Novel Non-Invasive Quantification and Imaging of Eumelanin and DHICA Subunit in Skin Lesions by Raman Spectroscopy and MCR Algorithm: Improving Dysplastic Nevi Diagnosis.

Malignant melanoma (MM) is the most aggressive form of skin cancer, and around 30% of them may develop from pre-existing dysplastic nevi (DN). Diagnosis of DN is a relevant clinical challenge, as these are intermediate lesions between benign and malignant tumors, and, up to date, few studies have focused on their diagnosis. In this study, the accuracy of Raman spectroscopy (RS) is assessed, together with multivariate analysis (MA), to classify 44 biopsies of MM, DN and compound nevus (CN) tumors. For this, we implement a novel methodology to non-invasively quantify and localize the eumelanin pigment, considered as a tumoral biomarker, by means of RS imaging coupled with the Multivariate Curve Resolution-Alternative Least Squares (MCR-ALS) algorithm. This represents a step forward with respect to the currently established technique for melanin analysis, High-Performance Liquid Chromatography (HPLC), which is invasive and cannot provide information about the spatial distribution of molecules. For the first time, we show that the 5, 6-dihydroxyindole (DHI) to 5,6-dihydroxyindole-2-carboxylic acid (DHICA) ratio is higher in DN than in MM and CN lesions. These differences in chemical composition are used by the Partial Least Squares-Discriminant Analysis (PLS-DA) algorithm to identify DN lesions in an efficient, non-invasive, fast, objective and cost-effective method, with sensitivity and specificity of 100% and 94.1%, respectively.

Linear unmixing protocol for hyperspectral image fusion analysis applied to a case study of vegetal tissues.

Hyperspectral imaging (HSI) is a useful non-invasive technique that offers spatial and chemical information of samples. Often, different HSI techniques are used to obtain complementary information from the sample by combining different image modalities (Image Fusion). However, issues related to the different spatial resolution, sample orientation or area scanned among platforms need to be properly addressed. Unmixing methods are helpful to analyze and interpret the information of HSI related to each of the components contributing to the signal. Among those, Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) offers very suitable features for image fusion, since it can easily cope with multiset structures formed by blocks of images coming from different samples and platforms and allows the use of optional and diverse constraints to adapt to the specific features of each HSI employed. In this work, a case study based on the investigation of cross-sections from rice leaves by Raman, synchrotron infrared and fluorescence imaging techniques is presented. HSI of these three different techniques are fused for the first time in a single data structure and analyzed by MCR-ALS. This example is challenging in nature and is particularly suitable to describe clearly the necessary steps required to perform unmixing in an image fusion context. Although this protocol is presented and applied to a study of vegetal tissues, it can be generally used in many other samples and combinations of imaging platforms.

Unravelling the Encapsulation of DNA and Other Biomolecules in HAp Microcalcifications of Human Breast Cancer Tissues by Raman Imaging.

Microcalcifications are detected through mammography screening and, depending on their morphology and distribution (BI-RADS classification), they can be considered one of the first indicators of suspicious cancer lesions. However, the formation of hydroxyapatite (HAp) calcifications and their relationship with malignancy remains unknown. In this work, we report the most detailed three-dimensional biochemical analysis of breast cancer microcalcifications to date, combining 3D Raman spectroscopy imaging and advanced multivariate analysis in order to investigate in depth the molecular composition of HAp calcifications found in 26 breast cancer tissue biopsies. We demonstrate that DNA has been naturally adsorbed and encapsulated inside HAp microcalcifications. Furthermore, we also show the encapsulation of other relevant biomolecules in HAp calcifications, such as lipids, proteins, cytochrome C and polysaccharides. The demonstration of natural DNA biomineralization, particularly in the tumor microenvironment, represents an unprecedented advance in the field, as it can pave the way to understanding the role of HAp in malignant tissues.

Fructose derived oligosaccharides prevent lipid membrane destabilization and DNA conformational alterations during vacuum-drying of Lactobacillus delbrueckii subsp. bulgaricus.

Dehydration of lactic acid bacteria for technological purposes conducts to multilevel damage of bacterial cells. The goal of this work was to determine at which molecular level fructose-oligosaccharides (FOS) and sucrose protect Lactobacillus delbrueckii subsp. bulgaricus CIDCA 333 during the vacuum-drying process. To achieve this aim, the cultivability and metabolic activity of vacuum-dried bacteria were firstly determined (plate counting and absorbance kinetics). Then, the membrane integrity and fluidity were assessed using propidium iodide and Laurdan probes (general polarization -GP-), respectively. Finally, bacterial structural alterations were determined using high throughput methods (fluorescence confocal microscopy and Raman spectroscopy coupled to Multivariate Curve Resolution analysis -MCR-). The vacuum-drying process directly affected the microorganism's cultivability and membrane integrity. Non-dehydrated cells and sugar protected bacteria (both with FOS or sucrose) presented high GP values typical from the gel state, as well as phospholipids microdomains laterally organized along the cytoplasmic membrane. On the contrary, bacteria dehydrated without protectants presented low GP values and greater water penetration, associated with membrane destabilization. Raman spectroscopy of vacuum-dried cells revealed DNA conformational changes, B-DNA conformations being associated to non-dehydrated or sugar protected bacteria, and A-DNA conformations being higher in bacteria vacuum-dried without protectants. These results support the role of FOS and sucrose as protective compounds, not only acting at the membrane organizational level but also preventing conformational alterations of intracellular structures, like DNA.

The nucleus measures shape changes for cellular proprioception to control dynamic cell behavior.

The physical microenvironment regulates cell behavior during tissue development and homeostasis. How single cells decode information about their geometrical shape under mechanical stress and physical space constraints within tissues remains largely unknown. Here, using a zebrafish model, we show that the nucleus, the biggest cellular organelle, functions as an elastic deformation gauge that enables cells to measure cell shape deformations. Inner nuclear membrane unfolding upon nucleus stretching provides physical information on cellular shape changes and adaptively activates a calcium-dependent mechanotransduction pathway, controlling actomyosin contractility and migration plasticity. Our data support that the nucleus establishes a functional module for cellular proprioception that enables cells to sense shape variations for adapting cellular behavior to their microenvironment.

3D and 4D Image Fusion: Coping with Differences in Spectroscopic Modes among Hyperspectral Images.

Image fusion is often oriented to solve differences in spatial scale and orientation among different spectroscopic platforms. However, an additional problem arises when the nature of the spectroscopic information differs in dimensionality as well. Indeed, most imaging systems, e.g., Raman, IR, MS, etc., allow acquisition of 3D images, with a linear spectrum per pixel, but new platforms have emerged, such as the recent excitation-emission fluorescence imaging platforms that provide 4D images, with a 2D spectral landscape per pixel. A proper 3D/4D image fusion needs to take into account the difference in the dimension of the spectral information and in the underlying models of both measurements (bilinear for 3D images and trilinear for 4D images). This work solves this image fusion problem through a new dedicated variant of the multivariate curve resolution-alternating least squares (MCR-ALS) algorithm for multiset analysis based on the incorporation of a hybrid bilinear/trilinear model that can handle the image fused structure preserving the natural behavior of the 3D and 4D imaging techniques coupled. The example is illustrated on the fusion of real 3D Raman and 4D fluorescence images recorded on cross sections of rice leaf samples.

GRP94 Is Involved in the Lipid Phenotype of Brain Metastatic Cells.

Metabolic adaptation may happen in response to the pressure exerted by the microenvironment and is a key step in survival of metastatic cells. Brain metastasis occurs as a consequence of the systemic dissemination of tumor cells, a fact that correlates with poor prognosis and high morbidity due to the difficulty in identifying biomarkers that allow a more targeted therapy. Previously, we performed transcriptomic analysis of human breast cancer patient samples and evaluated the differential expression of genes in brain metastasis (BrM) compared to lung, bone and liver metastasis. Our network approach identified upregulation of glucose-regulated protein 94 (GRP94) as well as proteins related to synthesis of fatty acids (FA) in BrM. Here we report that BrM cells show an increase in FA content and decreased saturation with regard to parental cells measured by Raman spectroscopy that differentiate BrM from other metastases. Moreover, BrM cells exerted a high ability to oxidize FA and compensate hypoglycemic stress due to an overexpression of proteins involved in FA synthesis and degradation (SREBP-1, LXRα, ACOT7). GRP94 ablation restored glucose dependence, down-regulated ACOT7 and SREBP-1 and decreased tumorigenicity in vivo. In conclusion, GRP94 is required for the metabolic stress survival of BrM cells, and it might act as a modulator of lipid metabolism to favor BrM progression.

Assessment of tissue-specific multifactor effects in environmental -omics studies of heterogeneous biological samples: Combining hyperspectral image information and chemometrics.

The use of hyperspectral imaging techniques in biological studies has increased in the recent years. Hyperspectral images (HSI) provide chemical information and preserve the morphology and original structure of heterogeneous biological samples, which can be potentially useful in environmental -omics studies when effects due to several factors, e.g., contaminant exposure, phenotype,…, at a specific tissue level need to be investigated. Yet, no available strategies exist to exploit adequately this kind of information. This work offers a novel chemometric strategy to pass from the raw image information to useful knowledge in terms of statistical assessment of the multifactor effects of interest in -omic studies. To do so, unmixing of the hyperspectral image measurement is carried out to provide tissue-specific information. Afterwards, several specific ANOVA-Simultaneous Component Analysis (ASCA) models are generated to properly assess and interpret the diverse effect of the factors of interest on the spectral fingerprints of the different tissues characterized. The unmixing step is performed by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) on multisets of biological images related to each studied condition and provides reliable HSI spectral signatures and related image maps for each specific tissue in the regions imaged. The variability associated with these signatures within a population is obtained through an MCR-based resampling step on representative pixel subsets of the images analyzed. All spectral fingerprints obtained for a particular tissue in the different conditions studied are used to obtain the related ASCA model that will help to assess the significance of the factors studied on the tissue and, if relevant, to describe the associated fingerprint modifications. The potential of the approach is assessed in a real case of study linked to the investigation of the effect of exposure time to chlorpyrifos-oxon (CPO) on ocular tissues of different phenotypes of zebrafish larvae from Raman HSI of eye cryosections. The study allowed the characterization of melanin, crystalline and internal eye tissue and the phenotype, exposure time and the interaction of the two factors were found to be significant in the changes found in all kind of tissues. Factor-related changes in the spectral fingerprint were described and interpreted per each kind of tissue characterized.

Raman spectroscopy quantification of eumelanin subunits in natural unaltered pigments.

Eumelanins are large polymers composed of 5,6-dihydroxyindole (DHI) and 5,6-dihydroxyindole-2-carboxylic acid (DHICA). Reactive oxygen species generation is higher during the synthesis of DHI than during the synthesis of DHICA; thus, the production of eumelanins with high relative DHI contents may compromise the viability of cells and organisms. Here, we show that dispersive Raman spectroscopy allows a noninvasive direct detection and quantification of DHI and DHICA in unaltered eumelanins that can be applied to the analysis of natural pigments in biological tissues. Using synthetic eumelanins purely composed of DHI or DHICA, we develop a model to determine the differential contribution of these subunits to the Raman spectra of mixed eumelanins, and exemplify the predictive capacity of the model with natural eumelanins deposited in black and gray feathers of fowl. We found that the wavenumber ranges of 550-1,200 and 1,650-2,300 cm-1 are significant to predict the DHI:DHICA ratio in unknown samples.

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.

Transcriptome analysis in tissue sectors with contrasting crocins accumulation provides novel insights into apocarotenoid biosynthesis and regulation during chromoplast biogenesis.

Crocins, the red soluble apocarotenoids of saffron, accumulate in the flowers of Crocus species in a developmental and tissue-specific manner. In Crocus sieberi, crocins accumulate in stigmas but also in a distinct yellow tepal sector, which we demonstrate contains chromoplast converted from amyloplasts. Secondary metabolites were analysed by LC-DAD-HRMS, revealing the progressive accumulation of crocetin and crocins in the yellow sector, which were also localized in situ by Raman microspectroscopy. To understand the underlying mechanisms of crocin biosynthesis, we sequenced the C. sieberi tepal transcriptome of two differentially pigmented sectors (yellow and white) at two developmental stages (6 and 8) by Illumina sequencing. A total of 154 million high-quality reads were generated and assembled into 248,099 transcripts. Differentially expressed gene analysis resulted in the identification of several potential candidate genes involved in crocin metabolism and regulation. The results provide a first profile of the molecular events related to the dynamics of crocetin and crocin accumulation during tepal development, and present new information concerning apocarotenoid biosynthesis regulators and their accumulation in Crocus. Further, reveals genes that were previously unknown to affect crocin formation, which could be used to improve crocin accumulation in Crocus plants and the commercial quality of saffron spice.

Interference with Clp protease impairs carotenoid accumulation during tomato fruit ripening.

Profound metabolic and structural changes are required for fleshy green fruits to ripen and become colorful and tasty. In tomato (Solanum lycopersicum), fruit ripening involves the differentiation of chromoplasts, specialized plastids that accumulate carotenoid pigments such as ß-carotene (pro-vitamin A) and lycopene. Here, we explored the role of the plastidial Clp protease in chromoplast development and carotenoid accumulation. Ripening-specific silencing of one of the subunits of the Clp proteolytic complex resulted in ß-carotene-enriched fruits that appeared orange instead of red when ripe. Clp-defective fruit displayed aberrant chromoplasts and up-regulated expression of nuclear genes encoding the tomato homologs of Orange (OR) and ClpB3 chaperones, most probably to deal with misfolded and aggregated proteins that could not be degraded by the Clp protease. ClpB3 and OR chaperones protect the carotenoid biosynthetic enzymes deoxyxylulose 5-phosphate synthase and phytoene synthase, respectively, from degradation, whereas OR chaperones additionally promote chromoplast differentiation by preventing the degradation of carotenoids such as ß-carotene. We conclude that the Clp protease contributes to the differentiation of chloroplasts into chromoplasts during tomato fruit ripening, acting in co-ordination with specific chaperones that alleviate protein folding stress, promote enzyme stability and accumulation, and prevent carotenoid degradation.

Combining hyperspectral imaging and chemometrics to assess and interpret the effects of environmental stressors on zebrafish eye images at tissue level.

Changes on an organism by the exposure to environmental stressors may be characterized by hyperspectral images (HSI), which preserve the morphology of biological samples, and suitable chemometric tools. The approach proposed allows assessing and interpreting the effect of contaminant exposure on heterogeneous biological samples monitored by HSI at specific tissue levels. In this work, the model example used consists of the study of the effect of the exposure of chlorpyrifos-oxon on zebrafish tissues. To assess this effect, unmixing of the biological sample images followed by tissue-specific classification models based on the unmixed spectral signatures is proposed. Unmixing and classification are performed by multivariate curve resolution-alternating least squares (MCR-ALS) and partial least squares-discriminant analysis (PLS-DA), respectively. Crucial aspects of the approach are: (1) the simultaneous MCR-ALS analysis of all images from 1 population to take into account biological variability and provide reliable tissue spectral signatures, and (2) the use of resolved spectral signatures from control and exposed populations obtained from resampling of pixel subsets analyzed by MCR-ALS multiset analysis as information for the tissue-specific PLS-DA classification models. Classification results diagnose the presence of a significant effect and identify the spectral regions at a tissue level responsible for the biological change.

Identification of Individual Exosome-Like Vesicles by Surface Enhanced Raman Spectroscopy.

Exosome-like vesicles (ELVs) are a novel class of biomarkers that are receiving a lot of attention for the detection of cancer at an early stage. In this study the feasibility of using a surface enhanced Raman spectroscopy (SERS) based method to distinguish between ELVs derived from different cellular origins is evaluated. A gold nanoparticle based shell is deposited on the surface of ELVs derived from cancerous and healthy cells, which enhances the Raman signal while maintaining a colloidal suspension of individual vesicles. This nanocoating allows the recording of SERS spectra from single vesicles. By using partial least squares discriminant analysis on the obtained spectra, vesicles from different origin can be distinguished, even when present in the same mixture. This proof-of-concept study paves the way for noninvasive (cancer) diagnostic tools based on exosomal SERS fingerprinting in combination with multivariate statistical analysis.

Rapid spontaneous Raman light sheet microscopy using cw-lasers and tunable filters.

We perform rapid spontaneous Raman 2D imaging in light-sheet microscopy using continuous wave lasers and interferometric tunable filters. By angularly tuning the filter, the cut-on/off edge transitions are scanned along the excited Stokes wavelengths. This allows obtaining cumulative intensity profiles of the scanned vibrational bands, which are recorded on image stacks; resembling a spectral version of the knife-edge technique to measure intensity profiles. A further differentiation of the stack retrieves the Raman spectra at each pixel of the image which inherits the 3D resolution of the host light sheet system. We demonstrate this technique using solvent solutions and composites of polystyrene beads and lipid droplets immersed in agar and by imaging the C-H (2800-3100cm(-1)) region in a C. elegans worm. The image acquisition time results in 4 orders of magnitude faster than confocal point scanning Raman systems, allowing the possibility of performing fast spontaneous Raman·3D-imaging on biological samples.

Dynamic molecular monitoring of retina inflammation by in vivo Raman spectroscopy coupled with multivariate analysis.

Retinal tissue is damaged during inflammation in Multiple Sclerosis. We assessed molecular changes in inflamed murine retinal cultures by Raman spectroscopy. Partial Least Squares-Discriminant analysis (PLS-DA) was able to classify retina cultures as inflamed with high accuracy. Using Multivariate Curve Resolution (MCR) analysis, we deconvolved 6 molecular components suffering dynamic changes along inflammatory process. Those include the increase of immune mediators (Lipoxygenase, iNOS and TNFα), changes in molecules involved in energy production (Cytochrome C, phenylalanine and NADH/NAD+) and decrease of Phosphatidylcholine. Raman spectroscopy combined with multivariate analysis allows monitoring the evolution of retina inflammation.

Direct observation of single DNA structural alterations at low forces with surface-enhanced Raman scattering.

DNA experiences numerous mechanical events, necessitating single-molecule force spectroscopy techniques to provide insight into DNA mechanics as a whole system. Inherent Brownian motion limits current force spectroscopy methods from observing possible bond level structural changes. We combine optical trapping and surface-enhanced Raman scattering to establish a direct relationship between DNA's extension and structure in the low force, entropic regime. A DNA molecule is trapped close to a surface-enhanced Raman scattering substrate to facilitate a detectable Raman signal. DNA Raman modes shift in response to applied force, indicating phosphodiester mechanical alterations. Molecular dynamic simulations confirm the local structural alterations and the Raman sensitive band identified experimentally. The combined Raman and force spectroscopy technique, to our knowledge, is a novel methodology that can be generalized to all single-molecule studies.

The lipid phenotype of breast cancer cells characterized by Raman microspectroscopy: towards a stratification of malignancy.

Although molecular classification brings interesting insights into breast cancer taxonomy, its implementation in daily clinical care is questionable because of its expense and the information supplied in a single sample allocation is not sufficiently reliable. New approaches, based on a panel of small molecules derived from the global or targeted analysis of metabolic profiles of cells, have found a correlation between activation of de novo lipogenesis and poorer prognosis and shorter disease-free survival for many tumors. We hypothesized that the lipid content of breast cancer cells might be a useful indirect measure of a variety of functions coupled to breast cancer progression. Raman microspectroscopy was used to characterize metabolism of breast cancer cells with different degrees of malignancy. Raman spectra from MDA-MB-435, MDA-MB-468, MDA-MB-231, SKBR3, MCF7 and MCF10A cells were acquired with an InVia Raman microscope (Renishaw) with a backscattered configuration. We used Principal Component Analysis and Partial Least Squares Discriminant Analyses to assess the different profiling of the lipid composition of breast cancer cells. Characteristic bands related to lipid content were found at 3014, 2935, 2890 and 2845 cm(-1), and related to lipid and protein content at 2940 cm(-1). A classificatory model was generated which segregated metastatic cells and non-metastatic cells without basal-like phenotype with a sensitivity of 90% and a specificity of 82.1%. Moreover, expression of SREBP-1c and ABCA1 genes validated the assignation of the lipid phenotype of breast cancer cells. Indeed, changes in fatty acid unsaturation were related with the epithelial-to-mesenchymal transition phenotype. Raman microspectroscopy is a promising technique for characterizing and classifying the malignant phenotype of breast cancer cells on the basis of their lipid profiling. The algorithm for the discrimination of metastatic ability is a first step towards stratifying breast cancer cells using this rapid and reagent-free tool.

Mechanochemistry of single red blood cells monitored using Raman tweezers.

Two microparticles were biochemically attached to a red blood cell at diametrically opposite parts and held by optical traps allowing to impose deformations. The cell deformation was monitored from the microscopy images. Raman spectra of the cell under tunable deformations were studied. Vibrational spectra analysis at different stretching states was supported with two statistical methods. Principal Component Analysis distinguishes the most prominent changes in spectra while 2D correlation technique monitors the evolution of Raman bands during stretching. The measurements show significant changes in the cell chemical structure with stretching however the changes saturate above 20% of cell deformation. Mechanical deformation of the cell mainly affects the bands corresponding to hemoglobin but contributions from spectrin and membrane proteins can not be excluded. The saturation of bands at higher deformations suggests some structural relaxation that RBC has to undergo to bear extra load. The results confirm widely accepted belief that spectrin released from membrane proteins allows for significant shape changes of the cells. We therefore tentatively suggest that interaction between membrane and cytoskeleton during deformation can be efficiently probed by confocal Raman spectroscopy, in particular via the peak around 1035 cm(-1).