Multivariate Statistical Data Analysis Research Articles

Metabolomic profile of children with recurrent respiratory infections

Recurrent respiratory infections (RRI) represent a widespread condition which has a severe social and economic impact. Immunostimulants are used for their prevention. It is crucial to better characterize children with RRI to refine their diagnosis and identify effective personalized prevention strategies. Metabolomics is a high-dimensional biological method that can be used for hypothesis-free biomarker profiling, examining a large number of metabolites in a given sample using spectroscopic techniques. Multivariate statistical data analysis then enables us to infer which metabolic information is relevant to the biological characterization of a given physiological or pathological condition. This can lead to the emergence of new, sometimes unexpected metabolites, and hitherto unknown metabolic pathways, enabling the formulation of new pathogenetic hypotheses, and the identification of new therapeutic targets.The aim of our pilot study was to apply mass-spectrometry-based metabolomics to the analysis of urine samples from children with RRI, comparing these children’s biochemical metabolic profiles with those of healthy peers. We also compared the RRI children’s and healthy controls’ metabolomic urinary profiles after the former had received pidotimod treatment for 3 months to see whether this immunostimulant was associated with biochemical changes in the RRI children’s metabolic profile.13 children (age range 3–6 yeas) with RRI and 15 matched per age healthy peers with no history of respiratory diseases or allergies were enrolled. Their metabolomic urine samples were compared before and after the RRI children had been treated with pidotimod for a period of 3 months. Metabolomic analyses on the urine samples were done using mass spectrometry combined with ultra-performance liquid chromatography (UPLC–MS). The resulting spectroscopic data then underwent multivariate statistical analysis and the most relevant variables characterizing the two groups were identified.Data modeling with post-transformation of PLS2-Discriminant Analysis (ptPLS2-DA) generated a robust model capable of discriminating the urine samples from children with RRI from those of healthy controls (R2=0.92,Q2CV7-fold=0.75, p-value<0.001). The dataset included 1502 time per mass variables, and 138 of them characterized the difference between the two groups. Thirty-five of these distinctive 138 variables persisted in the profiles of the children with RRI after pidotimod treatment.Metabolomics can discriminate children with RRI from healthy controls, suggesting that the former have a dysregulated metabolic profile. Among the variables characterizing children with RRI there are metabolites that may reflect the presence of a different microbiome.After pidotimod treatment, the metabolic profile of the children with RRI was no longer very different from that of the healthy controls, except for the persistence of some microbiome-related variables. We surmise that pidotimod partially “restores” the altered metabolic profile of children with RRI, without modifying the metabolites related to the composition of the gut microbiota. In the light of these results, we hypothesize a potential synergic effect of the combined use of immunostimulants and probiotics for the purpose of prevention in children with RRI.

Concurrent profiling of polar metabolites and lipids in human plasma using HILIC-FTMS.

Blood plasma is the most popularly used sample matrix for metabolite profiling studies, which aim to achieve global metabolite profiling and biomarker discovery. However, most of the current studies on plasma metabolite profiling focused on either the polar metabolites or lipids. In this study, a comprehensive analysis approach based on HILIC-FTMS was developed to concurrently examine polar metabolites and lipids. The HILIC-FTMS method was developed using mixed standards of polar metabolites and lipids, the separation efficiency of which is better in HILIC mode than in C5 and C18 reversed phase (RP) chromatography. This method exhibits good reproducibility in retention times (CVs < 3.43%) and high mass accuracy (<3.5 ppm). In addition, we found MeOH/ACN/Acetone (1:1:1, v/v/v) as extraction cocktail could achieve desirable gathering of demanded extracts from plasma samples. We further integrated the MeOH/ACN/Acetone extraction with the HILIC-FTMS method for metabolite profiling and smoking-related biomarker discovery in human plasma samples. Heavy smokers could be successfully distinguished from non smokers by univariate and multivariate statistical analysis of the profiling data, and 62 biomarkers for cigarette smoke were found. These results indicate that our concurrent analysis approach could be potentially used for clinical biomarker discovery, metabolite-based diagnosis, etc.

Untargeted Metabolomic Analysis of Amniotic Fluid in the Prediction of Preterm Delivery and Bronchopulmonary Dysplasia.

ObjectiveBronchopulmonary dysplasia (BPD) is a serious complication associated with preterm birth. A growing body of evidence suggests a role for prenatal factors in its pathogenesis. Metabolomics allows simultaneous characterization of low molecular weight compounds and may provide a picture of such a complex condition. The aim of this study was to evaluate whether an unbiased metabolomic analysis of amniotic fluid (AF) can be used to investigate the risk of spontaneous preterm delivery (PTD) and BPD development in the offspring.Study designWe conducted an exploratory study on 32 infants born from mothers who had undergone an amniocentesis between 21 and 28 gestational weeks because of spontaneous preterm labor with intact membranes. The AF samples underwent untargeted metabolomic analysis using mass spectrometry combined with ultra-performance liquid chromatography. The data obtained were analyzed using multivariate and univariate statistical data analysis tools.ResultsOrthogonally Constrained Projection to Latent Structures-Discriminant Analysis (oCPLS2-DA) excluded effects on data modelling of crucial clinical variables. oCPLS2-DA was able to find unique differences in select metabolites between term (n = 11) and preterm (n = 13) deliveries (negative ionization data set: R2 = 0.47, mean AUC ROC in prediction = 0.65; positive ionization data set: R2 = 0.47, mean AUC ROC in prediction = 0.70), and between PTD followed by the development of BPD (n = 10), and PTD without BPD (n = 11) (negative data set: R2 = 0.48, mean AUC ROC in prediction = 0.73; positive data set: R2 = 0.55, mean AUC ROC in prediction = 0.71).ConclusionsThis study suggests that amniotic fluid metabolic profiling may be promising for identifying spontaneous preterm birth and fetuses at risk for developing BPD. These findings support the hypothesis that some prenatal metabolic dysregulations may play a key role in the pathogenesis of PTD and the development of BPD.

Geochemical and mineralogical fingerprints to distinguish the exploited ferruginous mineralisations of Grotta della Monaca (Calabria, Italy)

This study examines the geochemical and mineralogical variations in the ferruginous mineralisations that crop out within Grotta della Monaca, which is considered to be the most striking and best known example of a prehistoric iron mine-cave from the southern Apennines (Calabria, Italy). Previous archaeological research identified three local and distinct ancient exploitation phases of these ferruginous mineralisations: (1) an Upper Palaeolithic phase; (2) a Late Neolithic phase; and (3) a post-Medieval phase. These materials, which have various forms of complex mineralogical admixtures and range in colour from yellow-orange to red and darker brown shades, mainly consist of iron oxides/hydroxides (essentially goethite and lepidocrocite), which are often mixed with subordinate and variable amounts of other matrix components (carbonates, sulphates, arsenates, silicates and organic matter). Such ferruginous mineralisations generally correspond to geochemically heterogeneous massive dyke/vein/mammillary/stratiform facies that are exposed within the local caves along open fractures and inclined bedding planes and that partially cover cave wall niches/notches/pockets and ceiling cupolas/holes. Selected samples/sub-samples are analysed through a multi-technique approach with a handheld portable X-ray Fluorescence, X-ray Diffraction, micro-Raman and Fourier Transform Infrared spectroscope (both conventional and attenuated total reflection), which is combined with subsequent multivariate statistical analysis of the elemental concentration data. The geochemical and mineralogical results are used to individualise similar compositional clusters. As expected, the identified groups, each of which has very specific geochemical-mineralogical “fingerprints” and spatial distributions, enable us to identify the sampled ferruginous mineralisations. These specific mineral resources can be compared to similar raw materials that are found in other neighbouring archaeological sites, with obvious implications toward understanding local exploitation strategies through time and the exchanges and kinship networks of these materials.

The Lipopolysaccharide-Induced Metabolome Signature in Arabidopsis thaliana Reveals Dynamic Reprogramming of Phytoalexin and Phytoanticipin Pathways.

Lipopolysaccharides (LPSs), as MAMP molecules, trigger the activation of signal transduction pathways involved in defence. Currently, plant metabolomics is providing new dimensions into understanding the intracellular adaptive responses to external stimuli. The effect of LPS on the metabolomes of Arabidopsis thaliana cells and leaf tissue was investigated over a 24 h period. Cellular metabolites and those secreted into the medium were extracted with methanol and liquid chromatography coupled to mass spectrometry was used for quantitative and qualitative analyses. Multivariate statistical data analyses were used to extract interpretable information from the generated multidimensional LC-MS data. The results show that LPS perception triggered differential changes in the metabolomes of cells and leaves, leading to variation in the biosynthesis of specialised secondary metabolites. Time-dependent changes in metabolite profiles were observed and biomarkers associated with the LPS-induced response were tentatively identified. These include the phytohormones salicylic acid and jasmonic acid, and also the associated methyl esters and sugar conjugates. The induced defensive state resulted in increases in indole—and other glucosinolates, indole derivatives, camalexin as well as cinnamic acid derivatives and other phenylpropanoids. These annotated metabolites indicate dynamic reprogramming of metabolic pathways that are functionally related towards creating an enhanced defensive capacity. The results reveal new insights into the mode of action of LPS as an activator of plant innate immunity, broadens knowledge about the defence metabolite pathways involved in Arabidopsis responses to LPS, and identifies specialised metabolites of functional importance that can be employed to enhance immunity against pathogen infection.

Distribution and variability of total mercury in snow cover-a case study from a semi-urban site in Poznań, Poland.

In the present paper, the inter-seasonal Hg variability in snow cover was examined based on multivariate statistical analysis of chemical and meteorological data. Samples of freshly fallen snow cover were collected at the semi-urban site in Poznań (central Poland), during 3-month field measurements in winter 2013. It was showed that concentrations of atmospherically deposited Hg were highly variable in snow cover, from 0.43 to 12.5 ng L−1, with a mean value of 4.62 ng L−1. The highest Hg concentration in snow cover coincided with local intensification of fossil fuel burning, indicating large contribution from various anthropogenic sources such as commercial and domestic heating, power generation plants, and traffic-related pollution. Moreover, the variability of Hg in collected snow samples was associated with long-range transport of pollutants, nocturnal inversion layer, low boundary layer height, and relatively low air temperature. For three snow episodes, Hg concentration in snow cover was attributed to southerly advection, suggesting significant contribution from the highly polluted region of Poland (Upper Silesia) and major European industrial hotspots. However, the peak Hg concentration was measured in samples collected during predominant N to NE advection of polluted air masses and after a relatively longer period without precipitation. Such significant contribution to the higher Hg accumulation in snow cover was associated with intensive emission from anthropogenic sources (coal combustion) and atmospheric conditions in this area. These results suggest that further measurements are needed to determine how the Hg transformation paths in snow cover change in response to longer/shorter duration of snow cover occurrence and to determine the interactions between mercury and absorbing carbonaceous aerosols in the light of climate change.

Signatures of Subacute Potentially Catastrophic Illness in the ICU: Model Development and Validation.

Patients in ICUs are susceptible to subacute potentially catastrophic illnesses such as respiratory failure, sepsis, and hemorrhage that present as severe derangements of vital signs. More subtle physiologic signatures may be present before clinical deterioration, when treatment might be more effective. We performed multivariate statistical analyses of bedside physiologic monitoring data to identify such early subclinical signatures of incipient life-threatening illness. We report a study of model development and validation of a retrospective observational cohort using resampling (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis type 1b internal validation) and a study of model validation using separate data (type 2b internal/external validation). University of Virginia Health System (Charlottesville), a tertiary-care, academic medical center. Critically ill patients consecutively admitted between January 2009 and June 2015 to either the neonatal, surgical/trauma/burn, or medical ICUs with available physiologic monitoring data. None. We analyzed 146 patient-years of vital sign and electrocardiography waveform time series from the bedside monitors of 9,232 ICU admissions. Calculations from 30-minute windows of the physiologic monitoring data were made every 15 minutes. Clinicians identified 1,206 episodes of respiratory failure leading to urgent unplanned intubation, sepsis, or hemorrhage leading to multi-unit transfusions from systematic individual chart reviews. Multivariate models to predict events up to 24 hours prior had internally validated C-statistics of 0.61-0.88. In adults, physiologic signatures of respiratory failure and hemorrhage were distinct from each other but externally consistent across ICUs. Sepsis, on the other hand, demonstrated less distinct and inconsistent signatures. Physiologic signatures of all neonatal illnesses were similar. Subacute potentially catastrophic illnesses in three diverse ICU populations have physiologic signatures that are detectable in the hours preceding clinical detection and intervention. Detection of such signatures can draw attention to patients at highest risk, potentially enabling earlier intervention and better outcomes.

GC-MS metabolomics analysis of mesenchymal stem cells treated with copper oxide nanoparticles

Human exposure to copper oxide (CuO) nanoparticles (NPs) is rapidly increasing and for this reason reliable toxicity test systems are urgently needed. Recently, the acute cytotoxicity of CuO NPs using the new toxicity test based on human bone marrow mesenchymal stem cells (hBMMSCs) has been evaluated. It was shown that CuO NPs are much more toxic when compared to CuO microparticles (MPs). Several studies associate CuO toxicity to a possible alteration of reactive oxygen species (ROS) system. Unluckily, the mechanism that causes the toxicity is still not clear. In this work, the polar metabolite pool of treated cells, at the corresponding IC50 value, for CuO micro and NPs has been studied by gas chromatography coupled to mass spectrometry (GC-MS) and multivariate statistical data analysis. By the same means, differences due to different treatments, on samples, were investigated. Results of discriminant analysis were considered with the aim of finding the relevant metabolites unique for each class. Serine, glyceric acid, and succinic acid were upregulated on samples treated with CuO microparticles, while glutamine was the only discriminant metabolite for the class of samples treated with nanoparticles.

Effect of the environment on the secondary metabolic profile of Tithonia diversifolia: a model for environmental metabolomics of plants.

Tithonia diversifolia is an invasive weed commonly found in tropical ecosystems. In this work, we investigate the influence of different abiotic environmental factors on the plant’s metabolite profile by multivariate statistical analyses of spectral data deduced by UHPLC-DAD-ESI-HRMS and NMR methods. Different plant part samples of T. diversifolia which included leaves, stems, roots, and inflorescences were collected from two Brazilian states throughout a 24-month period, along with the corresponding monthly environmental data. A metabolomic approach employing concatenated LC-MS and NMR data was utilised for the first time to study the relationships between environment and plant metabolism. A seasonal pattern was observed for the occurrence of metabolites that included sugars, sesquiterpenes lactones and phenolics in the leaf and stem parts, which can be correlated to the amount of rainfall and changes in temperature. The distribution of the metabolites in the inflorescence and root parts were mainly affected by variation of some soil nutrients such as Ca, Mg, P, K and Cu. We highlight the environment-metabolism relationship for T. diversifolia and the combined analytical approach to obtain reliable data that contributed to a holistic understanding of the influence of abiotic environmental factors on the production of metabolites in various plant parts.

Metabolite profiles of formula milk compared to breast milk

Breast milk (BM) feeding is the gold standard in neonate nutrition. When BM is not available it can be substituted or integrated with commercial formula milk (FM) usually sold under different brands and formulations. In this work, the low-molecular-weight hydrophilic compounds in milk were studied by gas chromatography electronic impact mass spectrometry (GC–MS), comparing eight different FM brands with BM samples. With the aid of multivariate statistical data analysis, a marked variability among FM brands, especially driven by the presence of prebiotics in their formulation, was highlighted. Quali-quantitative differences were found between FM and BM. Orotic acid and isomaltulose were found exclusively in FM, while phenylalanine and tyrosine levels were high in two FM brands. Moreover, higher levels of malic acid, sugars (glucose, fructose and galactose), and mannitol were detected in FM. On the other hand, BM showed a higher amino acid content. In conclusion, GC–MS proved to be a very sensitive analytical technique for the study of FM, highlighting metabolite differences among FM brands, and between FM and BM, that may have a possible strong impact on neonatal nutrition.

Metabolomics Analysis Reveals the Participation of Efflux Pumps and Ornithine in the Response of Pseudomonas putida DOT-T1E Cells to Challenge with Propranolol

Efflux pumps are critically important membrane components that play a crucial role in strain tolerance in Pseudomonas putida to antibiotics and aromatic hydrocarbons that result in these toxicants being expelled from the bacteria. Here, the effect of propranolol on P. putida was examined by sudden addition of 0.2, 0.4 and 0.6 mg mL-1 of this β-blocker to several strains of P. putida, including the wild type DOT-T1E and the efflux pump knockout mutants DOT-T1E-PS28 and DOT-T1E-18. Bacterial viability measurements reveal that the efflux pump TtgABC plays a more important role than the TtgGHI pump in strain tolerance to propranolol. Mid-infrared (MIR) spectroscopy was then used as a rapid, high-throughput screening tool to investigate any phenotypic changes resulting from exposure to varying levels of propranolol. Multivariate statistical analysis of these MIR data revealed gradient trends in resultant ordination scores plots, which were related to the concentration of propranolol. MIR illustrated phenotypic changes associated with the presence of this drug within the cell that could be assigned to significant changes that occurred within the bacterial protein components. To complement this phenotypic fingerprinting approach metabolic profiling was performed using gas chromatography mass spectrometry (GC-MS) to identify metabolites of interest during the growth of bacteria following toxic perturbation with the same concentration levels of propranolol. Metabolic profiling revealed that ornithine, which was only produced by P. putida cells in the presence of propranolol, presents itself as a major metabolic feature that has important functions in propranolol stress tolerance mechanisms within this highly significant and environmentally relevant species of bacteria.

Gas chromatography-mass spectrometry metabolomics of goat milk with different polymorphism at the αS1-casein genotype locus

Hyphenated gas chromatography-mass spectrometry (GC-MS) and multivariate data analysis techniques were used to uncover milk metabolite differences in different αS1-casein genotypes of goats. By a discriminant GC-MS metabolomics approach, we characterized milk polar metabolites of 28 goats. Animals were selected on the basis of their genotypes as 7 goats classified heterozygous for weak or null alleles, 5 for the genotype EE, 9 for the genotypes AE and BE, and finally 7 for the strong genotype AA. Low molecular weight polar metabolite profile was tightly related to the different goat genotypes, milk production, and protein levels. Results of multivariate statistical analysis of GC-MS data demonstrate that different heterozygous and homozygous genotypes expressed different metabolites such as citric and aconitic acid for the strong allele class with different sugars and polyols for the weak class.

Environmental Metabolic Footprinting: A novel application to study the impact of a natural and a synthetic β-triketone herbicide in soil

This study presents a novel approach for assessing the risk of agrochemicals in soil microcosms through the use of non-targeted metabolomics. The metabolome of treated soils was extracted and tested through LCMS profiling in order to generate an "Environmental Metabolic Footprint" (EMF). A dynamic characterization of pollution biomarkers was obtained through a multivariate statistical analysis of EMF data, where our results show the possible evolution towards a state of resilience. The EMF methodology was applied to two β-triketone herbicides in soil microcosms: one natural, leptospermone, and one synthetic, sulcotrione. In spite of a four-fold higher application dose, leptospermone exhibited a lower resilience time than did sulcotrione (ca. 30 days vs ca. 45 days respectively).

Compositional differences among Chinese soy sauce types studied by 13C NMR spectroscopy coupled with multivariate statistical analysis

Soy sauce a well known seasoning all over the world, especially in Asia, is available in global market in a wide range of types based on its purpose and the processing methods. Its composition varies with respect to the fermentation processes and addition of additives, preservatives and flavor enhancers. A comprehensive 1H NMR based study regarding the metabonomic variations of soy sauce to differentiate among different types of soy sauce available on the global market has been limited due to the complexity of the mixture. In present study, 13C NMR spectroscopy coupled with multivariate statistical data analysis like principle component analysis (PCA), and orthogonal partial least square-discriminant analysis (OPLS-DA) was applied to investigate metabonomic variations among different types of soy sauce, namely super light, super dark, red cooking and mushroom soy sauce. The main additives in soy sauce like glutamate, sucrose and glucose were easily distinguished and quantified using 13C NMR spectroscopy which were otherwise difficult to be assigned and quantified due to serious signal overlaps in 1H NMR spectra. The significantly higher concentration of sucrose in dark, red cooking and mushroom flavored soy sauce can directly be linked to the addition of caramel in soy sauce. Similarly, significantly higher level of glutamate in super light as compared to super dark and mushroom flavored soy sauce may come from the addition of monosodium glutamate.The study highlights the potentiality of 13C NMR based metabonomics coupled with multivariate statistical data analysis in differentiating between the types of soy sauce on the basis of level of additives, raw materials and fermentation procedures.

Nontargeted metabolomic analysis and “commercial-homophyletic” comparison-induced biomarkers verification for the systematic chemical differentiation of five different parts of Panax ginseng

A key segment in authentication of herbal medicines is the establishment of robust biomarkers that embody the intrinsic metabolites difference independent of the growing environment or processing technics. We present a strategy by nontargeted metabolomics and “Commercial-homophyletic” comparison-induced biomarkers verification with new bioinformatic vehicles, to improve the efficiency and reliability in authentication of herbal medicines. The chemical differentiation of five different parts (root, leaf, flower bud, berry, and seed) of Panax ginseng was illustrated as a case study. First, an optimized ultra-performance liquid chromatography/quadrupole time-of-flight-MSE (UPLC/QTOF-MSE) approach was established for global metabolites profiling. Second, UNIFI™ combined with search of an in-house library was employed to automatically characterize the metabolites. Third, pattern recognition multivariate statistical analysis of the MSE data of different parts of commercial and homophyletic samples were separately performed to explore potential biomarkers. Fourth, potential biomarkers deduced from commercial and homophyletic root and leaf samples were cross-compared to infer robust biomarkers. Fifth, discriminating models by artificial neutral network (ANN) were established to identify different parts of P. ginseng. Consequently, 164 compounds were characterized, and 11 robust biomarkers enabling the differentiation among root, leaf, flower bud, and berry, were discovered by removing those structurally unstable and possibly processing-related ones. The ANN models using the robust biomarkers managed to exactly discriminate four different parts and root adulterant with leaf as well. Conclusively, biomarkers verification using homophyletic samples conduces to the discovery of robust biomarkers. The integrated strategy facilitates authentication of herbal medicines in a more efficient and more intelligent manner.

Multivariate statistical analysis of hydrogeochemical data towards understanding groundwater flow systems in granites

The present study focuses on hydrogeochemical data from a Hercynian granitic mass where a new underground hydraulic circuit for a hydropower plant is being developed between two reservoirs and near another existing and operating underground hydraulic circuit. The main goal of this work is to assess the use of multivariate statistics to distinguish groundwater from different flow systems, to aid in evaluating the possible infiltration of water from the existing hydraulic circuit into the new one. Sampling points were selected inside the tunnels (eight points at the new circuit and one point at the existing circuit), near the surface (five points along the new circuit major axis) and on Venda Nova dam reservoir (one point). Based on laboratory determinations of major ions, three distinct groups of waters with different physicochemical characteristics and spatial distribution were established by cluster analyses. The relations of these groups with the characteristics of the occurrences are discussed with the support of principal component analysis. A hydrogeochemical conceptual model for groundwater flow systems related to infiltration and water–rock interaction in this granite rock mass is proposed based on multivariate statistical analyses and geological information. Supplementary material: Sample point coordinates, data results of physical–chemical parameters and elements, statistical data results of physical–chemical parameters and elements, and isotope data are available at https://doi.org/10.6084/m9.figshare.c.2857303.v1.

Accretion rates in coastal wetlands of the southeastern Gulf of California and their relationship with sea-level rise

Sea-level rise (SLR) is one of the most conspicuous examples of the environmental impact of recent climate change. Since SLR rates are not uniform around the planet, local and regional data are needed for proper adaptation plans. 210Pb-dated sediment cores were analyzed to determine the trends of sediment accretion rates (SARs) at three tropical saltmarshes in the Estero de Urias lagoon (Gulf of California, Mexico), in order to estimate the SLR trends during the past ~100 years, under the assumption that these ecosystems accrete at a similar rate to SLR. A chemometric approach, including multivariate statistical analysis (factor analysis) of geochemical data (including δ13C; δ15N; C/N ratios; and Br, Na, and Cl as proxies for marine transgression) was used to identify the marine transgression in the sediment records. Based on core geochemistry, only one of the three cores provided a long-term record attributable to marine transgression. SLR trends, estimated from SARs, showed increasing values, from a minimum of 0.73 ± 0.03 mm yr−1 at the beginning of the 20th century and up to 3.87 ± 0.12 mm yr−1 during the period 1990–2012. The estimated SLR trend between 1950 and 1970 was comparable to the tide gauge records in Mazatlan City for the same period. Results showed the caveats and strengths of this methodology to reconstruct decadal SLR trends from the sedimentary record, which can be used to estimate long-term SLR trends worldwide in regions where monitoring data are scarce or absent.

Abstract P2-12-20: Rapid evaporative ionisation mass spectrometry towards real time intraoperative oncological margin status determination in breast conserving surgery

Abstract Introduction: Positive tumour margins following attempted breast conserving surgery (BCS) is an important risk factor for local recurrence. Nationally in the United Kingdom on average approximately 25% of patients undergoing BCS require additional surgery for positive margins. Traditional techniques such as specimen xray, frozen section &amp; imprint cytology to optimise margin clearance have significant limitations. Various research methods under investigation include optical spectroscopy, high resolution imaging and radiofrequency spectroscopy. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) is a new method that uses mass spectrometric analysis of the tissue specific ionic content of the surgical diathermy smoke plume for the rapid identification of dissected breast tissues as an intelligent knife (iKnife). We investigate the ability of the "iKnife" to analyze heterogeneous breast tissue intraoperatively using mass spectrometric techniques. Method: The study involved three stages that comprised: method development, tissue specific ex-vivo database construction and intraoperative analysis. Smoke aerosol produced as a result of electrosurgical diathermy from a variety of frozen, fresh and in-vivo breast samples were aspirated into a mass spectrometer via a modified surgical handpiece. Tissue diagnosis was confirmed by subsequent histopathological validation. The data underwent computational analysis using multivariate statistics –predominantly Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), along with leave one patient out cross-validation. A total of 128 patients (n=40 method development, n=66 ex-vivo database, n=22 intraoperative analysis) undergoing breast surgery were enrolled in this study. Ethical approval was obtained from the Research Ethics Committee. Results: 40 patients contributed breast samples (normal and cancerous) for method optimisation to enable analysis of high intensity spectra from heterogeneous breast tissue. Following optimisation an ex-vivo database was constructed from 89 excised fresh breast tissue samples from 66 patients using 330 spectra (246 Normal, 60 Tumour – IDC, ILC, IMC and 24 Benign - fibroadenoma). Multivariate statistical analysis of data revealed classification of tumour compared to normal tissue with sensitivities of 93.0% and specificity of 91.9%. The iKnife was used intraoperatively during the entire operation of 25 surgeries. Spectral data was obtained within 1-2 seconds. Specific margin analysis correctly identified negative margins in 10 cases. Conclusions: The iKnife has been successfully developed for analysis of intraoperative heterogeneous breast tissue. Preliminary data suggests that this technique is suitable with high accuracy for the separation of normal, benign (fibroadenoma) and cancerous (invasive ductal and invasive lobular carcinoma) breast tissues. In comparison to the normal breast, cancerous tissues exhibit statistically different spectral profiles. Further work is aimed at the development of a real time algorithm able to match intraoperative data with the pre-existing database for the rapid interpretation and real time feedback of intraoperative data towards detecting positive margins intraoperatively. Citation Format: St John ER, Al-Khudairi R, Balog J, Rossi M, Gildea L, Speller A, Ramakrishnan R, Shousha S, Takats Z, Leff DR, Darzi A. Rapid evaporative ionisation mass spectrometry towards real time intraoperative oncological margin status determination in breast conserving surgery. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P2-12-20.

Spatial Distribution of Elemental Enrichments around Archaeological Sites: Insights from the Ancient City of Sagalassos in Southwest Turkey

Ancient human activities have resulted in important elemental enrichments in soils at archaeological sites. Nevertheless, the spatial extent of such elemental enrichment signals is rarely studied. Our research addresses this theme by studying the characteristics and extent of the geochemical enrichment halo around the monumental Colonnaded Street of the Hellenistic to Byzantine city of Sagalassos in southwest Turkey. Given the complex geology of the area, a strategy applying multivariate statistical data analysis techniques is proposed to identify whether the enrichments have a geological or an anthropogenic source. In addition, we evaluate how a wide variety of natural and anthropogenic processes may influence the distribution of elements around the site. In this paper, it is shown that enrichments of P and Pb are present up to a distance of 450 m and 150 m away from the city, respectively. Hence, we conclude that the extent of chemical enrichment haloes around archaeological sites may be element specific.

Metabolic fingerprinting reveals a novel candidate biomarker for prednisolone treatment in cattle

The use of glucocorticoids as growth promoters for meat-producing animals is strictly regulated within the European Union. However, in the past few years, a higher frequency of non-compliant bovine urine samples for prednisolone has been noticed, which could not be directly related to fraudulent use of prednisolone. As such, questions have risen about the origin of this compound. Unfortunately, at present, no decisive strategy has been established to discriminate between endogenous and exogenous prednisolone. In this study, an untargeted metabolomics strategy, based on Orbitrap and QqTOF mass spectrometry, was deployed to reveal urinary biomarkers, which are indicative for the exogenous administration of the synthetic glucocorticoid prednisolone. For this purpose, prednisolone was administered intramuscularly and per os to 12 bovines and a total of 2700 urine samples were collected before, during and after treatment. Multivariate statistical data analysis (i.e. OPLS-DA) revealed four differentiating metabolites that allowed discrimination between urine samples collected before and during prednisolone administration. None of these compounds were present in urine containing endogenous prednisolone, of which the formation was induced by the administration of a synthetic analogue of adrenocorticotropic hormone. Only one metabolite was retained as a highly suitable biomarker during growth-promoting and therapeutic prednisolone treatment, with 93.4 % sensitivity and 96.3 % specificity. Besides, this compound could be detected up to 4 days after a single therapeutic per os prednisolone administration. Based on accurate mass, isotope pattern, and MS/MS spectra, this compound was putatively annotated and is suggested as an actionable biomarker for exogenous prednisolone administration.