Characterization and analysis of mycobacteria and Gram-negative bacteria and co-culture mixtures by Raman microspectroscopy, FTIR, and atomic force microscopy

The molecular composition of mycobacteria and Gram-negative bacteria cell walls is structurally different. In this work, Raman microspectroscopy was applied to discriminate mycobacteria and Gram-negative bacteria by assessing specific characteristic spectral features. Analysis of Raman spectra indicated that mycobacteria and Gram-negative bacteria exhibit different spectral patterns under our experimental conditions due to their different biochemical components. Fourier transform infrared (FTIR) spectroscopy, as a supplementary vibrational spectroscopy, was also applied to analyze the biochemical composition of the representative bacterial strains. As for co-cultured bacterial mixtures, the distribution of individual cell types was obtained by quantitative analysis of Raman and FTIR spectral images and the spectral contribution from each cell type was distinguished by direct classical least squares analysis. Coupled atomic force microscopy (AFM) and Raman microspectroscopy realized simultaneous measurements of topography and spectral images for the same sampled surface. This work demonstrated the feasibility of utilizing a combined Raman microspectroscopy, FTIR, and AFM techniques to effectively characterize spectroscopic fingerprints from bacterial Gram types and mixtures.

Obtaining pure spectra of hemicellulose and cellulose from poplar cell wall Raman imaging data

A comprehensive understanding of the structural and chemical nature of plant cell walls is important from both the perspectives of plant biotechnology and of commercial utilization. The Raman imaging technique is a preferred solution for its ability to offer spatial and spectral information simultaneously. However, the exact spectra of hemicellulose and cellulose are difficult to directly discern from each other due to the strong spectral overlap. In the present study, we report for the first time that the spectra of hemicellulose and cellulose in poplar cell wall Raman imaging can be discriminated by using multivariate analysis. The semi-quantitative concentrations of polysaccharides are identified based on the corresponding Raman images. Results show that cellulose is mostly concentrated in the secondary wall of poplar fibres, whilst the distribution of hemicellulose is almost uniform throughout the cell wall of fibres except for a higher concentration found in the S1 and the outer S2 layer. The xylem ray and the vessel have relatively high hemicellulose concentrations which is comparable to the outer S2 layer of fibres, but the cellulose concentration is relatively low in these two cell types. This work not only contributes to broadening our knowledge about the distribution of polysaccharides in plant cell walls, but also provides a potential strategy to trace their dynamic changes during the bioconversion at a micro-level.     

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

A detailed comparison of six multivariate algorithms is presented to analyze and generate Raman microscopic images that consist of a large number of individual spectra. This includes the segmentation algorithms for hierarchical cluster analysis, fuzzy C-means cluster analysis, and k-means cluster analysis and the spectral unmixing techniques for principal component analysis and vertex component analysis (VCA). All algorithms are reviewed and compared. Furthermore, comparisons are made to the new approach N-FINDR. In contrast to the related VCA approach, the used implementation of N-FINDR searches for the original input spectrum from the non-dimension reduced input matrix and sets it as the endmember signature. The algorithms were applied to hyperspectral data from a Raman image of a single cell. This data set was acquired by collecting individual spectra in a raster pattern using a 0.5-??m step size via a commercial Raman microspectrometer. The results were also compared with a fluorescence staining of the cell including its mitochondrial distribution. The ability of each algorithm to extract chemical and spatial information of subcellular components in the cell is discussed together with advantages and disadvantages.     

Understanding the molecular information contained in principal component analysis of vibrational spectra of biological systems

K-means clustering followed by Principal Component Analysis (PCA) is employed to analyse Raman spectroscopic maps of single biological cells. K-means clustering successfully identifies regions of cellular cytoplasm, nucleus and nucleoli, but the mean spectra do not differentiate their biochemical composition. The loadings of the principal components identified by PCA shed further light on the spectral basis for differentiation but they are complex and, as the number of spectra per cluster is imbalanced, particularly in the case of the nucleoli, the loadings under-represent the basis for differentiation of some cellular regions. Analysis of pure bio-molecules, both structurally and spectrally distinct, in the case of histone, ceramide and RNA, and similarly in the case of the proteins albumin, collagen and histone, show the relative strong representation of spectrally sharp features in the spectral loadings, and the systematic variation of the loadings as one cluster becomes reduced in number. The more complex cellular environment is simulated by weighted sums of spectra, illustrating that although the loading becomes increasingly complex; their origin in a weighted sum of the constituent molecular components is still evident. Returning to the cellular analysis, the number of spectra per cluster is artificially balanced by increasing the weighting of the spectra of smaller number clusters. While it renders the PCA loading more complex for the three-way analysis, a pair wise analysis illustrates clear differences between the identified subcellular regions, and notably the molecular differences between nuclear and nucleoli regions are elucidated. Overall, the study demonstrates how appropriate consideration of the data available can improve the understanding of the information delivered by PCA.     

Cryoprobe 3D NMR of acetylated ball-milled pine cell walls

3D NMR of solubilized ball-milled pine cell walls reveals striking details of lignin units, right down to differentiating stereoisomers in the polymer. Such 3D spectral editing therefore overcomes the need to isolate wall components to study their structures.     

Chemistry at level of individual aerosol particle using multivariate curve resolution of confocal Raman image

Airborne particles with aerodynamic diameter in the 10-1 microm range have been collected in an industrial/urban zone by impaction and have been investigated by automated confocal Raman microspectrometry. The computer-microcontrolled XY scanning and Z focusing of Raman images provided many pixel Raman spectra which are characteristics of complex mixture at level of individual particle. The large heterogeneity was not resolved by the spatial resolution of the instrument which is limited by the optical diffraction. The severe spectral overlaps generated by heterogeneity were resolved by multivariate curve resolution (MCR) methods. The purity based method (SIMPLISMAX) was used to resolve both luminescence spectra and pure Raman spectra without prior information. The MCR-alternating least square (ALS) was used as a refined method of both spectra and spectral concentrations. The reconstructing Raman images of the respective spectral contribution supply a versatile potential to characterize the chemistry of atmospheric aerosols at the level of the individual particles.     

Fourier-transform Raman spectroscopic study of a Neolithic waterlogged wood assemblage

The use of Fourier-transform Raman spectroscopy for characterising lignocellulosics has increased significantly over the last twenty years. Here, an FT-Raman spectroscopic study of changes in the chemistry of waterlogged archaeological wood of Pinus sp. and Quercus sp. from a prehistoric assemblage recovered from northern Greece is presented. FT-Raman spectral features of biodeteriorated wood were associated with the depletion of lignin and/or carbohydrate polymers at various stages of deterioration. Spectra from the archaeological wood are presented alongside spectra of sound wood of the same taxa. A comparison of the relative changes in intensities of spectral bands associated with lignin and carbohydrates resulting from decay clearly indicated extensive deterioration of both the softwood and hardwood samples and the carbohydrates appear to be more deteriorated than the lignin. The biodeterioration of the archaeological timbers followed a pattern of initial preferential loss of carbohydrates causing significant loss of cellulose and hemicellulose, followed by the degradation of lignin.     

Methodology for fiber-optic Raman mapping and FTIR imaging of metastases in mouse brains

The objectives of this study were to optimize the preparation of pristine brain tissue to obtain reference information, to optimize the conditions for introducing a fiber-optic probe to acquire Raman maps, and to transfer previous results obtained from human brain tumors to an animal model. Brain metastases of malignant melanomas were induced by injecting tumor cells into the carotid artery of mice. The procedure mimicked hematogenous tumor spread in one brain hemisphere while the other hemisphere remained tumor free. Three series of sections were prepared consecutively from whole mouse brains: dried, thin sections for FTIR imaging, hematoxylin and eosin-stained thin sections for histopathological assessment, and pristine, 2-mm thick sections for Raman mapping. FTIR images were recorded using a spectrometer with a multi-channel detector. Raman maps were collected serially using a spectrometer coupled to a fiber-optic probe. The FTIR images and the Raman maps were segmented by cluster analysis. The color-coded cluster memberships coincided well with the morphology of mouse brains in stained tissue sections. More details in less time were resolved in FTIR images with a nominal resolution of 25 microm than in Raman maps collected with a laser focus 60 microm in diameter. The spectral contributions of melanin in tumor cells were resonance enhanced in Raman spectra on excitation at 785 nm which enabled their sensitive detection in Raman maps. Possible reasons why metastatic cells of malignant melanomas were not identified in FTIR images are discussed.     

Spatially resolved determination of the structure and composition of diatom cell walls by Raman and FTIR imaging

Vibrational spectroscopic imaging has developed into a versatile tool to study the local composition of various materials. Here, we present for the first time that Raman mapping and Fourier transform infrared imaging are useful tools to study diatom cell walls as is demonstrated for the species Stephanopyxis turris. The unicellular diatoms exhibit intricately micro- and nano-patterned cell walls, which consist of amorphous silica as well as various organic and inorganic constituents, thus making up an extremely interesting inorganic/organic hybrid material. The structure and composition of this material as well as the biochemical and biophysical processes leading to its formation remain to be challenges for ongoing research. Whereas the lateral resolution of Fourier transform infrared imaging is limited to 5 μm by diffraction, Raman maps are shown to be capable of detecting the spatial distribution of the silica as well as an additional inorganic component and the organic material down to 330-nm resolution. Due to the spherical shape of the sample with a radius of 40 μm and the requirement to accurately focus the laser before each Raman measurement within the micrometer range, Raman maps of whole diatom cell walls were registered after an adjustment of the axial position. The results reveal local differences in the cell wall composition of the honeycomb-like structures and the bottom layer.     

Detecting changes in arabidopsis cell wall composition using time‐of‐flight secondary ion mass spectrometry

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) was previously used to characterize lignocellulosic materials, including woody biomass. ToF‐SIMS can acquire both rapid spectral and spatial information about a sample's surface composition. In the present study, ToF‐SIMS was used to characterize the cell walls of stem tissue from the plant model organism, Arabidopsis thaliana. Using principal component analyses, ToF‐SIMS spectra from A. thaliana wild‐type (Col‐0), cellulose mutant (irx3), and lignin mutant (fah1) stem tissues were distinguished using ToF‐SIMS peaks annotated for wood‐derived lignocellulose, where spectra from the irx3 and fah1 were characterized by comparatively low polysaccharide and syringyl lignin content, respectively. Spatial analyses using ToF‐SIMS imaging furthermore differentiated interfascicular fiber and xylem vessels based on differences in the lignin content of corresponding cell walls. These new data support the applicability of ToF‐SIMS peak annotations based on woody biomass for herbaceous plants, including model plant systems like arabidopsis. Copyright © 2015 John Wiley & Sons, Ltd.     

Near infrared Raman spectroscopic mapping of native brain tissue and intracranial tumors

This study assessed the diagnostic potential of Raman spectroscopic mapping by evaluating its ability to distinguish between normal brain tissue and the human intracranial tumors gliomas and meningeomas. Seven Raman maps of native specimens were collected ex vivo by a Raman spectrometer with 785 nm excitation coupled to a microscope with a motorized stage. Variations within each Raman map were analyzed by cluster analysis. The dependence of tissue composition on the tissue type in cluster averaged Raman spectra was shown by linear combinations of reference spectra. Normal brain tissue was found to contain higher levels of lipids, intracranial tumors have more hemoglobin and lower lipid to protein ratios, meningeomas contain more collagen with maximum collagen content in normal meninges. One sample was studied without freezing. Whereas tumor regions did not change significantly, spectral changes were observed in the hemoglobin component after snap freezing and thawing to room temperature. The results constitute a basis for subsequent Raman studies to develop classification models for diagnosis of brain tissue.     

Direct visualization of straw cell walls by AFM

The structural relationship of cellulose, hemicellulose, and lignin in plant cell walls is still a mystery needing to be explored. By using atomic force microscopy (AFM) the surface of straw at different layers was directly observed, and the structural characteristics were analyzed by topographic analysis and FT-IR spectra. It was found that a compact layer of wax covered the outside of the straw, which protects the straw from insects and microorganisms. At the boundary of the primary and second wall there appears a network structure of cellulose and hemicellulose, with some lignin localised on the surface of the network. It is consistent with the model of a cell wall suggested by Vincent. Inside the second cell wall, there is a layer mainly composed of a cellulose crystalline region. High-resolution AFM observation reveals that the crystalline structure consists of both triclinic and monoclinic unit cells. An AFM phase image showing the structural relation between cellulose microfibrils, hemicellulose, and lignin in the straw cell wall.     

Crisp and soft multivariate methods visualize individual cell nuclei in Raman images of liver tissue sections

Raman and infrared spectroscopy have been recognized to be promising tools in clinical diagnostics because they provide molecular contrast without external stains. Here, vertex component analysis (VCA) was applied to Raman and Fourier transform infrared (FTIR) images of liver tissue sections and the results were compared with K-means cluster analysis, fuzzy C-means cluster analysis and principal component analysis. The main components of VCA from three Raman images were assigned to the central vein, periportal vein, cell nuclei, liver parenchyma and bile duct. After resonant Mie scattering correction, VCA of FTIR images identified veins, liver parenchyma, cracks, but no cell nuclei. The advantages of VCA in the context of tissue characterization by vibrational spectroscopic imaging are that the tissue architecture is visualized and the spectral information is reconstructed. Composite images were constructed that revealed a high molecular contrast and that can be interpreted in a similar way like hematoxylin and eosin stained tissue sections.     

Hyperspectral unmixing of Raman micro-images for assessment of morphological and chemical parameters in non-dried brain tumor specimens

Hyperspectral unmixing is an unsupervised algorithm to calculate a bilinear model of spectral endmembers and abundances of components from Raman images. Thirty-nine Raman images were collected from six glioma brain tumor specimens. The tumor grades ranged from astrocytoma WHO II to glioblastoma multiforme WHO IV. The abundance plots of the cell nuclei were processed by an image segmentation procedure to determine the average nuclei size, the number of nuclei, and the fraction of nuclei area. The latter two morphological parameters correlated with the malignancy. A combination of spectral unmixing and non-negativity constrained linear least squares fitting is introduced to assess chemical parameters. First, endmembers of the most abundant and most dissimilar components were defined that represent all data sets. Second, the content of the obtained components’ proteins, nucleic acids, lipids, and lipid to protein ratios were determined in all Raman images. Except for the protein content, all chemical parameters correlated with the malignancy. We conclude that the morphological and chemical information offer new ways to develop Raman-based classification approaches that can complement diagnosis of brain tumors. The role of non-linear Raman modalities to speed-up image acquisition is discussed.

Raman spectral imaging of single cancer cells: probing the impact of sample fixation methods

Raman spectroscopy has proven its potential for the analysis of cell constituents and processes. However, sample preparation methods compatible with clinical practice must be implemented for collection of accurate spectral information. This study aims at assessing, using micro-Raman imaging, the effects of some routinely used fixation methods such as formalin-fixation, formalin-fixation/air drying, cytocentrifugation, and air drying on intracellular spectral information. Data were compared with those acquired from single living cells. In parallel to these spectral information, cell morphological modifications that accompany sample preparation were compared. Spectral images of isolated cells were first analyzed in an unsupervised way using hierarchical cluster analysis (HCA), which allowed delimitation of the cellular compartments. The resulting nuclei cluster centers were compared and revealed at the molecular level that fixation induced changes in spectral information assigned to nucleic acids and proteins. In a second approach, a supervised fitting procedure using model spectra of DNA, RNA, and proteins, chemically extracted from living cells, revealed very small modifications at the level of the localization and quantification of these macromolecules. Finally, HCA and principal components analysis (PCA) performed on individual spectra randomly selected from the nuclear regions showed that formalin-fixation and cytocentrifugation are sample preparation methods that have little impact on the biochemical information as compared to living conditions. Any step involving cell air drying seems to accentuate the spectral deviations from the other preparation methods. It is therefore important in a future context of spectral cytology to take into account these variations.     

Overview of Popular Techniques of Raman Spectroscopy and Their Potential in the Study of Plant Tissues

Raman spectroscopy is one of the main analytical techniques used in optical metrology. It is a vibration, marker-free technique that provides insight into the structure and composition of tissues and cells at the molecular level. Raman spectroscopy is an outstanding material identification technique. It provides spatial information of vibrations from complex biological samples which renders it a very accurate tool for the analysis of highly complex plant tissues. Raman spectra can be used as a fingerprint tool for a very wide range of compounds. Raman spectroscopy enables all the polymers that build the cell walls of plants to be tracked simultaneously; it facilitates the analysis of both the molecular composition and the molecular structure of cell walls. Due to its high sensitivity to even minute structural changes, this method is used for comparative tests. The introduction of new and improved Raman techniques by scientists as well as the constant technological development of the apparatus has resulted in an increased importance of Raman spectroscopy in the discovery and defining of tissues and the processes taking place in them.     

活体小鼠耳朵的拉曼成像方法研究

利用扫描技术获取活体小鼠耳朵组织不同深度的微区拉曼光谱,选取分别归属于血糖、脂类、血红蛋白、蛋白质分子结构的物质的特征谱带1125,1300,1549和1660 cm"1进行峰面积计算,利用这些数据重建二维三维拉曼光谱图像。图像清晰显示了不同物质在活体组织中空间分布情况。实验表明,活体拉曼成像技术可以成为活体研究的新手段。     

Hyperspectral NIR imaging for calibration and prediction: a comparison between image and spectrometer data for studying organic and biological samples

A hyperspectral image in the near infrared contains thousands of position-referenced spectra. After imaging reference materials of known composition it is possible to build Partial Least Squares (PLS) regression models for predicting unknown compositions from new images or spectra. In this paper a comparison is made between spectra from a hyperspectral image and spectra from two spectrometers: a scanning grating instrument with rotating sample holders and an FT-NIR instrument utilizing a fiber-optic probe. The raw spectra and the quality of the PLS calibration models and predictions are compared. Two sample datasets consist of a set of 13 designed artificial mixtures of pure constituents and a selection of 13 sampled cheeses. The prediction error from the hyperspectral image spectra is between that of the two spectrometers. For a typical food sample, the average bias [and replicate standard deviation] was -0.6% [0.5%] for protein and -0.2% [1.3%] for fat. Comparable values for the best spectrometer were -0.2% bias for protein and -0.5% for fat. Some of the advantages of working with hyperspectral images are highlighted: the simultaneous exploration of representations of both spectral and spatial data, and the analysis of concentration profiles and concentration maps all contribute to better characterization of organic and biological materials.     

Investigation of sport supplements quality by Raman spectroscopy and principal component analysis

In this work, sport supplements were investigated by Raman spectroscopy. Samples were obtained from health foods shops, gyms and sports centers covering a wide range of available supplement powders. A systematic comparison of Raman spectra of the analyzed supplements allowed identifying the supplement type through the characteristic vibrational modes of carbohydrates and proteins. The protein supplements were identified by Raman bands at 1650, 1250 and 1004 cm⁻¹, while the spectral range between 1200 and 800 cm⁻¹ was useful to identify the carbohydrate supplements. Due to the diversity in composition of sport supplements, a chemometric tool such as principal component analysis (PCA) was employed to assist in the interpretation of Raman spectra, allowing also the identification of compounds present in sport supplements. Especially, the Raman scattering of aromatic and aliphatic amino acids residues contributes to the existence of bands characteristic for the different types of proteins. This kind of information is very important for the quality control of these products, for detecting the presence of fraud or a sample composition in disagreement with the label, thus ensuring the provenance of the supplements.     

Adaptive multiscale regression for reliable Raman quantitative analysis

This paper presents a novel methodology, adaptive multiscale regression (AMR), to adaptively process Raman spectra for quantitative analysis. The proposed methodology aims to construct an optimal calibration model for a Raman spectrum at hand, regardless of its structural characteristics, thus facilitating the application of Raman spectroscopy as a general tool for analytical chemistry. AMR firstly splits the spectra in a calibration set into frequency components at different scales using adaptive wavelet transform (AWT). Parallel member models constructed at different scales are then fused into a final prediction. The contributions of member models to a fusion model are straightforwardly estimated by a partial least square (PLS) model that emerges from a cross-validation results matrix (X) and reference values (Y). This procedure avoids information leakage by fully utilizing the multiscale nature of the input Raman spectra instead of arbitrarily removing some part of the spectral information by calibrating to selected features. Theoretically, we establish that AMR represents an automatic data-driven strategy that captures the Raman spectral structures adaptively and accurately. Our work tests and refines the AMR method by drawing upon the systematic analysis of spectra formulated to yield challenges representative of those encountered in common Raman analyses. AMR compares favorably with other popular preprocessing methods. Satisfactory calibration results suggest that AMR has the capacity to improve robustness and reliability of Raman spectral analysis, and may well extend to other spectroscopic techniques.