Metabolic fingerprinting in disease diagnosis: biomedical applications of infrared and Raman spectroscopy

Development of a Standard Reference Material for metabolomics research

The National Institute of Standards and Technology (NIST), in collaboration with the National Institutes of Health (NIH), has developed a Standard Reference Material (SRM) to support technology development in metabolomics research. SRM 1950 Metabolites in Human Plasma is intended to have metabolite concentrations that are representative of those found in adult human plasma. The plasma used in the preparation of SRM 1950 was collected from both male and female donors, and donor ethnicity targets were selected based upon the ethnic makeup of the U.S. population. Metabolomics research is diverse in terms of both instrumentation and scientific goals. This SRM was designed to apply broadly to the field, not toward specific applications. Therefore, concentrations of approximately 100 analytes, including amino acids, fatty acids, trace elements, vitamins, hormones, selenoproteins, clinical markers, and perfluorinated compounds (PFCs), were determined. Value assignment measurements were performed by NIST and the Centers for Disease Control and Prevention (CDC). SRM 1950 is the first reference material developed specifically for metabolomics research.

Label-free detection of insulin and glucagon within human islets of Langerhans using Raman spectroscopy

Intrahepatic transplantation of donor islets of Langerhans is a promising therapy for patients with type 1 diabetes. It is of critical importance to accurately monitor islet quality before transplantation, which is currently done by standard histological methods that are performed off-line and require extensive sample preparation. As an alternative, we propose Raman spectroscopy which is a non-destructive and label-free technique that allows continuous real-time monitoring of the tissue to study biological changes as they occur. By performing Raman spectroscopic measurements on purified insulin and glucagon, we showed that the 520 cm(-1) band assigned to disulfide bridges in insulin, and the 1552 cm(-1) band assigned to tryptophan in glucagon are mutually exclusive and could therefore be used as indirect markers for the label-free distinction between both hormones. High-resolution hyperspectral Raman imaging for these bands showed the distribution of disulfide bridges and tryptophan at sub-micrometer scale, which correlated with the location of insulin and glucagon as revealed by conventional immunohistochemistry. As a measure for this correlation, quantitative analysis was performed comparing the Raman images with the fluorescence images, resulting in Dice coefficients (ranging between 0 and 1) of 0.36 for insulin and 0.19 for glucagon. Although the use of separate microscope systems with different spatial resolution and the use of indirect Raman markers cause some image mismatch, our findings indicate that Raman bands for disulfide bridges and tryptophan can be used as distinctive markers for the label-free detection of insulin and glucagon in human islets of Langerhans.

Current developments in high-throughput analysis for microalgae cellular contents

Microalgae have emerged as one of the most promising feedstocks for biofuels and bio-based chemical production. However, due to the lack of effective tools enabling rapid and high-throughput analysis of the content of microalgae biomass, the efficiency of screening and identification of microalgae with desired functional components from the natural environment is usually quite low. Moreover, the real-time monitoring of the production of target components from microalgae is also difficult. Recently, research efforts focusing on overcoming this limitation have started. In this review, the recent development of high-throughput methods for analyzing microalgae cellular contents is summarized. The future prospects and impacts of these detection methods in microalgae-related processing and industries are also addressed.

Non-invasive metabolomic analysis using a commercial NIR instrument for embryo selection

CONTEXT:

Metabolomics was introduced in human in vitro fertilization (IVF) for noninvasive identification of viable embryos with the highest developmental competence.

AIMS:

To determine whether embryo selection using a commercial version of metabolomic analysis leads to increased implantation rates (IRs) with fetal cardiac activity (FCA) compared with morphology evaluation alone.

SETTING AND DESIGN:

Randomized controlled trial from April to December 2010 at a private IVF unit. The study was terminated prematurely due to the market withdrawal of the instrument.

MATERIALS AND METHODS:

IVF patients ≥18 and ≤43 years with ≥4 × 2PN were randomly allocated to metabolomic analysis combined with embryo morphology (ViaMetrics-E; metabolomics + morphology group) or embryo morphology alone (morphology group). Cycles with frozen embryos, oocyte donations, or testicular biopsy were excluded.

STATISTICAL ANALYSIS:

Categorical and continuous data were analyzed for statistical significance using 2-tailed Fisher's exact test and t-test, respectively. Statistical significance was accepted when P > 0.05.

RESULTS:

A total of 125 patients were included in the study; 39 patients were allocated to metabolomics + morphology group and 86 patients to morphology group. Patients were stratified according to the day of embryo transfer (Days 2, 3, or 5). IRs with FCA were similar for Days 2 and 3 transfers in both groups. For Day 5 transfers, IRs with FCA were significantly higher in the metabolomics + morphology group (46.8% vs. 28.9%; P = 0.041; 95% confidence intervalp [CI]: 1.09-34.18). Pregnancy and live births rates were similar for Days 2, 3, and 5 in both groups. The study was terminated early following the voluntary market withdrawal of ViaMetrics-E in December 2010.

CONCLUSIONS:

Metabolomic analysis using the commercial near-infrared (NIR) instrument does not appear to have a beneficial effect on pregnancy and live births, with improvement in IR with FCA for Day 5 transfers. However, no solid conclusions can be reached due to the lack of adequate study power.ClinicalTrials.gov Identifier: NCT01490515

The role of metabolomics in neonatal and pediatric laboratory medicine

Metabolomics consists of the quantitative analysis of a large number of low molecular mass metabolites involving substrates or products in metabolic pathways existing in all living systems. The analysis of the metabolic profile detectable in a human biological fluid allows to instantly identify changes in the composition of endogenous and exogenous metabolites caused by the interaction between specific physiopathological states, gene expression, and environment. In pediatrics and neonatology, metabolomics offers new encouraging perspectives for the improvement of critically ill patient outcome, for the early recognition of metabolic profiles associated with the development of diseases in the adult life, and for delivery of individualized medicine. In this view, nutrimetabolomics, based on the recognition of specific cluster of metabolites associated with nutrition and pharmacometabolomics, based on the capacity to personalize drug therapy by analyzing metabolic modifications due to therapeutic treatment may open new frontiers in the prevention and in the treatment of pediatric and neonatal diseases. This review summarizes the most relevant results published in the literature on the application of metabolomics in pediatric and neonatal clinical settings. However, there is the urgent need to standardize physiological and preanalytical variables, analytical methods, data processing, and result presentation, before establishing the definitive clinical value of results.

Detection and identification of cancerous murine fibroblasts, transformed by murine sarcoma virus in culture, using Raman spectroscopy and advanced statistical methods

BACKGROUND

Cancer is one of the leading worldwide causes of death. It may be induced by a variety of factors, including carcinogens, radiation, genetic factors, or DNA and RNA viruses. The early detection of cancer is critical for its successful therapy, which can result in complete recovery from some types of cancer.

METHODS

Raman spectroscopy has been widely used in medicine and biology. It is a noninvasive, nondestructive, and water-insensitive technique that can detect changes in cells and tissues that are caused by different disorders, such as cancer. In this study, Raman spectroscopy was used for the identification and characterization of murine fibroblast cell lines (NIH/3T3) and malignant fibroblast cells transformed by murine sarcoma virus (NIH-MuSV) cells.

RESULTS

Using principal component analysis and LDA it was possible to differentiate between the NIH/3T3 and NIH-MuSV cells with an 80-85% success rate based on their Raman shift spectra.

CONCLUSIONS

The best results for differentiation were achieved from spectra that were obtained from the rich membrane sites.

GENERAL SIGNIFICANCE

Because of its homogeneity and complete control of most factors affecting its growth, cell culture is a preferred model for the detection and identification of specific biomarkers related to cancer transformation or other cellular modifications.

Substrate contributions in micro-ATR of thin samples: implications for analysis of cells, tissue and biological fluids

Low-e microscope slides are a common substrate for biological samples. Typically they are used for transflection infrared microspectroscopy but increasingly they are also being used for micro-ATR experiments since it is assumed that the FTIR-ATR absorbance spectra of cells and tissue on low-e substrates will not contain any spectral contributions from the substrate materials. This, in part, is due to the expectation that all the infrared light will be reflected at the highly reflective surface. At low sample thicknesses, however (e.g. less than 2 μm) the electric field does indeed penetrate through the substrate layers and undergoes absorption, from the glass supporting layer making up the majority of the slide. In this paper we show experimental evidence of the substrate contributions in ATR spectra and also a theoretical model giving insight into the spectral contributions of the substrate as a function of sample thickness.

Illuminating disease and enlightening biomedicine: Raman spectroscopy as a diagnostic tool

The discovery of the Raman effect in 1928 not only aided fundamental understanding about the quantum nature of light and matter but also opened up a completely novel area of optics and spectroscopic research that is accelerating at a greater rate during the last decade than at any time since its inception. This introductory overview focuses on some of the most recent developments within this exciting field and how this has enabled and enhanced disease diagnosis and biomedical applications. We highlight a small number of stimulating high-impact studies in imaging, endoscopy, stem cell research, and other recent developments such as spatially offset Raman scattering amongst others. We hope this stimulates further interest in this already exciting field, by 'illuminating' some of the current research being undertaken by the latest in a very long line of dedicated experimentalists interested in the properties and potential beneficial applications of light.

FTIR spectroscopy of biofluids revisited: an automated approach to spectral biomarker identification

The extraction of disease specific information from Fourier transform infrared (FTIR) spectra of human body fluids demands the highest standards of accuracy and reproducibility of measurements because the expected spectral differences between healthy and diseased subjects are very small in relation to a large background absorbance of the whole sample. Here, we demonstrate that with the increased sensitivity of modern FTIR spectrometers, automatisation of sample preparation and modern bioinformatics, it is possible to identify and validate spectral biomarker candidates for distinguishing between urinary bladder cancer (UBC) and inflammation in suspected bladder cancer patients. The current dataset contains spectra of blood serum and plasma samples of 135 patients. All patients underwent cytology and pathological biopsy characterization to distinguish between patients without UBC (46) and confirmed UBC cases (89). A minimally invasive blood test could spare control patients a repeated cystoscopy including a transurethral biopsy, and three-day stationary hospitalisation. Blood serum, EDTA and citrate plasma were collected from each patient and processed following predefined strict standard operating procedures. Highly reproducible dry films were obtained by spotting sub-nanoliter biofluid droplets in defined patterns, which were compared and optimized. Particular attention was paid to the automatisation of sample preparation and spectral preprocessing to exclude errors by manual handling. Spectral biomarker candidates were identified from absorbance spectra and their 1(st) and 2(nd) derivative spectra using an advanced Random Forest (RF) approach. It turned out that the 2(nd) derivative spectra were most useful for classification. Repeat validation on 21% of the dataset not included in predictor training with Linear Discriminant Analysis (LDA) classifiers and Random Forests (RFs) yielded a sensitivity of 93 ± 10% and a specificity of 46 ± 18% for bladder cancer. The low specificity can be most likely attributed to the unbalanced and small number of control samples. Using this approach, spectral biomarker candidates in blood-derived biofluids were identified, which allow us to distinguish between cancer and inflammation, but the observed differences were tiny. Obviously, a much larger sample number has to be investigated to reliably validate such candidates.

A bloodspot-based diagnostic test for fibromyalgia syndrome and related disorders

The aim of this study was to investigate the ability of a rapid biomarker-based method for diagnosis of fibromyalgia syndrome (FM) using mid-infrared microspectroscopy (IRMS) to differentiate patients with FM from those with osteoarthritis (OA) and rheumatoid arthritis (RA), and to identify molecular species associated with the spectral patterns. Under IRB approval, blood samples were collected from patients diagnosed with FM (n = 14), RA (n = 15), or OA (n = 12). Samples were prepared, placed onto a highly reflective slide, and spectra were collected using IRMS. Spectra were analyzed using multivariate statistical modeling to differentiate groups. Aliquots of samples also were subjected to metabolomic analysis. IRMS separated subjects into classes based on spectral information with no misclassifications among FM and RA or OA patients. Interclass distances of 15.4 (FM vs. RA), 14.7 (FM vs. OA) and 2.5 (RA vs. OA) among subjects, demonstrating the ability of IRMS to achieve reliable resolution of unique spectral patterns specific to FM. Metabolomic analysis revealed that RA and OA groups were metabolically similar, whereas biochemical differences were identified in the FM that were quite distinctive from those found in the other two groups. Both IRMS and metabolomic analysis identified changes in tryptophan catabolism pathway that differentiated patients with FM from those with RA or OA.

Rapid assessment of quality parameters in processing tomatoes using hand-held and benchtop infrared spectrometers and multivariate analysis

Two portable infrared sensors were evaluated for the rapid determination of quality parameters in processing tomatoes. A total of 370 hot-break juices were prepared from ∼40 processing tomato varieties grown in 5 California counties. The levels of sugars, acids, soluble solids, titratable acidity, and pH in these juices were determined using standard reference methods. Juices were processed, filtered, and directly applied to the FT-IR crystal (15-40 μL) to obtain spectra. Partial least-squares regression (PLSR) was used to generate correlation models, both calibration and validation. The PLS validation models showed good ability (Rval > 0.80; <10% SEP) in estimating the sugars, acids, and especially soluble solids in tomato for both the transmission DialPath portable system and benchtop unit using triple-bounce attenuated total reflectance (ATR). The IR portable unit may provide the tomato processing industry with an efficient method for in-plant, high throughput quantification of quality parameters in tomatoes.

Plasmonic light harvesting for multicolor infrared thermal detection

Here we combined experiments and theory to study the optical properties of a plasmonic cavity consisting of a perforated metal film and a flat metal sheet separated by a semiconductor spacer. Three different types of optical modes are clearly identified-the propagating and localized surface plasmons on the perforated metal film and the Fabry-Perot modes inside the cavity. Interactions among them lead to a series of hybridized eigenmodes exhibiting excellent spectral tunability and spatially distinct field distributions, making the system particularly suitable for multicolor infrared light detections. As an example, we design a two-color detector protocol with calculated photon absorption efficiencies enhanced by more than 20 times at both colors, reaching ~42.8% at f1 = 20.0THz (15μm in wavelength) and ~46.2% at f2 = 29.5THz (~10.2μm) for a 1μm total thickness of sandwiched quantum wells.

Application of metabolomics approaches to the study of respiratory diseases

Metabolomics is the global unbiased analysis of all the small-molecule metabolites within a biological system, under a given set of conditions. These methods offer the potential for a holistic approach to clinical medicine, as well as improving disease diagnosis and understanding of pathological mechanisms. Respiratory diseases including asthma and chronic obstructive pulmonary disorder are increasing globally, with the latter predicted to become the third leading cause of global mortality by 2020. The root causes for disease onset remain poorly understood and no cures are available. This review presents an overview of metabolomics followed by in-depth discussion of its application to the study of respiratory diseases, including the design of metabolomics experiments, choice of clinical material collected and potentially confounding experimental factors. Particular challenges in the field are presented and placed within the context of the future of the applications of metabolomics approaches to the study of respiratory diseases.

Molecular conformation changes along the malignancy revealed by optical nanosensors

An interdisciplinary approach employing functionalized nanoparticles and ultrasensitive spectroscopic techniques is reported here to track the molecular changes in early stage of malignancy. Melanoma tissue tracking at molecular level using both labelled and unlabelled silver and gold nanoparticles has been achieved using surface enhanced Raman scattering (SERS) technique. We used skin tissue from ex vivo mice with induced melanoma. Raman and SERS molecular characterization of melanoma tissue is proposed here for the first time. Optical nanosensors based on Ag and Au nanoparticles with chemisorbed cresyl violet molecular species as labels revealed sensitive capability to tissues tagging and local molecular characterization. Sensitive information originating from surrounding native biological molecules is provided by the tissue SERS spectra obtained either with visible or NIR laser line. Labelled nanoparticles introduced systematic differences in tissue response compared with unlabelled ones, suggesting that the label functional groups tag specific tissue components revealed by proteins or nucleic acids bands. Vibrational data collected from tissue are presented in conjunction with the immunohistochemical analysis. The results obtained here open perspectives in applied plasmonic nanoparticles and SERS for the early cancer diagnostic based on the appropriate spectral databank.

NMR Metabolomics Analysis of Parkinson's Disease

Parkinson's disease (PD) is a neurodegenerative disease, which is characterized by progressive death of dopaminergic neurons in the substantia nigra pars compacta. Although mitochondrial dysfunction and oxidative stress are linked to PD pathogenesis, its etiology and pathology remain to be elucidated. Metabolomics investigates metabolite changes in biofluids, cell lysates, tissues and tumors in order to correlate these metabolomic changes to a disease state. Thus, the application of metabolomics to investigate PD provides a systematic approach to understand the pathology of PD, to identify disease biomarkers, and to complement genomics, transcriptomics and proteomics studies. This review will examine current research into PD mechanisms with a focus on mitochondrial dysfunction and oxidative stress. Neurotoxin-based PD animal models and the rationale for metabolomics studies in PD will also be discussed. The review will also explore the potential of NMR metabolomics to address important issues related to PD treatment and diagnosis.

Fourier-transform infrared spectroscopy for rapid screening and live-cell monitoring: application to nanotoxicology

A significant challenge to realize the full potential of nanotechnology for therapeutic and diagnostic applications is to understand and evaluate how live cells interact with an external stimulus, such as a nanosized particle, and the toxicity and broad risk associated with these stimuli. It is difficult to capture the complexity and dynamics of these interactions by following omics-based approaches exclusively, which can be expensive and time-consuming. Attenuated total reflectance-Fourier transform infrared spectroscopy is well suited to provide noninvasive live-cell monitoring of cellular responses to potentially toxic nanosized particles or other stimuli. This alternative approach provides the ability to carry out rapid toxicity screenings and nondisruptive monitoring of live-cell cultures. We review the technical basis of the approach, the instrument configuration and interface with the biological media, the various effects that impact the data, subsequent data analysis and toxicity, and present some preliminary results on live-cell monitoring.

Detection of cancer using advanced computerized analysis of infrared spectra of peripheral blood

We have developed a novel approach for detection of cancer based on biochemical analysis of peripheral blood plasma using Fourier transform infrared spectroscopy. This approach has proven to be quick, safe, minimal invasive, and effective. Our approach recognizes any signs of solid tumor presence, regardless of location in the body or cancer type by measuring a spectrum that gives information regarding the total molecular composition and structure of the peripheral blood samples. The analysis includes clinically relevant preprocessing and feature extraction with principal component analysis, and uses Fisher's linear discriminant analysis to classify between cancer patients and healthy controls. We evaluated our method with leave-one-out cross validation and were able to establish sensitivity of 93.33%, specificity of 87.8%, and overall accuracy of 90.7%. Using our method for cancer detection should result in fewer unnecessary invasive procedures and yield fast detection of solid tumors.

Multivariate Analysis in Metabolomics

Metabolomics aims to provide a global snapshot of all small-molecule metabolites in cells and biological fluids, free of observational biases inherent to more focused studies of metabolism. However, the staggeringly high information content of such global analyses introduces a challenge of its own; efficiently forming biologically relevant conclusions from any given metabolomics dataset indeed requires specialized forms of data analysis. One approach to finding meaning in metabolomics datasets involves multivariate analysis (MVA) methods such as principal component analysis (PCA) and partial least squares projection to latent structures (PLS), where spectral features contributing most to variation or separation are identified for further analysis. However, as with any mathematical treatment, these methods are not a panacea; this review discusses the use of multivariate analysis for metabolomics, as well as common pitfalls and misconceptions.

Raman microspectroscopy for species identification and mapping within bacterial biofilms

A new method of mapping multiple species of oral bacteria in intact biofilms has been developed, using the optical technique of confocal Raman microscopy. A species classification algorithm, developed on dried biofilms, was used to analyze spectra of hydrated biofilms containing two microbial species central to dental health: Streptococcus sanguinis and Streptococcus mutans. The algorithm transferred successfully to the hydrated environment, correctly identifying the species of origin of single-species biofilms. We then used the algorithm successfully both to detect the presence of two species in mixed biofilms and to create spatial maps within these biofilms.

Dupuytren's disease metabolite analyses reveals alterations following initial short-term fibroblast culturing

Dupuytren's disease (DD) is an ill-defined fibroproliferative disorder affecting the palm of the hand, resulting in progressive and irreversible digital contracture. In view of the abnormal gene dysregulation found in DD, and its potential effect on metabolites at a functional level, we chose to examine the metabolic profile involved in DD. Using Fourier transform infrared (FT-IR) spectroscopy to generate metabolic fingerprints of cultured cells, we compared the profiles of DD cords and nodules (1) against the unaffected transverse palmar fascia (internal control), (2) against carpal ligamentous fascia (external control), and (3) against fibroblasts from fat surrounding the nodule and skin overlying the nodule (environmental control). We also determined the effects of serial passaging of the cells on DD fingerprints. Subsequently, gas chromatography-mass spectrometry (GC-MS) was employed for metabolic profiling in order to identify metabolites characteristic of the DD tissue phenotypes. We developed a robust metabolomic analysis procedure of DD using cultured fibroblasts derived from DD tissues. Our carefully controlled culture conditions, combined with assessment of metabolic phenotypes by FT-IR and GC-MS, enabled us to demonstrate metabolic differences between DD and unaffected transverse palmar fascia and between DD and healthy control tissue. In early passage (0-3) the metabolic differences were clear, but cells from subsequent passages (4-6) started to lose this distinction between diseased and non-diseased origin. The dysregulated metabolites we identified were leucine, phenylalanine, lysine, cysteine, aspartic acid, glycerol-3-phosphate and the vitamin precursor to coenzyme A. Early passage DD cells exhibit a clear metabolic profile, in which central metabolic pathways appear to be involved. Experimental conditions have been identified in which these DD data are reproducible. The experimental reproducibility will be useful in DD diagnostics and for DD systems biology.

Rapid reagentless quantification of alginate biosynthesis in Pseudomonas fluorescens bacteria mutants using FT-IR spectroscopy coupled to multivariate partial least squares regression

Alginate is an important medical and commercial product and currently is isolated from seaweeds. Certain microorganisms also produce alginate and these polymers have the potential to replace seaweed alginates in some applications, mainly because such production will allow much better and more reproducible control of critical qualitative polymer properties. The research conducted here presents the development of a new approach to this problem by analysing a transposon insertion mutant library constructed in an alginate-producing derivative of the Pseudomonas fluorescens strain SBW25. The procedure is based on the non-destructive and reagent-free method of Fourier transform infrared (FT-IR) spectroscopy which is used to generate a complex biochemical infrared fingerprint of the medium after bacterial growth. First, we investigate the potential differences caused by the growth media fructose and glycerol on the bacterial phenotype and alginate synthesis in 193 selected P. fluorescens mutants and show that clear phenotypic differences are observed in the infrared fingerprints. In order to quantify the level of the alginate we also report the construction and interpretation of multivariate partial least squares regression models which were able to quantify alginate levels successfully with typical normalized root-mean-square error in predictions of only approximately 14%. We have demonstrated that this high-throughput approach can be implemented in alginate screens and we believe that this FT-IR spectroscopic methodology, when combined with the most appropriate chemometrics, could easily be modified for the quantification of other valuable microbial products and play a valuable screening role for synthetic biology.

Assessment of metabolic activity of human cells in solution and in polymer matrix with the use of metabolite-sensitive sensors

We developed metabolite-sensitive electrochemical sensors on the basis of electrodes modified with a thick film of carbon nanotubes. Modified electrodes provide efficient pre-adsorption of cellular metabolites and their sensitive detection using anodic square-wave voltammetry. On the electrode surface both adhered and non-adhered human cells produce three oxidation peaks at the potentials of +0.82, +1.05, and +1.17V attributed to three groups of cellular metabolites: amino acid-derived antioxidants including glutathione, guanine nucleotides, and also adenine nucleotides including ATP. The electrochemical response was well correlated with cell viability, intracellular ATP level and induction of apoptosis, as determined by independent assays. Developed sensors allow for robust and cost-effective assessment of ATP in cells in contrast to enzyme-based electrodes and conventional bioluminescent assay. Results can be used for rapid analysis of human cells for the purpose of medical diagnostics, transplantology, and toxicological screening. Additionally, we combined modified electrodes with human cells entrapped in agarose matrix. The resulting biosensor allowed for electrochemical monitoring of metabolic activity and death of cells within polymeric matrix that is of interest for tissue engineering applications.

Non-invasive glucose monitoring technology in diabetes management: a review

The frequent monitoring of glucose is an essential part of diabetes management. Despite the fact that almost all the commercially successful blood glucose monitoring devices are invasive, there is an immense need to develop non-invasive glucose monitoring (NGM) devices that will alleviate the pain and suffering of diabetics associated with the frequent pricking of skin for taking the blood sample for glucose testing. There have been numerous developments in the field of NGM during the last decade, which stress the need for a critical review. This manuscript aims to review the various NGM techniques and devices. The challenges and future trends in NGM are also discussed.

Metabolomics in the fields of oncology: a review of recent research

The study of all endogenously produced metabolites, known as metabolomics, is the youngest of the "omics" sciences. It is becoming increasingly clear that, of all of the "omics" techniques, metabolomic approaches will become increasingly useful in disease diagnosis and have potential power to improve our understanding of the underlying mechanisms of cancer. The primary aim of the review is to discuss the relationship between metabolomics and tumors are elucidated in detail. Then the review is also to introduce the technologies of metabolomics, especially emphasizing the application of metabolomics in the fields of oncology.

Vibrational spectroscopy: a tool being developed for the noninvasive monitoring of wound healing

Wound care and management accounted for over 1.8 million hospital discharges in 2009. The complex nature of wound physiology involves hundreds of overlapping processes that we have only begun to understand over the past three decades. The management of wounds remains a significant challenge for inexperienced clinicians. The ensuing inflammatory response ultimately dictates the pace of wound healing and tissue regeneration. Consequently, the eventual timing of wound closure or definitive coverage is often subjective. Some wounds fail to close, or dehisce, despite the use and application of novel wound-specific treatment modalities. An understanding of the molecular environment of acute and chronic wounds throughout the wound-healing process can provide valuable insight into the mechanisms associated with the patient's outcome. Pathologic alterations of wounds are accompanied by fundamental changes in the molecular environment that can be analyzed by vibrational spectroscopy. Vibrational spectroscopy, specifically Raman and Fourier transform infrared spectroscopy, offers the capability to accurately detect and identify the various molecules that compose the extracellular matrix during wound healing in their native state. The identified changes might provide the objective markers of wound healing, which can then be integrated with clinical characteristics to guide the management of wounds.

Chemically Specific Imaging Through Stimulated Raman Photoexcitation and Ultrasound Detection: Minireview

A powerful combination of chemically specific Raman excitation and deep tissue ultrasound imaging holds the promise to attain spatially resolved distribution of chemical compounds inside the scattering medium. In this report, an attempt is made to evaluate the recent achievements and possible challenges with an eye on potential clinical applications.

Metabolomics-assisted synthetic biology

As the world progresses from a fossil-fuel based economy to a more sustainable one, synthetic biology will become increasingly important for the production of high-value fine chemicals as well as low-value commodities in bulk. The integration of metabolomics and fluxomics within synthetic biology projects will be vital at all levels, including the initial design of the pathways to be generated, through to the optimisation of those pathways so that more efficient conversion of low-cost starting materials into highly desirable products can be achieved. This review highlights these areas and details the most important and exciting advances being made in this area.

Assessment of metabolic modulation in free-living versus endosymbiotic Symbiodinium using synchrotron radiation-based infrared microspectroscopy

The endosymbiotic relationship between coral hosts and dinoflagellates of the genus Symbiodinium is critical for the growth and productivity of coral reef ecosystems. Here, synchrotron radiation-based infrared microspectroscopy was applied to examine metabolite concentration differences between endosymbiotic (within the anemone Aiptasia pulchella) and free-living Symbiodinium over the light-dark cycle. Significant differences in levels of lipids, nitrogenous compounds, polysaccharides and putative cell wall components were documented. Compared with free-living Symbiodinium, total lipids, unsaturated lipids and polysaccharides were relatively enriched in endosymbiotic Symbiodinium during both light and dark photoperiods. Concentrations of cell wall-related metabolites did not vary temporally in endosymbiotic samples; in contrast, the concentrations of these metabolites increased dramatically during the dark photoperiod in free-living samples, possibly reflecting rhythmic cell-wall synthesis related to light-driven cell proliferation. The level of nitrogenous compounds in endosymbiotic cells did not vary greatly across the light-dark cycle and in general was significantly lower than that observed in free-living samples collected during the light. Collectively, these data suggest that nitrogen limitation is a factor that the host cell exploits to induce the biosynthesis of lipids and polysaccharides in endosymbiotic Symbiodinium.

Raman Spectroscopy for Clinical Oncology

Cancer is one of the leading causes of death throughout the world. Advancements in early and improved diagnosis could help prevent a significant number of these deaths. Raman spectroscopy is a vibrational spectroscopic technique which has received considerable attention recently with regards to applications in clinical oncology. Raman spectroscopy has the potential not only to improve diagnosis of cancer but also to advance the treatment of cancer. A number of studies have investigated Raman spectroscopy for its potential to improve diagnosis and treatment of a wide variety of cancers. In this paper the most recent advances in dispersive Raman spectroscopy, which have demonstrated promising leads to real world application for clinical oncology are reviewed. The application of Raman spectroscopy to breast, brain, skin, cervical, gastrointestinal, oral, and lung cancers is reviewed as well as a special focus on the data analysis techniques, which have been employed in the studies.

Label-free characterization of cancer-activated fibroblasts using infrared spectroscopic imaging

Glandular tumors arising in epithelial cells comprise the majority of solid human cancers. Glands are supported by stroma, which is activated in the proximity of a tumor. Activated stroma is often characterized by the molecular expression of α-smooth muscle actin (α-SMA) within fibroblasts. However, the precise spatial and temporal evolution of chemical changes in fibroblasts upon epithelial tumor signaling is poorly understood. Here we report a label-free method to characterize fibroblast changes by using Fourier transform infrared spectroscopic imaging and comparing spectra with α-SMA expression in primary normal human fibroblasts. We recorded the fibroblast activation process by spectroscopic imaging using increasingly tissue-like conditions: 1), stimulation with the growth factor TGFβ1; 2), coculture with MCF-7 human breast cancerous epithelial cells in Transwell coculture; and 3), coculture with MCF-7 in three-dimensional cell culture. Finally, we compared the spectral signatures of stromal transformation with normal and malignant human breast tissue biopsies. The results indicate that this approach reveals temporally complex spectral changes and thus provides a richer assessment than simple molecular imaging based on α-SMA expression. Some changes are conserved across culture conditions and in human tissue, providing a label-free method to monitor stromal transformations.

Use of near-infrared spectroscopic analysis of second trimester amniotic fluid to assess preterm births

This pilot study investigated the possibility that metabolomic differences exist in second trimester of women delivering at term (≥37 weeks, n = 216) and preterm (≤35 weeks, n = 11). For this retrospective study, biobanked AF samples underwent near-infrared (NIR) spectral analysis using wavelengths from 700 to 1050 nm. Spectral data was compressed then optimized by multilinear regression to create a calibration model. The resultant model was able to classify term and preterm births based on differing AF metabolomic profiles with a sensitivity and specificity of 100%. When groups were classified using a prematurity index (PI), there was a statistical difference (P < 0.001) between the predicted preterm group (PI 0.77 ± 0.08) and the term group (PI 1.00 ± 0.02). In conclusion, the 2nd trimester AF samples showed distinct differences in metabolomic profiles between patients delivering preterm as compared to those at term in functional groups related to proteins, carbohydrates, fats, polyols, and water.

Raman spectroscopy: lighting up the future of microbial identification

Over the last decade Raman spectroscopy has become established as a physicochemical technique for the rapid identification of microbes. This powerful analytical method generates a spectroscopic fingerprint from the microbial sample, which provides quantitative and qualitative information that can be used to characterize, discriminate and identify microorganisms, in both bacteria slurry and at the single-cell level. Recent developments in Raman spectroscopy have dramatically increased in recent years due to the enhancement of the signal by techniques including tip-enhanced Raman spectroscopy and coherent anti-Stokes Raman spectroscopy and due to the availability of user-friendly instrumentation and software. The result of this has been reduced cost and rapid collection time, and it has allowed the nonspecialist access to this physical sciences approach for biological applications. In this article, we will briefly explain the technique of Raman spectroscopy and discuss enhancement techniques, including the recent application of tip-enhanced Raman spectroscopy to microbiology, as well as the move towards rapid microbial identification with Raman spectroscopy. Furthermore, recent studies have combined Raman spectroscopy with microfluidic devices, giving greater control of sample conditions, which will no doubt have an important impact in the future development of Raman spectroscopy for microbial identification.

Study on gastric cancer blood plasma based on surface-enhanced Raman spectroscopy combined with multivariate analysis

A surface-enhanced Raman spectroscopy (SERS) method combined with multivariate analysis was developed for non-invasive gastric cancer detection. SERS measurements were performed on two groups of blood plasma samples: one group from 32 gastric patients and the other group from 33 healthy volunteers. Tentative assignments of the Raman bands in the measured SERS spectra suggest interesting cancer-specific biomolecular changes, including an increase in the relative amounts of nucleic acid, collagen, phospholipids and phenylalanine and a decrease in the percentage of amino acids and saccharide in the blood plasma of gastric cancer patients as compared with those of healthy subjects. Principal components analysis (PCA) and linear discriminant analysis (LDA) were employed to develop effective diagnostic algorithms for classification of SERS spectra between normal and cancer plasma with high sensitivity (79.5%) and specificity (91%). A receiver operating characteristic (ROC) curve was employed to assess the accuracy of diagnostic algorithms based on PCA-LDA. The results from this exploratory study demonstrate that SERS plasma analysis combined with PCA-LDA has tremendous potential for the non-invasive detection of gastric cancers.

Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry

Metabolism has an essential role in biological systems. Identification and quantitation of the compounds in the metabolome is defined as metabolic profiling, and it is applied to define metabolic changes related to genetic differences, environmental influences and disease or drug perturbations. Chromatography-mass spectrometry (MS) platforms are frequently used to provide the sensitive and reproducible detection of hundreds to thousands of metabolites in a single biofluid or tissue sample. Here we describe the experimental workflow for long-term and large-scale metabolomic studies involving thousands of human samples with data acquired for multiple analytical batches over many months and years. Protocols for serum- and plasma-based metabolic profiling applying gas chromatography-MS (GC-MS) and ultraperformance liquid chromatography-MS (UPLC-MS) are described. These include biofluid collection, sample preparation, data acquisition, data pre-processing and quality assurance. Methods for quality control-based robust LOESS signal correction to provide signal correction and integration of data from multiple analytical batches are also described.

Fourier transform infrared and Raman spectroscopies for the rapid detection, enumeration, and growth interaction of the bacteria Staphylococcus aureus and Lactococcus lactis ssp. cremoris in milk

Staphylococcus aureus is one of the main pathogenic microorganisms found in milk and dairy products and has been involved in bacterial foodborne outbreaks in the past. Current enumeration techniques for bacteria are very time-consuming, typically taking 24 h or longer, and bacterial antagonism in the form of lactic acid bacteria (LAB) may inhibit the growth of S. aureus . Therefore, the aim of this investigation was to establish the accuracy and sensitivity of rapid nondestructive metabolic fingerprinting techniques, such as Fourier transform infrared (FT-IR) spectroscopy and Raman spectroscopy (RS), in combination with multivariate analysis techniques, for the detection and enumeration of S. aureus in milk, as well as to study the growth interaction between S. aureus and Lactococcus lactis ssp. cremoris , a common LAB. The two bacterial species were investigated both in a pure monoculture and in a combined inoculated coculture after inoculation into ultraheated milk during the first 24 h of growth at 37 °C. Plating techniques were used to obtain primary reference data for viable bacteria counts. Principal component discriminant function analysis, canonical correlation analysis, partial least-squares (PLS), and kernel PLS (KPLS) multivariate statistical techniques were employed to analyze the data. FT-IR provided very reasonable quantification results both with PLS and KPLS, the latter providing marginally better predictions, with correlation coefficients in the test set (Q(2)) and training set (R(2)) varying from 0.64 to 0.76 and from 0.78 to 0.88 for different bacterial sample combinations. RS results were less encouraging with high degrees of error and poor correlation to viable bacterial counts. S. aureus growth was not inhibited by the presence of the LAB, but metabolic fingerprinting of the coculture indicated that the phenotype of this dual bacterial culture was closer to that of pure LAB cultures. In conclusion, FT-IR spectroscopy in combination with the above multivariate techniques appears to be a promising discrimination and enumeration analytical technique for the two bacterial species. In addition, it has been demonstrated that the L. cremoris metabolic effect in milk dominates that of S. aureus even though there was no growth antagonism observed.