Raman spectroscopy and regenerative medicine: a review

Cervical cancer biomarker screening based on Raman spectroscopy and multivariate statistical analysis

Cervical cancer (CC) stands as one of the most prevalent malignancies among females, and the examination of serum tumor markers(TMs) assumes paramount significance in both its diagnosis and treatment. This research delves into the potential of combining Surface-Enhanced Raman Spectroscopy (SERS) with Multivariate Statistical Analysis (MSA) to diagnose cervical cancer, coupled with the identification of prospective serum biomarkers. Serum samples were collected from 95 CC patients and 81 healthy subjects, with subsequent MSA employed to analyze the spectral data. The outcomes underscore the superior efficacy of Partial Least Squares Discriminant Analysis (PLS-DA) within the MSA framework, achieving predictive accuracy of 97.73 %, and exhibiting sensitivities and specificities of 100 % and 95.83 % respectively. Additionally, the PLS-DA model yields a Variable Importance in Projection (VIP) list, which, when coupled with the biochemical information of characteristic peaks, can be utilized for the screening of biomarkers. Here, the Random Forest (RF) model is introduced to aid in biomarker screening. The two findings demonstrate that the principal contributing features distinguishing cervical cancer Raman spectra from those of healthy individuals are located at 482, 623, 722, 956, 1093, and 1656 cm-1, primarily linked to serum components such as DNA, tyrosine, adenine, valine, D-mannose, and amide I. Predictive models are constructed for individual biomolecules, generating ROC curves. Remarkably, D-mannose of V (C-N) exhibited the highest performance, boasting an AUC value of 0.979. This suggests its potential as a serum biomarker for distinguishing cervical cancer from healthy subjects.

Seeing the unseen: The role of bioimaging techniques for the diagnostic interventions in intervertebral disc degeneration

Intervertebral Disc Degeneration is a pathophysiological condition that primarily affects the spinal discs, causing back pain and neurological deficits. It is caused by the contribution of several factors such as genetic predisposition, age-related degeneration, and lifestyle choices like obesity and physical activity. Even though there are medications to treat pain, there is a lack of medicines for a complete cure. The main difficulty lies in poor diagnosis of the morphological and functional changes in the disc. With the ever-increasing research on bioimaging techniques, new techniques are being developed and repurposed to evaluate disc shape and composition, and their defects like thinning or deformities on the disc, leading to the proper diagnostic intervention in intervertebral disc degeneration. In this review, we aim to present a comprehensive overview of the imaging techniques used in the pre-clinical and clinical stages for the diagnosis of intervertebral disc degeneration. First, we will discuss about patho-anatomy and the pathophysiology of degenerative disc disease with the significance and a brief description of various dyes and tracers utilized for bioimaging. Then we will shed light on the latest advancements in diagnostic modalities in intervertebral disc degeneration; concluded by an analysis of the repercussions of the methodologies and experimental systems employed in identifying mechanisms and developing therapeutic strategies in intervertebral disc degeneration.

Raman microscopy allows to follow internalization, subcellular accumulation and fate of iron oxide nanoparticles in cells

An important issue in the context of both potenial toxicity of iron oxide nanoparticles (IONP) and their medical applications is tracking of the internalization process of these nanomaterials into living cells, as well as their localization and fate within them. The typical methods used for this purpose are transmission electron microscopy, confocal fluorescence microscopy as well as light-scattering techniques including dark-field microscopy and flow cytometry. All the techniques mentioned have their advantages and disadvantages. Among the problems it is necessary to mention complicated sample preparation, difficult interpretation of experimental data requiring qualified and experienced personnel, different behavior of fluorescently labeled IONP comparing to those label-free or finally the lack of possibility of chemical composition characteristics of nanomaterials. The purpose of the present investigation was the assessment of the usefulness of Raman microscopy for the tracking of the internalization of IONP into cells, as well as the optimization of this process. Moreover, the study focused on identification of the potential differences in the cellular fate of superparamagnetic nanoparticles having magnetite and maghemite core. The Raman spectra of U87MG cells which internalized IONP presented additional bands which position depended on the used laser wavelength. They occurred at the wavenumber range 1700-2400 cm-1 for laser 488 nm and below the wavenumber of 800 cm-1 in case of laser 532 nm. The intensity of the mentioned Raman bands was higher for the green laser (532 nm) and their position, was independent and not characteristic on the primary core material of IONP (magnetite, maghemite). The obtained results showed that Raman microscopy is an excellent, non-destructive and objective technique that allows monitoring the process of internalization of IONP into cells and visualizing such nanoparticles and/or their metabolism products within them at low exposure levels. What is more, the process of tracking IONP using the technique may be further improved by using appropriate wavelength and power of the laser source.

Neural Network-Based Filter Design for Compressive Raman Classification of Cells

Cell-based therapies are bound to revolutionize medicine, but significant technical hurdles must be overcome before wider adoption. In particular, nondestructive, label-free methods to characterize cells in real time are needed to optimize the production process and improve quality control. Raman spectroscopy, which provides a fingerprint of a cell's chemical composition, would be an ideal modality but is too slow for high-throughput applications. Compressive Raman techniques, which measure only linear combinations of Raman intensities, can be fast but require careful optimization to deliver high performance. Here, we develop a neural network model to identify optimal parameters for a compressive sensing scheme that reduces measurement time by 2 orders of magnitude. In a data set containing Raman spectra of three different cell types, it achieves up to 90% classification accuracy using only five linear combinations of Raman intensities. Our method thus unlocks the power of Raman spectroscopy for the characterization of cell products.

Rapid detection of lung cancer based on serum Raman spectroscopy and a support vector machine: a case-control study

BACKGROUND

Early screening and detection of lung cancer is essential for the diagnosis and prognosis of the disease. In this paper, we investigated the feasibility of serum Raman spectroscopy for rapid lung cancer screening.

METHODS

Raman spectra were collected from 45 patients with lung cancer, 45 with benign lung lesions, and 45 healthy volunteers. And then the support vector machine (SVM) algorithm was applied to build a diagnostic model for lung cancer. Furthermore, 15 independent individuals were sampled for external validation, including 5 lung cancer patients, 5 benign lung lesion patients, and 5 healthy controls.

RESULTS

The diagnostic sensitivity, specificity, and accuracy were 91.67%, 92.22%, 90.56% (lung cancer vs. healthy control), 92.22%,95.56%,93.33% (benign lung lesion vs. healthy) and 80.00%, 83.33%, 80.83% (lung cancer vs. benign lung lesion), repectively. In the independent validation cohort, our model showed that all the samples were classified correctly.

CONCLUSION

Therefore, this study demonstrates that the serum Raman spectroscopy analysis technique combined with the SVM algorithm has great potential for the noninvasive detection of lung cancer.

Unveiling brain disorders using liquid biopsy and Raman spectroscopy

Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis and intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to the study of conventional brain tissues, liquid biopsy, which focuses on blood, tear, saliva, and cerebrospinal fluid (CSF), also unveils a myriad of underlying molecular processes, providing abundant predictive clinical information. In addition, liquid biopsy is minimally- to non-invasive, and highly repeatable, offering the potential for continuous monitoring. Raman spectroscopy (RS), with its ability to provide rich molecular information and cost-effectiveness, holds great potential for transformative advancements in early detection and understanding the biochemical changes associated with NDs and TBI. Recent developments in Raman enhancement technologies and advanced data analysis methods have enhanced the applicability of RS in probing the intricate molecular signatures within biological fluids, offering new insights into disease pathology. This review explores the growing role of RS as a promising and emerging tool for disease diagnosis in brain disorders, particularly through the analysis of liquid biopsy. It discusses the current landscape and future prospects of RS in the diagnosis of brain disorders, highlighting its potential as a non-invasive and molecularly specific diagnostic tool.

Evaluation of Ilex guayusa and Piper marginatum Extract Cytotoxicity on Human Dental Pulp Mesenchymal Stem Cells

BACKGROUND

Amelogenesis imperfecta is a hereditary disorder affecting dental enamel. Among its phenotypes, hypocalcified AI is characterized by mineral deficiency, leading to tissue wear and, consequently, dental sensitivity. Excessive fluoride intake (through drinking water, fluoride supplements, toothpaste, or by ingesting products such as pesticides or insecticides) can lead to a condition known as dental fluorosis, which manifests as stains and teeth discoloration affecting their structure. Our recent studies have shown that extracts from Colombian native plants, Ilex guayusa and Piper marginatum, deposit mineral ions such as phosphate and orthophosphate into the dental enamel structure; however, it is unknown whether these extracts produce toxic effects on the dental pulp.

OBJECTIVE

To assess cytotoxicity effects on human dental pulp stem cells (hDPSCs) exposed to extracts isolated from I. guayusa and P. marginatum and, hence, their safety for clinical use.

METHODS

Raman spectroscopy, fluorescence microscopy, and flow cytometry techniques were employed. For Raman spectroscopy, hDPSCs were seeded onto nanobiochips designed to provide surface-enhanced Raman spectroscopy (SERS effect), which enhances their Raman signal by several orders of magnitude. After eight days in culture, I. guayusa and P. marginatum extracts at different concentrations (10, 50, and 100 ppm) were added. Raman measurements were performed at 0, 12, and 24 h following extract application. Fluorescence microscopy was conducted using an OLIMPUS fv1000 microscope, a live-dead assay was performed using a kit employing a BD FACS Canto TM II flow cytometer, and data analysis was determined using a FlowJo program.

RESULTS

The Raman spectroscopy results showed spectra consistent with viable cells. These findings were corroborated using fluorescence microscopy and flow cytometry techniques, confirming high cellular viability.

CONCLUSIONS

The analyzed extracts exhibited low cytotoxicity, suggesting that they could be safely applied on enamel for remineralization purposes. The use of nanobiochips for SERS effect improved the cell viability assessment.

Characterization of individual spores of two biological insecticides, Bacillus thuringiensis and Lysinibacillus sphaericus, in response to glutaraldehyde using single-cell optical approaches

Bacillus thuringiensis (Bt) and Lysinibacillus sphaericus (Ls) are the most widely used microbial insecticides. Both encounter unfavorable environmental factors and pesticides in the field. Here, the responses of Bt and Ls spores to glutaraldehyde were characterized using Raman spectroscopy and differential interference contrast imaging at the single-cell level. Bt spores were more sensitive to glutaraldehyde than Ls spores under prolonged exposure: <1.0% of Bt spores were viable after 10 min of 0.5% (v/v) glutaraldehyde treatment, compared to ~ 20% of Ls spores. The Raman spectra of glutaraldehyde-treated Bt and Ls spores were almost identical to those of untreated spores; however, the germination process of individual spores was significantly altered. The time to onset of germination, the period of rapid Ca2+-2,6-pyridinedicarboxylic acid (CaDPA) release, and the period of cortex hydrolysis of treated Bt spores were significantly longer than those of untreated spores, with dodecylamine germination being particularly affected. Similarly, the germination of treated Ls spores was significantly prolonged, although the prolongation was less than that of Bt spores. Although the interiors of Bt and Ls spores were undamaged and CaDPA did not leak, proteins and structures involved in spore germination could be severely damaged, resulting in slower and significantly prolonged germination. This study provides insights into the impact of glutaraldehyde on bacterial spores at the single cell level and the variability in spore response to glutaraldehyde across species and populations.

Biomedical applications, perspectives and tag design concepts in the cell - silent Raman window

Spectroscopic studies increasingly employ Raman tags exhibiting a signal in the cell - silent region of the Raman spectrum (1800-2800 cm-1), where bands arising from biological molecules are inherently absent. Raman tags bearing functional groups which contain a triple bond, such as alkyne and nitrile or a carbon-deuterium bond, have a distinct vibrational frequency in this region. Due to the lack of spectral background and cell-associated bands in the specific area, the implementation of those tags can help overcome the inherently poor signal-to-noise ratio and presence of overlapping Raman bands in measurements of biological samples. The cell - silent Raman tags allow for bioorthogonal imaging of biomolecules with improved chemical contrast and they have found application in analyte detection and monitoring, biomarker profiling and live cell imaging. This review focuses on the potential of the cell - silent Raman region, reporting on the tags employed for biomedical applications using variants of Raman spectroscopy.

Trends in bioactivity: inducing and detecting mineralization of regenerative polymeric scaffolds

Due to limitations of biological and alloplastic grafts, regenerative engineering has emerged as a promising alternative to treat bone defects. Bioactive polymeric scaffolds are an integral part of such an approach. Bioactivity importantly induces hydroxyapatite mineralization that promotes osteoinductivity and osseointegration with surrounding bone tissue. Strategies to confer bioactivity to polymeric scaffolds utilize bioceramic fillers, coatings and surface treatments, and additives. These approaches can also favorably impact mechanical and degradation properties. A variety of fabrication methods are utilized to prepare scaffolds with requisite morphological features. The bioactivity of scaffolds may be evaluated with a broad set of techniques, including in vitro (acellular and cellular) and in vivo methods. Herein, we highlight contemporary and emerging approaches to prepare and assess scaffold bioactivity, as well as existing challenges.

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.

Complete characterization of RNA biomarker fingerprints using a multi-modal ATR-FTIR and SERS approach for label-free early breast cancer diagnosis

Breast cancer is a prevalent form of cancer worldwide, and the current standard screening method, mammography, often requires invasive biopsy procedures for further assessment. Recent research has explored microRNAs (miRNAs) in circulating blood as potential biomarkers for early breast cancer diagnosis. In this study, we employed a multi-modal spectroscopy approach, combining attenuated total reflection Fourier transform infrared (ATR-FTIR) and surface-enhanced Raman scattering (SERS) to comprehensively characterize the full-spectrum fingerprints of RNA biomarkers in the blood serum of breast cancer patients. The sensitivity of conventional FTIR and Raman spectroscopy was enhanced by ATR-FTIR and SERS through the utilization of a diamond ATR crystal and silver-coated silicon nanopillars, respectively. Moreover, a wider measurement wavelength range was achieved with the multi-modal approach than with a single spectroscopic method alone. We have shown the results on 91 clinical samples, which comprised 44 malignant and 47 benign cases. Principal component analysis (PCA) was performed on the ATR-FTIR, SERS, and multi-modal data. From the peak analysis, we gained insights into biomolecular absorption and scattering-related features, which aid in the differentiation of malignant and benign samples. Applying 32 machine learning algorithms to the PCA results, we identified key molecular fingerprints and demonstrated that the multi-modal approach outperforms individual techniques, achieving higher average validation accuracy (95.1%), blind test accuracy (91.6%), specificity (94.7%), sensitivity (95.5%), and F-score (94.8%). The support vector machine (SVM) model showed the best area under the curve (AUC) characterization value of 0.9979, indicating excellent performance. These findings highlight the potential of the multi-modal spectroscopy approach as an accurate, reliable, and rapid method for distinguishing between malignant and benign breast tumors in women. Such a label-free approach holds promise for improving early breast cancer diagnosis and patient outcomes.

Exploring the future of regenerative medicine: Unveiling the potential of optical microscopy for structural and functional imaging of stem cells

Regenerative medicine, which utilizes stem cells for tissue and organ repair, holds immense promise in healthcare. A comprehensive understanding of stem cell characteristics is crucial to unlock their potential. This study explores the pivotal role of optical microscopy in advancing regenerative medicine as a potent tool for stem cell research. Advanced optical microscopy techniques enable an in-depth examination of stem cell behavior, morphology, and functionality. The review encompasses current optical microscopy, elucidating its capabilities and constraints in stem cell imaging, while also shedding light on emerging technologies for improved stem cell visualization. Optical microscopy, complemented by techniques like fluorescence and multiphoton imaging, enhances our comprehension of stem cell dynamics. The introduction of label-free imaging facilitates noninvasive, real-time stem cell monitoring without external dyes or markers. By pushing the boundaries of optical microscopy, researchers reveal the intricate cellular mechanisms underpinning regenerative processes, thereby advancing more effective therapeutic strategies. The current study not only outlines the future of regenerative medicine but also underscores the pivotal role of optical microscopy in both structural and functional stem cell imaging.

Unraveling Molecular Composition in Biological Samples-Systematic Evaluation of Statistical Methods for the Analysis of Hyperspectral Raman Data

Recently, confocal Raman microscopy has gained popularity in biomedical research for studying tissues in healthy and diseased state due to its ability to acquire chemically selective data in a noninvasive approach. However, biological samples, such as brain tissue, are inherently difficult to analyze due to the superposition of molecules in the Raman spectra and low variation of spectral features within the sample. The analysis is further impeded by pathological hallmarks, for example beta-amyloid (Aβ) plaques in Alzheimer's disease, which are often solely characterized by subtle shifts in the respective Raman peaks. To unravel the underlying molecular information, convoluted statistical procedures are inevitable. Unfortunately, such statistical methods are often inadequately described, and most natural scientists lack knowledge of their appropriate use, causing unreproducible results and stagnation in the application of hyperspectral Raman imaging. Therefore, we have set out to provide a comprehensive guide to address these challenges with the example of a complex hyperspectral data set of brain tissue samples with Aβ plaques. Our study encompasses established as well as novel statistical methods, including univariate analysis, principal component analysis, cluster analysis, spectral unmixing, and 2D correlation spectroscopy, and critically compares the outcomes of each analysis. Moreover, we transparently demonstrate the effect of preprocessing decisions like denoising and scaling techniques, providing valuable insights into implications of spectral quality for data evaluation. Thereby, this study provides a comprehensive evaluation of analysis approaches for complex hyperspectral Raman data, laying out a blueprint for elucidating meaningful information from biological samples in chemical imaging.

Beyond the Bottle: Exploring Health-Promoting Compounds in Wine and Wine-Related Products-Extraction, Detection, Quantification, Aroma Properties, and Terroir Effects

Health-promoting compounds in wine and wine-related products are important due to their potential benefits to human health. Through an extensive literature review, this study explores the presence of these compounds in wine and wine-related products, examining their relationship with terroir and their impact on the aromatic and flavor properties that are perceived orally: sunlight exposure, rainfall patterns, and soil composition impact grapevines' synthesis and accumulation of health-promoting compounds. Enzymes, pH, and the oral microbiome are crucial in sensory evaluation and perception of health promotion. Moreover, their analysis of health-promoting compounds in wine and wine-related products relies on considerations such as the specific target compound, selectivity, sensitivity, and the complexity of the matrix.

Multimodal imaging of metabolic activities for distinguishing subtypes of breast cancer

Triple negative breast cancer (TNBC) is a highly aggressive form of cancer. Detecting TNBC early is crucial for improving disease prognosis and optimizing treatment. Unfortunately, conventional imaging techniques fall short in providing a comprehensive differentiation of TNBC subtypes due to their limited sensitivity and inability to capture subcellular details. In this study, we present a multimodal imaging platform that integrates heavy water (D2O)-probed stimulated Raman scattering (DO-SRS), two-photon fluorescence (TPF), and second harmonic generation (SHG) imaging. This platform allows us to directly visualize and quantify the metabolic activities of TNBC subtypes at a subcellular level. By utilizing DO-SRS imaging, we were able to identify distinct levels of de novo lipogenesis, protein synthesis, cytochrome c metabolic heterogeneity, and lipid unsaturation rates in various TNBC subtype tissues. Simultaneously, TPF imaging provided spatial distribution mapping of NAD[P]H and flavin signals in TNBC tissues, revealing a high redox ratio and significant lipid turnover rate in TNBC BL2 (HCC1806) samples. Furthermore, SHG imaging enabled us to observe diverse orientations of collagen fibers in TNBC tissues, with higher anisotropy at the tissue boundary compared to the center. Our multimodal imaging platform offers a highly sensitive and subcellular approach to characterizing not only TNBC, but also other tissue subtypes and cancers.

Exploring novel and fast stability or sameness evaluation tool for different categories of injectable formulations

The establishment of drug product stability and sameness is the heart of generic formulation development. For regulatory filing, various instrumental methods are used on a case basis to establish the generic and innovator product sameness in multiple aspects. Here in the present study, we explored the applicability of the Time-correlated single photon counting (TCS-PC) technique as a fast, reliable, and nondestructive method for establishing the sameness of three different categories of injectable formulations, namely, Amphotericin B liposome for injection, enoxaparin injection, and iron sucrose injection. All three category formulations were evaluated in their native and artificially induced post degradation state to identify the discrimination power of the used instrumental techniques. The degradation of materials were confirmed by high performance liquid chromatography (HPLC). Based on the product category, pre and post-degradation samples were evaluated by selective instrumental methods like differential scanning calorimetry (DSC), nuclear magnetic resonance (NMR), fluorescence spectroscopy, particle size analysis by dynamic light scattering (DLS), small angle X-ray scattering (SAXS), Raman spectroscopy, inductively coupled plasma optical-emission spectrometry (ICP-OES) and circular dichroism study. All pre and post-degradation samples were further analyzed by TCS-PC. We observed that, TCS-PC can identify the differences between the initial and post degradation samples in very less time with promising discrimination power across the different injectable formulation types. Thus TCS-PC can be used as a fast and promising stability or sameness evaluation tool for different injectable drug products.

Spectroscopic techniques for monitoring stem cell and organoid proliferation in 3D environments for therapeutic development

Spectroscopic techniques for monitoring stem cell and organoid proliferation have gained significant attention in therapeutic development. Spectroscopic techniques such as fluorescence, Raman spectroscopy, and infrared spectroscopy offer noninvasive and real-time monitoring of biochemical and biophysical changes that occur during stem cell and organoid proliferation. These techniques provide valuable insight into the underlying mechanisms of action of potential therapeutic agents, allowing for improved drug discovery and screening. This review highlights the importance of spectroscopic monitoring of stem cell and organoid proliferation and its potential impact on therapeutic development. Furthermore, this review discusses recent advances in spectroscopic techniques and their applications in stem cell and organoid research. Overall, this review emphasizes the importance of spectroscopic techniques as valuable tools for studying stem cell and organoid proliferation and their potential to revolutionize therapeutic development in the future.

Investigating biochemical and structural changes of glycated collagen using multimodal multiphoton imaging, Raman spectroscopy, and atomic force microscopy

Advanced glycation end products (AGEs) form extracellular crosslinking with collagenous proteins, which contributes to the development of diabetic complications. In this study, AGEs-related pentosidine (PENT) crosslinks-induced structural and biochemical changes are studied using multimodal multiphoton imaging, Raman spectroscopy and atomic force microscopy (AFM). Decellularized equine pericardium (EP) was glycated with four ribose concentrations ranging between 5 and 200 mM and monitored for up to 30 days. Two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) microscopic imaging probed elastin and collagen fibers, respectively. The glycated EP showed a decrease in the SHG intensities associated with loss of non-centrosymmetry of collagen and an increase of TPEF intensities associated with PENT crosslinks upon glycation. TPEF signals from elastin fibers were unaffected. A three-dimensional reconstruction with SHG + TPEF z-stack images visualized the distribution of collagen and elastin within the EP volume matrix. In addition, Raman spectroscopy (RS) detected changes in collagen-related bands and discriminated glycated from untreated EP. Furthermore, AFM scans showed that the roughness increases and the D-unit structure of fibers remained unchanged during glycation. The PENT crosslinked-induced changes are discussed in the context of previous studies of glutaraldehyde- and genipin-induced crosslinking and collagenase-induced digestion of collagen. We conclude that TPEF, SHG, RS, and AFM are effective, label-free, and non-destructive methods to investigate glycated tissues, differentiate crosslinking processes, and characterize general collagen-associated and disease-related changes, in particular by their RS fingerprints.

Fast Raman imaging through the combination of context-aware matrix completion and low spectral resolution

Raman hyperspectral imaging is an effective method for label-free imaging with chemical specificity, yet the weak signals and correspondingly long integration times have hindered its wide adoption as a routine analytical method. Recently, low resolution Raman imaging has been proposed to improve the spectral signal-to-noise ratio, which significantly improves the speed of Raman imaging. In this paper, low resolution Raman spectroscopy is combined with "context-aware" matrix completion, where regions of the sample that are not of interest are skipped, and the regions that are measured are under-sampled, then reconstructed with a low-rank constraint. Both simulations and experiment show that low-resolution Raman boosts the speed and image quality of the computationally-reconstructed Raman images, allowing deeper sub-sampling, reduced exposure time, and an overall >10-fold improvement in imaging speed, without sacrificing chemical specificity or spatial image quality. As the method utilizes traditional point-scan imaging, it retains full confocality and is "backwards-compatible" with pre-existing traditional Raman instruments, broadening the potential scope of Raman imaging applications.

Multi-scale sequential feature selection for disease classification using Raman spectroscopy data

Raman spectroscopy (RS) optical technology promises non-destructive and fast application in medical disease diagnosis in a single step. However, achieving clinically relevant performance levels remains challenging due to the inability to search for significant Raman signals at different scales. Here we propose a multi-scale sequential feature selection method that can capture global sequential features and local peak features for disease classification using RS data. Specifically, we utilize the Long short-term memory network (LSTM) module to extract global sequential features in the Raman spectra, as it can capture long-term dependencies present in the Raman spectral sequences. Meanwhile, the attention mechanism is employed to select local peak features that were ignored before and are the key to distinguishing different diseases. Experimental results on three public and in-house datasets demonstrate the superiority of our model compared with state-of-the-art methods for RS classification. In particular, our model achieves an accuracy of 97.9 ± 0.2% on the COVID-19 dataset, 76.3 ± 0.4% on the H-IV dataset, and 96.8 ± 1.9% on the H-V dataset.

Two-Dimensional Clustering of Spectral Changes for the Interpretation of Raman Hyperspectra

Two-dimensional correlation spectroscopy (2D-COS) is a technique that permits the examination of synchronous and asynchronous changes present in hyperspectral data. It produces two-dimensional correlation coefficient maps that represent the mutually correlated changes occurring at all Raman wavenumbers during an implemented perturbation. To focus our analysis on clusters of wavenumbers that tend to change together, we apply a k-means clustering to the wavenumber profiles in the perturbation domain decomposition of the two-dimensional correlation coefficient map. These profiles (or trends) reflect peak intensity changes as a function of the perturbation. We then plot the co-occurrences of cluster members two-dimensionally in a manner analogous to a two-dimensional correlation coefficient map. Because wavenumber profiles are clustered based on their similarity, two-dimensional cluster member spectra reveal which Raman peaks change in a similar manner, rather than how much they are correlated. Furthermore, clustering produces a discrete partitioning of the wavenumbers, thus a two-dimensional cluster member spectrum exhibits a discrete presentation of related Raman peaks as opposed to the more continuous representations in a two-dimensional correlation coefficient map. We demonstrate first the basic principles of the technique with the aid of synthetic data. We then apply it to Raman spectra obtained from a polystyrene perchlorate model system followed by Raman spectra from mammalian cells fixed with different percentages of methanol. Both data sets were designed to produce differential changes in sample components. In both cases, all the peaks pertaining to a given component should then change in a similar manner. We observed that component-based profile clustering did occur for polystyrene and perchlorate in the model system and lipids, nucleic acids, and proteins in the mammalian cell example. This confirmed that the method can translate to "real world" samples. We contrast these results with two-dimensional correlation spectroscopy results. To supplement interpretation, we present the cluster-segmented mean spectrum of the hyperspectral data. Overall, this technique is expected to be a valuable adjunct to two-dimensional correlation spectroscopy to further facilitate hyperspectral data interpretation and analysis.

Molecular Fingerprint Detection Using Raman and Infrared Spectroscopy Technologies for Cancer Detection: A Progress Review

Molecular vibrations play a crucial role in physical chemistry and biochemistry, and Raman and infrared spectroscopy are the two most used techniques for vibrational spectroscopy. These techniques provide unique fingerprints of the molecules in a sample, which can be used to identify the chemical bonds, functional groups, and structures of the molecules. In this review article, recent research and development activities for molecular fingerprint detection using Raman and infrared spectroscopy are discussed, with a focus on identifying specific biomolecules and studying the chemical composition of biological samples for cancer diagnosis applications. The working principle and instrumentation of each technique are also discussed for a better understanding of the analytical versatility of vibrational spectroscopy. Raman spectroscopy is an invaluable tool for studying molecules and their interactions, and its use is likely to continue to grow in the future. Research has demonstrated that Raman spectroscopy is capable of accurately diagnosing various types of cancer, making it a valuable alternative to traditional diagnostic methods such as endoscopy. Infrared spectroscopy can provide complementary information to Raman spectroscopy and detect a wide range of biomolecules at low concentrations, even in complex biological samples. The article concludes with a comparison of the techniques and insights into future directions.

Comprehensive Approach to Assessment of Liver Viability During Normothermic Machine Perfusion

Liver transplantation is the most effective treatment of advanced liver disease, and the use of extended criteria donor organs has broadened the source of available livers. Although normothermic machine perfusion (NMP) has become a useful tool in liver transplantation, there are no consistent criteria that can be used to evaluate the viability of livers during NMP. This review summarizes the criteria, indicators, and methods used to evaluate liver viability during NMP. The shape, appearance, and hemodynamics of the liver can be analyzed at a macroscopic level, while markers of liver injury, indicators of liver and bile duct function, and other relevant indicators can be evaluated by biochemical analysis. The liver can also be assessed by tissue biopsy at the microscopic level. Novel methods for assessment of liver viability are introduced. The limitations of evaluating liver viability during NMP are discussed and suggestions for future clinical practice are provided.

Quantitative chemometric phenotyping of three-dimensional liver organoids by Raman spectral imaging

Confocal Raman spectral imaging (RSI) enables high-content, label-free visualization of a wide range of molecules in biological specimens without sample preparation. However, reliable quantification of the deconvoluted spectra is needed. Here we develop an integrated bioanalytical methodology, qRamanomics, to qualify RSI as a tissue phantom calibrated tool for quantitative spatial chemotyping of major classes of biomolecules. Next, we apply qRamanomics to fixed 3D liver organoids generated from stem-cell-derived or primary hepatocytes to assess specimen variation and maturity. We then demonstrate the utility of qRamanomics for identifying biomolecular response signatures from a panel of liver-altering drugs, probing drug-induced compositional changes in 3D organoids followed by in situ monitoring of drug metabolism and accumulation. Quantitative chemometric phenotyping constitutes an important step in developing quantitative label-free interrogation of 3D biological specimens.

Rapid detection and quantification of paracetamol and its major metabolites using surface enhanced Raman scattering

Paracetamol (also known as acetaminophen) is an over-the-counter (OTC) drug that is commonly used as an analgesic for mild pain, headache, cold and flu. While in the short term it is a safe and effective medicine, it is sometimes used for attempted suicides particularly in young adults. In such circumstances it is important for rapid diagnosis of overdoses as antidotes can be given to limit liver damage from one of its primary metabolites N-acetyl-p-benzoquinone imine (NAPQI). Unfortunately, the demand for rapid and sensitive analytical techniques to accurately monitor the abuse of OTC drugs has significantly risen. Ideally these techniques would be highly specific, sensitive, reproducible, portable and rapid. In addition, an ideal point of care (PoC) test would enable quantitative detection of drugs and their metabolites present in body fluids. While Raman spectroscopy meets these specifications, there is a need for enhancement of the signal because the Raman effect is weak. In this study, we developed a surface-enhanced Raman scattering (SERS) methodology in conjunction with chemometrics to quantify the amount of paracetamol and its main primary metabolites (viz., paracetamol sulfate, p-acetamidophenyl β-D-glucuronide and NAPQI) in water and artificial urine. The enhancement of the SERS signals was achieved by mixing the drug or xenometabolites with a gold nanoparticle followed by aggregation with 0.045 M NaCl. We found that the SERS data could be collected directly, due to immediate analyte association with the Au surface and colloid aggregation. Accurate and precise measurements were generated, with a limit of detection (LoD) of paracetamol in water and artificial urine at 7.18 × 10^-6 M and 2.11 × 10^-5 M, respectively, which is well below the limit needed for overdose and indeed normal levels of paracetamol in serum after taking 1 g orally. The predictive values obtained from the analysis of paracetamol in water and artificial urine were also excellent, with the coefficient of determination (Q²) being 0.995 and 0.996, respectively (1 suggests a perfect model). It was noteworthy that when artificial urine was spiked with paracetamol, no aggregating agent was required due to the salt rich medium, which led to spontaneous aggregation. Moreover, for the xenometabolites of paracetamol excellent LoDs were obtained and these ranged from 2.6 × 10^-4 M to 5 × 10^-5 M with paracetamol sulfate and NAPQI having Q² values of 0.934 and 0.892 and for p-acetamidophenyl β-D-glucuronide this was slightly lower at 0.6437.

Accuracy of Raman spectroscopy in the diagnosis of Alzheimer's disease

Objective

To systematically evaluate the accuracy of Raman spectroscopy in the diagnosis of Alzheimer's disease.

Methods

Databases including Web of Science, PubMed, The Cochrane Library, EMbase, CBM, CNKI, Wan Fang Data, and VIP were electronically searched for studies on Raman spectroscopy in diagnosis of Alzheimer's disease from inception to November 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias in the included studies. Then, meta-analysis was performed using Meta-Disc1.4 and Stata 16.0 software.

Results

A total of eight studies were finally included. The pooled sensitivity of Raman spectroscopy was 0.86 [95% CI (0.80–0.91)], specificity was 0.87 [95% CI (0.79–0.92)], positive likelihood ratio was 5.50 [95% CI (3.55–8.51)], negative likelihood ratio was 0.17 [95% CI (0.09–0.34)], diagnosis odds ratio and area under the curve of SROC were 42.44 [95% CI (19.80–90.97)] and 0.931, respectively. Sensitivity analysis was carried out after each study was excluded one by one, and the results showed that pooled sensitivity and specificity had no significant change, indicating that the stability of the meta-analysis results was great.

Conclusions

Our findings indicated that Raman spectroscopy had high accuracy in the diagnosis of AD, though it still did not rule out the possibility of misdiagnosis and missed diagnosis. Limited by the quantity and quality of the included studies, the above conclusions need to be verified by more high-quality studies.

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.

The emerging applications and advancements of Raman spectroscopy in pediatric cancers

Although the survival rate of pediatric cancer has significantly improved, it is still an important cause of death among children. New technologies have been developed to improve the diagnosis, treatment, and prognosis of pediatric cancers. Raman spectroscopy (RS) is a non-destructive analytical technique that uses different frequencies of scattering light to characterize biological specimens. It can provide information on biological components, activities, and molecular structures. This review summarizes studies on the potential of RS in pediatric cancers. Currently, studies on the application of RS in pediatric cancers mainly focus on early diagnosis, prognosis prediction, and treatment improvement. The results of these studies showed high accuracy and specificity. In addition, the combination of RS and deep learning is discussed as a future application of RS in pediatric cancer. Studies applying RS in pediatric cancer illustrated good prospects. This review collected and analyzed the potential clinical applications of RS in pediatric cancers.

Study on the classification and identification of various carbonate and sulfate mineral medicines based on Raman spectroscopy combined with PCA-SVM algorithm

The efficacy of mineral medicines varies greatly between different origins. Therefore, investigating a method to quickly identify similar mineral medicines is meaningful. In this paper, a visual classification and identification model of Raman spectroscopy combined with principal component analysis (PCA) and support vector machine (SVM) algorithms was developed to rapidly classify and identify carbonate and sulfate mineral medicines. The results reveal that although the Raman spectra are too similar to distinguish by naked eye, the PCA-SVM algorithm can perform accurate classification and identification, and its accuracy, precision, recall and F1-score parameters all reach 100%. The proposed method is rapid, accurate, nondestructive, convenient, portable, and low cost, and has important application value for the classification, identification and quality supervision of various carbonate and sulfate mineral medicines.

Analysis of urine Raman spectra differences from patients with diabetes mellitus and renal pathologies

Background

Chronic kidney disease (CKD) poses a major public health burden. Diabetes mellitus (DM) is one of the major causes of CKD. In patients with DM, it can be difficult to differentiate diabetic kidney disease (DKD) from other causes of glomerular damage; it should not be assumed that all DM patients with decreased eGFR and/or proteinuria have DKD. Renal biopsy is the standard for definitive diagnosis, but other less invasive methods may provide clinical benefit. As previously reported, Raman spectroscopy of CKD patient urine with statistical and chemometric modeling may provide a novel, non-invasive methodology for discriminating between renal pathologies.

Methods

Urine samples were collected from renal biopsied and non-biopsied patients presenting with CKD secondary to DM and non-diabetic kidney disease. Samples were analyzed by Raman spectroscopy, baselined with the ISREA algorithm, and subjected to chemometric modeling. Leave-one-out cross-validation was used to assess the predictive capabilities of the model.

Results

This proof-of-concept study consisted of 263 samples, including renal biopsied, non-biopsied diabetic and non-diabetic CKD patients, healthy volunteers, and the Surine™ urinalysis control. Urine samples of DKD patients and those with immune-mediated nephropathy (IMN) were distinguished from one another with 82% sensitivity, specificity, positive-predictive value (PPV), and negative-predictive value (NPV). Among urine samples from all biopsied CKD patients, renal neoplasia was identified in urine with 100% sensitivity, specificity, PPV, and NPV, and membranous nephropathy was identified with 66.7% sensitivity, 96.4% specificity, 80.0% PPV, and 93.1% NPV. Finally, DKD was identified among a population of 150 patient urine samples containing biopsy-confirmed DKD, other biopsy-confirmed glomerular pathologies, un-biopsied non-diabetic CKD patients (no DKD), healthy volunteers, and Surine™ with 36.4% sensitivity, 97.8% specificity, 57.1% PPV, and 95.1% NPV. The model was used to screen un-biopsied diabetic CKD patients and identified DKD in more than 8% of this population. IMN in diabetic patients was identified among a similarly sized and diverse population with 83.3% sensitivity, 97.7% specificity, 62.5% PPV, and 99.2% NPV. Finally, IMN in non-diabetic patients was identified with 50.0% sensitivity, 99.4% specificity, 75.0% PPV, and 98.3% NPV.

Conclusions

Raman spectroscopy of urine with chemometric analysis may be able to differentiate between DKD, IMN, and other glomerular diseases. Future work will further characterize CKD stages and glomerular pathology, while assessing and controlling for differences in factors such as comorbidities, disease severity, and other lab parameters.

Targeted Suppression of Peptide Degradation in Ag-Based Surface-Enhanced Raman Spectra by Depletion of Hot Carriers

Sample degradation, in particular of biomolecules, frequently occurs in surface-enhanced Raman spectroscopy (SERS) utilizing supported silver SERS substrates. Currently, thermal and/or photocatalytic effects are considered to cause sample degradation. This paper establishes the efficient inhibition of sample degradation using iodide which is demonstrated by a systematic SERS study of a small peptide in aqueous solution. Remarkably, a distinct charge separation-induced surface potential difference is observed for SERS substrates under laser irradiation using Kelvin probe force microscopy. This directly unveils the photocatalytic effect of Ag-SERS substrates. Based on the presented results, it is proposed that plasmonic photocatalysis dominates sample degradation in SERS experiments and the suppression of typical SERS sample degradation by iodide is discussed by means of the energy levels of the substrate under mild irradiation conditions. This approach paves the way toward more reliable and reproducible SERS studies of biomolecules under physiological conditions.

Recent Advances in Raman Spectral Imaging in Cell Diagnosis and Gene Expression Prediction

Normal and tumor regions within cancer tissue can be distinguished using various methods, such as histological analysis, tumor marker testing, X-ray imaging, or magnetic resonance imaging. Recently, new discrimination methods utilizing the Raman spectra of tissues have been developed and put into practical use. Because Raman spectral microscopy is a non-destructive and non-labeling method, it is potentially compatible for use in the operating room. In this review, we focus on the basics of Raman spectroscopy and Raman imaging in live cells and cell type discrimination, as these form the bases for current Raman scattering-based cancer diagnosis. We also review recent attempts to estimate the gene expression profile from the Raman spectrum of living cells using simple machine learning. Considering recent advances in machine learning techniques, we speculate that cancer type discrimination using Raman spectroscopy will be possible in the near future.

Raman spectroscopy and supervised learning as a potential tool to identify high-dose-rate-brachytherapy induced biochemical profiles of prostate cancer

High-dose-rate-brachytherapy (HDR-BT) is an increasingly attractive alternative to external beam radiation-therapy for patients with intermediate risk prostate cancer. Despite this, no bio-marker based method currently exists to monitor treatment response, and the changes which take place at the biochemical level in hypo-fractionated HDR-BT remain poorly understood. The aim of this pilot study is to assess the capability of Raman spectroscopy (RS) combined with principal component analysis (PCA) and random-forest classification (RF) to identify radiation response profiles after a single dose of 13.5 Gy in a cohort of nine patients. We here demonstrate, as a proof-of-concept, how RS-PCA-RF could be utilised as an effective tool in radiation response monitoring, specifically assessing the importance of low variance PCs in complex sample sets. As RS provides information on the biochemical composition of tissue samples, this technique could provide insight into the changes which take place on the biochemical level, as result of HDR-BT treatment.

Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study

This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring.

Diagnostic Approaches For COVID-19: Lessons Learned and the Path Forward

Coronavirus disease 2019 (COVID-19) is a transmitted respiratory disease caused by the infection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although humankind has experienced several outbreaks of infectious diseases, the COVID-19 pandemic has the highest rate of infection and has had high levels of social and economic repercussions. The current COVID-19 pandemic has highlighted the limitations of existing virological tests, which have failed to be adopted at a rate to properly slow the rapid spread of SARS-CoV-2. Pandemic preparedness has developed as a focus of many governments around the world in the event of a future outbreak. Despite the largely widespread availability of vaccines, the importance of testing has not diminished to monitor the evolution of the virus and the resulting stages of the pandemic. Therefore, developing diagnostic technology that serves as a line of defense has become imperative. In particular, that test should satisfy three criteria to be widely adopted: simplicity, economic feasibility, and accessibility. At the heart of it all, it must enable early diagnosis in the course of infection to reduce spread. However, diagnostic manufacturers need guidance on the optimal characteristics of a virological test to ensure pandemic preparedness and to aid in the effective treatment of viral infections. Nanomaterials are a decisive element in developing COVID-19 diagnostic kits as well as a key contributor to enhance the performance of existing tests. Our objective is to develop a profile of the criteria that should be available in a platform as the target product. In this work, virus detection tests were evaluated from the perspective of the COVID-19 pandemic, and then we generalized the requirements to develop a target product profile for a platform for virus detection.

Label-free chemical imaging of cytochrome P450 activity by Raman microscopy

Although investigating drug modulation of cytochrome P450 (CYP) activity under physiological conditions is crucial in drug development to avoid severe adverse drug reactions, the current evaluation approaches that rely on the destructive and end-point analysis can be misleading due to invasive treatments and cellular heterogeneity. Here, we propose a non-destructive and high-content method for visualizing and quantifying intracellular CYP activity under drug administration by Raman microscopy. The redox-state and spin-state sensitive Raman measurement indicated that the induced CYPs in living hepatocytes were in oxidized and low-spin state, which is related to monooxygenase function of CYP. Moreover, glycogen depletion associated with CYP induction was simultaneously observed, indicating a relevant effect on glucose metabolism. By deciphering the overall changes in the biochemical fingerprints of hepatocytes, Raman microscopy offers a non-destructive and quantitative chemical imaging method to evaluate CYP activity at the single-cell level with the potential to facilitate future drug development schemes.

Nanotechnology Advances in the Detection and Treatment of Cancer: An Overview

Over the last few years, progress has been made across the nanomedicine landscape, in particular, the invention of contemporary nanostructures for cancer diagnosis and overcoming complexities in the clinical treatment of cancerous tissues. Thanks to their small diameter and large surface-to-volume proportions, nanomaterials have special physicochemical properties that empower them to bind, absorb and transport high-efficiency substances, such as small molecular drugs, DNA, proteins, RNAs, and probes. They also have excellent durability, high carrier potential, the ability to integrate both hydrophobic and hydrophilic compounds, and compatibility with various transport routes, making them especially appealing over a wide range of oncology fields. This is also due to their configurable scale, structure, and surface properties. This review paper discusses how nanostructures can function as therapeutic vectors to enhance the therapeutic value of molecules; how nanomaterials can be used as medicinal products in gene therapy, photodynamics, and thermal treatment; and finally, the application of nanomaterials in the form of molecular imaging agents to diagnose and map tumor growth.

Label-Free Monitoring of Drug-Induced Cytotoxicity and Its Molecular Fingerprint by Live-Cell Raman and Autofluorescence Imaging

Simultaneous observation of drug distribution at the effector site and subsequent cell response are essential in the drug development process. However, few studies have visualized the drug itself and biomolecular interactions in living cells. Here, we used label-free Raman microscopy to investigate drug-induced cytotoxicity and visualize drug uptake and subcellular localization by its specific molecular fingerprint. A redox-sensitive Raman microscope detected the decrease of reduced cytochrome c (cyt c) after Actinomycin D (ActD) treatment in a time-dependent and dose-dependent format. Immunofluorescence staining of cyt c suggested that the release of cyt c was not the major cause. Combining Raman microscopy with conventional biological methods, we reported that the oxidization of cyt c is an early cytotoxicity marker prior to the release of cyt c. Moreover, as the spectral properties of ActD are sensitive to the surrounding environment, subcellular localization of ActD was visualized sensitively by the weak autofluorescence, and the intercalation of ActD into DNA was detected by shifted Raman peaks, allowing for parallel observation of drug uptake and the mechanism of action. In this research, we achieved simultaneous observation of cytotoxicity and cellular drug uptake by Raman microscopy, which could facilitate a precise understanding of pharmacological effects and predict potential drug toxicity in the future.

Model-Based Optimization of Laser Excitation and Detection Improves Spectral Contrast in Noninvasive Diffuse Raman Spectroscopy

Spatially offset Raman spectroscopy (SORS) is a powerful technique for subsurface molecular analysis of optically turbid samples. Numerical modeling of light propagation has been used to investigate opportunities for improving spectral contrast and signal to noise ratio when imaging regions of interest located 0-4.5 mm below the surface in polymer bulk material. Two- and three-dimensional modeling results demonstrate that when analyzing a certain region of interest (ROI) of finite lateral dimensions below the sample surface, offsetting both the laser source and detector in opposite directions from the central point of the ROI can increase the spectral contrast as compared to conventional SORS approach where the detector or the laser source is maintained at the central point (centered SORS). The outlined modeling results have been validated experimentally using a bulk polymer sample with a trans-stilbene ROI (cylinder) below the sample surface. The results show that modeling of the spatial configurations of laser excitation and detection points can be used to optimize the instrument configuration to achieve significant improvements (up to 2.25-fold) in performance over the conventional centered SORS. Such optimal solutions can then be implemented, for example, using robust fiber optic probes, moveable optics, or flexible spatial light modulator instruments for specific applications.

Fibre optic Raman spectroscopy for the evaluation of disease state in Duchenne muscular dystrophy: an assessment using the mdx model and human muscle

Introduction/Aims

Raman spectroscopy is an emerging technique for the evaluation of muscle disease. In this study we evaluate the ability of in vivo intramuscular Raman spectroscopy to detect the effects of voluntary running in the mdx model of Duchenne muscular dystrophy (DMD). We also compare mdx data with muscle spectra from human DMD patients.

Methods

Thirty 90‐day‐old mdx mice were randomly allocated to an exercised group (48‐hour access to a running wheel) and an unexercised group (n = 15 per group). In vivo Raman spectra were collected from both gastrocnemius muscles and histopathological assessment subsequently performed. Raman data were analyzed using principal component analysis–fed linear discriminant analysis (PCA‐LDA). Exercised and unexercised mdx muscle spectra were compared with human DMD samples using cosine similarity.

Results

Exercised mice ran an average of 6.5 km over 48 hours, which induced a significant increase in muscle necrosis (P = .03). PCA‐LDA scores were significantly different between the exercised and unexercised groups (P < .0001) and correlated significantly with distance run (P = .01). Raman spectra from exercised mice more closely resembled human spectra than those from unexercised mice.

Discussion

Raman spectroscopy provides a readout of the biochemical alterations in muscle in both the mdx mouse and human DMD muscle.

The distinct phenotype of primary adipocytes and adipocytes derived from stem cells of white adipose tissue as assessed by Raman and fluorescence imaging

Spectroscopy-based analysis of chemical composition of cells is a tool still scarcely used in biological sciences, although it provides unique information about the cell identity accessible in vivo and in situ. Through time-lapse spectroscopic monitoring of adipogenesis in brown and white adipose tissue-derived stem cells we have demonstrated that considerable chemical and functional changes occur along with cells differentiation and maturation, yet yielding mature adipocytes with a similar chemical composition, independent of the cellular origin (white or brown adipose tissue). However, in essence, these stem cell-derived adipocytes have a markedly different chemical composition compared to mature primary adipocytes. The consequences of this different chemical (and, hence, functional) identity have great importance in the context of selecting a suitable methodology for adipogenesis studies, particularly in obesity-related research.

Bessel-beam illumination Raman microscopy

We demonstrate the use of Bessel beams for side illumination slit-scanning Raman imaging for label-free and hyperspectral analysis of cell spheroids. The background elimination by the side illumination and the aberration-resistant Bessel beam drastically improves the image contrast in Raman observation, allowing label-free investigation of intracellular molecules in thick biological samples. Live cell spheroids were observed to confirm the improvement in image contrast and background reduction with Bessel illumination compared to conventional epi-line illumination.

A Single-Cell Raman Spectroscopy Analysis of Bone Marrow Mesenchymal Stem/Stromal Cells to Identify Inter-Individual Diversity

The heterogeneity of stem cells represents the main challenge in regenerative medicine development. This issue is particularly pronounced when it comes to the use of primary mesenchymal stem/stromal cells (MSCs) due to a lack of identification markers. Considering the need for additional approaches in MSCs characterization, we applied Raman spectroscopy to investigate inter-individual differences between bone marrow MSCs (BM-MSCs). Based on standard biological tests, BM-MSCs of analyzed donors fulfill all conditions for their characterization, while no donor-related specifics were observed in terms of BM-MSCs morphology, phenotype, multilineage differentiation potential, colony-forming capacity, expression of pluripotency-associated markers or proliferative capacity. However, examination of BM-MSCs at a single-cell level by Raman spectroscopy revealed that despite similar biochemical background, fine differences in the Raman spectra of BM-MSCs of each donor can be detected. After extensive principal component analysis (PCA) of Raman spectra, our study revealed the possibility of this method to diversify BM-MSCs populations, whereby the grouping of cell populations was most prominent when cell populations were analyzed in pairs. These results indicate that Raman spectroscopy, as a label-free assay, could have a huge potential in understanding stem cell heterogeneity and sorting cell populations with a similar biochemical background that can be significant for the development of personalized therapy approaches.

Raman Spectroscopy—A Novel Method for Identification and Characterization of Microbes on a Single-Cell Level in Clinical Settings

Rapid and accurate identification of pathogens causing infections is one of the biggest challenges in medicine. Timely identification of causative agents and their antimicrobial resistance profile can significantly improve the management of infection, lower costs for healthcare, mitigate ever-growing antimicrobial resistance and in many cases, save lives. Raman spectroscopy was shown to be a useful—quick, non-invasive, and non-destructive —tool for identifying microbes from solid and liquid media. Modifications of Raman spectroscopy and/or pretreatment of samples allow single-cell analyses and identification of microbes from various samples. It was shown that those non-culture-based approaches could also detect antimicrobial resistance. Moreover, recent studies suggest that a combination of Raman spectroscopy with optical tweezers has the potential to identify microbes directly from human body fluids. This review aims to summarize recent advances in non-culture-based approaches of identification of microbes and their virulence factors, including antimicrobial resistance, using methods based on Raman spectroscopy in the context of possible use in the future point-of-care diagnostic process.

Simultaneous Infrared Spectroscopy, Raman Spectroscopy, and Luminescence Sensing: A Multispectroscopic Analytical Platform

Scientific questions in fields such as catalysis, monitoring of biological processes, or environmental chemistry demand analytical technologies combining orthogonal spectroscopies. Combined spectroscopic concepts facilitate in situ online monitoring of dynamic processes providing a better understanding of the involved reaction pathways. In the present study, a low-liquid-volume multispectroscopic platform was developed based on infrared attenuated total reflection (IR-ATR) spectroscopy combined with Raman spectroscopy and luminescence sensing. To demonstrate the measurement capabilities, exemplary analyte systems including water/heavy water and aqueous solutions of ammonium sulfate were analyzed as proof-of-principle studies. It was successfully demonstrated that three optical techniques may be integrated into a single analytical platform without interference providing synchronized and complementary data sets by probing the same minute sample volume. In addition, the developed assembly provides a gastight lid sealing the headspace above the probed liquid for monitoring the concentration of molecular oxygen also in the gas phase via luminescence quenching. Hence, the entire assembly may be operated at inert conditions, as required, for example, during the analysis of photocatalytic processes.

DO-SRS imaging of diet regulated metabolic activities in Drosophila during aging processes

Lipid metabolism plays crucial roles during aging processes, but how it is regulated by diets and how it interplays with aging still remain unclear. We proposed a new optical imaging platform by integrating heavy water (D2 O) probing with stimulated Raman scattering (DO-SRS) microscopy, for the first time, to directly visualize and quantify lipid metabolism regulated by different diets and insulin signaling pathway in Drosophila fat body during aging. We found that calorie restriction, low protein diet, and (moderately) high protein and high sucrose diets enhanced lipid turnover in flies at all ages, while (moderately) high fructose and glucose diets only promoted lipid turnover in aged flies. The measured lipid turnover enhancements under diverse diets were due to different mechanisms. High protein diet shortened the lifespan while all other diets extended the lifespan. Downregulating the insulin signaling pathway enhanced lipid turnover, which is likely related to lifespan increase, while upregulating insulin signaling pathway decreased lipid turnover that would shorten the lifespan. Our study offers the first approach to directly visualize spatiotemporal alterations of lipid turnover in aging Drosophila in situ, for a better understanding of the interconnections between lipid metabolism, diets, and aging.

Wafer-scale nanocracks enable single-molecule detection and on-site analysis

Large-area surface-enhanced Raman spectroscopy (SERS) sensing platforms displaying ultrahigh sensitivity and signal uniformity have potentially enormous sensing applicability, but they are still challenging to prepare in a scalable manner. In this study, silver nanopaste (AgNPA) was employed to prepare a wafer-scale, ultrasensitive SERS substrate. The self-generated, high-density Ag nanocracks (NCKs) with small gaps could be created on Si wafers via a spin-coating process, and provided extremely abundant hotspots for SERS analyses with ultrahigh sensitivity-down to the level of single molecules (enhancement factor: ca. 10¹⁰; detection limit: ca. 10^-18 M)-and great signal reproducibility (variation: ca. 3.6%). Moreover, the Ag NCK arrays demonstrated broad applicability and practicability for on-site detection when combined with a portable 785 Raman spectrometer. This method allowed the highly sensitive detection of a diverse range of analytes (benzo[a]pyrene, di-2-ethylhexyl phthalate, aflatoxins B1, zearalenone, ractopamine, salbutamol, sildenafil, thiram, dimethoate, and methamidophos). In particular, pesticides are used extensively in agricultural production. Unfortunately, they can affect the environment and human health as a result of acute toxicity. Therefore, the simultaneous label-free detection of three different pesticides was demonstrated. Finally, the SERS substrates are fabricated through a simple, efficient, and scalable process that offers new opportunities for mass production.

LC-MS Analysis Revealed the Significantly Different Metabolic Profiles in Spent Culture Media of Human Embryos with Distinct Morphology, Karyotype and Implantation Outcomes

In this study we evaluated possible differences in metabolomic profiles of spent embryo culture media (SECM) of human embryos with distinct morphology, karyotype, and implantation outcomes. A total of 153 samples from embryos of patients undergoing in vitro fertilization (IVF) programs were collected and analyzed by HPLC-MS. Metabolomic profiling and statistical analysis revealed clear clustering of day five SECM from embryos with different morphological classes and karyotype. Profiling of day five SECM from embryos with different implantation outcomes showed 241 significantly changed molecular ions in SECM of successfully implanted embryos. Separate analysis of paired SECM samples on days three and five revealed 46 and 29 molecular signatures respectively, significantly differing in culture media of embryos with a successful outcome. Pathway enrichment analysis suggests certain amino acids, vitamins, and lipid metabolic pathways to be crucial for embryo implantation. Differences between embryos with distinct implantation potential are detectable on the third and fifth day of cultivation that may allow the application of culture medium analysis in different transfer protocols for both fresh and cryopreserved embryos. A combination of traditional morphological criteria with metabolic profiling of SECM may increase implantation rates in assisted reproductive technology programs as well as improve our knowledge of the human embryo metabolism in the early stages of development.