Molecular component distribution imaging of living cells by multivariate curve resolution analysis of space-resolved Raman spectra

Single-Cell Level Raman Molecular Profiling Reveals the Classification of Growth Phases of Chaetoceros tenuissimus

Harmful algal blooms (HABs) are a natural phenomenon caused by outbreaks of algae, resulting in serious problems for aquatic ecosystems and the coastal environment. Chaetoceros tenuissimus (C. tenuissimus) is one of the diatoms responsible for HABs. The growth curve of C. tenuissimus can be observed from beginning to end of HABs: therefore, detailed analysis is necessary to characterize each growth phase of C. tenuissimus. It is important to examine the phenotype of each diatom cell individually, as they display heterogeneity even in the same growth phase. Raman spectroscopy is a label-free technique to elucidate biomolecular profiles and spatial information at the cellular level. Multivariate data analysis (MVA) is an efficient method for the analysis of complicated Raman spectra, to identify molecular features. Here, we utilized Raman microspectroscopy to identify the molecular information of each diatom cell, at the single-cell level. The MVA, together with a support vector machine, which is a machine learning technique, allowed the classification of proliferating and nonproliferating cells. The classification includes polyunsaturated fatty acids such as linoleic acid, eicosapentaenoic acid, and docosahexaenoic acid. This study indicated that Raman spectroscopy is an appropriate technique to examine C. tenuissimus at the single-cell level, providing relevant data to assess the correlation between the molecular details obtained from the Raman analysis, at each growth phase.

Determination of sugar content in honey using LC-Raman and programmable pump-Raman methods

We combined (i) liquid chromatography and Raman spectrometry (LC-Raman) and (ii) programmable pump and Raman spectrometry (PP-Raman) to separate and identify compounds in a mixture. These techniques were applied to conduct a quantitative analysis of the sugars in honey. The spectral and temporal axes of the LC-Raman data were analyzed using the MCR-ALS analysis procedure, which enabled the separation and identification of four sugars (glucose, fructose, sucrose, and trehalose). The PP-Raman method was employed to examine the sugar concentration dependence of the intensity pattern of the Raman spectrum, and the linear concentration dependence of the intensity was obtained. The sugar contents were quantitatively determined from the integrated area of the elution peaks. The result was consistent with those derived from mass spectrometry and previous studies. The origin of the errors in the derived sugar contents is discussed. Our study presents a novel quantitative LC-Raman spectrometric method that does not rely on resonance or surface enhancement effects.

Raman Spectroscopy for Chemical Biology Research

In chemical biology research, various fluorescent probes have been developed and used to visualize target proteins or molecules in living cells and tissues, yet there are limitations to this technology, such as the limited number of colors that can be detected simultaneously. Recently, Raman spectroscopy has been applied in chemical biology to overcome such limitations. Raman spectroscopy detects the molecular vibrations reflecting the structures and chemical conditions of molecules in a sample and was originally used to directly visualize the chemical responses of endogenous molecules. However, our initial research to develop "Raman tags" opens a new avenue for the application of Raman spectroscopy in chemical biology. In this Perspective, we first introduce the label-free Raman imaging of biomolecules, illustrating the biological applications of Raman spectroscopy. Next, we highlight the application of Raman imaging of small molecules using Raman tags for chemical biology research. Finally, we discuss the development and potential of Raman probes, which represent the next-generation probes in chemical biology.

Oral administration of Blautia wexlerae ameliorates obesity and type 2 diabetes via metabolic remodeling of the gut microbiota

The gut microbiome is an important determinant in various diseases. Here we perform a cross-sectional study of Japanese adults and identify the Blautia genus, especially B. wexlerae, as a commensal bacterium that is inversely correlated with obesity and type 2 diabetes mellitus. Oral administration of B. wexlerae to mice induce metabolic changes and anti-inflammatory effects that decrease both high-fat diet–induced obesity and diabetes. The beneficial effects of B. wexlerae are correlated with unique amino-acid metabolism to produce S-adenosylmethionine, acetylcholine, and l-ornithine and carbohydrate metabolism resulting in the accumulation of amylopectin and production of succinate, lactate, and acetate, with simultaneous modification of the gut bacterial composition. These findings reveal unique regulatory pathways of host and microbial metabolism that may provide novel strategies in preventive and therapeutic approaches for metabolic disorders.

Here, the authors inversely associate Blautia wexlerae with obesity and type 2 diabetes mellitus in humans and further show that administration of B. wexlerae to mice decrease both high-fat diet–induced obesity and diabetes via modulating gut microbial metabolism.

Raman Microspectroscopy Imaging Analysis of Extracellular Vesicles Biogenesis by Filamentous Fungus Penicilium chrysogenum

The mechanism of production of extracellular vesicles (EVs) and their molecular contents are of great interest due to their diverse roles in biological systems and are far from being completely understood. Even though cellular cargo releases mediated by EVs have been demonstrated in several cases, their role in secondary metabolite production and release remains elusive. In this study, this aspect is investigated in detail using Raman microspectroscopic imaging. Considerable evidence is provided to suggest that the release of antibiotic penicillin by the filamentous fungus Penicillium chrysogenum involves EVs. Further, the study also reveals morphological modifications of the fungal body during biogenesis, changes in cell composition at the locus of biogenesis, and major molecular contents of the released EVs. The results suggest a possible general role of EVs in the release of antibiotics from the producing organisms.

Single-cell metabolite detection and genomics reveals uncultivated talented producer

The production of bioactive metabolites is increasingly recognized as an important function of host-associated bacteria. An example is defensive symbiosis that might account for much of the chemical richness of marine invertebrates including sponges (Porifera), 1 of the oldest metazoans. However, most bacterial members of sponge microbiomes have not been cultivated or sequenced, and therefore, remain unrecognized. Unequivocally linking metabolic functions to a cellular source in sponge microbiomes is, therefore, a challenge. Here, we report an analysis pipeline of microfluidic encapsulation, Raman microscopy, and integrated digital genomics (MERMAID) for an efficient identification of uncultivated producers. We applied this method to the chemically rich bacteriosponge (sponge that hosts a rich bacterial community) Theonella swinhoei, previously shown to contain 'Entotheonella' symbionts that produce most of the bioactive substances isolated from the sponge. As an exception, the antifungal aurantosides had remained unassigned to a source. Raman-guided single-bacterial analysis and sequencing revealed a cryptic, distinct multiproducer, 'Candidatus Poriflexus aureus' from a new Chloroflexi lineage as the aurantoside producer. Its exceptionally large genome contains numerous biosynthetic loci and suggested an even higher chemical richness of this sponge than previously appreciated. This study highlights the importance of complementary technologies to uncover microbiome functions, reveals remarkable parallels between distantly related symbionts of the same host, and adds functional support for diverse chemically prolific lineages being present in microbial dark matter.

Ultrahigh-speed multiplex coherent anti-Stokes Raman scattering microspectroscopy using scanning elliptical focal spot

We present a beam-scanning multiplex coherent anti-Stokes Raman scattering (CARS) microspectroscopy system using parallel excitation and parallel detection schemes based on an elliptical focal spot, which enables highly efficient signal acquisition even for short exposures. The elliptical focal spot was used to simultaneously observe the CARS signals of an enlarged region and reduce the peak irradiance. The developed system realized an acquisition rate of 34 139 spectra/s and enabled ultrahigh-speed acquisition of a vibrational spectroscopic image, covering the fingerprint region of 930-1 830 cm^-1 with 256_(x) × 256_(y) × 512_(spectrum) pixels in 1.92 s or with 128_(x) × 128_(y) × 256_(spectrum) pixels in 0.54 s. We demonstrated ultrahigh-speed hyperspectral imaging of a mixture of polymer beads in liquid linoleic acid and living adipocytes using the developed system. All of the present demonstrations were performed with a low-peak irradiance excitation of ∼19 GW/cm², which has been reported in previous studies to cause less photodamage to living cells. The label-free and ultrahigh-speed identification and visualization of various molecules made possible by the present system will accelerate the development of practical live-cell investigation.

Deconstruction of Obscure Features in SVD-Decomposed Raman Images from P. chrysogenum Reveals Complex Mixing of Spectra from Five Cellular Constituents

Raman imaging has transcended in recent times from being an analytical tool to a molecular profiling technique. Biomedical applications of this technique often rely on singular-value decomposition (SVD), principal component analysis (PCA), etc. for data analysis. These methods, however, obliterate the molecular information contained in the original Raman data leading to speculative interpretations based on relative intensities. In the present study, SVD analysis of the Raman images from Penicillium chrysogenum resulted in 11 spectral components and corresponding images with highly distorted spectral features and complex image contrast, respectively. To interpret the SVD results in molecular terms, we have developed a combined multivariate approach. By applying this methodology, we have successfully extracted the contribution of five biomolecular constituents of the P. chrysogenum filamentous cell to the SVD vectors. Molecular interpretability will help SVD/PCA surpass the realm of variance-based classification to a more meaningful molecular domain.

Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells

To reveal the dynamic features of cellular systems, such as the correlation among phenotypes, a time or condition series set of samples is typically required. Here, we propose intra-ramanome correlation analysis (IRCA) to achieve this goal from just one snapshot of an isogenic population, via pairwise correlation among the cells of the thousands of Raman peaks in single-cell Raman spectra (SCRS), i.e., by taking advantage of the intrinsic metabolic heterogeneity among individual cells. For example, IRCA of Chlamydomonas reinhardtii under nitrogen depletion revealed metabolite conversions at each time point plus their temporal dynamics, such as protein-to-starch conversion followed by starch-to-triacylglycerol (TAG) conversion, and conversion of membrane lipids to TAG. Such among-cell correlations in SCRS vanished when the starch-biosynthesis pathway was knocked out yet were fully restored by genetic complementation. Extension of IRCA to 64 microalgal, fungal, and bacterial ramanomes suggests the IRCA-derived metabolite conversion network as an intrinsic metabolic signature of isogenic cellular population that is reliable, species-resolved, and state-sensitive. The high-throughput, low cost, excellent scalability, and general extendibility of IRCA suggest its broad applications.

Multivariate curve resolution combined with estimation by cosine similarity mapping of analytical data

We developed a multivariate curve resolution (MCR) calculation combined with the mapping of cosine similarity (cos-s) for estimating multiple mixture spectra of chemicals. The cos-s map was obtained by calculating the similarities of the variation of the signal intensities at each scanning parameter, such as the wavelength. The cos-s map was utilized for the initial estimation of the spectra of pure chemicals and also for the restriction of the iterative least-squares calculation of the MCR. These calculations were performed without arbitrary parameters by introducing soft clustering to the cos-s map. The chemically meaningful initial estimation could prevent the convergence at an incorrect local minimum, which frequently happens for the wrong initial estimation of spectra far away from the real answer. Herein, we demonstrated the robustness of this calculation method by applying it for UV/Vis spectra and XRD patterns of multiple unknown chemical mixtures, whose shapes were totally different (broad overlapped peaks and multiple complicated peaks). Pure spectra/patterns were recovered as >84% consistency with the reference spectra, and <6% accuracy of the concentration ratios was demonstrated.

Raman microspectroscopy and Raman imaging reveal biomarkers specific for thoracic aortic aneurysms

Aortic rupture and dissection are life-threatening complications of ascending thoracic aortic aneurysms (aTAAs), and risk assessment has been largely based on the monitoring of lumen size enlargement. Temporal changes in the extracellular matrix (ECM), which has a critical impact on aortic remodeling, are not routinely evaluated, and cardiovascular biomarkers do not exist to predict aTAA formation. Here, Raman microspectroscopy and Raman imaging are used to identify spectral biomarkers specific for aTAAs in mice and humans by multivariate data analysis (MVA). Multivariate curve resolution-alternating least-squares (MCR-ALS) combined with Lasso regression reveals elastic fiber-derived (Ce1) and collagen fiber-derived (Cc6) components that are significantly increased in aTAA lesions of murine and human aortic tissues. In particular, Cc6 detects changes in amino acid residues, including phenylalanine, tyrosine, tryptophan, cysteine, aspartate, and glutamate. Ce1 and Cc6 may serve as diagnostic Raman biomarkers that detect alterations of amino acids derived from aneurysm lesions.

Label-free Raman imaging of human/murine ascending thoracic aortic aneurysm (aTAA)

Multivariate analysis of Raman spectra allows detection of aTAA molecular features

Identification of spectral biomarkers for aTAA in elastic and collagen fibers

Alterations in amino acid spectra correlate with aTAA formation

Deuterium-labeled Raman tracking of glucose accumulation and protein metabolic dynamics in Aspergillus nidulans hyphal tips

Filamentous fungi grow exclusively at their tips, where many growth-related fungal processes, such as enzyme secretion and invasion into host cells, take place. Hyphal tips are also a site of active metabolism. Understanding metabolic dynamics within the tip region is therefore important for biotechnology and medicine as well as for microbiology and ecology. However, methods that can track metabolic dynamics with sufficient spatial resolution and in a nondestructive manner are highly limited. Here we present time-lapse Raman imaging using a deuterium (D) tracer to study spatiotemporally varying metabolic activity within the hyphal tip of Aspergillus nidulans. By analyzing the carbon-deuterium (C-D) stretching Raman band with spectral deconvolution, we visualize glucose accumulation along the inner edge of the hyphal tip and synthesis of new proteins from the taken-up D-labeled glucose specifically at the central part of the apical region. Our results show that deuterium-labeled Raman imaging offers a broadly applicable platform for the study of metabolic dynamics in filamentous fungi and other relevant microorganisms in vivo.

Detection of Penicillin G Produced by Penicillium chrysogenum with Raman Microspectroscopy and Multivariate Curve Resolution-Alternating Least-Squares Methods

Raman microspectroscopy is a minimally invasive technique that can identify molecules without labeling. In this study, we demonstrate the detection of penicillin G inside Penicillium chrysogenum KF425 fungal cells. Raman spectra acquired from the fungal cells had highly overlapped spectroscopic signatures and hence were analyzed with multivariate curve resolution by alternating least-squares (MCR-ALS) to extract the spectra of individual molecular constituents. In addition to detecting spatial distribution of multiple constituents such as proteins and lipids inside the fungal body, we could also observe the subcellular localization of penicillin G. This methodology has the potential to be employed in screening the production of bioactive compounds by microorganisms.

Molecular profiling of lipid droplets inside HuH7 cells with Raman micro-spectroscopy

Raman imaging has become an attractive technology in molecular biology because of its ability to detect multiple molecular components simultaneously without labeling. Two major limitations in accurately accounting for spectral features, viz., background removal and spectral unmixing, have been overcome by employing a modified and effective routine in multivariate curve resolution (MCR). With our improved strategy, we have spectrally isolated seven structurally specific biomolecules without any post-acquisition spectral treatments. Consequently, the isolated intensity profiles reflected concentrations of corresponding biomolecules with high statistical accuracy. Our study reveals the changes in the molecular composition of lipid droplets (LDs) inside HuH7 cells and its relation to the physiological state of the cell. Further, we show that the accurate separation of spectral components permits analysis of structural modification of molecules after cellular uptake. A detailed discussion is presented to highlight the potential of Raman spectroscopy with MCR in semi-quantitative molecular profiling of living cells.

Samuel, Miyaoka et al. investigate the changes in the molecular composition of lipid droplets inside HuH7 cells and its relation to the physiological state of the cell, using Raman spectroscopy and multivariate curve resolution. This study underscores the importance of separation of spectral components in semi-quantitative molecular profiling of living cells.

Raman spectroscopic signatures of carotenoids and polyenes enable label-free visualization of microbial distributions within pink biofilms

Pink biofilms are multispecies microbial communities that are commonly found in moist household environments. The development of this pink stain is problematic from an aesthetic point of view, but more importantly, it raises hygienic concerns because they may serve as a potential reservoir of opportunistic pathogens. Although there have been several studies of pink biofilms using molecular analysis and confocal laser scanning microscopy, little is known about the spatial distributions of constituent microorganisms within pink biofilms, a crucial factor associated with the characteristics of pink biofilms. Here we show that Raman spectroscopic signatures of intracellular carotenoids and polyenes enable us to visualize pigmented microorganisms within pink biofilms in a label-free manner. We measured space-resolved Raman spectra of a pink biofilm collected from a bathroom, which clearly show resonance Raman bands of carotenoids. Multivariate analysis of the Raman hyperspectral imaging data revealed the presence of typical carotenoids and structurally similar but different polyenes, whose spatial distributions within the pink biofilm were found to be mutually exclusive. Raman measurements on individual microbial cells isolated from the pink biofilm confirmed that these distributions probed by carotenoid/polyene Raman signatures are attributable to different pigmented microorganisms. The present results suggest that Raman microspectroscopy with a focus on microbial pigments such as carotenoids is a powerful nondestructive method for studying multispecies biofilms in various environments.

Organelle specific simultaneous Raman/green fluorescence protein microspectroscopy for living cell physicochemical studies

We demonstrate a novel bio-spectroscopic technique, "simultaneous Raman/GFP microspectroscopy". It enables organelle specific Raman microspectroscopy of living cells. Fission yeast, Schizosaccharomyces pombe, whose mitochondria are green fluorescence protein (GFP) labeled, is used as a test model system. Raman excitation laser and GFP excitation light irradiate the sample yeast cells simultaneously. GFP signal is monitored in the anti-Stokes region where interference from Raman scattering is negligibly small. Of note, 13 568 Raman spectra measured from different points of 19 living yeast cells are categorized according to their GFP fluorescence intensities, with the use of a two-component multivariate curve resolution with alternate least squares (MCR-ALS) analysis in the anti-Stokes region. This categorization allows us to know whether or not Raman spectra are taken from mitochondria. Raman spectra specific to mitochondria are obtained by an MCR-ALS analysis in the Stokes region of 1389 strongly GFP positive spectra. Two mitochondria specific Raman spectra have been obtained. The first one is dominated by protein Raman bands and the second by lipid Raman bands, being consistent with the known molecular composition of mitochondria. In addition, the second spectrum shows a strong band of ergosterol at 1602 cm^-1 , previously reported as "Raman spectroscopic signature of life of yeast."

Rapid inspection method for investigating the heat processing conditions employed for chicken meat using Raman spectroscopy

In Japan, the imports of meat products have been increasing every year. Heat processing of meat is the current standard method for ensuring domestic animal health, particularly in case of meat products from areas where infectious diseases are known to have occurred in domestic animals. The Animal Quarantine Service needs to establish a method that detects the temperature at which the meat has been heat-processed (endpoint temperature) to ensure that the standard protocol is followed at the production location. Here, we developed a Raman spectroscopy and multivariate statistics (viz. multivariate curve resolution (MCR))-based simple and rapid method for accurately estimating the end point temperature. We showed that the temperature-dependent secondary structure modification of proteins can serve as an accurate indicator of the temperature of heat processing. This methodology can be easily automated for effective utilization by someone who is not an expert in spectroscopy. We envisage a wider application of this method in food analysis, although the present research investigated the application of this method in chicken meat heat processing analysis.

Direct estimation of polymer crystallinity with Raman spectroscopy using ratio of scattering cross-sections estimated from variable temperature measurements

Physical properties of polymers (e.g. crystallinity, lamella thickness, thermodynamic properties etc.) can in principle be reliably estimated from their Raman spectral intensities by converting intensities to corresponding concentrations of conformers. However, such conversions are not straightforward due to the unknown scattering cross-sections. The study demonstrates that for several practical applications of Raman spectroscopy, a ratio of cross-sections can be used instead of the absolute values. A straight forwards method for accurately estimating ratio of scattering cross-section from variable temperature measurements is described here. In order to demonstrate its applicability, percent crystallinity (PC) of polyethylene has been directly estimated from Raman intensities without external calibration with other techniques. This general method can be applied to any polymer when there is a continuous change in composition of conformers over a range of temperatures.

Parallelized shifted-excitation Raman difference spectroscopy for fluorescence rejection in a temporary varying system

A fluorescence background is one of the common interference factors of the Raman spectroscopic analysis in the biology field. Shifted-excitation Raman difference spectroscopy (SERDS), in which a slow (typically 1 Hz) modulation to excitation wavelength is coupled with a sequential acquisition of alternating shifted-excitation spectra, has been used to separate Raman scattering from excitation-shift insensitive background. This sequential method is susceptible to spectral change and thus is limited only to stable samples. We incorporated a fast laser modulation (200 Hz) and a mechanical streak camera into SERDS to effectively parallelize the SERDS measurement in a single exposure. The developed system expands the scope of SERDS to include temporary varying system. The proof of concept is demonstrated using highly fluorescent samples, including living algae. Quantitative performance in fluorescence rejection and the robustness of the method to the dynamic spectral change during the measurement are manifested.

pyMCR: A Python Library for MultivariateCurve Resolution Analysis with Alternating Regression (MCR-AR)

pyMCR is a new open-source software library for performing multivariate curve resolution (MCR) analysis with an alternating regression scheme (MCR-AR). MCR is a chemometric method for elucidating measurement signatures of analytes and their relative abundance from a series of mixture measurements, without any knowledge of these values a priori. This software library, written in Python, enables users to perform MCR analysis with their choice of error functions for minimization, constraints, and regressors. Further, users can apply different constraints and regressors for signature and abundance calculations. Finally, this library enables users to develop their own constraints, regressors, and error functions or import them from existing libraries.

An initial estimation method using cosine similarity for multivariate curve resolution: application to NMR spectra of chemical mixtures

Mixture spectra is decomposed into pure spectra without prior knowledge, and the MCR calculation refines the spectra and provides the concentrations.

Biological and Medical Applications of Multivariate Curve Resolution Assisted Raman Spectroscopy

Biological specimens such as cells, tissues and biofluids (urine, blood) contain mixtures of many different biomolecules, all of which contribute to a Raman spectrum at any given point. The separation and identification of pure biochemical components remains one of the biggest challenges in Raman spectroscopy. Multivariate curve resolution, a matrix factorization method, is a powerful, yet flexible, method that can be used with constraints, such as non-negativity, to decompose a complex spectroscopic data matrix into a small number of physically meaningful pure spectral components along with their relative abundances. This paper reviews recent applications of multivariate curve resolution by alternating least squares analysis to Raman spectroscopic and imaging data obtained either in vivo or in vitro from biological and medical samples.

Toward a Noninvasive, Label-Free Screening Method for Determining Spore Inoculum Quality of Penicillium chrysogenum Using Raman Spectroscopy

We report on a label-free, noninvasive method for determination of spore inoculum quality of Penicillium chrysogenum prior to cultivation/germination. Raman microspectroscopy providing direct, molecule-specific information was used to extract information on the viability state of spores sampled directly from the spore inoculum. Based on the recorded Raman spectra, a supervised classification method was established for classification between living and dead spores and thus determining spore inoculum quality for optimized process control. A fast and simple sample preparation method consisting of one single dilution step was employed to eliminate interfering signals from the matrix and to achieve isolation of single spores on the sample carrier (CaF₂). Aiming to avoid any influence of the killing procedure in the Raman spectrum of the spore, spores were considered naturally dead after more than one year of storage time. Fluorescence staining was used as reference method. A partial least squares discriminant analysis classifier was trained with Raman spectra of 258 living and dead spores (178 spectra for calibration, 80 spectra for validation). The classifier showed good performance when being applied to a 1 µL droplet taken from a 1:1 mixture of living and dead spores. Of 135 recorded spectra, 51% were assigned to living spores while 49% were identified as dead spores by the classifier. The results obtained in this work are a fundamental step towards developing an automated, label-free, and noninvasive screening method for assessing spore inoculum quality.

Experimental Evaluation of the Density of Water in a Cell by Raman Microscopy

We report direct observation of a spatial distribution of water molecules inside of a living cell using Raman images of the O-H stretching band of water. The O-H Raman intensity of the nucleus was higher than that of the cytoplasm, indicating that the water density is higher in the nucleus than that in the cytoplasm. The shape of the O-H stretching band of the nucleus differed from that of the cytoplasm but was similar to that of the balanced salt solution surrounding cells, indicating less crowded environments in the nucleus. The concentration of biomolecules having C-H bonds was also estimated to be lower in the nucleus than that in the cytoplasm. These results indicate that the nucleus is less crowded with biomolecules than the cytoplasm.

Raman Microspectroscopic Mapping with Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) Applied to the High-Pressure Polymorph of Titanium Dioxide, TiO2-II

The high-pressure, α-PbO₂-structured polymorph of titanium dioxide (TiO₂-II) was recently identified in micrometer-sized grains recovered from four Neoarchean spherule layers deposited between ∼2.65 and ∼2.54 billion years ago. Several lines of evidence support the interpretation that these layers represent distal impact ejecta layers. The presence of shock-induced TiO₂-II provides physical evidence to further support an impact origin for these spherule layers. Detailed characterization of the distribution of TiO₂-II in these grains may be useful for correlating the layers, estimating the paleodistances of the layers from their source craters, and providing insight into the formation of the TiO₂-II. Here we report the investigation of TiO₂-II-bearing grains from these four spherule layers using multivariate curve resolution-alternating least squares (MCR-ALS) applied to Raman microspectroscopic mapping. Raman spectra provide evidence of grains consisting primarily of rutile (TiO₂) and TiO₂-II, as shown by Raman bands at 174 cm^-1 (TiO₂-II), 426 cm^-1 (TiO₂-II), 443 cm^-1 (rutile), and 610 cm^-1 (rutile). Principal component analysis (PCA) yielded a predominantly three-phase system comprised of rutile, TiO₂-II, and substrate-adhesive epoxy. Scanning electron microscopy (SEM) suggests heterogeneous grains containing polydispersed micrometer- and submicrometer-sized particles. Multivariate curve resolution-alternating least squares applied to the Raman microspectroscopic mapping yielded up to five distinct chemical components: three phases of TiO₂ (rutile, TiO₂-II, and anatase), quartz (SiO₂), and substrate-adhesive epoxy. Spectral profiles and spatially resolved chemical maps of the pure chemical components were generated using MCR-ALS applied to the Raman microspectroscopic maps. The spatial resolution of the Raman microspectroscopic maps was enhanced in comparable, cost-effective analysis times by limiting spectral resolution and optimizing spectral acquisition parameters. Using the resolved spectra of TiO₂-II generated from MCR-ALS analysis, a Raman spectrum for pure TiO₂-II was estimated to further facilitate its identification.

On-site Direct Detection of Astaxanthin from Salmon Fillet Using Raman Spectroscopy

A new technology employing Raman spectroscopy is attracting attention as a powerful biochemical technique for the detection of beneficial and functional food nutrients, such as carotenoids and unsaturated fatty acids. This technique allows for the dynamic characterization of food nutrient substances for the rapid determination of food quality. In this study, we attempt to detect and measure astaxanthin from salmon fillets using this technology. The Raman spectra showed specific bands corresponding to the astaxanthin present in salmon and the value of astaxanthin (Raman band, 1518 cm^-1) relative to those of protein/lipid (Raman band, 1446 cm^-1) in the spectra increased in a dose-dependent manner. A standard curve was constructed by the standard addition method using astaxanthin as the reference standard for its quantification by Raman spectroscopy. The calculation formula was established using the Raman bands typically observed for astaxanthin (i.e., 1518 cm^-1). In addition, we examined salmon fillets of different species (Atlantic salmon, coho salmon, and sockeye salmon) and five fillets obtained from the locations (from the head to tail) of an entire Atlantic salmon. Moreover, the sockeye salmon fillet exhibited the highest astaxanthin concentration (14.2 mg/kg), while coho salmon exhibited an intermediate concentration of 7.0 mg/kg. The Raman-based astaxanthin concentration in the five locations of Atlantic salmon was more strongly detected from the fillet closer to the tail. From the results, a rapid, convenient Raman spectroscopic method was developed for the detection of astaxanthin in salmon fillets.

Using Separable Nonnegative Matrix Factorization Techniques for the Analysis of Time-Resolved Raman Spectra

The key challenge of time-resolved Raman spectroscopy is the identification of the constituent species and the analysis of the kinetics of the underlying reaction network. In this work we present an integral approach that allows for determining both the component spectra and the rate constants simultaneously from a series of vibrational spectra. It is based on an algorithm for nonnegative matrix factorization that is applied to the experimental data set following a few pre-processing steps. As a prerequisite for physically unambiguous solutions, each component spectrum must include one vibrational band that does not significantly interfere with the vibrational bands of other species. The approach is applied to synthetic "experimental" spectra derived from model systems comprising a set of species with component spectra differing with respect to their degree of spectral interferences and signal-to-noise ratios. In each case, the species involved are connected via monomolecular reaction pathways. The potential and limitations of the approach for recovering the respective rate constants and component spectra are discussed.

Label-free Chemical Imaging of Fungal Spore Walls by Raman Microscopy and Multivariate Curve Resolution Analysis

Fungal cell walls are medically important since they represent a drug target site for antifungal medication. So far there is no method to directly visualize structurally similar cell wall components such as α-glucan, β-glucan and mannan with high specificity, especially in a label-free manner. In this study, we have developed a Raman spectroscopy based molecular imaging method and combined multivariate curve resolution analysis to enable detection and visualization of multiple polysaccharide components simultaneously at the single cell level. Our results show that vegetative cell and ascus walls are made up of both α- and β-glucans while spore wall is exclusively made of α-glucan. Co-localization studies reveal the absence of mannans in ascus wall but are distributed primarily in spores. Such detailed picture is believed to further enhance our understanding of the dynamic spore wall architecture, eventually leading to advancements in drug discovery and development in the near future.

Automatic and objective oral cancer diagnosis by Raman spectroscopic detection of keratin with multivariate curve resolution analysis

We have developed an automatic and objective method for detecting human oral squamous cell carcinoma (OSCC) tissues with Raman microspectroscopy. We measure 196 independent Raman spectra from 196 different points of one oral tissue sample and globally analyze these spectra using a Multivariate Curve Resolution (MCR) analysis. Discrimination of OSCC tissues is automatically and objectively made by spectral matching comparison of the MCR decomposed Raman spectra and the standard Raman spectrum of keratin, a well-established molecular marker of OSCC. We use a total of 24 tissue samples, 10 OSCC and 10 normal tissues from the same 10 patients, 3 OSCC and 1 normal tissues from different patients. Following the newly developed protocol presented here, we have been able to detect OSCC tissues with 77 to 92% sensitivity (depending on how to define positivity) and 100% specificity. The present approach lends itself to a reliable clinical diagnosis of OSCC substantiated by the “molecular fingerprint” of keratin.

When cells divide: Label-free multimodal spectral imaging for exploratory molecular investigation of living cells during cytokinesis

In vivo, molecular-level investigation of cytokinesis, the climax of the cell cycle, not only deepens our understanding of how life continues, but it will also open up new possibilities of diagnosis/prognosis of cancer cells. Although fluorescence-based methods have been widely employed to address this challenge, they require a fluorophore to be designed for a specific known biomolecule and introduced into the cell. Here, we present a label-free spectral imaging approach based on multivariate curve resolution analysis of Raman hyperspectral data that enables exploratory untargeted studies of mammalian cell cytokinesis. We derived intrinsic vibrational spectra and intracellular distributions of major biomolecular components (lipids and proteins) in dividing and nondividing human colon cancer cells. In addition, we discovered an unusual autofluorescent lipid component that appears predominantly in the vicinity of the cleavage furrow during cytokinesis. This autofluorescence signal could be utilized as an endogenous probe for monitoring and visualizing cytokinesis in vivo.

Raman Imaging from Microscopy to Nanoscopy, and to Macroscopy

Raman spectroscopy can not only provide intrinsic fingerprint information about a sample, but also utilize the merits of the narrow bandwidth and low background of Raman spectra, offering itself as a promising multiplex analytical technique. Raman microscopy has become particularly attractive recently because it has demonstrated itself as an important imaging technique for various samples, from biological samples and chemical systems to industrially important silicon-based wafers. In this Concept article, some of the most recent advances in Raman imaging techniques are critically reviewed, and the advantages and problems associated with the current techniques are discussed. Particular emphasis is placed on its future directions, from both the technical and application sides.

Raman spectroscopic study on the excystation process in a single unicellular organism amoeba (Acanthamoeba polyphaga)

An in vivo Raman spectroscopic study of amoeba (Acanthamoeba polyphaga) is presented. The changes of the spectra during the amoeba cyst activation and excystation are analyzed. The spectra show the changes of the relative intensities of bands corresponding to protein, lipid, and carotenoid components during cyst activation. The presence of carotenoids in the amoeba is observed via characteristic Raman bands. These signals in the Raman spectra are intense in cysts but decrease in intensity with cyst activation and exhibit a correlation with the life cycle of amoeba. This work demonstrates the feasibility of using Raman spectroscopy for the detection of single amoeba microorganisms in vivo and for the analysis of the amoeba life activity. The information obtained may have implications for the estimation of epidemiological situations and for the diagnostics and prognosis of the development of amoebic inflammations.