Seeding multivariate algorithms for spectral analysis, a data augmentation approach to enhance analytical performance

Seeding spectral datasets by augmenting the data matrix with either the full spectrum or selected spectral features in order to bias multivariate analysis towards the solution of interest is explored. It is demonstrated that such seeding can have a profound effect on the endpoint of the analysis. Using Raman spectroscopic data of human lung adenocarcinoma cells (A549) in vitro, systematic perturbations to the spectra are introduced to simulate dose dependent exposure to a drug (cisplatin), and/or cellular response, representing reduced viability. Taking Principal Components Analysis (PCA) as the first example, seeding with the known spectral profiles of the drug exposure is demonstrated to greatly increase the ability of the algorithm to differentiate two distinct data subsets, representing control and exposed. The improved differentiation is quantified by further Linear Discriminant Analysis of the PCA data. Other examples of where seeding may be applied include, simulated datasets consisting of simultaneous changes in the spectral markers of exposure dose and cellular response, which are used for Multivariate Curve Resolution - Alternating Least Squares analysis (MCR-ALS). In the example presented, adding pure components to the dataset improves the ability of the algorithm to both model the systematic variation of concentration dependent data and extract the component spectra more accurately than the unseeded dataset. The seeded approach thus provides improved performance for differential analysis of datasets, as well as spectral unmixing analyses, to monitor, for example, the kinetic evolution of a reaction mixture, or metabolic pathway.

Cross-platform detection of microplastics in human biological tissues: Comparing spectroscopic and chromatographic approaches

Microplastic (MP) contamination in ecosystems underscores concerns about human bioaccumulation, yet analytical challenges persist due to complex biological matrices and polymer diversity. To systematically evaluate the efficacy of complementary analytical platforms, we conducted this study to systematically evaluate Raman microscopy and pyrolysis gas chromatography-mass spectrometry (py-GC/MS) for complementary MP detection in human biological samples. Building upon prior research frameworks, 48 paired endometrial and urine samples from parturient women were analyzed under rigorously controlled protocols to minimize exogenous contamination. Raman microscopy identified six polymer types, with polytetrafluoroethylene (PTFE) and polystyrene (PS) constituting primary components across both sample types. Particle size distributions spanned 1.23-6.98 μm, exhibiting comparable mean diameters in urine (2.85 ± 1.26 μm) and endometrial samples (2.89 ± 1.40 μm). Subsequent py-GC/MS analysis revealed previously undetected polymer co-occurrences (PS, PC, PE, and PVC) in samples initially classified as single-polymer PTFE or PS via Raman spectroscopy, thereby exposing inherent disparities in method-specific sensitivity and resolution. The follow-up multi-method comparison demonstrates that Raman microscopy excels in particle-specific morphological characterization, while py-GC/MS provides polymer quantification and composite identification. Our findings underscore the necessity of integrating orthogonal analytical approaches to overcome methodological limitations and achieve comprehensive MP profiling in complex biological systems.

Laser wavelength selection in Raman spectroscopy

Research in Raman spectroscopy continues to abound in a diverse range of application spaces and concurrently, components of Raman systems have become increasingly sophisticated. Laser wavelength choice is a key question in any Raman spectroscopy experiment, and the wavelength required, or indeed wavelengths, depends on a number of factors. For instance, are trace compounds being interrogated and thus plasmonic enhancement required? Or, are the experiments targeted at a specific molecule, or class of analytes, which are resonant at a specific wavelength range? Safety, resolution, and ease of post-processing spectra, can also be crucial in the decision process. While laser vendors commonly offer guidance in terms of what to consider when picking lasers for Raman studies, advice tends to be succinct. In this article, we discuss these variables more comprehensively, alongside the needs within certain kinds of experiments, to assist the Raman spectroscopist in their laser choice.

Nanopore sensing of protein and peptide conformation for point-of-care applications

The global population's aging and growth will likely result in an increase in chronic aging-related diseases. Early diagnosis could improve the medical care and quality of life. Many diseases are linked to misfolding or conformational changes in biomarker peptides and proteins, which affect their function and binding properties. Current clinical methods struggle to detect and quantify these changes. Therefore, there is a need for sensitive conformational sensors that can detect low-concentration analytes in biofluids. Nanopore electrical detection has shown potential in sensing subtle protein and peptide conformation changes. This technique can detect single molecules label-free while distinguishing shape or physicochemical property changes. Its proven sensitivity makes nanopore sensing technology promising for ultra-sensitive, personalized point-of-care devices. We focus on the capability of nanopore sensing for detecting and quantifying conformational modifications and enantiomers in biomarker proteins and peptides and discuss this technology as a solution to future societal health challenges.

Rapid and high accuracy identification of culture medium by CNN of Raman spectra

Culture media are widely used for biological research and production. It is essential for the growth of microorganisms, cells, or tissues. It includes complex components like carbohydrates, proteins, vitamins, and minerals. The media's consistency is key for predictable outcomes in biology applications. However, traditional methods of analyzing media are costly and time-consuming by using chromatography or mass spectrometry. This study introduces an innovative approach using optimized convolutional neural networks (CNN) combined with Raman spectroscopy to identify culture media. Samples of culture media from different models and batches are prepared for identification experiment. Raman spectra of each culture media samples are captured with unique molecular vibrations and rotations by Raman spectrometer rapidly. After preprocessing of sample data, Raman spectra are input to CNN for identification training and validation. An optimized CNN with more layers is designed to enhance the identify ability for Raman spectra. In experiment, it compared the performance of PCA-SVM, the original CNN, and an optimized CNN for media identification. The PCA-SVM achieved high accuracy and precision rates of 99.19% and 98.39% respectively. The original CNN achieved an accuracy of 71.89% due to limited training dataset. The optimized CNN model achieved a perfect accuracy rate of 100% in identifying different culture media. To avoid overfitting risk, additional external test is performed with optimized CNN. The result confirmed that optimized CNN offering effectiveness in identifying media from different models and batches, with strong generalization ability. The findings in study may offer an efficient and cost-effective method for pharmaceutical companies, to ensure the consistency of culture media.

The role of salivary metabolomics in chronic periodontitis: bridging oral and systemic diseases

BACKGROUND

Chronic periodontitis is a condition impacting approximately 50% of the world's population. As chronic periodontitis progresses, the bacteria in the oral cavity change resulting in new microbial interactions which in turn influence metabolite production. Chronic periodontitis manifests with inflammation of the periodontal tissues, which is progressively developed due to bacterial infection and prolonged bacterial interaction with the host immune response. The bi-directional relationship between periodontitis and systemic diseases has been reported in many previous studies. Traditional diagnostic methods for chronic periodontitis and systemic diseases such as chronic kidney diseases (CKD) have limitations due to their invasiveness, requiring practised individuals for sample collection, frequent blood collection, and long waiting times for the results. More rapid methods are required to detect such systemic diseases, however, the metabolic profiles of the oral cavity first need to be determined.

AIM OF REVIEW

In this review, we explored metabolomics studies that have investigated salivary metabolic profiles associated with chronic periodontitis and systemic illnesses including CKD, oral cancer, Alzheimer's disease, Parkinsons's disease, and diabetes to highlight the most recent methodologies that have been applied in this field.

KEY SCIENTIFIC CONCEPTS OF THE REVIEW

Of the rapid, high throughput techniques for metabolite profiling, Nuclear magnetic resonance (NMR) spectroscopy was the most applied technique, followed by liquid chromatography-mass spectrometry (LC-MS) and gas chromatography-mass spectrometry (GC-MS). Furthermore, Raman spectroscopy was the most used vibrational spectroscopic technique for comparison of the saliva from periodontitis patients to healthy individuals, whilst Fourier Transform Infra-Red Spectroscopy (FT-IR) was not utilised as much in this field. A recommendation for cultivating periodontal bacteria in a synthetic medium designed to replicate the conditions and composition of saliva in the oral environment is suggested to facilitate the identification of their metabolites. This approach is instrumental in assessing the potential of these metabolites as biomarkers for systemic illnesses.

Disentangled global and local features of multi-source data variational autoencoder: An interpretable model for diagnosing IgAN via multi-source Raman spectral fusion techniques

A single Raman spectrum reflects limited molecular information. Effective fusion of the Raman spectra of serum and urine source domains helps to obtain richer feature information. However, most of the current studies on immunoglobulin A nephropathy (IgAN) based on Raman spectroscopy are based on small sample data and low signal-to-noise ratio. If a multi-source data fusion strategy is directly adopted, it may even reduce the accuracy of disease diagnosis. To this end, this paper proposes a data enhancement and spectral optimization method based on variational autoencoders to obtain reconstructed Raman spectra with doubled sample size and improved signal-to-noise ratio. In the diagnosis of IgAN in multi-source domain Raman spectra, this paper builds a global and local feature decoupled variational autoencoder (DMSGL-VAE) model based on multi-source data. First, the statistical features after spectral segmentation are extracted, and the latent variables obtained by the variational encoder are decoupled through the decoupling module. The global representation and local representation obtained represent the global shared information and local unique information of the serum and urine source domains, respectively. Then, the cross-source reconstruction loss and decoupling loss are used to constrain the decoupling, and the effectiveness of the decoupling is proved quantitatively and qualitatively. Finally, the features of different source domains were integrated to diagnose IgAN, and the results were analyzed for important features using the SHapley Additive exPlanations algorithm. The experimental results showed that the AUC value of the DMSGL-VAE model for diagnosing IgAN on the test set was as high as 0.9958. The SHAP algorithm was used to further prove that proteins, hydroxybutyrate, and guanine are likely to be common biological fingerprint substances for the diagnosis of IgAN by serum and urine Raman spectroscopy. In summary, the DMSGL-VAE model designed based on Raman spectroscopy in this paper can achieve rapid, non-invasive, and accurate screening of IgAN in terms of classification performance. And interpretable analysis may help doctors further understand IgAN and make more efficient diagnostic measures in the future.

Rapid mechanical phenotyping of breast cancer cells based on stochastic intracellular fluctuations

Predicting the phenotypic impact of genetic variants and treatments is crucial in cancer genetics and precision oncology. Here, we have developed a noise decorrelation method that enables quantitative phase imaging (QPI) with the capability for label-free noninvasive mapping of intracellular dry mass fluctuations within the millisecond-to-second timescale regime, previously inaccessible due to temporal phase noise. Applied to breast cancer cells, this method revealed regions driven by thermal forces and regions of intense activity fueled by ATP hydrolysis. Intriguingly, as malignancy increases, the cells strategically expand these active regions to satisfy increasing energy demands. We propose parameters encapsulating key information about the spatiotemporal distribution of intracellular fluctuations, enabling precise phenotyping. This technique addresses the need for accurate, rapid functional screening methods in cancer medicine.

Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders

Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a nondestructive, label-free manner. Many applications entail the unmixing of signals from mixtures of molecular species to identify the individual components present and their proportions, yet conventional methods for chemometrics often struggle with complex mixture scenarios encountered in practice. Here, we develop hyperspectral unmixing algorithms based on autoencoder neural networks, and we systematically validate them using both synthetic and experimental benchmark datasets created in-house. Our results demonstrate that unmixing autoencoders provide improved accuracy, robustness, and efficiency compared to standard unmixing methods. We also showcase the applicability of autoencoders to complex biological settings by showing improved biochemical characterization of volumetric Raman imaging data from a monocytic cell.

New insights into lipid and fatty acid metabolism from Raman spectroscopy

One of the challenges facing biology is to understand metabolic events at a single cellular level. While approaches to examine dynamics of protein distribution or report on spatiotemporal location of signalling molecules are well-established, tools for the dissection of metabolism in single living cells are less common. Advances in Raman spectroscopy, such as stimulated Raman scattering (SRS), are beginning to offer new insights into metabolic events in a range of experimental systems, including model organisms and clinical samples, and across a range of disciplines. Despite the power of Raman imaging, it remains a relatively under-used technique to approach biological problems, in part because of the specialised nature of the analysis. To raise the profile of this method, here we consider some key studies which illustrate how Raman spectroscopy has revealed new insights into fatty acid and lipid metabolism across a range of cellular systems. The powerful and non-invasive nature of this approach offers a new suite of tools for biomolecular scientists to address how metabolic events within cells informs on or underpins biological function. We illustrate potential biological applications, discuss some recent advances, and offer a direction of travel for metabolic research in this area.

Similarity Metrics for Subcellular Analysis of FRET Microscopy Videos

Understanding the heterogeneity of molecular environments within cells is an outstanding challenge of great fundamental and technological interest. Cells are organized into specialized compartments, each with distinct functions. These compartments exhibit dynamic heterogeneity under high-resolution microscopy, which reflects fluctuations in molecular populations, concentrations, and spatial distributions. To enhance our comprehension of the spatial relationships among molecules within cells, it is crucial to analyze images of high-resolution microscopy by clustering individual pixels according to their visible spatial properties and their temporal evolution. Here, we evaluate the effectiveness of similarity metrics based on their ability to facilitate fast and accurate data analysis in time and space. We discuss the capability of these metrics to differentiate subcellular localization, kinetics, and structures of protein-RNA interactions in Forster resonance energy transfer (FRET) microscopy videos, illustrated by a practical example from recent literature. Our results suggest that using the correlation similarity metric to cluster pixels of high-resolution microscopy data should improve the analysis of high-dimensional microscopy data in a wide range of applications.

Uncovering low-frequency vibrations in surface-enhanced Raman of organic molecules

Accessing the terahertz (THz) spectral domain through surface-enhanced Raman spectroscopy (SERS) is challenging and opens up the study of low-frequency molecular and electronic excitations. Compared to direct THz probing of heterogenous ensembles, the extreme plasmonic confinement of visible light to deep sub-wavelength scales allows the study of hundreds or even single molecules. We show that self-assembled molecular monolayers of a set of simple aromatic thiols confined inside single-particle plasmonic nanocavities can be distinguished by their low-wavenumber spectral peaks below 200 cm-1, after removal of a bosonic inelastic contribution and an exponential background from the spectrum. Developing environment-dependent density-functional-theory simulations of the metal-molecule configuration enables the assignment and classification of their THz vibrations as well as the identification of intermolecular coupling effects and of the influence of the gold surface configuration. Furthermore, we show dramatically narrower THz SERS spectra from individual molecules at picocavities, which indicates the possibility to study intrinsic vibrational properties beyond inhomogeneous broadening, further supporting the key role of local environment.

Antimicrobial photodynamic therapy against Escherichia coli by exploiting endogenously produced Protoporphyrin IX- In vitro study

Due to antimicrobial drug resistance, there is a growing interest in the development of light based alternative antibacterial therapies. This research work is focused on the inactivation of Escherichia coli (E. coli) by exploiting the absorption bands 405, 505, 542, 580 and 631 nm of its indigenously produced Protoporphyrin IX (PpIX) excited by three LEDs with broad emission bands at 418, 522 and 630 nm and two laser diodes with narrow emission bands at 405 and 635 nm. Fluorescence spectroscopy and plate count method have been employed for studying the inactivation rate of E. coli strain in autoclaved water suspension. It has been found that LEDs at 418, 522 and 630 nm produced pronounced antimicrobial photodynamic effect on E. coli strain comparing laser diodes at 405 and 635 nm, which might be attributed to the overlapping of broad emission bands of LEDs with the absorption bands of PpIX than narrow emission bands of laser diodes. Particular effect of LED at 522 nm has been noticed because its broad emission band overlaps three absorption bands 505, 542 and 580 nm of PpIX. The gold standard plate count method strongly correlates with Fluorescence spectroscopy, making it an innovative tool to administer bacterial inactivation. The experimental results suggested the development of a light source that entirely overlap absorption bands of PpIx to produce a pronounced antimicrobial photodynamic effect, which might become an effective modality for in vivo disinfection of antibiotic resistant microbes in wounds and lesions.

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.

RamanSPy: An Open-Source Python Package for Integrative Raman Spectroscopy Data Analysis

Raman spectroscopy is a nondestructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardization, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of tools for spectroscopic analysis that supports day-to-day tasks, integrative analyses, the development of methods and protocols, and the integration of advanced data analytics. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis, and machine learning in Python. RamanSPy is hosted at https://github.com/barahona-research-group/RamanSPy, supplemented with extended online documentation, available at https://ramanspy.readthedocs.io, that includes tutorials, example applications, and details about the real-world research applications presented in this paper.

Label-free differentiation of functional zones in mature mouse placenta using micro-Raman imaging

In histopathology, it is highly crucial to have chemical and structural information about tissues. Additionally, the segmentation of zones within a tissue plays a vital role in investigating the functions of these regions for better diagnosis and treatment. The placenta plays a vital role in embryonic and fetal development and in diagnosing some diseases associated with its dysfunction. This study provides a label-free approach to obtain the images of mature mouse placenta together with the chemical differences between the tissue compartments using Raman spectroscopy. To generate the Raman images, spectra of placental tissue were collected using a custom-built optical setup. The pre-processed spectra were analyzed using statistical and machine learning methods to acquire the Raman maps. We found that the placental regions called decidua and the labyrinth zone are biochemically distinct from the junctional zone. A histologist performed a comparison and evaluation of the Raman map with histological images of the placental tissue, and they were found to agree. The results of this study show that Raman spectroscopy offers the possibility of label-free monitoring of the placental tissue from mature mice while simultaneously revealing crucial structural information about the zones.

ResD-Net: A model for rapid prediction of antioxidant activity in gentian root using FT-IR spectroscopy

Gentian, an herb resource known for its antioxidant properties, has garnered significant attention. However, existing methods are time-consuming and destructive for assessing the antioxidant activity in gentian root samples. In this study, we propose a method for swiftly predicting the antioxidant activity of gentian root using FT-IR spectroscopy combined with chemometrics. We employed machine learning and deep learning models to establish the relationship between FT-IR spectra and DPPH free radical scavenging activity. The results of model fitting reveal that the deep learning model outperforms the machine learning model. The model's performance was enhanced by incorporating the Double-Net and residual connection strategy. The enhanced model, named ResD-Net, excels in feature extraction and also avoids gradient vanishing. The ResD-Net model achieves an R2 of 0.933, an RMSE of 0.02, and an RPD of 3.856. These results support the accuracy and applicability of this method for rapidly predicting antioxidant activity in gentian root samples.

Species identification and strain discrimination of fermentation yeasts Saccharomyces cerevisiae and Saccharomyces uvarum using Raman spectroscopy and convolutional neural networks

IMPORTANCE

The use of S. cerevisiae and S. uvarum yeast starter cultures is a common practice in the alcoholic beverage fermentation industry. As yeast strains from different or the same species have variable fermentation properties, rapid and reliable typing of yeast strains plays an important role in the final quality of the product. In this study, Raman spectroscopy combined with CNN achieved accurate identification of S. cerevisiae and S. uvarum isolates at both the species and strain levels in a rapid, non-destructive, and easy-to-operate manner. This approach can be utilized to test the identity of commercialized dry yeast products and to monitor the diversity of yeast strains during fermentation. It provides great benefits as a high-throughput screening method for agri-food and the alcoholic beverage fermentation industry. This proposed method has the potential to be a powerful tool to discriminate S. cerevisiae and S. uvarum strains in taxonomic, ecological studies and fermentation applications.

Automated calibration and in-line measurement of product quality during therapeutic monoclonal antibody purification using Raman spectroscopy

Current manufacturing and development processes for therapeutic monoclonal antibodies demand increasing volumes of analytical testing for both real-time process controls and high-throughput process development. The feasibility of using Raman spectroscopy as an in-line product quality measuring tool has been recently demonstrated and promises to relieve this analytical bottleneck. Here, we resolve time-consuming calibration process that requires fractionation and preparative experiments covering variations of product quality attributes (PQAs) by engineering an automation system capable of collecting Raman spectra on the order of hundreds of calibration points from two to three stock seed solutions differing in protein concentration and aggregate level using controlled mixing. We used this automated system to calibrate multi-PQA models that accurately measured product concentration and aggregation every 9.3 s using an in-line flow-cell. We demonstrate the application of a nonlinear calibration model for monitoring product quality in real-time during a biopharmaceutical purification process intended for clinical and commercial manufacturing. These results demonstrate potential feasibility to implement quality monitoring during GGMP manufacturing as well as to increase chemistry, manufacturing, and controls understanding during process development, ultimately leading to more robust and controlled manufacturing processes.

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

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

Digital Histopathology by Infrared Spectroscopic Imaging

Infrared (IR) spectroscopic imaging records spatially resolved molecular vibrational spectra, enabling a comprehensive measurement of the chemical makeup and heterogeneity of biological tissues. Combining this novel contrast mechanism in microscopy with the use of artificial intelligence can transform the practice of histopathology, which currently relies largely on human examination of morphologic patterns within stained tissue. First, this review summarizes IR imaging instrumentation especially suited to histopathology, analyses of its performance, and major trends. Second, an overview of data processing methods and application of machine learning is given, with an emphasis on the emerging use of deep learning. Third, a discussion on workflows in pathology is provided, with four categories proposed based on the complexity of methods and the analytical performance needed. Last, a set of guidelines, termed experimental and analytical specifications for spectroscopic imaging in histopathology, are proposed to help standardize the diversity of approaches in this emerging area.

Protocol for bacterial typing using Fourier transform infrared spectroscopy

The Fourier transform infrared (FT-IR) signals obtained from bacterial samples are specific and reproducible, making FT-IR an efficient tool for bacterial typing at a subspecies level. However, the typing accuracy could be affected by many factors, including sample preparation and spectral acquisition. Here, we present a unified protocol for bacterial typing based on FT-IR spectroscopy. We describe sample preparation from bacterial culture and FT-IR spectrum collection. We then detail FT-IR spectrum preprocessing and multivariate analysis of spectral data for bacterial typing.

Raman spectroscopy-based biomarker screening by studying the fingerprint and lipid characteristics of Polycythemıa Vera cases blood serum

This study aimed to develop a novel approach for diagnosing Polycythemia Vera (PV), a stem cell-derived neoplasm of the myeloid lineage. The approach utilized Raman spectroscopy coupled with multivariate analysis to analyze blood serum samples collected from PV patients. The results showed that PV serum exhibited lower protein and lipid levels and structural changes in the functional groups that comprise proteins and lipids. The study also demonstrated differences in lipid biosynthesis and protein levels in PV serum. Using the Partial Least Square Discriminant Analysis (PLS-DA) model, Raman-based multivariate analysis achieved high accuracy rates of 96.49% and 93.04% in the training sets and 93.10% and 89.66% in the test sets for the 800-1800 cm-1 and 2700-3000 cm-1 ranges, respectively. The area under the curve (AUC) values of the test datasets were calculated as 0.92 and 0.89 in the 800-1800 cm-1 and 2700-3000 cm-1 spectral regions, respectively, demonstrating the effectiveness of the PLS-DA models for the diagnosis of PV. This study highlights the potential of Raman spectroscopy-based analysis in the early and accurate diagnosis of PV, enabling the application of effective treatment strategies.

Automatic classification of Candida species using Raman spectroscopy and machine learning

One of the problems that most affect hospitals is infections by pathogenic microorganisms. Rapid identification and adequate, timely treatment can avoid fatal consequences and the development of antibiotic resistance, so it is crucial to use fast, reliable, and not too laborious techniques to obtain quick results. Raman spectroscopy has proven to be a powerful tool for molecular analysis, meeting these requirements better than traditional techniques. In this work, we have used Raman spectroscopy combined with machine learning algorithms to explore the automatic identification of eleven species of the genus Candida, the most common cause of fungal infections worldwide. The Raman spectra were obtained from more than 220 different measurements of dried drops from pure cultures of each Candida species using a Raman Confocal Microscope with a 532 nm laser excitation source. After developing a spectral preprocessing methodology, a study of the quality and variability of the measured spectra at the isolate and species level, and the spectral features contributing to inter-class variations, showed the potential to discriminate between those pathogenic yeasts. Several machine learning and deep learning algorithms were trained using hyperparameter optimization techniques to find the best possible classifier for this spectral data, in terms of accuracy and lowest possible overfitting. We found that a one-dimensional Convolutional Neural Network (1-D CNN) could achieve above 80 % overall accuracy for the eleven classes spectral dataset, with good generalization capabilities.

Age estimation of bloodstains based on Raman spectroscopy and chemometrics

The accurate estimation of the bloodstain age, which is one of the important biological evidence of crime scene, can provide a lot of information related to crime. How to extract the information quickly and accurately from bloodstains without damage has been a focused problem. In this study, a bloodstains age estimation method based on Raman spectroscopy and chemometrics was developed. As many as 11 simulated environments based on different temperature and humidity were constructed in the method, and bloodstains of three species including human were studied. The influence of environmental factors such as temperature and humidity on the variation of Raman spectral peaks during the aging process of bloodstains was analyzed using the e-index fitting. When the humidity was kept constant, the increase of temperature generally promoted the changes of the spectral peaks. When the temperature was kept constant, the increase of humidity generally slowed the changes of spectral peaks. These works provide data support for the further development of Raman spectroscopy for bloodstain age estimation and could accelerate its application in actual scenes.

Deciphering the microheterogeneous repartition effect of environmental matrix on surface-enhanced Raman spectroscopy (SERS) analysis for pollutants in natural waters

Although surface-enhanced Raman spectroscopy (SERS) offers a promising technology for sensitive detection of environmental pollutants in natural waters, its performance can be greatly affected by the environmental matrix. The lack of identification of the origin and the underlying mechanism of matrix effect hinders the application of SERS in practical environmental analysis. Herein, with silver nanoparticles (AgNPs) as a solution-based SERS substrate, the matrix effect from environmental waters on SERS analysis and the underlying mechanisms were investigated. It was found that natural water matrix could deteriorate SERS performance and cause artefacts in SERS spectra. Among various aqueous components, natural organic matter (NOM), including humic substances and proteins, mainly contributed to the matrix effect on SERS detection, while polysaccharides or inorganic ions had minor influence. The matrix effect from NOM was found to be prevalent for different analytes and SERS substrates. The mechanism of the matrix effect from NOM in the ternary system of analyte, NOM, and nanoparticles was investigated through three mutual interactions. The microheterogeneous repartition of analytes by NOM, other than the formation of NOM-corona or competitive adsorption between NOM and analytes on nanoparticles, was found to play the dominating role in interfering with SERS detection. This work illuminates the origin and underlying mechanisms of the matrix effect, which will promote the practical application of SERS technology in environmental analysis.

Trends in digital detection for the quality and safety of herbs using infrared and Raman spectroscopy

Herbs have been used as natural remedies for disease treatment, prevention, and health care. Some herbs with functional properties are also used as food or food additives for culinary purposes. The quality and safety inspection of herbs are influenced by various factors, which need to be assessed in each operation across the whole process of herb production. Traditional analysis methods are time-consuming and laborious, without quick response, which limits industry development and digital detection. Considering the efficiency and accuracy, faster, cheaper, and more environment-friendly techniques are highly needed to complement or replace the conventional chemical analysis methods. Infrared (IR) and Raman spectroscopy techniques have been applied to the quality control and safety inspection of herbs during the last several decades. In this paper, we generalize the current application using IR and Raman spectroscopy techniques across the whole process, from raw materials to patent herbal products. The challenges and remarks were proposed in the end, which serve as references for improving herb detection based on IR and Raman spectroscopy techniques. Meanwhile, make a path to driving intelligence and automation of herb products factories.

Accurate identification of traumatic lung injury (TLI) by ATR-FTIR spectroscopy combined with chemometrics

Traumatic lung injury (TLI), which is a common mechanical injury, is receiving increasing attention because of its serious hazards. In forensic practices, accurately identifying TLI is of great importance for investigations and case trials. The main goal of this research was to identify TLI utilizing attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy in combination with chemometrics. The macroscopic appearance of lung tissue showed that identifying TLI in lung tissue at the decomposition stage is not feasible by only visualization, and significant pulmonary hypostasis was observed in the lungs regardless of whether the lung tissue was injured. Average spectra and principal component analysis (PCA) suggested that the biochemical difference between injured lung tissue samples from the TLI group and noninjured lung tissue samples from the negative control group was mainly attributed to the different structures and contents of proteins. Partial least squares discriminant analysis (PLS-DA) was then utilized to identify TLI with an accuracy of 96.4% and 98.6% based on the training set and the test set, respectively. Next, we focused on samples that were misclassified in the model and proposed that the misclassification could be caused by the pulmonary hypostasis effect. Therefore, two additional PCA and PLS-DA models were created to identify the pulmonary hypostatic areas between the TLI group and the negative control group and the nonpulmonary hypostatic areas between the TLI group and the negative control group. The PCA results indicated that the biochemical difference between the two groups was still associated with proteins, and the two PLS-DA models achieved 100% accuracy based on both the training and test sets. This result indicated that when pulmonary hypostasis was considered and the lung tissue was divided into pulmonary hypostatic areas and nonpulmonary hypostatic areas for separate comparisons, TLI identification was achieved with a greater accuracy than that obtained when the two areas were combined. This research confirms that the combined application of ATR-FTIR spectroscopy and chemometrics can be utilized to accurately identify TLI.

Influence of measurement mode on the results of glioblastoma multiforme analysis with the FTIR microspectroscopy

Fourier Transform Infrared (FTIR) microspectroscopy is well known for its effectiveness in spectral and biochemical analyses of various materials. It enables to determine the sample biochemical composition by assigning detected frequencies, characteristic for functional groups of main biological macromolecules. In analysis of tissue sections one of two measurement modes, namely transmission and transflection, is usually applied. The first one has relatively straightforward geometry, hence it is considered to be more precise and accurate. However, IR-transparent media are very fragile and expensive. Transflection does not require expensive substrates, but is more prone to disruptive influence of Mie scattering as well as electric field standing wave effect. The excessive comparison of spectra' characteristics, obtained via both measurement modes, was performed in this paper. By the means of Mann-Whitney non-parametrical U test and PCA, the comparison of results obtained with both modes and assessment of usefulness of IR spectra obtained with transmission and transflection modes to differentiate between healthy and GBM-affected tissue, were performed. The main objective of the presented research is to compare the results of FTIR analysis of unfixed biological samples performed with transflection and transmission mode. In frame of the study we demonstrated the discrepancies between results of biochemical analysis performed based on data obtained with transmission and transflection. Such observation suggests that caution should be taken in drawing conclusions from the results obtained with transflection geometry, as its more prone to disruptive effects. Despite that, IR spectra developed with both modes allowed to distinguish GBM area from healthy tissue, which proves their diagnostic potential. Especially, application of the ME-EMSC correction of spectra before PCA enhances the performance of both methods to distinguish the analysed tissue areas.

Rapid identification of salmonella serovars by using Raman spectroscopy and machine learning algorithm

A widespread and escalating public health problem worldwide is foodborne illness, and foodborne Salmonella infection is one of the most common causes of human illness.For the three most pathogenic Salmonella serotypes, Raman spectroscopy was employed to acquire spectral data.As machine learning offers high efficiency and accuracy, we have chosen the convolutional neural network(CNN), which is suitable for solving multi-classification problems, to do in-depth mining and analysis of Raman spectral data.To optimize the instrument parameters, we compared three laser wavelengths: 532, 638, and 785 nm.Ultimately, the 532 nm wavelength was chosen as the most effective for detecting Salmonella.A pre-processing step is necessary to remove interference from the background noise of the Raman spectrum.Our study compared the effects of five spectral preprocessing methods, Savitzky-Golay smoothing (SG), Multivariate Scatter Correction (MSC), Standard Normal Variate (SNV), and Hilbert Transform (HT), on the predictive power of CNN models.Accuracy(ACC), Precision, Recall, and F1-score 4 machine learning evaluation indicators are used to evaluate the model performance under different preprocessing methods.In the results, SG combined with SNV was found to be the most accurate spectral pre-processing method for predicting Salmonella serotypes using Raman spectroscopy, achieving an accuracy of 98.7% for the training set and over 98.5% for the test set in CNN model.Pre-processing spectral data using this method yields higher accuracy than other methods.As a conclusion, the results of this study demonstrate that Raman spectroscopy when used in conjunction with a convolutional neural network model enables the rapid identification of three Salmonella serotypes at the single-cell level, and that the model has a great deal of potential for distinguishing between different serotypes of pathogenic bacteria and closely related bacterial species.This is vital to preventing outbreaks of foodborne illness and the spread of foodborne pathogens.

Raman microspectroscopy based TNM staging and grading of breast cancer

The tumor-node-metastasis (TNM) system is the most common way that doctors determine the anatomical extent of cancer on the basis of clinical and pathological criteria. In this study, a spectral histopathological study has been carried out to bridge Raman micro spectroscopy with the breast cancer TNM system. A total of seventy breast tissue samples, including healthy tissue, early, middle, and advanced cancer, were investigated to provide detailed insights into compositional and structural variations that accompany breast malignant evolution. After evaluating the main spectral variations in all tissue types, the generalized discriminant analysis (GDA) pathological diagnostic model was established to discriminate the TNM staging and grading information. Moreover, micro-Raman images were reconstructed by K-means clustering analysis (KCA) for visualizing the lobular acinar in healthy tissue and ductal structures in all early, middle and advanced breast cancer tissue groups. While, univariate imaging techniques were adapted to describe the distribution differences of biochemical components such as tryptophan, β-carotene, proteins, and lipids in the scanned regions. The achieved spectral histopathological results not only established a spectra-structure correlations via tissue biochemical profiles but also provided important data and discriminative model references for in vivo Raman-based breast cancer diagnosis.

Microfluidic SERS devices: brightening the future of bioanalysis

A new avenue has opened up for applications of surface-enhanced Raman spectroscopy (SERS) in the biomedical field, mainly due to the striking advantages offered by SERS tags. SERS tags provide indirect identification of analytes with rich and highly specific spectral fingerprint information, high sensitivity, and outstanding multiplexing potential, making them very useful in in vitro and in vivo assays. The recent and innovative advances in nanomaterial science, novel Raman reporters, and emerging bioconjugation protocols have helped develop ultra-bright SERS tags as powerful tools for multiplex SERS-based detection and diagnosis applications. Nevertheless, to translate SERS platforms to real-world problems, some challenges, especially for clinical applications, must be addressed. This review presents the current understanding of the factors influencing the quality of SERS tags and the strategies commonly employed to improve not only spectral quality but the specificity and reproducibility of the interaction of the analyte with the target ligand. It further explores some of the most common approaches which have emerged for coupling SERS with microfluidic technologies, for biomedical applications. The importance of understanding microfluidic production and characterisation to yield excellent device quality while ensuring high throughput production are emphasised and explored, after which, the challenges and approaches developed to fulfil the potential that SERS-based microfluidics have to offer are described.

Attenuated total reflection-Fourier transform infrared spectroscopy: a universal analytical technique with promising applications in forensic analyses

Contemporary criminal investigations are based on the statements made by the victim and the eyewitnesses. They also rely on the physical evidences found in the crime scene. These evidences, and more particularly biological ones, have a great judicial value in the courtroom. They are usually used to revoke the suspect's allegations and confirm or refute the statements made by the victim and the witnesses. Stains of body fluids are biological evidences highly sought by forensic investigators. In many criminal cases, the success of the investigation relies on the correct identification and classification of these stains. Therefore, the adoption of reliable and accurate forensic analytical methods seems to be of vital importance to attain this objective. Attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) is a modern and universal analytical technique capable of fingerprint recognition of the analyte using minimal amount of the test sample. The current systematic review aims to through light on the fundamentals of this technique and to illustrate its wide range of applications in forensic investigations. ATR-FTIR is a nondestructive technique which has demonstrated an exceptional efficiency in detecting, identifying and discriminating between stains of various types of body fluids usually encountered in crime scenes. The ATR-FTIR spectral data generated from bloodstains can be used to deduce a wealth of information related to the donor species, age, gender, and race. These data can also be exploited to discriminate between stains of different types of bloods including menstrual and peripheral bloods. In addition, ATR-FTIR has a great utility in the postmortem investigations. More particularly, in estimating the postmortem interval and diagnosing death caused by extreme weather conditions. It is also useful in diagnosing some ambiguous death causes such as fatal anaphylactic shock and diabetic ketoacidosis.

Pathological and ATR-FTIR spectral changes of delayed splenic rupture and medical significance

Traumatic delayed splenic rupture often follows by a "latent period" without typical symptoms after injury. During this period, though there are no obvious symptoms, the injury is still present and changing. In this study, we constructed an SD rat model of delayed splenic rupture; evaluated the model by HE staining, Perl's staining, Masson trichrome staining and immunohistochemical staining; observed the pathological changes of spleen tissue in delayed splenic rupture at different times after splenic injury; we found that pathological change of injured tissues were different from non-injured, and has phases-change patterns, it can be roughly divided into three phases: 2-7 d, 10-14 d, and 18-28.We then investigated the relationship between the pathological changes and FTIR spectroscopy by chemometric methods. The main distinction of injured and non-injured tissue was the protein secondary structure of amide I, and the main distinctions of different phases of delayed splenic rupture were protein secondary structures and content of amide I and amide II.A classification model developed by SVM-DA was used to infer three phases (2-7 days, 10-12 days and 14-28 days). According to the most probable class, the accuracy of external validation is 96.7%. The results indicate that FTIR spectroscopy combined with various types of pathological staining has a potential for forensic identification and can provide theoretical support and diagnostic reference on clinical persistent injury.

Comparison of Vibrational Spectroscopic Techniques for Quantification of Water in Natural Deep Eutectic Solvents

Vibrational spectroscopic techniques, i.e., attenuated total reflectance infrared (ATR-IR), near infrared spectroscopy (NIRS) and Raman spectroscopy (RS), coupled with Partial Least Squares Regression (PLSR), were evaluated as cost-effective label-free and reagent-free tools to monitor water content in Levulinic Acid/L-Proline (LALP) (2:1, mol/mol) Natural Deep Eutectic Solvent (NADES). ATR-IR delivered the best outcome of Root Mean Squared Error (RMSE) of Cross-Validation (CV) = 0.27% added water concentration, RMSE of Prediction (P) = 0.27% added water concentration and mean % relative error = 2.59%. Two NIRS instruments (benchtop and handheld) were also compared during the study, respectively yielding RMSECV = 0.35% added water concentration, RMSEP = 0.56% added water concentration and mean % relative error = 5.13% added water concentration, and RMECV = 0.36% added water concentration, RMSEP = 0.68% added water concentration and mean % relative error = 6.23%. RS analysis performed in quartz cuvettes enabled accurate water quantification with RMECV = 0.43% added water concentration, RMSEP = 0.67% added water concentration and mean % relative error = 6.75%. While the vibrational spectroscopic techniques studied have shown high performance in relation to reliable determination of water concentration, their accuracy is most likely related to their sensitivity to detect the LALP compounds in the NADES. For instance, whereas ATR-IR spectra display strong features from water, Levulinic Acid and L-Proline that contribute to the PLSR predictive models constructed, NIRS and RS spectra are respectively dominated by either water or LALP compounds, representing partial molecular information and moderate accuracy compared to ATR-IR. However, while ATR-IR instruments are common in chemistry and physics laboratories, making the technique readily transferable to water quantification in NADES, Raman spectroscopy offers promising potential for future development for in situ, sample withdrawal-free analysis for high throughput and online monitoring.

Raman Spectroscopic Differentiation of Streptococcus pneumoniae From Other Streptococci Using Laboratory Strains and Clinical Isolates

Streptococcus pneumoniae, commonly referred to as pneumococci, can cause severe and invasive infections, which are major causes of communicable disease morbidity and mortality in Europe and globally. The differentiation of S. pneumoniae from other Streptococcus species, especially from other oral streptococci, has proved to be particularly difficult and tedious. In this work, we evaluate if Raman spectroscopy holds potential for a reliable differentiation of S. pneumoniae from other streptococci. Raman spectra of eight different S. pneumoniae strains and four other Streptococcus species (S. sanguinis, S. thermophilus, S. dysgalactiae, S. pyogenes) were recorded and their spectral features analyzed. Together with Raman spectra of 59 Streptococcus patient isolates, they were used to train and optimize binary classification models (PLS-DA). The effect of normalization on the model accuracy was compared, as one example for optimization potential for future modelling. Optimized models were used to identify S. pneumoniae from other streptococci in an independent, previously unknown data set of 28 patient isolates. For this small data set balanced accuracy of around 70% could be achieved. Improvement of the classification rate is expected with optimized model parameters and algorithms as well as with a larger spectral data base for training.

Label-Free Imaging of Lipid Droplets in Prostate Cells Using Stimulated Raman Scattering Microscopy and Multivariate Analysis

Hyperspectral stimulated Raman scattering (SRS) microscopy is a powerful imaging modality for the analysis of biological systems. Here, we report the application of k-means cluster analysis (KMCA) of multi-wavelength SRS images in the high-wavenumber region of the Raman spectrum as a robust and reliable method for the segmentation of cellular organelles based on the intrinsic SRS spectrum. KMCA has been applied to the study of the endogenous lipid biochemistry of prostate cancer and prostate healthy cell models, while the corresponding SRS spectrum of the lipid droplet (LD) cluster enabled direct comparison of their composition. The application of KMCA in visualizing the LD content of prostate cell models following the inhibition of de novo lipid synthesis (DNL) using the acetyl-coA carboxylase inhibitor, 5-(tetradecyloxy)-2-furoic acid (TOFA), is demonstrated. This method identified a reliance of prostate cancer cell models upon DNL for metabolic requirements, with a significant reduction in the cellular LD content after treatment with TOFA, which was not observed in normal prostate cell models. SRS imaging combined with KMCA is a robust method for investigating drug–cell interactions in a label-free manner.

Marker-Free, Molecule Sensitive Mapping of Disturbed Falling Fluid Films Using Raman Imaging

Technical liquid flow films are the basic arrangement for gas fluid transitions of all kinds and are the basis of many chemical processes, such as columns, evaporators, dryers, and different other kinds of fluid/fluid separation units. This publication presents a new method for molecule sensitive, non-contact, and marker-free localized concentration mapping in vertical falling films. Using Raman spectroscopy, no label or marker is needed for the detection of the local composition in liquid mixtures. In the presented cases, the film mapping of sodium sulfate in water on a plain surface as well as an added artificial streaming disruptor with the shape of a small pyramid is scanned in three dimensions. The results show, as a prove of concept, a clear detectable spectroscopic difference between air, back plate, and sodium sulfate for every local point in all three dimensions. In conclusion, contactless Raman scanning on falling films for liquid mapping is realizable without any mechanical film interaction caused by the measuring probe. Surface gloss or optical reflections from a metallic back plate are suppressed by using only inelastic light scattering and the mathematical removal of background noise.

A Comparison of PCA-LDA and PLS-DA Techniques for Classification of Vibrational Spectra

Vibrational spectroscopies provide information about the biochemical and structural environment of molecular functional groups inside samples. Over the past few decades, Raman and infrared-absorption-based techniques have been extensively used to investigate biological materials under different pathological conditions. Interesting results have been obtained, so these techniques have been proposed for use in a clinical setting for diagnostic purposes, as complementary tools to conventional cytological and histological techniques. In most cases, the differences between vibrational spectra measured for healthy and diseased samples are small, even if these small differences could contain useful information to be used in the diagnostic field. Therefore, the interpretation of the results requires the use of analysis techniques able to highlight the minimal spectral variations that characterize a dataset of measurements acquired on healthy samples from a dataset of measurements relating to samples in which a pathology occurs. Multivariate analysis techniques, which can handle large datasets and explore spectral information simultaneously, are suitable for this purpose. In the present study, two multivariate statistical techniques, principal component analysis-linear discriminate analysis (PCA-LDA) and partial least square-discriminant analysis (PLS-DA) were used to analyse three different datasets of vibrational spectra, each one including spectra of two different classes: (i) a simulated dataset comprising control-like and exposed-like spectra, (ii) a dataset of Raman spectra measured for control and proton beam-exposed MCF10A breast cells and (iii) a dataset of FTIR spectra measured for malignant non-metastatic MCF7 and metastatic MDA-MB-231 breast cancer cells. Both PCA-LDA and PLS-DA techniques were first used to build a discrimination model by using calibration sets of spectra extracted from the three datasets. Then, the classification performance was established by using test sets of unknown spectra. The achieved results point out that the built classification models were able to distinguish the different spectra types with accuracy between 93% and 100%, sensitivity between 86% and 100% and specificity between 90% and 100%. The present study confirms that vibrational spectroscopy combined with multivariate analysis techniques has considerable potential for establishing reliable diagnostic models.

Combining Pharmacokinetics and Vibrational Spectroscopy: MCR-ALS Hard-and-Soft Modelling of Drug Uptake In Vitro Using Tailored Kinetic Constraints

Raman microspectroscopy is a label-free technique which is very suited for the investigation of pharmacokinetics of cellular uptake, mechanisms of interaction, and efficacies of drugs in vitro. However, the complexity of the spectra makes the identification of spectral patterns associated with the drug and subsequent cellular responses difficult. Indeed, multivariate methods that relate spectral features to the inoculation time do not normally take into account the kinetics involved, and important theoretical information which could assist in the elucidation of the relevant spectral signatures is excluded. Here, we propose the integration of kinetic equations in the modelling of drug uptake and subsequent cellular responses using Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and tailored kinetic constraints, based on a system of ordinary differential equations. Advantages of and challenges to the methodology were evaluated using simulated Raman spectral data sets and real Raman spectra acquired from A549 and Calu-1 human lung cells inoculated with doxorubicin, in vitro. The results suggest a dependency of the outcome on the system of equations used, and the importance of the temporal resolution of the data set to enable the use of complex equations. Nevertheless, the use of tailored kinetic constraints during MCR-ALS allowed a more comprehensive modelling of the system, enabling the elucidation of not only the time-dependent concentration profiles and spectral features of the drug binding and cellular responses, but also an accurate computation of the kinetic constants.

Featuring few essential Raman spectroscopic signatures between heterogeneous cells

Here we demonstrate it is instructive to quantify cell Raman spectroscopy by sparse regularization. To be able to extract the specific spectral differences in a heterogeneous cell system with great spectroscopic similarities derived from many common biomolecular components, the maximum information entropy probability was proposed and exemplified by identifying normal lymphocytes from leukemia cells. The essential spectroscopic features were observed to locate at three Raman peaks whose spectral signatures were commensurate. The applicability of the extracted features was acknowledged by that the predicted identification accuracy of up to 93% was still achieved when only two peaks were loaded into decision tree model, which may provide the possibility of a clinically rapid hematological malignancy detection.

Raman Spectroscopy in Prostate Cancer: Techniques, Applications and Advancements

Optical techniques are widely used tools in the visualisation of biological species within complex matrices, including biopsies, tissue resections and biofluids. Raman spectroscopy is an emerging analytical approach that probes the molecular signature of endogenous cellular biomolecules under biocompatible conditions with high spatial resolution. Applications of Raman spectroscopy in prostate cancer include biopsy analysis, assessment of surgical margins and monitoring of treatment efficacy. The advent of advanced Raman imaging techniques, such as stimulated Raman scattering, is creating opportunities for real-time in situ evaluation of prostate cancer. This review provides a focus on the recent preclinical and clinical achievements in implementing Raman-based techniques, highlighting remaining challenges for clinical applications. The research and clinical results achieved through in vivo and ex vivo Raman spectroscopy illustrate areas where these evolving technologies can be best translated into clinical practice.

Post-mortem evaluation of the pathological degree of myocardial infarction by Fourier transform infrared microspectroscopy

In clinical and forensic investigations, accurate post-mortem diagnosis of the pathological degree of myocardial infarction (MI) is critical. However, because of the observer variability, the diagnosis cannot be made objectively. Many studies have shown that Fourier transform infrared (FTIR) microspectroscopy is non-invasive, observer-independent, and label-free when analyzing biological tissues. In this study, we used FTIR microspectroscopy in combination with intelligent algorithms to identify the pathological phases in human infarcted cardiac tissues, including ischemia, necrotic, granulation, and fibrotic stages. First, a comparison of infrared spectra corresponding to infarcted tissue pathological categories revealed various spectral properties. The results of unsupervised principal component analysis (PCA) revealed a clear distinction between these four pathological stages and the normal stage. Then, to identify these five stages, an automatic artificial neural network (ANN) classifier was effectively created. Finally, two-dimensional pseudo-color images of two infarcted cardiac tissue sections visualized via the ANN classifier showed great agreement with their histological images. These findings demonstrate that FTIR microspectroscopy has the potential for the post-mortem evaluation of the pathological degree of MI.

High-Throughput Molecular Imaging via Deep-Learning-Enabled Raman Spectroscopy

Raman spectroscopy enables nondestructive, label-free imaging with unprecedented molecular contrast, but is limited by slow data acquisition, largely preventing high-throughput imaging applications. Here, we present a comprehensive framework for higher-throughput molecular imaging via deep-learning-enabled Raman spectroscopy, termed DeepeR, trained on a large data set of hyperspectral Raman images, with over 1.5 million spectra (400 h of acquisition) in total. We first perform denoising and reconstruction of low signal-to-noise ratio Raman molecular signatures via deep learning, with a 10× improvement in the mean-squared error over common Raman filtering methods. Next, we develop a neural network for robust 2-4× spatial super-resolution of hyperspectral Raman images that preserve molecular cellular information. Combining these approaches, we achieve Raman imaging speed-ups of up to 40-90×, enabling good-quality cellular imaging with a high-resolution, high signal-to-noise ratio in under 1 min. We further demonstrate Raman imaging speed-up of 160×, useful for lower resolution imaging applications such as the rapid screening of large areas or for spectral pathology. Finally, transfer learning is applied to extend DeepeR from cell to tissue-scale imaging. DeepeR provides a foundation that will enable a host of higher-throughput Raman spectroscopy and molecular imaging applications across biomedicine.

Reusable and highly sensitive SERS immunoassay utilizing gold nanostars and a cellulose hydrogel-based platform

The development of robust and sensitive point-of-care testing platforms is necessary to improve patient care and outcomes. Surface-enhanced Raman scattering (SERS)-based immunosensors are especially suited for this purpose. Here, we present a highly sensitive and selective SERS immunoassay, demonstrating for example the detection of horseradish peroxidase (HRP), in a sandwich format. The strength of our biosensor lies in merging: (i) SERS-immunotags based on gold nanostars, allowing exceptional intense SERS from attached Raman probes, covalent attachment of anti-HRP antibodies by a simple chemical method providing exceptional antigen binding activity; (ii) the ease of preparation of the capture platform from a regenerated cellulose-based hydrogel, a transparent material, ideal for microfluidics applications, with low background fluorescence and Raman signal, particularly suited for preserving high activity of the covalently bound anti-HRP antibodies. The sandwich complexes formed were characterised by atomic force microscopy, and by scanning electron microscopy coupled with electron diffraction spectroscopy; and (iii) the robustness of the simple Classical Least Squares method for SERS data analysis, resulting in superior discrimination of SERS signals from the background and much better data fitting, compared to the commonly used peak integral method. Our SERS immunoassay greatly improves the detection limits of traditional enzyme-linked immunosorbent assay approaches, and its performance is better or comparable to those of existing SERS-based immunosensors. Our approach successfully overcomes the main challenges of application at point-of-care, including increasing reproducibility, sensitivity, and specificity, associated with an environmentally friendly and robust design. Also, the proposed design withstands several cycles of regeneration, a feature absent in paper-SERS immunoassays and this opens the way for sensitive multiplexing applications on a microfluidic platform.

Tracking of the biochemical changes upon pleomorphic adenoma progression using vibrational microspectroscopy

Head and neck tumors can be very challenging to treat because of the risk of problems or complications after surgery. Therefore, prompt and accurate diagnosis is extremely important to drive appropriate treatment decisions, which may reduce the chance of recurrence. This paper presents the original research exploring the feasibility of Fourier transform infrared (FT-IR) and Raman spectroscopy (RS) methods to investigate biochemical alterations upon the development of the pleomorphic adenoma. Principal component analysis (PCA) was used for a detailed assessment of the observed changes and to determine the spectroscopic basis for salivary gland neoplastic pathogenesis. It is implied that within the healthy margin, as opposed to the tumoral tissue, there are parts that differ significantly in lipid content. This observation shed new light on the crucial role of lipids in tissue physiology and tumorigenesis. Thus, a novel approach that eliminates the influence of lipids on the elucidation of biochemical changes is proposed. The performed analysis suggests that the highly heterogeneous healthy margin contains more unsaturated triacylglycerols, while the tumoral section is rich in proteins. The difference in protein content was also observed for these two tissue types, i.e. the healthy tissue possesses more proteins in the anti-parallel β-sheet conformation, whereas the tumoral tissue is dominated by proteins rich in unordered random coils. Furthermore, the pathogenic tissue shows a higher content of carbohydrates and reveals noticeable differences in nucleic acid content. Finally, FT-IR and Raman spectroscopy methods were proposed as very promising methods in the discrimination of tumoral and healthy tissues of the salivary gland.

Multimodal Scanning Microscope Combining Optical Coherence Tomography, Raman Spectroscopy and Fluorescence Lifetime Microscopy for Mesoscale Label-Free Imaging of Tissue

Multimodal optical imaging of tissue has significant potential to become an indispensable diagnostic tool in clinical pathology. Conventional bright-field microscopy provides contrast based on attenuation or reflectance of light, having no depth-related information and no molecular specificity. Recent developments in biomedical optics have introduced a variety of optical modalities, such as Raman spectroscopy (RS), fluorescence lifetime imaging microscopy (FLIM) of endogenous fluorophores, optical coherence tomography (OCT), and others, which provide a distinct characteristic, i.e., molecular, chemical, and morphological information, of the sample. To harvest the full analytical potential of those modalities, we have developed a novel multimodal imaging system, which allows the co-registered acquisition of OCT/FLIM/RS on a single device. The present implementation allows the investigation of biological tissues in the mesoscale range, 0.1-5 mm in a correlated manner. Due to the co-registered acquisition of the modalities, it is possible to directly compare and evaluate the corresponding information between the three modalities. Moreover, by additionally preparing and characterizing entire pathological hematoxylin and eosin (H&E) slides of head and neck biopsies, it is also possible to correlate the multimodal spectroscopic information to any location of the ground truth H&E information. To the best of our knowledge, this is the first development and implementation of a compact and clinically applicable multimodal scanning microscope, which combines OCT, FLIM, and RS together with the possibility for co-registering H&E information for a morpho-chemical tissue characterization and a correlation with the pathological ground truth (H&E) of the underlying signal origin directly in a clinical environment.