Raman and infrared spectroscopy of carbohydrates: A review

Liquid saliva analysis using optofluidic photonic crystal fiber for detection of oral potentially malignant disorders

Oral potentially malignant disorders (OPMD) serve as early indicators of oral cancer. These conditions require reliable, non-invasive and real-time diagnostic methods for effective detection and screening. Saliva, as an easily accessible and non-invasive biofluid, has emerged as a promising tool to detect OPMD-associated biomarkers. This proof-of-concept study investigated the application of suspended-core optofluidic photonic crystal fibers (PCF) for Raman spectroscopy to distinguish between saliva from healthy controls and OPMD patients. This novel approach provides enhanced Raman signals through prolonged interactions between the excitation light and the saliva sample along the length of the PCF. Raman spectra of liquid saliva samples were collected from eleven participants, including six OPMD patients and five healthy controls. Notable spectral differences were identified at 1123 cm-1, 1251 cm-1, and 1454 cm-1, which correspond to carbohydrates, proteins, and lipids, respectively. In vivo tissue measurements were recorded as a reference for comparative analysis from the same patients. Our findings suggest that PCF-based Raman spectroscopy holds promise as a non-invasive diagnostic platform for oral cancer screening by enabling the monitoring of subtle molecular changes in liquid saliva.

Lipidomic profiling reveals the protective mechanism of nitrogen-controlled atmosphere on brown rice quality during storage

Rice, a globally important staple, requires effective preservation methods to maintain its quality during extended storage. This study explored the efficacy of nitrogen-controlled atmosphere (NCA) storage in preserving the quality of brown rice during a one-year period using UHPLC-MS/MS based lipidomic profiling. A total of 1013 lipids were identified and categorized into five main groups. Specific lipids including triglycerides (TG), diglycerides (DG), phosphatidylethanolamines (PE), cardiolipins (CL), and ceramides (Cer), were highlighted as potential biomarkers for assessing rice rancidity. NCA storage significantly suppressed lipase and lipoxygenase activities, reducing lipid hydrolysis and oxidation to effectively delayed rice quality deterioration. Furthermore, NCA regulated glycerolipid and glycerophospholipid metabolisms, promoting lipid remodeling while reducing the degradation of TGs and phospholipids. This regulation preserved cellular membrane integrity, limited fatty acid release, and mitigate rancidity and quality loss during storage. These findings elucidate the mechanism by which NCA storage delays deterioration and extends the stored rice shelf-life.

Quantitative evaluation of composition and biomolecular mapping of macrofungi spores by Raman spectroscopy

Raman spectroscopy is a widely used technique for detecting various chemical compounds in organic matter and creating high-resolution, label-free maps on the level of individual cells. One of the main advantages of this technique is its ability to study samples in vivo without special pretreatment. However, it is rarely used to determine the ratios between different substances or to conduct in-depth quantitative analysis of the obtained spectra. In our study, we establish ratiometric equations that enable the estimation of mass concentrations of triacylglycerols, proteins, sugars, polysaccharides, and DNA. We demonstrate that it is possible to determine the average unsaturation and chain length from the spectra of lipids and concentrations of phenylalanine, tyrosine, and tryptophan from the spectra of proteins. We apply the derived equations to the Raman spectra of fungal spores from over 70 different species of macrofungi, providing a comprehensive characterization of the lipids, proteins, and polysaccharides present in the spores. We believe that our study not only contributes valuable fundamental knowledge to the field of mycology but also lays the groundwork for the spectral quantification of any organic material. So, our approach may be applicable in areas such as food diagnostics.

Analysis of lipids by Raman spectroscopy and mass spectrometry provides a detection tool and mechanistic insight into imatinib resistance in CML-BC

Resistance to tyrosine kinase inhibitors (TKIs) is a major challenge in the treatment of chronic myeloid leukemia (CML). Established tests based on the known mechanisms of resistance in the initial chronic phase (CP) confirm resistance, reveal the underlying reason and thereby direct treatment modifications. In the terminal phase of blast crisis (BC), however, additional partially identified mechanisms of resistance exist which necessitates developing modalities to detect resistance regardless of the underlying mechanism and concurrent exploration of the resistance mechanism to assist in identification of appropriate drug targets. In this study both the clinical objectives were achieved by analysing lipids in BC cells, sensitive and resistant to TKIs, using the complementary strengths of distinct analytical technologies. Raman spectroscopy, through the spectral signatures with lipids as a significant differentiating component could segregate resistant from sensitive cells in the Principal Component Analysis (PCA) and Principal Component based Linear Discriminant Analysis (PC-LDA). This provided a tool to rapidly detect resistance in CML-BC despite unclear mechanism of TKI resistance. The depth of coverage achievable by mass spectrometry allowed the generation of quantitative differential profile of individual lipids in resistant cells. The alterations were in diverse classes of lipids which are involved in cell signalling and inhibition studies could link these alterations to modulation of phospholipase A2 (PLA2) levels mediated by p38 mitogen activated protein kinase (p38MAPK), which is causally associated with TKI resistance in CML-BC. Together, lipid analysis using the two platforms, contributed to the detection and mechanistic understanding of imatinib resistance in CML-BC.

Raman imaging investigation of hepatic LX-2 cell reversion under different lipidic treatments

Liver fibrosis resulting from chronic liver injury is characterized by increased extracellular matrix deposition and inflammation, which leads to excessive scar tissue formation. Targeting activated hepatic stellate cells (HSCs), which are the primary drivers of fibrogenesis, stands out as one of the most compelling therapeutic approaches in this regard. In a healthy liver, HSCs remain quiescent and store vitamin A in cytoplasmic lipid droplets. As a consequence of HSC activation and transdifferentiation to a proliferative myofibroblast-like state upon fibrotic stimuli, the distinctive phenotypic feature of the lipid droplets gets lost. While the reversal of activated HSCs is feasible, understanding the quiescent-like state following injury resolution is crucial for effective fibrosis treatment. This study explores the induced quiescent-like state of naïve immortalized human hepatic stellate (LX-2) cells when treated with soybean phospholipid that contains 75% phosphatidylcholine (S80). The lipid profile of the newly formed lipid droplets was analyzed using Raman imaging, which is a label-free technique well-suited for lipidomics. Results indicate the presence of distinct lipid profiles despite maintaining a quiescent-like state, suggesting that diverse mechanisms govern the active-to-inactive state transition. Additionally, our findings support the fact that each hepatic cell state is composed of heterogeneous subpopulations. This emphasizes the complexity of liver fibrosis and highlights the need for a comprehensive understanding of cellular states to develop targeted therapies.

Discussion on the comparison of Raman spectroscopy and cardiovascular disease-related imaging techniques and the future applications of Raman technology: a systematic review

Cardiovascular disease (CVD) is a major cause of unnatural death worldwide, so timely diagnosis of CVD is crucial for improving patient outcomes. Although the traditional diagnostic tools can locate plaque and observe inner wall of blood vessel structure, they commonly have radioactivity and cannot detect the chemical composition of the plaque accurately. Recently emerging Raman techniques can detect the plaque composition precisely, and have the advantages of being fast, high-resolution and marker-free. This makes Raman have great potential for detecting blood samples, understanding disease conditions, and real-time monitoring. This review summarizes the origin and state-of-art of Raman techniques, including the following aspects: (a) the principle and technical classification of Raman techniques; (b) the applicability of Raman techniques and its comparison with traditional diagnostic tools at different diagnosis targets; (c) the applicability of Raman spectroscopy in advanced CVD. Lastly, we highlight the possible future applications of Raman techniques in CVD diagnosis.

Preliminary study demonstrating cancer cells detection at the margins of whole glioblastoma specimens with Raman spectroscopy imaging

Intraoperative Raman spectroscopy uses near-infrared laser light to gain molecular information without causing damage. It can be used in vivo or ex vivo without exogenous contrast agents. Clinically, the technique was primarily used with machine learning for in situ tumor detection with fiberoptics probes analyzing tissue at sub-millimeter scales one point at the time. Here we report the development of a whole-specimen spectroscopic imaging system designed to detect cancer cells at the margins of surgical specimens. The system has a field of view covering a square area of side one centimeter with a pixel size of a quarter of a millimeter . First, a tumor detection model was developed from data acquired using a point-probe in 24 glioblastoma patients that had a detection sensitivity of 90% and a specificity of 95%. That model was then used to produce cancer prediction maps of nine glioblastoma specimens from five patients with validation based on histopathology analyses. The results preliminarily demonstrate the instrument was able to detect tissue areas associated with cancer cells from the Raman peaks associated with the amino acids phenylalanine and tryptophan as well as the relative concentration of lipids and proteins linked with deformations of the CH2 and CH3 bonds.

Comparison of Different Vibrational Spectroscopic Probes (ATR-FTIR, O-PTIR, Micro-Raman, and AFM-IR) of Lipids and Other Compounds Found in Environmental Samples: Case Study of Substrate-Deposited Sea Spray Aerosols

The use of vibrational spectroscopy to probe environmental samples is increasing with the development of new methods, including microspectroscopic probes. In this study, we compare different vibrational methods to interrogate lipids and other compounds found in environmental samples. In particular, we compare the vibrational spectra for different lipids that include fatty acids (protonated and deprotonated forms), fatty alcohols, and fatty esters by utilizing attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy, optical photothermal infrared (O-PTIR) spectroscopy, micro-Raman spectroscopy, and atomic force microscopy infrared (AFM-IR) spectroscopy. We show the utility of infrared methods to clearly delineate the structure of the lipid, i.e., whether it is an acid, alcohol, or ester. In contrast, it is difficult to differentiate these from micro-Raman spectroscopy. Furthermore, in the case of fatty acids, the protonation state can also be determined by infrared methods. In most cases, there is a high correlation between the three different infrared techniques as seen for ATR-FTIR and O-PTIR spectroscopy; however, this is not always true with AFM-IR spectroscopy for samples with low signal-to-noise or in a liquid phase state. Additionally, substrate-deposited aerosols were collected from the Scripps Ocean-Atmosphere Research Simulator (SOARS) and examined with both the O-PTIR and micro-Raman spectroscopy to show how these two vibrational probes together can provide essential chemical insights into environmental samples that are difficult to achieve otherwise.

Activation dynamics study of trapped platelets using a lab-built optical tweezers micro-raman spectrometer

It is well documented that platelet disorders can result from various causes and can lead to different disease conditions such as cardiovascular diseases (CVDs), thrombocythemia, thrombocytopenia, Autoimmune diseases, Alzheimer's disease (AD), and even cancer, to name a few. The diagnosis of many of these diseases mainly depends on imaging examinations, clinical analysis and neuropsychological tests, these may be time taking procedures and, have a high chances of false positive/false negative results. The Raman tweezers spectroscopy can provide trust worthy results without much time delay. In the present study the activation dynamics of platelets were studied and observed that the activation leads to biochemical and morphological changes, such as the formation of filopodia on the platelet surface, transformation in the shape from discoid to spherical, and translocation of aminophospholipids from inner leaflet to the outer leaflet of the plasma membrane. The Raman bands corresponding to phospholipids shows remarkable intensity variations during activation. The detailed knowledge regarding the activation dynamics of platelets will be important in monitoring CVDs, ADs, etc. and this paper illustrates a prospective method that can be incorporated into clinical settings in the near future to study and analyze platelet activation. This study will be the first to display the trapping of platelets in its live form to study their activation dynamics using an in-house assembled optical tweezers micro-Raman spectrometer.

Unraveling biochemical differences in the membrane of functional RBCs and elliptocytes using vortex beam-based micro-Raman spectroscopy

Understanding the complexity of membrane biochemical changes in in-vitro-induced elliptocytosis can be interesting as it may mimic those in hereditary elliptocytosis. Studying the membrane biochemical changes in metabolically active elliptocytes can be crucial, but most modern methods, such as ektacytometry and EMA binding tests, fail to do so. This study employs single-cell Raman spectroscopy, a proven technique to study biochemical changes in individual functional cells to investigate biochemical modifications in the membrane and cytoskeleton of elliptocytes. This was possible by applying a vortex beam, which can probe the RBC membrane with a reduced contribution from hemoglobin, which otherwise dominates the cell spectrum. Raman spectral variations in elliptocytes indicated changes in proteins, lipids, and lipid-protein interactions. The study also presented an incidental observation of diversity in membrane components and membrane-hemoglobin interaction among tested individuals.

Cervical cancer diagnosis model using spontaneous Raman and Coherent anti-Stokes Raman spectroscopy with artificial intelligence

Cervical cancer is the fourth most common cancer worldwide. Histopathology, which is currently considered the gold standard for cervical cancer diagnosis, can be time-consuming and subjective. Therefore, there is an urgent need for a rapid, objective, and non-destructive cervical cancer detection technique. In this study, high-wavenumber spontaneous Raman spectroscopy was used to detect cervical squamous cell carcinoma and normal tissues. The levels of lipids, fatty acids, and proteins in cervical cancerous tissues were found to be higher than those in normal tissues. Raman difference spectroscopy revealed the most significant difference at 2928 cm-1. Additionally, a Coherent anti-Stokes Raman spectroscopy (CARS) instrument was employed to enhance the wavenumber signal intensity and sensitivity. The intrinsic relationship between CARS imaging and cervical lesions was established. The CARS images indicated that the intensity of normal cervical squamous cells was zero, whereas the intensities of keratinized and non-keratinized cervical squamous cell carcinoma tissues were significantly higher. Consequently, diagnostic outcomes could be obtained by observing CARS images with the naked eye. Furthermore, the characteristic structure of keratin pearls in keratinized cervical cancer could serve as a marker for subdividing cervical cancer types. Finally, a ConvNeXt network, a machine-learning model built from CARS images, was utilized to classify different types of tissue images. The results indicated a verification accuracy of 100 %, with a loss function of 0.0927. These findings suggest that the diagnostic model established using CARS images could efficiently diagnose cervical cancer, providing novel insights into the pathological diagnosis of this disease.

Investigation on morphological and molecular fingerprints of penguin brain using label-free optical imaging and spectroscopic techniques

The morphology and molecular study of the penguin brain are crucial to define its survival in the extreme conditions of Antarctica. The present study focusses on extracting different optical parameters of the penguin brain using label-free optical imaging and spectroscopic techniques. In label-free optical imaging, we have used quantitative phase imaging, which provides morphological information about the neurons in brain tissue, giving the quantitative phase value of 5 to 20 radians corresponding to the 8 µm tissue section. In label-free spectroscopic techniques, we have used autofluorescence and Raman spectroscopy. Autofluorescence spectroscopy provides molecular information about nicotinamide dinucleotide, flavins, lipofuscins, and porphyrins in the brain's spectral range of 420 nm to 700 nm. Raman spectroscopy provides multiple peaks associated with different molecules in the brain; among them, few signals are observed at approximately 1305 cm-1, 1448 cm-1, and 1661 cm-1, which correspond to vibrational modes indicative of vibrational features within lipids and protein structures, as well as the presence of amide groups within brain tissue constituents. All these techniques provide the microscopic and molecular fingerprint of the penguin brain, which can be useful for understanding penguin's anatomical, physiological, and social behavior.

Raman signatures of type A and B influenza viruses: molecular origin of the "catch and kill" inactivation mechanism mediated by micrometric silicon nitride powder

A multiomic study of the structural characteristics of type A and B influenza viruses by means of highly spectrally resolved Raman spectroscopy is presented. Three virus strains, A H1N1, A H3N2, and B98, were selected because of their known structural variety and because they have co-circulated with variable relative prevalence within the human population since the re-emergence of the H1N1 subtype in 1977. Raman signatures of protein side chains tyrosine, tryptophan, and histidine revealed unequivocal and consistent differences for pH characteristics at the virion surface, while different conformations of two C-S bond configurations in gauche and trans methionine rotamers provided distinct low-wavenumber fingerprints for different virus lineages/subtypes. Short-term exposure to a few percent fraction of silicon nitride (Si3N4) micrometric powder in an aqueous environment completely inactivated the influenza virions, independent of lineage/subtype dependent characteristics. The molecular-scale details of the inactivation process were studied by Raman spectroscopy and interpreted in terms of a "catch and kill" mechanism, in which the hydrolyzing ceramic surface first attracts virions with high efficiency through electrochemical interactions (mimicking cellular sialic acid) and then "poisons" the viruses by local hydrolytic elution of ammonia and nitrogen radicals. The latter event causes severe damage to the virions' structures, including structural degradation of RNA purines, rotameric scrambling of methionine residues, formation of sulfhydryl and ionized carboxyl groups, and deprotonation/torsional deformation of tyrosine, tryptophan, and histidine residues. This study confirmed the antiviral effectiveness of Si3N4 powder, which is safe to the human body and simply activated by water molecules. Raman spectroscopy was confirmed as a powerful tool in molecular virology, complementary to genomics and unique in providing direct information on virus structures at the molecular scale.

Comparison of biochemical changes induced in radioresistant prostate cancer cells by X-rays, radiosensitizing drugs, and a combined therapy using Raman microspectroscopy

Cancer radioresistance is a major problem in radiotherapy. Many strategies have been proposed to overcome this process including the use of radiosensitizing drugs such as C75 or silibinin. The overall result of all treatments (radiotherapy, chemotherapy, and combined treatment) is cancer cell death. On the other hand, each treatment affects cancer cells differently at the molecular level. However, little is known about biochemical changes induced in cancer cells by these treatments (especially in combined therapy) at the submicroscale. In this study, Raman microspectroscopy was applied to follow such changes induced in radioresistant prostate cancer cells by X-rays, radiosensitizing drugs (C75, silibinin), and a combined treatment. The analysis was supported by the Partial Least Squares Regression method to reveal spectral changes induced by an increasing dose of X-rays and concentrations of the drugs. The obtained regression coefficient (β) plots were compared to each other using a correlation coefficient (R). Our results show that PC-3 cells exhibit dose- and concentration-dependent responses to the treatment with different biochemical changes induced by X-rays in the presence of C75 and silibinin. Moreover, both drugs affect the cells differently at the submicroscale and independently from the X-ray's presence. Finally, C75 shows significant efficiency in the reduction of cell radioresistance.

Penile Cancer: Innovations in Ultrastructural and Vibrational Markers

Penile cancer (PCa) is a disease that manifests predominantly as squamous cell carcinomas (SCCs), which, although rare, represents a significant public health problem, especially in regions with less socioeconomic development. One of the biggest challenges in managing this disease is the difficulty in differentiating tumor subtypes, making accurate diagnosis and treatment challenging. In this context, new characterization techniques are needed to investigate these tumors more completely. Atomic force microscopy (AFM) and Raman spectroscopy (RS) are valuable in this context, providing quantitative and qualitative ultrastructural data and vibrational signatures of the analyzed samples. In this study, AFM and RS techniques were employed to investigate subtypes of penile cancer, including the highly aggressive basaloid subtype, which is closely associated with human papillomavirus (HPV), and the sarcomatoid subtype, comparing them with nontumorous tissues. The AFM results revealed nanoscale changes in the ultrastructural properties of tumor samples, such as increased roughness in tumor tissues, with emphasis on the basaloid type associated with the HPV virus, and reduction in the surface area and volume of tumor tissues at the nanoscale, suggesting deeper tissue infiltration and greater deformability of tumor samples at the nanoscale. RS results detected significant spectral differences between normal and cancerous tissues and between tumor subtypes, particularly in vibrational modes related to proteins and lipids. Principal component analysis (PCA) confirmed a strong discriminative power between control and PCa groups. The data presented here offers new insights into the characteristics of penile tumors that, when integrated with clinical analyses, could improve the understanding of penile cancer behavior, contributing to more accurate diagnostic methods and targeted treatments.

Probing the effects of dextran-coated CeO2 nanoparticles on lung fibroblasts using multivariate single-cell Raman spectroscopy

In this study, we investigated the cytotoxic effect of highly soluble dextran-coated CeO2 nanoparticles on human fetal lung fibroblasts MRC-5. We examined individual nanoparticle-treated cells by Raman spectroscopy and analyzed Raman spectra using non-negative principal component analysis and k-means clustering. In this way, we determined dose-dependent differences between treated cells, which were reflected through the intensity change of lipid, phospholipid and RNA-related Raman modes. Performing standard biological tests for cell growth, viability and induction of apoptosis in parallel, these changes were correlated with nanoparticle-induced apoptotic processes. The cells with specific spectral characteristics, referring to non-apoptotic, but possibly autophagic cell death modality, were also detected. Additionally, Raman imaging combined with principal component and vertex component analysis was used to map the spatial distribution of biological molecules in treated and untreated cells. This work provided the description of different resulting states of the treated cells depending on the dextran-coated CeO2 nanoparticles dose, which can be later used in the design of the nanoparticles for industrial or medical applications. The wide content of information resulting from single-cell Raman spectroscopy has the potential to detect biochemical changes caused by nanoparticles that would otherwise require a series of expensive and time-consuming standard biological techniques.

Alteration of Lipid Metabolism in Hypoxic Cancer Cells

Due to uncontrolled cell proliferation and disrupted vascularization, many cancer cells in solid tumors have limited oxygen supply. The hypoxic microenvironments of tumors lead to metabolic reprogramming of cancer cells, contributing to therapy resistance and metastasis. To identify better targets for the effective removal of hypoxia-adaptive cancer cells, it is crucial to understand how cancer cells alter their metabolism in hypoxic conditions. Here, we studied lipid metabolic changes in cancer cells under hypoxia using coherent Raman scattering (CRS) microscopy. We discovered the accumulation of lipid droplets (LDs) in the endoplasmic reticulum (ER) in hypoxia. Time-lapse CRS microscopy revealed the release of old LDs and the reaccumulated LDs in the ER during hypoxia exposure. Additionally, we explored the impact of carbon sources on LD formation and found that MIA PaCa2 cells preferred fatty acid uptake for LD formation, while glucose was essential to alleviate lipotoxicity. Hyperspectral-stimulated Raman scattering (SRS) microscopy revealed a reduction in cholesteryl ester content and a decrease in lipid saturation levels of LDs in hypoxic MIA PaCa2 cancer cells. This alteration in LD content is linked to reduced efficacy of treatments targeting cholesteryl ester formation. This study unveils important lipid metabolic changes in hypoxic cancer cells, providing insights that could lead to better treatment strategies for hypoxia-resistant cancer cells.

Noninvasive Optical Sensing of Aging and Diet Preferences Using Raman Spectroscopy

Effective dietary strategies and interventions for monitoring dietary exposures require accurate and noninvasive methods to understand how diet modulates health and risk of obesity; advances in technology are transforming the landscape and enabling more specific tailored approaches to nutritional guidance. This study explores the use of Raman spectroscopy (RS), a noninvasive and nondestructive analytical technique, to identify changes in the mice skin in response to constant dietary exposures. We found that RS is highly accurate to determine body composition as a result of habitual dietary patterns, specifically Vegan, Typical American, and Ketogenic diets, all very common in the US context. RS is based on major differences in the intensities of vibrational bands that originate from collagen. Moreover, RS could be used to predict folate deficiency and identify the sex of the animals. Finally, we found that RS could be used to track the chronological age of the mice. Considering the hand-held nature of the utilized spectrometer, one can expect that RS could be used to monitor and, consequently, personalize effects of diet on the body composition.

Raman spectroscopy to investigate early biochemical alterations in human tears caused by contact lenses

CLINICAL RELEVANCE

The interaction between contact lenses and tear film plays a critical role in lens biocompatibility and can influence discomfort during wear. Early detection of biochemical changes is important for improving ocular surface health, preventing complications, and developing advanced lens materials to enhance long-term comfort.

BACKGROUND

The pursuit of biocompatible lens materials requires a thorough understanding of their interaction with tear environment. The aim of this study is to evaluate different approaches to investigate early alterations in tear biomolecular profiles induced by lens wear.

METHODS

Two participants wore different CLs and underwent ocular analysis before CL insertion (T0), after 2-hour wear (T2), and two hours after removing the CL (T4), evaluating non-invasive tear break-up time (NIBUT), tear meniscus height (TMH), and osmolarity. Forty CLs were used. At the same time points, tears were collected and analysed by Raman spectroscopy.

RESULTS

Tear film stability, as measured by NIBUT, decreased post-lens wear (T0 vs T2, p = 0.034 and 0.025 for S1 and S2, respectively) but showed recovery within 2 hours (T0 vs T4, p > 0.05 for both S1 and S2), while TMH and osmolarity remained unchanged (T0 vs T2 vs T4, p > 0.05 for both S1 and S2). Raman spectroscopy highlighted notable changes in the biomolecular profile of tears after lens wear, particularly in the 2700-3200 cm-1 spectral window related to C-H stretching, which is sensitive to CH, CH2 and CH3 groups, their functional chemistry group, and the surrounding microenvironment.

CONCLUSIONS

Contact lens wear induces subtle biochemical, subclinical alterations in tears that can be detected early by Raman spectroscopy. The method is promising for analysing the subject-specific responses to lenses, also with possible individual variability in the timing of these changes. In addition, this approach can provide insights for designing biocompatible materials and for the early detection of subclinical biomarkers.

Raman imaging for monitoring deuterated squalene-gemcitabine nanomedicines in single living breast cancer cells

We have investigated the impact of gemcitabine (Gem) and deuterated gemcitabine-squalene (GemSQ-d6) nanoparticles (NPs) on MCF7 and MDA-MB-231 breast cancer cell lines by Raman spectroscopy. Quantification of LDL expression levels in both cell lines revealed a four-fold increase in MDA-MB-231 cells compared to MCF7 cells. In in vitro antitumor assessments, Gem displayed 13.5 times more effectiveness than GemSQ NPs against MCF7 cells, whereas GemSQ NPs induced a 14-fold increase in cytotoxicity compared to Gem for MDA-MB-231 cells. Oil Red O staining revealed that the treatment with GemSQ-d6 NPs induced a higher accumulation of lipid droplets at the periphery of the nucleus in MDA-MB-231 cells compared to MCF7 cells. Raman spectroscopy was employed to assess the impact of these drugs (50 µM, 24 hrs) on these breast cancer cell lines. By using the silent region (2000-2400 cm-1), we demonstrated that the accumulation of the GemSQ-d6 bioconjugate was higher in the cytoplasm of MDA-MB-231 cells than in MCF7 cells. This difference in drug accumulation is likely correlated with their expression levels of low-density lipoprotein receptors (LDLR). However, no information was obtained on Gem in this spectral region. We identified Raman features of squalene (SQ) in 700-1800 cm-1 fingerprint region that allowed us to observe almost the same distribution of GemSQ as that observed in the silent region for both cell lines treated with GemSQ-d6 or SQ-d6. Subsequently, the effects of Gem and GemSQ-d6 on cellular components such as proteins, nucleic acids, and cytochrome C were monitored within the fingerprint spectral region. Our results revealed distinct features in the subcellular accumulation of these biomolecules in response to Gem and GemSQ treatments.

Polyanhydride Copolymer-Based Niclosamide Nanoparticles for Inhibiting Triple-Negative Breast Cancer: Metabolic Responses and Synergism with Paclitaxel

The heterogeneity of tumors and the lack of effective therapies have resulted in triple-negative breast cancer (TNBC) exhibiting the least favorable outcomes among breast cancer subtypes. TNBC is characterized by its aggressive nature, often leading to high rates of relapse, metastasis, and mortality. Niclosamide (Nic), an Food and Drug Administration-approved anthelmintic drug, has been repurposed for cancer treatment; however, its application for TNBC is hindered by significant challenges, including strong hydrophobicity, poor aqueous solubility, and low bioavailability. This study aimed to develop Nic nanoparticles (Nic NPs) using biodegradable and biocompatible polyanhydride copolymers to enhance Nic's bioavailability and therapeutic efficacy. Nic NPs effectively inhibited migration, proliferation, and clonogenicity in both murine and human TNBC cells, inducing apoptosis and suppressing STAT3 signaling. For the first time, we utilized Raman spectroscopy and Seahorse extracellular flux assays to demonstrate the metabolic responses of TNBC cells to Nic NPs, revealing significant metabolic alterations, including the inhibition of mitochondrial respiration and glycolysis. Additionally, this study is the first to explore the combination therapy of repurposed Nic with the approved chemotherapeutic agent paclitaxel in the 4T1 TNBC immunocompetent mouse model. The combination of Nic NPs and paclitaxel significantly reduced tumor growth without adversely affecting the body weight of tumor-bearing mice. In summary, these findings suggest that Nic NPs could serve as a promising component in combination therapies for the effective treatment of TNBC.

Quantitatively Detecting Camellia Oil Products Adulterated by Rice Bran Oil and Corn Oil Using Raman Spectroscopy: A Comparative Study Between Models Utilizing Machine Learning Algorithms and Chemometric Algorithms

The fast and accurate quantitative detection of camellia oil products is significant for multiple reasons. In this study, rice bran oil and corn oil, whose Raman spectra both hold great similarities with camellia oil, are blended with camellia oil, and the concentration of each composition is predicted by models with varying feature extraction methods and regression algorithms. Back propagation neural network (BPNN), which has been rarely investigated in previous work, is used to construct regression models, the performances of which are compared with models using random forest (RF) and partial least squares regression (PLSR). Independent component analysis (ICA), competitive adaptive reweighing sampling (CARS), and their dual combinations served to extract spectral features. In camellia oil adulteration with rice bran oil, both the ICA-BPNN and ICA-PLSR models are found to achieve satisfactory performances. For camellia oil adulteration with rice bran oil and corn oil, on the other hand, the performances of BPNN-based models are substantially deteriorated, and the best prediction accuracy is achieved by a PLSR model coupled with CARS-ICA. In addition to performance fluctuations with varying regression algorithms, the output for feature extraction method also played a vital role in ultimate prediction performance.

Anti-inflammatory effects of cyclodextrin nanoparticles enable macrophage repolarization and reduce inflammation

Inflammation plays a critical role in the pathophysiology of many diseases, and dysregulation of the involved signaling cascades often culminates in uncontrollable disease progression and, ultimately, chronic manifestation. Addressing these disorders requires balancing inflammation control while preserving essential immune functions. Cyclodextrins (CDs), particularly β-CD, have gained attention as biocompatible biomaterials with intrinsic anti-inflammatory properties, and chemical modification of their backbone offers a promising strategy to enhance their physicochemical properties, adaptability, and therapeutic potential. This study evaluated and characterized the immunomodulatory effects of amphiphilic CD derivatives, which self-assemble into nanoparticles, compared to soluble parent β-CD. In a human macrophage model, CD nanoparticles demonstrated superior anti-inflammatory activity, with derivative-specific effects tied to their physicochemical properties, surpassing the soluble β-CD control. Alongside the downregulation of key pro-inflammatory markers, significant reductions in inflammasome activation and changes in lipid profiles were observed. The findings of this study underscore the potential of cyclodextrin-based nanoparticles as versatile biomaterials for treating the complex pathophysiology of various acute and chronic inflammation-associated disorders.

Lighting the Path: Raman Spectroscopy's Journey Through the Microbial Maze

The rapid and precise identification of microorganisms is essential in environmental science, pharmaceuticals, food safety, and medical diagnostics. Raman spectroscopy, valued for its ability to provide detailed chemical and structural information, has gained significant traction in these fields, especially with the adoption of various excitation wavelengths and tailored optical setups. The choice of wavelength and setup in Raman spectroscopy is influenced by factors such as applicability, cost, and whether bulk or single-cell analysis is performed, each impacting sensitivity and specificity in bacterial detection. In this study, we investigate the potential of different excitation wavelengths for bacterial identification, utilizing a mock culture composed of six bacterial species: three Gram-positive (S. warneri, S. cohnii, and E. malodoratus) and three Gram-negative (P. stutzeri, K. terrigena, and E. coli). To improve bacterial classification, we applied machine learning models to analyze and extract unique spectral features from Raman data. The results indicate that the choice of excitation wavelength significantly influences the bacterial spectra obtained, thereby impacting the accuracy and effectiveness of the subsequent classification results.

High-throughput single-cell sorting by stimulated Raman-activated cell ejection

Raman-activated cell sorting isolates single cells in a nondestructive and label-free manner, but its throughput is limited by small spontaneous Raman scattering cross section. Coherent Raman scattering integrated with microfluidics enables high-throughput cell analysis, but faces challenges with small cells (<3 μm) and tissue sections. Here, we report stimulated Raman-activated cell ejection (S-RACE) that enables high-throughput single-cell sorting by integrating stimulated Raman imaging, in situ image decomposition, and laser-induced cell ejection. S-RACE allows ejection of live bacteria or fungi guided by their Raman signatures. Furthermore, S-RACE successfully sorted lipid-rich Rhodotorula glutinis cells from a cell mixture with a throughput of ~13 cells per second, and the sorting results were confirmed by downstream quantitative polymerase chain reaction. Beyond single cells, S-RACE shows high compatibility with tissue sections. Incorporating a closed-loop feedback control circuit further enables real-time SRS imaging-identification-ejection. In summary, S-RACE opens exciting opportunities for diverse single-cell sorting applications.

Analytical tools to assess polymer biodegradation: A critical review and recommendations

Many petroleum-derived plastic materials are highly recalcitrant and persistent in the environment, posing significant threats to human and ecological receptors due to their accumulation in ecosystems. In recent years, research efforts have focused on advancing biological methods for polymer degradation. Enzymatic depolymerization has emerged as particularly relevant for biobased plastic recycling, potentially scalable for industrial use. Biodegradation involves adsorption to the plastic solid surface, followed by an interfacial reaction, resulting in cleavage of bonds of polymer chains exposed on the surface. Here, widely varying substrate-specific kinetics are observed, with the polymer's properties possessing a significant impact on the rate of this interfacial catalysis. Thus, there is a critical need for sensitive and accurate characterization of the material surface during and after interfacial depolymerization to fully understand the reaction mechanisms. Here, we provide a critical review of a range of techniques used in the analysis of material surfaces to characterize the chemical, topological, and morphological features relevant to the study of enzymatic biocatalysis, including microscopy techniques, spectroscopic techniques (e.g., X-ray diffraction analysis, Fourier transform infrared attenuated total reflectance spectroscopy, and mass spectrometry detection of analytes associated with degradation). Techniques for evaluation of surface energy and topology in their relevancy for sensitive detection of biological surface modifications are also discussed. In addition, this paper provides an overview of the strengths of these techniques and compares their performance in both sensitivity and throughput, including emerging techniques, which can be useful, particularly for the rapid analysis of the surface properties of polymeric materials in high-throughput screening of candidate biocatalysts. This research serves as a starting point in selecting and applying appropriate methodologies that provide direct evidence to the ongoing biotic degradation of polymeric materials.

Unraveling the Nanomechanical and Vibrational Properties of the Mayaro Virus

Mayaro virus (MAYV) is an emerging mosquito-borne viral pathogen whose infection results in arthritogenic disease. Despite ongoing research efforts, MAYV biology is largely unknown. Physical virology can assess MAYV nanoparticle metastability, assembly/disassembly, and polymorphism, allowing us to understand virion architecture and dynamics. Here, we employ atomic force microscopy (AFM) and surface enhancement Raman spectroscopy (SERS) to assess MAYV nanomechanical properties, including maps of adhesion force and Young's modulus on individual viral particles. We established topographic maps of MAYV in two and three dimensions, revealing the three-dimensional arrangement and distribution of charges on viral spikes at the virus surface. Furthermore, the organization of the densely packaged RNA, which affords the viral particle exceptional mechanical resistance compared to chikungunya (CHIKV), was observed using MAYV adsorption patterns. The vibrational signature of MAYV particles differs from CHIKV, with more intense protein modes matching the distribution of E1/E2 dimers and the nucleocapsid, which are well structured and suggestive of mechanical strength.

Raman spectroscopy in lung cancer diagnostics: Can an in vivo setup compete with ex vivo applications?

Lung carcinoma remains the leading cause of cancer death worldwide. The tactic to change this unfortunate rate may be a timely and rapid diagnostic, which may in many cases improve patient prognosis. In our study, we focus on the comparison of two novel methods of rapid lung carcinoma diagnostics, label-free in vivo and ex vivo Raman spectroscopy of the epithelial tissue, and assess their feasibility in clinical practice. As these techniques are sensitive not only to the basic molecular composition of the analyzed sample but also to the secondary structure of large biomolecules, such as tissue proteins, they represent suitable candidate methods for epithelial cancer diagnostics. During routine bronchoscopy, we collected 78 in vivo Raman spectra of normal and cancerous lung tissue and 37 samples of endobronchial pathologies, which were subsequently analyzed ex vivo. Using machine learning techniques, namely principal component analysis (PCA) and support vector machines (SVM), we were able to reach 87.2% (95% CI, 79.8-94.6%) and 100.0% (95% CI, 92.1-100.0%) of diagnostic accuracy for in vivo and ex vivo setup, respectively. Although the ex vivo approach provided superior results, the rapidity of in vivo Raman spectroscopy might become unmatchable in the acceleration of the diagnostic process.

Mapping lipid species remodeling in high fat diet-fed mice: Unveiling adipose tissue dysfunction with Raman microspectroscopy

Dysregulated lipid metabolism in obesity leads to adipose tissue expansion, a major contributor to metabolic dysfunction and chronic disease. Lipid metabolism and fatty acid changes play vital roles in the progression of obesity. In this proof-of-concept study, Raman techniques combined with histochemical imaging methods were utilized to analyze the impact of a high-fat diet (HFD) on different types of adipose tissue in mice, using a small sample size (n = 3 per group). After six weeks of high-fat diet (HFD) feeding, our findings showed hypertrophy, elevated collagen levels, and increased macrophage presence in the adipose tissues of the HFD group compared to the low-fat diet (LFD) group. Statistical analysis of Raman spectra revealed significantly lower unsaturated lipid levels and higher lipid to protein content in different fat pads (brown adipose tissue (BAT), subcutaneous white adipose tissue (SWAT), and visceral white adipose tissue (VWAT)) with HFD. Raman images of adipose tissues were analyzed using Empty modeling and DCLS methods to spatially profile unsaturated and saturated lipid species in the tissues. It revealed elevated levels of ω-3, ω-6, cholesterol, and triacylglycerols in BAT adipose tissues of HFD compared to LFD tissues. These findings indicated that while cholesterol, ω-6/ω-3 ratio, and triacylglycerol levels have risen in the SWAT and VWAT adipose tissues of the HFD group, the levels of ω-3 and ω-6 have decreased following the HFD. The study showed that Raman spectroscopy provided invaluable information at the molecular level for investigating lipid species remodeling and spatial mapping of adipose tissues during HFD.

Combining Multiple Spectroscopic Techniques to Reveal the Effects of Staphylococcus aureus Infection on Human Bone Tissues

Osteomyelitis (OM) and periprosthetic joint infections (PJIs) are major public health concerns in Western countries due to increased life expectancy. Infections usually occur due to bacterial spread through fractures, implants, or blood-borne transmission. The pathogens trigger an inflammatory response that hinders bone tissue regeneration. Treatment requires surgical intervention, which involves the precise removal of infected tissue, wound cleansing, and local and systemic antibiotic administration. Staphylococcus aureus (SA) is one of the most common pathogens causing infection-induced OM and PJIs. It forms antimicrobial-resistant biofilms and is frequently found in healthcare settings. In this proof-of-concept, we present an approach based on multiple spectroscopic techniques aimed at investigating the effects of SA infection on bone tissue, as well as identifying specific markers useful to detect early bacterial colonization on the tissue surface. A cross-section of a human femoral diaphysis, with negative-culture results, was divided into three parts, and the cortical and trabecular regions were separated from each other. Two portions of each bone tissue type were infected with SA for one and seven days, respectively. Multiple techniques were used to investigate the impact of the infection on bone tissue, Brillouin-Raman microspectroscopy and attenuated total reflection Fourier transform infrared spectroscopy were employed to assess and develop a new noninvasive diagnostic method to detect SA by targeting the bone of the host. The results indicate that exposure to SA infection significantly alters the bone structure, especially in the case of the trabecular type, even after just one day. Moreover, Raman spectral markers of the tissue damage were identified, indicating that this technique can detect the effect of the pathogens' presence in bone biopsies and pave the way for potential application during surgery, due to its nondestructive and contactless nature.

Non-invasive in vivo sensing of bacterial implant infection using catalytically-optimised gold nanocluster-loaded liposomes for urinary readout

Staphylococcus aureus is a leading cause of nosocomial implant-associated infections, causing significant morbidity and mortality, underscoring the need for rapid, non-invasive, and cost-effective diagnostics. Here, we optimise the synthesis of renal-clearable gold nanoclusters (AuNCs) for enhanced catalytic activity with the aim of developing a sensitive colourimetric diagnostic for bacterial infection. All-atom molecular dynamics (MD) simulations confirm the stability of glutathione-coated AuNCs and surface access for peroxidase-like activity in complex physiological environments. We subsequently develop a biosensor by encapsulating these optimised AuNCs in bacterial toxin-responsive liposomes, which is extensively studied by various single-particle techniques. Upon exposure to S. aureus toxins, the liposomes rupture, releasing AuNCs that generate a colourimetric signal after kidney-mimetic filtration. The biosensor is further validated in vitro and in vivo using a hyaluronic acid (HA) hydrogel implant infection model. Urine samples collected from mice with bacteria-infected HA hydrogel implants turn blue upon substrate addition, confirming the suitability of the sensor for non-invasive detection of implant-associated infections. This platform has significant potential as a versatile, cost-effective diagnostic tool.

Evaluating pathological levels of intracellular cholesterol through Raman and surface-enhanced Raman spectroscopies

Versatile methods for the quantification of intracellular cholesterol are essential for understanding cellular physiology and for diagnosing disorders linked to cholesterol metabolism. Here we used Raman spectroscopy (RS) and surface-enhanced Raman spectroscopy (SERS) to measure changes in cholesterol after incubating human fibroblasts with increasing concentrations of cholesterol-methyl-β-cyclodextrin. RS and SERS were sensitive and accurate enough to detect high levels of cholesterol in fibroblasts from patients affected by type C Niemann-Pick disease (NPC), a lysosomal storage disorder characterized by the primary accumulation of cholesterol. Moreover, SERS was able to distinguish between fibroblasts from different NPC patients, demonstrating higher accuracy than RS and standard fluorescent labeling of cholesterol with filipin III. We show that the type of gold nanoparticles used as signal enhancer surfaces in our SERS measurements are internalized by the cells and are eventually found in lysosomes, the main site of accumulation of cholesterol in NPC fibroblasts. The higher sensitivity of SERS can thus be attributed to the specific trafficking of our gold nanoparticles into these organelles. Our results indicate that RS and SERS can be used as sensitive and accurate methods for the evaluation of intracellular cholesterol content, allowing for the potential development of an optical detection tool for the ex-vivo screening and monitoring of those diseases characterized by abnormal modification in cholesterol levels.

Silibinin-Loaded Liposomes: The Influence of Modifications on Physicochemical Characteristics, Stability, and Bioactivity Associated with Dermal Application

BACKGROUND/OBJECTIVES

The aims of the presented study were the development of four types of silibinin-loaded liposomes (multilamellar liposomes-MLVs, sonicated small unilamellar liposomes-SUVs, UV-irradiated liposomes, and lyophilized liposomes) and their physicochemical characterization and biological potential related to skin health benefits.

METHODS

The characterization was performed via the determination of the encapsulation efficiency (EE), particle size, polydispersity index, zeta potential, conductivity, mobility, storage stability, density, surface tension, viscosity, FT-IR, and Raman spectra. In addition, cytotoxicity on the keratinocytes and antioxidant and anti-inflammatory potential were also determined.

RESULTS

UV irradiation significantly changed the rheological and chemical properties of the liposomes and increased their cytotoxic effect. The lyophilization of the liposomes caused significant changes in their EE and physical characteristics, decreased their ABTS and DPPH radical scavenging potential, and increased their potential to reduce the expression of interleukin 1 beta (IL-1β) in cells treated with bacterial lipopolysaccharide. Sonication significantly changed the EE and physical and rheological properties of the liposomes, and slightly increased their cytotoxicity and reduction effect on IL-1β, while the anti-ABTS and anti-DPPH capacity of the liposomes significantly increased. All developed liposomes showed an increasing trend in particle size and a decreasing trend in zeta potential (absolute values) during storage.

CONCLUSIONS

Silibinin-loaded liposomes (MLVs and lyophilized) showed promising antioxidant activity (toward reactive oxygen species generated in cells) and anti-inflammatory effects (reducing macrophage inhibitory factor expression) on keratinocytes and did not lead to a change in their viability. Future perspectives will focus on wound healing, anti-aging, and other potential of developed liposomes with silibinin in sophisticated cell-based models of skin diseases, wounds, and aging.

Label-Free Prediction of Tumor Metastatic Potential via Ramanome

Assessing metastatic potential is crucial for cancer treatment strategies. However, current methods are time-consuming, labor-intensive, and have limited sample accessibility. Therefore, this study aims to investigate the urgent need for rapid and accurate approaches by proposing a Ramanome-based metastasis index (RMI) using machine learning of single-cell Raman spectra to rapidly and accurately assess tumor cell metastatic potential. Validation with various cultured tumor cells and a mouse orthotopic model of pancreatic ductal adenocarcinoma show a Kendall rank correlation coefficient of 1 compared to Transwell experiments and histopathological assessments. Significantly, lipid-related Raman peaks are most influential in determining RMI. The lipidomic analysis confirmed strong correlations between metastatic potential and phosphatidylcholine, phosphatidylethanolamine, cholesteryl ester, ceramide, and bis(monoacylglycero)phosphate, crucial in cell membrane composition or signal transduction. Therefore, RMI is a valuable tool for predicting tumor metastatic potential and providing insights into metastasis mechanisms.

Chemical Landscape of Adipocytes Derived from 3T3-L1 Cells Investigated by Fourier Transform Infrared and Raman Spectroscopies

Adipocytes derived from 3T3-L1 cells are a gold standard for analyses of adipogenesis processes and the metabolism of fat cells. A widely used histological and immunohistochemical staining and mass spectrometry lipidomics are mainly aimed for examining lipid droplets (LDs). Visualizing other cellular compartments contributing to the cellular machinery requires additional cell culturing for multiple labeling. Here, we present the localization of the intracellular structure of the 3T3-L1-derived adipocytes utilizing vibrational spectromicroscopy, which simultaneously illustrates the cellular compartments and provides chemical composition without extensive sample preparation and in the naïve state. Both vibrational spectra (FTIR-Fourier transform infrared and RS-Raman scattering spectroscopy) extended the gathered chemical information. We proved that both IR and RS spectra provide distinct chemical information about lipid content and their structure. Despite the expected presence of triacylglycerols and cholesteryl esters in lipid droplets, we also estimated the length and unsaturation degree of the fatty acid acyl chains that were congruent with known MS lipidomics of these cells. In addition, the clustering of spectral images revealed that the direct surroundings around LDs attributed to lipid-associated proteins and a high abundance of mitochondria. Finally, by using quantified markers of biomolecules, we showed that the fixative agents, paraformaldehyde and glutaraldehyde, affected the cellular compartment differently. We concluded that PFA preserves LDs better, while GA fixation is better for cytochromes and unsaturated lipid analysis. The proposed analysis of the spectral images constitutes a complementary tool for investigations into the structural and molecular features of fat cells.

Comparative analysis of biofilm structures in Salmonella Typhimurium DMC4 strain and its dam and seqA gene mutants using Fourier transform infrared spectroscopy (FT-IR) and Raman spectroscopy methods

It is well-established that the dam and seqA genes act in the biofilm production in Salmonella. However, the molecular basis underlying this activity remains unexplored. This study aims to address this gap in the literature. In this study, comparative Fourier Transform Infrared (FT-IR) Spectroscopy and Raman spectral analyses were conducted to investigate the molecular basis of decreases in swimming, swarming motility, and biofilm characteristics observed in the dam and seqA gene mutants of S. Typhimurium DMC4 wild-type strain. The comparative analysis revealed a pronounced reduction in proteins, lipids, carbohydrates, and nucleic acids within the biofilm structures of mutant strains. These findings confirm that these macromolecules are crucial for the integrity and functionality of biofilm structures. FT-IR analysis showed that while amide-I bands decreased in the biofilm structures of mutant strains, amide-II bands increased compared to the wild-type strain. Similarly, Raman analyses indicated an increase in amide-IV bonds and a decrease in amide-V bonds. The parallelism between FT-IR and Raman spectral analysis results, particularly regarding amide I, amide V, amide II, and amide IV bands, is noteworthy. Additionally, these findings may lead to the development of markers for rapidly diagnosing transitions from planktonic to biofilm form in Salmonella. The substantial decrease in β-glucans and lipids, including cellulose, within the biofilm matrix of mutant strains highlights the critical role these polymers play in swimming and swarming motility. Given the clinical and industrial importance of Salmonella biofilms, it is crucial to develop strategies to prevent biofilm formation and identify target molecules that can inhibit biofilm formation. The results of our study suggest that β-glucans and amides are essential targets in the effort to combat Salmonella biofilms.

Baseline Raman Spectral Fingerprints of Zebrafish Embryos and Larvae

As a highly sensitive vibrational technique, Raman spectroscopy (RS) can provide valuable chemical and molecular data useful to characterise animal cell types, tissues and organs. As a label-free, rapid detection method, RS has been considered a valuable asset in forensics, biology and medicine. The technique has been applied to zebrafish for various purposes, including physiological, biochemical or bioaccumulation analyses. The available data point out its potential for the early diagnosis of detrimental effects elicited by toxicant exposure. Nevertheless, no baseline spectra are available for zebrafish embryos and larvae that could allow for suitable planning of toxicological assessments, comparison with toxicant-elicited spectra or mechanistic understanding of biochemical and physiological responses to the exposures. With this in mind, this work carried out a baseline characterisation of Raman spectra of zebrafish embryos and larvae throughout early development. Raman spectra were recorded from the iris, forebrain, melanocytes, heart, muscle and swim bladder between 24 and 168 h post-fertilisation. A chemometrics approach, based on partial least-squares discriminant analysis (PLS-DA), was used to obtain a Raman characterisation of each tissue or organ. In total, 117 Raman bands were identified, of which 24 were well represented and, thus, retained in the data analysed. Only three bands were found to be common to all organs and tissues. The PLS-DA provided a tentative Raman spectral fingerprint typical of each tissue or organ, reflecting the ongoing developmental dynamics. The bands showed frequencies previously assigned to collagen, cholesterol, various essential amino acids, carbohydrates and nucleic acids.

Raman-based machine-learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma

BACKGROUND

Formalin-fixed, paraffin-embedded (FFPE) tissue slides are routinely used in cancer diagnosis, clinical decision-making, and stored in biobanks, but their utilization in Raman spectroscopy-based studies has been limited due to the background coming from embedding media.

METHODS

Spontaneous Raman spectroscopy was used for molecular fingerprinting of FFPE tissue from 46 patient samples with known methylation subtypes. Spectra were used to construct tumor/non-tumor, IDH1WT/IDH1mut, and methylation-subtype classifiers. Support vector machine and random forest were used to identify the most discriminatory Raman frequencies. Stimulated Raman spectroscopy was used to validate the frequencies identified. Mass spectrometry of glioma cell lines and TCGA were used to validate the biological findings.

RESULTS

Here, we develop APOLLO (rAman-based PathOLogy of maLignant gliOma)-a computational workflow that predicts different subtypes of glioma from spontaneous Raman spectra of FFPE tissue slides. Our novel APOLLO platform distinguishes tumors from nontumor tissue and identifies novel Raman peaks corresponding to DNA and proteins that are more intense in the tumor. APOLLO differentiates isocitrate dehydrogenase 1 mutant (IDH1mut) from wild-type (IDH1WT) tumors and identifies cholesterol ester levels to be highly abundant in IDHmut glioma. Moreover, APOLLO achieves high discriminative power between finer, clinically relevant glioma methylation subtypes, distinguishing between the CpG island hypermethylated phenotype (G-CIMP)-high and G-CIMP-low molecular phenotypes within the IDH1mut types.

CONCLUSIONS

Our results demonstrate the potential of label-free Raman spectroscopy to classify glioma subtypes from FFPE slides and to extract meaningful biological information thus opening the door for future applications on these archived tissues in other cancers.

Liquid saliva-based Raman spectroscopy device with on-board machine learning detects COVID-19 infection in real-time

With greater population density, the likelihood of viral outbreaks achieving pandemic status is increasing. However, current viral screening techniques use specific reagents, and as viruses mutate, test accuracy decreases. Here, we present the first real-time, reagent-free, portable analysis platform for viral detection in liquid saliva, using COVID-19 as a proof-of-concept. We show that vibrational molecular spectroscopy and machine learning (ML) detect biomolecular changes consistent with the presence of viral infection. Saliva samples were collected from 470 individuals, including 65 that were infected with COVID-19 (28 from hospitalized patients and 37 from a walk-in testing clinic) and 251 that had a negative polymerase chain reaction (PCR) test. A further 154 were collected from healthy volunteers. Saliva measurements were achieved in 6 minutes or less and led to machine learning models predicting COVID-19 infection with sensitivity and specificity reaching 90%, depending on volunteer symptoms and disease severity. Machine learning models were based on linear support vector machines (SVM). This platform could be deployed to manage future pandemics using the same hardware but using a tunable machine learning model that could be rapidly updated as new viral strains emerge.

Raman spectroscopy of large extracellular vesicles derived from human microvascular endothelial cells to detect benzo[a]pyrene exposure

Extracellular vesicles (EVs) have shown great potential as biomarkers since they reflect the physio-pathological status of the producing cell. In the context of cytotoxicity, it has been found that exposing cells to toxicants leads to changes in protein expression and the cargo of the EVs they produce. Here, we studied large extracellular vesicles (lEVs) derived from human microvascular endothelial cells (HMEC-1) to detect the modifications induced by cell exposure to benzo[a]pyrene (B[a]P). We used a custom CaF2-based biochip which allowed hyphenated techniques of investigation: surface plasmon resonance imaging (SPRi) to monitor the adsorption of objects, atomic force microscopy (AFM) to characterise EVs' size and morphology, and Raman spectroscopy to detect molecular modifications. Results obtained on EVs by Raman microscopy and tip-enhanced Raman spectroscopy (TERS) showed significant differences induced by B[a]P in the high wavenumber region of Raman spectra (2800 to 3000 cm-1), corresponding mainly to lipid modifications. Two types of spectra were detected in the control sample. A support vector machine (SVM) model was trained on the pre-processed spectral data to differentiate between EVs from cells exposed or not to B[a]P at the spectrum level; this model could achieve a sensitivity of 88% and a specificity of 99.5%. Thus, this experimental setup facilitated the distinction between EVs originating from two cell culture conditions and enabled the discrimination of EV subsets within one cell culture condition.

Diagnosis of pregnancy disorder in the first-trimester patient plasma with Raman spectroscopy and protein analysis

Gestational diabetes mellitus (GDM) is a pregnancy disorder associated with short- and long-term adverse outcomes in both mothers and infants. The current clinical test of blood glucose levels late in the second trimester is inadequate for early detection of GDM. Here we show the utility of Raman spectroscopy (RS) for rapid and highly sensitive maternal metabolome screening for GDM in the first trimester. Key metabolites, including phospholipids, carbohydrates, and major amino acids, were identified with RS and validated with mass spectrometry, enabling insights into associated metabolic pathway enrichment. Using classical machine learning (ML) approaches, we showed the performance of the RS metabolic model (cross-validation AUC 0.97) surpassed that achieved with patients' clinical data alone (cross-validation AUC 0.59) or prior studies with single biomarkers. Further, we analyzed novel proteins and identified fetuin-A as a promising candidate for early GDM prediction. A correlation analysis showed a moderate to strong correlation between multiple metabolites and proteins, suggesting a combined protein-metabolic analysis integrated with ML would enable a powerful screening platform for first trimester diagnosis. Our study underscores RS metabolic profiling as a cost-effective tool that can be integrated into the current clinical workflow for accurate risk stratification of GDM and to improve both maternal and neonatal outcomes.

Protein-metal interactions due to fretting corrosion at the taper junction of hip implants: An in vitro investigation using Raman spectroscopy

Modular hip implants are a clinically successful and widely used treatment for patients with arthritis. Despite ongoing retrieval studies the understanding of the fundamental physico-chemical mechanisms of friction and wear within the head-taper interface is still limited. Here, we Raman-spectroscopically analyze structural features of the biotribological material which is formed within the taper joint between Ti6Al4V and low-carbon cobalt alloy or high-nitrogen steel surfaces in in vitro gross-slip fretting corrosion tests with bovine calf serum. As a function of the fretting duration, we investigate short and long aliphatic chains and their adsorption behavior on the cobalt- and steel-type surfaces. Using the intensity and frequency shifts of the amide I and III Raman bands, we furthermore identify progressive protein folding and unfolding including the secondary structures of α-helix, β-sheet, and random-coil configuration as well as the formation of proteinaceous clusters depending on the hydrophilicity of the metallic surfaces. We additionally find a mixture of chromates and iron oxides with tryptophan and tyrosine at the worn cobalt alloy and high-nitrogen steel surfaces, respectively. Also, for long fretting duration, sp2 hybridized amorphous carbon is formed due to fretting-induced cleavage of proteins. STATEMENT OF SIGNIFICANCE: Despite efforts enhancing the biomedical tribology of hip implants, the impact of the organic environment on friction and wear at the femoral head-stem taper interface is limitedly understood. Using Raman spectroscopy we resolve structural changes within the biotribological material agglomerated at biomedical-grade metal alloys due to metal-organic interactions during in vitro fretting corrosion tests. Adsorption of short and long aliphatic chains, progressive protein (un)folding and proteinaceous cluster formation depend to a distinguishable extent on the fretting duration and type of alloy. Chromates and iron oxides are mixed with tryptophan and tyrosine, and amorphous carbon is formed resulting from a fretting-induced cleavage of serum proteins. Such information spectroscopically gleaned from biotribological material are vital to improve the design and performance of taper junctions.

Heterogeneity of Endothelial Cells Impacts the Functionality of Human Pancreatic In Vitro Models

Endothelial cells (ECs) play a crucial role in maintaining tissue homeostasis and functionality. Depending on their tissue of origin, ECs can be highly heterogeneous regarding their morphology, gene and protein expression, functionality, and signaling pathways. Understanding the interaction between organ-specific ECs and their surrounding tissue is therefore critical when investigating tissue homeostasis, disease development, and progression. In vitro models often lack organ-specific ECs, potentially limiting the translatability and validity of the obtained results. The goal of this study was to assess the differences between commonly used EC sources in tissue engineering applications, including human umbilical vein ECs (HUVECs), human dermal microvascular ECs (hdmvECs), and human foreskin microvascular ECs (hfmvECs), and organ-specific human pancreatic microvascular ECs (hpmvECs), and test their impact on functionality within an in vitro pancreas test system used for diabetes research. Utilizing high-resolution Raman microspectroscopy and Raman imaging in combination with established protein and gene expression analyses and exposure to defined physical signals within microfluidic cultures, we identified that ECs exhibit significant differences in their biochemical composition, relevant protein expression, angiogenic potential, and response to the application of mechanical shear stress. Proof-of-concept results showed that the coculture of isolated human islets of Langerhans with hpmvECs significantly increased the functionality when compared with control islets and islets cocultured with HUVECs. Our study demonstrates that the choice of EC type significantly impacts the experimental results, which needs to be considered when implementing ECs into in vitro models.

Raman Spectroscopic Algorithms for Assessing Virulence in Oral Candidiasis: The Fight-or-Flight Response

This study aimed to test the effectiveness of Raman spectroscopy in the characterization of the degrees of physiological stress and virulence in clinical swab samples collected from patients affected by oral candidiasis. Raman experiments were conducted on a series of eight isolates, both in an as-collected state and after biofilm purification followed by 3 days of culture. The outputs were matched to optical microscopy observations and the results of conventional chromogenic medium assays. A statistically significant series of ten Raman spectra were collected for each clinical sample, and their averages were examined and interpreted as multiomic snapshots for albicans and non-albicans species. Spectroscopic analyses based on selected Raman parameters previously developed for standard Candida samples revealed an extreme structural complexity for all of the clinical samples, which arose from the concurrent presence of a variety of biofilms and commensal bacteria in the samples, as well as a number of other biochemical circumstances affecting the cells in their physiological stress state. However, three Raman algorithms survived such complexity, which enabled insightful classifications of Candida cells from clinical samples, in terms of their physiological stress and morphogenic state, membrane permeability, and virulence. These three characteristics, in turn, converged into a seemingly "fight or flight" response of the Candida cells. Although yet preliminary, the present study points out criticalities and proposes solutions regarding the potential utility of Raman spectroscopy in fast bedside analyses of surveillance samples.

Hyperspectral oblique plane microscopy enables spontaneous, label-free imaging of biological dynamic processes in live animals

Spontaneous Raman imaging has emerged as powerful label-free technique for investigating the molecular composition of medicines and biological specimens. Although Raman imaging can facilitate understanding of complex biological phenomena in vivo, current imaging modalities are limited in speed and sample compatibility. Here, we introduce a single-objective line-scanning light-sheet microscope, named [Formula: see text]-OPM, which records Raman images on a timescale of minutes to seconds. To demonstrate its function, we use [Formula: see text]-OPM to map and identify microplastic particles based on their Raman spectral characteristics. In live zebrafish embryos, we show that [Formula: see text]-OPM can capture wound dynamics at five-minute intervals, revealing rapid changes in cellular and extracellular matrix composition in the wounded region. Finally, we use [Formula: see text]-OPM to synchronize and average 36,800 individual frames to obtain hyperspectral videos of a zebrafish embryo's beating heart at an effective 28 frames per second, recording compositional changes throughout the cardiac cycle.

Metabolic Response to Small Molecule Therapy in Colorectal Cancer Tracked with Raman Spectroscopy and Metabolomics

Despite numerous screening tools for colorectal cancer (CRC), 25 % of patients are diagnosed with advanced disease. Novel diagnostic technologies that are early, accurate, and rapid are imperative to assess the therapeutic efficacy of clinical drugs and identify new biomarkers of treatment response. Here Raman spectroscopy (RS) was used to track metabolic reprogramming in KRAS-mutant HCT116 and SW837 cells, and KRAS wild-type CC cells. RS combined with multivariate analysis methods distinguished nonresponsive, partially responsive, and responsive cells treated with cetuximab, a monoclonal antibody for EGFR inhibition, sotorasib, a clinically approved KRAS inhibitor, and various doses of trametinib, an inhibitor of the MAPK pathway. Cells treated with a combination of subtoxic doses of trametinib and BKM120, an inhibitor of the PI3K pathway, showed a synergistic response between the two pathways. Using a supervised machine learning regression model, we established a scoring methodology trained to a priori predict therapeutic response to new treatment combinations. RS metabolites were verified with mass spectrometry, and enrichment pathways were identified, including amino acid, purine, and nicotinate and nicotinamide metabolism that differentiated monotherapy from combination therapy. Our approach may ultimately be applicable to patient-derived primary cells and cultures of patient tumors to predict effective drugs for individualized care.

Gaining insights into the responses of individual yeast cells to ethanol fermentation using Raman tweezers and chemometrics

Saccharomyces cerevisiae is the most common microbe used for the industrial production of bioethanol, and it encounters various stresses that inhibit cell growth and metabolism during fermentation. However, little is currently known about the physiological changes that occur in individual yeast cells during ethanol fermentation. Therefore, in this work, Raman spectroscopy and chemometric techniques were employed to monitor the metabolic changes of individual yeast cells at distinct stages during high gravity ethanol fermentation. Raman tweezers was used to acquire the Raman spectra of individual yeast cells. Multivariate curve resolution-alternating least squares (MCR-ALS) and principal component analysis were employed to analyze the Raman spectra dataset. MCR-ALS extracted the spectra of proteins, phospholipids, and triacylglycerols and their relative contents in individual cells. Changes in intracellular biomolecules showed that yeast cells undergo three distinct physiological stages during fermentation. In addition, heterogeneity among yeast cells significantly increased in the late fermentation period, and different yeast cells may respond to ethanol stress via different mechanisms. Our findings suggest that the combination of Raman tweezers and chemometrics approaches allows for characterizing the dynamics of molecular components within individual cells. This approach can serve as a valuable tool in investigating the resistance mechanism and metabolic heterogeneity of yeast cells during ethanol fermentation.

Carotenoids dispersed in gypsum rock as a result of algae adaptation to the extreme conditions of the Atacama Desert

The high-altitude pre-Andean region of the Atacama Desert is characterized by its stark volcanic rock formations and unique hydrothermal gypsum outcrops (gypcrete) that it hosts. This study delves into the biomolecular composition of the endolithic phototrophic microbes that thrive within these gypcretes. Using advanced Raman spectroscopy techniques, including Raman imaging (complemented by microscopic and 3D microscopic observations), herein we unveil new insights into the adaptive strategies of these gypsum-inhabiting algae. Our Raman imaging results provide a detailed chemical map of carotenoids associated with microbial colonization. This map reveals a significant gradient in pigment content, highlighting a critical survival mechanism for algae and cyanobacteria in this polyextreme environment. Intriguingly, we detected signals for carotenoids not only in the algae-colonized layer, but also deeper within the gypsum matrix - indicating pigment migration following cell disruption. In addition, we conducted an in-depth analysis of individual algal cells from the Trebouxiaceae family, noting their color variations from green to orange, plus describing the spectral differences in detail. This investigation identified in-vivo pigments (carotenoids, chlorophyll) and lipids at the cellular level, offering a comprehensive view of the molecular adaptations enabling life in one of the Earth's most extreme habitats.

The impact of transmembrane peptides on lipid bilayer structure and mechanics: A study of the transmembrane domain of the influenza A virus M2 protein

Transmembrane peptides play important roles in many biological processes by interacting with lipid membranes. This study investigates how the transmembrane domain of the influenza A virus M2 protein, M2TM, affects the structure and mechanics of model lipid bilayers. Atomic force microscopy (AFM) imaging revealed small decreases in bilayer thickness with increasing peptide concentrations. AFM-based force spectroscopy experiments complemented by theoretical model analysis demonstrated significant decreases in bilayer's Young's modulus (E) and lateral area compressibility modulus (KA). This suggests that M2TM disrupts the cohesive interactions between neighboring lipid molecules, leading to a decrease in both the bilayer's resistance to indentation (E) and its ability to resist lateral compression/expansion (KA). The large decreases in bilayer elastic parameters (i.e., E and KA) contrast with small changes in bilayer thickness, implying that bilayer mechanics are not solely dictated by bilayer thickness in the presence of transmembrane peptides. The observed significant reduction in bilayer mechanical properties suggests a softening effect on the bilayer, potentially facilitating membrane curvature generation, a crucial step for M2-mediated viral budding. In parallel, our Raman spectroscopy revealed small but statistically significant changes in hydrocarbon chain vibrational dynamics, indicative of minor disordering in lipid chain conformation. Our findings provide useful insights into the complex interplay between transmembrane peptides and lipid bilayers, highlighting the significance of peptide-lipid interactions in modulating membrane structure, mechanics, and molecular dynamics.

Feasibility of a Fiber-Dispersive Raman Spectrometer for Biomarker Detection

Raman spectroscopy is among the top analytical techniques for ultra-low-dense organic matter, crucial to the search for life and analysis of celestial body surfaces in space exploration missions. Achieving the ultimate sensitivity of in-situ Raman spectroscopy necessitates a breakthrough in detecting inelastically scattered light. Single-photon detectors (SPDs) operating in photon counting mode, which can differentiate between Raman and luminescence responses, are promising candidates for the challenging scientific requirements. Since large SPD arrays are not yet commercially available, a dispersive element can be adapted to a single-pixel detector. By exploiting chromatic dispersion in optical fibers and picosecond-pulsed excitation, we delay the arrivals of different spectral components onto a single-pixel SPD. This method also separates weak Raman signals from stronger luminescence through correlated time-domain measurements. We study the impact of fiber properties and the excitation wavelength of a pulsed laser on the spectral resolution of the fiber-dispersive Raman spectrometer (FDRS). Additionally, we demonstrate the FDRS's potential for studying biomarkers and discuss its feasibility for analyzing inclusions in ice matrices.