Raman Spectroscopy and Imaging in Bioanalytics

Unveiling the Molecular Secrets: A Comprehensive Review of Raman Spectroscopy in Biological Research

Raman spectroscopy has been proven to be a fast, convenient, and nondestructive technique for advancing our understanding of biological systems. The Raman effect originates from the inelastic scattering of light which directly probe vibration/rotational states in biological molecules and materials. Despite numerous advantages over infrared spectroscopy and continuous technical as well as operational improvement in Raman spectroscopy, an advanced development of the device and more applications have become possible. In this review, we explore the principles, techniques, and myriad applications of Raman spectroscopy in the realm of biology. We begin by providing an overview of Raman spectroscopy, highlighting its significance in unraveling the complexities of biological research. The focus of this review is on Raman spectroscopy concepts and methods, clarifying the fundamentals of Raman scattering and spectral interpretation. The review also highlights the key experimental considerations for productive biological applications. We explore the broad range of Raman applications including molecular structure, biomolecular composition, disease detection, and medication discovery. The Raman imaging and mapping can also be used to visualize biological samples at the molecular level. Raman spectroscopy is still developing, giving fresh insights and remedies, from biosensing to its use in tissue engineering and regenerative medicine. This review sheds light on the past, present, and future of Raman spectroscopy; it also highlights promising directions of future research developments and serves as a thorough resource for all researchers.

Targeting Reactive Oxygen Species for Diagnosis of Various Diseases

Reactive oxygen species (ROS) are generated predominantly during cellular respiration and play a significant role in signaling within the cell and between cells. However, excessive accumulation of ROS can lead to cellular dysfunction, disease progression, and apoptosis that can lead to organ dysfunction. To overcome the short half-life of ROS and the relatively small amount produced, various imaging methods have been developed, using both endogenous and exogenous means to monitor ROS in disease settings. In this review, we discuss the molecular mechanisms underlying ROS production and explore the methods and materials that could be used to detect ROS overproduction, including iron-based materials, ROS-responsive chemical bond containing polymers, and ROS-responsive molecule containing biomaterials. We also discuss various imaging and imaging techniques that could be used to target and detect ROS overproduction. We discuss the ROS imaging potentials of established clinical imaging methods, such as magnetic resonance imaging (MRI), sonographic imaging, and fluorescence imaging. ROS imaging potentials of other imaging methods, such as photoacoustic imaging (PAI) and Raman imaging (RI) that are currently in preclinical stage are also discussed. Finally, this paper focuses on various diseases that are associated with ROS overproduction, and the current and the future clinical applications of ROS-targeted imaging. While the most widely used clinical condition is cardiovascular diseases, its potential extends into non-cardiovascular clinical conditions, such as neurovascular, neurodegenerative, and other ROS-associated conditions, such as cancers, skin aging, acute kidney injury, and inflammatory arthritis.

Evaluation methods for decellularized tissues: A focus on human amniotic membrane

Tissue engineering, a multidisciplinary research field aiming to revolutionize regenerative medicine, relies on scaffolds for optimal cell cultures and organ development. Decellularized tissue extracellular matrices (dECM) scaffolds, particularly from human amniotic membrane (hAM), show promise in clinical applications. This review discusses the significance of scaffolds, emphasizing dECM-based hAM scaffolds, delving into ECM complexities, decellularization processes, and evaluation methods. Raman spectroscopy emerges as a non-destructive tool for evaluating ECM preservation, presenting potential for quantifying ECM components in hAM before and after decellularization. The review explores the role of hAM as a biomaterial, detailing its composition and characteristics and emphasizes the importance of evaluating ultrastructural components and suggests Raman spectroscopy as a valuable technique for this purpose.

Advancing cerumen analysis: exploring innovative vibrational spectroscopy techniques with respect to their potential as new point-of-care diagnostic tools

Cerumen, commonly known as earwax, is a complex mixture composed of secretions from ceruminous glands. These secretions are heterogeneous mixtures mainly composed of lipids and proteins. Despite its prevalence, the potential diagnostic value of cerumen remains largely unexplored. Here, we present an in-depth analysis of cerumen utilizing well-known vibrational approaches such as conventional Raman spectroscopy or surface-enhanced Raman spectroscopy (SERS) together with advanced vibrational spectroscopy techniques such as coherent Raman scattering (CRS), i.e. broadband coherent anti-Stokes Raman scattering (CARS) or stimulated Raman scattering (SRS), as well as optical photothermal infrared (OPTIR) spectroscopy. Through the integration of these vibrational spectroscopic methods, lipids and proteins can be identified as the main components of cerumen; however, they contribute to the final spectral information to various extents depending on the vibrational detection scheme applied. The inherently weak Raman signal could be enhanced by linear (SERS) and non-linear (CRS) processes, resulting in efficient acquisition of fingerprint information and allowing for the detection of marker modes, which cannot be addressed by conventional Raman spectroscopy. OPTIR spectroscopy provides complementary information to Raman spectroscopy, however, without the contribution of a fluorescence background. Our findings underscore the utility of these cutting-edge techniques in unveiling the intricate molecular landscape of cerumen, paving the way for novel point-of-care diagnostic methodologies and therapeutic interventions.

Diagnosis of pregnancy disorder in the first-trimester patient plasma with Raman spectroscopy and protein analysis

Gestational diabetes mellitus (GDM) is a pregnancy disorder associated with short- and long-term adverse outcomes in both mothers and infants. The current clinical test of blood glucose levels late in the second trimester is inadequate for early detection of GDM. Here we show the utility of Raman spectroscopy (RS) for rapid and highly sensitive maternal metabolome screening for GDM in the first trimester. Key metabolites, including phospholipids, carbohydrates, and major amino acids, were identified with RS and validated with mass spectrometry, enabling insights into associated metabolic pathway enrichment. Using classical machine learning (ML) approaches, we showed the performance of the RS metabolic model (cross-validation AUC 0.97) surpassed that achieved with patients' clinical data alone (cross-validation AUC 0.59) or prior studies with single biomarkers. Further, we analyzed novel proteins and identified fetuin-A as a promising candidate for early GDM prediction. A correlation analysis showed a moderate to strong correlation between multiple metabolites and proteins, suggesting a combined protein-metabolic analysis integrated with ML would enable a powerful screening platform for first trimester diagnosis. Our study underscores RS metabolic profiling as a cost-effective tool that can be integrated into the current clinical workflow for accurate risk stratification of GDM and to improve both maternal and neonatal outcomes.

Multi-Biomarker Profiling for Precision Diagnosis of Lung Cancer

Multi-biomarker analysis can enhance the accuracy of the single-biomarker analysis by reducing the errors caused by genetic and environmental differences. For this reason, multi-biomarker analysis shows higher accuracy in early and precision diagnosis. However, conventional analysis methods have limitations for multi-biomarker analysis because of their long pre-processing times, inconsistent results, and large sample requirements. To solve these, a fast and accurate precision diagnostic method is introduced for lung cancer by multi-biomarker profiling using a single drop of blood. For this, surface-enhanced Raman spectroscopic immunoassay (SERSIA) is employed for the accurate, quick, and reliable quantification of biomarkers. Then, it is checked the statistical relation of the multi-biomarkers to differentiate between healthy controls and lung cancer patients. This approach has proven effective; with 20 µL of blood serum, lung cancer is diagnosed with 92% accuracy. It also accurately identifies the type and stage of cancer with 87% and 85%, respectively. These results show the importance of multi-biomarker analysis in overcoming the challenges posed by single-biomarker diagnostics. Furthermore, it markedly improves multi-biomarker-based analysis methods, illustrating its important impact on clinical diagnostics.

Unveiling intracellular phase separation: advances in optical imaging of biomolecular condensates

Intracellular biomolecular condensates, which form via phase separation, display a highly organized ultrastructure and complex properties. Recent advances in optical imaging techniques, including super-resolution microscopy and innovative microscopic methods that leverage the intrinsic properties of the molecules observed, have transcended the limitations of conventional microscopies. These advances facilitate the exploration of condensates at finer scales and in greater detail. The deployment of these emerging but sophisticated imaging tools allows for precise observations of the multiphasic organization and physicochemical properties of these condensates, shedding light on their functions in cellular processes. In this review, we highlight recent progress in methodological innovations and their profound implications for understanding the organization and dynamics of intracellular biomolecular condensates.

3D nanoplasmonic structure for ultrahigh enhanced SERS with less variability, polarization independence, and multimodal sensing applied to picric acid detection

Surface-enhanced Raman scattering (SERS) is recognized as a powerful analytical method. However, its efficacy is hindered by considerable signal variability stemming from factors like surface irregularities, temporal instability of the substrate, interference with substrate signal, polarization sensitivity and uneven molecular distribution. To address these challenges, a new strategy is employed to enhance the reproducibility of SERS signals. Initially, a periodic 3D metallic structure is utilized to achieve polarization-independent ultrahigh enhancement. Additionally, signal averaging over multiple points and normalization are implemented. The integration of these techniques enables multimodal sensing (SERS, SEF, SPR) using a plasmonic chip, demonstrating ultrahigh enhancement through the interaction of extended and localized plasmons alongside nanoantenna-type resonances. The chip comprises a periodic silver 2D grating adorned with Au nanocubes, behaving as a 3D metasurface to amplify plasmonic local fields, thus facilitating SERS. Its uniformity and polarization independence together with signal averaging and normalization mitigate signal variability. Fabricated via electron beam lithography, the chip's performance is evaluated for surface-enhanced fluorescence (SEF) and SERS using Rhodamine 6G as the target molecule. Results exhibit two orders of magnitude enhancement factor for SEF and 2.5 × 107 for SERS. For chemical sensing, the chip is tested for picric acid detection across a concentration range from nanomolar to millimolar, demonstrating a detection limit of approximately 3 nM.

Microlens Hollow-Core Fiber Probes for Operando Raman Spectroscopy

We introduce a flexible microscale all-fiber-optic Raman probe which can be embedded into devices to enable operando in situ spectroscopy. The facile-constructed probe is composed of a nested antiresonant nodeless hollow-core fiber combined with an integrated high refractive index barium titanate microlens. Pump laser 785 nm excitation and near-infrared collection are independently characterized, demonstrating an excitation spot of full-width-half-maximum 1.1 μm. Since this is much smaller than the effective collection area, it has the greatest influence on the collected Raman scattering. Our characterization scheme provides a suitable protocol for testing the efficacy of these fiber probes using various combinations of fiber types and microspheres. Raman measurements on a surface-enhanced Raman spectroscopy sample and a copper battery electrode demonstrate the viability of the fiber probe as an alternative to bulk optic Raman microscopes, giving comparable collection to a 10 objective, thus paving the way for operando Raman studies in applications such as lithium battery monitoring.

Simultaneous Protein Colorful Imaging via Raman Signal Classification

Protein imaging aids diagnosis and drug development by revealing protein-drug interactions or protein levels. However, the challenges of imaging multiple proteins, reduced sensitivity, and high reliance on specific protein properties such as Raman peaks or refractive index hinder the understanding. Here, we introduce multiprotein colorful imaging through Raman signal classification. Our method utilized machine learning-assisted classification of Raman signals, which are the distinctive features of label-free proteins. As a result, three types of proteins could be imaged simultaneously. In addition, we could quantify individual proteins from a mixture of multiple proteins over a wide detection range (10 fg/mL-1 μg/mL). These results showed a 1000-fold improvement in sensitivity and a 30-fold increase in the upper limit of detection compared to existing methods. These advances will enhance our understanding of biology and facilitate the development of disease diagnoses and treatments.

Approaching Maximum Resolution in Structured Illumination Microscopy via Accurate Noise Modeling

Biological images captured by microscopes are characterized by heterogeneous signal-to-noise ratios (SNRs) due to spatially varying photon emission across the field of view convoluted with camera noise. State-of-the-art unsupervised structured illumination microscopy (SIM) reconstruction algorithms, commonly implemented in the Fourier domain, do not accurately model this noise and suffer from high-frequency artifacts, user-dependent choices of smoothness constraints making assumptions on biological features, and unphysical negative values in the recovered fluorescence intensity map. On the other hand, supervised methods rely on large datasets for training, and often require retraining for new sample structures. Consequently, achieving high contrast near the maximum theoretical resolution in an unsupervised, physically principled, manner remains an open problem. Here, we propose Bayesian-SIM (B-SIM), an unsupervised Bayesian framework to quantitatively reconstruct SIM data, rectifying these shortcomings by accurately incorporating known noise sources in the spatial domain. To accelerate the reconstruction process, we use the finite extent of the point-spread-function to devise a parallelized Monte Carlo strategy involving chunking and restitching of the inferred fluorescence intensity. We benchmark our framework on both simulated and experimental images, and demonstrate improved contrast permitting feature recovery at up to 25% shorter length scales over state-of-the-art methods at both high- and low-SNR. B-SIM enables unsupervised, quantitative, physically accurate reconstruction without the need for labeled training data, democratizing high-quality SIM reconstruction and expands the capabilities of live-cell SIM to lower SNR, potentially revealing biological features in previously inaccessible regimes.

Unveiling brain disorders using liquid biopsy and Raman spectroscopy

Brain disorders, including neurodegenerative diseases (NDs) and traumatic brain injury (TBI), present significant challenges in early diagnosis and intervention. Conventional imaging modalities, while valuable, lack the molecular specificity necessary for precise disease characterization. Compared to the study of conventional brain tissues, liquid biopsy, which focuses on blood, tear, saliva, and cerebrospinal fluid (CSF), also unveils a myriad of underlying molecular processes, providing abundant predictive clinical information. In addition, liquid biopsy is minimally- to non-invasive, and highly repeatable, offering the potential for continuous monitoring. Raman spectroscopy (RS), with its ability to provide rich molecular information and cost-effectiveness, holds great potential for transformative advancements in early detection and understanding the biochemical changes associated with NDs and TBI. Recent developments in Raman enhancement technologies and advanced data analysis methods have enhanced the applicability of RS in probing the intricate molecular signatures within biological fluids, offering new insights into disease pathology. This review explores the growing role of RS as a promising and emerging tool for disease diagnosis in brain disorders, particularly through the analysis of liquid biopsy. It discusses the current landscape and future prospects of RS in the diagnosis of brain disorders, highlighting its potential as a non-invasive and molecularly specific diagnostic tool.

High-Resolution Raman Imaging of >300 Patient-Derived Cells from Nine Different Leukemia Subtypes: A Global Clustering Approach

Leukemia comprises a diverse group of bone marrow tumors marked by cell proliferation. Current diagnosis involves identifying leukemia subtypes through visual assessment of blood and bone marrow smears, a subjective and time-consuming method. Our study introduces the characterization of different leukemia subtypes using a global clustering approach of Raman hyperspectral maps of cells. We analyzed bone marrow samples from 19 patients, each presenting one of nine distinct leukemia subtypes, by conducting high spatial resolution Raman imaging on 319 cells, generating over 1.3 million spectra in total. An automated preprocessing pipeline followed by a single-step global clustering approach performed over the entire data set identified relevant cellular components (cytoplasm, nucleus, carotenoids, myeloperoxidase (MPO), and hemoglobin (HB)) enabling the unsupervised creation of high-quality pseudostained images at the single-cell level. Furthermore, this approach provided a semiquantitative analysis of cellular component distribution, and multivariate analysis of clustering results revealed the potential of Raman imaging in leukemia research, highlighting both advantages and challenges associated with global clustering.

Self-Assembly of Single-Virus SERS Hotspots for Highly Sensitive In Situ Detection of SARS-CoV-2 on Solid Surfaces

Microbial surface transmission has aroused great attention since the pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Developing a simple in situ detection method for viruses on solid surfaces is of great significance for timely public health surveillance. Taking advantage of the natural structure of SARS-CoV-2, we reported the assembly of Au@AgNPs on the surface of a single virus by the specific aptamer-spike protein interaction. Multiple hotspots can be created between the neighboring Au@AgNPs for the highly sensitive surface-enhanced Raman scattering (SERS) detection of SARS-CoV-2. Using two different aptamers labeled with Cy3 and Au@AgNPs, in situ SERS detection of pseudotyped SARS-CoV-2 (PSV) on packaging surfaces was achieved within 20 min, with a detection limit of 5.26 TCID50/mL. For the blind testing of 20 PSV-contaminated packaging samples, this SERS aptasensor had a sensitivity of 100% and an accuracy of 100%. This assay has been successfully applied to in situ detection of PSV on the surfaces of different packaging materials, suggesting its potential applicability.

Genetic algorithm-based semisupervised convolutional neural network for real-time monitoring of Escherichia coli fermentation of recombinant protein production using a Raman sensor

As a non-destructive sensing technique, Raman spectroscopy is often combined with regression models for real-time detection of key components in microbial cultivation processes. However, achieving accurate model predictions often requires a large amount of offline measurement data for training, which is both time-consuming and labor-intensive. In order to overcome the limitations of traditional models that rely on large datasets and complex spectral preprocessing, in addition to the difficulty of training models with limited samples, we have explored a genetic algorithm-based semi-supervised convolutional neural network (GA-SCNN). GA-SCNN integrates unsupervised process spectral labeling, feature extraction, regression prediction, and transfer learning. Using only an extremely small number of offline samples of the target protein, this framework can accurately predict protein concentration, which represents a significant challenge for other models. The effectiveness of the framework has been validated in a system of Escherichia coli expressing recombinant ProA5M protein. By utilizing the labeling technique of this framework, the available dataset for glucose, lactate, ammonium ions, and optical density at 600 nm (OD600) has been expanded from 52 samples to 1302 samples. Furthermore, by introducing a small component of offline detection data for recombinant proteins into the OD600 model through transfer learning, a model for target protein detection has been retrained, providing a new direction for the development of associated models. Comparative analysis with traditional algorithms demonstrates that the GA-SCNN framework exhibits good adaptability when there is no complex spectral preprocessing. Cross-validation results confirm the robustness and high accuracy of the framework, with the predicted values of the model highly consistent with the offline measurement results.

Unlocking Preclinical Alzheimer's: A Multi-Year Label-Free In Vitro Raman Spectroscopy Study Empowered by Chemometrics

Alzheimer's disease is a progressive neurodegenerative disorder, the early detection of which is crucial for timely intervention and enrollment in clinical trials. However, the preclinical diagnosis of Alzheimer's encounters difficulties with gold-standard methods. The current definitive diagnosis of Alzheimer's still relies on expensive instrumentation and post-mortem histological examinations. Here, we explore label-free Raman spectroscopy with machine learning as an alternative to preclinical Alzheimer's diagnosis. A special feature of this study is the inclusion of patient samples from different cohorts, sampled and measured in different years. To develop reliable classification models, partial least squares discriminant analysis in combination with variable selection methods identified discriminative molecules, including nucleic acids, amino acids, proteins, and carbohydrates such as taurine/hypotaurine and guanine, when applied to Raman spectra taken from dried samples of cerebrospinal fluid. The robustness of the model is remarkable, as the discriminative molecules could be identified in different cohorts and years. A unified model notably classifies preclinical Alzheimer's, which is particularly surprising because of Raman spectroscopy's high sensitivity regarding different measurement conditions. The presented results demonstrate the capability of Raman spectroscopy to detect preclinical Alzheimer's disease for the first time and offer invaluable opportunities for future clinical applications and diagnostic methods.

Power of Light: Raman Spectroscopy and Machine Learning for the Detection of Lung Cancer

Lung cancer is the leading cause of cancer-related deaths worldwide, emphasizing the urgent need for reliable and efficient diagnostic methods. Conventional approaches often involve invasive procedures and can be time-consuming and costly, thereby delaying the effective treatment. The current study explores the potential of Raman spectroscopy, as a promising noninvasive technique, by analyzing human blood plasma samples from lung cancer patients and healthy controls. In a benchmark study, 16 machine learning models were evaluated by employing four strategies: the combination of dimensionality reduction with classifiers; application of feature selection prior to classification; stand-alone classifiers; and a unified predictive model. The models showed different performances due to the inherent complexity of the data, achieving accuracies from 0.77 to 0.85 and areas under the curve for receiver operating characteristics from 0.85 to 0.94. Hybrid methods incorporating dimensionality reduction and feature selection algorithms present the highest figures of merit. Nevertheless, all machine learning models deliver creditable scores and demonstrate that Raman spectroscopy represents a powerful method for future in vitro diagnostics of lung cancer.

An intuitive approach for spike removal in Raman spectra based on peaks' prominence and width

BACKGROUND

Raman spectroscopists are familiar with the challenge of dealing with spikes caused by cosmic rays. These artifacts may lead to errors in subsequent data processing steps, such as for example calibration, normalization or spectral search. Spike removal is therefore a fundamental step in Raman spectral data pre-treatment, but access to publicly accessible code for spike removal tools is limited, and their performance for spectra correction often unknown. Therefore, there is a need for development and testing open-source and easy-to-implement algorithms that improve the Raman data processing workflow.

RESULTS

In this work, we present and validate two approaches for spike detection and correction in Raman spectral data from graphene: i) An algorithm based on the peaks' widths and prominences and ii) an algorithm based on the ratio of these two peak features. The first algorithm provides an efficient and reliable approach for spike detection in real and synthetic Raman spectra by imposing thresholds on the peaks' width and prominence. The second approach leverages the prominence/width ratio for outlier detection. It relies on the calculation of a limit of detection based on either one or several spectra, enabling the automatic identification of cosmic ray and low-intensity noise-originated spikes alike. Both algorithms detect low-intensity spikes down to at least ≈10% of the highest Raman peak of spectra with different noise levels. To address their limitations and prove their versatility, the algorithms were further tested in Raman spectra from calcite and polystyrene.

SIGNIFICANCE

Our intuitive, open-source algorithms have been validated and allow automatic correction for a given set of samples. They do not require any pre-processing steps such as calibration or baseline subtraction, and their implementation with Python libraries is computationally efficient, allowing for immediate utilization within existing open-source packages for Raman spectra processing.

Elucidating Iron Metabolism through Molecular Imaging

Iron is essential for many physiological processes, and the dysregulation of its metabolism is implicated in the pathogenesis of various diseases. Recent advances in iron metabolism research have revealed multiple complex pathways critical for maintaining iron homeostasis. Molecular imaging, an interdisciplinary imaging technique, has shown considerable promise in advancing research on iron metabolism. Here, we comprehensively review the multifaceted roles of iron at the cellular and systemic levels (along with the complex regulatory mechanisms of iron metabolism), elucidate appropriate imaging methods, and summarize their utility and fundamental principles in diagnosing and treating diseases related to iron metabolism. Utilizing molecular imaging technology to deeply understand the complexities of iron metabolism and its critical role in physiological and pathological processes offers new possibilities for early disease diagnosis, treatment monitoring, and the development of novel therapies. Despite technological limitations and the need to ensure the biological relevance and clinical applicability of imaging results, molecular imaging technology's potential to reveal the iron metabolic process is unparalleled, providing new insights into the link between iron metabolism abnormalities and various diseases.

SERS probing of fungal HET-s fibrils formed at neutral and acidic pH conditions

Advances in precision medical diagnostics require accurate and sensitive characterization of pathogens. In particular, health conditions associated with protein misfolding require an identification of proteinaceous amyloid fibrils or their precursors. These pathogenic entities express specific molecular structures, which require ultra-sensitive, molecular-level detection methods. A potentially transformative technique termed nanoplasmonics employs electro-optical phenomena in the vicinity of specially engineered metal nanostructures. A signature application of nanoplasmonics exploits enhancement of inelastic scattering of light in specific locations near metallic nanostructures, known as surface-enhanced Raman scattering (SERS). We applied SERS complemented with confocal microscopy imaging for ultra-sensitive, non-invasive, and label-free characterization of the fungal prion HET-s (218-289) as a model for β-sheet rich amyloid structures. This characterization employed Au-coated dielectric supports as plasmonic substrates. After confirming the formation of HET-s fibrils at both pH 7.5 and 2.8 using negative staining transmission electron microscopy, we subjected the fibril-containing solutions to multimodal analysis using confocal microscopy and SERS. The SERS spectral fingerprints from all HET-s samples expressed vibrational markers for β-structure, unstructured backbone, and aromatic side-chains. However, relative intensities of major SERS bands were pronouncedly different for the two pH levels. We have analyzed potential origins of the most pronounced SERS bands and proposed hypothetical mechanistic models that could explain the observed SERS fingerprints from HET-s fibrils grown at pH 7.5 and 2.8.

Combination of Machine Learning and Raman Spectroscopy for Determination of the Complex of Whey Protein Isolate with Hyaluronic Acid

Macromolecules and their complexes remain interesting topics in various fields, such as targeted drug delivery and tissue regeneration. The complex chemical structure of such substances can be studied with a combination of Raman spectroscopy and machine learning. The complex of whey protein isolate (WPI) and hyaluronic acid (HA) is beneficial in terms of drug delivery. It provides HA properties with the stability obtained from WPI. However, differences between WPI-HA and WPI solutions can be difficult to detect by Raman spectroscopy. Especially when the low HA (0.1, 0.25, 0.5% w/v) and the constant WPI (5% w/v) concentrations are used. Before applying the machine learning techniques, all the collected data were divided into training and test sets in a ratio of 3:1. The performances of two ensemble methods, random forest (RF) and gradient boosting (GB), were evaluated on the Raman data, depending on the type of problem (regression or classification). The impact of noise reduction using principal component analysis (PCA) on the performance of the two machine learning methods was assessed. This procedure allowed us to reduce the number of features while retaining 95% of the explained variance in the data. Another application of these machine learning methods was to identify the WPI Raman bands that changed the most with the addition of HA. Both the RF and GB could provide feature importance data that could be plotted in conjunction with the actual Raman spectra of the samples. The results show that the addition of HA to WPI led to changes mainly around 1003 cm-1 (correspond to ring breath of phenylalanine) and 1400 cm-1, as demonstrated by the regression and classification models. For selected Raman bands, where the feature importance was greater than 1%, a direct evaluation of the effect of the amount of HA on the Raman intensities was performed but was found not to be informative. Thus, applying the RF or GB estimators to the Raman data with feature importance evaluation could detect and highlight small differences in the spectra of substances that arose from changes in the chemical structure; using PCA to filter out noise in the Raman data could improve the performance of both the RF and GB. The demonstrated results will make it possible to analyze changes in chemical bonds during various processes, for example, conjugation, to study complex mixtures of substances, even with small additions of the components of interest.

Illuminating the Tiny World: A Navigation Guide for Proper Raman Studies on Microorganisms

Raman spectroscopy is an emerging method for the identification of bacteria. Nevertheless, a lot of different parameters need to be considered to establish a reliable database capable of identifying real-world samples such as medical or environmental probes. In this review, the establishment of such reliable databases with the proper design in microbiological Raman studies is demonstrated, shining a light into all the parts that require attention. Aspects such as the strain selection, sample preparation and isolation requirements, the phenotypic influence, measurement strategies, as well as the statistical approaches for discrimination of bacteria, are presented. Furthermore, the influence of these aspects on spectra quality, result accuracy, and read-out are discussed. The aim of this review is to serve as a guide for the design of microbiological Raman studies that can support the establishment of this method in different fields.

Two-in-one sensor of refractive index and Raman scattering using hollow-core microstructured optical waveguides for colloid characterization

Hollow-core microstructured optical waveguides (HC-MOW) have recently emerged in sensing technologies, including the gas and liquid detection for industrial as well as clinical applications. Antiresonant HC-MOW provide capabilities for applications in refractive index (RI) sensing, while the long optical path for analyte-light interaction in HC-MOW leads to increased sensitivity of sensor based on Raman scattering signal measurements. In this study, we developed a two-in-one sensor device using HC-MOW for RI and Raman scattering detection. The performance of the sensor was evaluated by characterizing protein-copolymer multicomponent colloids, specifically, bovine serum albumin (BSA) and poly(N - vinyl-2 -pyrrolidone-co-acrylic acid) P(VP-AA) nano-sized complexes and microbubbles of the corresponding shell. Monocomponent solutions showed linear dependencies of RI and characteristic Raman peak intensities on mass concentration. Multicomponent Raman sensing of BSA@P(VP-AA) complexes and microbubbles revealed that changes in P(VP-AA) characteristic peak intensities can describe interactions between components needed to produce colloid systems. RI sensing of multicomponent colloids demonstrated linear dependence on total mass concentrations for BSA@P(VP-AA) complexes, while corresponding BSA@P(VP-AA) microbubbles can be detected with concentrations as high as 4.0 × 108 MB/mL. Therefore, the developed two-in-one sensor of RI and Raman scattering can be used the robust characterization of albumin-based colloids designed for therapeutic and diagnostic needs.

Emergence and impact of theranostic-nanoformulation of triple therapeutics for combination cancer therapy

Cancer remains a major global health threat necessitating the multipronged approaches for its prevention and management. Traditional approaches in the form of chemotherapy, surgery, and radiotherapy are often encountered with poor patient outcomes evidenced by high mortality and morbidity, compelling the need for precision medicine for cancer patients to enable personalized and targeted cancer treatment. There has been an emergence of smart multimodal theranostic nanoformulation for triple combination cancer therapy in the last few years, which dramatically enhances the overall safety of the nanoformulation for in vivo and potential clinical applications with minimal toxicity. However, it is imperative to gain insight into the limitations of this system in terms of clinical translation, cost-effectiveness, accessibility, and multidisciplinary collaboration. This review paper aims to highlight and compare the impact of the recent theranostic nanoformulations of triple therapeutics in a single nanocarrier for effective management of cancer and provide a new dimension for diagnostic and treatment simultaneously.

Prussian blue (PB) modified gold nanoparticles as a SERS-based sensing platform for capturing and detection of pyrazinoic acid (POA)

Pyrazinoic acid (POA) is a metabolite of the anti-tuberculosis drug pyrazinamide (PZA), and its detection can be used to assess the resistance of Mycobacterium tuberculosis in cultures, as only sensitive strains of the bacteria can metabolize PZA into POA. Prussian blue is a well-known metal-organic framework compound widely used in various sensing platforms such as electrochemical, photochemical, and magnetic sensors. In this study, we present a novel sensing platform based on Prussian blue-modified gold nanoparticles (AuNPs) designed to enhance the affinity of POA towards the sensing surface and to capture POA molecules from aqueous solutions. This SERS-based method allows for the selective enrichment of POA, which can be detected in both pure aqueous solution and in the presence of its pro-drug PZA. The limit of detection (LOD) for POA was estimated to be 1.08 μM in pure aqueous solution and 0.18 mM in the presence of PZA. Furthermore, the precision of the SERS method was verified by the relative standard deviation (RSD) of 3.34-12.02% for three parallel samples using different matrices, i.e. aqueous solution, spiked river water and spiked simulated saliva. The recoveries of the samples ranged from 92.65 to 118.51%. These all demonstrate the potential application of the proposed detection scheme in medical research.

Simulated gastrointestinal digestion of polylactic acid (PLA) biodegradable microplastics and their interaction with the gut microbiota

The accumulation of microplastics (MPs) in the environment as well as their presence in foods and humans highlight the urgent need for studies on the effects of these particles on humans. Polylactic acid (PLA) is the most widely used bioplastic in the food industry and medical field. Despite its biodegradability, biocompatibility, and "Generally Recognized As Safe" (GRAS) status, recent animal model studies have shown that PLA MPs can alter the intestinal microbiota; however, to date, no studies have been reported on the possible gut and health consequences of its intake by humans. This work simulates the ingestion of a realistic daily amount of PLA MPs and their pass through the gastrointestinal tract by combining the INFOGEST method and the gastrointestinal simgi® model to evaluate possible effects on the human colonic microbiota composition (16S rRNA gene sequencing analysis) and metabolic functionality (lactic acid and short-chain fatty acids (SCFA) production). Although PLA MPs did not clearly alter the microbial community homeostasis, increased Bifidobacterium levels tended to increase in presence of millimetric PLA particles. Furthermore, shifts detected at the functional level suggest an alteration of microbial metabolism, and a possible biotransformation of PLA by the human microbial colonic community. Raman spectroscopy and field emission scanning electron microscopy (FESEM) characterization revealed morphological changes on the PLA MPs after the gastric phase of the digestion, and the adhesion of organic matter as well as a microbial biofilm, with surface biodegradation, after the intestinal and colonic phases. With this evidence and the emerging use of bioplastics, understanding their impact on humans and potential biodegradation through gastrointestinal digestion and the human microbiota merits critical investigation.

Microbial interaction-induced siderophore dynamics lead to phenotypic differentiation of Staphylococcus aureus

This study investigated the impact of microbial interactions on siderophore dynamics and phenotypic differentiation of Staphylococcus aureus under iron-deficient conditions. Optimization of media demonstrated that the glycerol alanine salts medium was best suited for analyzing the dynamics of siderophore production because of its stable production of diverse siderophore types. The effects of pH and iron concentration on siderophore yield revealed a maximum yield at neutral pH and low iron concentration (10 µg). Microbial interaction studies have highlighted variations in siderophore production when different strains (Staphylococcus epidermidis, Pseudomonas aeruginosa, and Escherichia coli) are co-cultured with S. aureus. Co-culture of S. aureus with P. aeruginosa eliminated siderophore production in S. aureus, while co-culture of S. aureus with E. coli and S. epidermidis produced one or two siderophores, respectively. Raman spectroscopy revealed that microbial interactions and siderophore dynamics play a crucial role in directing the phenotypic differentiation of S. aureus, especially under iron-deficient conditions. Our results suggest that microbial interactions profoundly influence siderophore dynamics and phenotypic differentiation and that the study of these interactions could provide valuable insights for understanding microbial survival strategies in iron-limited environments.

Dual-Modal Apoptosis Assay Enabling Dynamic Visualization of ATP and Reactive Oxygen Species in Living Cells

ATP and reactive oxygen species (ROS) are considered significant indicators of cell apoptosis. However, visualizing the interplay between apoptosis-related ATP and ROS is challenging. Herein, we developed a metal-organic framework (MOF)-based nanoprobe for an apoptosis assay using duplex imaging of cellular ATP and ROS. The nanoprobe was fabricated through controlled encapsulation of gold nanorods with a thin zirconium-based MOF layer, followed by modification of the ROS-responsive molecules 2-mercaptohydroquinone and 6-carboxyfluorescein-labeled ATP aptamer. The nanoprobe enables ATP and ROS visualization via fluorescence and surface-enhanced Raman spectroscopy, respectively, avoiding the mutual interference that often occurs in single-mode methods. Moreover, the dual-modal assay effectively showed dynamic imaging of ATP and ROS in cancer cells treated with various drugs, revealing their apoptosis-related pathways and interactions that differ from those under normal conditions. This study provides a method for studying the relationship between energy metabolism and redox homeostasis in cell apoptosis processes.

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.

Core-shell SERS nanotags-based western blot

Western blot (WB) is the most commonly used scheme for protein identification in life science, but it still faces great challenges in the accurate quantitative detection of low-abundance proteins. Here, we proposed a novel surface-enhanced Raman scattering-based Western blot (SERS-WB) to solve this challenge. SERS nanotags were used as quantitative labels of proteins, which were composed of gold-silver core-shell nanoparticles, and Nile blue A (NBA) molecules were anchored on the interface of the core and shell. The results show that the SERS-WB possessed excellent sensitivity with detection limit of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) protein of 0.15 pg, as well as wide linear dynamic range (LDR) of 382 fg to 382 ng. In addition, the target protein on nitrocellulose (NC) membrane could be directly identified by colorimetric signal due to the aggregation effect of nanoparticles, which greatly simplifies the procedure. This as-proposed strategy will bring new thoughts to technological innovation of WB.

Hydrophobic expanded graphite-covered support to construct flexible and stable SERS substrate for sensitive determination by paste-sampling from irregular surfaces

Surface enhanced Raman spectroscopy (SERS) is a promising technique for trace determination. More and more attention is focused on hybrid SERS substrates, which coupled with noble metal nanoparticles and carbon-based materials. Herein, expanded graphite (EG) is used to prepare EG-covered support by ultrasonic washing and filtration. Such support is flexible and can be cut into any shape. And the contact angle (θe) for Au nanorods (Au NRs) sol on the EG-covered support was 108.2° and the hydrophobic surface is helpful for Au NRs to construct 'hot spots' during evaporation. The limits of detection (LOD) for crystal violet (CV), thiram, malachite green (MG) and methylene blue (MB) were as low as 1 ppb, 50 ppb, 1 ppb and 1 ppb, respectively. Moreover, a fast and convenient 'paste-sampling' method could be employed for trace contaminants on real samples, because EG-based Au NRs substrate is of flexibility and porosity. Thus, CV residue on shrimp could be determined lower than 1 ppb and thiram residue on grapes could be identified lower than 50 ppb. In addition to high sensitivity, the stability of EG-based Au NRs substrate is also very good. Even after acid/alkali pretreatment (pH = 4∼10) or 30 min of thermal treatment (T = 20∼100 °C), the enhancement of the substrate remained stable. What's more, the substrate could be stored as long as 30 days. The highly stable, sensitive, cost-effective and easy-to-produce EG-based Au NRs substrates exhibit a great potential to promote application of SERS for routine analysis.

Raman Spectroscopy in Open-World Learning Settings Using the Objectosphere Approach

Raman spectroscopy, combined with machine learning techniques, holds great promise for many applications as a rapid, sensitive, and label-free identification method. Such approaches perform well when classifying spectra of chemical species that were encountered during the training phase. That is, species that are known to the neural network. However, in real-world settings, such as in clinical applications, there will always be substances whose spectra have not yet been taken. When the neural network encounters these new species during the testing phase, the number of false positives becomes uncontrollable, limiting the usefulness of these techniques, especially in public safety applications. To overcome these barriers, we implemented the recently introduced Entropic Open Set and Objectosphere loss functions. To demonstrate the efficacy and efficiency of this approach, we compiled a database of hyperspectral Raman images of 40 chemical species separating them into three class categorizations. The known class consisted of 20 biologically relevant species comprising amino acids, the ignored class was 10 "irrelevant" species comprising bio-related chemicals, and the never seen before class was 10 various chemical species that the neural network had not seen before. We show that this approach not only enables the network to effectively separate the unknown species while preserving high accuracy on the known ones and reducing false positives but also performs better than the current gold standards in machine learning techniques. This opens the door to using Raman spectroscopy, combined with our novel machine learning algorithm, in a variety of practical applications. Availability and implementation: freely available on the web at https://github.com/BalytskyiJaroslaw/RamanOpenSet.git.

High-Resolution Low-Power Hyperspectral Line-Scan Imaging of Fast Cellular Dynamics Using Azo-Enhanced Raman Scattering Probes

Small-molecule Raman probes for cellular imaging have attracted great attention owing to their sharp peaks that are sensitive to environmental changes. The small cross section of molecular Raman scattering limits dynamic cellular Raman imaging to expensive and complex coherent approaches that acquire single-channel images and lose hyperspectral Raman information. We introduce a new method, dynamic azo-enhanced Raman imaging (DAERI), to couple the new class of azo-enhanced Raman probes with a high-speed line-scan Raman imaging system. DAERI achieved high-resolution low-power imaging of fast cellular dynamics resolved at ∼270 nm along the confocal direction, 75 μW/μm² and 3.5 s/frame. Based on the azo-enhanced Raman probes with characteristic signals 10²-10⁴ stronger than classic Raman labels, DAERI was not restricted to the cellular Raman-silent region as in prior work and enabled multiplex visualization of organelle motions and interactions. We anticipate DAERI to be a powerful tool for future studies in biophysics and cell biology.

In Vitro Fiber-Probe-Based Identification of Pathogens in Biofilms by Raman Spectroscopy

Biofilms are the preferred habitat of microorganisms on living and artificial surfaces. Biofilm-related infections, such as infections of medical implants, are difficult to treat, and due to a reduced cultivability of the included bacteria, difficult to diagnose. Therefore, it is highly important to rapidly identify and investigate biofilms on implant surfaces, e.g., during surgery. In this study, we present fiber-probe-based Raman spectroscopy with an excitation wavelength of 785 nm, which was applied to investigate six different pathogen species involved in biofilm-related infections. Biofilms were cultivated in a drip flow reactor, which can model a biofilm growth environment. The signals collected from a fiber probe allowed us to collect Raman spectra not only from the embedded bacterial and yeast cells but also the surrounding extracellular polymeric substance matrix. This information was used in a classification model. The model consists of a principal component analysis in combination with linear discriminant analysis and was examined by applying a leave-one-batch-out cross-validation. This model achieved a classification accuracy of 93.8%. In addition, the identification accuracy increased up to 97.5% when clinical strains were used for identification. A fiber-probe-based Raman spectroscopy method combined with a chemometric analysis might therefore serve as a fast, accurate, and portable strategy for the species identification of biofilm-related infections, e.g., during surgical procedures.

Raman spectroscopy for the differentiation of Enterobacteriaceae: a comparison of two methods

A comprehensive dataset of bacteria of the family Enterobacteriaceae was collected and measured with Raman spectroscopy. Fiber-probe based Raman spectroscopy enabled classification with 100% accuracy and remained robust with a validation dataset.