Spectral unmixing and clustering algorithms for assessment of single cells by Raman microscopic imaging

Raman micro-spectroscopy reveals the spatial distribution of fumarate in cells and tissues

Aberrantly accumulated metabolites elicit intra- and inter-cellular pro-oncogenic cascades, yet current measurement methods require sample perturbation/disruption and lack spatio-temporal resolution, limiting our ability to fully characterize their function and distribution. Here, we show that Raman spectroscopy (RS) can directly detect fumarate in living cells in vivo and animal tissues ex vivo, and that RS can distinguish between Fumarate hydratase (Fh1)-deficient and Fh1-proficient cells based on fumarate concentration. Moreover, RS reveals the spatial compartmentalization of fumarate within cellular organelles in Fh1-deficient cells: consistent with disruptive methods, we observe the highest fumarate concentration (37 ± 19 mM) in mitochondria, where the TCA cycle operates, followed by the cytoplasm (24 ± 13 mM) and then the nucleus (9 ± 6 mM). Finally, we apply RS to tissues from an inducible mouse model of FH loss in the kidney, demonstrating RS can classify FH status. These results suggest RS could be adopted as a valuable tool for small molecule metabolic imaging, enabling in situ non-destructive evaluation of fumarate compartmentalization.

Identification and visualization of environmental microplastics by Raman imaging based on hyperspectral unmixing coupled machine learning

Microplastics (MPs) are ubiquitous contaminants that have become an emerging pollutant of concern, potentially threatening human health and ecosystem environments. Although current detection methods can accurately identify various types of MPs, it remains necessary to develop non-destructive and rapid methods to meet growing demands for detection. Herein, we combine a hyperspectral unmixing method and machine learning to analyse Raman imaging data of environmental MPs. Five MPs types including poly(butylene adipate-co-terephthalate) (PBAT), poly(butylene succinate) (PBS), p-polyethylene (PE), polystyrene (PS) and polypropylene (PP) were visualized and identified. Individual or mixed pure or aged MPs along with environmental samples were analysed by Raman imaging. Alternating volume maximization (AVmax) combined with unconstrained least squares (UCLS) method estimated end members and abundance maps of each of the MPs in the samples. Pearson correlation coefficients (r) were used as the evaluation index; the results showed that there is a high similarity between the raw spectra and the average spectra calculated by AVmax. This indicates that Raman imaging based on machine learning and hyperspectral unmixing is a novel imaging analysis method that can directly identify and visualize MPs in the environment.

Unraveling Molecular Composition in Biological Samples-Systematic Evaluation of Statistical Methods for the Analysis of Hyperspectral Raman Data

Recently, confocal Raman microscopy has gained popularity in biomedical research for studying tissues in healthy and diseased state due to its ability to acquire chemically selective data in a noninvasive approach. However, biological samples, such as brain tissue, are inherently difficult to analyze due to the superposition of molecules in the Raman spectra and low variation of spectral features within the sample. The analysis is further impeded by pathological hallmarks, for example beta-amyloid (Aβ) plaques in Alzheimer's disease, which are often solely characterized by subtle shifts in the respective Raman peaks. To unravel the underlying molecular information, convoluted statistical procedures are inevitable. Unfortunately, such statistical methods are often inadequately described, and most natural scientists lack knowledge of their appropriate use, causing unreproducible results and stagnation in the application of hyperspectral Raman imaging. Therefore, we have set out to provide a comprehensive guide to address these challenges with the example of a complex hyperspectral data set of brain tissue samples with Aβ plaques. Our study encompasses established as well as novel statistical methods, including univariate analysis, principal component analysis, cluster analysis, spectral unmixing, and 2D correlation spectroscopy, and critically compares the outcomes of each analysis. Moreover, we transparently demonstrate the effect of preprocessing decisions like denoising and scaling techniques, providing valuable insights into implications of spectral quality for data evaluation. Thereby, this study provides a comprehensive evaluation of analysis approaches for complex hyperspectral Raman data, laying out a blueprint for elucidating meaningful information from biological samples in chemical imaging.

Multimodal Imaging of Pancreatic Cancer Microenvironment in Response to an Antiglycolytic Drug

Lipid metabolism and glycolysis play crucial roles in the progression and metastasis of cancer, and the use of 3-bromopyruvate (3-BP) as an antiglycolytic agent has shown promise in killing pancreatic cancer cells. However, developing an effective strategy to avoid chemoresistance requires the ability to probe the interaction of cancer drugs with complex tumor-associated microenvironments (TAMs). Unfortunately, no robust and multiplexed molecular imaging technology is currently available to analyze TAMs. In this study, the simultaneous profiling of three protein biomarkers using SERS nanotags and antibody-functionalized nanoparticles in a syngeneic mouse model of pancreatic cancer (PC) is demonstrated. This allows for comprehensive information about biomarkers and TAM alterations before and after treatment. These multimodal imaging techniques include surface-enhanced Raman spectroscopy (SERS), immunohistochemistry (IHC), polarized light microscopy, second harmonic generation (SHG) microscopy, fluorescence lifetime imaging microscopy (FLIM), and untargeted liquid chromatography and mass spectrometry (LC-MS) analysis. The study reveals the efficacy of 3-BP in treating pancreatic cancer and identifies drug treatment-induced lipid species remodeling and associated pathways through bioinformatics analysis.

Rapid identification of pathogens in blood serum via Raman tweezers in combination with advanced processing methods

Pathogenic microbes contribute to several major global diseases that kill millions of people every year. Bloodstream infections caused by these microbes are associated with high morbidity and mortality rates, which are among the most common causes of hospitalizations. The search for the "Holy Grail" in clinical diagnostic microbiology, a reliable, accurate, low cost, real-time, and easy-to-use diagnostic method, is one of the essential issues in clinical practice. These very critical conditions can be met by Raman tweezers in combination with advanced analysis methods. Here, we present a proof-of-concept study based on Raman tweezers combined with spectral mixture analysis that allows for the identification of microbial strains directly from human blood serum without user intervention, thus eliminating the influence of a data analyst.

Quantitative Raman chemical imaging of intracellular drug-membrane aggregates and small molecule drug precipitates in cytoplasmic organelles

Raman confocal microscopes have been used to visualize the distribution of small molecule drugs within different subcellular compartments. This visualization allows the discovery, characterization, and detailed analysis of the molecular transport phenomena underpinning the Volume of Distribution - a key parameter governing the systemic pharmacokinetics of small molecule drugs. In the specific case of lipophilic small molecules with large Volumes of Distribution, chemical imaging studies using Raman confocal microscopes have revealed how weakly basic, poorly soluble drug molecules can accumulate inside cells by forming stable, supramolecular complexes in association with cytoplasmic membranes or by precipitating out within organelles. To study the self-assembly and function of the resulting intracellular drug inclusions, Raman chemical imaging methods have been developed to measure and map the mass, concentration, and ionization state of drug molecules at a microscopic, subcellular level. Beyond the field of drug delivery, Raman chemical imaging techniques relevant to the study of microscopic drug precipitates and drug-lipid complexes which form inside cells are also being developed by researchers with seemingly unrelated scientific interests. Highlighting advances in data acquisition, calibration methods, and computational data management and analysis tools, this review will cover a decade of technological developments that enable the conversion of spectral signals obtained from Raman confocal microscopes into new discoveries and information about previously unknown, concentrative drug transport pathways driven by soluble-to-insoluble phase transitions occurring within the cytoplasmic organelles of eukaryotic cells.

Fundamentals and Applications of Raman-Based Techniques for the Design and Development of Active Biomedical Materials

Raman spectroscopy is an analytical method based on light-matter interactions that can interrogate the vibrational modes of matter and provide representative molecular fingerprints. Mediated by its label-free, non-invasive nature, and high molecular specificity, Raman-based techniques have become ubiquitous tools for in situ characterization of materials. This review comprehensively describes the theoretical and practical background of Raman spectroscopy and its advanced variants. The numerous facets of material characterization that Raman scattering can reveal, including biomolecular identification, solid-to-solid phase transitions, and spatial mapping of biomolecular species in bioactive materials, are highlighted. The review illustrates the potential of these techniques in the context of active biomedical material design and development by highlighting representative studies from the literature. These studies cover the use of Raman spectroscopy for the characterization of both natural and synthetic biomaterials, including engineered tissue constructs, biopolymer systems, ceramics, and nanoparticle formulations, among others. To increase the accessibility and adoption of these techniques, the present review also provides the reader with practical recommendations on the integration of Raman techniques into the experimental laboratory toolbox. Finally, perspectives on how recent developments in plasmon- and coherently-enhanced Raman spectroscopy can propel Raman from underutilized to critical for biomaterial development are provided.

Unsupervised segmentation of biomedical hyperspectral image data: tackling high dimensionality with convolutional autoencoders

Information about the structure and composition of biopsy specimens can assist in disease monitoring and diagnosis. In principle, this can be acquired from Raman and infrared (IR) hyperspectral images (HSIs) that encode information about how a sample's constituent molecules are arranged in space. Each tissue section/component is defined by a unique combination of spatial and spectral features, but given the high dimensionality of HSI datasets, extracting and utilising them to segment images is non-trivial. Here, we show how networks based on deep convolutional autoencoders (CAEs) can perform this task in an end-to-end fashion by first detecting and compressing relevant features from patches of the HSI into low-dimensional latent vectors, and then performing a clustering step that groups patches containing similar spatio-spectral features together. We showcase the advantages of using this end-to-end spatio-spectral segmentation approach compared to i) the same spatio-spectral technique not trained in an end-to-end manner, and ii) a method that only utilises spectral features (spectral k-means) using simulated HSIs of porcine tissue as test examples. Secondly, we describe the potential advantages/limitations of using three different CAE architectures: a generic 2D CAE, a generic 3D CAE, and a 2D convolutional encoder-decoder architecture inspired by the recently proposed UwU-net that is specialised for extracting features from HSI data. We assess their performance on IR HSIs of real colon samples. We find that all architectures are capable of producing segmentations that show good correspondence with HE stained adjacent tissue slices used as approximate ground truths, indicating the robustness of the CAE-driven spatio-spectral clustering approach for segmenting biomedical HSI data. Additionally, we stress the need for more accurate ground truth information to enable a precise comparison of the advantages offered by each architecture.

Chemometrics for Raman Spectroscopy Harmonization

Raman spectroscopy is used in a wide variety of fields, and in a plethora of different configurations. Raman spectra of simple analytes can often be analyzed using univariate approaches and interpreted in a straightforward manner. For more complex spetral data such as time series or line profiles (1D), Raman maps (2D), or even volumes (3D), multivariate data analysis (MVDA) becomes a requirement. Even though there are some existing standards for creation, implementation, and validation of methods and models employed in industry and academics, further research and development in the field must contribute to their improvement. This review will cover, in broad terms, existing techniques as well as new developments for MVDA for Raman spectroscopic data, and in particular the use associated with instrumentation and data calibration. Chemometric models are often generated via fusion of analytical data from different sources, which enhances model discrimination and prediction abilities as compared to models derived from a single data source. For Raman spectroscopy, raw or unprocessed data is rarely ever used. Instead, spectra are usually corrected and manipulated,¹ often by case-specific rather than universal methods. Calibration models can be used to characterize qualitatively and/or quantitatively samples measured with the same instrumentation that was used to create the model. However, regular validation is required to ensure that aging or incorrect maintenance of the instrument does not alter the model's predictions, particularly when applied in regulated fields such as pharmaceuticals. Furthermore, a model transfer may be required for different reasons, such as replacement or significant repair of the instrumentation. Modeling can also be used to consistently harmonize Raman spectroscopic data across several instrumental designs, accounting for variations in the resulting spectrum induced by different components. Data for Raman harmonization models should be processed in a protocolled manner, and the original data accessible to allow for model reconstruction or transfer when new data is added. Important processing steps will be the calibration of the spectral axes and instrument dependent effects, such as spectral resolution. In addition, data fusion and model transfer are essential for allowing new instrumentation to build on existing models to harmonize their own data. Ideally, an open access database would be created and maintained, for the purpose of allowing for continued harmonization of new Raman instruments using an outlined and accepted protocol.

Fast and Noninvasive Hair Test for Preliminary Diagnosis of Mood Disorders

The main objective of this study was to develop a test for the fast and noninvasive prediagnosis of mood disorders based on the noninvasive analysis of hair samples. The database included 75 control subjects (who were not diagnosed with depression) and 40 patients diagnosed with mood disorders such as depression or bipolar disorder. Both women and men, aged 18-65 years, participated in the research. After taking the hair samples, they were washed (methanol-water-methanol by shaking in a centrifuge for two min) and air-dried in a fume hood. Each hair collection was analyzed using Fourier transform infrared spectroscopy attenuated total reflection (ATR-FTIR) spectroscopy. Subsequently, the results obtained were analyzed based on chemometric methods: hierarchical cluster analysis (HCA) and principal component analysis (PCA). As a results of the research conducted, potential differences were noticed. There was a visible change in the spectra intensity at around 2800-3100 cm^-1 and smaller differences around 1460 cm^-1; the bands can be assigned to protein vibrations. However, these are preliminary studies that provide a good basis for the development of a test for the initial diagnosis of mood disorders.

Investigating the cellular responses of osteosarcoma to cisplatin by confocal Raman microspectroscopy

Confocal Raman Microspectroscopy (CRM) was employed to clarify the cellular response of cisplatin in osteosarcoma (OS) cells with different dosages and incubation times. The K7M2 mouse osteosarcoma cells were treated by cisplatin in 0 μM (UT group), 20 μM (20 T group), and 40 μM (40 T group) doses for 24-h (24H group) and 48-h (48H group), respectively. Raman spectroscopy was utilized to analyze the drug induced variations of intracellular biochemical components in osteosarcoma cells. The spectral results shows that the main changes in its biochemical composition come from nucleic acids. By adopting three different kernel functions (linear, polynomial, and Gaussian radial basis function (RBF)), principal component analysis combined with support vector machine models (PCA-SVM) was built to address the spectral variations among all investigated groups. Meanwhile, multivariate curve resolution alternating least squares (MCR-ALS) was further utilized to discuss on the chemical interpretation on the acquired spectral results. Moreover, Raman spectral images, which is reconstructed by K-means cluster analysis (KCA) with point-scanned hyperspectral dataset, was applied to illustrate the drug induced compositional and morphological variations in each subcellular region. The achieved results not only prove the application potential of Raman based analytical technique in non-labeled intracellular studies, but also illustrate the detailed compositional and structural information of cisplatin induced OS cell responses from the perspective of multivariate analysis and imaging of Raman spectroscopy.

Chemometric analysis in Raman spectroscopy from experimental design to machine learning-based modeling

Raman spectroscopy is increasingly being used in biology, forensics, diagnostics, pharmaceutics and food science applications. This growth is triggered not only by improvements in the computational and experimental setups but also by the development of chemometric techniques. Chemometric techniques are the analytical processes used to detect and extract information from subtle differences in Raman spectra obtained from related samples. This information could be used to find out, for example, whether a mixture of bacterial cells contains different species, or whether a mammalian cell is healthy or not. Chemometric techniques include spectral processing (ensuring that the spectra used for the subsequent computational processes are as clean as possible) as well as the statistical analysis of the data required for finding the spectral differences that are most useful for differentiation between, for example, different cell types. For Raman spectra, this analysis process is not yet standardized, and there are many confounding pitfalls. This protocol provides guidance on how to perform a Raman spectral analysis: how to avoid these pitfalls, and strategies to circumvent problematic issues. The protocol is divided into four parts: experimental design, data preprocessing, data learning and model transfer. We exemplify our workflow using three example datasets where the spectra from individual cells were collected in single-cell mode, and one dataset where the data were collected from a raster scanning-based Raman spectral imaging experiment of mice tissue. Our aim is to help move Raman-based technologies from proof-of-concept studies toward real-world applications.

Towards Raman-Based Screening of Acute Lymphoblastic Leukemia-Type B (B-ALL) Subtypes

Simple Summary

Acute lymphoblastic leukemia (ALL) is the most common pediatric malignancy originating from abnormal lymphoid progenitor cells. Since ALL is genetically highly heterogenous, more sensitive and rapid methods for identifying the molecular subtype of ALL are still being searched, and Raman spectroscopy (RS) has a chance of becoming a valuable tool for this purpose. Herein, the RS was applied to analyze normal B cells and three subtypes of B-ALL, characterized by the presence of the product of gene fusion, i.e., BCR-ABL1, TEL-AML1, and TCF3-PBX1. The classification and discrimination of normal and neoplastic cells were carried out with the chemometric approach. Normal B cells were characterized mostly by bands assigned to nucleic acids and proteins, whereas three subtypes of ALL appeared to contain a higher lipid content. Spectral differences between particular ALL subtypes were modest. The results lead to the conclusion that RS has the potential as a diagnostic tool in clinical practice.

Abstract

Acute lymphoblastic leukemia (ALL) is the most common type of malignant neoplasms in the pediatric population. B-cell precursor ALLs (BCP-ALLs) are derived from the progenitors of B lymphocytes. Traditionally, risk factors stratifying therapy in ALL patients included age at diagnosis, initial leukocytosis, and the response to chemotherapy. Currently, treatment intensity is modified according to the presence of specific gene alterations in the leukemic genome. Raman imaging is a promising diagnostic tool, which enables the molecular characterization of cells and differentiation of subtypes of leukemia in clinical samples. This study aimed to characterize and distinguish cells isolated from the bone marrow of patients suffering from three subtypes of BCP-ALL, defined by gene rearrangements, i.e., BCR-ABL1 (Philadelphia-positive, t(9;22)), TEL-AML1 (t(12;21)) and TCF3-PBX1 (t(1;19)), using single-cell Raman imaging combined with multivariate statistical analysis. Spectra collected from clinical samples were compared with single-cell spectra of B-cells collected from healthy donors, constituting the control group. We demonstrated that Raman spectra of normal B cells strongly differ from spectra of their malignant counterparts, especially in the intensity of bands, which can be assigned to nucleic acids. We also showed that the identification of leukemia subtypes could be automated with the use of chemometric methods. Results prove the clinical suitability of Raman imaging for the identification of spectroscopic markers characterizing leukemia cells.

Micro-Raman spectroscopy of lipid halo and dense-core amyloid plaques: aging process characterization in the Alzheimer's disease APPswePS1ΔE9 mouse model

The deposition of amyloid plaques is considered one of the main microscopic features of Alzheimer's disease (AD). Since plaque formation can precede extensive neurodegeneration and it is the main clinical manifestation of AD, it constitutes a relevant target for new treatment and diagnostic approaches. Micro-Raman spectroscopy, a label-free technique, is an accurate method for amyloid plaque identification and characterization. Here, we present a high spatial resolution micro-Raman hyperspectral study in transgenic APPswePS1ΔE9 mouse brains, showing details of AD tissue biochemical and histological changes without staining. First we used stimulated micro-Raman scattering to identify the lipid-rich halo surrounding the amyloid plaque, and then proceeded with spontaneous (conventional) micro-Raman spectral mapping, which shows a cholesterol and sphingomyelin lipid-rich halo structure around dense-core amyloid plaques. The detailed images of this lipid halo relate morphologically well with dystrophic neurites surrounding plaques. Principal Component Analysis (PCA) of the micro-Raman hyperspectral data indicates the feasibility of the optical biomarkers of AD progression with the potential for discriminating transgenic groups of young adult mice (6-month-old) from older ones (12-month-old). Frequency-specific PCA suggests that plaque-related neurodegeneration is the predominant change captured by Raman spectroscopy, and the main differences are highlighted by vibrational modes associated with cholesterol located majorly in the lipid halo.

Multivariate analysis of Brillouin imaging data by supervised and unsupervised learning

Brillouin imaging relies on the reliable extraction of subtle spectral information from hyperspectral datasets. To date, the mainstream practice has been to use line fitting of spectral features to retrieve the average peak shift and linewidth parameters. Good results, however, depend heavily on sufficient signal-to-noise ratio and may not be applicable in complex samples that consist of spectral mixtures. In this work, we thus propose the use of various multivariate algorithms that can be used to perform supervised or unsupervised analysis of the hyperspectral data, with which we explore advanced image analysis applications, namely unmixing, classification and segmentation in a phantom and live cells. The resulting images are shown to provide more contrast and detail, and obtained on a timescale ∼10² faster than fitting. The estimated spectral parameters are consistent with those calculated from pure fitting.

Falcarindiol Purified From Carrots Leads to Elevated Levels of Lipid Droplets and Upregulation of Peroxisome Proliferator-Activated Receptor-γ Gene Expression in Cellular Models

Falcarindiol (FaDOH) is a cytotoxic and anti-inflammatory polyacetylenic oxylipin found in food plants of the carrot family (Apiaceae). FaDOH has been shown to activate PPARγ and to increase the expression of the cholesterol transporter ABCA1 in cells, both of which play an important role in lipid metabolism. Thus, a common mechanism of action of the anticancer and antidiabetic properties of FaDOH may be due to a possible effect on lipid metabolism. In this study, the effect of sub-toxic concentration (5 μM) of FaDOH inside human mesenchymal stem cells (hMSCs) was studied using white light microscopy and Raman imaging. Our results show that FaDOH increases lipid content in the hMSCs cells as well as the number of lipid droplets (LDs) and that this can be explained by increased expression of PPARγ2 as shown in human colon adenocarcinoma cells. Activation of PPARγ can lead to increased expression of ABCA1. We demonstrate that ABCA1 is upregulated in colorectal neoplastic rat tissue, which indicates a possible role of this transporter in the redistribution of lipids and increased formation of LDs in cancer cells that may lead to endoplasmic reticulum stress and cancer cell death.

A fingerprint of amyloid plaques in a bitransgenic animal model of Alzheimer's disease obtained by statistical unmixing analysis of hyperspectral Raman data

The global prevalence of Alzheimer's disease (AD) points to endemic levels, especially considering the increase of average life expectancy worldwide. AD diagnosis based on early biomarkers and better knowledge of related pathophysiology are both crucial in the search for medical interventions that are able to modify AD progression. In this study we used unsupervised spectral unmixing statistical techniques to identify the vibrational spectral signature of amyloid β aggregation in neural tissues, as early biomarkers of AD in an animal model. We analyzed spectral images composed of a total of 55 051 Raman spectra obtained from the frontal cortex and hippocampus of five bitransgenic APPswePS1ΔE9 mice, and colocalized amyloid β plaques by other fluorescence techniques. The Raman signatures provided a multifrequency fingerprint consistent with the results of synthesized amyloid β fibrils. The fingerprint obtained from unmixed analysis in neural tissues is shown to provide a detailed image of amyloid plaques in the brain, with the potential to be used as biomarkers for non-invasive early diagnosis and pathophysiology studies in AD on the retina.

Immune cell type, cell activation, and single cell heterogeneity revealed by label-free optical methods

Measurement techniques that allow the global analysis of cellular responses while retaining single-cell sensitivity are increasingly needed in order to understand complex and dynamic biological processes. In this context, compromises between sensitivity, degree of multiplexing, throughput, and invasiveness are often unavoidable. We present here a noninvasive optical approach that can retrieve quantitative biomarkers of both morphological and molecular phenotypes of individual cells, based on a combination of quantitative phase imaging and Raman spectroscopy measurements. We then develop generalized statistical tools to assess the influence of both controlled (cell sub-populations, immune stimulation) and uncontrolled (culturing conditions, animal variations, etc.) experimental parameters on the label-free biomarkers. These indicators can detect different macrophage cell sub-populations originating from different progenitors as well as their activation state, and how these changes are related to specific differences in morphology and molecular content. The molecular indicators also display further sensitivity that allow identification of other experimental conditions, such as differences between cells originating from different animals, allowing the detection of outlier behaviour from given cell sub-populations.

Lactoferrin translocates to the nucleus of bovine rectal epithelial cells in the presence of Escherichia coli O157:H7

Enterohemorrhagic Escherichia coli (EHEC) O157:H7 is a foodborne pathogen which causes illness in humans. Ruminants are the main reservoirs and EHEC predominantly colonizes the epithelium of the recto-anal junction of cattle. Immunosuppression by EHEC promotes re-infection of cattle. However, bovine lactoferrin (bLF) apparently can overrule the immunosuppression by inducing EHEC-specific IgA responses at the mucosal site. The IgA responses are significantly correlated with reduced EHEC shedding and the absence of colonization at the rectal mucosa following re-infection. Therefore, to examine the interaction between bLF and bovine rectal epithelial cells, we first developed a method to establish a primary cell culture of epithelial cells of the rectum of cattle. Furthermore, we used LC–MS/MS to demonstrate the presence of secreted lactoferrin in bovine milk and the absence of a “delta” isoform which is known to translocate to the nucleus of cells. Nevertheless, lactoferrin derived from bovine milk was internalized by rectal epithelial cells and translocated to the nuclei. Moreover, nuclear translocation of bLF was significantly enhanced when the epithelial cells were inoculated with EHEC, as demonstrated by confocal fluorescence microscopy and confirmed by Raman microscopy and 3D imaging.

High-Resolution Raman Microscopic Detection of Follicular Thyroid Cancer Cells with Unsupervised Machine Learning

We use Raman microscopic images with high spatial and spectral resolution to investigate differences between human follicular thyroid (Nthy-ori 3-1) and follicular thyroid carcinoma (FTC-133) cells, a well-differentiated thyroid cancer. Through comparison to classification of single-cell Raman spectra, the importance of subcellular information in the Raman images is emphasized. Subcellular information is extracted through a coarse-graining of the spectra at high spatial resolution (∼1.7 μm²), producing a set of characteristic spectral groups representing locations having similar biochemical compositions. We develop a cell classifier based on the frequencies at which the characteristic spectra appear within each of the single cells. Using this classifier, we obtain a more accurate (89.8%) distinction of FTC-133 and Nthy-ori 3-1, in comparison to single-cell spectra (77.6%). We also infer which subcellular components are important to cellular distinction; we find that cancerous FTC-133 cells contain increased populations of lipid-containing components and decreased populations of cytochrome-containing components relative to Nthy-ori 3-1, and that the regions containing these contributions are largely outside the cell nuclei. In addition to increased classification accuracy, this approach provides rich subcellular information about biochemical differences and cellular locations associated with the distinction of the normal and cancerous follicular thyroid cells.

A Strategy to Assess Quality Consistency of Drug Products

Herein, we aimed to develop a strategy to assess quality consistency of a drug product, with a focus on two typical cases of injection. Multi-variable analysis using a sequencing combination of factor analysis, one-way analysis of variance and cluster analysis identified all potential Critical Quality Attributes (CQAs) for each manufacturing process, which were identified from the attributes of quality standard (QAs) using supervised (cefazolin sodium pentahydrate, α-CEZ-Na) or unsupervised (cephathiamidine, CETD) analysis. All CQAs from QAs were applied to set up an integrated index, quality consistency attribute (QCA), to evaluate product quality consistency in a specific aspect. Meanwhile, real-time analysis by chemometrics-assisted near-infrared spectroscopy (NIR) was used to obtain useful information corresponding to the CQAs from the process attributes (PAs) of some of the critical processes. The quantitative results of characteristic signals of NIR by multiple linear regression was defined as the process consistency attribute (PrCA), and was used to assess the product quality consistency in another aspect. Therefore, either values of QCA or PrCA displayed sensitivity to changes in product quality, allowing us to establish a strategy with strong practicality, comprehensiveness and visualization to demonstrate the quality consistency of a specific product. Such strategy is not only conducive to the improvement of quality standards, but to the retrospective investigation of manufacturing processes which ultimately allowed maintenance of product consistency.

Analyzing Raman spectroscopic data

This chapter is a short introduction into the data analysis pipeline, which is typically utilized to analyze Raman spectra. We empathized in the chapter that this data analysis pipeline must be tailored to the specific application of interest. Nevertheless, the tailored data analysis pipeline consists always of the same general procedures applied sequentially. The utilized procedures correct for artefacts, standardize the measured spectral data and translate the spectroscopic signals into higher level information. These computational procedures can be arranged into separate groups namely data pre-treatment, pre-processing and modeling. Thereby the pre-treatment aims to correct for non-sample-dependent artefacts, like cosmic spikes and contributions of the measurement device. The block of procedures, which needs to be applied next, is called pre-processing. This group consists of smoothing, baseline correction, normalization and dimension reduction. Thereafter, the analysis model is constructed and the performance of the models is evaluated. Every data analysis pipeline should be composed of procedures of these three groups and we describe every group in this chapter. After the description of data pre-treatment, pre-processing and modeling, we summarized trends in the analysis of Raman spectra namely model transfer approaches and data fusion. At the end of the chapter we tried to condense the whole chapter into guidelines for the analysis of Raman spectra.

Resolving Interobserver Discrepancies in Lung Cancer Diagnoses by Spectral Histopathology

This paper reports the results of a collaborative lung cancer study between City of Hope Cancer Center (Duarte, California) and CIRECA, LLC (Cambridge, Massachusetts), comprising 328 samples from 249 patients, that used an optical technique known as spectral histopathology (SHP) for tissue classification. Because SHP is based on a physical measurement, it renders diagnoses on a more objective and reproducible basis than methods based on assessing cell morphology and tissue architecture. This report demonstrates that SHP provides distinction of adenocarcinomas from squamous cell carcinomas of the lung with an accuracy comparable to that of immunohistochemistry and highly reliable classification of adenosquamous carcinoma. Furthermore, this report shows that SHP can be used to resolve interobserver differences in lung pathology. Spectral histopathology is based on the detection of changes in biochemical composition, rather than morphologic features, and is therefore more akin to methods such as matrix-assisted laser desorption ionization time-of-flight mass spectrometry imaging. Both matrix-assisted laser desorption ionization time-of-flight mass spectrometry and SHP imaging modalities demonstrate that changes in tissue morphologic features observed in classical pathology are accompanied by, and may be correlated to, changes in the biochemical composition at the cellular level. Thus, these imaging methods provide novel insight into biochemical changes due to disease.

Raman tweezers microspectroscopy of circa 100 nm extracellular vesicles

The technique of Raman tweezers microspectroscopy (RTM) for the global biomolecular content characterization of a single extracellular vesicle (EV) or a small number of EVs or other nanoscale bioparticles in an aqueous dispersion in the difficult-to-access size range of near 100 nm is described in detail. The particularities and potential of RTM are demonstrated using the examples of DOPC liposomes, exosomes from human urine and rat hepatocytes, and a mixed sample of the transfection reagent FuGENE in diluted DNA solution. The approach of biomolecular component analysis for the estimation of the main biomolecular contributions (proteins, lipids, nucleic acids, carotenoids, etc.) is proposed and discussed. Direct Raman evidence for strong intra-sample biomolecular heterogeneity of individual optically trapped EVs, due to variable contributions from nucleic acids and carotenoids in some preparations, is reported. On the basis of the results obtained, we are making an attempt to convince the scientific community that RTM is a promising method of single-EV research; to our knowledge, it is the only technique available at the moment that provides unique information about the global biomolecular composition of a single vesicle or a small number of vesicles, thus being capable of unravelling the high diversity of EV subpopulations, which is one of the most significant urgent challenges to overcome. Possible RTM applications include, among others, searching for DNA biomarkers, cancer diagnosis, and discrimination between different subpopulations of EVs, lipid bodies, protein aggregates and viruses.

Distributions of Silica and Biopolymer Structural Components in the Spore Elater of Equisetum arvense, an Ancient Silicifying Plant

Equisetum species are primitive vascular plants that benefit from the biogenesis of silica bio-organic inclusions in their tissues and participate in the annual biosilica turnover in local eco-systems. As means of Equisetum reproduction and propagation, spores are expected to reflect the evolutionary adaptation of the plants to the climatic conditions at different times of the year. Combining methods of Raman and scanning electron microscopy and assisted with density functional theory, we conducted material spatial-spectral correlations to characterize the distribution of biopolymers and silica based structural elements that contribute to the bio-mineral content of the elater. The elater tip has underlying skeletal-like structural elements where cellulose fibers provide strength and flexibility, both of which are necessary for locomotion. The surface of the elater tips is rich with less ordered pectin like polysaccharide and shows a ridged, folded character. At the surface we observe silica of amorphous, colloidal form in nearly spherical structures where the silica is only a few layers thick. We propose the observed expansion of elater tips upon germination and the form of silica including encapsulated biopolymers are designed for ready dispersion, release of the polysaccharide-arginine rich content and to facilitate silica uptake to the developing plant. This behavior would help to condition local soil chemistry to facilitate competitive rooting potential and stem propagation.

Characterization of Solid-State Drug Polymorphs and Real-Time Evaluation of Crystallization Process Consistency by Near-Infrared Spectroscopy

Herein, we aimed to develop a strategy for evaluating the consistency of pharmaceutically important crystallization processes in real time, focusing on two typical cases of polymorphism. Theoretical analysis using a combination of ¹³C solid-state nuclear magnetic resonance spectroscopy with other polymorphism analysis techniques identified a number of marker signals, the changes of which revealed the presence of two or more structural orientations (lattices and/or molecular conformations) in both cefazolin sodium pentahydrate (α-CEZ-Na) and cephathiamidine (CETD). The proportions of these forms were shown to be batch-dependent and were defined as critical quality attributes (CQAs) to evaluate process consistency. Subsequently, real-time analysis by chemometrics-assisted near-infrared spectroscopy (NIR) was used to obtain useful information corresponding to CQAs. The pretreated spectra of representative samples were transformed by first derivative and vector normalization methods and used to calculate standard deviations at each wavelength and thus detect significant differences. As a result, vibrational responses of H₂O, CH₃, and CH₂ moieties (at 5,280, 4,431, and 4,339 cm^-1, respectively) were shown to be sensitive to the CQAs of α-CEZ-Na, which allowed us to establish a highly accurate discrimination model. Moreover, signals of H₂O, CONH, and COOH moieties (at 5,211, 5,284, and 5,369 cm^-1, respectively) played the same role in the case of CETD, as confirmed by theoretical results. Thus, we established a technique for the rapid evaluation of crystallization process consistency and deepened our understanding of crystallization behavior by using NIR in combination with polymorphism analysis techniques.

Rapid acquisition of mean Raman spectra of eukaryotic cells for a robust single cell classification

Raman spectroscopy has previously been used to identify eukaryotic and prokaryotic cells. While prokaryotic cells are small in size and can be assessed by a single Raman spectrum, the larger size of eukaryotic cells and their complex organization requires the acquisition of multiple Raman spectra to properly characterize them. A Raman spectrum from a diffraction-limited spot at an arbitrary location within a cell results in spectral variations that affect classification approaches. To probe whole cells with Raman imaging at high spatial resolution is time consuming, because a large number of Raman spectra need to be collected, resulting in low cell throughput and impairing statistical analysis due to low cell numbers. Here we propose a method to overcome the effects of cellular heterogeneity by acquiring integrated Raman spectra covering a large portion of a cell. The acquired spectrum represents the mean macromolecular composition of a cell with an exposure time that is comparable to acquisition of a single Raman spectrum. Data sets were collected from T lymphocyte Jurkat cells, and pancreatic cell lines Capan1 and MiaPaca2. Cell classification by support vector machines was compared for single spectra, spectra of images and integrated Raman spectra of cells. The integrated approach provides better and more stable prediction for individual cells, and in the current implementation, the mean macromolecular information of a cell can be acquired faster than with the acquisition of individual spectra from a comparable region. It is expected that this approach will have a major impact on the implementation of Raman based cell classification.

Hierarchical deep convolutional neural networks combine spectral and spatial information for highly accurate Raman-microscopy-based cytopathology

Hierarchical variants of so-called deep convolutional neural networks (DCNNs) have facilitated breakthrough results for numerous pattern recognition tasks in recent years. We assess the potential of these novel whole-image classifiers for Raman-microscopy-based cytopathology. Conceptually, DCNNs facilitate a flexible combination of spectral and spatial information for classifying cellular images as healthy or cancer-affected cells. As we demonstrate, this conceptual advantage translates into practice, where DCNNs exceed the accuracy of both conventional classifiers based on pixel spectra as well as classifiers based on morphological features extracted from Raman microscopic images. Remarkably, accuracies exceeding those of all previously proposed classifiers are obtained while using only a small fraction of the spectral information provided by the dataset. Overall, our results indicate a high potential for DCNNs in medical applications of not just Raman, but also infrared microscopy.

Unravelling the Metabolic Progression of Breast Cancer Cells to Bone Metastasis by Coupling Raman Spectroscopy and a Novel Use of Mcr-Als Algorithm

Raman spectroscopy (RS) has shown promise as a tool to reveal biochemical changes that occur in cancer processes at the cellular level. However, when analyzing clinical samples, RS requires improvements to be able to resolve biological components from the spectra. We compared the strengths of Multivariate Curve Resolution (MCR) versus Principal Component Analysis (PCA) to deconvolve meaningful biological components formed by distinct mixtures of biological molecules from a set of mixed spectra. We exploited the flexibility of the MCR algorithm to easily accommodate different initial estimates and constraints. We demonstrate the ability of MCR to resolve undesired background signals from the RS that can be subtracted to obtain clearer cancer cell spectra. We used two triple negative breast cancer cell lines, MDA-MB 231 and MDA-MB 435, to illustrate the insights obtained by RS that infer the metabolic changes required for metastasis progression. Our results show that increased levels of amino acids and lower levels of mitochondrial signals are attributes of bone metastatic cells, whereas lung metastasis tropism is characterized by high lipid and mitochondria levels. Therefore, we propose a method based on the MCR algorithm to achieve unique biochemical insights into the molecular progression of cancer cells using RS.

Simultaneous isolation and detection of single breast cancer cells using surface-enhanced Raman spectroscopy

Nowadays, cancer is one of the most dangerous and deadly disease all around the world. Cancer that is diagnosed at early stages is more likely to be treated successfully. Treatment of progressed cancer is very difficult, and generally surviving rates are much lower. Therefore, much research has been focused on developing non-invasive methods for detection of cancer and monitoring of its progress. Within this contribution, we present a novel strategy for selective isolation and detection of breast cancer cell lines (MCF-7 and BT-20) based on surface enhanced Raman scattering (SERS). A simplified protocol based on cell-aptamer interaction has been developed in which core-shell (Au@Fe₃O₄) nanoparticles (CSNs) were functionalized with a mucin 1 (MUC1) specific aptamer (Apt1) to capture cells through the interaction between Apt1 and overexpressed protein (MUC1) on the surface of the tumor cells. Meanwhile, a SERS nano-tag, synthesized by the conjugation of Apt1 to the surface of BSA coated and with 4-mercaptopyridine (4-Mpy) functionalized gold nanoparticles, was used to detect the isolated cells. As a conclusion, the proposed strategy can be extended to isolate and detect cells more precisely based on the detection of different kinds of biomarkers on the surface of cancer cells, simultaneously.

Noninvasive detection of macrophage activation with single-cell resolution through machine learning

We present a method enabling the noninvasive study of minute cellular changes in response to stimuli, based on the acquisition of multiple parameters through label-free microscopy. The retrieved parameters are related to different attributes of the cell. Morphological variables are extracted from quantitative phase microscopy and autofluorescence images, while molecular indicators are retrieved via Raman spectroscopy. We show that these independent parameters can be used to build a multivariate statistical model based on logistic regression, which we apply to the detection at the single-cell level of macrophage activation induced by lipopolysaccharide (LPS) exposure and compare their respective performance in assessing the individual cellular state. The models generated from either morphology or Raman can reliably and independently detect the activation state of macrophage cells, which is validated by comparison with their cytokine secretion and intracellular expression of molecules related to the immune response. The independent models agree on the degree of activation, showing that the features provide insight into the cellular response heterogeneity. We found that morphological indicators are linked to the phenotype, which is mostly related to downstream effects, making the results obtained with these variables dose-dependent. On the other hand, Raman indicators are representative of upstream intracellular molecular changes related to specific activation pathways. By partially inhibiting the LPS-induced activation using progesterone, we could identify several subpopulations, showing the ability of our approach to identify the effect of LPS activation, specific inhibition of LPS, and also the effect of progesterone alone on macrophage cells.

New insights of Raman spectroscopy for oral clinical applications

Oral injuries are currently diagnosed by histopathological analysis of biopsy, which is an invasive procedure and does not give immediate results.

Real-time Raman and SRS imaging of living human macrophages reveals cell-to-cell heterogeneity and dynamics of lipid uptake

Monitoring living cells in real-time is important in order to unravel complex dynamic processes in life sciences. In particular the dynamics of initiation and progression of degenerative diseases is intensely studied. In atherosclerosis the thickening of arterial walls is related to high lipid levels in the blood stream, which trigger the lipid uptake and formation of droplets as neutral lipid reservoirs in macrophages in the arterial wall. Unregulated lipid uptake finally results in foam cell formation, which is a hallmark of atherosclerosis. In previous studies, the uptake and storage of different fatty acids was monitored by measuring fixed cells. Commonly employed fluorescence staining protocols are often error prone because of cytotoxicity and unspecific fluorescence backgrounds. By following living cells with Raman spectroscopic imaging, lipid uptake of macrophages was studied with real-time data acquisition. Isotopic labeling using deuterated palmitic acid has been combined with spontaneous and stimulated Raman imaging to investigate the dynamic process of fatty acid storage in human macrophages for incubation times from 45 min to 37 h. Striking heterogeneity in the uptake rate and the total concentration of deuterated palmitic acid covering two orders of magnitude is detected in single as well as ensembles of cultured human macrophages. SRS signal of deuterated palmitic acid measured at the CD vibration band after incorporation into living macrophages.

Surface-enhanced hyper Raman hyperspectral imaging and probing in animal cells

Hyper Raman scattering, that is, spontaneous, two-photon excited Raman scattering, of organic molecules becomes strong when it occurs as surface-enhanced hyper Raman scattering (SEHRS), in the proximity of plasmonic nanostructures. Its advantages over one-photon excited surface-enhanced Raman scattering (SERS) include complementary vibrational information resulting from different selection rules, probing of very small focal volumes, and beneficial excitation with long wavelengths. Here, imaging of macrophage cells by SEHRS is demonstrated, using SEHRS labels consisting of silver nanoparticles and two different molecules, 2-naphthalenethiol and para-mercaptobenzoic acid, that are excited off-resonance. The vibrational signatures of the molecules are discriminated using hyperspectral analysis and provide information about the subcellular localization of the SEHRS probes. The SEHRS based hyperspectral imaging approach presented here uses principal component analysis (PCA) to localize the reporter molecules inside the cells and is augmented by hierarchical cluster analysis (HCA). The high sensitivity of SEHRS spectra with respect to small environmental changes can be utilized for mapping of physiological parameters in the endosomal system of the cells. This is illustrated by discussing the spatial distribution of endosomes of varying pH inside the cytosol.

A machine learning framework to analyze hyperspectral stimulated Raman scattering microscopy images of expressed human meibum

We develop and discuss a methodology for batch-level analysis of hyperspectral stimulated Raman scattering (hsSRS) data sets of human meibum in the CH-stretching vibrational range. The analysis consists of two steps. The first step uses a training set (n=19) to determine chemically meaningful reference spectra that jointly constitute a basis set for the sample. This procedure makes use of batch-level vertex component analysis (VCA), followed by unsupervised k-means clustering to express the data set in terms of spectra that represent lipid and protein mixtures in changing proportions. The second step uses a random forest classifier to rapidly classify hsSRS stacks in terms of the pre-determined basis set. The overall procedure allows a rapid quantitative analysis of large hsSRS data sets, enabling a direct comparison among samples using a single set of reference spectra. We apply this procedure to assess 50 specimens of expressed human meibum, rich in both protein and lipid, and show that the batch-level analysis reveals marked variation among samples that potentially correlate with meibum health quality.

Quantitative volumetric Raman imaging of three dimensional cell cultures

The ability to simultaneously image multiple biomolecules in biologically relevant three-dimensional (3D) cell culture environments would contribute greatly to the understanding of complex cellular mechanisms and cell-material interactions. Here, we present a computational framework for label-free quantitative volumetric Raman imaging (qVRI). We apply qVRI to a selection of biological systems: human pluripotent stem cells with their cardiac derivatives, monocytes and monocyte-derived macrophages in conventional cell culture systems and mesenchymal stem cells inside biomimetic hydrogels that supplied a 3D cell culture environment. We demonstrate visualization and quantification of fine details in cell shape, cytoplasm, nucleus, lipid bodies and cytoskeletal structures in 3D with unprecedented biomolecular specificity for vibrational microspectroscopy.

Protein expression guided chemical profiling of living cells by the simultaneous observation of Raman scattering and anti-Stokes fluorescence emission

Our current understanding of molecular biology provides a clear picture of how the genome, transcriptome and proteome regulate each other, but how the chemical environment of the cell plays a role in cellular regulation remains much to be studied. Here we show an imaging method using hybrid fluorescence-Raman microscopy that measures the chemical micro-environment associated with protein expression patterns in a living cell. Simultaneous detection of fluorescence and Raman signals, realised by spectrally separating the two modes through the single photon anti-Stokes fluorescence emission of fluorescent proteins, enables the accurate correlation of the chemical fingerprint of a specimen to its physiological state. Subsequent experiments revealed the slight chemical differences that enabled the chemical profiling of mouse embryonic stem cells with and without Oct4 expression. Furthermore, using the fluorescent probe as localisation guide, we successfully analysed the detailed chemical content of cell nucleus and Golgi body. The technique can be further applied to a wide range of biomedical studies for the better understanding of chemical events during biological processes.

Bacterial Community and PHB-Accumulating Bacteria Associated with the Wall and Specialized Niches of the Hindgut of the Forest Cockchafer (Melolontha hippocastani)

A characterization of the bacterial community of the hindgut wall of two larval and the adult stages of the forest cockchafer (Melolontha hippocastani) was carried out using amplicon sequencing of the 16S rRNA gene fragment. We found that, in second-instar larvae, Caulobacteraceae and Pseudomonadaceae showed the highest relative abundances, while in third-instar larvae, the dominant families were Porphyromonadaceae and Bacteroidales-related. In adults, an increase of the relative abundance of Bacteroidetes, Proteobacteria (γ- and δ- classes) and the family Enterococcaceae (Firmicutes) was observed. This suggests that the composition of the hindgut wall community may depend on the insect’s life stage. Additionally, specialized bacterial niches hitherto very poorly described in the literature were spotted at both sides of the distal part of the hindgut chamber. We named these structures “pockets.” Amplicon sequencing of the 16S rRNA gene fragment revealed that the pockets contained a different bacterial community than the surrounding hindgut wall, dominated by Alcaligenaceae and Micrococcaceae-related families. Poly-β-hydroxybutyrate (PHB) accumulation in the pocket was suggested in isolated Achromobacter sp. by Nile Blue staining, and confirmed by gas chromatography–mass spectrometry analysis (GC-MS) on cultured bacterial mass and whole pocket tissue. Raman micro-spectroscopy allowed to visualize the spatial distribution of PHB accumulating bacteria within the pocket tissue. The presence of this polymer might play a role in the colonization of these specialized niches.

Raman imaging of macrophages incubated with triglyceride-enriched oxLDL visualizes translocation of lipids between endocytic vesicles and lipid droplets

Raman spectroscopic imaging was used to investigate the uptake of oxidized LDLs (oxLDLs) by human macrophages. To better understand the endocytic pathway and the intracellular fate of modified lipoproteins is of foremost interest with regard to the development of atherosclerotic plaques. To obtain information on the storage process of lipids caused by oxLDL uptake, Raman spectroscopic imaging was used because of its unique chemical specificity, especially for lipids. For the present study, a protocol was established to incorporate deuterated tripalmitate into oxLDL. Subsequently, human THP-1 macrophages were incubated for different time points and their chemical composition was analyzed using Raman spectroscopic imaging. β-Carotene was found to be a reliable marker molecule for the uptake of lipoproteins into macrophages. In addition, lipoprotein administration led to small endocytic vesicles with different concentrations of deuterated lipids within the cells. For the first time, the translocation of deuterated lipids from endocytic vesicles into lipid droplets over time is reported in mature human THP-1 macrophages.

Raman technologies in cancer diagnostics

Despite significant effort, cancer still remains a leading cause of death worldwide. In order to reduce its burden, the development and improvement of noninvasive strategies for early detection and diagnosis of cancer are urgently needed. Raman spectroscopy, an optical technique that relies on inelastic light scattering arising from molecular vibrations, is one such strategy, as it can noninvasively probe cancerous markers using only endogenous contrast. In this review, spontaneous, coherent and surface enhanced Raman spectroscopies and imaging, as well as the fundamental principles governing the successful use of these techniques, are discussed. Methods for spectral data analysis are also highlighted. Utilization of the discussed Raman techniques for the detection and diagnosis of cancer in vitro, ex vivo and in vivo is described. The review concludes with a discussion of the future directions of Raman technologies, with particular emphasis on their clinical translation.

Compressed sensing laser scanning microscopy

We present a measurement and reconstruction method for laser-scanning microscopy based on compressed sensing, which enables significantly higher frame rates and reduced photobleaching. The image reconstruction accuracy is ensured by including a model of the physical imaging process into the compressed sensing reconstruction procedure. We demonstrate its applicability to unmodified commercial confocal fluorescence microscopy systems and for Raman imaging, showing a potential data reduction of 10-15 times, which directly leads to improvements in acquisition speed, or reduction of photobleaching, without significant loss of spatial resolution. Furthermore, the reconstruction model is also robust to noise, and effective for low-light applications. This method has promising applications for all imaging modalities based on laser-scanning acquisition, including fluorescence, Raman, and nonlinear microscopy.

Bioactive secondary metabolites with multiple activities from a fungal endophyte

In order to replace particularly biohazardous nematocides, there is a strong drive to finding natural product‐based alternatives with the aim of containing nematode pests in agriculture. The metabolites produced by the fungal endophyte Fusarium oxysporum 162 when cultivated on rice media were isolated and their structures elucidated. Eleven compounds were obtained, of which six were isolated from a Fusarium spp. for the first time. The three most potent nematode‐antagonistic compounds, 4‐hydroxybenzoic acid, indole‐3‐acetic acid (IAA) and gibepyrone D had LC₅₀ values of 104, 117 and 134 μg ml⁻¹, respectively, after 72 h. IAA is a well‐known phytohormone that plays a role in triggering plant resistance, thus suggesting a dual activity, either directly, by killing or compromising nematodes, or indirectly, by inducing defence mechanisms against pathogens (nematodes) in plants. Such compounds may serve as important leads in the development of novel, environmental friendly, nematocides.

Protein clustering in chemically stressed HeLa cells studied by infrared nanospectroscopy

Photo-Thermal Induced Resonance (PTIR) nanospectroscopy, tuned towards amide-I absorption, was used to study the distribution of proteic material in 34 different HeLa cells, of which 18 were chemically stressed by oxidative stress with Na₃AsO₃. The cell nucleus was found to provide a weaker amide-I signal than the surrounding cytoplasm, while the strongest PTIR signal comes from the perinuclear region. AFM topography shows that the cells exposed to oxidative stress undergo a volume reduction with respect to the control cells, through an accumulation of the proteic material around and above the nucleus. This is confirmed by the PTIR maps of the cytoplasm, where the pixels providing a high amide-I signal were identified with a space resolution of ∼300 × 300 nm. By analyzing their distribution with two different statistical procedures we found that the probability to find protein clusters smaller than 0.6 μm in the cytoplasm of stressed HeLa cells is higher by 35% than in the control cells. These results indicate that it is possible to study proteic clustering within single cells by label-free optical nanospectroscopy.

Spatially resolved spectroscopic differentiation of hydrophilic and hydrophobic domains on individual insulin amyloid fibrils

The formation of insoluble β-sheet-rich protein structures known as amyloid fibrils is associated with numerous neurodegenerative diseases, such as Alzheimer's and Parkinson's disease. A detailed understanding of the molecular structure of the fibril surface is of interest as the first contact with the physiological environment in vivo and plays a decisive role in biological activity and associated toxicity. Recent studies reveal that the inherent sensitivity and specificity of tip-enhanced Raman scattering (TERS) renders this technique a compelling method for fibril surface analysis at the single-particle level. Here, the reproducibility of TERS is demonstrated, indicating its relevance for detecting molecular variations. Consequently, individual fibrils are systematically investigated at nanometer spatial resolution. Spectral parameters were obtained by band-fitting, particularly focusing on the identification of the secondary structure via the amide III band and the differentiation of hydrophobic and hydrophilic domains on the surface. In addition multivariate data analysis, specifically the N-FINDR procedure, was employed to generate structure-specific maps. The ability of TERS to localize specific structural domains on fibril surfaces shows promise to the development of new fibril dissection strategies and can be generally applied to any (bio)chemical surface when structural variations at the nanometer level are of interest.

Confocal raman microspectral imaging of ex vivo human spinal cord tissue

Confocal Raman microspectral imaging (CRMI) provides a versatile tool to illustrate the biochemical nature and structure of biological tissue without introducing any external labels. In this work, a precise correlation was established between the biochemical profile and histological architecture of ex vivo human spinal cord tissue by using CRMI with 633nm excitation. After precisely linking the spectral features to the chemical constituents, much information about the molecular composition of both gray and white matter were revealed. Two-dimensional Raman images were generated by integrating the intensities of the characteristic Raman bands in the area of the intermediate column and ventral horn. K-mean cluster analysis was further applied to visualize the underlying morphological basis of spinal cord tissue by chemical component types and their distribution pattern. Lipid-rich white matter could be visually distinguished from gray matter considering a CH2 bending/scissoring band at 1445cm(-1) and an amide III band at 1250cm(-1). Meanwhile, the formation and distribution pattern of perineuronal nets (PNNs) in the scanning area was validated by the integration of saccharides (617cm(-1)) and amide III bands. Moreover, the heme profile indicated a higher degree of vascularization in gray matter. All of the results obtained testified to the possibility that gray matter could be more susceptible to spinal cord injury (SCI) because of capillary network distribution and glycosaminoglycans (GAGs) aggregation. These findings are important for interpreting the morphological specificity of human spinal cord tissue, and also for studying the molecular basis of SCI.

Imaging of Osteoarthritic Human Articular Cartilage using Fourier Transform Infrared Microspectroscopy Combined with Multivariate and Univariate Analysis

The changes in chemical composition of human articular cartilage (AC) caused by osteoarthritis (OA) were investigated using Fourier transform infrared microspectroscopy (FTIR-MS). We demonstrate the sensitivity of FTIR-MS for monitoring compositional changes that occur with OA progression. Twenty-eight AC samples from tibial plateaus were imaged with FTIR-MS. Hyperspectral images of all samples were combined for K-means clustering. Partial least squares regression (PLSR) analysis was used to compare the spectra with the OARSI grade (histopathological grading of OA). Furthermore, the amide I and the carbohydrate regions were used to estimate collagen and proteoglycan contents, respectively. Spectral peak at 1338 cm(-1) was used to estimate the integrity of the collagen network. The layered structure of AC was revealed using the carbohydrate region for clustering. Statistically significant correlation was observed between the OARSI grade and the collagen integrity in the superficial (r = -0.55) and the deep (r = -0.41) zones. Furthermore, PLSR models predicted the OARSI grade from the superficial (r = 0.94) and the deep (r = 0.77) regions of the AC with high accuracy. Obtained results suggest that quantitative and qualitative changes occur in the AC composition during OA progression, and these can be monitored by the use of FTIR-MS.