Vibrational biospectroscopy coupled with multivariate analysis extracts potentially diagnostic features in blood plasma/serum of ovarian cancer patients

Infrared Spectroscopy for Rapid Triage of Cancer Using Blood Derivatives: A Reality Check

Infrared (IR) spectroscopy of serum/plasma represents an alluring molecular diagnostic tool, especially for cancer, as it can provide a molecular fingerprint of clinical samples based on vibrational modes of chemical bonds. However, despite the superior performance, the routine adoption of this technique for clinical settings has remained elusive. This is due to the potential confounding factors that are often overlooked and pose a significant barrier to clinical translation. In this Perspective, we summarize the concerns associated with various confounding factors, such as fluid sampling, optical effects, hemolysis, abnormal cardiovascular and/or hepatic functions, infections, alcoholism, diet style, age, and gender of a patient or normal control cohort, and improper selection of numerical methods that ultimately would lead to improper spectral diagnosis. We also propose some precautionary measures to overcome the challenges associated with these confounding factors.

Combined analytical approach empowers precise spectroscopic interpretation of subcellular components of pancreatic cancer cells

The lack of specific and sensitive early diagnostic options for pancreatic cancer (PC) results in patients being largely diagnosed with late-stage disease, thus inoperable and burdened with high mortality. Molecular spectroscopic methodologies, such as Raman or infrared spectroscopies, show promise in becoming a leader in screening for early-stage cancer diseases, including PC. However, should such technology be introduced, the identification of differentiating spectral features between various cancer types is required. This would not be possible without the precise extraction of spectra without the contamination by necrosis, inflammation, desmoplasia, or extracellular fluids such as mucous that surround tumor cells. Moreover, an efficient methodology for their interpretation has not been well defined. In this study, we compared different methods of spectral analysis to find the best for investigating the biomolecular composition of PC cells cytoplasm and nuclei separately. Sixteen PC tissue samples of main PC subtypes (ductal adenocarcinoma, intraductal papillary mucinous carcinoma, and ampulla of Vater carcinoma) were collected with Raman hyperspectral mapping, resulting in 191,355 Raman spectra and analyzed with comparative methodologies, specifically, hierarchical cluster analysis, non-negative matrix factorization, T-distributed stochastic neighbor embedding, principal components analysis (PCA), and convolutional neural networks (CNN). As a result, we propose an innovative approach to spectra classification by CNN, combined with PCA for molecular characterization. The CNN-based spectra classification achieved over 98% successful validation rate. Subsequent analyses of spectral features revealed differences among PC subtypes and between the cytoplasm and nuclei of their cells. Our study establishes an optimal methodology for cancer tissue spectral data classification and interpretation that allows precise and cognitive studies of cancer cells and their subcellular components, without mixing the results with cancer-surrounding tissue. As a proof of concept, we describe findings that add to the spectroscopic understanding of PC.

Surface-enhanced Raman spectroscopy for the characterization of different anatomical subtypes of oral cavity cancer

BACKGROUND

The prognosis for oral cancer patients is still very poor worldwide. Early detection and treatment therapy remain the key issue to be addressed for improved patient survival. The characteristic Raman spectral features associated with the biochemical changes in the blood serum samples can be used for the diagnosis of diseases, particularly for oral cancer. Surface-enhanced Raman spectroscopy (SERS) is a promising technique for non-invasive and early detection of oral cancer by analyzing molecular changes in body fluids.

OBJECTIVES

To detect oral cavity anatomical subsites (buccal mucosa, cheek, hard palate, lips, mandible, maxilla, tongue and tonsillar region) cancers by using blood serum samples, SERS with principal component analysis is used.

MATERIAL AND METHOD

SERS is employed with silver nanoparticles for the analysis and detection of oral cancer serum samples by comparing with healthy serum samples. SERS spectra are recorded by Raman instrument and preprocessed using the statistical tool. Principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA) are used to discriminate between oral cancer serum samples and control serum samples.

RESULTS

Some major SERS peaks are observed at 1136 cm^-1 (Phospholipids) and 1006 cm^-1 (Phenylalanine) remain higher in intensities for oral cancer spectra as compared to healthy spectra. The peak at 1241 cm^-1 (amide III) is observed only in oral cancer serum samples while absent in healthy serum samples. Higher protein and DNA contents were detected in SERS mean spectra of oral cancer. Moreover, PCA is used to identify the biochemical differences in the form of SERS features which is used to differentiate between oral cancer and healthy blood serum samples, while PLS-DA is used to build differentiation model of oral cancer serum samples and healthy control serum samples. PLS-DA provides successful differentiation with 94% specificity and 95.5% sensitivity.

CONCLUSIONS

SERS can be used for the diagnosis of oral cancer and to identify metabolic changes that occur during disease development.

Vibrational spectroscopy – are we close to finding a solution for early pancreatic cancer diagnosis?

Pancreatic cancer (PC) is an aggressive and lethal neoplasm, ranking seventh in the world for cancer deaths, with an overall 5-year survival rate of below 10%. The knowledge about PC pathogenesis is rapidly expanding. New aspects of tumor biology, including its molecular and morphological heterogeneity, have been reported to explain the complicated “cross-talk” that occurs between the cancer cells and the tumor stroma or the nature of pancreatic ductal adenocarcinoma-associated neural remodeling. Nevertheless, currently, there are no specific and sensitive diagnosis options for PC. Vibrational spectroscopy (VS) shows a promising role in the development of early diagnosis technology. In this review, we summarize recent reports about improvements in spectroscopic methodologies, briefly explain and highlight the drawbacks of each of them, and discuss available solutions. The important aspects of spectroscopic data evaluation with multivariate analysis and a convolutional neural network methodology are depicted. We conclude by presenting a study design for systemic verification of the VS-based methods in the diagnosis of PC.

ATR-IR Spectroscopy Application to Diagnostic Screening of Advanced Endometriosis

Endometriosis is one of the most common gynecological diseases among young women of reproductive age. Thus far, it has not been possible to define a parameter that is sensitive and specific enough to be a recognized biomarker for diagnosing this disease. Nonspecific symptoms of endometriosis and delayed diagnosis are impulses for researching noninvasive methods of differentiating endometriosis from other gynecological disorders. We compared three groups of individuals in our research: women with endometriosis (E), patients suffering from other gynecological disorders (nonendometriosis, NE), and healthy women from the control group (C). Partial least squares discriminant analysis (PLS-DA) models were developed based on selected serum biochemical parameters, specific regions of the serum’s infrared attenuated total reflectance (FTIR ATR) spectra, and combined data. Incorporating the spectral data into the models significantly improved differentiation among the three groups, with an overall accuracy of 87.5%, 97.3%, and 98.5%, respectively. This study shows that infrared spectroscopy and discriminant analysis can be used to differentiate serum samples among women with advanced endometriosis, women without this disease, i.e., healthy women, and, most importantly, also women with other benign gynecological disorders.

Vibrational Biospectroscopy: An Alternative Approach to Endometrial Cancer Diagnosis and Screening

Endometrial cancer (EC) is the sixth most common cancer and the fourth leading cause of death among women worldwide. Early detection and treatment are associated with a favourable prognosis and reduction in mortality. Unlike other common cancers, however, screening strategies lack the required sensitivity, specificity and accuracy to be successfully implemented in clinical practice and current diagnostic approaches are invasive, costly and time consuming. Such limitations highlight the unmet need to develop diagnostic and screening alternatives for EC, which should be accurate, rapid, minimally invasive and cost-effective. Vibrational spectroscopic techniques, Mid-Infrared Absorption Spectroscopy and Raman, exploit the atomic vibrational absorption induced by interaction of light and a biological sample, to generate a unique spectral response: a “biochemical fingerprint”. These are non-destructive techniques and, combined with multivariate statistical analysis, have been shown over the last decade to provide discrimination between cancerous and healthy samples, demonstrating a promising role in both cancer screening and diagnosis. The aim of this review is to collate available evidence, in order to provide insight into the present status of the application of vibrational biospectroscopy in endometrial cancer diagnosis and screening, and to assess future prospects.

Disposable Coverslip for Rapid Throughput Screening of Malaria Using Attenuated Total Reflection Spectroscopy

Malaria is considered to be one of the most catastrophic health issues in the whole world. Vibrational spectroscopy is a rapid, robust, label-free, inexpensive, highly sensitive, nonperturbative, and nondestructive technique with high diagnostic potential for the early detection of disease agents. In particular, the fingerprinting capability of attenuated total reflection spectroscopy is promising as a point-of-care diagnostic tool in resource-limited areas. However, improvements are required to expedite the measurements of biofluids, including the drying procedure and subsequent cleaning of the internal reflection element to enable high throughput successive measurements. As an alternative, we propose using an inexpensive coverslip to reduce the sample preparation time by enabling multiple samples to be collectively dried together under the same temperature and conditions. In conjunction with partial least squares regression, attenuated total reflection spectroscopy was able to detect and quantify the parasitemia with root mean square error of cross-validation and R² values of 0.177 and 0.985, respectively. Here, we characterize an inexpensive, disposable coverslip for the high throughput screening of malaria parasitic infections and thus demonstrate an alternative approach to direct deposition of the sample onto the internal reflection element.

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.

Raman spectroscopy of blood and urine liquid biopsies for ovarian cancer diagnosis: identification of chemotherapy effects

Blood plasma and serum Raman spectroscopy for ovarian cancer diagnosis has been applied in pilot studies, with promising results. Herein, a comparative analysis of these biofluids, with a novel assessment of urine, was conducted by Raman spectroscopy application in a large patient cohort. Spectra were obtained through samples measurements from 116 ovarian cancer patients and 307 controls. Principal component analysis identified significant spectral differences between cancers without previous treatment (n = 71) and following neo-adjuvant chemotherapy (NACT), (n = 45). Application of five classification algorithms achieved up to 73% sensitivity for plasma, high specificities and accuracies for both blood biofluids, and lower performance for urine. A drop in sensitivities for the NACT group in plasma and serum, with an opposite trend in urine, suggest that Raman spectroscopy could identify chemotherapy-related changes. This study confirms that biofluids' Raman spectroscopy can contribute in ovarian cancer's diagnostic work-up and demonstrates its potential in monitoring treatment response.

Detection of ovarian cancer (± neo-adjuvant chemotherapy effects) via ATR-FTIR spectroscopy: comparative analysis of blood and urine biofluids in a large patient cohort

Ovarian cancer remains the most lethal gynaecological malignancy, as its timely detection at early stages remains elusive. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy of biofluids has been previously applied in pilot studies for ovarian cancer diagnosis, with promising results. Herein, these initial findings were further investigated by application of ATR-FTIR spectroscopy in a large patient cohort. Spectra were obtained by measurements of blood plasma and serum, as well as urine, from 116 patients with ovarian cancer and 307 patients with benign gynaecological conditions. A preliminary chemometric analysis revealed significant spectral differences in ovarian cancer patients without previous chemotherapy (n = 71) and those who had received neo-adjuvant chemotherapy-NACT (n = 45), so these groups were compared separately with benign controls. Classification algorithms with blind predictive model validation demonstrated that serum was the best biofluid, achieving 76% sensitivity and 98% specificity for ovarian cancer detection, whereas urine exhibited poor performance. A drop in sensitivities for the NACT ovarian cancer group in plasma and serum indicates the potential of ATR-FTIR spectroscopy to identify chemotherapy-related spectral changes. Comparisons of regression coefficient plots for identification of biomarkers suggest that glycoproteins (such as CA125) are the main classifiers for ovarian cancer detection and responsible for smaller differences in spectra between NACT patients and benign controls. This study confirms the capacity of biofluids' ATR-FTIR spectroscopy (mainly blood serum) to diagnose ovarian cancer with high accuracy and demonstrates its potential in monitoring response to chemotherapy, which is reported for the first time. ATR-FTIR spectroscopy of blood serum achieves good segregation of ovarian cancers from benign controls, with attenuation of differences following neo-adjuvant chemotherapy.

Infrared Spectroscopy of Blood

The magnitude of infectious diseases in the twenty-first century created an urgent need for point-of-care diagnostics. Critical shortages in reagents and testing kits have had a large impact on the ability to test patients with a suspected parasitic, bacteria, fungal, and viral infections. New point-of-care tests need to be highly sensitive, specific, and easy to use and provide results in rapid time. Infrared spectroscopy, coupled to multivariate and machine learning algorithms, has the potential to meet this unmet demand requiring minimal sample preparation to detect both pathogenic infectious agents and chronic disease markers in blood. This focal point article will highlight the application of Fourier transform infrared spectroscopy to detect disease markers in blood focusing principally on parasites, bacteria, viruses, cancer markers, and important analytes indicative of disease. Methodologies and state-of-the-art approaches will be reported and potential confounding variables in blood analysis identified. The article provides an up to date review of the literature on blood diagnosis using infrared spectroscopy highlighting the recent advances in this burgeoning field.

Fourier Transform Infrared Spectroscopy: An Innovative Method for the Diagnosis of Ovarian Cancer

Ovarian cancer is the most lethal gynecologic malignancy due to the late diagnoses at advanced stages, drug resistance and the high recurrence rate. Thus, there is an urgent need to develop new techniques to diagnose and monitor ovarian cancer patients. Fourier transform infrared (FTIR) spectroscopy has great potential in the diagnosis of this disease, as well as the real-time monitoring of cancer development and chemoresistance. As a noninvasive, simple and convenient technique, it can not only distinguish the molecular differences between normal and malignant tissues, but also be used to identify the characteristics of different types of ovarian cancer. FTIR spectroscopy is also widely used in monitoring cancer cells in response to antitumor drugs, distinguishing cells in different growth states, and identifying new synthetic drugs. In this paper, the applications of FTIR spectroscopy for ovarian cancer diagnosis and other works carried out so far are described in detail.

Raman spectroscopy of follicular fluid and plasma with machine-learning algorithms for polycystic ovary syndrome screening

Polycystic ovary syndrome (PCOS) is the main cause of anovulatory infertility and affects women throughout their lives. The specific diagnostic method is still under investigation. In the present study, we aimed to identify the metabolic tracks of the follicular fluid and plasma samples from women with PCOS by performing Raman spectroscopy with principal component analysis and spectral classification models. Follicular fluid and plasma samples obtained from 50 healthy (non-PCOS) and 50 PCOS women were collected and measured by Raman spectroscopy. Multivariate statistical methods and different machine-learning algorithms based on the Raman spectra were established to analyze the results. The principal component analysis of the Raman spectra showed differences in the follicular fluid between the non-PCOS and PCOS groups. The stacking classification models based on the k-nearest-neighbor, random forests and extreme gradient boosting algorithms yielded a higher accuracy of 89.32% by using follicular fluid than the accuracy of 74.78% obtained with plasma samples in classifying the spectra from the two groups. In this regard, PCOS may lead to the changes of metabolic profiles that can be detected by Raman spectroscopy. As a novel, rapid and affordable method, Raman spectroscopy combined with advanced machine-learning algorithms have potential to analyze and characterize patients with PCOS.

Vibrational Spectroscopic Investigation of Blood Plasma and Serum by Drop Coating Deposition for Clinical Application

In recent decades, vibrational spectroscopic methods such as Raman and FT-IR spectroscopy are widely applied to investigate plasma and serum samples. These methods are combined with drop coating deposition techniques to pre-concentrate the biomolecules in the dried droplet to improve the detected vibrational signal. However, most often encountered challenge is the inhomogeneous redistribution of biomolecules due to the coffee-ring effect. In this study, the variation in biomolecule distribution within the dried-sample droplet has been investigated using Raman and FT-IR spectroscopy and fluorescence lifetime imaging method. The plasma-sample from healthy donors were investigated to show the spectral differences between the inner and outer-ring region of the dried-sample droplet. Further, the preferred location of deposition of the most abundant protein albumin in the blood during the drying process of the plasma has been illustrated by using deuterated albumin. Subsequently, two patients with different cardiac-related diseases were investigated exemplarily to illustrate the variation in the pattern of plasma and serum biomolecule distribution during the drying process and its impact on patient-stratification. The study shows that a uniform sampling position of the droplet, both at the inner and the outer ring, is necessary for thorough clinical characterization of the patient’s plasma and serum sample using vibrational spectroscopy.

Use of Fourier-Transform Infrared Spectroscopy (FT-IR) for Monitoring Experimental Helicobacter pylori Infection and Related Inflammatory Response in Guinea Pig Model

Infections due to Gram-negative bacteria Helicobacter pylori may result in humans having gastritis, gastric or duodenal ulcer, and even gastric cancer. Investigation of quantitative changes of soluble biomarkers, correlating with H. pylori infection, is a promising tool for monitoring the course of infection and inflammatory response. The aim of this study was to determine, using an experimental model of H. pylori infection in guinea pigs, the specific characteristics of infrared spectra (IR) of sera from H. pylori infected (40) vs. uninfected (20) guinea pigs. The H. pylori status was confirmed by histological, molecular, and serological examination. The IR spectra were measured using a Fourier-transform (FT)-IR spectrometer Spectrum 400 (PerkinElmer) within the range of wavenumbers 3000–750 cm⁻¹ and converted to first derivative spectra. Ten wavenumbers correlated with H. pylori infection, based on the chi-square test, were selected for a K-nearest neighbors (k-NN) algorithm. The wavenumbers correlating with infection were identified in the W2 and W3 windows associated mainly with proteins and in the W4 window related to nucleic acids and hydrocarbons. The k-NN for detection of H. pylori infection has been developed based on chemometric data. Using this model, animals were classified as infected with H. pylori with 100% specificity and 97% sensitivity. To summarize, the IR spectroscopy and k-NN algorithm are useful for monitoring experimental H. pylori infection and related inflammatory response in guinea pig model and may be considered for application in humans.

In search of the correlation between nanomechanical and biomolecular properties of prostate cancer cells with different metastatic potential

Nanomechanical properties of living cells, as measured with atomic force microscopy (AFM), are increasingly recognized as criteria that differentiate normal and pathologically altered cells. Locally measured cell elastic properties, described by the parameter known as Young's modulus, are currently proposed as a new diagnostic parameter that can be used at the early stage of cancer detection. In this study, local mechanical properties of normal human prostate (RWPE-1) cells and a range of malignant (22Rv1) and metastatic prostate cells (LNCaP, Du145 and PC3) were investigated. It was found that non-malignant prostate cells are stiffer than cancer cells while the metastatic cells are much softer than malignant cells from the primary tumor site. Next, the biochemical properties of the cells were measured using confocal Raman (RS) and Fourier-transform infrared (FT-IR) spectroscopies to reveal these cells' biochemical composition as malignant transformation proceeds. Nanomechanical and biochemical profiles of five different prostate cell lines were subsequently analyzed using partial least squares regression (PLSR) in order to identify which spectral features of the RS and FT-IR spectra correlate with the cell's elastic properties. The PLSR-based model could predict Young's modulus values based on both RS and FT-IR spectral information. These outcomes show not only that AFM, RS and FT-IR techniques can be used for discrimination between normal and cancer cells, but also that a linear correlation between mechanical response and biomolecular composition of the cells that undergo malignant transformation can be found. This knowledge broadens our understanding of how prostate cancer cells evolve thorough the multistep process of tumor pathogenesis.

A comparative analysis of different biofluids towards ovarian cancer diagnosis using Raman microspectroscopy

Biofluids, such as blood plasma or serum, are currently being evaluated for cancer detection using vibrational spectroscopy. These fluids contain information of key biomolecules, such as proteins, lipids, carbohydrates and nucleic acids, that comprise spectrochemical patterns to differentiate samples. Raman is a water-free and practically non-destructive vibrational spectroscopy technique, capable of recording spectrochemical fingerprints of biofluids with minimum or no sample preparation. Herein, we compare the performance of these two common biofluids (blood plasma and serum) together with ascitic fluid, towards ovarian cancer detection using Raman microspectroscopy. Samples from thirty-eight patients were analysed (n = 18 ovarian cancer patients, n = 20 benign controls) through different spectral pre-processing and discriminant analysis techniques. Ascitic fluid provided the best class separation in both unsupervised and supervised discrimination approaches, where classification accuracies, sensitivities and specificities above 80% were obtained, in comparison to 60–73% with plasma or serum. Ascitic fluid appears to be rich in collagen information responsible for distinguishing ovarian cancer samples, where collagen-signalling bands at 1004 cm⁻¹ (phenylalanine), 1334 cm⁻¹ (CH₃CH₂ wagging vibration), 1448 cm⁻¹ (CH₂ deformation) and 1657 cm⁻¹ (Amide I) exhibited high statistical significance for class differentiation (P < 0.001). The efficacy of vibrational spectroscopy, in particular Raman spectroscopy, combined with ascitic fluid analysis, suggests a potential diagnostic method for ovarian cancer.

Raman Analysis Reveals Biochemical Differences in Plasma of Crohn's Disease Patients

BACKGROUNDS AND AIMS

There is no accurate and reliable circulating biomarker to diagnose Crohn's disease [CD]. Raman spectroscopy is a relatively new approach that provides information on the biochemical composition of samples in minutes and virtually without any sample preparation. We aimed to test the use of Raman spectroscopy analysis of plasma samples as a potential diagnostic tool for CD.

METHODS

We analysed by Raman spectroscopy dry plasma samples obtained from 77 CD patients [CD] and 45 healthy controls [HC]. In the dataset obtained, we analysed spectra differences between CD and HC, as well as among CD patients with different disease behaviours. We also developed a method, based on principal component analysis followed by a linear discrimination analysis [PCA-LDA], for the automatic classification of individuals based on plasma spectra analysis.

RESULTS

Compared with HC, the CD spectra were characterised by less intense peaks corresponding to carotenoids [p <10-4] and by more intense peaks corresponding to proteins with β-sheet secondary structure [p <10-4]. Differences were also found on Raman peaks relative to lipids [p = 0.0007] and aromatic amino acids [p <10-4]. The predictive model we developed was able to classify CD and HC subjects with 83.6% accuracy [sensitivity 80.0% and specificity 85.7%] and F1-score of 86.8%.

CONCLUSIONS

Our results indicate that Raman spectroscopy of blood plasma can identify metabolic variations associated with CD and it could be a rapid pre-screening tool to use before further specific evaluation.

Rapid analysis of disease state in liquid human serum combining infrared spectroscopy and "digital drying"

In recent years, the diagnosis of brain tumors has been investigated with attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy on dried human serum samples to eliminate spectral interferences of the water component, with promising results. This research evaluates ATR-FTIR on both liquid and air-dried samples to investigate "digital drying" as an alternative approach for the analysis of spectra obtained from liquid samples. Digital drying approaches, consisting of water subtraction and least-squares method, have demonstrated a greater random forest (RF) classification performance than the air-dried spectra approach when discriminating cancer vs control samples, reaching sensitivity values higher than 93.0% and specificity values higher than 83.0%. Moreover, quantum cascade laser infrared (QCL-IR) based spectroscopic imaging is utilized on liquid samples to assess the implications of a deep-penetration light source on disease classification. The RF classification of QCL-IR data has provided sensitivity and specificity amounting to 85.1% and 75.3% respectively.

Raman spectroscopy-based biomarker screening by studying the fingerprint characteristics of chronic lymphocytic leukemia and diffuse large B-cell lymphoma

Raman spectroscopy (RS) can provide fingerprint-type information on biochemical molecules. RS-based blood plasma analysis of solid tumors has been reported in recent years; however, there are no studies on the use of this analysis for detecting blood diseases. We studied the features of blood plasma in patients with diffuse large B-cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL) by RS with the aim of developing a simple blood test for noninvasive DLBCL and CLL detection. We analyzed blood plasma from 33 DLBCL patients, 39 CLL patients and 30 healthy volunteers. Orthogonal partial least squares discriminant analysis (OPLS-DA) could build two clusters with almost no overlap between DLBCL/CLL and the controls. We used the prediction set to test the model built by OPLS-DA. For the CLL model, the sensitivity was 92.86%, and the specificity was 100%, whereas for the DLBCL model, the sensitivity was 80% and the specificity was 92.31%. We found Raman bands specific to both DLBCL and CLL patients in comparison with the healthy volunteers. Most importantly, we found that the combination of the 1445 cm^-1 and 1655 cm^-1 Raman shifts could discriminate DLBCL from CLL and even the other solid tumors reported to date. Further analysis of the assignments of 1655 cm^-1 also gave us a clue to find potential important variables hemoglobin and serum albumin related with the CLL prognosis. Our exploratory study primarily demonstrated the great potential of developing RS blood plasma analysis as a novel clinical tool for the noninvasive detection of DLBCL and CLL.

Vibrational spectroscopy as a powerful tool for follow-up immunoadsorption therapy treatment of dilated cardiomyopathy - a case report

Dilated cardiomyopathy (DCM) is a leading cardiomyopathy condition and is the leading reason for heart transplantation. Due to high etiologic and genetic heterogeneity of the pathologies, different therapeutic treatment strategies are available and have been successful for different treatments. Immunoadsorption (IA) therapy removes the circulating anticardiac antibodies and improves the left ventricular function in substantial proportion of DCM patients. Powerful, non-invasive analytical tools are highly desired to investigate the efficiency and success of IA therapy. In this contribution, we followed the changes of a female DCM patient undergoing IA therapy at different treatment time points in a label-free, non-invasive manner from blood samples (plasma and serum) on the basis of vibrational spectroscopy (Raman scattering and IR absorption). Chemometric methods, including dimension reduction and statistical modeling, were used to interpret spectral data. The impact of different time points of the IA treatment can be identified in both the plasma and serum, using both techniques, with high accuracy. The removal of antibodies of immunoglobulin G (IgG) group during IA therapy and their restoration was reflected in both Raman and FTIR spectra. Relative changes in the spectral bands assigned to IgG agreed well with the immunoturbidimetry measurement of total IgG. Successful clinical treatment was accompanied by spectral differences between vibrational spectra obtained at initial disease state and 11 months after the IA treatment. The long-term follow-up of the patient reveals the stabilization of the health state after therapy. It is noteworthy that the treatment time points were distinguished with a better accuracy using spectra from plasma compared to those from serum samples, which might indicate the involvement of corresponding proteins in the coagulation. Vibrational spectroscopy is a powerful tool for personalized medicine to follow-up the treatment success of IA therapy for the DCM disorder.

Discrimination of fresh frozen non-tumour and tumour brain tissue using spectrochemical analyses and a classification model

Introduction: In order for brain tumours to be successfully treated, maximal resection is beneficial. A method to detect infiltrative tumour edges intraoperatively, improving on current methods would be clinically useful. Vibrational spectroscopy offers the potential to provide a handheld, reagent-free method for tumour detection.Purpose: This study was designed to determine the ability of both Raman and Fourier-transform infrared (FTIR) spectroscopy towards differentiating between normal brain tissue, glioma or meningioma.Method: Unfixed brain tissue, which had previously only been frozen, comprising normal, glioma or meningioma tissue was placed onto calcium fluoride slides for analysis using Raman and attenuated total reflection (ATR)-FTIR spectroscopy. Matched haematoxylin and eosin slides were used to confirm tumour areas. Analyses were then conducted to generate a classification model.Results: This study demonstrates the ability of both Raman and ATR-FTIR spectroscopy to discriminate tumour from non-tumour fresh frozen brain tissue with 94% and 97.2% of cases correctly classified, with sensitivities of 98.8% and 100%, respectively. This decreases when spectroscopy is used to determine tumour type.Conclusion: The study demonstrates the ability of both Raman and ATR-FTIR spectroscopy to detect tumour tissue from non-tumour brain tissue with a high degree of accuracy. This demonstrates the ability of spectroscopy when targeted for a cancer diagnosis. However, further improvement would be required for a classification model to determine tumour type using this technology, in order to make this tool clinically viable.

Fourier transform infrared spectroscopic signature of blood plasma in the progression of breast cancer with simultaneous metastasis to lungs

Despite advanced diagnostic techniques used for detecting cancer, this disease still remains a leading cause of death in the developed world. What is more, the greatest danger for patients is not related with growing of tumor but rather with metastasis of cancer cells to the distant organs. In this study, Fourier transform infrared (FTIR) spectroscopy was used to track chemical changes in blood plasma to find spectral markers of metastatic breast cancer during the disease progression. Plasma samples were taken 1-5 weeks after orthotropic inoculation of 4T1 metastatic breast cancer cells to mice. The earliest changes detected by FTIR spectroscopy in plasma were correlated with unsaturation of phospholipids and secondary structures of proteins that appeared 2 and 3 weeks, respectively, after 4T1 cells inoculation (micrometastatic phase). Significant alternations in the content and structure of lipids and carbohydrates were identified in plasma at the later stages (macrometastatic phase). When large primary tumors in breast and macrometastases in lung were developed, all bands in FTIR spectra significantly differed from those at earlier phases of the cancer progression. In conclusion, we showed that each phase of the breast cancer progression and its pulmonary metastasis can be characterized by a specific panel of spectral markers.

Fourier transform infrared and Raman-based biochemical profiling of different grades of pure foetal-type hepatoblastoma

The biomolecular events resulting from the progression of hepatoblastoma remain to be elucidated. Fourier-transform infrared (FTIR) and Raman spectroscopies are capable of noninvasively and accurately capturing the biochemical properties of biological tissue from its pathological status. Our aim was to probe critial biomolecular changes of liver accompanying the progression of pure foetal hepatoblastoma (PFH) by FTIR and Raman spectroscopies. Herein, biochemical alterations were both evident in the FTIR spectra (regions of 3100-2800 cm^-1 and 1800-900 cm^-1 ) and the Raman spectra (region of 1800-400 cm^-1 ) among normal, borderline and malignant liver tissues. Compared with normal tissues, the ratios of protein-to-lipid, α-helix-to-β-sheet, RNA-to-DNA, CH3 methyl-to-CH2 methylene, glucose-to-phospholipids, and unsaturated-to-saturated lipids intensities were significantly higher in malignant tissues, while the ratios of RNA-to-Amide II, DNA-to-Amide II, glycogen-to-cholesterol and Amide I-to-Amide II intensities were remarkably lower. These biochemical alterations in the transition from normal to malignant have profound implications not only for cyto-pathological classification but also for molecular understanding of PFH progression. The successive changes of the spectral characteristics have been shown to be consistent with the development of PFH, indicating that FTIR and Raman spectroscopies are excellent tools to interrogate the biochemical features of different grades of PFH.

Raman spectroscopy and chemometrics: A potential universal method for diagnosing cancer

Cancer is the second-leading cause of death worldwide. It affects an unfathomable number of people, with almost 16 million Americans currently living with it. While many cancers can be detected, current diagnostic efforts exhibit definite room for improvement. It is imperative that a person be diagnosed with cancer as early on in its progression as possible. An earlier diagnosis allows for the best treatment and intervention options available to be presented. Unfortunately, existing methods for diagnosing cancer can be expensive, invasive, inconclusive or inaccurate, and are not always made during initial stages of the disease. As such, there is a crucial unmet need to develop a singular universal method that is reliable, cost-effective, and non-invasive and can diagnose all forms of cancer early-on. Raman spectroscopy in combination with advanced statistical analysis is offered here as a potential solution for this need. This review covers recently published research in which Raman spectroscopy was used for the purpose of diagnosing cancer. The benefits and the risks of the methodology are presented; however, there is overwhelming evidence that suggests Raman spectroscopy is highly suitable for becoming the first universal method to be used for diagnosing cancer.

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.

Postmortem diagnosis of fatal hypothermia/hyperthermia by spectrochemical analysis of plasma

Postmortem diagnosis of extreme-weather-related deaths is a challenging forensic task. Here, we present a state-of-the-art study that employed attenuated total reflection (ATR) Fourier transform infrared (FTIR) spectroscopy in combination with Chemometrics for postmortem diagnosis of fatal hypothermia/hyperthermia by biochemical investigation of plasma in rats. The results of principal component analysis (PCA) and spectral analysis revealed that plasma samples from the fatal hypothermia, fatal hyperthermia, and control groups, are substantially different from each other based on the spectral variations associated with the lipid, carbohydrate and nucleic acid components. Two partial least squares-discriminant analysis (PLS-DA) classification models (hypothermia-nonhypothermia and hyperthermia-nonhyperthermia binary models) with a 100% accuracy rate were constructed. Subsequently, internal cross-validation was performed to assess the robustness of these two models, which resulted in 98.1 and 100% accuracy. Ultimately, classification predictions of 42 unknown plasma samples were performed by these two models, and both models achieved 100% accuracy. Additionally, our results demonstrated that hemolysis and postmortem hypothermic/hyperthermic effects did not weaken the prediction ability of these two classification models. In summary, this work demonstrates ATR-FTIR spectroscopy's great potential for postmortem diagnosis of fatal hypothermia/hyperthermia.

Standardization of complex biologically derived spectrochemical datasets

Spectroscopic techniques such as Fourier-transform infrared (FTIR) spectroscopy are used to study interactions of light with biological materials. This interaction forms the basis of many analytical assays used in disease screening/diagnosis, microbiological studies, and forensic/environmental investigations. Advantages of spectrochemical analysis are its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, an urgent need exists for repetition and validation of these methods in large-scale studies and across different research groups, which would bring the method closer to clinical and/or industrial implementation. For this to succeed, it is important to understand and reduce the effect of random spectral alterations caused by inter-individual, inter-instrument and/or inter-laboratory variations, such as variations in air humidity and CO₂ levels, and aging of instrument parts. Thus, it is evident that spectral standardization is critical to the widespread adoption of these spectrochemical technologies. By using calibration transfer procedures, in which the spectral response of a secondary instrument is standardized to resemble the spectral response of a primary instrument, different sources of variation can be normalized into a single model using computational-based methods, such as direct standardization (DS) and piecewise direct standardization (PDS); therefore, measurements performed under different conditions can generate the same result, eliminating the need for a full recalibration. Here, we have constructed a protocol for model standardization using different transfer technologies described for FTIR spectrochemical applications. This is a critical step toward the construction of a practical spectrochemical analysis model for daily routine analysis, where uncertain and random variations are present.

Ex Vivo Raman Spectrochemical Analysis Using a Handheld Probe Demonstrates High Predictive Capability of Brain Tumour Status

With brain tumour incidence increasing, there is an urgent need for better diagnostic tools. Intraoperatively, brain tumours are diagnosed using a smear preparation reported by a neuropathologist. These have many limitations, including the time taken for the specimen to reach the pathology department and for results to be communicated to the surgeon. There is also a need to assist with resection rates and identifying infiltrative tumour edges intraoperatively to improve clearance. We present a novel study using a handheld Raman probe in conjunction with gold nanoparticles, to detect primary and metastatic brain tumours from fresh brain tissue sent for intraoperative smear diagnosis. Fresh brain tissue samples sent for intraoperative smear diagnosis were tested using the handheld Raman probe after application of gold nanoparticles. Derived Raman spectra were inputted into forward feature extraction algorithms to build a predictive model for sensitivity and specificity of outcome. These results demonstrate an ability to detect primary from metastatic tumours (especially for normal and low grade lesions), in which accuracy, sensitivity and specificity were respectively equal to 98.6%, 94.4% and 99.5% for normal brain tissue; 96.1%, 92.2% and 97.0% for low grade glial tumours; 90.3%, 89.7% and 90.6% for high grade glial tumours; 94.8%, 63.9% and 97.1% for meningiomas; 95.4%, 79.2% and 98.8% for metastases; and 99.6%, 88.9% and 100% for lymphoma, based on smear samples (κ = 0.87). Similar results were observed when compared to the final formalin-fixed paraffin embedded tissue diagnosis (κ = 0.85). Overall, our results have demonstrated the ability of Raman spectroscopy to match results provided by intraoperative smear diagnosis and raise the possibility of use intraoperatively to aid surgeons by providing faster diagnosis. Moving this technology into theatre will allow it to develop further and thus reach its potential in the clinical arena.

4-Nonylphenol effects on rat testis and sertoli cells determined by spectrochemical techniques coupled with chemometric analysis

Herein, vibrational spectroscopy has been applied for qualitative identification of biomolecular alterations that occur in cells and tissues following chemical treatment. Towards this end, we combined attenuated total reflection Fourier-transform infrared (ATR-FTIR) and Raman spectroscopy to assess testicular toxicology after 4-nonylphenol (NP) exposure, an estrogenic endocrine disruptor affecting testicular function in rats and other species. Rats aged 21, 35 or 50 days received NP at intra-peritoneal doses of 0, 25, 50 or 100 mg/kg for 20 consecutive days. Primary Sertoli cells (SCs) were treated with NP at various concentrations (0, 2.5, 5, 10 or 20 μM) for 12 h. Post-exposure, testicular cells, interstitial tissue and SCs were interrogated respectively using spectrochemical techniques coupled with multivariate analysis. Distinct biomolecular segregation between the NP-exposed samples vs. control were observed based on infrared (IR) spectral regions of 3200-2800 cm^-1 and 1800-900 cm^-1, and the Raman spectral region of 1800-900 cm^-1. For in vivo experiments, the main wavenumbers responsible for segregation varied significantly among the three age classes. The main IR and Raman band differences between NP-exposed and control groups were observed for Amide (proteins), lipids and DNA/RNA. An interesting finding was that the peptide aggregation level, Amide Ӏ-to-Amide II ratio, and phosphate-to-carbohydrate ratio were considerably reduced in ex vivo NP-exposed testicular cells or SCs in vitro. This study demonstrates that ATR-FTIR and Raman spectroscopy techniques can be applied towards analysing NP-induced testicular biomolecular alterations.

Spectral classification for diagnosis involving numerous pathologies in a complex clinical setting: A neuro-oncology example

Much effort is currently being placed into developing new blood tests for cancer diagnosis in the hope of moving cancer diagnosis earlier and by less invasive means than current techniques, e.g., biopsy. Current methods are expected to diagnose and begin treatment of cancer within 62 days of patient presentation, though due to high volume and pressures within the NHS in the UK any technique that can reduce time to diagnosis would allow reduction in the time to treat for patients. The use of vibrational spectroscopy, notably infrared (IR) spectroscopy, has been under investigation for many years with varying success. This technique holds promise as is would combine a generally well accepted test (a blood test) with analysis that is reagent free and cheap to run. It has been demonstrated that, when asked simple clinical questions (i.e., cancer vs. no cancer), results from spectroscopic studies are promising. However, in order to become a clinically useful tool, it is important that the test differentiates a variety of cancer types from healthy patients. This study has analysed plasma samples with attenuated total reflection Fourier-transform IR spectroscopy (ATR-FTIR), to establish if the technique is able to distinguish normal from primary or metastatic brain tumours. We have shown that when asked specific questions, i.e., high-grade glioma vs. low-grade glioma, the results show a significantly high accuracy (100%). Crucially, when combined with meningiomas and metastatic lesions, the accuracy remains high (88-100%) with only minimal overlap between the two metastatic adenocarcinoma groups. Therefore in a clinical setting, this novel technique demonstrates potential benefit when used in conjuction with existing diagnostic methods.

A comparative profiling of oral cancer patients and high risk niswar users using FT-IR and chemometric analysis

Oral cancer is one of the major cancer types, which has increased sustainably in Southeast Asian countries due to the extensive use of a variety of tobacco and betel nut products. The current study is focused on developing an easy, efficient and cost-effective method for plasma profiling of oral cancer patients and tobacco users in order to have a progressive picture towards oral cancer. For this purpose, the profiling of 147 plasma samples including 67 oral cancer patients' samples, 60 "niswar" (a dipping tobacco product) user samples, and 20 healthy controls using attenuated total reflectance-fourier transform infrared spectroscopy (ATR-FTIR), and chemometric analysis was carried out. Fingerprint region (500-1500 cm^-1) of all three groups showed interesting variations in peaks pattern. From these observations, height ratios of two bands H1646/H1550 and H1080/H1024 with p value of 2.01 × 10^-6 and 8.39 × 10^-7, respectively, showed a pattern between healthy to oral cancer and "niswar" user samples. Chemometric analysis of the data showed a clean separation among the groups. PLS-DA and OPLS-DA models provided 87.7% and 89.5% classification rate, respectively. Area under the curve (AUC) for healthy control, oral cancer and "niswar" users were found to be 0.97, 0.95 and 0.92%, respectively. The results of the present study indicate that FT-IR spectroscopy, in conjunction with chemometric data, can be effectively used for the preliminary differentiation of plasma samples of oral cancer patients, "niswar" users and control samples of healthy persons.

Diagnosis of malignant pleural mesothelioma from pleural fluid by Fourier transform-infrared spectroscopy coupled with chemometrics

This study was conducted to differentiate malignant pleural mesothelioma (MPM) from lung cancer (LC) and benign pleural effusion (BPE) from pleural fluids using the diagnostic power of Fourier transform-infrared spectroscopy with attenuated total reflectance mode coupled with chemometrics. Infrared spectra of MPM (n  =  24), LC (n  =  20), and BPE (n  =  25) were collected, and hierarchical cluster analysis (HCA) and principal component analysis (PCA) were applied to their spectra. HCA results indicated that MPM was differentiated from LC with 100% sensitivity and 100% specificity and from BPE, with 100% sensitivity and 88% specificity, which were also confirmed by PCA score plots. PCA loading plots indicated that these separations originated mainly from lipids, proteins, and nucleic acids-related spectral bands. There was significantly higher lipid, protein, nucleic acid, and glucose contents in the MPM and LC. However, the significant changes in triglyceride and cholesterol ester content, protein and nucleic acid structure, a lower membrane fluidity, and higher membrane order were only observed in the MPM. To check the classification success of some test samples/each group, soft independent modeling of class analogies was performed and 96.2% overall classification success was obtained. This approach can provide a rapid and inexpensive methodology for the efficient differentiation of MPM from other pleural effusions.

Characterization of ovarian cancer cells and tissues by Fourier transform infrared spectroscopy

Background

Ovarian cancer is the most lethal of gynecological malignancies. Fourier Transform Infrared (FTIR) spectroscopy has gradually developed as a convenient, inexpensive and non-destructive technique for the study of many diseases. In this study, FTIR spectra of normal and several heterogeneous ovarian cancer cell lines as well as ovarian cancer tissue samples were compared in the spectral region of 4000 cm^− 1 - 600 cm^− 1.

Methods

Cell samples were collected from human ovarian surface epithelial cell line (HOSEpiC) and five ovarian cancer cell lines (ES2, A2780, OVCAR3, SKOV3 and IGROV1). Validation spectra were performed on normal and cancerous tissue samples from 12 ovarian cancer patients. FTIR spectra were collected from a NICOLET iN10 MX spectrometer and the spectral data were analyzed by OMNIC 8.0 software.

Results

Spectral features discriminating malignant tissues from normal tissues were integrated by cell line data and tissue data. In particular changes in cancerous tissues, the decrease in the amount of lipids and nucleic acids were observed. Protein conformation and composition were also altered in some cancer cells. The band intensity ratio of 1454/1400 was higher in normal cells/tissues and lower in cancer cells/tissues.

Conclusion

The spectral features revealed the important molecular characteristics about ovarian cancer cells/tissues. These findings demonstrate the possible diagnostic use of FTIR spectroscopy, providing the research model and evidences, and supporting the future study on more tissue samples to establish a data bank of spectra features for the possible discrimination of ovarian cancers.

Comments on recent reports on infrared spectral detection of disease markers in blood components

The search for disease markers in whole blood, or easily accessible blood components by spectral methods is a highly important aspect in the field of biophotonic research for disease diagnostics and screening, since it promises a minimally invasive approach to assess an individual's state of health. Fourier transform infrared spectroscopy, in particular, promises to be a fast, inexpensive method to search for markers of disease, since it detects variation in the proteome, lipidome and metabolome of biofluids, or activation of immune cells. However, the analysis of any materials by spectral methods is confounded by external factors such as those related to sample deposition and data acquisition, and by inherent variations in blood plasma concentration of small molecules (lactate, carbonate, phosphate, glucose) that varies between individual subjects and even for a given individual, as a function of time. Furthermore, observed differences in spectral patterns between patient samples and the control group may be due to the body's immune response (in particular, to the albumin to globulin ratio) and therefore, may not be specific to disease. These factors need to be accounted for in any effort to reliably detect much smaller variations in the concentration of disease-specific markers.

Discrimination of patients with different serological evolution of HIV and co-infection with HCV using metabolic fingerprinting based on Fourier transform infrared

Human immunodeficiency virus (HIV) is a retrovirus that weakens the immune system and permits opportunistic diseases such as hepatitis C (HCV) to enter the body. These diseases induce metabolic disorders in the patients and it is therefore logical to approach them from a holistic, functional perspective, studying the metabolome comprehensively to identify metabolic signatures associated with certain disease states. The metabolomics strategy here proposed involves metabolic fingerprinting using Fourier transform infrared spectroscopy and chemometric tools on 72 plasma samples (subdivided into 63 training and 9 test samples) to differentiate between healthy subjects and patients with different disease stages. Several options, relating to the variable selection method used in linear discriminant analysis and the number of categories being considered, were explored to optimize discrimination ability. A total of 18 bands enabled differentiation between control subjects, HIV patients and the group that encompassed patients with acquired immune deficiency syndrome (AIDS), AIDS/HCV and HIV/HCV, providing overall classification and internal prediction rates of 97.67% and 93.65%, respectively. Only 9 bands were required to further discriminate between AIDS, AIDS/HCV and HIV/HCV, with 99.20% (training) and 89.66% (cross-validation) correct classifications. The simplicity and effectiveness of the classification methodology proposed was reinforced by the satisfactory results obtained in external prediction.

Relapsing-Remitting Multiple Sclerosis diagnosis from cerebrospinal fluids via Fourier transform infrared spectroscopy coupled with multivariate analysis

Multiple sclerosis (MS) is a chronic, progressive, inflammatory and degenerative disease of central nervous system. Here, we aimed to develop a method for differential diagnosis of Relapsing-Remitting MS (RRMS) and clinically isolated syndrome (CIS) patients, as well as to identify CIS patients who will progress to RRMS, from cerebrospinal fluid (CSF) by infrared (IR) spectroscopy and multivariate analysis. Spectral analyses demonstrated significant differences in the molecular contents, especially in the lipids and Z conformation of DNA of CSF from CIS, CIS to RRMS transformed (TCIS) and RRMS groups. These changes enables the discrimination of diseased groups and controls (individuals with no neurological disease) from each other using hierarchical cluster and principal component analysis. Some CIS samples were consistently clustered in RRMS class, which may indicate that these CIS patients potentially will transform to RRMS over time. Z-DNA band at 795 cm⁻¹ that is existent only in diseased groups and significant increase in carbonyl amount, decrease in amideI/amide II and lipid/protein ratios observed only for RRMS groups can be used as diagnostic biomarkers. The results of the present study shed light on the early diagnosis of RRMS by IR spectroscopy complemented with multivariate analysis tools.

Challenges in application of Raman spectroscopy to biology and materials

Raman spectroscopy has become an essential tool for chemists, physicists, biologists and materials scientists. In this article, we present the challenges in unravelling the molecule-specific Raman spectral signatures of different biomolecules like proteins, nucleic acids, lipids and carbohydrates based on the review of our work and the current trends in these areas. We also show how Raman spectroscopy can be used to probe the secondary and tertiary structural changes occurring during thermal denaturation of protein and lysozyme as well as more complex biological systems like bacteria. Complex biological systems like tissues, cells, blood serum etc. are also made up of such biomolecules. Using mice liver and blood serum, it is shown that different tissues yield their unique signature Raman spectra, owing to a difference in the relative composition of the biomolecules. Additionally, recent progress in Raman spectroscopy for diagnosing a multitude of diseases ranging from cancer to infection is also presented. The second part of this article focuses on applications of Raman spectroscopy to materials. As a first example, Raman spectroscopy of a melt cast explosives formulation was carried out to monitor the changes in the peaks which indicates the potential of this technique for remote process monitoring. The second example presents various modern methods of Raman spectroscopy such as spatially offset Raman spectroscopy (SORS), reflection, transmission and universal multiple angle Raman spectroscopy (UMARS) to study layered materials. Studies on chemicals/layered materials hidden in non-metallic containers using the above variants are presented. Using suitable examples, it is shown how a specific excitation or collection geometry can yield different information about the location of materials. Additionally, it is shown that UMARS imaging can also be used as an effective tool to obtain layer specific information of materials located at depths beyond a few centimeters.

This paper reviews various facets of Raman spectroscopy. This encompasses biomolecule fingerprinting and conformational analysis, discrimination of healthy vs. diseased states, depth-specific information of materials and 3D Raman imaging.

Applications of mid-infrared spectroscopy in the clinical laboratory setting

Fourier transform mid-infrared (MIR-FTIR) spectroscopy is a nondestructive, label-free, highly sensitive and specific technique that provides complete information on the chemical composition of biological samples. The technique both can offer fundamental structural information and serve as a quantitative analysis tool. Therefore, it has many potential applications in different fields of clinical laboratory science. Although considerable technological progress has been made to promote biomedical applications of this powerful analytical technique, most clinical laboratory analyses are based on spectroscopic measurements in the visible or ultraviolet (UV) spectrum and the potential role of FTIR spectroscopy still remains unexplored. In this review, we present some general principles of FTIR spectroscopy as a useful method to study molecules in specimens by MIR radiation together with a short overview of methods to interpret spectral data. We aim at illustrating the wide range of potential applications of the proposed technique in the clinical laboratory setting with a focus on its advantages and limitations and discussing the future directions. The reviewed applications of MIR spectroscopy include (1) quantification of clinical parameters in body fluids, (2) diagnosis and monitoring of cancer and other diseases by analysis of body fluids, cells, and tissues, (3) classification of clinically relevant microorganisms, and (4) analysis of kidney stones, nails, and faecal fat.

Raman micro-spectroscopy applied to treatment resistant and sensitive human ovarian cancer cells

Despite the many advances intended to enhance the response to treatment, the survival rate of patients with ovarian cancer has only marginally improved in the past few decades. One major cause for this, is the lack of diagnostics for platinum-resistant disease. The goal of this study was to determine whether Raman micro-spectroscopy in conjunction with multivariate statistical analysis could discriminate between chemically fixed cisplatin-resistant (A2780cp) and cisplatin-sensitive (A2780s) human ovarian carcinoma cells. Raman spectra collected from individual cells were pre-processed and subsequently analyzed with Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA). Statistically significant differences (P <  0.0001) were observed between the Raman spectra of A2780s and A2780cp cells. A diagnostic accuracy of 82% was obtained using the PCA-LDA classifier model for the discrimination between the A2780s and A2780cp cells. The loading plot analysis suggests that relative increases in proteins and glutathione in the cisplatin-resistant cells compared to the cisplatin-sensitive cells are most likely the major source of discrimination between the two types of cells. These results support the potential application of Raman spectroscopy in the identification of chemo-resistant tumors prior to treatment.

Random Forest-Based Evaluation of Raman Spectroscopy for Dengue Fever Analysis

This work presents the evaluation of Raman spectroscopy using random forest (RF) for the analysis of dengue fever in the infected human sera. A total of 100 dengue suspected blood samples, collected from Holy Family Hospital, Rawalpindi, Pakistan, have been used in this study. Out of these samples, 45 were dengue-positive based on immunoglobulin M (IgM) capture enzyme-linked immunosorbent assay (ELISA) tests. For highlighting the spectral differences between normal and infected samples, an effective machine learning system is developed that automatically learns the pattern of the shift in spectrum for the dengue compared to normal cases and thus is able to predict the unknown class based on the known example. In this connection, dimensionality reduction has been performed with the principal component analysis (PCA), while RF is used for automatic classification of dengue samples. For the determination of diagnostic capabilities of Raman spectroscopy based on RF, sensitivity, specificity, and accuracy have been calculated in comparison to normally performed IgM capture ELISA. According to the experiment, accuracy of 91%, sensitivity of 91%, and specificity of 91% were achieved for the proposed RF-based model.

Subtle effects of environmental stress observed in the early life stages of the Common frog, Rana temporaria

Worldwide amphibian populations are declining due to habitat loss, disease and pollution. Vulnerability to environmental contaminants such as pesticides will be dependent on the species, the sensitivity of the ontogenic life stage and hence the timing of exposure and the exposure pathway. Herein we investigated the biochemical tissue 'fingerprint' in spawn and early-stage tadpoles of the Common frog, Rana temporaria, using attenuated total reflection-Fourier-transform infrared (ATR-FTIR) spectroscopy with the objective of observing differences in the biochemical constituents of the respective amphibian tissues due to varying water quality in urban and agricultural ponds. Our results demonstrate that levels of stress (marked by biochemical constituents such as glycogen that are involved in compensatory metabolic mechanisms) can be observed in tadpoles present in the pond most impacted by pollution (nutrients and pesticides), but large annual variability masked any inter-site differences in the frog spawn. ATR-FTIR spectroscopy is capable of detecting differences in tadpoles that are present in selected ponds with different levels of environmental perturbation and thus serves as a rapid and cost effective tool in assessing stress-related effects of pollution in a vulnerable class of organism.

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.

Modern trends in biophotonics for clinical diagnosis and therapy to solve unmet clinical needs

This contribution covers recent original research papers in the biophotonics field. The content is organized into main techniques such as multiphoton microscopy, Raman spectroscopy, infrared spectroscopy, optical coherence tomography and photoacoustic tomography, and their applications in the context of fluid, cell, tissue and skin diagnostics. Special attention is paid to vascular and blood flow diagnostics, photothermal and photodynamic therapy, tissue therapy, cell characterization, and biosensors for biomarker detection.

Alterations in plasma biochemical composition in NO deficiency induced by L-NAME in mice analysed by Fourier Transform Infrared Spectroscopy

Mouse model of nitric oxide deficiency, induced by prolonged treatment with N^G -nitro-L-arginine methyl ester (L-NAME) was used for infrared spectroscopy (FTIR) analysis of plasma. L-NAME leads to increased peripheral resistance and systemic hypertension. Classification of spectral response was by principal component analysis (PCA) and linear discriminant analysis (LDA). PCA allowed to separate each animal group showing that FTIR spectra are sensitive to development of NO-deficiency on contrary to blood pressure values indicating hypertension. Globally, the most pronounced spectral alternations were observed in the second and third week of L-NAME treatment indicating that infrared signature of blood plasma can serve as indicator of early and late stages of the disease. The PLS-DA method provided >95% classification accuracy. Spectral features characteristic for L-NAME treatment were mainly associated with an elevated level of proteins accompanied by a decrease of a tyrosine content and changes in lipids/phospholipid concentration. In our work we discuss these changes for which statistically significant differences (p < 0.05 - 0.005) were observed between spectra collected for each time-point of the L-NAME treatment versus control subjects. We demonstrated for the first time that NO-deficiency and hypertension resulted in changes in biochemical profile of plasma that was detected by FTIR spectroscopy.

Towards Translation of Discrete Frequency Infrared Spectroscopic Imaging for Digital Histopathology of Clinical Biopsy Samples

Fourier transform infrared (FT-IR) spectroscopic imaging has been widely tested as a tool for stainless digital histology of biomedical specimens, including for the identification of infiltration and fibrosis in endomyocardial biopsy samples to assess transplant rejection. A major barrier in clinical translation has been the slow speed of imaging. To address this need, we tested and report here the viability of using high speed discrete frequency infrared (DFIR) imaging to obtain stain-free biochemical imaging in cardiovascular samples collected from patients. Images obtained by this method were classified with high accuracy by a Bayesian classification algorithm trained on FT-IR imaging data as well as on DFIR data. A single spectral feature correlated with instances of fibrosis, as identified by the pathologist, highlights the advantage of the DFIR imaging approach for rapid detection. The speed of digital pathologic recognition was at least 16 times faster than the fastest FT-IR imaging instrument. These results indicate that a fast, on-site identification of fibrosis using IR imaging has potential for real time assistance during surgeries. Further, the work describes development and applications of supervised classifiers on DFIR imaging data, comparing classifiers developed on FT-IR and DFIR imaging modalities and identifying specific spectral features for accurate identification of fibrosis. This addresses a topic of much debate on the use of training data and cross-modality validity of IR measurements. Together, the work is a step toward addressing a clinical diagnostic need at acquisition time scales that make IR imaging technology practical for medical use.