Tear-Based Vibrational Spectroscopy Applied to Amyotrophic Lateral Sclerosis

Raman spectroscopy assisted tear analysis: A label free, optical approach for noninvasive disease diagnostics

In recent times, tear fluid analysis has garnered considerable attention in the field of biomarker-based diagnostics due to its noninvasive sample collection method. Tears encompass a reservoir of biomarkers that assist in diagnosing not only ocular disorders but also a diverse list of systemic diseases. This highlights the necessity for sensitive and dependable screening methods to employ tear fluid as a potential noninvasive diagnostic specimen in clinical environments. Considerable research has been conducted to investigate the potential of Raman spectroscopy-based investigations for tear analysis in various diagnostic applications. Raman Spectroscopy (RS) is a highly sensitive and label free spectroscopic technique which aids in investigating the molecular structure of samples by evaluating the vibrational frequencies of molecular bonds. Due to the distinct chemical compositions of different samples, it is possible to obtain a sample-specific spectral fingerprint. The distinctive spectral fingerprints obtained from Raman spectroscopy enable researchers to identify specific compounds or functional groups present in a sample, aiding in diverse biomedical applications. Its sensitivity to changes in molecular structure or environment provides invaluable insights into subtle alterations associated with various diseases. Thus, Raman Spectroscopy has the potential to assist in diagnosis and treatment as well as prognostic evaluation. Raman spectroscopy possesses several advantages, such as the non-destructive examination of samples, remarkable sensitivity to structural variations, minimal prerequisites for sample preparation, negligible interference from water, and the aptness for real-time investigation of tear samples. The purpose of this review is to highlight the potential of Raman spectroscopic technique in facilitating the clinical diagnosis of various ophthalmic and systemic disorders through non-invasive tear analysis. Additionally, the review delves into the advancements made in Raman spectroscopy with regards to paper-based sensing substrates and tear analysis methods integrated into contact lenses. Furthermore, the review also addresses the obstacles and future possibilities associated with implementing Raman spectroscopy as a routine diagnostic tool based on tear analysis in clinical settings.

Iron bioleaching and polymers accumulation by an extreme acidophilic bacterium

In many European regions, both local metallic and non-metallic raw materials are poorly exploited due to their low quality and the lack of technologies to increase their economic value. In this context, the development of low cost and eco-friendly approaches, such as bioleaching of metal impurities, is crucial. The acidophilic strain Acidiphilium sp. SJH reduces Fe(III) to Fe(II) by coupling the oxidation of an organic substrate to the reduction of Fe(III) and can therefore be applied in the bioleaching of iron impurities from non-metallic raw materials. In this work, the physiology of Acidiphilium sp. SJH and the reduction of iron impurities from quartz sand and its derivatives have been studied during growth on media supplemented with various carbon sources and under different oxygenation conditions, highlighting that cell physiology and iron reduction are tightly coupled. Although the organism is known to be aerobic, maximum bioleaching performance was obtained by cultures cultivated until the exponential phase of growth under oxygen limitation. Among carbon sources, glucose has been shown to support faster biomass growth, while galactose allowed highest bioleaching. Moreover, Acidiphilium sp. SJH cells can synthesise and accumulate Poly-β-hydroxybutyrate (PHB) during the process, a polymer with relevant application in biotechnology. In summary, this work gives an insight into the physiology of Acidiphilium sp. SJH, able to use different carbon sources and to synthesise a technologically relevant polymer (PHB), while removing metals from sand without the need to introduce modifications in the process set up.

Conformational fingerprinting with Raman spectroscopy reveals protein structure as a translational biomarker of muscle pathology

Neuromuscular disorders are a group of conditions that can result in weakness of skeletal muscles. Examples include fatal diseases such as amyotrophic lateral sclerosis and conditions associated with high morbidity such as myopathies (muscle diseases). Many of these disorders are known to have abnormal protein folding and protein aggregates. Thus, easy to apply methods for the detection of such changes may prove useful diagnostic biomarkers. Raman spectroscopy has shown early promise in the detection of muscle pathology in neuromuscular disorders and is well suited to characterising the conformational profiles relating to protein secondary structure. In this work, we assess if Raman spectroscopy can detect differences in protein structure in muscle in the setting of neuromuscular disease. We utilise in vivo Raman spectroscopy measurements from preclinical models of amyotrophic lateral sclerosis and the myopathy Duchenne muscular dystrophy, together with ex vivo measurements of human muscle samples from individuals with and without myopathy. Using quantitative conformation profiling and matrix factorisation we demonstrate that quantitative 'conformational fingerprinting' can be used to identify changes in protein folding in muscle. Notably, myopathic conditions in both preclinical models and human samples manifested a significant reduction in α-helix structures, with concomitant increases in β-sheet and, to a lesser extent, nonregular configurations. Spectral patterns derived through non-negative matrix factorisation were able to identify myopathy with a high accuracy (79% in mouse, 78% in human tissue). This work demonstrates the potential of conformational fingerprinting as an interpretable biomarker for neuromuscular disorders.

Tear biomarkers

An extensive exploration of lacrimal fluid molecular biomarkers in understanding and diagnosing a spectrum of ocular and systemic diseases is presented. The chapter provides an overview of lacrimal fluid composition, elucidating the roles of proteins, lipids, metabolites, and nucleic acids within the tear film. Pooled versus single-tear analysis is discussed to underline the benefits and challenges associated with both approaches, offering insights into optimal strategies for tear sample analysis. Subsequently, an in-depth analysis of tear collection methods is presented, with a focus on Schirmer's test strips and microcapillary tubes methods. Alternative tear collection techniques are also explored, shedding light on their applicability and advantages. Variability factors, including age, sex, and diurnal fluctuations, are examined in the context of their impact on tear biomarker analysis. The main body of the chapter is dedicated to discussing specific biomarkers associated with ocular discomfort and a wide array of ocular diseases. From dry eye disease and thyroid-associated ophthalmopathy to keratoconus, age-related macular degeneration, diabetic retinopathy, and glaucoma, the intricate relationship between molecular biomarkers and these conditions is thoroughly dissected. Expanding beyond ocular pathologies, the chapter explores the applicability of tear biomarkers in diagnosing systemic diseases such as multiple sclerosis, amyotrophic lateral sclerosis, Alzheimer's disease, Parkinson's disease, and cancer. This broader perspective underscores the potential of lacrimal fluid analysis in offering non-invasive diagnostic tools for conditions with far-reaching implications.

Recent developments and future perspectives of microfluidics and smart technologies in wearable devices

Wearable devices are gaining popularity in the fields of health monitoring, diagnosis, and drug delivery. Recent advances in wearable technology have enabled real-time analysis of biofluids such as sweat, interstitial fluid, tears, saliva, wound fluid, and urine. The integration of microfluidics and emerging smart technologies, such as artificial intelligence (AI), machine learning (ML), and Internet of Things (IoT), into wearable devices offers great potential for accurate and non-invasive monitoring and diagnosis. This paper provides an overview of current trends and developments in microfluidics and smart technologies in wearable devices for analyzing body fluids. The paper discusses common microfluidic technologies in wearable devices and the challenges associated with analyzing each type of biofluid. The paper emphasizes the importance of combining smart technologies with microfluidics in wearable devices, and how they can aid diagnosis and therapy. Finally, the paper covers recent applications, trends, and future developments in the context of intelligent microfluidic wearable devices.

Translating Biospectroscopy Techniques to Clinical Settings: A New Paradigm in Point-of-Care Screening and/or Diagnostics

As healthcare tools increasingly move towards a more digital and computational format, there is an increasing need for sensor-based technologies that allow for rapid screening and/or diagnostics [...].

Monitoring Metabolic Changes in Response to Chemotherapies in Cancer with Raman Spectroscopy and Metabolomics

Resistance to clinical therapies remains a major barrier in cancer management. There is a critical need for rapid and highly sensitive diagnostic tools that enable early prediction of treatment response to allow accurate clinical decisions. Here, Raman spectroscopy was employed to monitor changes in key metabolites as early predictors of response in KRAS-mutant colorectal cancer (CRC) cells, HCT116, treated with chemotherapies. We show at the single cell level that HCT116 is resistant to cetuximab (CTX), the first-line treatment in CRC, but this resistance can be overcome with pre-sensitization of cells with oxaliplatin (OX). In combination treatment of CTX + OX, sequential delivery of OX followed by CTX rather than simultaneous administration of drugs was observed to be critical for effective therapy. Our results demonstrated that metabolic changes are well aligned to cellular mechanical changes where Young's modulus decreased after effective treatment, indicating that both changes in mechanical properties and metabolism in cells are likely responsible for cancer proliferation. Raman findings were verified with mass spectrometry (MS) metabolomics, and both platforms showed changes in lipids, nucleic acids, and amino acids as predictors of resistance/response. Finally, key metabolic pathways enriched were identified when cells are resistant to CTX but downregulated with effective treatment. This study highlights that drug-induced metabolic changes both at the single cell level (Raman) and ensemble level (MS) have the potential to identify mechanisms of response to clinical cancer therapies.

The Emerging Role of Raman Spectroscopy as an Omics Approach for Metabolic Profiling and Biomarker Detection toward Precision Medicine

Omics technologies have rapidly evolved with the unprecedented potential to shape precision medicine. Novel omics approaches are imperative toallow rapid and accurate data collection and integration with clinical information and enable a new era of healthcare. In this comprehensive review, we highlight the utility of Raman spectroscopy (RS) as an emerging omics technology for clinically relevant applications using clinically significant samples and models. We discuss the use of RS both as a label-free approach for probing the intrinsic metabolites of biological materials, and as a labeled approach where signal from Raman reporters conjugated to nanoparticles (NPs) serve as an indirect measure for tracking protein biomarkers in vivo and for high throughout proteomics. We summarize the use of machine learning algorithms for processing RS data to allow accurate detection and evaluation of treatment response specifically focusing on cancer, cardiac, gastrointestinal, and neurodegenerative diseases. We also highlight the integration of RS with established omics approaches for holistic diagnostic information. Further, we elaborate on metal-free NPs that leverage the biological Raman-silent region overcoming the challenges of traditional metal NPs. We conclude the review with an outlook on future directions that will ultimately allow the adaptation of RS as a clinical approach and revolutionize precision medicine.

Current State and Future Directions in the Therapy of ALS

Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative disorder affecting upper and lower motor neurons, with death resulting mainly from respiratory failure three to five years after symptom onset. As the exact underlying causative pathological pathway is unclear and potentially diverse, finding a suitable therapy to slow down or possibly stop disease progression remains challenging. Varying by country Riluzole, Edaravone, and Sodium phenylbutyrate/Taurursodiol are the only drugs currently approved in ALS treatment for their moderate effect on disease progression. Even though curative treatment options, able to prevent or stop disease progression, are still unknown, recent breakthroughs, especially in the field of targeting genetic disease forms, raise hope for improved care and therapy for ALS patients. In this review, we aim to summarize the current state of ALS therapy, including medication as well as supportive therapy, and discuss the ongoing developments and prospects in the field. Furthermore, we highlight the rationale behind the intense research on biomarkers and genetic testing as a feasible way to improve the classification of ALS patients towards personalized medicine.

Synchrotron-Based Fourier-Transform Infrared Micro-Spectroscopy of Cerebrospinal Fluid from Amyotrophic Lateral Sclerosis Patients Reveals a Unique Biomolecular Profile

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, with the most common adult-onset neurodegenerative disorder affecting motoneurons. Although disruptions in macromolecular conformation and homeostasis have been described in association with ALS, the underlying pathological mechanisms are still not completely understood, and unambiguous biomarkers are lacking. Fourier Transform Infrared Spectroscopy (FTIR) of cerebrospinal fluid (CSF) is appealing to extensive interest due to its potential to resolve biomolecular conformation and content, as this approach offers a non-invasive, label-free identification of specific biologically relevant molecules in a few microliters of CSF sample. Here, we analyzed the CSF of 33 ALS patients compared to 32 matched controls using FTIR spectroscopy and multivariate analysis and demonstrated major differences in the molecular contents. A significant change in the conformation and concentration of RNA is demonstrated. Moreover, significantly increased glutamate and carbohydrates are found in ALS. Moreover, key markers of lipid metabolism are strongly altered; specifically, we find a decrease in unsaturated lipids and an increase in peroxidation of lipids in ALS, whereas the total amount of lipids compared to proteins is reduced. Our study demonstrates that FTIR characterization of CSF could represent a powerful tool for ALS diagnosis and reveals central features of ALS pathophysiology.

Accuracy of Raman spectroscopy in the diagnosis of Alzheimer's disease

Objective

To systematically evaluate the accuracy of Raman spectroscopy in the diagnosis of Alzheimer's disease.

Methods

Databases including Web of Science, PubMed, The Cochrane Library, EMbase, CBM, CNKI, Wan Fang Data, and VIP were electronically searched for studies on Raman spectroscopy in diagnosis of Alzheimer's disease from inception to November 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias in the included studies. Then, meta-analysis was performed using Meta-Disc1.4 and Stata 16.0 software.

Results

A total of eight studies were finally included. The pooled sensitivity of Raman spectroscopy was 0.86 [95% CI (0.80–0.91)], specificity was 0.87 [95% CI (0.79–0.92)], positive likelihood ratio was 5.50 [95% CI (3.55–8.51)], negative likelihood ratio was 0.17 [95% CI (0.09–0.34)], diagnosis odds ratio and area under the curve of SROC were 42.44 [95% CI (19.80–90.97)] and 0.931, respectively. Sensitivity analysis was carried out after each study was excluded one by one, and the results showed that pooled sensitivity and specificity had no significant change, indicating that the stability of the meta-analysis results was great.

Conclusions

Our findings indicated that Raman spectroscopy had high accuracy in the diagnosis of AD, though it still did not rule out the possibility of misdiagnosis and missed diagnosis. Limited by the quantity and quality of the included studies, the above conclusions need to be verified by more high-quality studies.

Non-negative matrix factorisation of Raman spectra finds common patterns relating to neuromuscular disease across differing equipment configurations, preclinical models and human tissue

Raman spectroscopy shows promise as a biomarker for complex nerve and muscle (neuromuscular) diseases. To maximise its potential, several challenges remain. These include the sensitivity to different instrument configurations, translation across preclinical/human tissues and the development of multivariate analytics that can derive interpretable spectral outputs for disease identification. Nonnegative matrix factorisation (NMF) can extract features from high-dimensional data sets and the nonnegative constraint results in physically realistic outputs. In this study, we have undertaken NMF on Raman spectra of muscle obtained from different clinical and preclinical settings. First, we obtained and combined Raman spectra from human patients with mitochondrial disease and healthy volunteers, using both a commercial microscope and in-house fibre optic probe. NMF was applied across all data, and spectral patterns common to both equipment configurations were identified. Linear discriminant models utilising these patterns were able to accurately classify disease states (accuracy 70.2-84.5%). Next, we applied NMF to spectra obtained from the mdx mouse model of a Duchenne muscular dystrophy and patients with dystrophic muscle conditions. Spectral fingerprints common to mouse/human were obtained and able to accurately identify disease (accuracy 79.5-98.8%). We conclude that NMF can be used to analyse Raman data across different equipment configurations and the preclinical/clinical divide. Thus, the application of NMF decomposition methods could enhance the potential of Raman spectroscopy for the study of fatal neuromuscular diseases.

Cheese-whey permeate improves the fitness of Escherichia coli cells during recombinant protein production

BACKGROUND

Escherichia coli cells are the most frequently used hosts in recombinant protein production processes and mainly require molecules such as IPTG or pure lactose as inducers of heterologous expression. A possible way to reduce the production costs is to replace traditional inducers with waste materials such as cheese whey permeate (CWP). CWP is a secondary by-product generated from the production of the valuable whey proteins, which are obtained from ultrafiltration of cheese whey, a main by-product of the dairy industry, which is rich in lactose.

RESULTS

The effects of CWP collected from an Italian plant were compared with those of traditional inducers on the production of two model proteins (i.e., green fluorescent protein and the toxic Q55 variant of ataxin-3), in E. coli BL21 (DE3) cells. It was found that the high lactose content of CWP (165 g/L) and the antioxidant properties of its micronutrients (vitamins, cofactors and osmolytes) sustain production yields similar to those obtained with traditional inducers, accompanied by the improvement of cell fitness.

CONCLUSIONS

CWP has proven to be an effective and low-cost alternative inducer to produce recombinant proteins. Its use thus combines the advantage of exploiting a waste product with that of reducing the production costs of recombinant proteins.

Identification of circulating biomarkers of Crohn's disease and spondyloarthritis using Fourier transform infrared spectroscopy

Crohn's disease (CD) and spondyloarthritis (SpA) are two inflammatory diseases sharing many common features (genetic polymorphism, armamentarium). Both diseases lack diagnostic markers of certainty. While the diagnosis of CD is made by a combination of clinical, and biological criteria, the diagnosis of SpA may take several years to be confirmed. Based on the hypothesis that CD and SpA alter the biochemical profile of plasma, the objective of this study was to evaluate the analytical capability of Fourier transform infrared spectroscopy (FTIR) in identifying spectral biomarkers. Plasma from 104 patients was analyzed. After data processing of the spectra by Extended Multiplicative Signal Correction and linear discriminant analysis, we demonstrated that it was possible to distinguish CD and SpA from controls with an accuracy of 97% and 85% respectively. Spectral differences were mainly associated with proteins and lipids. This study showed that FTIR analysis is efficient to identify plasma biosignatures specific to CD or SpA.

Potential of vibrational spectroscopy coupled with machine learning as a non-invasive diagnostic method for COVID-19

BACKGROUND AND OBJECTIVE

Efforts to alleviate the ongoing coronavirus disease 2019 (COVID-19) crisis showed that rapid, sensitive, and large-scale screening is critical for controlling the current infection and that of ongoing pandemics.

METHODS

Here, we explored the potential of vibrational spectroscopy coupled with machine learning to screen COVID-19 patients in its initial stage. Herein presented is a hybrid classification model called grey wolf optimized support vector machine (GWO-SVM). The proposed model was tested and comprehensively compared with other machine learning models via vibrational spectroscopic fingerprinting including saliva FTIR spectra dataset and serum Raman scattering spectra dataset.

RESULTS

For the unknown vibrational spectra, the presented GWO-SVM model provided an accuracy, specificity and F1_score value of 0.9825, 0.9714 and 0.9778 for saliva FTIR spectra dataset, respectively, while an overall accuracy, specificity and F1_score value of 0.9085, 0.9552 and 0.9036 for serum Raman scattering spectra dataset, respectively, which showed superiority than those of state-of-the-art models, thereby suggesting the suitability of the GWO-SVM model to be adopted in a clinical setting for initial screening of COVID-19 patients.

CONCLUSIONS

Prospectively, the presented vibrational spectroscopy based GWO-SVM model can facilitate in screening of COVID-19 patients and alleviate the medical service burden. Therefore, herein proof-of-concept results showed the chance of vibrational spectroscopy coupled with GWO-SVM model to help COVID-19 diagnosis and have the potential be further used for early screening of other infectious diseases.

A Global Picture of Molecular Changes Associated to LPS Treatment in THP-1 Derived Human Macrophages by Fourier Transform Infrared Microspectroscopy

Macrophages are among the first immune cells involved in the initiation of the inflammatory response to protect the host from pathogens. THP-1 derived macrophages (TDM) are used as a model to study the pro-inflammatory effects of lipopolysaccharide (LPS) exposure. Intact TDM cells were analysed by Fourier transform infrared (FTIR) microspectroscopy, supported by multivariate analysis, to obtain a snapshot of the molecular events sparked by LPS stimulation in macrophage-like cells. This spectroscopic analysis enabled the untargeted identification of the most significant spectral components affected by the treatment, ascribable mainly to lipid, protein, and sulfated sugar bands, thus stressing the fundamental role of these classes of molecules in inflammation and in immune response. Our study, therefore, shows that FTIR microspectroscopy enabled the identification of spectroscopic markers of LPS stimulation and has the potential to become a tool to assess those global biochemical changes related to inflammatory and anti-inflammatory stimuli of synthetic and natural immunomodulators different from LPS.

Contact Lens Wear Induces Alterations of Lactoferrin Functionality in Human Tears

The tear film is a complex matrix composed of several molecular classes, from small metal ions to macromolecules. Contact lens (CL) wear can affect the protein homeostasis of the tear film, by accumulating deposits on the CL surface and/or altering their structural and functional properties. This work investigates the effect of CL wear on lactoferrin (Lf), one of the most abundant tear proteins, known as an unspecific biomarker of inflammation. Tears from eight volunteers were collected and analyzed after alternated periods of CL wear and without CL. The experimental approach is to probe Lf into unprocessed human tears by the peculiar fluorescence emission originating from complex formation of Lf with terbium (Tb³⁺) at the iron-binding sites. The experimental data indicate that CL wear does not significantly affect the total amount of Lf. On the other hand, Lf affinity for Tb³⁺ is reduced upon CL wear, suggesting relevant changes in Lf structure and possible alterations of protein functionality. Future studies based on this approach will help define CL features (material, lens-care solution, wearing time, etc.) with minimal effects on tear protein activity, in order to obtain more biocompatible and comfortable devices.

Viscoelastic properties of the human tear film

When considering eye blinking from a tribological perspective, tear viscosity is expected to play a fundamental role. The application of rheological techniques to describe the tear film dates back to the late 1980s, but there has been a continuous need of reappraisal due to new findings in tear film biochemistry and to the development of new methods of physico-chemical characterization. This review provides an overview on tear rheological behavior by analyzing the peer-reviewed literature on this topic. Specifically, examples of in-vitro and in-vivo viscosity measurements are detailed, highlighting experimental criticalities and the need of a standard convention for rheological techniques to compare data across different studies, of analyses on tears of single individuals even within the limits of the low volume available, and of a further development of in-vivo techniques. Then, the controversial role of specific tear components on viscosity is discussed, together with the alterations associated to dry eye disease and contact lens wear. Finally, an updated focus is reported on the viscosity of artificial tears formulations.

Contribution of Infrared Spectroscopy to the Understanding of Amyloid Protein Aggregation in Complex Systems

Infrared (IR) spectroscopy is a label-free and non-invasive technique that probes the vibrational modes of molecules, thus providing a structure-specific spectrum. The development of infrared spectroscopic approaches that enable the collection of the IR spectrum from a selected sample area, from micro- to nano-scale lateral resolutions, allowed to extend their application to more complex biological systems, such as intact cells and tissues, thus exerting an enormous attraction in biology and medicine. Here, we will present recent works that illustrate in particular the applications of IR spectroscopy to the in situ characterization of the conformational properties of protein aggregates and to the investigation of the other biomolecules surrounding the amyloids. Moreover, we will discuss the potential of IR spectroscopy to the monitoring of cell perturbations induced by protein aggregates. The essential support of multivariate analyses to objectively pull out the significant and non-redundant information from the spectra of highly complex systems will be also outlined.