Discrimination of Healthy and Neoplastic Human Colon Tissues by ex Vivo HR-MAS NMR Spectroscopy and Chemometric Analyses

A new technological approach in diagnostic pathology: mass spectrometry imaging-based metabolomics for biomarker detection in urachal cancer

Urachal adenocarcinomas (UrC) are rare but aggressive. Despite being of profound therapeutic relevance, UrC cannot be differentiated by histomorphology alone from other adenocarcinomas of differential diagnostic importance. As no reliable tissue-based diagnostic biomarkers are available, we aimed to detect such by integrating mass-spectrometry imaging-based metabolomics and digital pathology, thus allowing for a multimodal approach on the basis of spatial information. To achieve this, a cohort of UrC (n = 19) and colorectal adenocarcinomas (CRC, n = 27) as the differential diagnosis of highest therapeutic relevance was created, tissue micro-arrays (TMAs) were constructed, and pathological data was recorded. Hematoxylin and eosin (H&E) stained tissue sections were scanned and annotated, enabling an automized discrimination of tumor and non-tumor areas after training of an adequate algorithm. Spectral information within tumor regions, obtained via matrix-assisted laser desorption/ionization (MALDI)-Orbitrap-mass spectrometry imaging (MSI), were subsequently extracted in an automated workflow. On this basis, metabolic differences between UrC and CRC were revealed using machine learning algorithms. As a result, the study demonstrated the feasibility of MALDI-MSI for the evaluation of FFPE tissue in UrC and CRC with the potential to combine spatial metabolomics data with annotated histopathological data from digitalized H&E slides. The detected Area under the curve (AUC) of 0.94 in general and 0.77 for the analyte taurine alone (diagnostic accuracy for taurine: 74%) makes the technology a promising tool in this differential diagnostic dilemma situation. Although the data has to be considered as a proof-of-concept study, it presents a new adoption of this technology that has not been used in this scenario in which reliable diagnostic biomarkers (such as immunohistochemical markers) are currently not available.

A metabolomic data fusion approach to support gliomas grading

Magnetic resonance imaging (MRI) is the current gold standard for the diagnosis of brain tumors. However, despite the development of MRI techniques, the differential diagnosis of central nervous system (CNS) primary pathologies, such as lymphoma and glioblastoma or tumor-like brain lesions and glioma, is often challenging. MRI can be supported by in vivo magnetic resonance spectroscopy (MRS) to enhance its diagnostic power and multiproject-multicenter evaluations of classification of brain tumors have shown that an accuracy around 90% can be achieved for most of the pairwise discrimination problems. However, the survival rate for patients affected by gliomas is still low. The High-Resolution Magic-Angle-Spinning Nuclear Magnetic Resonance (HR-MAS NMR) metabolomics studies may be helpful for the discrimination of gliomas grades and the development of new strategies for clinical intervention. Here, we propose to use T₂ -filtered, diffusion-filtered and conventional water-presaturated spectra to try to extract as much information as possible, fusing the data gathered by these different NMR experiments and applying a chemometric approach based on Multivariate Curve Resolution (MCR). Biomarkers important for glioma's discrimination were found. In particular, we focused our attention on cystathionine (Cyst) that shows promise as a biomarker for the better prognosis of glioma tumors.

Metabolic models predict bacterial passengers in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is a complex multifactorial disease. Increasing evidence suggests that the microbiome is involved in different stages of CRC initiation and progression. Beyond specific pro-oncogenic mechanisms found in pathogens, metagenomic studies indicate the existence of a microbiome signature, where particular bacterial taxa are enriched in the metagenomes of CRC patients. Here, we investigate to what extent the abundance of bacterial taxa in CRC metagenomes can be explained by the growth advantage resulting from the presence of specific CRC metabolites in the tumor microenvironment.

METHODS

We composed lists of metabolites and bacteria that are enriched on CRC samples by reviewing metabolomics experimental literature and integrating data from metagenomic case-control studies. We computationally evaluated the growth effect of CRC enriched metabolites on over 1500 genome-based metabolic models of human microbiome bacteria. We integrated the metabolomics data and the mechanistic models by using scores that quantify the response of bacterial biomass production to CRC-enriched metabolites and used these scores to rank bacteria as potential CRC passengers.

RESULTS

We found that metabolic networks of bacteria that are significantly enriched in CRC metagenomic samples either depend on metabolites that are more abundant in CRC samples or specifically benefit from these metabolites for biomass production. This suggests that metabolic alterations in the cancer environment are a major component shaping the CRC microbiome.

CONCLUSION

Here, we show with in sillico models that supplementing the intestinal environment with CRC metabolites specifically predicts the outgrowth of CRC-associated bacteria. We thus mechanistically explain why a range of CRC passenger bacteria are associated with CRC, enhancing our understanding of this disease. Our methods are applicable to other microbial communities, since it allows the systematic investigation of how shifts in the microbiome can be explained from changes in the metabolome.

Assessing Heterogeneity of Osteolytic Lesions in Multiple Myeloma by ¹H HR-MAS NMR Metabolomics

Multiple myeloma (MM) is a malignancy of plasma cells characterized by multifocal osteolytic bone lesions. Macroscopic and genetic heterogeneity has been documented within MM lesions. Understanding the bases of such heterogeneity may unveil relevant features of MM pathobiology. To this aim, we deployed unbiased ¹H high-resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) metabolomics to analyze multiple biopsy specimens of osteolytic lesions from one case of pathological fracture caused by MM. Multivariate analyses on normalized metabolite peak integrals allowed clusterization of samples in accordance with a posteriori histological findings. We investigated the relationship between morphological and NMR features by merging morphological data and metabolite profiling into a single correlation matrix. Data-merging addressed tissue heterogeneity, and greatly facilitated the mapping of lesions and nearby healthy tissues. Our proof-of-principle study reveals integrated metabolomics and histomorphology as a promising approach for the targeted study of osteolytic lesions.

Investigation of relative metabolic changes in the organs and plasma of rats exposed to X-ray radiation using HR-MAS (1)H NMR and solution (1)H NMR

Excess exposure to ionizing radiation generates reactive oxygen species and increases the cellular inflammatory response by modifying various metabolic pathways. However, an investigation of metabolic perturbations and organ-specific responses based on the amount of radiation during the acute phase has not been conducted. In this study, high-resolution magic-angle-spinning (HR-MAS) NMR and solution NMR-based metabolic profiling were used to investigate dose-dependent metabolic changes in multiple organs and tissues--including the jejunum, spleen, liver, and plasma--of rats exposed to X-ray radiation. The organs, tissues, and blood samples were obtained 24, 48, and 72 h after exposure to low-dose (2 Gy) and high-dose (6 Gy) X-ray radiation and subjected to metabolite profiling and multivariate analyses. The results showed the time course of the metabolic responses, and many significant changes were detected in the high-dose compared with the low-dose group. Metabolites with antioxidant properties showed acute responses in the jejunum and spleen after radiation exposure. The levels of metabolites related to lipid and protein metabolism were decreased in the jejunum. In addition, amino acid levels increased consistently at all post-irradiation time points as a consequence of activated protein breakdown. Consistent with these changes, plasma levels of tricarboxylic acid cycle intermediate metabolites decreased. The liver did not appear to undergo remarkable metabolic changes after radiation exposure. These results may provide insight into the major metabolic perturbations and mechanisms of the biological systems in response to pathophysiological damage caused by X-ray radiation.

Separation of small metabolites and lipids in spectra from biopsies by diffusion-weighted HR-MAS NMR: a feasibility study

High Resolution Magic Angle Spinning (HR-MAS) NMR allows metabolic characterization of biopsies. HR-MAS spectra from tissues of most organs show strong lipid contributions that are overlapping metabolite regions, which hamper metabolite estimation. Metabolite quantification and analysis would benefit from a separation of lipids and small metabolites. Generally, a relaxation filter is used to reduce lipid contributions. However, the strong relaxation filter required to eliminate most of the lipids also reduces the signals for small metabolites. The aim of our study was therefore to investigate different diffusion editing techniques in order to employ diffusion differences for separating lipid and small metabolite contributions in the spectra from different organs for unbiased metabonomic analysis. Thus, 1D and 2D diffusion measurements were performed, and pure lipid spectra that were obtained at strong diffusion weighting (DW) were subtracted from those obtained at low DW, which include both small metabolites and lipids. This subtraction yielded almost lipid free small metabolite spectra from muscle tissue. Further improved separation was obtained by combining a 1D diffusion sequence with a T2-filter, with the subtraction method eliminating residual lipids from the spectra. Similar results obtained for biopsies of different organs suggest that this method is applicable in various tissue types. The elimination of lipids from HR-MAS spectra and the resulting less biased assessment of small metabolites have potential to remove ambiguities in the interpretation of metabonomic results. This is demonstrated in a reproducibility study on biopsies from human muscle.

Spatially Localized Two-Dimensional J-Resolved NMR Spectroscopy via Intermolecular Double-Quantum Coherences for Biological Samples at 7 T

Background and Purpose

Magnetic resonance spectroscopy (MRS) constitutes a mainstream technique for characterizing biological samples. Benefiting from the separation of chemical shifts and J couplings, spatially localized two-dimensional (2D) J-resolved spectroscopy (JPRESS) shows better identification of complex metabolite resonances than one-dimensional MRS does and facilitates the extraction of J coupling information. However, due to variations of macroscopic magnetic susceptibility in biological samples, conventional JPRESS spectra generally suffer from the influence of field inhomogeneity. In this paper, we investigated the implementation of the localized 2D J-resolved spectroscopy based on intermolecular double-quantum coherences (iDQCs) on a 7 T MRI scanner.

Materials and Methods

A γ-aminobutyric acid (GABA) aqueous solution, an intact pig brain tissue, and a whole fish (Harpadon nehereus) were explored by using the localized iDQC J-resolved spectroscopy (iDQCJRES) method, and the results were compared to those obtained by using the conventional 2D JPRESS method.

Results

Inhomogeneous line broadening, caused by the variations of macroscopic magnetic susceptibility in the detected biological samples (the intact pig brain tissue and the whole fish), degrades the quality of 2D JPRESS spectra, particularly when a large voxel is selected and some strongly structured components are included (such as the fish spinal cord). By contrast, high-resolution 2D J-resolved information satisfactory for metabolite analyses can be obtained from localized 2D iDQCJRES spectra without voxel size limitation and field shimming. From the contrastive experiments, it is obvious that the spectral information observed in the localized iDQCJRES spectra acquired from large voxels without field shimming procedure (i.e. in inhomogeneous fields) is similar to that provided by the JPRESS spectra acquired from small voxels after field shimming procedure (i.e. in relatively homogeneous fields).

Conclusion

The localized iDQCJRES method holds advantage for recovering high-resolution 2D J-resolved information from inhomogeneous fields caused by external non-ideal field condition or internal macroscopic magnetic susceptibility variations in biological samples, and it is free of voxel size limitation and time-consuming field shimming procedure. This method presents a complementary way to the conventional JPRESS method for MRS measurements on MRI systems equipped with broad inner bores, and may provide a promising tool for in vivo MRS applications.

Discrimination of healthy and osteoarthritic articular cartilages by Fourier transform infrared imaging and partial least squares-discriminant analysis

Fourier transform infrared imaging (FTIRI) combined with chemometrics algorithm has strong potential to obtain complex chemical information from biology tissues. FTIRI and partial least squares-discriminant analysis (PLS-DA) were used to differentiate healthy and osteoarthritic (OA) cartilages for the first time. A PLS model was built on the calibration matrix of spectra that was randomly selected from the FTIRI spectral datasets of healthy and lesioned cartilage. Leave-one-out cross-validation was performed in the PLS model, and the fitting coefficient between actual and predicted categorical values of the calibration matrix reached 0.95. In the calibration and prediction matrices, the successful identifying percentages of healthy and lesioned cartilage spectra were 100% and 90.24%, respectively. These results demonstrated that FTIRI combined with PLS-DA could provide a promising approach for the categorical identification of healthy and OA cartilage specimens.

Prediction of survival for patients with advanced colorectal cancer using (1) H High-resolution magic angle spinning nuclear MR spectroscopy

PURPOSE

To evaluate whether the metabolic profiles of colorectal cancer specimens can be used for prediction of survival.

MATERIALS AND METHODS

The metabolic profiles of colorectal cancer tissues were determined using the high-resolution magic angle spinning (HR MAS) nuclear magnetic resonance (NMR) technique (16.4 T). HR MAS analysis was performed for 52 tissues taken from patients classified as survivors and nonsurvivors (30). Quantitative analysis was performed for each spectrum. Receiver operating characteristic (ROC) curves were used to evaluate the potential to predict patient survival over 5.5 years.

RESULTS

Analysis of (1) H NMR spectra led to the identification and quantitative analysis of 30 metabolites. A significant increase in the Tau/Gly and Tau/MI ratios were associated with long-term survival (P = 0.004 and P = 0.003, respectively). ROC analysis indicated that the Tau/MI ratio had the best predictive value for survival (sensitivity 64.7% and specificity 100%). Good predictive value of survival was found for Tau/Gly ratio (sensitivity 63.6% and specificity 96.3%). Moreover, the Glu/Gln metabolic ratio with a cutoff level of 1.74 was predictive of survival with a sensitivity of 83.3% and a specificity of 85.7%.

CONCLUSION

Our results indicate that HR MAS spectroscopy is potentially useful for survival prediction in advanced colorectal cancer.

Metabonomics of human colorectal cancer: new approaches for early diagnosis and biomarker discovery

Colorectal cancer (CRC) is one of the most common cancers in the world, having both high prevalence and mortality. It is usually diagnosed at advanced stages due to the limitations of current screening methods used in the clinic. There is an urgent need to develop new biomarkers and modalities to detect, diagnose, and monitor the disease. Metabonomics, an approach that involves the comprehensive profiling of the full complement of endogenous metabolites in a biological system, has demonstrated its great potential for use in the early diagnosis and personalized treatment of various cancers including CRC. By applying advanced analytical techniques and bioinformatics tools, the metabolome is mined for biomarkers that are associated with carcinogenesis and prognosis. This review provides an overview of the metabonomics workflow and studies, with a focus on recent advances and findings in biomarker discovery for the early diagnosis and prognosis of CRC.

Characterization of metabolites determined by means of 1H HR MAS NMR in intervertebral disc degeneration

Object

The objective of this study is the identification of metabolites by means of ¹H high resolution magic angle spinning nuclear magnetic resonance (¹H HR MAS NMR) spectroscopy and the evaluation of their applicability in distinguishing between healthy and degenerated disc tissues.

Materials and methods

Differences between the metabolic profiles of healthy and degenerated disc tissues were studied by means of ¹H HR MAS NMR. Analysis was performed for 81 disc tissue samples (control samples n = 21, degenerated disc tissue samples n = 60). Twenty six metabolites (amino acids, carbohydrates, and alcohols) were identified and quantified.

Results

The results indicate that the metabolic profile of degenerated discs is characterized by the presence of 2-propanol and the absence of scyllo-inositol and taurine. The concentrations of 2-propanol and lactate increase with age.

Conclusion

PCA analysis of ex vivo ¹H HR MAS NMR data revealed the occurrence of two groups: healthy and degenerative disc tissues. The effects of insufficient nutrient supply of discs, leading to their degeneration and back pain, are discussed.

Electronic supplementary material

The online version of this article (doi:10.1007/s10334-014-0457-0) contains supplementary material, which is available to authorized users.

Rapid diagnosis and staging of colorectal cancer via high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy of intact tissue biopsies

OBJECTIVE

To develop novel metabolite-based models for diagnosis and staging in colorectal cancer (CRC) using high-resolution magic angle spinning nuclear magnetic resonance (HR-MAS NMR) spectroscopy.

BACKGROUND

Previous studies have demonstrated that cancer cells harbor unique metabolic characteristics relative to healthy counterparts. This study sought to characterize metabolic properties in CRC using HR-MAS NMR spectroscopy.

METHODS

Between November 2010 and January 2012, 44 consecutive patients with confirmed CRC were recruited to a prospective observational study. Fresh tissue samples were obtained from center of tumor and 5 cm from tumor margin from surgical resection specimens. Samples were run in duplicate where tissue volume permitted to compensate for anticipated sample heterogeneity. Samples were subjected to HR-MAS NMR spectroscopic profiling and acquired spectral data were imported into SIMCA and MATLAB statistical software packages for unsupervised and supervised multivariate analysis.

RESULTS

A total of 171 spectra were acquired (center of tumor, n = 88; 5 cm from tumor margin, n = 83). Cancer tissue contained significantly increased levels of lactate (P < 0.005), taurine (P < 0.005), and isoglutamine (P < 0.005) and decreased levels of lipids/triglycerides (P < 0.005) relative to healthy mucosa (R2Y = 0.94; Q2Y = 0.72; area under the curve, 0.98). Colon cancer samples (n = 49) contained higher levels of acetate (P < 0.005) and arginine (P < 0.005) and lower levels of lactate (P < 0.005) relative to rectal cancer samples (n = 39). In addition unique metabolic profiles were observed for tumors of differing T-stage.

CONCLUSIONS

HR-MAS NMR profiling demonstrates cancer-specific metabolic signatures in CRC and reveals metabolic differences between colonic and rectal cancers. In addition, this approach reveals that tumor metabolism undergoes modification during local tumor advancement, offering potential in future staging and therapeutic approaches.

High-resolution two-dimensional J-resolved NMR spectroscopy for biological systems

NMR spectroscopy is a principal tool in metabolomic studies and can, in theory, yield atom-level information critical for understanding biological systems. Nevertheless, NMR investigations on biological tissues generally have to contend with field inhomogeneities originating from variations in macroscopic magnetic susceptibility; these field inhomogeneities broaden spectral lines and thereby obscure metabolite signals. The congestion in one-dimensional NMR spectra of biological tissues often leads to ambiguities in metabolite identification and quantification. We propose an NMR approach based on intermolecular double-quantum coherences to recover high-resolution two-dimensional (2D) J-resolved spectra from inhomogeneous magnetic fields, such as those created by susceptibility variations in intact biological tissues. The proposed method makes it possible to acquire high-resolution 2D J-resolved spectra on intact biological samples without recourse to time-consuming shimming procedures or the use of specialized hardware, such as magic-angle-spinning probes. Separation of chemical shifts and J couplings along two distinct dimensions is achieved, which reduces spectral crowding and increases metabolite specificity. Moreover, the apparent J coupling constants observed are magnified by a factor of 3, facilitating the accurate measurement of small J couplings, which is useful in metabolic analyses. Dramatically improved spectral resolution is demonstrated in our applications of the technique on pig brain tissues. The resulting spectra contain a wealth of chemical shift and J-coupling information that is invaluable for metabolite analyses. A spatially localized experiment applied on an intact fish (Crossocheilus siamensis) reveals the promise of the proposed method in in vivo metabolite studies. Moreover, the proposed method makes few demands on spectrometer hardware and therefore constitutes a convenient and effective manner for metabonomics study of biological systems.

Altered tissue metabolites correlate with microbial dysbiosis in colorectal adenomas

Several studies have linked bacterial dysbiosis with elevated risk of colorectal adenomas and cancer. However, the functional implications of gut dysbiosis remain unclear. Gut bacteria contribute to nutrient metabolism and produce small molecules termed the “metabolome”, which may contribute to the development of neoplasia in the large bowel. We assessed the metabolome in normal rectal mucosal biopsies of 15 subjects with colorectal adenomas and 15 nonadenoma controls by liquid chromatography and gas chromatography time-of-flight mass spectrometry. Quantitative real-time PCR was used to measure abundances of specific bacterial taxa. We identified a total of 274 metabolites. Discriminant analysis suggested a separation of metabolomic profiles between adenoma cases and nonadenoma controls. Twenty-three metabolites contributed to the separation, notably an increase in adenoma cases of the inflammatory metabolite prostaglandin E2 and a decrease in antioxidant-related metabolites 5-oxoproline and diketogulonic acid. Pathway analysis suggested that differential metabolites were significantly related to cancer, inflammatory response, carbohydrate metabolism, and GI disease pathways. Abundances of six bacterial taxa assayed were increased in cases. The 23 differential metabolites demonstrated correlations with bacteria that were different between cases and controls. These findings suggest that metabolic products of bacteria may be responsible for the development of colorectal adenomas and CRC.

Metabolomic profiling in colorectal cancer: opportunities for personalized medicine

Colorectal cancer (CRC) is one of the most common types of cancer in the world, with high prevalence and mortality. Understanding the alterations of cancer metabolism and identifying reliable biomarkers would facilitate the development of novel technologies of CRC screening and early diagnosis, as well as new approaches to providing personalized medicine. Metabolomics, as an emerging molecular phenotyping approach, provides a clinical platform technology with an unprecedented amount of metabolic readout information, which is ideal for theranostic biomarker discovery. Metabolic signatures can link the unique pathophysiological states of patients to personalized health monitoring and intervention strategies. This article presents an overview of the metabolomic studies of CRC with a focus on recent advances in the biomarker discovery in serum, urine, fecal water and tissue samples for cancer diagnosis. The development and application of metabolomics towards personalized medicine, including early diagnosis, cancer staging, treatment and drug discovery are also discussed.

Metabolomics of colorectal cancer: past and current analytical platforms

Metabolomics is coming of age as an important area of investigation which may help reveal answers to questions left unanswered or only partially understood from proteomic or genomic approaches. Increased knowledge of the relationship of genes and proteins to smaller biomolecules (metabolites) will advance our ability to diagnose, treat, and perhaps prevent cancer and other diseases that have eluded scientists for generations. Colorectal tumors are the second leading cause of cancer mortality in the USA, and the incidence is rising. Many patients present late, after the onset of symptoms, when the tumor has spread from the primary site. Once metastases have occurred, the prognosis is significantly worse. Understanding alterations in metabolic profiles that occur with tumor onset and progression could lead to better diagnostic tests as well as uncover new approaches to treat or even prevent colorectal cancer (CRC). In this review, we explore the various analytical technologies that have been applied in CRC metabolomics research and summarize all metabolites measured in CRC and integrate them into metabolic pathways. Early studies with nuclear magnetic resonance and gas-chromatographic mass spectrometry suggest that tumor cells are characterized by aerobic glycolysis, increased purine metabolism for DNA synthesis, and protein synthesis. Liquid chromatography, capillary electrophoresis, and ion mobility, each coupled with mass spectrometry, promise to advance the field and provide new insight into metabolic pathways used by cancer cells. Studies with improved technology are needed to identify better biomarkers and targets for treatment or prevention of CRC.

Bacterial responses to a simulated colon tumor microenvironment

One of the few bacteria that have been consistently linked to colorectal cancer (CRC) is the opportunistic pathogen Streptococcus gallolyticus. Infections with this bacterium are generally regarded as an indicator for colonic malignancy, while the carriage rate of this bacterium in the healthy large intestine is relatively low. We speculated that the physiological changes accompanying the development of CRC might favor the colonization of this bacterium. To investigate whether colon tumor cells can support the survival of S. gallolyticus, this bacterium was grown in spent medium of malignant colonocytes to simulate the altered metabolic conditions in the CRC microenvironment. These in vitro simulations indicated that S. gallolyticus had a significant growth advantage in these spent media, which was not observed for other intestinal bacteria. Under these conditions, bacterial responses were profiled by proteome analysis and metabolic shifts were analyzed by (1)H-NMR-spectroscopy. In silico pathway analysis of the differentially expressed proteins and metabolite analysis indicated that this advantage resulted from the increased utilization of glucose, glucose derivates, and alanine. Together, these data suggest that tumor cell metabolites facilitate the survival of S. gallolyticus, favoring its local outgrowth and providing a possible explanation for the specific association of S. gallolyticus with colonic malignancy.

Towards the human colorectal cancer microbiome

Multiple factors drive the progression from healthy mucosa towards sporadic colorectal carcinomas and accumulating evidence associates intestinal bacteria with disease initiation and progression. Therefore, the aim of this study was to provide a first high-resolution map of colonic dysbiosis that is associated with human colorectal cancer (CRC). To this purpose, the microbiomes colonizing colon tumor tissue and adjacent non-malignant mucosa were compared by deep rRNA sequencing. The results revealed striking differences in microbial colonization patterns between these two sites. Although inter-individual colonization in CRC patients was variable, tumors consistently formed a niche for Coriobacteria and other proposed probiotic bacterial species, while potentially pathogenic Enterobacteria were underrepresented in tumor tissue. As the intestinal microbiota is generally stable during adult life, these findings suggest that CRC-associated physiological and metabolic changes recruit tumor-foraging commensal-like bacteria. These microbes thus have an apparent competitive advantage in the tumor microenvironment and thereby seem to replace pathogenic bacteria that may be implicated in CRC etiology. This first glimpse of the CRC microbiome provides an important step towards full understanding of the dynamic interplay between intestinal microbial ecology and sporadic CRC, which may provide important leads towards novel microbiome-related diagnostic tools and therapeutic interventions.

Towards the human colorectal cancer microbiome

Multiple factors drive the progression from healthy mucosa towards sporadic colorectal carcinomas and accumulating evidence associates intestinal bacteria with disease initiation and progression. Therefore, the aim of this study was to provide a first high-resolution map of colonic dysbiosis that is associated with human colorectal cancer (CRC). To this purpose, the microbiomes colonizing colon tumor tissue and adjacent non-malignant mucosa were compared by deep rRNA sequencing. The results revealed striking differences in microbial colonization patterns between these two sites. Although inter-individual colonization in CRC patients was variable, tumors consistently formed a niche for Coriobacteria and other proposed probiotic bacterial species, while potentially pathogenic Enterobacteria were underrepresented in tumor tissue. As the intestinal microbiota is generally stable during adult life, these findings suggest that CRC-associated physiological and metabolic changes recruit tumor-foraging commensal-like bacteria. These microbes thus have an apparent competitive advantage in the tumor microenvironment and thereby seem to replace pathogenic bacteria that may be implicated in CRC etiology. This first glimpse of the CRC microbiome provides an important step towards full understanding of the dynamic interplay between intestinal microbial ecology and sporadic CRC, which may provide important leads towards novel microbiome-related diagnostic tools and therapeutic interventions.

In vivo biodistribution and biological impact of injected carbon nanotubes using magnetic resonance techniques

Background:

Single-walled carbon nanotubes (SWCNT) hold promise for applications as contrast agents and target delivery carriers in the field of nanomedicine. When administered in vivo, their biodistribution and pharmacological profile needs to be fully characterized. The tissue distribution of carbon nanotubes and their potential impact on metabolism depend on their shape, coating, and metallic impurities. Because standard radiolabeled or fluorescently-labeled pharmaceuticals are not well suited for long-term in vivo follow-up of carbon nanotubes, alternative methods are required.

Methods:

In this study, noninvasive in vivo magnetic resonance imaging (MRI) investigations combined with high-resolution magic angle spinning (HR-MAS), Raman spectroscopy, iron assays, and histological analysis ex vivo were proposed and applied to assess the biodistribution and biological impact of intravenously injected pristine (raw and purified) and functionalized SWCNT in a 2-week longitudinal study. Iron impurities allowed raw detection of SWCNT in vivo by susceptibility-weighted MRI.

Results:

A transitional accumulation in the spleen and liver was observed by MRI. Raman spectroscopy, iron assays, and histological findings confirmed the MRI readouts. Moreover, no acute toxicological effect on the liver metabolic profile was observed using the HR-MAS technique, as confirmed by quantitative real-time polymerase chain reaction analysis.

Conclusion:

This study illustrates the potential of noninvasive MRI protocols for longitudinal assessment of the biodistribution of SWCNT with associated intrinsic metal impurities. The same approach can be used for any other magnetically-labeled nanoparticles.

Potential of magnetic resonance spectroscopy in assessing the effect of fatty acids on inflammatory bowel disease in an animal model

People with inflammatory bowel disease (IBD) are at risk for developing colorectal cancer, and this risk increases at a rate of 1% per year after 8-10 years of having the disease. Saturated and omega-6 polyunsaturated fatty acids (PUFAs) have been implicated in its causation. Conversely, omega-3 PUFAs may have the potential to confer therapeutic benefit. Since proton magnetic resonance spectroscopy ((1)H MRS) combined with pattern recognition methods could be a valuable adjunct to histology, the objective of this study was to analyze the potential of (1)H MRS in assessing the effect of dietary fatty acids on colonic inflammation. Forty male Sprague-Dawley rats were administered one of the following dietary regimens for 2 weeks: low-fat corn oil (omega-6), high-fat corn oil (omega-6), high-fat flaxseed oil (omega-3) or high-fat beef tallow (saturated fatty acids). Half of the animals were fed 2% carrageenan to induce colonic inflammation similar to IBD. (1)H MRS and histology were performed on ex vivo colonic samples, and the (1)H MR spectra were analyzed using a statistical classification strategy (SCS). The histological and/or MRS studies revealed that different dietary fatty acids modulate colonic inflammation differently, with high-fat corn oil being the most inflammatory and high-fat flaxseed oil the least inflammatory. (1)H MRS is capable of identifying the biochemical changes in the colonic tissue as a result of inflammation, and when combined with SCS, this technique accurately differentiated the inflamed colonic mucosa based on the severity of the inflammation. This indicates that MRS could serve as a valuable adjunct to histology in accurately assessing colonic inflammation. Our data also suggest that both the type and the amount of fatty acids in the diet are critical in modulating IBD.

Characterizing Human Cancer Metabolomics with ex vivo 1H HRMAS MRS

Publications of proton high resolution magic angle spinning (1 H HRMAS) magnetic resonance spectroscopy (MRS) and its role in identification of metabolic markers for human cancer reported between 2005 and 2009 are reviewed according the anatomic sites of cancer: lung, breast, prostate, brain, colorectal, and cervical. Limited and insufficient screening options for the general public have indicated a need for more advanced tests that can identify and locate cancer at an early stage. 1 H HRMAS MRS is a valuable tool that can elucidate relevant biological metabolite information that is being used to distinguish cancer from benign tissue, and even classify types of tumors. Researchers are working to translate this ex vivo spectroscopy information into an in vivo system that could be implemented in cancer clinics. For instance, in the case of lung cancer, the goal is to identify the at risk population through a simple blood test, which would be the first level of screening. From these tests, patients identified as at risk will be able to undergo further non-invasive radiological testing for diagnostic purposes. Not only will this ex vivo technology become a valuable diagnostic tool, it will also provide a way to monitor treatments on an individual basis so they can be adjusted accordingly for the best possible outcome.

Detection of cancer in cervical tissue biopsies using mobile lipid resonances measured with diffusion-weighted (1)H magnetic resonance spectroscopy

The purpose of this study was to implement a diffusion-weighted sequence for visualisation of mobile lipid resonances (MLR) using high resolution magic angle spinning (HR-MAS) (1)H MRS and to evaluate its use in establishing differences between tissues from patients with cervical carcinoma that contain cancer from those that do not. A stimulated echo sequence with bipolar gradients was modified to allow T(1) and T(2) measurements and optimised by recording signal loss in HR-MAS spectra as a function of gradient strength in model lipids and tissues. Diffusion coefficients, T(1) and apparent T(2) relaxation times were measured in model lipid systems. MLR profiles were characterised in relation to T(1) and apparent T(2) relaxation in human cervical cancer tissue samples. Diffusion-weighted (DW) spectra of cervical biopsies were quantified and peak areas analysed using linear discriminant analysis (LDA). The optimised sequence reduced spectral overlap by suppressing signals originating from low molecular weight metabolites and non-lipid contributions. Significantly improved MLR visualisation allowed visualisation of peaks at 0.9, 1.3, 1.6, 2.0, 2.3, 2.8, 4.3 and 5.3 ppm. MLR analysis of DW spectra showed at least six peaks arising from saturated and unsaturated lipids and those arising from triglycerides. Significant differences in samples containing histologically confirmed cancer were seen for peaks at 0.9 (p < 0.006), 1.3 (p < 0.04), 2.0 (p < 0.03), 2.8 (p < 0.003) and 4.3 ppm (p < 0.0002). LDA analysis of MLR peaks from DW spectra almost completely separated two clusters of cervical biopsies (cancer, 'no-cancer'), reflecting underlying differences in MLR composition. Generated Receiver Operating Characteristic (ROC) curves and calculated area under the curve (0.962) validated high sensitivity and specificity of the technique. Diffusion-weighting of HR-MAS spectroscopic sequences is a useful method for characterising MLR in cancer tissues and displays an accumulation of lipids arising during tumourigenesis and an increase in the unsaturated lipid and triglyceride peaks with respect to saturated MLR.

Multivariate modeling and prediction of breast cancer prognostic factors using MR metabolomics

Axillary lymph node status together with estrogen and progesterone receptor status are important prognostic factors in breast cancer. In this study, the potential of using MR metabolomics for prediction of these prognostic factors was evaluated. Biopsies from breast cancer patients (n = 160) were excised during surgery and analyzed by high resolution magic angle spinning MR spectroscopy (HR MAS MRS). The spectral data were preprocessed and variable stability (VAST) scaled, and training and test sets were generated using the Kennard-Stone and SPXY sample selection algorithms. The data were analyzed by partial least-squares discriminant analysis (PLS-DA), probabilistic neural networks (PNNs) and Bayesian belief networks (BBNs), and blind samples (n = 50) were predicted for verification. Estrogen and progesterone receptor status was successfully predicted from the MR spectra, and were best predicted by PLS-DA with a correct classification of 44 of 50 and 39 of 50 samples, respectively. Lymph node status was best predicted by BBN with 34 of 50 samples correctly classified, indicating a relationship between metabolic profile and lymph node status. Thus, MR profiles contain prognostic information that may be of benefit in treatment planning, and MR metabolomics may become an important tool for diagnosis of breast cancer patients.