Vibrational spectroscopic methods for cytology and cellular research

Wavenumber Calibration Protocol for Raman Spectrometers Using Physical Modelling and a Fast Search Algorithm

A wavenumber calibration protocol is proposed that replaces polynomial fitting to relate the detector axis and the wavenumber shift. The physical model of the Raman spectrometer is used to derive a mathematical expression relating the detector plane to the wavenumber shift, in terms of the system parameters including the spectrograph focal length, the grating angle, and the laser wavelength; the model is general to both reflection and transmission gratings. A fast search algorithm detects the set of parameters that best explains the position of spectral lines recorded on the detector for a known reference standard. Using three different reference standards, four different systems, and hundreds of spectra recorded with a rotating grating, we demonstrate the superior accuracy of the technique, especially in bands outside of the outermost reference peaks when compared with polynomial fitting. We also provide a thorough review of wavenumber calibration for Raman spectroscopy and we introduce several new evaluation metrics to this field borrowed from chemometrics, including leave-one-out and leave-half-out cross-validation.

Vibrational phenomics decoding of the stem cell stepwise aging process at single-cell resolution

We introduce vibrational spectroscopy to quantitatively measure the phenotypic heterogeneity of senescent stem cells in the aging process at the single cell level. Using an aging model of serially passaged human mesenchymal stem cells (MSCs), we characterized the phenotypic changes of MSCs during different aged stages and discovered a stepwise aging process with several distinct subtypes.

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

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

A review of innovative electrochemical strategies for bioactive molecule detection and cell imaging: Current advances and challenges

Cellular heterogeneity poses a major challenge for tumor theranostics, requiring high-resolution intercellular bioanalysis strategies. Over the past decades, the advantages of electrochemical analysis, such as high sensitivity, good spatio-temporal resolution, and ease of use, have made it the preferred method to uncover cellular differences. To inspire more creative research, herein, we highlight seminal works in electrochemical techniques for biomolecule analysis and bioimaging. Specifically, micro/nano-electrode-based electrochemical techniques enable real-time quantitative analysis of electroactive substances relevant to life processes in the micro-nanostructure of cells and tissues. Nanopore-based technique plays a vital role in biosensing by utilizing nanoscale pores to achieve high-precision detection and analysis of biomolecules with exceptional sensitivity and single-molecule resolution. Electrochemiluminescence (ECL) technology is utilized for real-time monitoring of the behavior and features of individual cancer cells, enabling observation of their dynamic processes due to its capability of providing high-resolution and highly sensitive bioimaging of cells. Particularly, scanning electrochemical microscopy (SECM) and scanning ion conductance microscopy (SICM) which are widely used in real-time observation of cell surface biological processes and three-dimensional imaging of micro-nano structures, such as metabolic activity, ion channel activity, and cell morphology are introduced in this review. Furthermore, the expansion of the scope of cellular electrochemistry research by innovative functionalized electrodes and electrochemical imaging models and strategies to address future challenges and potential applications is also discussed in this review.

Application of spectrochemical analysis with chemometrics to profile biochemical alterations in nanoplastic-exposed HepG2 cells

Humans are routinely exposed to nanoplastics (NPs) in various ways, and this exposure presents a significant health risk. Nevertheless, there remain gaps in our knowledge, particularly in the mechanisms of toxicity of NPs with different surface charges at very low environmental concentrations. Herein, a spectrochemical approach was used to profile the cytotoxicity of NPs with different surface charges in HepG2 cells. It was found that all three NPs can cause some biomolecular alterations in cells, affecting cellular lipids, proteins, amino acids, and genetic material. Of these, PS and PS-COOH led to a non-linear dose-response, which may be related to a biphasic dose-response, whereas PS-NH2 led to a linear dose-response with a gradual increase in toxicity with increasing exposure concentration. In addition, the spectroscopic results showed that surface modifications led to cellular biochemical changes and caused adverse biological effects, with PS-NH2 exhibiting higher toxicity compared to PS or PS-COOH along with an inhibition of cell proliferation. Surprisingly PS-COOH, although considered the least toxic NP, appears to cause DNA damage. Overall, the toxic effects of different surface-modified NPs in cells were detected for the first time by applying spectrochemical techniques, and these findings provide important data towards understanding the emerging widespread environmental pollution of NPs and their effects on humans.

Multivariate curve Resolution-Alternating least squares coupled with Raman microspectroscopy: new insights into the kinetic response of primary oral squamous carcinoma cells to cisplatin

Raman MicroSpectroscopy (RMS) is a powerful label-free tool to probe the effects of drugs at a cellular/subcellular level. It is important, however, to be able to extract relevant biochemical and kinetic spectroscopic signatures of the specific cellular responses. In the present study, a combination of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis (PCA) is used to analyse the RMS data for the example of exposure of primary Oral Squamous Carcinoma Cells (OSCC) to the chemotherapeutic agent cisplatin. Dosing regimens were established by cytotoxicity assays, and the effects of the drug on cellular spectral profiles were monitored from 16 to 72 hours post-exposure using an apoptosis assay, to establish the relative populations of viable (V), early (EA) and late apoptotic/dead (LA/D) cells after the drug treatment. Based on a kinetic model of the progression from V > EA > D, MCR-ALS regression analysis of the RMS responses was able to extract spectral profiles associated with each stage of the cellular responses, enabling a quantitative comparison of the response rates for the respective drug treatments. Moreover, PCA was used to compare the spectral profiles of the viable cells exposed to the drug. Spectral differences were highlighted in the early stages (16 hours exposure), indicative of the initial cellular response to the drug treatment, and also in the late stages (48-72 hours exposure), representing the cell death pathway. The study demonstrates that RMS coupled with multivariate analysis can be used to quantitatively monitor the progression of cellular responses to different drugs, towards future applications for label-free, in vitro, pre-clinical screening.

Quantitative Assessment of Low-Dose Photodynamic Therapy Effects on Diabetic Wound Healing Using Raman Spectroscopy

One of challenges that faces diabetes is the wound healing process. The delayed diabetic wound healing is caused by a complicated molecular mechanism involving numerous physiological variables. Low-dose photodynamic therapy (LDPDT) provides excellent results in rejuvenation and wound healing. In this study, the LDPDT effect on diabetic wounds in mice was studied using two photosensitizers, 5-aminolevulinic acid and methylene blue, and two laser dose expositions of 1 J/cm² and 4 J/cm² by Raman spectroscopy (RS). The latter was used as a noninvasive method, providing specific information about tissue state based on the fundamental vibrational modes of its molecular components. RS allows high spatial resolution acquisition of biochemical and structural information through the generation of point spectra or spectral images. An approach to in vivo quantitative assessment of diabetic wound healing state was developed. This approach is based on an application of the principal component analysis combined with the Mahalanobis metrics to skin Raman spectra, in particular, intensities of the amide I and CH₂ bands.

Synchrotron radiation-based Fourier transform infrared microspectroscopy investigation of WRL68 cells treated with doxorubicin

Doxorubicin is an effective chemotherapeutic agent applied in a wide variety of cancers. Despite its potent anticancer activity towards cancer cells, doxorubicin is also toxic to noncancerous cells. Therefore, doxorubicin can cause serious side effects in various organs, especially when dose escalation is required for patients with advanced disease. The liver is the major detoxification organ that metabolizes drugs, and hepatotoxicity is one of the most common adverse effects of doxorubicin administration. However, the exact mechanisms of doxorubicin-induced hepatotoxicity have not been clearly identified, and how doxorubicin treatment affects the biomolecular contents of normal human hepatocytes has rarely been studied. Synchrotron radiation-based Fourier transform infrared (SR-FTIR) microspectroscopy is a state-of-the-art analytical technique for characterizing the biomolecules present in cells. In this research, the biomolecular alterations of doxorubicin-treated normal human hepatocytes compared to untreated control cells were investigated at the single-cell level by combining SR-FTIR microspectroscopy with the Cell Counting Kit-8 (CCK-8) assay and flow cytometry. WRL68 human normal embryonic liver cells, which have been shown to be very promising for assessing the cytotoxicity of toxic compounds and investigating hepato-toxicology, were used in this research. Principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were used to further analyse the biomolecular contents of WRL68 cells. The order of lipid acyl chains and protein α-helix structures in doxorubicin-treated WRL68 cells was found to be distinctly changed, while the nucleic acids were altered relatively less. No alteration in the carbohydrate content was distinguishable after doxorubicin treatment. These results provide more comprehensive information about the biomolecular changes in hepatocytes induced by doxorubicin treatment and help to elucidate the mechanism of doxorubicin-induced hepatotoxicity. This research also proves that SR-FTIR microspectroscopy, combined with PCA and OPLS-DA, is a promising approach for investigating drug-cell interaction systems.

Plasma versus Serum Analysis by FTIR Spectroscopy to Capture the Human Physiological State

Fourier Transform InfraRed spectroscopy of serum and plasma has been highly explored for medical diagnosis, due to its general simplicity, and high sensitivity and specificity. To evaluate the plasma and serum molecular fingerprint, as obtained by FTIR spectroscopy, to acquire the system metabolic state, serum and plasma spectra were compared to characterize the metabolic state of 30 human volunteers, between 90 days consumption of green tea extract rich in Epigallocatechin-3-gallate (EGCG). Both plasma and serum spectra enabled the high impact of EGCG consumption on the biofluid spectra to be observed, as analyzed by the spectra principal component analysis, hierarchical-cluster analysis, and univariate data analysis. Plasma spectra resulted in the prediction of EGCG consumption with a slightly higher specificity, accuracy, and precision, also pointing to a higher number of significant spectral bands that were different between the 90 days period. Despite this, the lipid regions of the serum spectra were more affected by EGCG consumption than the corresponding plasma spectra. Therefore, in general, if no specific compound analysis is highlighted, plasma is in general the advised biofluid to capture by FTIR spectroscopy the general metabolic state. If the lipid content of the biofluid is relevant, serum spectra could present some advantages over plasma spectra.

New Insights into the Multivariate Analysis of SER Spectra Collected on Blood Samples for Prostate Cancer Detection: Towards a Better Understanding of the Role Played by Different Biomolecules on Cancer Screening: A Preliminary Study

Simple Summary

In recent years, research on biofluids using Raman and SERS has expanded dramatically, indicating the enormous promise of this technology as a high-throughput tool for identifying cancer and other disorders. In the investigations thus far, researchers have concentrated on a specific illness or condition, but the techniques employed to acquire experimental spectra prevent direct comparison of the data. This necessitates comparative research of a variety of diseases and an increase in scientific cooperation to standardize experimental conditions. In our study, positive results were reached by applying a combined SERS multivariate analysis (MVA) to the urgent problem of prostate cancer diagnosis that was directly linked to real-world settings in healthcare. Moreover, in comparison to the prostate-specific antigen (PSA) test, which has a high sensitivity but limited specificity, our combined SERS-MVA method has greater specificity, which may assist in preventing the overtreatment of patients.

Abstract

It is possible to obtain diagnostically relevant data on the changes in biochemical elements brought on by cancer via the use of multivariate analysis of vibrational spectra recorded on biological fluids. Prostate cancer and control groups included in this research generated almost similar SERS spectra, which means that the values of peak intensities present in SERS spectra can only give unspecific and limited information for distinguishing between the two groups. Our diagnostic algorithm for prostate cancer (PCa) differentiation was built using principal component analysis and linear discriminant analysis (PCA-LDA) analysis of spectral data, which has been widely used in spectral data management in many studies and has shown promising results so far. In order to fully utilize the entire SERS spectrum and automatically determine the most meaningful spectral features that can be used to differentiate PCa from healthy patients, we perform a multivariate analysis on both the entire and specific spectral intervals. Using the PCA-LDA model, the prostate cancer and control groups are clearly distinguished in our investigation. The separability of the following two data sets is also evaluated using two alternative discrimination techniques: principal least squares discriminant analysis (PLS-DA) and principal component analysis—support vector machine (PCA-SVM).

Infrared spectrometric biomarkers for ulcerative colitis screening using human serum samples

This study uses infrared spectrometry coupled with data analysis techniques to understand colitis-induced alterations in the molecular components of serum samples. Using samples from 18 ulcerative colitis patients and 28 healthy volunteers, we assessed features such as absorbance values at wavenumbers of 1033 and 1076 cm^-1 , and the ratios at 1121 versus 1020 cm^-1 and 1629 versus 1737 cm^-1 . Through the deconvolution of the amide I band, protein secondary structure analysis was performed. Colitis-induced alterations are reflected as fluctuations in the vibrational modes, and are used to identify associated spectral signatures. The results of the study show statistically significant differences in five identifying spectral signatures. Among them, the sensitivity and specificity of the spectral signature, I₁₁₂₁ /I₁₀₂₀ , were 100% and 86%, respectively. These findings resemble our earlier proof-of-concept investigations in mouse models and provide preliminary evidence that this could be a reliable diagnostic test for human patients.

Raman spectroscopy and multivariate analysis as potential tool to follow Alzheimer's disease progression

Raman spectroscopy is an emerging tool in the research and diagnosis of different diseases, including neurodegenerative disorders. In this work, blood serum samples collected from healthy controls and dementia patients were analysed by Raman spectroscopy to develop a classification model for the diagnosis of dementia of Alzheimer’s type (DAT). Raman spectra were processed by means of multivariate tools for multivariate analysis. Lower concentration levels of carotenoids were detected in blood serum from patients, which allowed for a good discrimination with respect to controls, such as 93% of correct predictions on the test set with random forest. We also hypothesize that carotenoid levels might be informative about the severity and progression of the disease, since the intensity of carotenoid signals decreased from the early stage to more severe patients. These encouraging results suggest the possibility to use Raman spectroscopy for the analysis of alternative biofluids (e.g. saliva) and the unobtrusive diagnosis of other neurodegenerative disorders.

Multi- and transgenerational biochemical effects of low-dose exposure to bisphenol A and 4-nonylphenol on testicular interstitial (Leydig) cells

Bisphenol A (BPA) and 4-nonylphenol (NP) are well-known endocrine-disrupting chemicals (EDCs) that have been proven to affect Leydig cell (LC) functions and testosterone production, but whether BPA and NP have multi- and transgenerational biochemical effects on Leydig cells (LCs) is unknown. Fourier transform infrared (FTIR) spectroscopy is a powerful analytical technique that enables label-free and non-destructive analysis of the tissue specimen. Herein we employed FTIR coupled with chemometrics analysis to identify biomolecular changes in testicular interstitial (Leydig) cells of rats after chronic exposure to low doses of BPA and NP. Cluster segregations between exposed and control groups were observed based on the fingerprint region of 1800-900 cm^-1 in all generations. The main biochemical alterations for segregation were amide I, amide II and nucleic acids. BPA and NP single and co-exposure induced significant differences in the ratio of amide I to amide II compared to the corresponding control group in all generations. BPA exposure resulted in remarkable changes of cellular gene transcription and DNA oxidative damage across all generations. Direct exposure to BPA, NP, and BPA&NP of F0 and F1 generations could significantly decrease lipid accumulation in LCs in the F2 and F3 generations. The overall findings revealed that single or co-exposure to BPA and NP at environmental concentrations affects the biochemical structures and properties of LCs.

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

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

Prospects of Surface-Enhanced Raman Spectroscopy for Biomarker Monitoring toward Precision Medicine

Future precision medicine will be undoubtedly sustained by the detection of validated biomarkers that enable a precise classification of patients based on their predicted disease risk, prognosis, and response to a specific treatment. Up to now, genomics, transcriptomics, and immunohistochemistry have been the main clinically amenable tools at hand for identifying key diagnostic, prognostic, and predictive biomarkers. However, other molecular strategies, including metabolomics, are still in their infancy and require the development of new biomarker detection technologies, toward routine implementation into clinical diagnosis. In this context, surface-enhanced Raman scattering (SERS) spectroscopy has been recognized as a promising technology for clinical monitoring thanks to its high sensitivity and label-free operation, which should help accelerate the discovery of biomarkers and their corresponding screening in a simpler, faster, and less-expensive manner. Many studies have demonstrated the excellent performance of SERS in biomedical applications. However, such studies have also revealed several variables that should be considered for accurate SERS monitoring, in particular, when the signal is collected from biological sources (tissues, cells or biofluids). This Perspective is aimed at piecing together the puzzle of SERS in biomarker monitoring, with a view on future challenges and implications. We address the most relevant requirements of plasmonic substrates for biomedical applications, as well as the implementation of tools from artificial intelligence or biotechnology to guide the development of highly versatile sensors.

Brain DNA damage analysis in pesticide exposed wistar albino rats (Rattus norvegicus): a chemometric approach

Brain the most important organ which controls most of the regulations in the body is composed of neurons and glia. As brain has a high metabolic rate and reduced cell renewal capability, the lipids, proteins and nucleic acids become the major targets of damage. In the present study carbofuran (CF) induced brain DNA damage in male wistar albino rats at sub-lethal doses (Control-A; B-1.0, C-0.5 and D-0.3 mg/kg BW) while the groups B1,C1, D1, B2, C2, D2 and B3, C3, D3 represents the exposure duration 30, 60 and 90 days each respectively. FTIR spectroscopy based chemometric analysis of functional groups in nucleic acids are reported, changes in band area and band frequencies were examined to understand the DNA damage by constructing heat map. Significant changes were observed in the band frequency, band areas, bandwidth and intensity values (p < 0.05, 0.01, 0.001). The principal component analysis was analyzed to study the alterations at the molecular level, which revealed maximum variance of 74% in groups A, B1, C1, D1 and C2 and 13.7% variance in B2, D2, B3, C3 and D3. The present study provides significant details to analyse DNA damage using non-conventional approach and can also be used for detecting DNA damage in several neural diseases and disorders and emphasizes on using FTIR spectral data through chemometric approach to analyse the DNA damage.Communicated by Ramaswamy H. Sarma.

Screening ovarian cancers with Raman spectroscopy of blood plasma coupled with machine learning data processing

The mortality of ovarian cancer is closely related to its poor rate of early detection. In the search of an efficient diagnosis method, Raman spectroscopy of blood features as a promising technique allowing simple, rapid, minimally-invasive and cost-effective detection of cancers, in particular ovarian cancer. Although Raman spectroscopy has been demonstrated to be effective to detect ovarian cancers with respect to normal controls, a binary classification remains idealized with respect to the real clinical practice. This work considered a population of 95 woman patients initially suspected of an ovarian cancer and finally fixed with a cancer or a cyst. Additionally, 79 normal controls completed the ensemble of samples. Such sample collection proposed us a study case where a ternary classification should be realized with Raman spectroscopy of the collected blood samples coupled with suitable spectroscopic data treatment algorithms. In the medical as well as data points of view, the appearance of the cyst case considerably reduces the distances among the different populations and makes their distinction much more difficult, since the intermediate cyst case can share the specific features of the both cancer and normal cases. After a proper spectrum pretreatment, we first demonstrated the evidence of different behaviors among the Raman spectra of the 3 types of samples. Such difference was further visualized in a high dimensional space, where the data points of the cancer and the normal cases are separately clustered, whereas the data of the cyst case were scattered into the areas respectively occupied by the cancer and normal cases. We finally developed and tested an ensemble of models for a ternary classification with 2 consequent steps of binary classifications, based on machine learning algorithms, allowing identification with sensitivity and specificity of 81.0% and 97.3% for cancer samples, 63.6% and 91.5% for cyst samples, 100% and 90.6% for normal samples.

Automated Raman Micro-Spectroscopy of Epithelial Cell Nuclei for High-Throughput Classification

Simple Summary

We demonstrate an automated Raman cytology system designed for high-throughput and reproducibility. The system uses a Raman spectroscopy system integrated into a conventional microscope, all controlled electronically via and open source software, Micro-Manager. The system can automatically identify and probe epithelial cell nuclei for Raman spectroscopy. 6426 HT1197 (high-grade bladder cancer) cell spectra, and 7499 RT112 (low-grade bladdercancer) cell spectra were recorded. The data was subsequently culled and processed for denoising and artifact removal. We demonstrate, using multivariate statistical analysis, that the cells can be distinguished, using a variety of approaches with accuracy, sensitivity and specificity in excess of 95%.

Abstract

Raman micro-spectroscopy is a powerful technique for the identification and classification of cancer cells and tissues. In recent years, the application of Raman spectroscopy to detect bladder, cervical, and oral cytological samples has been reported to have an accuracy greater than that of standard pathology. However, despite being entirely non-invasive and relatively inexpensive, the slow recording time, and lack of reproducibility have prevented the clinical adoption of the technology. Here, we present an automated Raman cytology system that can facilitate high-throughput screening and improve reproducibility. The proposed system is designed to be integrated directly into the standard pathology clinic, taking into account their methodologies and consumables. The system employs image processing algorithms and integrated hardware/software architectures in order to achieve automation and is tested using the ThinPrep standard, including the use of glass slides, and a number of bladder cancer cell lines. The entire automation process is implemented, using the open source Micro-Manager platform and is made freely available. We believe that this code can be readily integrated into existing commercial Raman micro-spectrometers.

An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms

Although some neurodegenerative diseases can be identified by behavioral characteristics relatively late in disease progression, we currently lack methods to predict who has developed disease before the onset of symptoms, when onset will occur, or the outcome of therapeutics. New biomarkers are needed. Here we describe spectral phenotyping, a new kind of biomarker that makes disease predictions based on chemical rather than biological endpoints in cells. Spectral phenotyping uses Fourier Transform Infrared (FTIR) spectromicroscopy to produce an absorbance signature as a rapid physiological indicator of disease state. FTIR spectromicroscopy has over the past been used in differential diagnoses of manifest disease. Here, we report that the unique FTIR chemical signature accurately predicts disease class in mouse with high probability in the absence of brain pathology. In human cells, the FTIR biomarker accurately predicts neurodegenerative disease class using fibroblasts as surrogate cells.

Rapid characterization of hypanthium and seed in wild and cultivated rosehip: application of Raman microscopy combined with multivariate analysis

Rosehip (pseudo-fruit) of dog rose (Rosa canina L.) is highly valued, and owing to nutritional and sensory properties it has a significant place in the food industry. This work represents an innovative report focusing on the evaluation of the phytochemical composition of rosehips (hypanthium and seed) grown in different locations in Serbia, using Raman microspectroscopy combined with multivariate data analysis. Some significant differences arose between the analysed rosehip samples with regard to the chemical profile of both hypanthium parenchyma cells and seed, although no evident discrimination was recorded between the samples of wild and cultivated rosehip. The differences between the hypanthium samples compared were mainly determined by the content of carotenoids, phenolic compounds and polysaccharides, whereas phenolics, polysaccharides (pectin, cellulose and hemicellulose) and lipids (to a lower extent) contributed to the seed sample discrimination. The differences observed between the rosehip samples may be attributed to abiotic factors (growing, ripening and storage conditions), which had a significant impact on the carotenoid and polyphenols biosynthesis.

Discrimination of bacteria using whole organism fingerprinting: the utility of modern physicochemical techniques for bacterial typing

This review compares and contrasts MALDI-MS, FT-IR spectroscopy and Raman spectroscopy for whole organism fingerprinting and bacterial typing.

Raman-guided subcellular pharmaco-metabolomics for metastatic melanoma cells

Non-invasively probing metabolites within single live cells is highly desired but challenging. Here we utilize Raman spectro-microscopy for spatial mapping of metabolites within single cells, with the specific goal of identifying druggable metabolic susceptibilities from a series of patient-derived melanoma cell lines. Each cell line represents a different characteristic level of cancer cell de-differentiation. First, with Raman spectroscopy, followed by stimulated Raman scattering (SRS) microscopy and transcriptomics analysis, we identify the fatty acid synthesis pathway as a druggable susceptibility for differentiated melanocytic cells. We then utilize hyperspectral-SRS imaging of intracellular lipid droplets to identify a previously unknown susceptibility of lipid mono-unsaturation within de-differentiated mesenchymal cells with innate resistance to BRAF inhibition. Drugging this target leads to cellular apoptosis accompanied by the formation of phase-separated intracellular membrane domains. The integration of subcellular Raman spectro-microscopy with lipidomics and transcriptomics suggests possible lipid regulatory mechanisms underlying this pharmacological treatment. Our method should provide a general approach in spatially-resolved single cell metabolomics studies.

Single-cell metabolomics can offer deep insights into the metabolic reprogramming that accompanies disease states. Here, the authors use Raman spectro-microscopy for non-invasive metabolite analysis and identification of druggable metabolic susceptibilities in single live melanoma cells.

Dark-field hyperspectral imaging for label free detection of nano-bio-materials

Nanomaterials are playing an increasingly important role in cancer diagnosis and treatment. Nanoparticle (NP)-based technologies have been utilized for targeted drug delivery during chemotherapies, photodynamic therapy, and immunotherapy. Another active area of research is the toxicity studies of these nanomaterials to understand the cellular uptake and transport of these materials in cells, tissues, and environment. Traditional techniques such as transmission electron microscopy, and mass spectrometry to analyze NP-based cellular transport or toxicity effect are expensive, require extensive sample preparation, and are low-throughput. Dark-field hyperspectral imaging (DF-HSI), an integration of spectroscopy and microscopy/imaging, provides the ability to investigate cellular transport of these NPs and to quantify the distribution of them within bio-materials. DF-HSI also offers versatility in non-invasively monitoring microorganisms, single cell, and proteins. DF-HSI is a low-cost, label-free technique that is minimally invasive and is a viable choice for obtaining high-throughput quantitative molecular analyses. Multimodal imaging modalities such as Fourier transform infrared and Raman spectroscopy are also being integrated with HSI systems to enable chemical imaging of the samples. HSI technology is being applied in surgeries to obtain molecular information about the tissues in real-time. This article provides brief overview of fundamental principles of DF-HSI and its application for nanomaterials, protein-detection, single-cell analysis, microbiology, surgical procedures along with technical challenges and future integrative approach with other imaging and measurement modalities. This article is categorized under: Diagnostic Tools > in vitro Nanoparticle-Based Sensing Diagnostic Tools > in vivo Nanodiagnostics and Imaging Implantable Materials and Surgical Technologies > Nanoscale Tools and Techniques in Surgery.

Optimizing infrared spectral discrimination to enhance disease diagnostics: monitoring the signatures of inflammatory bowel diseases with anti-TNFα therapy

This study presents an application of infrared spectroscopy of sera for monitoring the efficacy of anti-TNFα therapy for inflammatory bowel diseases. Understanding the therapeutic response includes the analysis of absorption bands representing constituent molecules. Interleukin-10 knockout mouse model of the diseases with anti-TNFα treatment was used. The discrimination potential is optimized by analyzing data with curve fitting. It shows; antibody therapy markedly ameliorated the disease, concurring with earlier mucosal immunology and pathophysiologic studies. This technique may thus also be useful for the evaluation of mucosal healing or other therapeutic modalities of gastrointestinal tract diseases keeping the endoscopic tests as confirmatory.

Transmission Fourier Transform Infrared Spectroscopic Imaging, Mapping, and Synchrotron Scanning Microscopy with Zinc Sulfide Hemispheres on Living Mammalian Cells at Sub-Cellular Resolution

Fourier transform infrared (FT-IR) spectroscopic imaging and microscopy of single living cells are established label-free technique for the study of cell biology. The constant driver to improve the spatial resolution of the technique is due to the diffraction limit given by infrared (IR) wavelength making subcellular study challenging. Recently, we have reported, with the use of a prototype zinc sulfide (ZnS) transmission cell made of two hemispheres, that the spatial resolution is improved by the factor of the refractive index of ZnS, achieving a λ/2.7 spatial resolution using the synchrotron-IR microscopy with a 36× objective with numerical aperture of 0.5. To refine and to demonstrate that the ZnS hemisphere transmission device can be translated to standard bench-top FT-IR imaging systems, we have, in this work, modified the device to achieve a more precise path length, which has improved the spectral quality of the living cells, and showed for the first time that the device can be applied to study live cells with three different bench-top FT-IR imaging systems. We applied focal plane array (FPA) imaging, linear array, and a synchrotron radiation single-point scanning method and demonstrated that in all cases, subcellular details of individual living cells can be obtained. Results have shown that imaging with the FPA detector can measure the largest area in a given time, while measurements from the scanning methods produced a smoother image. Synchrotron radiation single-point mapping produced the best quality image and has the flexibility to introduce over sampling to produce images of cells with great details, but it is time consuming in scanning mode. In summary, this work has demonstrated that the ZnS hemispheres can be applied in all three spectroscopic approaches to improve the spatial resolution without any modification to the existing microscopes.

An FTIR Microspectroscopy Ratiometric Approach for Monitoring X-ray Irradiation Effects on SH-SY5Y Human Neuroblastoma Cells

The ability of Fourier transform infrared (FTIR) spectroscopy in analyzing cells at a molecular level was exploited for investigating the biochemical changes induced in protein, nucleic acid, lipid, and carbohydrate content of cells after irradiation by graded X-ray doses. Infrared spectra from in vitro SH-SY5Y neuroblastoma cells following exposure to X-rays (0, 2, 4, 6, 8, 10 Gy) were analyzed using a ratiometric approach by evaluating the ratios between the absorbance of significant peaks. The spectroscopic investigation was performed on cells fixed immediately (t0 cells) and 24 h (t24 cells) after irradiation to study both the initial radiation-induced damage and the effect of the ensuing cellular repair processes. The analysis of infrared spectra allowed us to detect changes in proteins, lipids, and nucleic acids attributable to X-ray exposure. The ratiometric analysis was able to quantify changes for the protein, lipid, and DNA components and to suggest the occurrence of apoptosis processes. The ratiometric study of Amide I band indicated also that the secondary structure of proteins was significantly modified. The comparison between the results from t0 and t24 cells indicated the occurrence of cellular recovery processes. The adopted approach can provide a very direct way to monitor changes for specific cellular components and can represent a valuable tool for developing innovative strategies to monitor cancer radiotherapy outcome.

Mid-Infrared Frequency Comb Generation and Spectroscopy with Few-Cycle Pulses and χ^{(2)} Nonlinear Optics

The mid-infrared atmospheric window of 3-5.5  μm holds valuable information regarding molecular composition and function for fundamental and applied spectroscopy. Using a robust, mode-locked fiber-laser source of <11  fs pulses in the near infrared, we explore quadratic (χ^{(2)}) nonlinear optical processes leading to frequency comb generation across this entire mid-infrared atmospheric window. With experiments and modeling, we demonstrate intrapulse difference frequency generation that yields few-cycle mid-infrared pulses in a single pass through periodically poled lithium niobate. Harmonic and cascaded χ^{(2)} nonlinearities further provide direct access to the carrier-envelope offset frequency of the near infrared driving pulse train. The high frequency stability of the mid-infrared frequency comb is exploited for spectroscopy of acetone and carbonyl sulfide with simultaneous bandwidths exceeding 11 THz and with spectral resolution as high as 0.003  cm^{-1}. The combination of low noise and broad spectral coverage enables detection of trace gases with concentrations in the part-per-billion range.

Spectroscopic Methods Used in Implant Material Studies

It is recognized that interactions between most materials are governed by their surface properties and manifest themselves at the interface formed between them. To gain more insight into this thin layer, several methods have been deployed. Among them, spectroscopic methods have been thoroughly evaluated. Due to their exceptional sensitivity, data acquisition speed, and broad material tolerance they have been proven to be invaluable tools for surface analysis, used by scientists in many fields, for example, implant studies. Today, in modern medicine the use of implants is considered standard practice. The past two decades of constant development has established the importance of implants in dentistry, orthopedics, as well as extended their applications to other areas such as aesthetic medicine. Fundamental to the success of implants is the knowledge of the biological processes involved in interactions between an implant and its host tissue, which are directly connected to the type of implant material and its surface properties. This review aims to demonstrate the broad applications of spectroscopic methods in implant material studies, particularly discussing hard implants, surface composition studies, and surface-cell interactions.

VIS-NIR spectral signature and quantitative analysis of HeLa and DU145 cell line

Cancer is increasing in incidence and the leading cause of death worldwide. Controlling and reducing cancer requires early detection and technique to accurately detect and quantify predictive biomarkers. Optical spectroscopy has shown promising non-destructive ability to display distinctive spectral characteristics between cancerous and normal tissues from different part of human organ. Nonetheless, not many information is available on spectroscopic properties of cancer cell lines. In this research, the visible-near infrared (VIS-NIR) absorbance spectroscopy measurement of cultured cervical cancer (HeLa) and prostate cancer cells (DU145) lines has been performed to develop spectral signature of cancer cells and to generate algorithm to quantify cancer cells. Spectroscopic measurement on mouse skin fibroblast (L929) was also taken for comparative purposes. In visible region, the raw cells' spectra do not produce any noticeable peak absorbance that provides information on color because the medium used for cells is colorless and transparent. NIR wavelength between 950 and 975 nm exhibit significant peak due to water absorbance by the medium. Development of spectral signature for the cells through the application of regression technique significantly enhances the diverse characteristics between L929, HeLa and DU145. The application of multiple linear regression allows high measurement accuracy of the cells with coefficient of determination above 0.94.

Vibrational Spectroscopy Fingerprinting in Medicine: from Molecular to Clinical Practice

In the last two decades, Fourier Transform Infrared (FTIR) and Raman spectroscopies turn out to be valuable tools, capable of providing fingerprint-type information on the composition and structural conformation of specific molecular species. Vibrational spectroscopy’s multiple features, namely highly sensitive to changes at the molecular level, noninvasive, nondestructive, reagent-free, and waste-free analysis, illustrate the potential in biomedical field. In light of this, the current work features recent data and major trends in spectroscopic analyses going from in vivo measurements up to ex vivo extracted and processed materials. The ability to offer insights into the structural variations underpinning pathogenesis of diseases could provide a platform for disease diagnosis and therapy effectiveness evaluation as a future standard clinical tool.

Probing primary mesenchymal stem cells differentiation status by micro-Raman spectroscopy

We have employed micro-Raman spectroscopy to get insight into intrinsic biomolecular profile of individual mesenchymal stem cell isolated from periodontal ligament. Furthermore, these cells were stimulated towards adipogenic, chondrogenic, and osteogenic lineages and their status of differentiation was assessed using micro-Raman spectroscopy. In both cases, glass coverslips were used as substrates, due to their wide availability and cost effectiveness. In all sample groups, the same type of behavior was observed, manifested as changes in Raman spectra: the increase of relative intensity of protein/lipid bands and decrease of nucleic acid bands. Comprehensive statistical analysis in the form of principal component analysis was performed, which revealed noticeable grouping of cells with the similar features. Despite the inhomogeneity of primary stem cells and their differentiated lineages, we demonstrated that micro-Raman spectroscopy is sufficient for distinguishing cells' status, which can be valuable for medical and clinical application.

Development of a high throughput (HT) Raman spectroscopy method for rapid screening of liquid blood plasma from prostate cancer patients

Extensive research has been undertaken on the examination of tissue biopsies using vibrational spectroscopic techniques. However, fewer studies have focused on less invasive and commonly acquired blood samples. Recent studies have shown the ability of Raman and Fourier transform infrared (FTIR) spectroscopy to discriminate between non-cancer controls and cancer cases using blood serum or plasma. Even though many studies have proposed Raman spectroscopy as a potential diagnostic tool in various cancers, the Raman spectroscopic technique has not been introduced as a routine clinical technology. This is due to multiple drawbacks with the application of the technique, including sample preparation, the requirement for expensive substrates and long acquisition times. The current study aims to overcome these limitations and focuses on the translation of Raman spectroscopy into a high throughput clinical diagnostic tool for prostate cancer. In this study, the effect of different instrumental and sample preparation parameters were investigated, with the aim of identifying a combination that would reduce the overall acquisition time for spectra from peripheral blood plasma, reduce the complexity of sample preparation and retain the classification accuracy from Raman spectroscopic diagnostics. A high throughput (HT) system was developed and Raman spectroscopic measurements were performed on plasma samples from 10 prostate cancer patients and 10 healthy volunteers. The spectra were pre-processed and classified by principal component analysis - linear discriminant analysis (PCA-LDA) in the R environment. Statistically significant differences were observed between Raman spectra of prostate cancer patients and non-cancer controls. The (HT) classification resulted in a sensitivity and specificity of 96.5% and 95% respectively. Overall, this study has overcome some of the limitations associated with clinical translation of Raman spectroscopy. The HT-Raman spectroscopy method developed in this study can be used for rapid and accurate diagnosis of prostate cancer using liquid plasma samples.

Raman micro-spectroscopy for accurate identification of primary human bronchial epithelial cells

Live cell Raman micro-spectroscopy is emerging as a promising bioanalytical technique for label-free discrimination of a range of different cell types (e.g. cancer cells and fibroblasts) and behaviors (e.g. apoptosis). The aim of this study was to determine whether confocal Raman micro-spectroscopy shows sufficient sensitivity and specificity for identification of primary human bronchial epithelial cells (HBECs) to be used for live cell biological studies in vitro. We first compared cell preparation substrates and media, considering their influence on lung cell proliferation and Raman spectra, as well as methods for data acquisition, using different wavelengths (488 nm, 785 nm) and scan protocols (line, area). Evaluating these parameters using human lung cancer (A549) and fibroblast (MRC5) cell lines confirmed that line-scan data acquisition at 785 nm using complete cell media on a quartz substrate gave optimal performance. We then applied our protocol to acquisition of data from primary human bronchial epithelial cells (HBEC) derived from three independent sources, revealing an average sensitivity for different cell types of 96.3% and specificity of 95.2%. These results suggest that Raman micro-spectroscopy is suitable for delineating primary HBEC cell cultures, which in future could be used for identifying different lung cell types within co-cultures and studying the process of early carcinogenesis in lung cell culture.

Detection of mycoplasma in contaminated mammalian cell culture using FTIR microspectroscopy

Mycoplasma contamination represents a significant problem to the culture of mammalian cells used for research as it can cause disastrous effects on eukaryotic cells by altering cellular parameters leading to unreliable experimental results. Mycoplasma cells are very small bacteria therefore they cannot be detected by visual inspection using a visible light microscope and, thus, can remain unnoticed in the cell cultures for long periods. The detection techniques used nowadays to reveal mycoplasma contamination are time consuming and expensive with each having significant drawbacks. The ideal detection should be simple to perform with minimal preparation time, rapid, inexpensive, and sensitive. To our knowledge, for the first time, we employed Fourier transform infrared (FTIR) microspectroscopy to investigate whether we can differentiate between control cells and the same cells which have been infected with mycoplasmas during the culturing process. Chemometric methods such as HCA and PCA were used for the data analysis in order to detect spectral differences between control and intentionally infected cells, and spectral markers were revealed even at low contamination level. The preliminary results showed that FTIR has the potential to be used in the future as a reliable complementary detection technique for mycoplasma-infected cells. Graphical abstract FTIR microspectroscopy is able to differentiate between mycoplasma infected cells (LC for low contamination and HC for high contamination) and control non-infected cells (CN).

Restoring the biophysical properties of decellularized patches through recellularization

Various extracellular matrix (ECM) scaffolds, isolated through decellularization, were suggested as ideal biomimetic materials for 'Functional tissue engineering' (FTE). The decellularization process comprises a compromise between damaging and preserving the ultrastructure and composition of ECM-previously shown to affect cell survival, proliferation, migration, organization, differentiation and maturation. Inversely, the effects of cells on the ECM constructs' biophysical properties, under physiological-like conditions, remain still largely unknown. We hypothesized that by re-cellularizing porcine cardiac ECM (pcECM, as a model scaffold) some of the original biophysical properties of the myocardial tissue can be restored, which are related to the scaffold's surface and the bulk modifications consequent to cellularization. We performed a systematic biophysical assessment of pcECM scaffolds seeded with human mesenchymal stem cells (MSCs), a common multipotent cell source in cardiac regenerative medicine. We report a new type of FTE study in which cell interactions with a composite-scaffold were evaluated from the perspective of their contribution to the biophysical properties of the construct surface (FTIR, WETSEM™) and bulk (DSC, TGA, and mechanical testing). The results obtained were compared with acellular pcECM and native ventricular tissue serving as negative and positive controls, respectively. MSC recellularization resulted in an inter-fiber plasticization effect, increased protein density, masking of acylated glycosaminoglycans (GAGs) and active pcECM remodelling which further stabilized the reseeded construct and increased its denaturation resistance. The systematic approach presented herein, therefore, identifies cells as "biological plasticizers" and yields important methodologies, understanding, and data serving both as a reference as well as possible 'design criteria' for future studies in FTE.

Biofluid spectroscopic disease diagnostics: A review on the processes and spectral impact of drying

The complex patterns observed from evaporated liquid drops have been examined extensively over the last 20 years. Complete understanding of drop deposition is vital in many medical processes, and one which is essential to the translation of biofluid spectroscopic disease diagnostics. The promising use of spectroscopy in disease diagnosis has been hindered by the complicated patterns left by dried biological fluids which may inhibit the clinical translation of this technology. Coffee-ring formation, cracking and gelation patterns have all been observed in biofluid drops, and with surface homogeneity being a key element to many spectroscopic techniques, experimental issues have been found to arise. A better understanding of the fundamental processes involved in a drying droplet could allow efficient progression in this research field, and ultimately benefit the population with the development of a reliable cancer diagnostic.

Infrared micro-spectroscopy of human tissue: principles and future promises

This article summarizes the methods employed, and the progress achieved over the past two decades in applying vibrational (Raman and IR) micro-spectroscopy to problems of medical diagnostics and cellular biology. During this time, several research groups have verified the enormous information contained in vibrational spectra; in fact, information on protein, lipid and metabolic composition of cells and tissues can be deduced by decoding the observed vibrational spectra. This decoding process is aided by the availability of computer workstations and advanced algorithms for data analysis. Furthermore, commercial instrumentation for the fast collection of both Raman and infrared micro-spectral data has enabled the collection of images of cells and tissues based solely on vibrational spectroscopic data. The progress in the field has been manifested by a steady increase in the number and quality of publications submitted by established and new research groups in vibrational spectroscopy in the biological and biomedical arenas.

Developing Raman spectroscopy as a diagnostic tool for label-free antigen detection

For several decades, a multitude of studies have documented the ability of Raman spectroscopy (RS) to differentiate between tissue types and identify pathological changes to tissues in a range of diseases. Furthermore, spectroscopists have illustrated that the technique is capable of detecting disease-specific alterations to tissue before morphological changes become apparent to the pathologist. This study draws comparisons between the information that is obtainable using RS alongside immunohistochemistry (IHC), since histological examination is the current GOLD standard for diagnosing a wide range of diseases. Here, Raman spectral maps were generated using formalin-fixed, paraffin-embedded colonic tissue sections from healthy patients and spectral signatures from principal components analysis (PCA) were compared with several IHC markers to confirm the validity of their localizations. PCA loadings identified a number of signatures that could be assigned to muscle, DNA and mucin glycoproteins and their distributions were confirmed with antibodies raised against anti-Desmin, anti-Ki67 and anti-MUC2, respectively. The comparison confirms that there is excellent correlation between RS and the IHC markers used, demonstrating that the technique is capable of detecting compositional changes in tissue in a label-free manner, eliminating the need for antibodies.

Feature driven classification of Raman spectra for real-time spectral brain tumour diagnosis using sound

Spectroscopic diagnostics have been shown to be an effective tool for the analysis and discrimination of disease states from human tissue. Furthermore, Raman spectroscopic probes are of particular interest as they allow for in vivo spectroscopic diagnostics, for tasks such as the identification of tumour margins during surgery. In this study, we investigate a feature-driven approach to the classification of metastatic brain cancer, glioblastoma (GB) and non-cancer from tissue samples, and we provide a real-time feedback method for endoscopic diagnostics using sound. To do this, we first evaluate the sensitivity and specificity of three classifiers (SVM, KNN and LDA), when trained with both sub-band spectral features and principal components taken directly from Raman spectra. We demonstrate that the feature extraction approach provides an increase in classification accuracy of 26.25% for SVM and 25% for KNN. We then discuss the molecular assignment of the most salient sub-bands in the dataset. The most salient sub-band features are mapped to parameters of a frequency modulation (FM) synthesizer in order to generate audio clips from each tissue sample. Based on the properties of the sub-band features, the synthesizer was able to maintain similar sound timbres within the disease classes and provide different timbres between disease classes. This was reinforced via listening tests, in which participants were able to discriminate between classes with mean classification accuracy of 71.1%. Providing intuitive feedback via sound frees the surgeons' visual attention to remain on the patient, allowing for greater control over diagnostic and surgical tools during surgery, and thus promoting clinical translation of spectroscopic diagnostics.