Vibrational spectroscopic mapping and imaging of tissues and cells

Vibrational spectroscopic mapping (point-by-point measurement) and imaging of biological samples (cells and tissues) covering Fourier-transform infrared (FTIR) and Raman spectroscopies has opened up many exciting new avenues to explore biochemical architecture and processes within healthy and diseased cells and tissues, including medical diagnostics and drug design.     

High throughput PCR detection of Xylella fastidiosa directly from almond tissues

Xylella fastidiosa, the causal agent of almond leaf scorch disease (ALSD), is currently re-emerging as a serious concern in California. Efficient pathogen detection is critical for ALSD epidemiological studies, particularly when a large sample size is involved. We here report a PCR procedure to detect X. fastidiosa directly from infected almond tissue without the laborious DNA extraction. Plant samples were prepared by freeze-drying and pulverized. Appropriate dilutions of the pulverized freeze-dried tissue (PFT) were determined to minimize the effect of enzyme inhibitors from plant tissue and retain PCR detection of X. fastdiosa cells at a single digit number level. This PFT-PCR procedure was evaluated by comparing to the in vitro cultivation method using 102 symptomatic samples and resulted in a predictive value of 90.8%. PFT-PCR was further applied to monitor the seasonal occurrence of X. fastidiosa from four selected almond trees in two orchards in 2005. The results matched with those of the cultivation method at 92.3%. Considering the simplicity and reliability, we conclude that PFT-PCR is a valuable option for high throughput rapid detection of X. fastidiosa.     

Adaptogenic activity of withaferin A on human cervical carcinoma cells using high-definition vibrational spectroscopic imaging

Despite invaluable advances in cervical cancer therapy, treatment regimens for recurrent or persistent cancers and low-toxicity alternative treatment options are scarce. In recent years, substances classified as adaptogens have been identified as promising drug sources for preventing and treating cancer-based diseases on their ability to attack multiple molecular targets.This paper establishes the effectiveness of inhibition of the neoplastic process by a withaferin A (WFA), an adaptogenic substance, based on an in vitro model of cervical cancer. This study explores for the first time the potential of high-definition vibrational spectroscopy methods, i.e. Fourier-transform infrared (FT-IR) and Raman spectroscopic (RS) imaging at the single-cell level to evaluate the efficacy of the adaptogenic drug. HeLa cervical cancer cells were incubated with various concentrations of WFA at different incubation times.The multimodal spectroscopic approach combined with partial least squares (PLS) regression allowed the identification of molecular changes (e.g., lipids, protein secondary structures, or nucleic acids) induced by WFA at the cellular level. The results clearly illustrate the enormous potential of WFA in inhibiting the proliferation of cervical cancer cells. WFA inhibited the growth of the studied cancer cell line in a dose-dependent manner. Such studies provide comprehensive information on the sensitivity of cells to adaptogenic drugs. This is a fundamental step towards determining the rate and nature of adaptogen-induced changes in cancer cells.     

Bayesian estimation of the number of protonation sites for urinary metabolites from NMR spectroscopic data

Introduction

To aid the development of better algorithms for \(^1\)H NMR data analysis, such as alignment or peak-fitting, it is important to characterise and model chemical shift changes caused by variation in pH. The number of protonation sites, a key parameter in the theoretical relationship between pH and chemical shift, is traditionally estimated from the molecular structure, which is often unknown in untargeted metabolomics applications.

Objective

We aim to use observed NMR chemical shift titration data to estimate the number of protonation sites for a range of urinary metabolites.

Methods

A pool of urine from healthy subjects was titrated in the range pH 2–12, standard \(^1\)H NMR spectra were acquired and positions of 51 peaks (corresponding to 32 identified metabolites) were recorded. A theoretical model of chemical shift was fit to the data using a Bayesian statistical framework, using model selection procedures in a Markov Chain Monte Carlo algorithm to estimate the number of protonation sites for each molecule.

Results

The estimated number of protonation sites was found to be correct for 41 out of 51 peaks. In some cases, the number of sites was incorrectly estimated, due to very close pKa values or a limited amount of data in the required pH range.

Conclusions

Given appropriate data, it is possible to estimate the number of protonation sites for many metabolites typically observed in \(^1\)H NMR metabolomics without knowledge of the molecular structure. This approach may be a valuable resource for the development of future automated metabolite alignment, annotation and peak fitting algorithms.

     

High resolution imaging of photosynthetic activities of tissues, cells and chloroplasts in leaves

Through imaging of chlorophyll fluorescence, it is possible to produce parameterized fluorescence images that estimate the operating quantum efficiency of photosystem II (PSII) photochemistry and which can be used to reveal heterogeneous patterns of photosynthetic performance within leaves. The operating quantum efficiency of PSII photochemistry is dependent upon the effective absorption cross-section of the light-harvesting system of PSII and the photochemical capacity of PSII. The effective absorption cross-section is decreased by the process of down-regulation, which is widely thought to operate within the pigment matrices of PSII and which results in non-photochemical quenching of chlorophyll fluorescence. The photochemical capacity is non-linearly related to the proportion of PSII centres in the 'open' state and results in photochemical quenching of chlorophyll fluorescence. Examples of heterogeneity of the operating quantum efficiency of PSII photochemistry during the induction of photosynthesis in maize leaves and in the chloroplast populations of stomatal guard cells of a leaf of Tradescantia albifora are presented, together with analyses of the factors determining this heterogeneity. A comparison of the operating quantum efficiency of PSII photochemistry within guard cells and adjacent mesophyll cells of Commelina communis is also made, before and after stomatal closure through a change in ambient humidity.     

Composition of carrageenan blends inferred from C-NMR and infrared spectroscopic analysis

Carrageenan blends mainly composed of kappa, iota and lambda carrageenan were investigated by ¹³C-NMR and infrared (IR) spectroscopy. ¹³C-NMR was shown to be a powerful tool for quantifying the kappa-iota ratio in a carrageenan blend. The technique, however, was not helpful for identifying lambda carrageenan. Consequently, IR spectroscopy was used to achieve a semi-quantitative determination of lambda carrageenan. By combining both techniques, the carrageenan composition of several commercial samples was established.     

Time-resolved spectroscopic fluorescence imaging, transient absorption and vibrational spectroscopy of intact and photo-inhibited photosynthetic tissue.

Fluorescence, absorption and vibrational spectroscopic techniques were used to study spinach at the photosystem II (PS II), chloroplast and cellular levels and to determine the effects and mechanisms of ultraviolet-B (UV-B) photoinhibition of these structures. Two-photon fluorescence spectroscopic imaging of intact chloroplasts shows significant spatial variations in the component fluorescence spectra in the range 640-740 nm, indicating that the type and distribution of chlorophylls vary markedly with position in the chloroplast. The chlorophyll distributions and excitonic behaviour in chloroplasts and whole plant tissue were studied using picosecond time-gated fluorescence imaging, which also showed UV-induced kinetic changes that clearly indicate that UV-B induces both structural and excitonic uncoupling of chlorophylls within the light-harvesting complexes. Transient absorption measurements and low-frequency infrared and Raman spectroscopy show that the predominant sites of UV-B damage in PS II are at the oxygen-evolving centre (OEC) itself, as well as at specific locations near the OEC-binding sites.     

Fourier transform infrared spectroscopic imaging and multivariate regression for prediction of proteoglycan content of articular cartilage

Fourier Transform Infrared (FT-IR) spectroscopic imaging has been earlier applied for the spatial estimation of the collagen and the proteoglycan (PG) contents of articular cartilage (AC). However, earlier studies have been limited to the use of univariate analysis techniques. Current analysis methods lack the needed specificity for collagen and PGs. The aim of the present study was to evaluate the suitability of partial least squares regression (PLSR) and principal component regression (PCR) methods for the analysis of the PG content of AC. Multivariate regression models were compared with earlier used univariate methods and tested with a sample material consisting of healthy and enzymatically degraded steer AC. Chondroitinase ABC enzyme was used to increase the variation in PG content levels as compared to intact AC. Digital densitometric measurements of Safranin O-stained sections provided the reference for PG content. The results showed that multivariate regression models predict PG content of AC significantly better than earlier used absorbance spectrum (i.e. the area of carbohydrate region with or without amide I normalization) or second derivative spectrum univariate parameters. Increased molecular specificity favours the use of multivariate regression models, but they require more knowledge of chemometric analysis and extended laboratory resources for gathering reference data for establishing the models. When true molecular specificity is required, the multivariate models should be used.     

Using artificially generated spectral data to improve protein secondary structure prediction from Fourier transform infrared spectra of proteins

Secondary structures of proteins have been predicted using neural networks from their Fourier transform infrared spectra. To improve the generalization ability of the neural networks, the training data set has been artificially increased by linear interpolation. The leave-one-out approach has been used to demonstrate the applicability of the method. Bayesian regularization has been used to train the neural networks and the predictions have been further improved by the maximum-likelihood estimation method. The networks have been tested and standard error of prediction (SEP) of 4.19% for alpha helix, 3.49% for beta sheet, and 3.15% for turns have been achieved. The results indicate that there is a significant decrease in the SEP for each type of structure parameter compared to previous works.     

How the worm got its pharynx: phylogeny, classification and Bayesian assessment of character evolution in Acoela

Acoela are marine microscopic worms currently thought to be the sister taxon of all other bilaterians. Acoels have long been used as models in evolutionary scenarios, and generalized conclusions about acoel and bilaterian ancestral features are frequently drawn from studies of single acoel species. There is no extensive phylogenetic study of Acoela and the taxonomy of the 380 species is chaotic. Here we use two nuclear ribosomal genes and one mitochondrial gene in combination with 37 morphological characters in an analysis of 126 acoel terminals (about one-third of the described species) to estimate the phylogeny and character evolution of Acoela. We present an estimate of posterior probabilities for ancestral character states at 31 control nodes in the phylogeny. The overall reconstruction signal based on the shape of the posterior distribution of character states was computed for all morphological characters and control nodes to assess how well these were reconstructed. The body-wall musculature appears more clearly reconstructed than the reproductive organs. Posterior similarity to the root was calculated by averaging the divergence between the posterior distributions at the nodes and the root over all morphological characters. Diopisthoporidae is the sister group to all other acoels and has the highest posterior similarity to the root. Convolutidae, including several "model" acoels, is most divergent. Finally, we present a phylogenetic classification of Acoela down to the family level where six previous family level taxa are synonymized.     

Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data

MOTIVATION: Selecting a small number of relevant genes for accurate classification of samples is essential for the development of diagnostic tests. We present the Bayesian model averaging (BMA) method for gene selection and classification of microarray data. Typical gene selection and classification procedures ignore model uncertainty and use a single set of relevant genes (model) to predict the class. BMA accounts for the uncertainty about the best set to choose by averaging over multiple models (sets of potentially overlapping relevant genes). RESULTS: We have shown that BMA selects smaller numbers of relevant genes (compared with other methods) and achieves a high prediction accuracy on three microarray datasets. Our BMA algorithm is applicable to microarray datasets with any number of classes, and outputs posterior probabilities for the selected genes and models. Our selected models typically consist of only a few genes. The combination of high accuracy, small numbers of genes and posterior probabilities for the predictions should make BMA a powerful tool for developing diagnostics from expression data. AVAILABILITY: The source codes and datasets used are available from our Supplementary website.     

A novel application of Fourier-transformed infrared spectroscopy: classification of slime from staphylococci

It has been proposed that the virulence of nosocomial Staphylococcus infections associated with indwelling medical devices is related to the ability of the bacterium to colonise these materials by forming a biofilm composed of multilayered cell clusters embedded in a slime matrix. However, the pathogenic role of exopolysaccharide biofilms is not fully understood. A new method was sought for differentiating the structure of slime from two closely related bacterial strains, Staphylococcus aureus and Staphylococcus epidermidis. Using PCR it was confirmed that these strains were positive for the icaA and icaD genes and the complete ica operon (2.7 kb). Monosaccharide analysis by thin-layer chromatography revealed an identical profile for both strains, with xylose and glucose present among the four visible bands. Using Fourier-transformed infrared spectroscopy and hierarchical cluster analysis, three of four S. aureus samples (75%), and four of five S. epidermidis samples were grouped according to species. A novel FTIR approach in classifying slime produced by S. aureus and S. epidermidis is reported.     

A novel application of Fourier-transformed infrared spectroscopy: classification of slime from staphylococci

Abstract

It has been proposed that the virulence of nosocomial Staphylococcus infections associated with indwelling medical devices is related to the ability of the bacterium to colonise these materials by forming a biofilm composed of multilayered cell clusters embedded in a slime matrix. However, the pathogenic role of exopolysaccharide biofilms is not fully understood. A new method was sought for differentiating the structure of slime from two closely related bacterial strains, Staphylococcus aureus and Staphylococcus epidermidis. Using PCR it was confirmed that these strains were positive for the icaA and icaD genes and the complete ica operon (2.7 kb). Monosaccharide analysis by thin-layer chromatography revealed an identical profile for both strains, with xylose and glucose present among the four visible bands. Using Fourier-transformed infrared spectroscopy and hierarchical cluster analysis, three of four S. aureus samples (75%), and four of five S. epidermidis samples were grouped according to species. A novel FTIR approach in classifying slime produced by S. aureus and S. epidermidis is reported.     

Metabolite profile analysis: from raw data to regression and classification

Successful metabolic profile analysis will aid in the fundamental understanding of physiology. Here, we present a possible analysis workflow. Initially, the procedure to transform raw data into a data matrix containing relative metabolite levels for each sample is described. Given that, because of experimental issues in the technical equipment, the levels of some metabolites cannot be universally determined or that different experiments need to be compared, missing value estimation and normalization are presented as helpful preprocessing steps. Regression methods are presented in this review as tools to relate metabolite levels with other physiological properties like biomass and gene expression. As the number of measured metabolites often exceeds the number of samples, dimensionality reduction methods are required. Two of these methods are discussed in detail in this review. Throughout this article, practical examples illustrating the application of the aforementioned methods are given. We focus on the uncovering the relationship between metabolism and growth-related properties.     

Bayesian estimation of movement and survival probabilities from capture-recapture data

The Arnason–Schwarz model is usually used for estimatingsurvival and movement probabilities of animal populations fromcapture-recapture data. The missing data structure of this capture-recapturemodel is exhibited and summarised via a directed graph representation.Taking advantage of this structure we implement a Gibbs samplingalgorithm from which Bayesian estimates and credible intervalsfor survival and movement probabilities are derived. Convergenceof the algorithm is proved using a duality principle. We illustrateour approach through a real example.     

Scale dependence of immigration rates: models, metrics and data

1. We examine the relationship between immigration rate and patch area for different types of movement behaviours and detection modes. Theoretical models suggest that the scale dependence of the immigration rate per unit area (I/A) can be described by a power model I/A = i*Area(zeta), where zeta describes the strength of the scale dependence. 2. Three types of scaling were identified. Area scaling (zeta = 0) is expected for passively dispersed organisms that have the same probability of landing anywhere in the patch. Perimeter scaling (-0.30 > zeta > -0.45) is expected when patches are detected from a very short distance and immigrants arrive over the patch boundary, whereas diameter scaling (zeta = -0.5) is expected if patches are detected from a long distance or if search is approximately linear. 3. A meta-analysis of published empirical studies of the scale dependence of immigration rates in terrestrial insects suggests that butterflies show diameter scaling, aphids show area scaling, and the scaling of beetle immigration is highly variable. We conclude that the scaling of immigration rates in many cases can be predicted from search behaviour and the mode of patch detection.     

Application of airborne imaging spectrometry system data to intertidal seaweed classification and mapping

Hydrobiologia - The aim of this paper is to test the ability of imaging radiometers to describe the principal seaweed and seagrass beds along the coast of Brittany (France). In this work we used...     

CLASI-FISH: Principles of combinatorial labeling and spectral imaging

Although the number of phylotypes present in a microbial community may number in the hundreds or more, until recently, fluorescence in situ hybridization has been used to label, at most, only a handful of different phylotypes in a single sample. We recently developed a technique, CLASI-FISH for combinatorial labeling and spectral imaging – fluorescence in situ hybridization, to greatly expand the number of distinguishable taxa in a single FISH experiment. The CLASI technique involves labeling microbes of interest with combinations of probes coupled with spectral imaging to allow the use of fluorophores with highly overlapping excitation and emission spectra. Here, we present the basic principles and theory of CLASI-FISH along with some guidelines for performing CLASI-FISH experiments. We further include a protocol for creating fluorescence spectral reference standards, a vital component of successful CLASI-FISH.