Label-free Molecular Imaging and Analysis by Raman Spectroscopy

Spectral insights: Navigating the frontiers of biomedical and microbiological exploration with Raman spectroscopy

Raman spectroscopy (RS), a powerful analytical technique, has gained increasing recognition and utility in the fields of biomedical and biological research. Raman spectroscopic analyses find extensive application in the field of medicine and are employed for intricate research endeavors and diagnostic purposes. Consequently, it enjoys broad utilization within the realm of biological research, facilitating the identification of cellular classifications, metabolite profiling within the cellular milieu, and the assessment of pigment constituents within microalgae. This article also explores the multifaceted role of RS in these domains, highlighting its distinct advantages, acknowledging its limitations, and proposing strategies for enhancement.

Label-Free, Noninvasive Bone Cell Classification by Hyperspectral Confocal Raman Microscopy

Characterizing and identifying cells in multicellular in vitro models remain a substantial challenge. Here, we utilize hyperspectral confocal Raman microscopy and principal component analysis coupled with linear discriminant analysis to form a label-free, noninvasive approach for classifying bone cells and osteosarcoma cells. Through the development of a library of hyperspectral Raman images of the K7M2-wt osteosarcoma cell lines, 7F2 osteoblast cell lines, RAW 264.7 macrophage cell line, and osteoclasts induced from RAW 264.7 macrophages, we built a linear discriminant model capable of correctly identifying each of these cell types. The model was cross-validated using a k-fold cross validation scheme. The results show a minimum of 72% accuracy in predicting cell type. We also utilize the model to reconstruct the spectra of K7M2 and 7F2 to determine whether osteosarcoma cancer cells and normal osteoblasts have any prominent differences that can be captured by Raman. We find that the main differences between these two cell types are the prominence of the β-sheet protein secondary structure in K7M2 versus the α-helix protein secondary structure in 7F2. Additionally, differences in the CH2 deformation Raman feature highlight that the membrane lipid structure is different between these cells, which may affect the overall signaling and functional contrasts. Overall, we show that hyperspectral confocal Raman microscopy can serve as an effective tool for label-free, nondestructive cellular classification and that the spectral reconstructions can be used to gain deeper insight into the differences that drive different functional outcomes of different cells.

Cardiac multiscale bioimaging: from nano- through micro- to mesoscales

Cardiac multiscale bioimaging is an emerging field that aims to provide a comprehensive understanding of the heart and its functions at various levels, from the molecular to the entire organ. It combines both physiologically and clinically relevant dimensions: from nano- and micrometer resolution imaging based on vibrational spectroscopy and high-resolution microscopy to assess molecular processes in cardiac cells and myocardial tissue, to mesoscale structural investigations to improve the understanding of cardiac (patho)physiology. Tailored super-resolution deep microscopy with advanced proteomic methods and hands-on experience are thus strategically combined to improve the quality of cardiovascular research and support future medical decision-making by gaining additional biomolecular information for translational and diagnostic applications.

Review of optical methods for noninvasive imaging of skin fibroblasts-From in vitro to ex vivo and in vivo visualization

Fibroblasts are among the most common cell types in the stroma responsible for creating and maintaining the structural organization of the extracellular matrix in the dermis, skin regeneration, and a range of immune responses. Until now, the processes of fibroblast adaptation and functioning in a varying environment have not been fully understood. Modern laser microscopes are capable of studying fibroblasts in vitro and ex vivo. One-photon- and two-photon-excited fluorescence microscopy, Raman spectroscopy/microspectroscopy are well-suited noninvasive optical methods for fibroblast imaging in vitro and ex vivo. In vivo staining-free fibroblast imaging is not still implemented. The exception is fibroblast imaging in tattooed skin. Although in vivo noninvasive staining-free imaging of fibroblasts in the skin has not yet been implemented, it is expected in the future. This review summarizes the state-of-the-art in fibroblast visualization using optical methods and discusses the advantages, limitations, and prospects for future noninvasive imaging.

RaMALDI: Enabling simultaneous Raman and MALDI imaging of the same tissue section

Multimodal tissue imaging techniques that integrate two complementary modalities are powerful discovery tools for unraveling biological processes and identifying biomarkers of disease. Combining Raman spectroscopic imaging (RSI) and matrix-assisted laser-desorption/ionization (MALDI) mass spectrometry imaging (MSI) to obtain fused images with the advantages of both modalities has the potential of providing spatially resolved, sensitive, specific biomolecular information, but has so far involved two separate sample preparations, or even consecutive tissue sections for RSI and MALDI MSI, resulting in images with inherent disparities. We have developed RaMALDI, a streamlined, integrated, multimodal imaging workflow of RSI and MALDI MSI, performed on a single tissue section with one sample preparation protocol. We show that RaMALDI imaging of various tissues effectively integrates molecular information acquired from both RSI and MALDI MSI of the same sample, which will drive discoveries in cell biology, biomedicine, and pathology, and advance tissue diagnostics.

Application of serum Raman spectroscopy combined with classification model for rapid breast cancer screening

Introduction

This study aimed to evaluate the feasibility of using general Raman spectroscopy as a method to screen for breast cancer. The objective was to develop a machine learning model that utilizes Raman spectroscopy to detect serum samples from breast cancer patients, benign cases, and healthy subjects, with puncture biopsy as the gold standard for comparison. The goal was to explore the value of Raman spectroscopy in the differential diagnosis of breast cancer, benign lesions, and healthy individuals.

Methods

In this study, blood serum samples were collected from a total of 333 participants. Among them, there were 129 cases of tumors (pathologically diagnosed as breast cancer and labeled as cancer), 91 cases of benign lesions (pathologically diagnosed as benign and labeled as benign), and 113 cases of healthy controls (labeled as normal). Raman spectra of the serum samples from each group were collected. To classify the normal, benign, and cancer sample groups, principal component analysis (PCA) combined with support vector machine (SVM) was used. The SVM model was evaluated using a cross-validation method.

Results

The results of the study revealed significant differences in the mean Raman spectra of the serum samples between the normal and tumor/benign groups. Although the mean Raman spectra showed slight variations between the cancer and benign groups, the SVM model achieved a remarkable prediction accuracy of up to 98% for classifying cancer, benign, and normal groups.

Discussion

In conclusion, this exploratory study has demonstrated the tremendous potential of general Raman spectroscopy as a clinical adjunctive diagnostic and rapid screening tool for breast cancer.

Multifocal Raman Spectrophotometer for Examining Drug-Induced and Chemical-Induced Cellular Changes in 3D Cell Spheroids

Cell spheroids offer alternative in vitro cell models to monolayer cultured cells because they express complexities similar to those of in vivo tissues, such as cellular responses to drugs and chemicals. Raman spectroscopy emerged as a powerful analytical tool for detecting chemical changes in living cells because it nondestructively provides vibrational information regarding a target. Although multiple iterations are required in drug screening to determine drugs to treat cell spheroids and assess the inter-spheroid heterogeneity, current Raman applications used in spheroids analysis allow the observation of only a few spheroids owing to the low throughput of Raman spectroscopy. In this study, we developed a multifocal Raman spectrophotometer that enables simultaneous analysis of multiple spheroids in separate wells of a regular 96-well plate. By utilizing 96 focal spots excitation and parallel signal collection, our system can improve the throughput by approximately 2 orders of magnitude compared to a conventional single-focus Raman microscope. The Raman spectra of HeLa cell spheroids treated with anticancer drugs and HepG2 cell spheroids treated with free fatty acids were measured simultaneously, and concentration-dependent cellular responses were observed in both studies. Using the multifocal Raman spectrophotometer, we rapidly observed chemical changes in spheroids, and thus, this system can facilitate the application of Raman spectroscopy in analyzing the cellular responses of spheroids.

Detection of primary myelofibrosis in blood serum via Raman spectroscopy assisted by machine learning approaches; correlation with clinical diagnosis

Primary myelofibrosis (PM) is one of the myeloproliferative neoplasm, where stem cell-derived clonal neoplasms was noticed. Diagnosis of this disease is based on: physical examination, peripheral blood findings, bone marrow morphology, cytogenetics, and molecular markers. However, the molecular marker of PM, which is a mutation in the JAK2V617F gene, was observed also in other myeloproliferative neoplasms such as polycythemia vera and essential thrombocythemia. Therefore, there is a need to find methods that provide a marker unique to PM and allow for higher accuracy of PM diagnosis and consequently the treatment of the disease. Continuing, in this study, we used Raman spectroscopy, Principal Components Analysis (PCA), and Partial Least Squares (PLS) analysis as helpful diagnostic tools for PM. Consequently, we used serum collected from PM patients, which were classified using clinical parameters of PM such as the dynamic international prognostic scoring system (DIPSS) for primary myelofibrosis plus score, the JAK2V617F mutation, spleen size, bone marrow reticulin fibrosis degree and use of hydroxyurea drug features. Raman spectra showed higher amounts of C-H, C-C and C-C/C-N and amide II and lower amounts of amide I and vibrations of CH3 groups in PM patients than in healthy ones. Furthermore, shifts of amides II and I vibrations in PM patients were noticed. Machine learning methods were used to analyze Raman regions: (i) 800 cm-1 and 1800 cm-1, (ii) 1600 cm-1-1700 cm-1, and (iii) 2700 cm-1-3000 cm-1 showed 100 % accuracy, sensitivity, and specificity. Differences in the spectral dynamic showed that differences in the amide II and amide I regions were the most significant in distinguishing between PM and healthy subjects. Importantly, until now, the efficacy of Raman spectroscopy has not been established in clinical diagnostics of PM disease using the correlation between Raman spectra and PM clinical prognostic scoring. Continuing, our results showed the correlation between Raman signals and bone marrow fibrosis, as well as JAKV617F. Consequently, the results revealed that Raman spectroscopy has a high potential for use in medical laboratory diagnostics to quantify multiple biomarkers simultaneously, especially in the selected Raman regions.

High-Speed Stimulated Raman Scattering Microscopy Using Inertia-Free AOD Scanning

High-throughput stimulated Raman scattering (SRS) microscopy is highly desired for large tissue imaging with chemical specificity. However, the mapping speed remains as the major short board of conventional SRS, primarily owing to the mechanical inertia existing in galvanometers or other laser scanning alternatives. Here, we developed inertia-free acousto-optic deflector (AOD)-based high-speed large-field stimulated Raman scattering microscopy, in which both the speed and integration time are ensured by immune of the mechanical response time. To avoid laser beam distortion induced by the intrinsic spatial dispersion of AODs, two spectral compression systems are implemented to compress the broad-band femtosecond pulse to picosecond laser. We achieved an SRS imaging of a 12 × 8 mm² mouse brain slice in only 8 min at an image resolution of approximately 1 μm and 32 slices from a whole brain in 12 h. The AOD-based inertia-free SRS mapping can be much faster after further upgrading and allow broad-spectrum applications of chemical imaging in the future.

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.

High-throughput line-illumination Raman microscopy with multislit detection

Raman microscopy is an emerging tool for molecular imaging and analysis of living samples. Use of Raman microscopy in life sciences is, however, still limited because of its slow measurement speed for spectral imaging and analysis. We developed a multiline-illumination Raman microscope to achieve ultrafast Raman spectral imaging. A spectrophotometer equipped with a periodic array of confocal slits detects Raman spectra from a sample irradiated by multiple line illuminations. A comb-like Raman hyperspectral image is formed on a two-dimensional detector in the spectrophotometer, and a hyperspectral Raman image is acquired by scanning the sample with multiline illumination array. By irradiating a sample with 21 simultaneous illumination lines, we achieved high-throughput Raman hyperspectral imaging of mouse brain tissue, acquiring 1108800 spectra in 11.4 min. We also measured mouse kidney and liver tissue as well as conducted label-free live-cell molecular imaging. The ultrafast Raman hyperspectral imaging enabled by the presented technique will expand the possible applications of Raman microscopy in biological and medical fields.

Raman imaging of rat nonalcoholic fatty liver tissues reveals distinct biomolecular states

An essential challenge in diagnosing states of nonalcoholic fatty liver disease (NAFLD) is the early prediction of progression from nonalcoholic fatty liver (NAFL) to nonalcoholic steatohepatitis (NASH) before the disease progresses. Histological diagnoses of NAFLD rely on the appearance of anomalous tissue morphologies, and it is difficult to segment the biomolecular environment of the tissue through a conventional histopathological approach. Here, we show that hyperspectral Raman imaging provides diagnostic information on NAFLD in rats, as spectral changes among disease states can be detected before histological characteristics emerge. Our results demonstrate that Raman imaging of NAFLD can be a useful tool for histopathologists, offering biomolecular distinctions among tissue states that cannot be observed through standard histopathological means.

Lipid droplet accumulation and adipophilin expression in follicular thyroid carcinoma

Follicular neoplasms of the thyroid include follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA). However, the differences in cytological findings between FTC and FTA remain undetermined. Here, we aimed to evaluate the accumulation of lipid droplets (LDs) and the expression of adipophilin (perilipin 2/ADRP/ADFP), a known LD marker, in cultured FTC cells. We also immunohistochemically compared adipophilin expression in the FTC and FTA of resected human thyroid tissues. Cultured FTC (FTC-133 and RO82W-1) possessed increased populations of LDs compared to thyroid follicular epithelial (Nthy-ori 3-1) cells. In vitro treatment with phosphatidylinositol-3-kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR) signaling inhibitors (LY294002, MK2206, and rapamycin) in FTC-133 cells downregulated the PI3K/Akt/mTOR/sterol regulatory element-binding protein 1 (SREBP1) signaling pathway, resulting in a significant reduction in LD accumulation. SREBP1 is a master transcription factor that controls lipid metabolism. Fluorescence immunocytochemistry revealed adipophilin expression in the LDs of FTC-133 cells. Immunohistochemical analysis of surgically resected human thyroid tissues revealed significantly increased expression of adipophilin in FTC compared with FTA and adjacent non-tumorous thyroid epithelia. Taken together, LDs and adipophilin were abundant in cultured FTC; the evaluation of adipophilin expression can help distinguish FTC from FTA in surgical specimens.

Regulation of organelle size and organization during development

During early embryogenesis, as cells divide in the developing embryo, the size of intracellular organelles generally decreases to scale with the decrease in overall cell size. Organelle size scaling is thought to be important to establish and maintain proper cellular function, and defective scaling may lead to impaired development and disease. However, how the cell regulates organelle size and organization are largely unanswered questions. In this review, we summarize the process of size scaling at both the cell and organelle levels and discuss recently discovered mechanisms that regulate this process during early embryogenesis. In addition, we describe how some recently developed techniques and Xenopus as an animal model can be used to investigate the underlying mechanisms of size regulation and to uncover the significance of proper organelle size scaling and organization.

Dual-Mode Tumor Imaging Using Probes That Are Responsive to Hypoxia-Induced Pathological Conditions

Hypoxia in solid tumors is associated with poor prognosis, increased aggressiveness, and strong resistance to therapeutics, making accurate monitoring of hypoxia important. Several imaging modalities have been used to study hypoxia, but each modality has inherent limitations. The use of a second modality can compensate for the limitations and validate the results of any single imaging modality. In this review, we describe dual-mode imaging systems for the detection of hypoxia that have been reported since the start of the 21st century. First, we provide a brief overview of the hallmarks of hypoxia used for imaging and the imaging modalities used to detect hypoxia, including optical imaging, ultrasound imaging, photoacoustic imaging, single-photon emission tomography, X-ray computed tomography, positron emission tomography, Cerenkov radiation energy transfer imaging, magnetic resonance imaging, electron paramagnetic resonance imaging, magnetic particle imaging, and surface-enhanced Raman spectroscopy, and mass spectrometric imaging. These overviews are followed by examples of hypoxia-relevant imaging using a mixture of probes for complementary single-mode imaging techniques. Then, we describe dual-mode molecular switches that are responsive in multiple imaging modalities to at least one hypoxia-induced pathological change. Finally, we offer future perspectives toward dual-mode imaging of hypoxia and hypoxia-induced pathophysiological changes in tumor microenvironments.

Recent advances in optical label-free characterization of extracellular vesicles

Extracellular vesicles (EVs) are complex biological nanoparticles endogenously secreted by all eukaryotic cells. EVs carry a specific molecular cargo of proteins, lipids, and nucleic acids derived from cells of origin and play a significant role in the physiology and pathology of cells, organs, and organisms. Upon release, they may be found in different body fluids that can be easily accessed via noninvasive methodologies. Due to the unique information encoded in their molecular cargo, they may reflect the state of the parent cell and therefore EVs are recognized as a rich source of biomarkers for early diagnostics involving liquid biopsy. However, body fluids contain a mixture of EVs released by different types of healthy and diseased cells, making the detection of the EVs of interest very challenging. Recent research efforts have been focused on the detection and characterization of diagnostically relevant subpopulations of EVs, with emphasis on label-free methods that simplify sample preparation and are free of interfering signals. Therefore, in this paper, we review the recent progress of the label-free optical methods employed for the detection, counting, and morphological and chemical characterization of EVs. We will first briefly discuss the biology and functions of EVs, and then introduce different optical label-free techniques for rapid, precise, and nondestructive characterization of EVs such as nanoparticle tracking analysis, dynamic light scattering, atomic force microscopy, surface plasmon resonance spectroscopy, Raman spectroscopy, and SERS spectroscopy. In the end, we will discuss their applications in the detection of neurodegenerative diseases and cancer and provide an outlook on the future impact and challenges of these technologies to the field of liquid biopsy via EVs.

Lipidomic and Membrane Mechanical Signatures in Triple-Negative Breast Cancer: Scope for Membrane-Based Theranostics

Triple-negative breast cancer (TNBC) is a highly aggressive form of breast cancer associated with poor prognosis, higher grade, and a high rate of metastatic occurrence. Limited therapeutic interventions and the compounding issue of drug resistance in triple-negative breast cancer warrants the discovery of novel therapeutic targets and diagnostic modules. To this view, in addition to proteins, lipids also regulate cellular functions via the formation of membranes that modulate membrane protein function, diffusion, and their localization; thus, orchestrating signaling hot spots enriched in specific lipids/proteins on cell membranes. Lipid deregulation in cancer leads to reprogramming of the membrane dynamics and functions impacting cell proliferation, metabolism, and metastasis, providing exciting starting points for developing lipid-based approaches for treating TNBC. In this review, we provide a detailed account of specific lipidic changes in breast cancer, link the altered lipidome with membrane structure and mechanical properties, and describe how these are linked to subsequent downstream functions implicit in cancer progression, metastasis, and chemoresistance. At the fundamental level, we discuss how the lipid-centric findings in TNBC are providing cues for developing lipid-inspired theranostic strategies while bridging existing gaps in our understanding of the functional involvement of lipid membranes in cancer.

Label-free discrimination of tumorigenesis stages using in vitro prostate cancer bone metastasis model by Raman imaging

Metastatic prostate cancer colonizes the bone to pave the way for bone metastasis, leading to skeletal complications associated with poor prognosis and morbidity. This study demonstrates the feasibility of Raman imaging to differentiate between cancer cells at different stages of tumorigenesis using a nanoclay-based three-dimensional (3D) bone mimetic in vitro model that mimics prostate cancer bone metastasis. A comprehensive study comparing the classification of as received prostate cancer cells in a two-dimensional (2D) model and cancer cells in a 3D bone mimetic environment was performed over various time intervals using principal component analysis (PCA). Our results showed distinctive spectral differences in Raman imaging between prostate cancer cells and the cells cultured in 3D bone mimetic scaffolds, particularly at 1002, 1261, 1444, and 1654 cm-1, which primarily contain proteins and lipids signals. Raman maps capture sub-cellular responses with the progression of tumor cells into metastasis. Raman feature extraction via cluster analysis allows for the identification of specific cellular constituents in the images. For the first time, this work demonstrates a promising potential of Raman imaging, PCA, and cluster analysis to discriminate between cancer cells at different stages of metastatic tumorigenesis.

Label-free Assessment of the Nascent State of Rat Non-alcoholic Fatty Liver Disease Using Spontaneous Raman Microscopy

Spontaneous Raman microscopy, which can detect molecular vibrations in cells and tissues, could be a useful tool for the label-free assessment of non-alcoholic fatty liver disease (NAFLD). However, it is unclear whether it can be used to evaluate the nascent state of NAFLD. To address this, we analyzed the Raman spectra of rat liver tissues in the nascent state of NAFLD upon excitation at 532 nm. Raman and histochemical analyses were performed of liver tissues from rats fed a high-fat, high-cholesterol diet (HFHCD). Raman microscopic imaging analysis of formalin-fixed thin tissue slices showed hepatic steatosis, as revealed by the Raman band at 2,854 cm⁻¹, whereas lipid droplets were not detectable by hematoxylin-eosin staining of images until 3 days after feeding a HFHCD. Raman signals of retinol at 1,588 cm⁻¹ emitted from hepatic stellate cells were distributed alongside hepatic cords; the retinol content rapidly decreased after feeding a HFHCD, whereas hepatic lipid content increased inversely. Raman microscopic analysis of the surface of fresh ex vivolivers enabled early detection of lipid accumulation after a 1-day feeding a HFHCD. In conclusion, spontaneous Raman microscopy can be applied to the label-free evaluation of the nascent state of NAFLD liver tissues.

Direct Imaging of Lipid Metabolic Changes in Drosophila Ovary During Aging Using DO-SRS Microscopy

Emerging studies have shown that lipids and proteins play versatile roles in various aspects of aging. High-resolution in situ optical imaging provides a powerful approach to study the metabolic dynamics of lipids and proteins during aging. Here, we integrated D2O probing and stimulated Raman scattering (DO-SRS) microscopy to directly visualize metabolic changes in aging Drosophila ovary. The subcellular spatial distribution of de novo protein synthesis and lipogenesis in ovary was quantitatively imaged and examined. Our Raman spectra showed that early stages follicles were protein-enriched whereas mature eggs were lipid-enriched. DO-SRS imaging showed a higher protein synthesis in the earlier developing stages and an increased lipid turned over at the late stage. Aged (35 days) flies exhibited a dramatic decrease in metabolic turnover activities of both proteins and lipids, particularly, in the germ stem cell niche of germarium. We found an accumulation of unsaturated lipids in the nurse cells and oocytes in old flies, suggesting that unsaturated lipids may play an important role in the processes of oocyte maturation. We further detected changes in mitochondrial morphology and accumulation of Cytochrome c during aging. To our knowledge, this is the first study that directly visualizes spatiotemporal changes in lipid and protein metabolism in Drosophila ovary during development and aging processes. Our study not only demonstrates the application of a new imaging platform in visualizing metabolic dynamics of lipids and proteins in situ but also unravels how the metabolic activity and lipid distribution change in Drosophila ovary during aging.

Multiwindow SRS Imaging Using a Rapid Widely Tunable Fiber Laser

Spectroscopic stimulated Raman scattering (SRS) imaging has become a useful tool finding a broad range of applications. Yet, wider adoption is hindered by the bulky and environmentally sensitive solid-state optical parametric oscillator (OPO) in a current SRS microscope. Moreover, chemically informative multiwindow SRS imaging across C-H, C-D, and fingerprint Raman regions is challenging due to the slow wavelength tuning speed of the solid-state OPO. In this work, we present a multiwindow SRS imaging system based on a compact and robust fiber laser with rapid and wide tuning capability. To address the relative intensity noise intrinsic to a fiber laser, we implemented autobalanced detection, which enhances the signal-to-noise ratio of stimulated Raman loss imaging by 23 times. We demonstrate high-quality SRS metabolic imaging of fungi, cancer cells, and Caenorhabditis elegans across the C-H, C-D, and fingerprint Raman windows. Our results showcase the potential of the compact multiwindow SRS system for a broad range of applications.

Vibrational line shape effects in plasmon-enhanced stimulated Raman spectroscopies

A density matrix treatment of plasmon-enhanced (PE) stimulated Raman spectroscopies is developed. Specifically, PE stimulated Raman Gain/Loss (PE-SRG/L) and coherent anti-Stokes Raman scattering (PE-CARS) due to monochromatic excitation and PE femtosecond stimulated Raman spectroscopy (PE-FSRS) are considered. A Lorentz oscillator model is used to explicitly describe the time dependence of plasmon-enhanced optical fields. These temporal characteristics are required for a density matrix based description of all plasmon-enhanced nonlinear molecular spectroscopies. Dispersive vibrational line shapes in PE-SRG/L and PE-FSRS spectra are shown to result primarily from terms proportional to the square of the complex optical field enhancement factor. The dependence on the plasmon resonance, picosecond and femtosecond pulse characteristics, and molecular vibrational properties are evident in the density matrix derived PE-FSRS intensity expression. The difference in signal detection mechanisms accounts for the lack of dispersive line shapes in PE spontaneous Raman spectroscopy. This density matrix treatment of PE-FSRS line shapes is compared with prior coupled wave results.

Raman spectroscopy in skeletal tissue disorders and tissue engineering: present and prospective

Musculoskeletal disorders are the most common reason of chronic pain and disability representing worldwide an enormous socio-economic burden. In this review, new biomedical application fields for Raman spectroscopy (RS) technique related to skeletal tissues are discussed showing that it can provide a comprehensive profile of tissue composition in situ, in a rapid, label-free, and non-destructive manner. RS can be used as a tool to study tissue alterations associated to aging, pathologies, and disease treatments. The main advantage with respect to currently applied methods in clinics is its ability to provide specific information on molecular composition, which goes beyond other diagnostic tools. Being compatible with water, RS can be performed without pre-treatment on unfixed, hydrated tissue samples, without any labelling and chemical fixation used in histochemical methods. This review provides first the description of basic principles of RS as a biotechnology tool and introduces into the field of currently available RS based techniques, developed to enhance Raman signal. The main spectral processing statistical tools, fingerprint identification and available databases are mentioned. The recent literature has been analysed for such applications of RS as tendon and ligaments, cartilage, bone, and tissue engineered constructs for regenerative medicine. Several cases of proof-of-concept preclinical studies have been described. Finally, advantages, limitations, future perspectives, and challenges for translation of RS into clinical practice have been also discussed.

Raman spectroscopy on blood serum samples of patients with end-stage liver disease

Raman spectroscopy has shown to be a promising method for the examination of biomedical samples. However, until now, its efficacy has not been established in clinical diagnostics. In this study, Raman spectroscopy’s potential application in medical laboratories is evaluated for a large variety (38) of biomarkers. Given 234 serum samples from a cohort of patients with different stages of liver disease, we performed Raman spectroscopy at 780nm excitation wavelength. The Raman spectra were analyzed in combination with the results of routine diagnostics using specifically developed complex mathematical algorithms, including fluorescence filtering, frequency subset selection and several overfitting circumventing strategies, such as independent validation. With the results of this cohort, which were validated in 328 independent samples, a significant proof-of-concept study was completed. This study highlights the need to prevent overfitting and to use independent data for validation. The results reveal that Raman spectroscopy has high potential for use in medical laboratory diagnostics to simultaneously quantify multiple biomarkers.

Real-time surface-enhanced Raman scattering-based live cell monitoring of the changes in mitochondrial membrane potential

Obtaining molecular information on cells in real time has been a critical challenge in studying the interaction between molecules of interest and intracellular components. Fluorescence-based methods have long served as excellent tools to study such important interactions. In this paper, we introduce a Raman scattering-based method as a promising platform to achieve the real-time monitoring of subtle molecular changes occurring within cells. We found that the Raman scattering-based method enabled monitoring changes in the mitochondrial membrane potential at the single-cell level in rheumatoid arthritis synovial fibroblasts induced by tumor necrosis factor-alpha (TNF-α) protein, various chemicals (MgCl₂, FCCP, and sodium pyruvate), and a non-chemical stimulus (i.e., light). The triphenylphosphine-modified gold nanoparticles were selectively localized in the mitochondria and showed the characteristic Raman spectrum of cytochrome C and other Raman spectra of molecular components inside the cell. The surface-enhanced Raman spectrum originating from mitochondria was sensitively changed over time when mitochondrial depolarization was induced by the addition of TNF-α, or chemicals known to induce mitochondrial depolarization. The Raman-based signal changes were well matched with results of the conventional fluorescence-based analysis. However, in contrast to the conventional approach, the Raman-based method enables monitoring such changes in real time and provides detailed molecular information in terms of the interaction of molecules. Therefore, these results highlight the possibility of surface-enhanced Raman scattering-based live cell analysis for future proteomics or drug-screening applications.

Non-invasive SERS serum detection technology combined with multivariate statistical algorithm for simultaneous screening of cervical cancer and breast cancer

Surface-enhanced Raman scattering (SERS), as a rapid, reliable and non-destructive spectral detection technology, has made a series of breakthrough achievements in screening and pre-diagnosis of various cancerous tumors. In this paper, high-performance gold nanoparticles/785 porous silicon photonic crystals (Au NPs/785 PSi PhCs) active SERS substrates were specially designed for serum testing, and realized highly sensitive detection of serum from healthy people, patients with cervical cancer and breast cancer. Based on the SERS spectra of the three groups of serum, the significant differences between the healthy group and cancer group at 1030 cm^-1 and 1051 cm^-1 were analyzed, and the similar but different serum SERS spectra of cervical cancer and breast cancer patients were compared. In addition, the spectral difference detected by SERS technology combined with a multivariate statistical algorithm was used to distinguish three kinds of serum. The serum SERS spectral sensitive bands were extracted by recursive weighted partial least squares (rPLS), and the three classification diagnosis models were established by combining orthogonal partial least squares discriminant analysis (OPLS-DA), linear discriminant analysis (LDA) and principal component analysis support vector machine (PCA-SVM) for synchronous classification and discrimination of the three groups of serum. The diagnostic results showed that the overall screening accuracy of three models were 93.28%, 97.77% and 94.78%, respectively. These above results confirmed that the Au NPs/785 PSi PhCs can realize super-sensitive detection of serum, and the established diagnostic model has great potential for pre-diagnosis and simultaneous screening of cervical cancer and breast cancer.

Improved Nerve Visualization in Head and Neck Surgery Using Mueller Polarimetric Imaging: Preclinical Feasibility Study in a Swine Model

BACKGROUND AND OBJECTIVES

Meticulous dissection and identification of nerves during head and neck surgery are crucial for preventing nerve damage. At present, nerve identification relies heavily on the surgeon's knowledge of anatomy, optionally combined with intraoperative neuromonitoring. Recently, optical techniques such as Mueller polarimetric imaging (MPI) have shown potential to improve nerve identification.

STUDY DESIGN/MATERIALS AND METHODS

With institutional approval, seven 25-35 kg Yorkshire pigs underwent cervical incision in the central neck. Intraoperative images were obtained using our in-house MPI system. Birefringence maps from the MPI system were processed to quantify the values between 0 and 255 from different tissue types; an active contour model was applied to further improve nerve visualization on the corresponding color images.

RESULTS

Among the seven pigs, the vagus nerves and recurrent laryngeal nerves were successfully differentiated with a mean intensity of 130.954 ± 20.611, which was significantly different (P < 0.05) from those of arteries (78.512 ± 27.78) and other surrounding tissues (82.583 ± 35.547). There were no imaging-related complications during the procedure. © 2021 Wiley Periodicals LLC.

CONCLUSIONS

MPI is a potentially complementary intraoperative tool for nerve identification in adjacent tissues.

Chemical Imaging of Single Anisotropic Polystyrene/Poly (Methacrylate) Microspheres with Complex Hierarchical Architecture

Monodisperse polystyrene spheres are functional materials with interesting properties, such as high cohesion strength, strong adsorptivity, and surface reactivity. They have shown a high application value in biomedicine, information engineering, chromatographic fillers, supercapacitor electrode materials, and other fields. To fully understand and tailor particle synthesis, the methods for characterization of their complex 3D morphological features need to be further explored. Here we present a chemical imaging study based on three-dimensional confocal Raman microscopy (3D-CRM), scanning electron microscopy (SEM), focused ion beam (FIB), diffuse reflectance infrared Fourier transform (DRIFT), and nuclear magnetic resonance (NMR) spectroscopy for individual porous swollen polystyrene/poly (glycidyl methacrylate-co-ethylene di-methacrylate) particles. Polystyrene particles were synthesized with different co-existing chemical entities, which could be identified and assigned to distinct regions of the same particle. The porosity was studied by a combination of SEM and FIB. Images of milled particles indicated a comparable porosity on the surface and in the bulk. The combination of standard analytical techniques such as DRIFT and NMR spectroscopies yielded new insights into the inner structure and chemical composition of these particles. This knowledge supports the further development of particle synthesis and the design of new strategies to prepare particles with complex hierarchical architectures.

Raman Spectroscopic Assessment of Myocardial Viability in Langendorff-Perfused Ischemic Rat Hearts

Spontaneous Raman spectroscopy, which senses changes in cellular contents of reduced cytochrome c, could be a powerful tool for label-free evaluation of ischemic hearts. However, undetermined is whether it is applicable to evaluation of myocardial viability in ischemic hearts. To address this issue, we investigated sequential changes in Raman spectra of the subepicardial myocardium in the Langendorff-perfused rat heart before and during ligation of the left coronary artery and its subsequent release and re-ligation. Under 532-nm wavelength excitation, the Raman peak intensity of reduced cytochrome c at 747 cm^-1 increased quickly after the coronary ligation, and reached a quasi-steady state within 30 min. Subsequent reperfusion of the heart after a short-term (30-min) ligation that simulates reversible conditions resulted in quick recovery of the peak intensity to the baseline. Further re-ligation resulted in resurgence of the peak intensity to nearly the identical value to the first ischemia value. In contrast, reperfusion after prolonged (120-min) ligation that assumes irreversible states resulted in incomplete recovery of the peak intensity, and re-ligation resulted in inadequate resurgence. Electron microscopic observations confirmed the spectral findings. Together, the Raman spectroscopic measurement for cytochrome c could be applicable to evaluation of viability of the ischemic myocardium without labeling.

Advances in stimulated Raman scattering imaging for tissues and animals

Stimulated Raman scattering (SRS) microscopy has emerged in the last decade as a powerful optical imaging technology with high chemical selectivity, speed, and subcellular resolution. Since the invention of SRS microscopy, it has been extensively employed in life science to study composition, structure, metabolism, development, and disease in biological systems. Applications of SRS in research and the clinic have generated new insights in many fields including neurobiology, tumor biology, developmental biology, metabolomics, pharmacokinetics, and more. Herein we review the advances and applications of SRS microscopy imaging in tissues and animals, as well as envision future applications and development of SRS imaging in life science and medicine.

Comparison of surface enhanced Raman spectroscopy and Raman spectroscopy for the detection of breast cancer based on serum samples

In this study, surface enhanced Raman spectroscopy (SERS) and Raman spectroscopy (RS), are employed for the classification of different stages of breast cancer using clinically diagnosed serum samples from breast cancer patients and healthy individuals. These serum samples are compared for their spectral features acquired by SERS and RS to establish spectral features that can be considered as spectral markers of breast cancer diagnosis and classification. SERS features related to DNA, proteins and lipids were observed which are solely observed in the serum samples of patients at different stages of breast cancer as compared to healthy samples. In order to explore the capability of SERS and RS and their comparison as an analytical tool for the efficient understanding of the progression of breast cancer, Principal Component Analysis (PCA) is done for the SERS and RS spectra of control, stage 2, stage 3 and stage 4. Furthermore, the Partial Least Squares-Discriminant Analysis (PLS-DA) was performed to compare the diagnostic performance of SERS and Raman spectroscopy for the classification of disease positive samples and healthy ones. The sensitivity and specificity and area under receiver operating characteristic (AUROC) curve values for SERS data were 90%, 98.4%, and 94% respectively which were higher as compared to Raman spectral data for which these values were found to be 88.2%, 97.7%, and 83.4% respectively.

Mammalian cell and tissue imaging using Raman and coherent Raman microscopy

Direct imaging of metabolism in cells or multicellular organisms is important for understanding many biological processes. Raman scattering (RS) microscopy, particularly, coherent Raman scattering (CRS) such as coherent anti-Stokes Raman scattering (CARS) and stimulated Raman scattering (SRS), has emerged as a powerful platform for cellular imaging due to its high chemical selectivity, sensitivity, and imaging speed. RS microscopy has been extensively used for the identification of subcellular structures, metabolic observation, and phenotypic characterization. Conjugating RS modalities with other techniques such as fluorescence or infrared (IR) spectroscopy, flow cytometry, and RNA-sequencing can further extend the applications of RS imaging in microbiology, system biology, neurology, tumor biology and more. Here we overview RS modalities and techniques for mammalian cell and tissue imaging, with a focus on the advances and applications of CARS and SRS microscopy, for a better understanding of the metabolism and dynamics of lipids, protein, glucose, and nucleic acids in mammalian cells and tissues. This article is categorized under: Laboratory Methods and Technologies > Imaging Biological Mechanisms > Metabolism Analytical and Computational Methods > Analytical Methods.

Data mining in Raman imaging in a cellular biological system

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Working flow of data mining in Raman imaging of cell system described.

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Pre-processing, pattern recognition and validation discussed.

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Machine learning methods applied at each step discussed.

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Single-cell visualization, cell type classification and quantification applications.

The distribution and dynamics of biomolecules in the cell is of critical interest in biological research. Raman imaging techniques have expanded our knowledge of cellular biological systems significantly. The technological developments that have led to the optimization of Raman instrumentation have helped to improve the speed of the measurement and the sensitivity. As well as instrumental developments, data mining plays a significant role in revealing the complicated chemical information contained within the spectral data. A number of data mining methods have been applied to extract the spectral information and translate them into biological information. Single-cell visualization, cell classification and biomolecular/drug quantification have all been achieved by the application of data mining to Raman imaging data. Herein we summarize the framework for Raman imaging data analysis, which involves preprocessing, pattern recognition and validation. There are multiple methods developed for each stage of analysis. The characteristics of these methods are described in relation to their application in Raman imaging of the cell. Furthermore, we summarize the software that can facilitate the implementation of these methods. Through its careful selection and application, data mining can act as an essential tool in the exploration of information-rich Raman spectral data.

Micro-Raman Spectroscopy Analysis of Optically Trapped Erythrocytes in Jaundice

Derangements in bilirubin metabolism and/or dysfunctions in the hepato-biliary system lead to the unhealthy buildup of bilirubin in blood, resulting in jaundice. During the course of this disorder, circulating red cells are invariably subjected to toxic effects of serum bilirubin and an array of inflammatory compounds. This study aimed to investigate the vibrational spectroscopy of live red cells in jaundice using micro-Raman spectroscopy combined with optical-trap. Red cells from blood samples of healthy volunteers and patients with jaundice were optically immobilized and micro-Raman probed using a 785 nm diode laser. Raman signatures from red cells in jaundice exhibited significant variations from the normal and the spectral-markers were obtained from multivariate analytical methods. This research gives insightful views on how different pathologies can act as "stress-milieus" for red cells in circulation, possibly impeding their normal functions and also exasperating anemia. Raman spectroscopy, an emerging bio-analytical technique, is sensitive in detecting molecular-conformations in situ, at cellular-levels and in real-time. This study could pave way in understanding fundamental red cell behavior in different diseases by analyzing Raman markers.

Detection of Age-Related Changes in Tendon Molecular Composition by Raman Spectroscopy-Potential for Rapid, Non-Invasive Assessment of Susceptibility to Injury

The lack of clinical detection tools at the molecular level hinders our progression in preventing age-related tendon pathologies. Raman spectroscopy can rapidly and non-invasively detect tissue molecular compositions and has great potential for in vivo applications. In biological tissues, a highly fluorescent background masks the Raman spectral features and is usually removed during data processing, but including this background could help age differentiation since fluorescence level in tendons increases with age. Therefore, we conducted a stepwise analysis of fluorescence and Raman combined spectra for better understanding of the chemical differences between young and old tendons. Spectra were collected from random locations of vacuum-dried young and old equine tendon samples (superficial digital flexor tendon (SDFT) and deep digital flexor tendon (DDFT), total n = 15) under identical instrumental settings. The fluorescence-Raman spectra showed an increase in old tendons as expected. Normalising the fluorescence-Raman spectra further indicated a potential change in intra-tendinous fluorophores as tendon ages. After fluorescence removal, the pure Raman spectra demonstrated between-group differences in CH2 bending (1450 cm-1) and various ring-structure and carbohydrate-associated bands (1000-1100 cm-1), possibly relating to a decline in cellular numbers and an accumulation of advanced glycation end products in old tendons. These results demonstrated that Raman spectroscopy can successfully detect age-related tendon molecular differences.

Cavenee W, S. Mischel P, Shibata N. Codependency of Metabolism and Epigenetics Drives Cancer Progression: A Review

Cancer is widely considered to be a set of genetic diseases that are currently classified by tissue and cell type of origin and, increasingly, by its molecular characteristics. This latter aspect is based primarily upon oncogene gains, tumor suppressor losses, and associated transcriptional profiles. However, cancers are also characterized by profound alterations in cellular metabolism and epigenetic landscape. It is particularly noteworthy that cancer-causing genomic defects not only activate cell cycle progression, but regulate the opportunistic uptake and utilization of nutrients, effectively enabling tumors to maximize growth and drug resistance in changing tissue and systemic microenvironments. Shifts in chromatin architecture are central to this dynamic behavior. Further, changes in nutrient uptake and utilization directly affect chromatin structure. In this review, we describe a set of recent discoveries of metabolic and epigenetic reprogramming in cancer, and especially focus on the genomically well-characterized brain tumor, glioblastoma. Further, we discuss a new mode of metabolic regulation driven by epigenetic mechanisms, that enables cancer cells to autonomously activate iron metabolism for their survival. Together, these underscore the integration of genetic mutations with metabolic reprogramming and epigenetic shifts in cancer, suggesting a new means to identifying patient subsets suitable for specific precision therapeutics.

Contribution of Raman Spectroscopy to Diagnosis and Grading of Chondrogenic Tumors

In the last decade, Raman Spectroscopy has demonstrated to be a label-free and non-destructive optical spectroscopy able to improve diagnostic accuracy in cancer diagnosis. This is because Raman spectroscopic measurements can reveal a deep molecular understanding of the biochemical changes in cancer tissues in comparison with non-cancer tissues. In this pilot study, we apply Raman spectroscopy imaging to the diagnosis and grading of chondrogenic tumors, including enchondroma and chondrosarcomas of increasing histologic grades. The investigation included the analysis of areas of 50×50 μm² to approximately 200×200 μm², respectively. Multivariate statistical analysis, based on unsupervised (Principal Analysis Components) and supervised (Linear Discriminant Analysis) methods, differentiated between the various tumor samples, between cells and extracellular matrix, and between collagen and non-collagenous components. The results dealt out basic biochemical information on tumor progression giving the possibility to grade with certainty the malignant cartilaginous tumors under investigation. The basic processes revealed by Raman Spectroscopy are the progressive degrading of collagen type-II components, the formation of calcifications and the cell proliferation in tissues ranging from enchondroma to chondrosarcomas. This study highlights that Raman spectroscopy is particularly effective when cartilaginous tumors need to be subjected to histopathological analysis.

Raman spectroscopy links differentiating osteoblast matrix signatures to pro-angiogenic potential

Mineralization of bone is achieved by the sequential maturation of the immature amorphous calcium phase to mature hydroxyapatite (HA) and is central in the process of bone development and repair. To study normal and dysregulated mineralization in vitro, substrates are often coated with poly-l-lysine (PLL) which facilitates cell attachment. This study has used Raman spectroscopy to investigate the effect of PLL coating on osteoblast (OB) matrix composition during differentiation, with a focus on collagen specific proline and hydroxyproline and precursors of HA. Deconvolution analysis of murine derived long bone OB Raman spectra revealed collagen species were 4.01-fold higher in OBs grown on PLL. Further, an increase of 1.91-fold in immature mineral species (amorphous calcium phosphate) was coupled with a 9.32-fold reduction in mature mineral species (carbonated apatite) on PLL versus controls. These unique low mineral signatures identified in OBs were linked with reduced alkaline phosphatase enzymatic activity, reduced Alizarin Red staining and altered osteogenic gene expression. The promotion of immature mineral species and restriction of mature mineral species of OB grown on PLL were linked to increased cell viability and pro-angiogenic vascular endothelial growth factor (VEGF) production. These results demonstrate the utility of Raman spectroscopy to link distinct matrix signatures with OB maturation and VEGF release. Importantly, Raman spectroscopy could provide a label-free approach to clinically assess the angiogenic potential of bone during fracture repair or degenerative bone loss.

Research advances of Tetrasphaera in enhanced biological phosphorus removal: A review

The processes of enhanced biological phosphorus removal (EBPR) have been widely applied in wastewater treatment plants (WWTPs). However, meeting the increasingly stringent effluent discharge standards requires a more stable EBPR performance. Under the circumstances, the identification of genus Tetrasphaera as potential phosphate accumulating organisms (PAOs) has aroused much research interests on them. In practice, a large biovolume of genus Tetrasphaera has been reliably observed in a number (up to 80) of WWTPs around the world. Tetrasphaera show a phenotype of aerobic polyphosphate (poly-P) accumulation at the condition of assimilating glucose and/or amino acids anaerobically in advance. Moreover, Tetrasphaera also present versatile physiologies, of which some show no net orthophosphate removal. While there are certainly some contradictory results and gaps in our knowledge concerning Tetrasphaera, this review summarizes the discovery, abundance in WWTPs, functions on EBPR, and biochemistry of the genus Tetrasphaera in the existing literature. It is expected to present the state-of-art progress about the genus Tetrasphaera, and to guide future R & D work.

Microfluidics and catalyst particles

In this review article, we discuss the latest advances and future perspectives of microfluidics for micro/nanoscale catalyst particle synthesis and analysis. In the first section, we present an overview of the different methods to synthesize catalysts making use of microfluidics and in the second section, we critically review catalyst particle characterization using microfluidics. The strengths and challenges of these approaches are highlighted with various showcases selected from the recent literature. In the third section, we give our opinion on the future perspectives of the combination of catalytic nanostructures and microfluidics. We anticipate that in the synthesis and analysis of individual catalyst particles, generation of higher throughput and better understanding of transport inside individual porous catalyst particles are some of the most important benefits of microfluidics for catalyst research.

Inhomogeneous Distribution of Components in Solid Protein Pharmaceuticals: Origins, Consequences, Analysis, and Resolutions

Successful development of stable solid protein formulations usually requires the addition of one or several excipients to achieve optimal stability. In these products, there is a potential risk of an inhomogeneous distribution of the various ingredients, specifically the ratio of protein and stabilizer may vary. Such inhomogeneity can be detrimental for stability but is mostly neglected in literature. In the past, it was challenging to analyze inhomogeneous component distribution, but recent advances in analytical techniques have revealed new options to investigate this phenomenon. This paper aims to review fundamental aspects of the inhomogeneous distribution of components of freeze-dried and spray-dried protein formulations. Four key topics will be presented and discussed, including the sources of component inhomogeneity, its consequences on protein stability, the analytical methods to reveal component inhomogeneity, and possible solutions to prevent or mitigate inhomogeneity.

A Toolbox for Organelle Mechanobiology Research-Current Needs and Challenges

Mechanobiology studies from the last decades have brought significant insights into many domains of biological research, from development to cellular signaling. However, mechano-regulation of subcellular components, especially membranous organelles, are only beginning to be unraveled. In this paper, we take mitochondrial mechanobiology as an example to discuss recent advances and current technical challenges in this field. In addition, we discuss the needs for future toolbox development for mechanobiological research of intracellular organelles.

Surface-enhanced raman spectroscopy: A non invasive alternative procedure for early detection in childhood asthma biomarkers in saliva

The early detection of bronchial inflammation in asthma, through a non-invasive, simple method and under a subclinical state, could lead to a more effective control of this condition. The aim of this study was to identify biomarkers of bronchial inflammation in the saliva of children with asthma through immunoassay and Surface Enhanced Raman Spectroscopy (SERS). We conducted an analytical cross-sectional study in 44 children ages 6-12; the diagnosis of asthma was made according to Global Initiative for Asthma (GINA) standards. The children's saliva was analyzed by immunoassay for the quantification of 37 cytokines, as well as SERS analysis in a confocal Raman microscope at 785 nm. We found a significant association between bronchial obstruction and IL-8 (p = 0.004), IL-10 (p = 0.008) and sCD163 (p = 0.003). The Raman spectra showed significant amplification in the region of 760 to 1750 cm^-1. The Principal Component Analysis and Linear Discriminant Analysis (PCA-LDA) method has a sensitivity of 85%, specificity of 82% and an accuracy of 84% for the diagnosis of asthma. These results demonstrate the presence of a subclinical inflammatory state, suggestive of bronchial remodeling in the population studied. The SERS method is a potential tool for identifying bronchial inflammation and its endotype, allowing for a highly sensitive and specific diagnosis.

High-Throughput Cell Imaging and Classification by Narrowband and Low-Spectral-Resolution Raman Microscopy

We investigated the use of narrowband Raman spectra for rapid label-free molecular imaging aimed at cell classification using principal component regression and linear discriminant analysis. In the classification of breast nontumorigenic epithelial and cancer cell lines, the classification accuracies using a spectral range of 100 cm^-1 were equivalent to or better than that with using the fingerprint and high-wavenumber regions. Narrowing the Raman spectral range for analysis allows reduction of the charge-coupled device (CCD) pixels required for spectrum detection, resulting in the improvement of image acquisition speed with adequate classification accuracy. Our measurements revealed that the wavenumber region at 1397-1501 cm^-1 can provide molecular information sufficient for cell classification without causing notable errors in the baseline-correction. A spectral resolution of ∼9 cm^-1 was found to be sufficient to provide high accuracy in cell classification, which allowed us to apply pixel binning at the CCD readout for further acceleration of the imaging speed. As a result, the acquisition time for a 1200 × 1500 pixels Raman hyperspectral image at 1397-1501 cm^-1 was reduced to 21 min. Under this condition, different cell lines were classified at accuracies higher than 90%. The presented approach will improve throughput of cell and tissue analysis and classification using Raman spectroscopy and extend practical uses of Raman imaging in biology and medicine.

Breast Cancer Diagnosis by Surface-Enhanced Raman Scattering (SERS) of Urine

Background: There is an ongoing research for breast cancer diagnostic tools that are cheaper, more accurate and more convenient than mammography. Methods: In this study, we employed surface-enhanced Raman scattering (SERS) for analysing urine from n = 53 breast cancer patients and n = 22 controls, with the aim of discriminating between the two groups using multivariate data analysis techniques such as principal component analysis—linear discriminant analysis (PCA-LDA). The SERS spectra were acquired using silver nanoparticles synthesized by reduction with hydroxylamine hydrochloride, which were additionally activated with Ca2+ 10−4 M. Results: The addition of Ca(NO3)2 10−4 M promoted the specific adsorption to the metal surface of the anionic purine metabolites such as uric acid, xanthine and hypoxanthine. Moreover, the SERS spectra of urine were acquired without any filtering or processing step for removing protein traces and other contaminants. Using PCA-LDA, the SERS spectra of urine from breast cancer patients were classified with a sensitivity of 81%, a specificity of 95% and an overall accuracy of 88%. Conclusion: The results of this preliminary study contribute to the translation of SERS in the clinical setting and highlight the potential of SERS as a novel screening strategy for breast cancer.