Infrared spectroscopy: a reagent-free method to distinguish Alzheimer's disease patients from normal-aging subjects

FTIR Spectroscopy and Blood-Derived Extracellular Vesicles Duo in Alzheimer's Disease

Background

Alzheimer's disease (AD) diagnosis is difficult, and new accurate tools based on peripheral biofluids are urgently needed. Extracellular vesicles (EVs) emerged as a valuable source of biomarker profiles for AD, since their cargo is disease-specific and these can be easily isolated from easily accessible biofluids, as blood. Fourier Transform Infrared (FTIR) spectroscopy can be employed to analyze EVs and obtain the spectroscopic profiles from different regions of the spectra, simultaneously characterizing carbohydrates, nucleic acids, proteins, and lipids.

Objective

The aim of this study was to identify blood-derived EVs (bdEVs) spectroscopic signatures with AD discriminatory potential.

Methods

Herein, FTIR spectra of bdEVs from two biofluids (serum and plasma) and distinct sets of Controls and AD cases were acquired, and EVs' spectra analyzed.

Results

Analysis of bdEVs second derivative peaks area revealed differences between Controls and AD cases in distinct spectra regions, assigned to carbohydrates and nucleic acids, amides, and lipids.

Conclusions

EVs' spectroscopic profiles presented AD discriminatory value, supporting the use of bdEVs combined with FTIR as a screening or complementary tool for AD diagnosis.

Quantitative biochemical phenotypic heterogeneity of macrophages after myelin debris phagocytosis at a single cell level by synchrotron radiation fourier transform infrared microspectroscopy

During the immuno-inflammatory pathophysiological process of spinal cord injury, traumatic brain injury, and ischemic stroke, macrophages play an important role in phagocytizing and clearing degenerated myelin debris. After phagocytizing myelin debris, the biochemical phenotypes related to the biological function of macrophages show vast heterogeneity; however, it is not fully understood. Detecting biochemical changes after myelin debris phagocytosis by macrophages at a single-cell level is helpful to characterize phenotypic and functional heterogeneity. In this study, based on the cell model of myelin debris phagocytosis by macrophages in vitro, the biochemical changes in macrophages were investigated using Synchrotron radiation-based Fourier transform infrared (SR-FTIR) microspectroscopy. Infrared spectrum fluctuations, principal component analysis, and cell-to-cell Euclidean distance statistical analysis of specific spectrum regions revealed dynamic and significant changes in proteins and lipids within macrophages after myelin debris phagocytosis. Thus, SR-FTIR microspectroscopy is a powerful identification toolkit for exploring biochemical phenotype heterogeneity transformation that may be of great importance to providing an evaluation strategy for studying cell functions related to cellular substance distribution and metabolism.

Classification of Systemic Lupus Erythematosus Using Raman Spectroscopy of Blood and Automated Computational Detection Methods: A Novel Tool for Future Diagnostic Testing

The aim of this study was to explore the proof of concept for using Raman spectroscopy as a diagnostic platform in the setting of systemic lupus erythematosus (SLE). We sought to identify unique Raman signatures in serum blood samples to successfully segregate SLE patients from healthy controls (HC). In addition, a retrospective audit was undertaken to assess the clinical utility of current testing platforms used to detect anti-double stranded DNA (dsDNA) antibodies (n = 600). We examined 234 Raman spectra to investigate key variances between SLE patients (n = 8) and HC (n = 4). Multi-variant analysis and classification model construction was achieved using principal component analysis (PCA), PCA-linear discriminant analysis and partial least squares-discriminant analysis (PLS-DA). We achieved the successful segregation of Raman spectra from SLE patients and healthy controls (p-value < 0.0001). Classification models built using PLS-DA demonstrated outstanding performance characteristics with 99% accuracy, 100% sensitivity and 99% specificity. Twelve statistically significant (p-value < 0.001) wavenumbers were identified as potential diagnostic spectral markers. Molecular assignments related to proteins and DNA demonstrated significant Raman intensity changes between SLE and HC groups. These wavenumbers may serve as future biomarkers and offer further insight into the pathogenesis of SLE. Our audit confirmed previously reported inconsistencies between two key methodologies used to detect anti-dsDNA, highlighting the need for improved laboratory testing for SLE. Raman spectroscopy has demonstrated powerful performance characteristics in this proof-of-concept study, setting the foundations for future translation into the clinical setting.

Alzheimer's disease diagnosis by blood plasma molecular fluorescence spectroscopy (EEM)

Despite tremendous research advances in detecting Alzheimer's disease (AD), traditional diagnostic tests remain expensive, time-consuming or invasive. The search for a low-cost, rapid, and minimally invasive test has marked a new era of research and technological developments toward establishing blood-based AD biomarkers. The current study has employed excitation-emission matrices (EEM) of fluorescence spectroscopy combined with machine learning to diagnose AD using blood plasma samples from 230 individuals (83 AD patients from 147 healthy controls). To evaluate the performance of the classification algorithms, we calculated the commonly used figures of merit (accuracy, sensitivity and specificity) and figures of merit that take into account the samples unbalance and the discrimination power of the models, as F₂-score (F₂), Matthews correlation coefficient (MCC) and test effectiveness (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta$$\end{document}δ). The classification models achieved satisfactory results: Parallel Factor Analysis with Quadratic Discriminant Analysis (PARAFAC-QDA) with 83.33% sensitivity, 100% specificity, 86.21% F₂; and Tucker3-QDA with 91.67% sensitivity, 95.45% specificity and 91.67% F₂. In addition, the classifiers show high overall performance with 94.12% accuracy and 0.87 MCC. Regarding the discrimination power between healthy and AD patients, the classification algorithms showed high effectiveness with the mean scores separated by three or more standard deviations. The PARAFAC's spectral profiles and the wavelength values from both models loading profiles can be used in future research to relate this information to plasma AD biomarkers. Our results point to a rapid, low-cost and minimally invasive blood-based method for AD diagnosis.

Association of F2-isoprostane levels with Alzheimer's disease in observational studies: A systematic review and meta-analysis

INTRODUCTION

The association between F₂-isoprostanes and Alzheimer's disease (AD) has been controversially discussed in the literature since the 1990s. However, no systematic review has been performed so far.

METHODS

A systematic review of observational studies on the associations of F₂-isoprostanes and the specific biomarker 8-iso-prostaglandin F_2α with AD were conducted. Random-effects model meta-analyses were performed.

RESULTS

29 studies were included in the systematic review, including four longitudinal studies. In an overall meta-analysis of the 25 cross-sectional studies, F₂-isoprostane levels were statistically significantly associated with AD (Hedge's g [95% confidence interval]: 1.00 [0.69-1.32]). When studies were grouped by biomarker and sample specimen, F₂-isoprostane and 8-iso-prostaglandin F_2α levels were statistically significantly elevated in tissue samples of the frontal lobe of AD patients. Moreover, F₂-isoprostane levels in cerebrospinal fluid and 8-iso-prostaglandin F_2α levels in blood samples of AD patients were significantly increased. Meta-analyses of the few longitudinal studies did not reach statistical significance.

DISCUSSION

Increased concentrations of F₂-isoprostanes were found in AD patients. However, due to the lack of adjustment in most cross-sectional case-control studies, results must be interpreted carefully. In addition, the causality of the association is uncertain because evidence from well-conducted longitudinal studies was conflicting, and further longitudinal studies are required to reinforce the results.

A robust metabolomics approach for the evaluation of human embryos from in vitro fertilization

The identification of the most competent embryos for transfer to the uterus constitutes the main challenge of in vitro fertilization (IVF). We established a metabolomic-based approach by applying Fourier transform infrared (FTIR) spectroscopy on 130 samples of 3-day embryo culture supernatants from 26 embryos that implanted and 104 embryos that failed. On examining the internal structure of the data by unsupervised multivariate analysis, we found that the supernatant spectra of nonimplanted embryos constituted a highly heterogeneous group. Whereas ∼40% of these supernatants were spectroscopically indistinguishable from those of successfully implanted embryos, ∼60% exhibited diverse, heterogeneous metabolic fingerprints. This observation proved to be the direct result of pregnancy's multifactorial nature, involving both intrinsic embryonic traits and external characteristics. Our data analysis strategy thus involved one-class modelling techniques employing soft independent modelling of class analogy that identified deviant fingerprints as unsuitable for implantation. From these findings, we could develop a noninvasive Fourier-transform-infrared-spectroscopy-based approach that represents a shift in the fundamental paradigm for data modelling applied in assisted-fertilization technologies.

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.

The mid-infrared spectroscopy: A novel non-invasive diagnostic tool for NASH diagnosis in severe obesity

There is an urgent medical need to develop non-invasive tests for non-alcoholic steatohepatitis (NASH). This study evaluates the diagnostic performance of an innovative model based on mid-infrared (MIR) spectroscopy for the diagnosis of NASH.

Methods

Severely obese patients who underwent a bariatric procedure at the University Hospital of Nice, France (n = 395) were prospectively recruited. The clinico-biological characteristics were measured prior to surgery. Liver biopsies were collected during the surgical procedure and assessed by a pathologist. A training group (316 patients, NASH: 16.8%) and a validation group (79 patients, NASH: 16.5%) were randomly defined. MIR spectra were acquired by fiber evanescent wave spectroscopy, using chalcogenide glass fiber optic sensors and a spectrometer. This absorption spectroscopic technique delivers a spectrum that identifies the molecular composition of a sample, defining a patient's metabolic fingerprint.

Results

The areas under the receiver operating curve (AUROC) for the diagnosis of NASH were 0.82 and 0.77 in the training and validation groups, respectively. The best threshold was 0.15, which was associated with a sensitivity of 0.75 and 0.69, and a specificity of 0.72 and 0.76. Negative predictive values of 0.94 and 0.93 and positive predictive values of 0.35 and 0.36, as well as correctly classified patient rates of 72% and 75% were obtained in the training and validation groups, respectively. A composite model using aspartate aminotransferase level, triglyceride level and waist circumference alongside the MIR spectra led to an increase in AUROC (0.88 and 0.84 for the training and validations groups, respectively).

Conclusions

MIR spectroscopy provides good sensitivity and negative predictive values for NASH screening in patients with severe obesity.

Lay summary

There is an urgent need for tools to non-invasively diagnose and monitor non-alcoholic steatohepatitis (NASH). This study evaluates the performance of a new tool for fast NASH diagnosis based on mid-infrared (MIR) spectroscopy. Using serum samples from severely obese patients who underwent a bariatric procedure, which enabled a concomitant liver biopsy to be performed, the MIR spectroscopy model performed well in screening patients for NASH compared to a traditional, histological diagnosis.

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There is no validated non-invasive diagnostic tool for NASH in routine care.

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NASH follow-up requires a non-invasive diagnostic method.

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Using a simple drop of serum, the mid-infrared spectrum captures a patient’s metabolic fingerprint.

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A model based on mid-infrared spectroscopy provides efficient NASH screening for patients with severe obesity.

New approach to investigate Common Variable Immunodeficiency patients using spectrochemical analysis of blood

Common variable immune deficiency (CVID) is a primary immunodeficiency disease, characterized by hypogammaglobulinemia, recurrent infections and various complications. The clinical heterogeneity of CVID has hindered identification of an underlying immune defect; diagnosis relies on clinical judgement, alongside evidence-based criteria. The lack of pathognomonic clinical or laboratory features leads to average diagnostic delays of 5 years or more from the onset. Vibrational spectroscopic techniques such as Fourier-transform infrared (FTIR) spectroscopy have recently gained increasing clinical importance, being rapid-, non-invasive and inexpensive methods to obtain information on the content of biological samples. This has led us to apply FTIR spectroscopy to the investigation of blood samples from a cohort of CVID patients; revealing spectral features capable of stratifying CVID patients from healthy controls with sensitivities and specificities of 97% and 93%, respectively for serum, and 94% and 95%, respectively for plasma. Furthermore we identified several discriminating spectral biomarkers; wavenumbers in regions indicative of nucleic acids (984 cm⁻¹, 1053 cm⁻¹, 1084 cm⁻¹, 1115 cm⁻¹, 1528 cm⁻¹, 1639 cm⁻¹), and a collagen-associated biomarker (1528 cm⁻¹), which may represent future candidate biomarkers and provide new knowledge on the aetiology of CVID. This proof-of-concept study provides a basis for developing a novel diagnostic tool for CVID.

Blood-based molecular signature of Alzheimer's disease via spectroscopy and metabolomics

OBJECTIVES

With over 35 million cases worldwide, Alzheimer's disease (AD) represents the main cause of dementia. The differentiation of AD from other types of dementia is challenging and its early diagnosis is complicated. The established biomarkers are not only based on the invasive collection of cerebrospinal fluid, but also lack sufficient sensitivity and specificity. Therefore, much current effort is aimed at the identification of new biomarkers of AD in peripheral blood.

DESIGN AND METHODS

We focused on blood-based analyses using chiroptical spectroscopy (Raman optical activity, electronic circular dichroism) supplemented with conventional vibrational spectroscopy (infrared, Raman) and metabolomics (high-performance liquid chromatography with a high-resolution mass detection).

RESULTS

This unique approach enabled us to identify the spectral pattern of AD and variations in metabolite levels. Subsequent linear discriminant analysis of the spectral data resulted in differentiation between the AD patients and control subjects.

CONCLUSIONS

It may be stated that this less invasive approach has strong potential for the identification of disease-related changes within essential plasmatic biomolecules and metabolites.

Blood-based near-infrared spectroscopy for the rapid low-cost detection of Alzheimer's disease

Alzheimer's disease (AD) is currently under-diagnosed and is predicted to affect a great number of people in the future, due to the unrestrained aging of the population. An accurate diagnosis of AD at an early stage, prior to (severe) symptomatology, is of crucial importance as it would allow the subscription of effective palliative care and/or enrolment into specific clinical trials. Today, new analytical methods and research initiatives are being developed for the on-time diagnosis of this devastating disorder. During the last decade, spectroscopic techniques have shown great promise in the robust diagnosis of various pathologies, including neurodegenerative diseases and dementia. In the current study, blood plasma samples were analysed with near-infrared (NIR) spectroscopy as a minimally-invasive method to distinguish patients with AD (n = 111) from non-demented volunteers (n = 173). After applying multivariate classification models (principal component analysis with quadratic discriminant analysis - PCA-QDA), AD individuals were correctly identified with 92.8% accuracy, 87.5% sensitivity and 96.1% specificity. Our results show the potential of NIR spectroscopy as a simple and cost-effective diagnostic tool for AD. Robust and early diagnosis may be a first step towards tackling this disease by allowing timely intervention.

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.

Progress and Challenges in the Diagnosis of Dementia: A Critical Review

Longer life expectancies have led to an increased number of neurodegenerative disease cases globally. Accurate diagnosis of this devastating disorder is of crucial importance but is still feasible only by a brain biopsy after death. An enormous amount of attention and research has been in place over the years toward the better understanding of the mechanisms, as well as the early diagnosis, of neurodegeneration. However, numerous studies have been contradictory from time to time, while new diagnostic methods are constantly developed in a tireless effort to tackle the disease. Nonetheless, there is not yet a conclusive report covering a broader range of techniques for the diagnosis of different types of dementia. In this paper, we critically review current knowledge on the different hypotheses about the pathogenesis of distinct types of dementia, as well as risk factors and current diagnostic approaches in a clinical setting, including neuroimaging, cerebrospinal (CSF), and blood tests. Encouraging research results for the diagnosis and investigation of neurodegenerative disorders are also reported. Particular attention is given to the field of spectroscopy as an emerging tool to detect dementias, follow-up patients, and potentially monitor the patients' response to a therapeutic approach. Spectroscopic techniques, such as infrared and Raman spectroscopy, have facilitated numerous disease-related studies, including neurodegenerative disorders, and are currently undergoing trials for clinical implementation. This review constitutes a comprehensive report with an in-depth focus on promising imaging, molecular biomarker and spectroscopic tests in the field of dementive diseases.

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.

Mid-infrared spectroscopy of serum, a promising non-invasive method to assess prognosis in patients with ascites and cirrhosis

Background & aims

Prognostic tests are critical in the management of patients with cirrhosis and ascites. Biological tests or scores perform poorly in that situation. Mid-infrared fibre evanescent wave spectroscopy (MIR-FEWS) which allows for global serum metabolic profiling may provide more relevant information by measuring a wider range of metabolic parameters in serum. Here we present the accuracy of a MIR-FEWS based predictive model for the prognosis of 6 months survival in patients with ascites and cirrhosis.

Methods

Patients with ascites were prospectively included and followed up for 6 months. MIR-FEWS spectra were measured in serum samples. The most informative spectral variables obtained by MIR-FEWS were selected by FADA algorithm and then used to build the MIR model. Accuracy of this model was assessed by ROC curves and 90%/10% Monte Carlo cross-validation. MIR model accuracy for 6 months survival was compared to that of the Child-Pugh and MELD scores.

Results

119 patients were included. The mean age was 57.36±13.70, the MELD score was 16.32±6.26, and the Child-Pugh score was 9.5±1.83. During follow-up, 23 patients died (20%). The MIR model had an AUROC for 6 months mortality of 0.90 (CI95: 0.88–0.91), the MELD 0.77 (CI95: 0.66–0.89) and Child-Pugh 0.76 (CI95: 0.66–0.88). MELD and Child-Pugh AUROCs were significantly lower than that of the MIR model (p = 0.02 and p = 0.02 respectively). Multivariate logistic regression analysis showed that MELD (p<0.05, OR:0.86;CI95:0.76–0.97), Beta blockers (p = 0.036;OR:0.20;CI95:0.04–0.90), and the MIR model (p<0.001; OR:0.50; CI95:0.37–0.66), were significantly associated with 6 months mortality.

Conclusions

In this pilot study MIR-FEWS more accurately assess the 6-month prognosis of patients with ascites and cirrhosis than the MELD or Child-Pugh scores. These promising results, if confirmed by a larger study, suggest that mid infrared spectroscopy could be helpful in the management of these patients.

Differential diagnosis of Alzheimer's disease using spectrochemical analysis of blood

The progressive aging of the world's population makes a higher prevalence of neurodegenerative diseases inevitable. The necessity for an accurate, but at the same time, inexpensive and minimally invasive, diagnostic test is urgently required, not only to confirm the presence of the disease but also to discriminate between different types of dementia to provide the appropriate management and treatment. In this study, attenuated total reflection FTIR (ATR-FTIR) spectroscopy combined with chemometric techniques were used to analyze blood plasma samples from our cohort. Blood samples are easily collected by conventional venepuncture, permitting repeated measurements from the same individuals to monitor their progression throughout the years or evaluate any tested drugs. We included 549 individuals: 347 with various neurodegenerative diseases and 202 age-matched healthy individuals. Alzheimer's disease (AD; n = 164) was identified with 70% sensitivity and specificity, which after the incorporation of apolipoprotein ε4 genotype (APOE ε4) information, increased to 86% when individuals carried one or two alleles of ε4, and to 72% sensitivity and 77% specificity when individuals did not carry ε4 alleles. Early AD cases (n = 14) were identified with 80% sensitivity and 74% specificity. Segregation of AD from dementia with Lewy bodies (DLB; n = 34) was achieved with 90% sensitivity and specificity. Other neurodegenerative diseases, such as frontotemporal dementia (FTD; n = 30), Parkinson's disease (PD; n = 32), and progressive supranuclear palsy (PSP; n = 31), were included in our cohort for diagnostic purposes. Our method allows for both rapid and robust diagnosis of neurodegeneration and segregation between different dementias.

FTIR and Raman Spectroscopy Applied to Dementia Diagnosis Through Analysis of Biological Fluids

To date, it is still difficult to perform an early and accurate diagnosis of dementia, therefore significant research has focused on finding new dementia biomarkers that can aid in this respect. There is an urgent need for non-invasive, rapid, and relatively inexpensive procedures for early diagnostics. Studies have demonstrated that of spectroscopic techniques, such as Fourier Transform Infrared Spectroscopy (FTIR) and Raman Spectroscopy could be a useful and accurate procedure to diagnose dementia. Given that several biochemical mechanisms related to neurodegeneration and dementia can lead to changes in plasma components and others peripheral body fluids; blood-based samples coupled to spectroscopic analyses can be used as a simple and less invasive approach.

Metabolic profiling of recombinant Escherichia coli cultivations based on high-throughput FT-MIR spectroscopic analysis

Escherichia coli is one of the most used host microorganism for the production of recombinant products, such as heterologous proteins and plasmids. However, genetic, physiological and environmental factors influence the plasmid replication and cloned gene expression in a highly complex way. To control and optimize the recombinant expression system performance, it is very important to understand this complexity. Therefore, the development of rapid, highly sensitive and economic analytical methodologies, which enable the simultaneous characterization of the heterologous product synthesis and physiologic cell behavior under a variety of culture conditions, is highly desirable. For that, the metabolic profile of recombinant E. coli cultures producing the pVAX-lacZ plasmid model was analyzed by rapid, economic and high-throughput Fourier Transform Mid-Infrared (FT-MIR) spectroscopy. The main goal of the present work is to show as the simultaneous multivariate data analysis by principal component analysis (PCA) and direct spectral analysis could represent a very interesting tool to monitor E. coli culture processes and acquire relevant information according to current quality regulatory guidelines. While PCA allowed capturing the energetic metabolic state of the cell, e.g. by identifying different C-sources consumption phases, direct FT-MIR spectral analysis allowed obtaining valuable biochemical and metabolic information along the cell culture, e.g. lipids, RNA, protein synthesis and turnover metabolism. The information achieved by spectral multivariate data and direct spectral analyses complement each other and may contribute to understand the complex interrelationships between the recombinant cell metabolism and the bioprocess environment towards more economic and robust processes design according to Quality by Design framework. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:285-298, 2017.

Fourier Transform Infrared Spectroscopy and Photoacoustic Spectroscopy for Saliva Analysis

Saliva provides a valuable tool for assessing oral and systemic diseases, but concentrations of salivary components are very small, calling the need for precise analysis methods. In this work, Fourier transform infrared (FT-IR) spectroscopy using transmission and photoacoustic (PA) modes were compared for quantitative analysis of saliva. The performance of these techniques was compared with a calibration series. The linearity of spectrum output was verified by using albumin-thiocyanate (SCN(-)) solution at different SCN(-) concentrations. Saliva samples used as a comparison were obtained from healthy subjects. Saliva droplets of 15 µL were applied on the silicon sample substrate, 6 drops for each specimen, and dried at 37 ℃ overnight. The measurements were carried out using an FT-IR spectrometer in conjunction with an accessory unit for PA measurements. The findings with both transmission and PA modes mirror each other. The major bands presented were 1500-1750 cm(-1) for proteins and 1050-1200 cm(-1) for carbohydrates. In addition, the distinct spectral band at 2050 cm(-1) derives from SCN(-) anions, which is converted by salivary peroxidases to hypothiocyanate (OSCN(-)). The correlation between the spectroscopic data with SCN(-) concentration (r > 0.990 for transmission and r = 0.967 for PA mode) was found to be significant (P < 0.01), thus promising to be utilized in future applications.

Synchrotron FTIR microspectroscopy reveals early adipogenic differentiation of human mesenchymal stem cells at single-cell level

Human mesenchymal stem cells (hMSCs) have been used as an ideal in vitro model to study human adipogenesis. However, little knowledge of the early stage differentiation greatly hinders our understanding on the mechanism of the adipogenesis processes. In this study, synchrotron radiation-based Fourier transform infrared (SR-FTIR) microspectroscopy was applied to track the global structural and compositional changes of lipids, proteins and nucleic acids inside individual hMSCs along the time course. The multivariate analysis of the SR-FTIR spectra distinguished the dynamic and significant changes of the lipids and nucleic acid at early differentiation stage. Importantly, changes of lipid structure during early days (Day 1-3) of differentiation might serve as a potential biomarker in identifying the state in early differentiation at single cell level. These results proved that SR-FTIR is a powerful tool to study the stem cell fate determination and early lipogenesis events.

Developing and understanding biofluid vibrational spectroscopy: a critical review

Biofluid vibrational spectroscopy, a promising tool for rapid disease diagnosis.

Plasma biomarkers of pulmonary hypertension identified by Fourier transform infrared spectroscopy and principal component analysis

The main goal of this study was to find specific plasma spectral markers associated with pulmonary arterial hypertension (PAH) induced by monocrotaline injection in rats. FTIR was used to monitor biochemical changes in plasma caused by PAH as compared with the systemic hypertension induced by partial ligation on the left artery and with the control group. Both pathologies, systemic and pulmonary hypertension, induced a unique response in the biochemical content of plasma, mainly related to the composition and secondary structure of plasma proteins. For PAH, β-pleated sheet components of plasma proteins were identified whereas the protein composition in systemic hypertension was dominated by unordered structures. In addition, a higher concentration of tyrosine-rich proteins was found in plasma in PAH than in systemic hypertension. The differences between both pathologies were identified also in terms of lipid composition/metabolism as well as in the content of RNA and glucose, suggesting that lipid peroxidation appears upon pulmonary hypertension development. In summary, this work demonstrates that FTIR spectroscopy supported by principal component analysis (PCA) has the potential to become a fast and non-destructive method for biochemical characterization of plasma that consequently could have a diagnostic significance in pulmonary hypertension.

Structural investigation of donor age effect on human bone marrow mesenchymal stem cells: FTIR spectroscopy and imaging

Stem cell studies hold enormous potential for development of new therapies for tissue regeneration and repair. Bone marrow mesenchymal stem cells (BM-MSCs) can differentiate into a variety of non-hematopoietic tissues and contribute maintenance of healthy hematopoiesis by providing supportive cellular microenvironment into BM. Here, we investigated age-related differences in BM-MSCs by using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and FTIR imaging together with hierarchical clustering as a novel methods to clarify global alterations in the structure and function of macromolecules in characterized BM-MSCs of different aged donors. The results may contribute to identification of age-related new molecular marker(s) to determine the effects of donor age on MSCs. The spectral results reflected that there were significant increases in the concentration of saturated lipids, proteins, glycogen, and nucleic acids in children and adolescent group BM-MSCs when compared to the infants and early and mid adults. The concentration of mentioned macromolecules in adult (early and mid) BM-MSCs were significantly lower than the concentrations in the children and adolescents. These results were attributed to the increase in the proliferation activity in younger BM-MSCs. The distribution of macromolecules into the cells was shown as in the form of chemical maps by FTIR imaging, and the results are in agreement with the ATR-FTIR spectroscopy results. The cellular activity degree was determined by the thiazolyl blue tetrazolium bromide (MTT) proliferation assay to support ATR-FTIR spectroscopy results. BM-MSCs of five different age groups were discriminated by making the hierarchical cluster analysis where the spectral data according to alterations in structure and composition of macromolecules were considered.

Bone marrow mesenchymal stem cells in patients with beta thalassemia major: molecular analysis with attenuated total reflection-Fourier transform infrared spectroscopy study as a novel method

Bone marrow mesenchymal stem cells (BM-MSCs) are the main cellular components of the bone marrow, providing a supportive cellular microenvironment to maintain healthy hematopoiesis. β-thalassemia major (β-TM) is characterized by anemia that is caused by a genetic defect in hemoglobin synthesis and results in ineffective erythropoiesis (IE). The alterations in the microenvironment in thalassemic bone marrow during IE can cause changes in BM-MSCs. This study aimed to investigate global structural and compositional changes in BM-MSCs in β-TM that may provide a basis in understanding interactions of hematopoietic stem cells (HSCs)-MSCs in such a pathological bone marrow microenvironment. Following characterization of morphological, immunophenotypical, and differentiation properties, the changes in healthy and thalassemic BM-MSCs before and after bone marrow transplantation (BMT) were examined by attenuated total reflection-Fourier transform infrared (ATR-FTIR). The significant increase in lipid, protein, glycogen, and nucleic acid contents in thalassemic BM-MSCs with respect to healthy BM-MSCs was attributed to enhanced cell proliferation and BM activity during IE. The significant decreases in the content of mentioned macromolecules in post-transplant group BM-MSCs versus pre-transplant BM-MSCs was interpreted as restoring effect of BMT therapy on IE and defective BM microenvironment. These alterations were also supported by ELISA results of erythropoietin (EPO) and growth differentiation factor (GDF15) in bone marrow plasma samples as a reflection of IE and by MTT proliferation assay on BM-MSCs. Based on these changes, sampling groups were discriminated by cluster analysis. These results provide information for the studies that concentrate on interactions between HSCs-MSCs in bone marrow.

Biological markers and Alzheimer disease: a canadian perspective

Decreased β-amyloid_1-42 and increased phospho-tau protein levels in the cerebrospinal fluid (CSF) are currently the most accurate chemical neurodiagnostics of sporadic Alzheimer disease (AD). A report (2007) of the Third Canadian Consensus Conference on the Diagnosis and Treatment of Dementia (2006) recommended that biological markers should not be currently requisitioned by primary care physicians in the routine investigation of subjects with memory complaints. Consideration for such testing should prompt patient referral to a specialist engaged in dementia evaluations or a Memory Clinic. The specialist should consider having CSF biomarkers (β-amyloid_1-42 and phospho-tau) measured at a reputable facility in restricted cases presenting with atypical features and diagnostic confusion, but not as a routine procedure in all individuals with typical sporadic AD phenotypes. We submit that developments in the field of AD biomarker discovery since publication of the 3rd CCCDTD consensus data do not warrant revision of the 2007 recommendations.

Diabetes-related molecular signatures in infrared spectra of human saliva

BACKGROUND

There is an ongoing need for improvements in non-invasive, point-of-care tools for the diagnosis and prognosis of diabetes mellitus. Ideally, such technologies would allow for community screening.

METHODS

In this study, we employed infrared spectroscopy as a novel diagnostic tool in the prediction of diabetic status by analyzing the molecular and sub-molecular spectral signatures of saliva collected from subjects with diabetes (n = 39) and healthy controls (n = 22).

RESULTS

Spectral analysis revealed differences in several major metabolic components - lipid, proteins, glucose, thiocyanate and carboxylate - that clearly demarcate healthy and diseased saliva. The overall accuracy for the diagnosis of diabetes based on infrared spectroscopy was 100% on the training set and 88.2% on the validation set. Therefore, we have established that infrared spectroscopy can be used to generate complex biochemical profiles in saliva and identify several potential diabetes-associated spectral features.

CONCLUSIONS

Infrared spectroscopy may represent an appropriate tool with which to identify novel diseases mechanisms, risk factors for diabetic complications and markers of therapeutic efficacy. Further study into the potential utility of infrared spectroscopy as diagnostic and prognostic tool for diabetes is warranted.