time and the corrected spectrum being biased towards an artificial reference spectrum.     

Diagnosing colorectal abnormalities using scattering coefficient maps acquired from optical coherence tomography

Optical coherence tomography (OCT) has shown potential in differentiating normal colonic mucosa from neoplasia. In this study of 33 fresh human colon specimens, we report the first use of texture features and computer vision-based imaging features acquired from en face scattering coefficient maps to characterize colorectal tissue. En face scattering coefficient maps were generated automatically using a new fast integral imaging algorithm. From these maps, a gray-level cooccurrence matrix algorithm was used to extract texture features, and a scale-invariant feature transform algorithm was used to derive novel computer vision-based features. In total, 25 features were obtained, and the importance of each feature in diagnosis was evaluated using a random forest model. Two classifiers were assessed on two different classification tasks. A support vector machine model was found to be optimal for distinguishing normal from abnormal tissue, with 94.7% sensitivity and 94.0% specificity, while a random forest model performed optimally in further differentiating abnormal tissues (i.e., cancerous tissue and adenomatous polyp) with 86.9% sensitivity and 85.0% specificity. These results demonstrated the potential of using OCT to aid the diagnosis of human colorectal disease.     

Direct spectroscopic determination of functional sulphydryl groups on intact cell surfaces by surface-enhanced resonance Raman scattering

Semi-quantitative and direct determination of labelled sulphydryl groups on the surface of intact erythrocytes has been accomplished for the first time with surface-enhanced resonance Raman scattering (SERRS). The method, which involves the use of citrate-reduced silver colloids, is sensitive and selective. A 10^–8 M effective concentration of picomole quantities of sulphydryl groups was determined in the presence of the normally overwhelming signal from haemoglobin. This seminal study suggests that SERRS may be applied to other in situ, site-directed labelling experiments. Correspondence to: W.E. Smith     

Individual Split Au Square Nanorings for Surface-Enhanced Raman and Hyper-Raman Scattering

In this paper, individual split Au square nanorings were numerically proposed as novel substrates for surface-enhanced Raman and hyper-Raman scattering (SERS and SEHRS) simultaneously. The peak wavelengths of their localized surface plasmon resonance (LSPR) fall in the near-infrared and visible light regions, respectively, which are able to be finely tuned to match well with the wavelengths of the incident laser and hyper-Raman scattered light beams. Their SEHRS and SERS performances along with electromagnetic (EM) field distributions are numerically investigated by finite element method. With the enhancement of near electric-fields generated by LSPRs, the maximum SEHRS and SERS enhancement factors are demonstrated to reach 1.22?×?10¹² and 10⁸, respectively. Meanwhile, the corresponding SERS-based refractive index (RI) sensitivity factor reaches as high as 258 nm/RIU and 893 nm/RIU, at visible and near-infrared wavelengths, respectively. The proposed structure holds great promise both for developing SEHRS- and SERS-based RI sensing substrates, which shows strong potential applications in nanosensing and enhanced Raman scattering.

     

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.     

Inside Cover: Comparison between high definition FT‐IR,Raman and AFM‐IR for subcellular chemical imaging of cholesteryl esters in prostate cancer cells (J. Biophotonics 5/2020)

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. Three vibrational spectroscopy techniques (High Definition FT‐IR, Raman and 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. Further details can be found in the article by Maciej Roman, Tomasz P. Wrobel, Czeslawa Paluszkiewicz, and Wojciech M. Kwiatek ( e201960094 ).

     

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.     

Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering (CARS)

Coherent Raman imaging techniques have seen a dramatic increase in activity over the past decade due to their promise to enable label-free optical imaging with high molecular specificity ¹. The sensitivity of these techniques, however, is many orders of magnitude weaker than fluorescence, requiring milli-molar molecular concentrations ^1,2. Here, we describe a technique that can enable the detection of weak or low concentrations of Raman-active molecules by amplifying their signal with that obtained from strong or abundant Raman scatterers. The interaction of short pulsed lasers in a biological sample generates a variety of coherent Raman scattering signals, each of which carry unique chemical information about the sample. Typically, only one of these signals, e.g. Coherent Anti-stokes Raman scattering (CARS), is used to generate an image while the others are discarded. However, when these other signals, including 3-color CARS and four-wave mixing (FWM), are collected and compared to the CARS signal, otherwise difficult to detect information can be extracted ³. For example, doubly-resonant CARS (DR-CARS) is the result of the constructive interference between two resonant signals ⁴. We demonstrate how tuning of the three lasers required to produce DR-CARS signals to the 2845 cm^-1 CH stretch vibration in lipids and the 2120 cm^-1 CD stretching vibration of a deuterated molecule (e.g. deuterated sugars, fatty acids, etc.) can be utilized to probe both Raman resonances simultaneously. Under these conditions, in addition to CARS signals from each resonance, a combined DR-CARS signal probing both is also generated. We demonstrate how detecting the difference between the DR-CARS signal and the amplifying signal from an abundant molecule''s vibration can be used to enhance the sensitivity for the weaker signal. We further demonstrate that this approach even extends to applications where both signals are generated from different molecules, such that e.g. using the strong Raman signal of a solvent can enhance the weak Raman signal of a dilute solute.     

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.     

Designable nanoplasmonic biomarkers for direct microscopy cytopathology diagnostics

Direct microscopy interpretation of fine‐needle biopsy cytological samples is routinely used by practicing cytopathologists. Adding possibility to identify selective and multiplexed biomarkers on the same samples and with the same microscopy technique can greatly improve diagnostic accuracy. In this article, we propose to use biomarkers based on designable plasmonic nanoparticles (NPs) with unique optical properties and excellent chemical stability that can satisfy the above‐mentioned requirements. By finely controlling the size and composition of gold‐silver alloy NPs and gold nanorods, the NPs plasmonic resonance properties, such as scattering efficiency and resonance peak spectral position, are adjusted in order to provide reliable identification and chromatic differentiation by conventional direct microscopy. Efficient darkfield NPs imaging is performed by using a novel circular side illumination adaptor that can be easily integrated into any microscopy setup while preserving standard cytopathology visualization method. The efficiency of the proposed technology for fast visual detection and differentiation of three spectrally distinct NP‐markers is demonstrated in different working media, thus confirming the potential application in conventional cytology preparations. It is worth emphasizing that the presented technology does not interfere with standard visualization with immunohistochemical staining, but should rather be considered as a second imaging modality to confirm the diagnostics.