Publications
90 results found
Galea D, Laponogov I, Veselkov K, 2018, Exploiting and assessing multi-source data for supervised biomedical named entity recognition, Bioinformatics, Vol: 34, Pages: 2472-2482, ISSN: 1367-4803
Motivation:Recognition of biomedical entities from scientific text is a critical component of naturallanguage processing and automated information extraction platforms. Modern named entity recognitionapproaches rely heavily on supervised machine learning techniques, which are critically dependent onannotated training corpora. These approaches have been shown toperform well when trained and testedon the same source. However, in such scenario, the performanceand evaluation of these models may beoptimistic, as such models may not necessarily generalize to independent corpora, resulting in potentialnon-optimal entity recognition for large-scale tagging of widely diverse articles in databases such asPubMed.Results:Here we aggregated published corpora for the recognition of biomolecular entities (such asgenes, RNA, proteins, variants, drugs, and metabolites), identified entity class overlap and performedleave-corpus-out cross validation strategy to test the efficiency of existing models. We demonstratethat accuracies of models trained on individual corpora decrease substantially for recognition of thesame biomolecular entity classes in independent corpora. This behavior is possibly due to limitedgeneralizability of entity-class-related features captured by individual corpora (model “overtraining”) whichwe investigated further at the orthographic level, as well as potential annotation standard differences.We show that the combined use of multi-source training corpora results in overall more generalizablemodels for named entity recognition, while achieving comparable individual performance. By performinglearning-curve-based power analysis we further identified thatperformance is often not limited by thequantity of the annotated data.
Galea D, Laponogov I, Veselkov KA, 2018, Sub-word information in pre-trained biomedical word representations: evaluation and hyper-parameter optimization, Annual Computational Linguistics
Laponogov I, Sadawi N, Galea D, et al., 2018, ChemDistiller: an engine for metabolite annotation in mass spectrometry, Bioinformatics, Vol: 34, Pages: 2096-2102, ISSN: 1367-4803
MotivationHigh-resolution mass spectrometry permits simultaneous detection of thousands of different metabolites in biological samples; however, their automated annotation still presents a challenge due to the limited number of tailored computational solutions freely available to the scientific community.ResultsHere, we introduce ChemDistiller, a customizable engine that combines automated large-scale annotation of metabolites using tandem MS data with a compiled database containing tens of millions of compounds with pre-calculated ‘fingerprints’ and fragmentation patterns. Our tests using publicly and commercially available tandem MS spectra for reference compounds show retrievals rates comparable to or exceeding the ones obtainable by the current state-of-the-art solutions in the field while offering higher throughput, scalability and processing speed.
Varshavi D, Scott FH, Varshavi D, et al., 2018, Metabolic biomarkers of ageing in C57BL/6J wild-type and flavin-containing monooxygenase 5 (FMO5)-knockout mice, Frontiers in Molecular Biosciences, Vol: 5, ISSN: 2296-889X
It was recently demonstrated in mice that knockout of the flavin-containing monooxygenase 5 gene, Fmo5, slows metabolic ageing via pleiotropic effects. We have now used an NMR-based metabonomics approach to study the effects of ageing directly on the metabolic profiles of urine and plasma from male, wild-type C57BL/6J and Fmo5-/- (FMO5 KO) mice back-crossed onto the C57BL/6J background. The aim of this study was to identify metabolic signatures that are associated with ageing in both these mouse lines and to characterize the age-related differences in the metabolite profiles between the FMO5 KO mice and their wild-type counterparts at equivalent time points. We identified a range of age-related biomarkers in both urine and plasma. Some metabolites, including urinary 6-hydroxy-6-methylheptan-3-one (6H6MH3O), a mouse sex pheromone, showed similar patterns of changes with age, regardless of genetic background. Others, however, were altered only in the FMO5 KO, or only in the wild-type mice, indicating the impact of genetic modifications on mouse ageing. Elevated concentrations of urinary taurine represent a distinctive, ageing-related change observed only in wild-type mice.
Veselkov KA, Sleeman J, Claude E, et al., 2018, BASIS: High-performance bioinformatics platform for processing of large-scale mass spectrometry imaging data in chemically augmented histology, Scientific Reports, Vol: 8, ISSN: 2045-2322
Mass Spectrometry Imaging (MSI) holds significant promise in augmenting digital histopathologic analysis by generating highly robust big data about the metabolic, lipidomic and proteomic molecular content of the samples. In the process, a vast quantity of unrefined data, that can amount to several hundred gigabytes per tissue section, is produced. Managing, analysing and interpreting this data is a significant challenge and represents a major barrier to the translational application of MSI. Existing data analysis solutions for MSI rely on a set of heterogeneous bioinformatics packages that are not scalable for the reproducible processing of large-scale (hundreds to thousands) biological sample sets. Here, we present a computational platform (pyBASIS) capable of optimized and scalable processing of MSI data for improved information recovery and comparative analysis across tissue specimens using machine learning and related pattern recognition approaches. The proposed solution also provides a means of seamlessly integrating experimental laboratory data with downstream bioinformatics interpretation/analyses, resulting in a truly integrated system for translational MSI.
Charkoftaki G, Rattray NJW, Andrén PE, et al., 2018, Yale School of Public Health Symposium on tissue imaging mass spectrometry: illuminating phenotypic heterogeneity and drug disposition at the molecular level., Human Genomics, Vol: 12, Pages: 10-10, ISSN: 1479-7364
Bhome R, Goh RW, Bullock MD, et al., 2017, Exosomal microRNAs derived from colorectal cancer-associated fibroblasts: role in driving cancer progression, Aging-US, Vol: 9, Pages: 2666-2694, ISSN: 1945-4589
Colorectal cancer is a global disease with increasing incidence. Mortality is largely attributed to metastatic spread and therefore, a mechanistic dissection of the signals which influence tumor progression is needed. Cancer stroma plays a critical role in tumor proliferation, invasion and chemoresistance. Here, we sought to identify and characterize exosomal microRNAs as mediators of stromal-tumor signaling. In vitro, we demonstrated that fibroblast exosomes are transferred to colorectal cancer cells, with a resultant increase in cellular microRNA levels, impacting proliferation and chemoresistance. To probe this further, exosomal microRNAs were profiled from paired patient-derived normal and cancer-associated fibroblasts, from an ongoing prospective biomarker study. An exosomal cancer-associated fibroblast signature consisting of microRNAs 329, 181a, 199b, 382, 215 and 21 was identified. Of these, miR-21 had highest abundance and was enriched in exosomes. Orthotopic xenografts established with miR-21-overexpressing fibroblasts and CRC cells led to increased liver metastases compared to those established with control fibroblasts. Our data provide a novel stromal exosome signature in colorectal cancer, which has potential for biomarker validation. Furthermore, we confirmed the importance of stromal miR-21 in colorectal cancer progression using an orthotopic model, and propose that exosomes are a vehicle for miR-21 transfer between stromal fibroblasts and cancer cells.
Galea D, Inglese P, Cammack L, et al., 2017, Translational utility of a hierarchical classification strategy in biomolecular data analytics., Scientific Reports, Vol: 7, ISSN: 2045-2322
Hierarchical classification (HC) stratifies and classifies data from broad classes into more specific classes. Unlike commonly used data classification strategies, this enables the probabilistic prediction of unknown classes at different levels, minimizing the burden of incomplete databases. Despite these advantages, its translational application in biomedical sciences has been limited. We describe and demonstrate the implementation of a HC approach for "omics-driven" classification of 15 bacterial species at various taxonomic levels achieving 90-100% accuracy, and 9 cancer types into morphological types and 35 subtypes with 99% and 76% accuracy, respectively. Unknown bacterial species were probabilistically assigned with 100% accuracy to their respective genus or family using mass spectra (n = 284). Cancer types were predicted by mRNA data (n = 1960) for most subtypes with 95-100% accuracy. This has high relevance in clinical practice where complete datasets are difficult to compile with the continuous evolution of diseases and emergence of new strains, yet prediction of unknown classes, such as bacterial species, at upper hierarchy levels may be sufficient to initiate antimicrobial therapy. The algorithms presented here can be directly translated into clinical-use with any quantitative data, and have broad application potential, from unlabeled sample identification, to hierarchical feature selection, and discovery of new taxonomic variants.
Kinross J, Mirnezami R, Alexander J, et al., 2017, A prospective analysis of mucosal microbiome-metabonome interactions in colorectal cancer using a combined MAS 1HNMR and metataxonomic strategy, Scientific Reports, Vol: 7, ISSN: 2045-2322
Colon cancer induces a state of mucosal dysbiosis with associated niche specific changes in the gut microbiota. However, the key metabolic functions of these bacteria remain unclear. We performed a prospective observational study in patients undergoing elective surgery for colon cancer without mechanical bowel preparation (n = 18). Using 16 S rRNA gene sequencing we demonstrated that microbiota ecology appears to be cancer stage-specific and strongly associated with histological features of poor prognosis. Fusobacteria (p < 0.007) and ε- Proteobacteria (p < 0.01) were enriched on tumour when compared to adjacent normal mucosal tissue, and fusobacteria and β-Proteobacteria levels increased with advancing cancer stage (p = 0.014 and 0.002 respecitvely). Metabonomic analysis using 1H Magic Angle Spinning Nuclear Magnetic Resonsance (MAS-NMR) spectroscopy, demonstrated increased abundance of taurine, isoglutamine, choline, lactate, phenylalanine and tyrosine and decreased levels of lipids and triglycerides in tumour relative to adjacent healthy tissue. Network analysis revealed that bacteria associated with poor prognostic features were not responsible for the modification of the cancer mucosal metabonome. Thus the colon cancer mucosal microbiome evolves with cancer stage to meet the demands of cancer metabolism. Passenger microbiota may play a role in the maintenance of cancer mucosal metabolic homeostasis but these metabolic functions may not be stage specific.
Tillner J, Wu V, Jones EA, et al., 2017, Faster, more reproducible DESI-MS for biological tissue imaging, Journal of The American Society for Mass Spectrometry, Vol: 28, Pages: 2090-2098, ISSN: 1044-0305
A new, more robust sprayer for desorption electrospray ionization (DESI) mass spectrometry imaging is presented. The main source of variability in DESI is thought to be the uncontrolled variability of various geometric parameters of the sprayer, primarily the position of the solvent capillary, or more specifically, its positioning within the gas capillary or nozzle. If the solvent capillary is off-center, the sprayer becomes asymmetrical, making the geometry difficult to control and compromising reproducibility. If the stiffness, tip quality, and positioning of the capillary are improved, sprayer reproducibility can be improved by an order of magnitude. The quality of the improved sprayer and its potential for high spatial resolution imaging are demonstrated on human colorectal tissue samples by acquisition of images at pixel sizes of 100, 50, and 20 μm, which corresponds to a lateral resolution of 40-60 μm, similar to the best values published in the literature. The high sensitivity of the sprayer also allows combination with a fast scanning quadrupole time-of-flight mass spectrometer. This provides up to 30 times faster DESI acquisition, reducing the overall acquisition time for a 10 mm × 10 mm rat brain sample to approximately 1 h. Although some spectral information is lost with increasing analysis speed, the resulting data can still be used to classify tissue types on the basis of a previously constructed model. This is particularly interesting for clinical applications, where fast, reliable diagnosis is required. Graphical Abstract ᅟ.
Poynter LR, Veselkov K, Galea D, et al., 2017, Network-driven analytics of published tissue-based biomarkers to predict response to neoadjuvant therapy in rectal cancer, Annual Meeting of the American-Association-for-Cancer-Research (AACR), Publisher: AMER ASSOC CANCER RESEARCH, ISSN: 0008-5472
Antcliffe D, Jimenez B, Veselkov K, et al., 2017, Metabolic profiling in patients with pneumonia on intensive care, EBioMedicine, Vol: 18, Pages: 244-253, ISSN: 2352-3964
Clinical features and investigations lack predictive value when diagnosing pneumonia, especially when patients are ventilated and when patients develop ventilator associated pneumonia (VAP). New tools to aid diagnosis are important to improve outcomes. This pilot study examines the potential for metabolic profiling to aid the diagnosis in critical care.In this prospective observational study ventilated patients with brain injuries or pneumonia were recruited in the intensive care unit and serum samples were collected soon after the start of ventilation. Metabolic profiles were produced using 1D 1H NMR spectra. Metabolic data were compared using multivariate statistical techniques including Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA).We recruited 15 patients with pneumonia and 26 with brain injuries, seven of whom went on to develop VAP. Comparison of metabolic profiles using OPLS-DA differentiated those with pneumonia from those with brain injuries (R2Y = 0.91, Q2Y = 0.28, p = 0.02) and those with VAP from those without (R2Y = 0.94, Q2Y = 0.27, p = 0.05). Metabolites that differentiated patients with pneumonia included lipid species, amino acids and glycoproteins.Metabolic profiling shows promise to aid in the diagnosis of pneumonia in ventilated patients and may allow a more timely diagnosis and better use of antibiotics.
Inglese P, McKenzie JS, Mroz A, et al., 2017, Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer, Chemical Science, Vol: 8, Pages: 3500-3511, ISSN: 2041-6539
Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.
Doria ML, McKenzie JS, Mroz A, et al., 2016, Epithelial ovarian carcinoma diagnosis by desorption electrospray ionization mass spectrometry imaging, Scientific Reports, Vol: 6, ISSN: 2045-2322
Ovarian cancer is highly prevalent among European women, and is the leading cause of gynaecological cancer death. Current histopathological diagnoses of tumour severity are based on interpretation of, for example, immunohistochemical staining. Desorption electrospray mass spectrometry imaging (DESI-MSI) generates spatially resolved metabolic profiles of tissues and supports an objective investigation of tumour biology. In this study, various ovarian tissue types were analysed by DESI-MSI and co-registered with their corresponding haematoxylin and eosin (H&E) stained images. The mass spectral data reveal tissue type-dependent lipid profiles which are consistent across the n = 110 samples (n = 107 patients) used in this study. Multivariate statistical methods were used to classify samples and identify molecular features discriminating between tissue types. Three main groups of samples (epithelial ovarian carcinoma, borderline ovarian tumours, normal ovarian stroma) were compared as were the carcinoma histotypes (serous, endometrioid, clear cell). Classification rates >84% were achieved for all analyses, and variables differing statistically between groups were determined and putatively identified. The changes noted in various lipid types help to provide a context in terms of tumour biochemistry. The classification of unseen samples demonstrates the capability of DESI-MSI to characterise ovarian samples and to overcome existing limitations in classical histopathology.
Veselkov KA, Inglese, Galea D, et al., 2016, Statistical Tools for Molecular Covariance Spectroscopy, Encyclopedia of Spectroscopy and Spectrometry, Editors: Lindon, Tranter, Koppenaal, Publisher: Elsevier B.V., Pages: 243-249, ISBN: 978-0-12-803224-4
One major application of modern spectroscopic and spectrometric techniques is to measure hundreds to thousands of molecules in biological specimens as part of a process of metabolic phenotyping. Statistical spectroscopy covers a range of techniques used for the recovery of correlated intensity patterns within and between molecules. This plays an essential role in the annotation of molecular features of potential biological or diagnostic significance. The article introduces a variety of univariate and multivariate statistical tools for molecular covariance spectroscopy.
Cauet E, Laponogov I, McKenzie J, et al., 2016, Computer assisted identification of metabolite mass spectra: How can machine learning and quantum mechanics help?, AMERICAN CHEMICAL SOCIETY, Publisher: AMER CHEMICAL SOC, ISSN: 0065-7727
Alexander J, Gildea L, Balog J, et al., 2016, A novel methodology for in vivo endoscopic phenotyping of colorectal cancer based on real-time analysis of the mucosal lipidome: a prospective observational study of the iKnife, Surgical Endoscopy and Other Interventional Techniques, Vol: 31, Pages: 1361-1370, ISSN: 1432-2218
Background:This pilot study assessed the diagnostic accuracy of rapid evaporative ionization mass spectrometry (REIMS) in colorectal cancer (CRC) and colonic adenomas.Methods:Patients undergoing elective surgical resection for CRC were recruited at St. Mary’s Hospital London and The Royal Marsden Hospital, UK. Ex vivo analysis was performed using a standard electrosurgery handpiece with aspiration of the electrosurgical aerosol to a Xevo G2-S iKnife QTof mass spectrometer (Waters Corporation). Histological examination was performed for validation purposes. Multivariate analysis was performed using principal component analysis and linear discriminant analysis in Matlab 2015a (Mathworks, Natick, MA). A modified REIMS endoscopic snare was developed (Medwork) and used prospectively in five patients to assess its feasibility during hot snare polypectomy.Results:Twenty-eight patients were recruited (12 males, median age 71, range 35–89). REIMS was able to reliably distinguish between cancer and normal adjacent mucosa (NAM) (AUC 0.96) and between NAM and adenoma (AUC 0.99). It had an overall accuracy of 94.4 % for the detection of cancer versus adenoma and an adenoma sensitivity of 78.6 % and specificity of 97.3 % (AUC 0.99) versus cancer. Long-chain phosphatidylserines (e.g., PS 22:0) and bacterial phosphatidylglycerols were over-expressed on cancer samples, while NAM was defined by raised plasmalogens and triacylglycerols expression and adenomas demonstrated an over-expression of ceramides. REIMS was able to classify samples according to tumor differentiation, tumor budding, lymphovascular invasion, extramural vascular invasion and lymph node micrometastases (AUC’s 0.88, 0.87, 0.83, 0.81 and 0.81, respectively). During endoscopic deployment, colonoscopic REIMS was able to detect target lipid species such as ceramides during hot snare polypectomy.Conclusion:REIMS demonstrates high diagnostic accuracy for tumor type and for established histological featur
Abbassi-Ghadi N, Golf O, Kumar S, et al., 2016, Imaging of esophageal lymph node metastases by desorption electrospray ionization mass spectrometry, Cancer Research, Vol: 76, Pages: 5647-5656, ISSN: 1538-7445
Histopathological assessment of lymph node metastases (LNM) depends on subjective analysis of cellular morphology with inter-/intra-observer variability. In this study, LNM from esophageal adenocarcinoma was objectively detected using desorption electrospray ionization-mass spectrometry imaging (DESI-MSI). Ninety lymph nodes and their primary tumor biopsies from 11 esophago-gastrectomy specimens were examined and analyzed by DESI-MSI. Images from mass spectrometry and corresponding histology were co-registered and analyzed using multivariate statistical tools. The MSIs revealed consistent lipidomic profiles of individual tissue types found within lymph nodes. Spatial mapping of the profiles showed identical distribution patterns as per the tissue types in matched immunohistochemistry images. Lipidomic profile comparisons of LNM versus the primary tumor revealed a close association in contrast to benign lymph node tissue types. This similarity was used for the objective prediction of LNM in mass spectrometry images utilizing the average lipidomic profile of esophageal adenocarcinoma. The multivariate statistical algorithm developed for LNM identification demonstrated a sensitivity, specificity, positive predictive value and negative predictive value of 89.5, 100, 100 and 97.2 per-cent, respectively, when compared to gold-standard immunohistochemistry. DESI-MSI has the potential to be a diagnostic tool for peri-operative identification of LNM and compares favorably with techniques currently used by histopathology experts.
Mirnezami R, Veselkov K, Strittmatter N, et al., 2016, Spatially resolved profiling of colorectal cancer lipid biochemistry via DESI imaging mass spectrometry to reveal morphology-dependent alterations in fatty acid metabolism, Annual Meeting of the American-Society-of-Clinical-Oncology (ASCO), Publisher: American Society of Clinical Oncology, ISSN: 0732-183X
Background: Lipid metabolic alterations are recognised as potential oncogenic triggers that promote malignant transformation. Here we performed spatially-resolved profiling of lipid signatures in colorectal cancer (CRC) tissue and matched healthy mucosa using desorption electrospray ionisation imaging mass spectrometry (DESI-MSI). The objectives of this study were to comprehensively define the CRC ‘lipidome’ and to assess lipid signatures in discrete histological regions-of-interest, specifically morphologically bland peri-tumoural epithelium (PT-e) and tumour stroma (T-s). Methods: Fresh frozen tissue sections from 42 patients with confirmed CRC were subjected to negative-ion mode DESI-MSI analysis. Mass spectra in the 200-1000 m/zrange were collated from CRC epithelium (CRC-e), PT-e, T-s and healthy tumour-remote epithelium (TR-e). Spectral signatures were subjected to multivariate analysis using a recursive maximum margin criterion (RMMC) algorithm operating in MATLAB. Results: Increased levels of long/very-long chain fatty acids (LCFA/VLCFA) were seen in CRC-e compared with TR-e(AUC = 0.99). Correspondingly, increased expression of lipogenic and elongase enzymes was found on IHC. Transmission electron microscopy was performed to evaluate peroxisomal distribution and morphology in CRC-e, as these organelles metabolise LCFA/VLCFA through β-oxidation, to negligibly low levels, in healthy cells. No discernible difference in peroxisomal distribution, abundance or structure was found between CRC-e and TR-e. PT-e demonstrated a lipid expression pattern almost identical to that of CRC-e, and markedly different from TR-e (AUC = 0.89). Conclusions: A shift towards increased LCFA/VLCFA production may be an important metabolic trait in CRC facilitated through upregulation of de novo lipogenesis and fatty acid elongation and concurrent impairment of peroxisomal β-oxidation. This phenotype was also observed in morphologically bland PT-e, suggesting that
Alexander JL, Scott A, Mroz A, et al., 2016, 91 Mass Spectrometry Imaging (MSI) of Microbiome-Metabolome Interactions in Colorectal Cancer, 2016 Digestive Diseases Week, Publisher: Elsevier, Pages: S23-S23, ISSN: 0016-5085
Alexander JL, Scott A, Mroz A, et al., 2016, Mass Spectrometry Imaging (MSI) of Microbiome-Metabolome Interactions in Colorectal Cancer, Digestive Disease Week (DDW), Publisher: W B SAUNDERS CO-ELSEVIER INC, Pages: S23-S23, ISSN: 0016-5085
Tillner J, McKenzie JS, Jones EA, et al., 2016, Investigation of the Impact of Desorption Electrospray Ionization Sprayer Geometry on Its Performance in Imaging of Biological Tissue., Analytical Chemistry, Vol: 88, Pages: 4808-4816, ISSN: 0003-2700
In this study, the impact of sprayer design and geometry on performance in desorption electrospray ionization mass spectrometry (DESI-MS) is assessed, as the sprayer is thought to be a major source of variability. Absolute intensity repeatability, spectral composition, and classification accuracy for biological tissues are considered. Marked differences in tissue analysis performance are seen between the commercially available and a lab-built sprayer. These are thought to be associated with the geometry of the solvent capillary and the resulting shape of the primary electrospray. Experiments with a sprayer with a fixed solvent capillary position show that capillary orientation has a crucial impact on tissue complex lipid signal and can lead to an almost complete loss of signal. Absolute intensity repeatability is compared for five lab-built sprayers using pork liver sections. Repeatability ranges from 1 to 224% for individual sprayers and peaks of different spectral abundance. Between sprayers, repeatability is 16%, 9%, 23%, and 34% for high, medium, low, and very low abundance peaks, respectively. To assess the impact of sprayer variability on tissue classification using multivariate statistical tools, nine human colorectal adenocarcinoma sections are analyzed with three lab-built sprayers, and classification accuracy for adenocarcinoma versus the surrounding stroma is assessed. It ranges from 80.7 to 94.5% between the three sprayers and is 86.5% overall. The presented results confirm that the sprayer setup needs to be closely controlled to obtain reliable data, and a new sprayer setup with a fixed solvent capillary geometry should be developed.
Dona AC, Kyriakides M, Scott F, et al., 2016, A guide to the identification of metabolites in NMR-based metabonomics/metabolomics experiments., Computational and Structural Biotechnology Journal, Vol: 14, Pages: 135-153, ISSN: 2001-0370
Metabonomics/metabolomics is an important science for the understanding of biological systems and the prediction of their behaviour, through the profiling of metabolites. Two technologies are routinely used in order to analyse metabolite profiles in biological fluids: nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), the latter typically with hyphenation to a chromatography system such as liquid chromatography (LC), in a configuration known as LC-MS. With both NMR and MS-based detection technologies, the identification of the metabolites in the biological sample remains a significant obstacle and bottleneck. This article provides guidance on methods for metabolite identification in biological fluids using NMR spectroscopy, and is illustrated with examples from recent studies on mice.
McPhail MJW, Shawcross D, Lewis MR, et al., 2016, Mutlivariate metabotyping of plasma accurately predicts survival in decompensated cirrhosis, Journal of Hepatology, Vol: 64, Pages: 1058-1067, ISSN: 1600-0641
Background & AimsPredicting survival in decompensated cirrhosis (DC) is important in decision making for liver transplantation and resource allocation. We investigated whether high-resolution metabolic profiling can determine a metabolic phenotype associated with 90-day survival.MethodsTwo hundred and forty-eight subjects underwent plasma metabotyping by 1H nuclear magnetic resonance (NMR) spectroscopy and reversed-phase ultra-performance liquid chromatography coupled to time-of-flight mass spectrometry (UPLC-TOF-MS; DC: 80-derivation set, 101-validation; stable cirrhosis (CLD) 20 and 47 healthy controls (HC)).Results1H NMR metabotyping accurately discriminated between surviving and non-surviving patients with DC. The NMR plasma profiles of non-survivors were attributed to reduced phosphatidylcholines and lipid resonances, with increased lactate, tyrosine, methionine and phenylalanine signal intensities. This was confirmed on external validation (area under the receiver operating curve [AUROC] = 0.96 (95% CI 0.90–1.00, sensitivity 98%, specificity 89%). UPLC-TOF-MS confirmed that lysophosphatidylcholines and phosphatidylcholines [LPC/PC] were downregulated in non-survivors (UPLC-TOF-MS profiles AUROC of 0.94 (95% CI 0.89–0.98, sensitivity 100%, specificity 85% [positive ion detection])). LPC concentrations negatively correlated with circulating markers of cell death (M30 and M65) levels in DC. Histological examination of liver tissue from DC patients confirmed increased hepatocyte cell death compared to controls. Cross liver sampling at time of liver transplantation demonstrated that hepatic endothelial beds are a source of increased circulating total cytokeratin-18 in DC.ConclusionPlasma metabotyping accurately predicts mortality in DC. LPC and amino acid dysregulation is associated with increased mortality and severity of disease reflecting hepatocyte cell death.
Veselkov KA, Inglese P, Galea D, et al., 2016, Statistical tools for molecular covariance spectroscopy, Encyclopedia of Spectroscopy and Spectrometry, Pages: 243-249, ISBN: 9780128032244
One major application of modern spectroscopic and spectrometric techniques is to measure hundreds to thousands of molecules in biological specimens as part of a process of metabolic phenotyping. Statistical spectroscopy covers a range of techniques used for the recovery of correlated intensity patterns within and between molecules. This plays an essential role in the annotation of molecular features of potential biological or diagnostic significance. The article introduces a variety of univariate and multivariate statistical tools for molecular covariance spectroscopy.
Kumar S, Huang J, Abbassi-Ghadi N, et al., 2015, Mass Spectrometric Analysis of Exhaled Breath for the Identification of Volatile Organic Compound Biomarkers in Esophageal and Gastric Adenocarcinoma, ANNALS OF SURGERY, Vol: 262, Pages: 981-990, ISSN: 0003-4932
Veselkov KA, McKenzie JS, Nicholson JK, 2015, Multivariate Data Analysis Methods for NMR-based Metabolic Phenotyping in Pharmaceutical and Clinical Research, NMR in Pharmaceutical Science, Editors: Everett, Harris, Lindon, Wilson, Publisher: John Wiley & Sons, Pages: 323-334, ISBN: 9781118660256
High-resolution NMR spectroscopy is applied for molecular phenotyping across a range of pharmaceutical and clinical applications such as drug toxicity, disease diagnostics, and personalized healthcare studies. A typical NMR profile of a biological sample contains tens of thousands of signals arising from hundreds of endogenous and exogenous metabolites. The generated data requires advanced computational workflows to translate raw spectroscopic data into pharmacology and clinically useful information. This article outlines various chemoinformatics strategies that maximize disease and pharmacologically relevant molecular information recovery from one-dimensional NMR spectra of biological samples. In broad terms, the outlined strategies involve (i) raw analytical signal preprocessing for improved information recovery, (ii) multivariate statistical explorative and predictive analyses of NMR biological spectra, and (iii) time-course analyses to address a range of pharmaceutically and clinically relevant questions.
Oetjen J, Veselkov K, Watrous J, et al., 2015, Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry, GigaScience, Vol: 4, ISSN: 2047-217X
Background: Three-dimensional (3D) imaging mass spectrometry (MS) is an analytical chemistry technique for the3D molecular analysis of a tissue specimen, entire organ, or microbial colonies on an agar plate. 3D-imaging MS hasunique advantages over existing 3D imaging techniques, offers novel perspectives for understanding the spatialorganization of biological processes, and has growing potential to be introduced into routine use in both biologyand medicine. Owing to the sheer quantity of data generated, the visualization, analysis, and interpretation of 3Dimaging MS data remain a significant challenge. Bioinformatics research in this field is hampered by the lack ofpublicly available benchmark datasets needed to evaluate and compare algorithms.Findings: High-quality 3D imaging MS datasets from different biological systems at several labs were acquired,supplied with overview images and scripts demonstrating how to read them, and deposited into MetaboLights,an open repository for metabolomics data. 3D imaging MS data were collected from five samples using two typesof 3D imaging MS. 3D matrix-assisted laser desorption/ionization imaging (MALDI) MS data were collected frommurine pancreas, murine kidney, human oral squamous cell carcinoma, and interacting microbial colonies culturedin Petri dishes. 3D desorption electrospray ionization (DESI) imaging MS data were collected from a human colorectaladenocarcinoma.Conclusions: With the aim to stimulate computational research in the field of computational 3D imaging MS, selectedhigh-quality 3D imaging MS datasets are provided that could be used by algorithm developers as benchmark datasets.
Guenther S, Muirhead LJ, Speller AVM, et al., 2015, Spatially resolved metabolic phenotyping of breast cancer by desorption electrospray ionization mass spectrometry, Cancer Research, Vol: 75, Pages: 1828-1837, ISSN: 0008-5472
Breast cancer is a heterogeneous disease characterized by varying responses to therapeutic agents and significant differences in long-term survival. Thus, there remains an unmet need for early diagnostic and prognostic tools and improved histologic characterization for more accurate disease stratification and personalized therapeutic intervention. This study evaluated a comprehensive metabolic phenotyping method in breast cancer tissue that uses desorption electrospray ionization mass spectrometry imaging (DESI MSI), both as a novel diagnostic tool and as a method to further characterize metabolic changes in breast cancer tissue and the tumor microenvironment. In this prospective single-center study, 126 intraoperative tissue biopsies from tumor and tumor bed from 50 patients undergoing surgical resections were subject to DESI MSI. Global DESI MSI models were able to distinguish adipose, stromal, and glandular tissue based on their metabolomic fingerprint. Tumor tissue and tumor-associated stroma showed evident changes in their fatty acid and phospholipid composition compared with normal glandular and stromal tissue. Diagnosis of breast cancer was achieved with an accuracy of 98.2% based on DESI MSI data (PPV 0.96, NVP 1, specificity 0.96, sensitivity 1). In the tumor group, correlation between metabolomic profile and tumor grade/hormone receptor status was found. Overall classification accuracy was 87.7% (PPV 0.92, NPV 0.9, specificity 0.9, sensitivity 0.92). These results demonstrate that DESI MSI may be a valuable tool in the improved diagnosis of breast cancer in the future. The identified tumor-associated metabolic changes support theories of de novo lipogenesis in tumor tissue and the role of stroma tissue in tumor growth and development and overall disease prognosis. Cancer Res; 75(9); 1828–37. ©2015 AACR.
Abbassi-Ghadi N, Jones EA, Veselkov KA, et al., 2015, Repeatability and reproducibility of desorption electrospray ionization-mass spectrometry (DESI-MS) for the imaging analysis of human cancer tissue: a gateway for clinical applications, Analytical Methods: advancing methods and applications, Vol: 7, Pages: 71-80, ISSN: 1759-9660
In this study, we aim to demonstrate the repeatability and reproducibility of DESI-MS for the imaging analysis of human cancer tissue using a set of optimal geometric and electrospray solvent parameters. Oesophageal cancer tissue was retrieved from four quadrants of a freshly removed tumor specimen, snap frozen, cryo-sectioned and mounted on glass slides for DESI-MS image acquisition. Prior to assessing precision, optimal geometric and electrospray solvent parameters were determined to maximize the number of detected lipid species and associated Total Ion Count (TIC). The same settings were utilized for all subsequent experiments. Repeatability measurements were performed using the same instrument, by the same operator on a total of 16 tissue sections (four from each quadrant of the tumor). Reproducibility measurements were determined in a different laboratory, on a separate DESI-MS platform and by an independent operator on 4 sections of one quadrant and compared to the corresponding measurements made for the repeatability experiments. The mean ± SD CV of lipid ion intensities was found to be 22 ± 7% and 18 ± 8% as measures of repeatability and reproducibility, respectively. In conclusion, DESI-MS has acceptable levels of reproducibility for the analysis of lipids in human cancer tissue and is suitable for the purposes of clinical research and diagnostics.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.