Assessing endothelial cytotoxicity induced by tyrosine kinase inhibitors: insights from Raman and fluorescence imaging

Since their approval, tyrosine kinase inhibitors (TKIs) have been widely used in antitumor therapy for chronic myeloblastic leukemia. Despite being approved by the FDA in 2001 to treat a rare cancer called chronic myeloid leukemia (CML), imatinib and other TKIs remain subjects of research for several reasons, such as their long-term effects, resistance, or molecular mechanisms. This study uses Raman and fluorescence imaging to investigate the in vitro cytotoxic effects of two TKIs, imatinib and dasatinib, on human aortic endothelial cells (HAECs). A comprehensive range of concentrations for these TKIs was applied to assess their cytotoxic impact based on viability, inflammation, and biochemical profile. Detailed data analysis revealed alterations in the biochemical profiles of cellular components, even though the viability of HAECs was around 80-90%. These changes indicate that, despite the cells retaining viability, they are experiencing considerable sub-lethal stress. Specifically, cells exposed to clinically relevant TKI concentrations showed increased signals from proteins and saturated lipids alongside decreased signals from nucleic acids, cytochromes, and unsaturated lipids. The subcellular analysis highlighted prominent changes in the perinuclear area, dominated by the endoplasmic reticulum and the cytoplasm. These findings suggest that TKIs are cytotoxic to vascular endothelium at concentrations close to those that are clinically observed. The predominant mechanism appears to involve oxidative stress-mediated inflammation, as evidenced by increased lipid content in treated cells and ICAM-1 staining. This cytotoxicity may contribute to the cardiotoxic effects observed during TKI therapy.

Machine Learning Classification of Integrin-Expression-Based Magnetic Sorted SW 620 Cells by Simultaneous O-PTIR and SERS

Immortalized cell lines are commonly used for in vitro studies such as drug efficacy, toxicology, and life cycle due to their cost effectiveness and accessibility; however, subpopulations within a cell line can arise from random mutations or asynchronous cell cycles which may lead to results that make interpretation difficult. A method that could classify these differences and separate unique subpopulations would increase our understanding of heterogeneous cellular responses. In the present work, we explore spectroscopic signals associated with subpopulations of cells magnetically sorted on the basis of α5β1 integrin binding to cyclic-RGDfC which mimics fibronectin in the extracellular matrix. SW620 colon cancer cells were incubated with cyclic-RGDfC functionalized gold-coated, iron core nanoparticles and magnetically sorted. The subpopulations from the sort were imaged (N = 10 positive and N = 10 negative, number of cells) via simultaneous surface-enhanced Raman scattering (SERS) and optical-photothermal infrared spectroscopy (O-PTIR). Pearson correlations of the standard peptide-protein interaction in the SERS channel allowed for visualization of the cyclic RGDfC-integrin α5β1 interaction. Partial least-squares discriminant analysis of the O-PTIR spectra collected from cell maps successfully classified the positively or negatively sorted cells. These results demonstrate that biochemical changes within a single cell line can be sorted via an integrin-activity-based assay using simultaneous SERS and O-PTIR.

Label-free detection of the cytotoxicity effect of cisplatin in human leukemic cells using Raman spectroscopy in conjunction with multivariate analysis

The single-cell Raman spectra of human leukemic Jurkat cells can be obtained by confocal microscopy Raman spectroscopy, including cell groups treated with different doses of cisplatin (0, 3.5, 7, 10.5 and 14 μmol L-1) for 24 hours and those treated with 10.5 μmol L-1 cisplatin for different times (0, 6, 12, 24 and 36 hours). The structure and amount of protein, nucleic acid and other major molecules from different cell groups show special changes in the percentage of biochemical constituents. Compared with the control group, the two protein Raman bands (1449 and 1659 cm-1) and two DNA bands (1303 and 1338 cm-1) in the treatment groups decrease in intensity with the increase of the drug dose and treatment time of cisplatin. Partial least squares combined with support vector machines was used to develop diagnostic algorithms for distinguishing between control and treatment groups. The support vector machines for classification between the control group (0 μmol L-1) and cell groups treated with 10.5 and 14 μmol L-1 cisplatin for 24 hours have achieved good diagnostic results with a high sensitivity of 100%, specificity of 100% and accuracy of 100%, respectively, indicating that 10.5 μmol L-1 can be used as an appropriate therapeutic dose. Using the same method, the diagnostic sensitivity, specificity and accuracy between the control group (0 hours) and cell groups treated with 10.5 μmol L-1 cisplatin for 24 and 36 hours are all 100%, showing that 24 hours can be used as an appropriate therapeutic time. These results showed that Raman spectroscopy in conjunction with multivariate statistical analysis could be a useful tool for evaluating the cytotoxicity induced by cisplatin in human leukemic cells.

Vibrational microspectroscopy as a tool to unveil new chemotherapeutic strategies against osteosarcoma

Over the years, osteosarcoma therapy has had a significative improvement with the use of a multidrug regime strategy, increasing the survival rates from less than 20 % to circa 70 %. Different types of development of new antineoplastic agents are critical to achieve irreversible damage to cancer cells, while preserving the integrity of their healthy counterparts. In the present study, complexes with two and three Pd(II) centres linked by the biogenic polyamines: spermine (Pd2SpmCl4) and spermidine (Pd3Spd2Cl6) were tested against non-malignant (osteoblasts, HOb) and cancer (osteosarcoma, MG-63) human cell lines. Either alone or in combination according to the EURAMOS-1 protocol, they were used versus cisplatin as a drug reference. By evaluating the cytotoxic effects of both therapeutic approaches (single and drug combination) in HOb and MG-63 cell lines, the selective anti-tumoral potential is assessed. To understand the different treatments at a molecular level, Synchrotron Radiation Fourier Transform Infrared and Raman microspectroscopies were applied. Principal component analysis and hierarchical cluster analysis are applied to the vibrational data, revealing the major metabolic changes caused by each drug, which were found to rely on DNA, lipids, and proteins, acting as biomarkers of drug-to-cell impact. The main changes were observed for the B-DNA native conformation to either Z-DNA (higher in the presence of polynuclear complexes) or A-DNA (preferably after cisplatin exposure). Additionally, a higher effect upon variation in proteins content was detected in drug combination when compared to single drug administration proving the efficacy of the EURAMOS-1 protocol with the new drugs tested.

Efficacy of tyrosine kinase inhibitors examined by a combination of Raman micro-spectroscopy and a deep wavelet scattering-based multivariate analysis framework

HER2 is a crucial therapeutic target in breast cancer, and the survival rate of breast cancer patients has increased because of this receptor's inhibition. However, tumors have shown resistance to this therapeutic strategy due to oncogenic mutations that decrease the binding of several HER2-targeted drugs, including lapatinib, and confer resistance to this drug. Neratinib can overcome this drug resistance and effectively inhibit HER2 signaling and tumor growth. In the present study, we examined the efficacy of lapatinib and neratinib using breast cancer cells by Raman microscopy combined with a deep wavelet scattering-based multivariate analysis framework. This approach discriminated between control cells and drug-treated cells with high accuracy, compared to classical principal component analysis. Both lapatinib and neratinib induced changes in the cellular biochemical composition. Furthermore, the Raman results were compared with the results of several in vitro assays. For instance, drug-treated cells exhibited (i) inhibition of ERK and AKT phosphorylation, (ii) inhibition of cellular proliferation, (iii) cell-cycle arrest, and (iv) apoptosis as indicated by western blotting, real-time cell analysis (RTCA), cell-cycle analysis, and apoptosis assays. Thus, the observed Raman spectral changes are attributed to cell-cycle arrest and apoptosis. The results also indicated that neratinib is more potent than lapatinib. Moreover, the uptake and distribution of lapatinib in cells were visualized through its label-free marker bands in the fingerprint region using Raman spectral imaging. These results show the prospects of Raman microscopy in drug evaluation and presumably in drug discovery.

Correlation analysis between Raman spectral signature and transcriptomic features of carbapenem-resistant Klebsiella pneumoniae

The Raman microspectroscopy technology has been successfully applied to evaluate the molecular composition of living cells for identifying cell types and states, but the rationale behind it was not well investigated. In this study, we acquired single-cell Raman spectra (SCRS) of three Klebsiella pneumoniae (K. pneumoniae) strains with different Carbapenem resistant mechanisms and analyzed them with machine learning algorithm. Two carbapenem resistant Klebsiella pneumoniae (CRKP) strains can be successfully distinguished from susceptible strain and CRKP with KPC or IMP carbapenemases can be classified with an overall accuracy achieving 100 %. Furthermore, we performed a correlation analysis between transcriptome and Raman spectra, and found that Raman shifts such as 752 and 1039 cm-1 highly correlated with drug resistance genes expression and could be regarded as Raman biomarkers for CRKP with different mechanisms. The findings of the study provide a theoretical basis for identifying the relationship between Raman spectra and transcriptome of bacteria, as well as a novel method for rapid identification of CRKP and their carbapenemases types.

Early-stage diagnosis of bladder cancer using surface-enhanced Raman spectroscopy combined with machine learning algorithms in a rat model

Early diagnosis and accurate assessment of tumor development facilitate early bladder cancer resection and initiation of drug therapy. This study enabled an early, accurate, label-free, noninvasive diagnosis of bladder tumors by analyzing nano-biomarkers in a single drop of urine through surface-enhanced Raman spectroscopy (SERS). In a standard N-butyl-N-4-hydroxybutyl nitrosamine-induced rat model of bladder cancer, cancer stage and polyp tumor development were monitored using a small endoscope with a diameter of 1.2 mm in a minimally invasive manner without the need to kill the rats. Samples were divided into cancer-free, early-stage, and polyp-form cancer. Training data were classified according to micro-cystoscopic 5-aminolevulinic acid fluorescence diagnosis, and specimens were postmortem verified through histopathological analysis. A drop of urine from each sample group was placed on an Au-coated zinc oxide nanoporous chip to filter nano-biomaterials and selectively enhance the Raman signals of nanoscale analytes via SERS. Principal component analysis was used to reduce the dimensionality of the collected Raman spectra, and partial least squares discriminant analysis was used to find diagnostic clusters based on the labeled samples. The combination of SERS and machine learning achieved an accuracy ≥99.6% in diagnosing both early- and polyp-stage bladder tumors. With an area under the receiver operating characteristic curve greater than 0.996, the accuracy of the diagnosis in the rat model suggests that SERS-based diagnostic methods are promising when coupled with machine learning. Low-cost, label-free, and noninvasive surface-enhanced Raman spectra are ideal for developing clinically relevant point-of-care diagnostic techniques.

Cancer detection in dogs using rapid Raman molecular urinalysis

Introduction

The presence of cancer in dogs was detected by Raman spectroscopy of urine samples and chemometric analysis of spectroscopic data. The procedure created a multimolecular spectral fingerprint with hundreds of features related directly to the chemical composition of the urine specimen. These were then used to detect the broad presence of cancer in dog urine as well as the specific presence of lymphoma, urothelial carcinoma, osteosarcoma, and mast cell tumor.

Methods

Urine samples were collected via voiding, cystocentesis, or catheterization from 89 dogs with no history or evidence of neoplastic disease, 100 dogs diagnosed with cancer, and 16 dogs diagnosed with non-neoplastic urinary tract or renal disease. Raman spectra were obtained of the unprocessed bulk liquid urine samples and were analyzed by ISREA, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) were applied using the Rametrix®Toolbox software.

Results and discussion

The procedure identified a spectral fingerprint for cancer in canine urine, resulting in a urine screening test with 92.7% overall accuracy for a cancer vs. cancer-free designation. The urine screen performed with 94.0% sensitivity, 90.5% specificity, 94.5% positive predictive value (PPV), 89.6% negative predictive value (NPV), 9.9 positive likelihood ratio (LR+), and 0.067 negative likelihood ratio (LR-). Raman bands responsible for discerning cancer were extracted from the analysis and biomolecular associations were obtained. The urine screen was more effective in distinguishing urothelial carcinoma from the other cancers mentioned above. Detection and classification of cancer in dogs using a simple, non-invasive, rapid urine screen (as compared to liquid biopsies using peripheral blood samples) is a critical advancement in case management and treatment, especially in breeds predisposed to specific types of cancer.

Species identification and strain discrimination of fermentation yeasts Saccharomyces cerevisiae and Saccharomyces uvarum using Raman spectroscopy and convolutional neural networks

IMPORTANCE

The use of S. cerevisiae and S. uvarum yeast starter cultures is a common practice in the alcoholic beverage fermentation industry. As yeast strains from different or the same species have variable fermentation properties, rapid and reliable typing of yeast strains plays an important role in the final quality of the product. In this study, Raman spectroscopy combined with CNN achieved accurate identification of S. cerevisiae and S. uvarum isolates at both the species and strain levels in a rapid, non-destructive, and easy-to-operate manner. This approach can be utilized to test the identity of commercialized dry yeast products and to monitor the diversity of yeast strains during fermentation. It provides great benefits as a high-throughput screening method for agri-food and the alcoholic beverage fermentation industry. This proposed method has the potential to be a powerful tool to discriminate S. cerevisiae and S. uvarum strains in taxonomic, ecological studies and fermentation applications.

Sub-Cellular Dynamic Analysis of BGC823 Cells after Treatment with the Multi-Component Drug CKI Using Raman Spectroscopy

Multi-component drugs (MCDs) can induce various cellular changes covering multiple levels, from molecular and subcellular structure to cell morphology. A "non-invasive" method for comprehensively detecting the dynamic changes of cellular fine structure and chemical components on the subcellular level is highly desirable for MCD studies. In this study, the subcellular dynamic processes of gastric cancer BGC823 cells after treatment with a multi-component drug, Compound Kushen Injection (CKI), were investigated using a homemade, high-resolution, confocal Raman spectroscopy (RS) device combined with bright-field imaging. The Raman spectra of the nucleus, cytoplasm and intracellular vesicles (0.4-1 μm) were collected simultaneously for each cell treated with CKI at different times and doses. The RS measurements showed that CKI decreased the DNA signatures, which the drug is known to inhibit. Meanwhile, the CKI-induced subcellular dynamic changes in the appearance of numerous intracellular vesicles and the deconstruction of cytoplasm components were observed and discussed. The results demonstrated that high-resolution subcellular micro-Raman spectroscopy has potential for detecting fine cellular dynamic variation induced by drugs and the screening of MCDs in cancer therapy.

Monitoring and modelling the glutamine metabolic pathway: a review and future perspectives

BACKGROUND

Analysis of the glutamine metabolic pathway has taken a special place in metabolomics research in recent years, given its important role in cell biosynthesis and bioenergetics across several disorders, especially in cancer cell survival. The science of metabolomics addresses the intricate intracellular metabolic network by exploring and understanding how cells function and respond to external or internal perturbations to identify potential therapeutic targets. However, despite recent advances in metabolomics, monitoring the kinetics of a metabolic pathway in a living cell in situ, real-time and holistically remains a significant challenge.

AIM

This review paper explores the range of analytical approaches for monitoring metabolic pathways, as well as physicochemical modeling techniques, with a focus on glutamine metabolism. We discuss the advantages and disadvantages of each method and explore the potential of label-free Raman microspectroscopy, in conjunction with kinetic modeling, to enable real-time and in situ monitoring of the cellular kinetics of the glutamine metabolic pathway.

KEY SCIENTIFIC CONCEPTS

Given its important role in cell metabolism, the ability to monitor and model the glutamine metabolic pathways are highlighted. Novel, label free approaches have the potential to revolutionise metabolic biosensing, laying the foundation for a new paradigm in metabolomics research and addressing the challenges in monitoring metabolic pathways in living cells.

A novel alternative strategy for monitoring and insight into liver fibrosis progression: The combination of surface-enhanced Raman spectroscopy (SERS) and gut microbiota

Nowadays, the studies on the interaction and relationship between the intestinal microorganisms and liver diseases are increasing. However, it is still a huge challenge for the in-depth investigation and dynamic monitoring of such a complex network. Herein, a significant discovery was made. A strong association between gut microbial structural and functional genomics and SERS spectra of hepatocytes were revealed. Based on the study of gut microbes and SERS spectra, complementary information could be provided for the mechanism analysis of related diseases. Liver fibrosis, a chronic liver disease that lack specific cure was thus comprehensive studied. Liver targeting gold nanoparticle dimers were prepared as the SERS tags, and abundant SERS peak signals were acquired. Meanwhile, the gut microbiomes were also comparative studied. The changes of carbohydrates and lipids in liver cells were observed at the early stages of liver fibrosis, and TLR4 (toll-like receptors 4) was activated to elicit immune responses. Then again, oxidative stress, endotoxin and serum inflammatory factors were the major observations at the late stages. The SERS signals and the microbiome analysis were well confirmed and complemented each other, which suggested that the detection strategy could be another valuable method for the "gut-liver axis" study.

Assessment of glycosaminoglycan content in bone using Raman spectroscopy

Glycosaminoglycans (GAGs) are responsible for preserving bone tissue toughness as well as regulating collagen formation and mineralization in the extracellular matrix. However, current methods for characterization of GAGs in bone are destructive, thus unable to capture in situ changes or differences in GAGs between experimental groups. As an alternative, Raman spectroscopy is a non-destructive method and can detect concurrent changes in GAGs and other bone constituents. In this study, we hypothesized that the two most prominent Raman peaks of sulfated GAGs (at ~1066 cm^-1 and at ~1378 cm^-1) could be used to detect differences in GAGs content of bone. To test this hypothesis, three experimental models were utilized: an in vitro model (enzymatic removal of GAGs from human cadaver bone), an in vivo mouse model (biglycan KO vs. WT), and an ex vivo aging model (comparing cadaveric bone samples from young and old donors). All Raman measurements were compared to Alcian blue measurements to confirm the validity of Raman spectroscopy in detecting GAGs changes in bone. Irrespective of different models, it was found that the ~1378 cm^-1 peak in Raman spectra of bone was uniquely sensitive to changes of GAGs content in bone when normalized with respect to the phosphate phase (~960 cm^-1); i.e., 1378 cm^-1/960 cm^-1 (peak intensity ratio) or 1370-1385 cm^-1/930-980 cm^-1 (integrated peak area ratio). In contrast, the 1070 cm^-1 peak, which includes another major peak of GAGs (1066 cm^-1), seemed to be compromised to detect changes of GAGs in bone due to concurrent changes of carbonate (CO₃) in the similar peak range. This study validates the ability of Raman spectroscopy to detect in situ treatment-, genotype-, and age-related changes in GAG levels of bone matrix.

Pd-Based Hybrid Nanoparticles As Multimodal Theranostic Nanomedicine

A nanodelivery system based on palladium nanoparticles (PdNP) and cisplatin (CisPt) was developed by physisorption of the drug onto the PdNP synthesized via a green redox process, using d-glucose and polyvinylpyrrolidone (PVP) as reducing and stabilizing/capping agents, respectively. UV-vis analysis and H₂-evolution measurements were carried out to prove the nanoparticles' capability to act as bimodal theranostic nanomedicine, i.e., having both plasmonic and photocatalytic properties. XPS, XRD, and TEM allowed light to be shed on the chemical composition and morphology of the PdNP. The analysis of the UV-visible spectra evidenced plasmonic peak changes for the hybrid nanoparticle-drug assembly (Pd@CisPt), which pointed to a significant interaction of CisPt with the NP surface. The drug loading was quantitatively estimated by ICP-OES measurements, while DLS and AFM confirmed the strong association of the drug with the nanoparticle surface. The test of SOD-like activity in a cell-free environment proved the maintenance of the antioxidant capability of PdNP also in the Pd@CisPt systems. Finally, Pd@CisPt tested in prostate cancer cells (PC-3 line) unveiled the antitumoral action of the developed nanomedicine, related to reactive oxygen species (ROS) generation, with a condition of protein misfolding/unfolding and DNA damage, as evidenced by cytotoxicity and MitoSOX assays, as well as Raman microspectroscopy, respectively. Cell imaging by confocal microscopy evidenced cellular uptake of the nanoparticles, as well as dynamic processes of copper ion accumulation at the level of subcellular compartments. Finally, cell migration studies upon treatment with Pd@CisPt evidenced a tunable response between the inhibitory effect of CisPt and the enhanced rate of cell migration for the metal NP alone, which pointed out the promising potential of the developed theranostic nanomedicine in tissue regeneration.

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.

In situ identification of environmental microorganisms with Raman spectroscopy

Microorganisms in natural environments are crucial in maintaining the material and energy cycle and the ecological balance of the environment. However, it is challenging to delineate environmental microbes' actual metabolic pathways and intraspecific heterogeneity because most microorganisms cannot be cultivated. Raman spectroscopy is a culture-independent technique that can collect molecular vibration profiles from cells. It can reveal the physiological and biochemical information at the single-cell level rapidly and non-destructively in situ. The first part of this review introduces the principles, advantages, progress, and analytical methods of Raman spectroscopy applied in environmental microbiology. The second part summarizes the applications of Raman spectroscopy combined with stable isotope probing (SIP), fluorescence in situ hybridization (FISH), Raman-activated cell sorting and genomic sequencing, and machine learning in microbiological studies. Finally, this review discusses expectations of Raman spectroscopy and future advances to be made in identifying microorganisms, especially for uncultured microorganisms.

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.

Combining Pharmacokinetics and Vibrational Spectroscopy: MCR-ALS Hard-and-Soft Modelling of Drug Uptake In Vitro Using Tailored Kinetic Constraints

Raman microspectroscopy is a label-free technique which is very suited for the investigation of pharmacokinetics of cellular uptake, mechanisms of interaction, and efficacies of drugs in vitro. However, the complexity of the spectra makes the identification of spectral patterns associated with the drug and subsequent cellular responses difficult. Indeed, multivariate methods that relate spectral features to the inoculation time do not normally take into account the kinetics involved, and important theoretical information which could assist in the elucidation of the relevant spectral signatures is excluded. Here, we propose the integration of kinetic equations in the modelling of drug uptake and subsequent cellular responses using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and tailored kinetic constraints, based on a system of ordinary differential equations. Advantages of and challenges to the methodology were evaluated using simulated Raman spectral data sets and real Raman spectra acquired from A549 and Calu-1 human lung cells inoculated with doxorubicin, in vitro. The results suggest a dependency of the outcome on the system of equations used, and the importance of the temporal resolution of the data set to enable the use of complex equations. Nevertheless, the use of tailored kinetic constraints during MCR-ALS allowed a more comprehensive modelling of the system, enabling the elucidation of not only the time-dependent concentration profiles and spectral features of the drug binding and cellular responses, but also an accurate computation of the kinetic constants.

Determination of IC50 values of anticancer drugs on cells by D2O - single cell Raman spectroscopy

A simple, sensitive and repeatable D₂O-single cell Raman spectroscopy method is developed to quantify the inhibitory activity of anticancer drugs on cancer cell metabolism. The IC₅₀ values obtained from A549 cells incubated with cisplatin and taxol are comparable with results of CCK-8 and ATP luminescent cell viability assays.

Investigating the cellular responses of osteosarcoma to cisplatin by confocal Raman microspectroscopy

Confocal Raman Microspectroscopy (CRM) was employed to clarify the cellular response of cisplatin in osteosarcoma (OS) cells with different dosages and incubation times. The K7M2 mouse osteosarcoma cells were treated by cisplatin in 0 μM (UT group), 20 μM (20 T group), and 40 μM (40 T group) doses for 24-h (24H group) and 48-h (48H group), respectively. Raman spectroscopy was utilized to analyze the drug induced variations of intracellular biochemical components in osteosarcoma cells. The spectral results shows that the main changes in its biochemical composition come from nucleic acids. By adopting three different kernel functions (linear, polynomial, and Gaussian radial basis function (RBF)), principal component analysis combined with support vector machine models (PCA-SVM) was built to address the spectral variations among all investigated groups. Meanwhile, multivariate curve resolution alternating least squares (MCR-ALS) was further utilized to discuss on the chemical interpretation on the acquired spectral results. Moreover, Raman spectral images, which is reconstructed by K-means cluster analysis (KCA) with point-scanned hyperspectral dataset, was applied to illustrate the drug induced compositional and morphological variations in each subcellular region. The achieved results not only prove the application potential of Raman based analytical technique in non-labeled intracellular studies, but also illustrate the detailed compositional and structural information of cisplatin induced OS cell responses from the perspective of multivariate analysis and imaging of Raman spectroscopy.

Surface-enhanced Raman spectroscopy for analysis of PCR products of viral RNA of hepatitis C patients

In the current study, for a qualitative and quantitative study of Polymerase Chain Reaction (PCR) products of viral RNA of Hepatitis C virus (HCV) infection, surface-enhanced Raman spectroscopy (SERS) methodology has been developed. SERS was used to identify the spectral features associated with the PCR products of viral RNA of Hepatitis C in various samples of HCV-infected patients with predetermined viral loads. The measurements for SERS were performed on 30 samples of PCR products, which included three PCR products of RNA of healthy individuals, six negative controls, and twenty-one HCV positive samples of varying viral loads (VLs) using Silver nanoparticles (Ag NPs) as a SERS substrates. Additionally, on SERS spectral data, the multivariate data analysis methods including Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR) were also carried out which help to illustrate the diagnostic capabilities of this method. The PLSR model is designed to predict HCV viral loads based on biochemical changes observed as SERS spectral features which can be associated directly with HCV RNA. Several SERS characteristic features are observed in the RNA of HCV which are not detected in the spectra of healthy RNA/controls. PCA is found helpful to differentiate the SERS spectral data sets of HCV RNA samples from healthy and negative controls. The PLSR model is found to be 99% accurate in predicting VLs of HCV RNA samples of unknown samples based on SERS spectral changes associated with the Hepatitis C development.

SERS diagnosis of liver fibrosis in the early stage based on gold nanostar liver targeting tags

In order to realize the accurate and early diagnosis of liver fibrosis, a long slow pathological process which may lead to cirrhosis or even liver cancer, liver targeting tags made up of gold nanostars and glycyrrhetinic acid are reported in this paper. Gold nanostars (GNSs) and GNS liver targeting tags (GLTTs) were injected into model mice with stage S1 liver fibrosis and normal mice via the tail vein respectively, then the SERS spectra were collected. GLTTs had a better detection effect on liver tissue than unmodified GNSs (12.85 times), and better detection reproducibility as well. Moreover, according to the MTT and survival analysis experiments, GLTTs also had better biocompatibility. Hence, the changes of 10 SERS signals and other substances in the early stage of liver fibrosis were analyzed at the molecular level, and the SERS characteristic peaks that could be used for the diagnosis of early liver fibrosis were screened out. Revealed by the experimental results, the GLTTs designed and prepared were applicable to the efficient SERS detection of early liver fibrosis in mice, and the strategy we have proposed might be a potential approach for the early diagnosis of this disease in clinics.

A pilot study for early detection of oral premalignant diseases using oral cytology and Raman micro-spectroscopy: Assessment of confounding factors

This study demonstrates the efficacy of Raman micro-spectroscopy of oral cytological samples for differentiating dysplastic, potentially malignant lesions from those of normal, healthy donors. Cells were collected using brush biopsy from healthy donors (n = 20) and patients attending a Dysplasia Clinic (n = 20). Donors were sampled at four different sites (buccal mucosa, tongue, alveolus, gingiva), to ensure matched normal sites for all lesions, while patient samples were taken from clinically evident, histologically verified dysplastic lesions. Spectra were acquired from the nucleus and cytoplasm of individual cells of all samples and subjected to partial least squares-discriminant analysis. Discriminative sensitivities of 94% and 86% and specificity of 85% were achieved for the cytoplasm and nucleus, respectively, largely based on lipidic contributions of dysplastic cells. Alveolar/gingival samples were differentiated from tongue/buccal samples, indicating that anatomical site is potentially a confounding factor, while age, gender, smoking and alcohol consumption were confirmed not to be.

Unveiling dose- and time-dependent osteosarcoma cell responses to the γ-secretase inhibitor, DAPT, by confocal Raman microscopy

Using confocal Raman micro-spectroscopy, this study aims to elucidate the cellular responses of the γ-secretase inhibitor, N-[N-(3,5-difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl ester (DAPT), in osteosarcoma (OS) cells in a dose- and time-dependent manner. The K7M2 murine OS cell line was treated with different DAPT doses (0, 10, 20, and 40 μM) for 24 and 48 hours before investigations. Significant compositional changes (nucleic acids, protein and lipid) after DAPT treatment were addressed, which testified inhibitory effect of DAPT on the growth of OS cells. Moreover, both partial least squares-discriminant analysis (PLS-DA) and principal component analysis-linear discriminant analysis (PCA-LDA) analyses revealed governing composition variations among groups by distinguishing their spectral characteristics. Furthermore, by adopting leave-one-out cross validation method, it is shown that PLS-DA exhibited more classification capacity than PCA-LDA algorithm. Hence, by understanding the DAPT-based cellular variations, the achieved results provided an experimental foundation to establish new DAPT-based anticancer therapeutic strategies, and preclinical Raman analytical methodologies on drug-cell interactions.

Arcobacter Identification and Species Determination Using Raman Spectroscopy Combined with Neural Networks

Rapid and accurate identification of Arcobacter is of great importance because it is considered an emerging food- and waterborne pathogen and potential zoonotic agent. Raman spectroscopy can differentiate bacteria based on Raman scattering spectral patterns of whole cells in a fast, reagentless, and easy-to-use manner. We aimed to detect and discriminate Arcobacter bacteria at the species level using confocal micro-Raman spectroscopy (785 nm) coupled with neural networks. A total of 82 reference and field isolates of 18 Arcobacter species from clinical, environmental, and agri-food sources were included. We determined that the bacterial cultivation time and growth temperature did not significantly influence the Raman spectral reproducibility and discrimination capability. The genus Arcobacter could be successfully differentiated from the closely related genera Campylobacter and Helicobacter using principal-component analysis. For the identification of Arcobacter to the species level, an accuracy of 97.2% was achieved for all 18 Arcobacter species using Raman spectroscopy combined with a convolutional neural network (CNN). The predictive capability of Raman-CNN was further validated using an independent data set of 12 Arcobacter strains. Furthermore, a Raman spectroscopy-based fully connected artificial neural network (ANN) was constructed to determine the actual ratio of a specific Arcobacter species in a bacterial mixture ranging from 5% to 100% by biomass (regression coefficient >0.99). The application of both CNN and fully connected ANN improved the accuracy of Raman spectroscopy for bacterial species determination compared to the conventional chemometrics. This newly developed approach enables rapid identification and species determination of Arcobacter within an hour following cultivation.IMPORTANCE Rapid identification of bacterial pathogens is critical for developing an early warning system and performing epidemiological investigation. Arcobacter is an emerging foodborne pathogen and has become more important in recent decades. The incidence of Arcobacter species in the agro-ecosystem is probably underestimated mainly due to the limitation in the available detection and characterization techniques. Raman spectroscopy combined with machine learning can accurately identify Arcobacter at the species level in a rapid and reliable manner, providing a promising tool for epidemiological surveillance of this microbe in the agri-food chain. The knowledge elicited from this study has the potential to be used for routine bacterial screening and diagnostics by the government, food industry, and clinics.

Detection and time-tracking activation of a photosensitiser on live single colorectal cancer cells using Raman spectroscopy

Raman spectroscopy has been used to observe uptake, metabolism and response of single-cells to drugs. Photodynamic therapy is based on the use of light, a photosensitiser and oxygen to destroy tumour tissue. Here, we used single-cell Raman spectroscopy to study the uptake and intracellular degradation of a novel photosensitiser with a diphenylacetylene structure, DC473, in live single-cells from colorectal adenocarcinoma cell lines SW480, HT29 and SW620. DC473 was seen to predominantly accumulate in lipid droplets, showing higher accumulation in HT29 and SW620 cells than in SW480 cells, with a broader DC473 peak shifted to higher wavenumbers. DC473 activation and effects were tracked on live single-cells for 5 minutes. Upon exposure to UV light, the DC473 signal intensity dropped, with remaining DC473 shifting towards higher wavenumbers and widening, with a lifetime of approximately 50 seconds. Morphologically, SW480 and SW620 cells showed changes upon photodynamic therapy, whereas HT29 cells showed no changes. Morphological changes correlated with higher remaining DC473 signal after UV exposure. Our research suggests that DC473 forms aggregates within the cells that disaggregate following activation, showing the potential of Raman spectroscopy for the study of time-dependent single-cell pharmacodynamics.

Study on the chemodrug-induced effect in nasopharyngeal carcinoma cells using laser tweezer Raman spectroscopy

To explore the effect in nasopharyngeal carcinoma (NPC) cells after treatment with chemodrugs, Raman profiles were characterized by laser tweezer Raman spectroscopy. Two NPC cell lines (CNE2 and C666-1) were treated with gemcitabine, cisplatin, and paclitaxel, respectively. The high-quality Raman spectra of cells without or with treatments were recorded at the single-cell level with label-free laser tweezers Raman spectroscopy (LTRS) and analyzed for the differences of alterations of Raman profiles. Tentative assignments of Raman peaks indicated that the cellular specific biomolecular changes associated with drug treatment include changes in protein structure (e.g. 1655 cm^-1), changes in DNA/RNA content and structure (e.g. 830 cm^-1), destruction of DNA/RNA base pairs (e.g. 785 cm^-1), and reduction in lipids (e.g. 970 cm^-1). Besides, both principal components analysis (PCA) combined with linear discriminant analysis (LDA) and the classification and regression trees (CRT) algorithms were employed to further analyze and classify the spectral data between control group and treated group, with the best discriminant accuracy of 96.7% and 90.0% for CNE2 and C666-1 group treated with paclitaxel, respectively. This exploratory work demonstrated that LTRS technology combined with multivariate statistical analysis has promising potential to be a novel analytical strategy at the single-cell level for the evaluation of NPC-related chemotherapeutic drugs.

Can ethanol affect the cell structure? A dynamic molecular and Raman spectroscopy study

The role that tobacco consumption plays in the etiology of oral cancer carcinogenesis, and of alcohol consumption acting as a co-factor, have been well established. However, in recent years, the contribution of alcohol consumption alone to oral cancer has been proposed. In fact, a high percentage of patients who develop oral cancer have both habits (tobacco and alcohol consumption), and other small patient groups only consume alcohol or do not have any other identifiable bad habits. In the present study we demonstrate, using a combination of dynamic molecular modelling and Raman spectroscopy, that ethanol has a significant effect on oral cells in vitro, mainly interacting with the lipids of the cell membrane, changing their conformation. Thus, it is possible to conclude that ethanol can affect the cell permeability, and by consequence serve as a possible trigger in oral carcinogenesis.

Molecular Spectroscopic Markers of DNA Damage

Every cell in a living organism is constantly exposed to physical and chemical factors which damage the molecular structure of proteins, lipids, and nucleic acids. Cellular DNA lesions are the most dangerous because the genetic information, critical for the identity and function of each eukaryotic cell, is stored in the DNA. In this review, we describe spectroscopic markers of DNA damage, which can be detected by infrared, Raman, surface-enhanced Raman, and tip-enhanced Raman spectroscopies, using data acquired from DNA solutions and mammalian cells. Various physical and chemical DNA damaging factors are taken into consideration, including ionizing and non-ionizing radiation, chemicals, and chemotherapeutic compounds. All major spectral markers of DNA damage are presented in several tables, to give the reader a possibility of fast identification of the spectral signature related to a particular type of DNA damage.

Liver changes induced by cadmium poisoning distinguished by confocal Raman imaging

Heavy metal pollution has become an important issue threatening human health and the liver is a very important metabolic organ. Here, we use label-free Raman confocal imaging to study the alterations of the liver tissue after cadmium pollution. Raman imaging has been performed on 100μmx100μm liver tissues to study the distribution of important macromolecules and the average Raman spectrum of the entire region has been used to characterize and quantize the change of biochemical compositions in liver tissue. The poisoned livers displayed a significant decrease in the intensity of 748 cm^-1, 1128 cm^-1 and 1585 cm^-1 bands of cytochrome C, in comparison to the control. The collagen peak at 1082 cm^-1 is significantly higher than that of control, suggesting the increasing fibrosis of Cd liver tissues. To confirm the results, we selected a 30μmx15μm liver cell area for high-resolution Raman imaging. We observed a substantial increase of lipids and proteins at specific points of hepatocytes. The confocal Raman imaging of liver tissues provided a unique tool to better understand disease-induced changes in the biochemical phenotype of primary liver tissues. Our study provides valuable references as in vitro models for studying Cd accumulation and toxicity in human liver.

In vivo, ex vivo and in vitro assessment of buccal permeation of drugs from delivery systems

Introduction: Buccal mucosa has been described as an attractive site for local and systemic drug delivery, owing its accessibility, safety, and excellent blood supply. The absorption of drugs through buccal mucosa has been assessed by in vivo, ex vivo and in vitro permeability studies, using animal and cell-based models with close resemblance to the human buccal mucosa.Areas covered: This paper focuses on the current in vivo, ex vivo and in vitro permeability studies to analyze the absorption of compounds of interest through buccal mucosa, as well as their advantages and limitations in the preclinical studies of the drugs absorption profiles. The techniques for preparation and preservation of the animal buccal tissue are also discussed to evaluate their interference in the integrity and permeability of the tissues.Expert opinion: Overall, the permeability studies have been useful to evaluate the drugs absorption and to clarify the mechanism of transport of drugs across human buccal mucosa, as well as to explain the enhancement of permeability provided by certain dosage forms. Currently, several researchers have demonstrated particular interest in ex vivo permeability studies, due to their effectiveness in the evaluation of drug absorption and low costs in the acquisition of buccal mucosa samples.

Principal components analysis of Raman spectral data for screening of Hepatitis C infection

In the current study, Raman spectroscopy is employed for the identification of the biochemical changes taking place during the development of Hepatitis C. The Raman spectral data acquired from the human blood plasma samples of infected and healthy individuals is analysed by Principal Components Analysis and the Raman spectral markers of the Hepatitis C Virus (HCV) infection are identified. Spectral changes include those associated with nucleic acidsat720 cm^-1, 1077 cm^-1 1678 (CO stretching mode of dGTP of RNA), 1778 cm^-1 (RNA), with proteins at 1641 cm^-1(amide-I), 1721 cm^-1(CC stretching of proteins) and lipids at 1738 cm^-1(CO of ester group in lipids). These differences in Raman spectral features of blood plasma samples of the patients and healthy volunteers can be associated with the development of the biochemical changes during HCV infection.

Qualitative and quantitative analysis of therapeutic solutions using Raman and infrared spectroscopy

Anticancer drugs are prescribed and administrated to an increasing number of patients on a daily basis. As a consequence, a number of concerns have been raised about the patient health and safety in the case that the drugs administered are not at the required concentration or even worse not the correct ones. Quality control of therapeutic solutions has therefore been extensively implemented in hospital environments, in order to avoid any failure in the intense workflow faced by administering pharmacists. In the present study, infrared (IR) and Raman spectroscopy have been employed for the analysis of 3 commercially available therapeutic solutions TEVA®, MYLAN®, CERUBIDINE®, respectively containing doxorubicin, epirubicin and daunorubicin. They perfectly illustrate the analytical difficulties encountered, as these 3 chemotherapeutic drugs are isomers, hardly distinguishable with conventional approaches such as UV/VIS spectrometry. Any analytical failure to identify these molecules can lead to delays in patient treatment. While Partial Least Squares Regression analysis demonstrates that both Raman and IR can deliver satisfactory quantitative analysis in the clinical range, with respective Root Mean Square Error of Cross Validation (RMSECV) between 0.0127 - 0.0220 g·L^-1 and 0.0573 - 0.0759 g·L^-1, the identification rate between the 2 techniques differs substantially. Indeed, Principal Component Analysis - Factorial Discriminant Analysis (PCA-FDA) highlights that, depending on the data preprocessing applied to Raman spectra, the discrimination between the 3 drugs is decreased, with in some cases specificity and sensitivity below 50%. However, IR analysis displays encouraging results with an overall specificity and sensitivity between 99 and 100%, suggesting that reliable validation of the therapeutic solution for administration to patients can be achieved. IR and Raman spectroscopy could assist and support quality control of chemotherapeutic solutions prepared in personalised concentrations for each patient. The effective and reliable characterisation of therapeutic solutions could have a lot to offer to improve current practices in a near future.

Two-dimensional correlation analysis of Raman microspectroscopy of subcellular interactions of drugs in vitro

Two-dimensional (2D) correlation analysis is explored to data mine the time evolution of the characteristic Raman microspectroscopic signatures of the subcellular responses of the nucleoli of human lung cancer cells to the uptake of doxorubicin. A simulated dataset of experimental control spectra, perturbed with systematically time-dependent spectral changes, constituted by a short-term response which represents the initial binding of the drug in the nucleolus, followed by a longer term response of the organelle metabolism, is used to validate the analysis protocol. Applying 2D correlation analysis, the in phase, synchronous correlation coefficients are seen to contain contributions of both response profiles, whereas they can be independently extracted from the out of phase, asynchronous correlation coefficients. The methodology is applied to experimental data of the uptake of doxorubicin in human lung cell lines to differentiate the signatures of chemical binding and subsequent cellular response.

Raman spectroscopy based differentiation of typhoid and dengue fever in infected human sera

This study investigated the potential of Raman spectroscopy, to differentiate between two different types of human pathological conditions with some symptom similarity; typhoid and dengue. Raman spectra were recorded from 20 Salmonella Typhi infected and 22 dengue virus (DENV) infected sera samples using laser wavelength 785 nm. When Raman spectrum (540 to 1830 cm^-1) of serum samples was compared it showed significant variations. Twelve characteristic Raman bands at 562, 649, 716, 780, 838, 1099, 1144, 1156, 1260, 1386, 1556 and 1746 cm^-1 were recorded specifically in case of the typhoid infected samples, whereas four Raman bands at 756, 1218, 1672 and 1686 cm^-1 were found specifically in the DENV infected samples. For statistical differentiation principal component analysis (PCA) and linear discriminant analysis (LDA) was applied which successfully separated two sets of data. These disease specific Raman signatures may be labeled as biomarkers for identification as well as differential diagnosis of typhoid and dengue infection. Hence we propose that Raman spectroscopy has the potential to not only identify but also to differentiate between different pathological conditions, having some similar symptoms like high grade fever in this case.

Raman micro-spectroscopy monitors acquired resistance to targeted cancer therapy at the cellular level

Monitoring the drug efficacy or resistance in vitro is usually carried out by measuring the response of single few proteins. However, observation of single proteins instead of an integral cell response may lead to results that are not consistent with patient’s response to a drug. We present a Raman spectroscopic method that detects the integral cell response to drugs such as tyrosine kinase inhibitors (TKIs). Non-small cell lung cancer (NSCLC) patients with EGFR mutations develop acquired resistance to first (erlotinib)- and third (osimertinib)-generation TKIs. Large erlotinib-induced differences were detected by Raman micro-spectroscopy in NSCLC cells without T790M EGFR mutation but not in cells with this mutation. Additionally, Raman difference spectra detected the response of NSCLC cells with T790M EGFR mutation to second- (neratinib) and third-generation (osimertinib) TKIs, and the resistance of cells with T790M/C797S EGFR mutation to osimertinib. Thus, the in vitro Raman results indicated that NSCLC cells with T790M and T790M/C797S EGFR mutations are resistant to erlotinib- and osimertinib, respectively, consistent with the observed responses of patients. This study shows the potential of Raman micro-spectroscopy to monitor drug resistance and opens a new door to in vitro companion diagnostics for screening personalized therapies.

Advancing Raman microspectroscopy for cellular and subcellular analysis: towards in vitro high-content spectralomic analysis

In the confocal mode, Raman microspectroscopy can profile the biochemical content of biological cells at a subcellular level, and any changes to it by exogenous agents, such as therapeutic drugs or toxicants. As an exploration of the potential of the technique as a high-content, label-free analysis technique, this report reviews work to monitor the spectroscopic signatures associated with the uptake and response pathways of commercial chemotherapeutic agents and polymeric nanoparticles by human lung cells. It is demonstrated that the signatures are reproducible and characteristic of the cellular event, and can be used, for example, to identify the mode of action of the agent as well as the subsequent cell death pathway, and even mechanisms of cellular resistance. Data mining approaches are discussed and a spectralomics approach is proposed.

Raman spectral analysis for rapid screening of dengue infection

Infection with the dengue virus is currently clinically detected according to different biomarkers in human blood plasma, commonly measured by enzyme linked immunosorbent assays, including non-structural proteins (Ns1), immunoglobulin M (IgM) and immunoglobulin G (IgG). However, there is little or no mutual correlation between the biomarkers, as demonstrated in this study by a comparison of their levels in samples from 17 patients. As an alternative, the label free, rapid screening technique, Raman spectroscopy has been used for the characterisation/diagnosis of healthy and dengue infected human blood plasma samples. In dengue positive samples, changes in specific Raman spectral bands associated with lipidic and amino acid/protein content are observed and assigned based on literature and these features can be considered as markers associated with dengue development. Based on the spectroscopic analysis of the current, albeit limited, cohort of samples, Principal Components Analysis (PCA) coupled Factorial Discriminant Analysis, yielded values of 97.95% sensitivity and 95.40% specificity for identification of dengue infection. Furthermore, in a comparison of the normal samples to the patient samples which scored low for only one of the biomarker tests, but high or medium for either or both of the other two, PCA-FDA demonstrated a sensitivity of 97.38% and specificity of 86.18%, thus providing an unambiguous screening technology.

Chemotherapeutic response to cisplatin-like drugs in human breast cancer cells probed by vibrational microspectroscopy

Studies of drug-cell interactions in cancer model systems are essential in the preclinical stage of rational drug design, which relies on a thorough understanding of the mechanisms underlying cytotoxic activity and biological effects, at a molecular level. This study aimed at applying complementary vibrational spectroscopy methods to evaluate the cellular impact of two Pt(ii) and Pd(ii) dinuclear chelates with spermine (Pt2Spm and Pd2Spm), using cisplatin (cis-Pt(NH3)2Cl2) as a reference compound. Their effects on cellular metabolism were monitored in a human triple-negative metastatic breast cancer cell line (MDA-MB-231) by Raman and synchrotron-radiation infrared microspectroscopies, for different drug concentrations (2-8 μM) at 48 h exposure. Multivariate data analysis was applied (unsupervised PCA), unveiling drug- and concentration-dependent effects: apart from discrimination between control and drug-treated cells, a clear separation was obtained for the different agents studied - mononuclear vs. polynuclear, and Pt(ii) vs. Pd(ii). Spectral biomarkers of drug action were identified, as well as the cellular response to the chemotherapeutic insult. The main effect of the tested compounds was found to be on DNA, lipids and proteins, the Pd(ii) agent having a more significant impact on proteins while its Pt(ii) homologue affected the cellular lipid content at lower concentrations, which suggests the occurrence of distinct and unconventional pathways of cytotoxicity for these dinuclear polyamine complexes. Raman and FTIR microspectroscopies were confirmed as powerful non-invasive techniques to obtain unique spectral signatures of the biochemical impact and physiological reaction of cells to anticancer agents.

In vitro label-free screening of chemotherapeutic drugs using Raman microspectroscopy: Towards a new paradigm of spectralomics

This overview groups some of the recent studies highlighting the potential application of Raman microspectroscopy as an analytical technique in preclinical development to predict drug mechanism of action and in clinical application as a companion diagnostic and in personalised therapy due to its capacity to predict cellular resistance and therefore to optimise chemotherapeutic treatment efficacy. Notably, the anthracyclines, doxorubicin and actinomycin D, elicit similar spectroscopic signatures of subcellular interaction characteristic of the mode of action of intercalation. Although cisplatin and vincristine show markedly different signatures, at low exposure doses, their signatures at higher doses show marked similarities to those elicited by the intercalating anthracyclines, confirming that anticancer agents can have different modes of action with different spectroscopic signatures, depending on the dose. The study demonstrates that Raman microspectroscopy can elucidate subcellular transport and accumulation pathways of chemotherapeutic agents, characterise and fingerprint their mode of action, and potentially identify cell-resistant strains. The consistency of the spectroscopic signatures for drugs of similar modes of action, in different cell lines, suggests that this fingerprint can be considered a "spectralome" of the drug-cell interaction suggesting a new paradigm of representing spectroscopic responses.

Exploring the efficacy and cellular uptake of sorafenib in colon cancer cells by Raman micro-spectroscopy

This study demonstrates the efficacy and distribution of sorafenib in colon cancer cells by label-free Raman microscopy.

Raman spectroscopy for the evaluation of the effects of different concentrations of Copper on the chemical composition and biological activity of basil essential oil

The present study is performed to evaluate the effect of different concentrations of Cu as fertilizer on the chemical composition of basil essential oil and its biological activity including antioxidant and antifungal activities by employing Raman spectroscopy. Moreover, the effect of Cu is also determined on the vegetative growth and essential oil yield. Both, antifungal and antioxidant activities were found to be maximum with essential oils obtained at 0.04mg/l concentration of Cu fertilizer. The results of the GC-MS and Raman spectroscopy have revealed that the linalool and estragole are found to be as a major chemical compound in basil essential oil. The Raman spectral changes associated with these biological components lead to the conclusion that estragole seems to have dominating effect in the biological activities of the basil essential oil as compared to linalool although the latter is observed in greater concentration.

Ultra-filtration of human serum for improved quantitative analysis of low molecular weight biomarkers using ATR-IR spectroscopy

Monitoring of changes in the concentrations of the low molecular weight constituents enhanced by abundant proteins depletion.

Screening the low molecular weight fraction of human serum using ATR-IR spectroscopy

Vibrational spectroscopic techniques can detect small variations in molecular content, linked with disease, showing promise for screening and early diagnosis. Biological fluids, particularly blood serum, are potentially valuable for diagnosis purposes. The so-called Low Molecular Weight Fraction (LMWF) contains the associated peptidome and metabolome and has been identified as potentially the most relevant molecular population for disease-associated biomarker research. Although vibrational spectroscopy can deliver a specific chemical fingerprint of the samples, the High Molecular Weight Fraction (HMWF), composed of the most abundant serum proteins, strongly dominates the response and ultimately makes the detection of minor spectral variations a challenging task. Spectroscopic detection of potential serum biomarkers present at relatively low concentrations can be improved using pre-analytical depletion of the HMWF. In the present study, human serum fractionation by centrifugal filtration was used prior to analysis by Attenuated Total Reflection infrared spectroscopy. Using a model sample based on glycine spiked serum, it is demonstrated that the screening of the LMWF can be applied to quantify blinded concentrations up to 50 times lower. Moreover, the approach is easily transferable to different bodily fluids which would support the development of more efficient and suitable clinical protocols exploring vibrational spectroscopy based ex-vivo diagnostic tools. Revealing serum LMWF for spectral serological diagnostic applications.