21 results found
Correia GDS, Takis PG, Sands CJ, et al., 2022, 1H NMR Signals from urine excreted protein are a source of bias in probabilistic quotient normalization, Analytical Chemistry, ISSN: 0003-2700
Normalization to account for variation in urinary dilution is crucial for interpretation of urine metabolic profiles. Probabilistic quotient normalization (PQN) is used routinely in metabolomics but is sensitive to systematic variation shared across a large proportion of the spectral profile (>50%). Where 1H nuclear magnetic resonance (NMR) spectroscopy is employed, the presence of urinary protein can elevate the spectral baseline and substantially impact the resulting profile. Using 1H NMR profile measurements of spot urine samples collected from hospitalized COVID-19 patients in the ISARIC 4C study, we determined that PQN coefficients are significantly correlated with observed protein levels (r2 = 0.423, p < 2.2 × 10–16). This correlation was significantly reduced (r2 = 0.163, p < 2.2 × 10–16) when using a computational method for suppression of macromolecular signals known as small molecule enhancement spectroscopy (SMolESY) for proteinic baseline removal prior to PQN. These results highlight proteinuria as a common yet overlooked source of bias in 1H NMR metabolic profiling studies which can be effectively mitigated using SMolESY or other macromolecular signal suppression methods before estimation of normalization coefficients.
Pruski P, Dos Santos Correia G, Lewis H, et al., 2021, Direct on-swab metabolic profiling of vaginal microbiome host interactions during pregnancy and preterm birth, Nature Communications, Vol: 12, ISSN: 2041-1723
The pregnancy vaginal microbiome contributes to risk of preterm birth, the primary cause of death in children under 5 years of age. Here we describe direct on-swab metabolic profiling by Desorption Electrospray Ionization Mass Spectrometry (DESI-MS) for sample preparation-free characterisation of the cervicovaginal metabolome in two independent pregnancy cohorts (VMET, n = 160; 455 swabs; VMET II, n = 205; 573 swabs). By integrating metataxonomics and immune profiling data from matched samples, we show that specific metabolome signatures can be used to robustly predict simultaneously both the composition of the vaginal microbiome and host inflammatory status. In these patients, vaginal microbiota instability and innate immune activation, as predicted using DESI-MS, associated with preterm birth, including in women receiving cervical cerclage for preterm birth prevention. These findings highlight direct on-swab metabolic profiling by DESI-MS as an innovative approach for preterm birth risk stratification through rapid assessment of vaginal microbiota-host dynamics.
Wolfer AM, Correia GDS, Sands CJ, et al., 2021, peakPantheR, an R package for large-scale targeted extraction and integration of annotated metabolic features in LC-MS profiling datasets, BIOINFORMATICS, Vol: 37, Pages: 4886-4888, ISSN: 1367-4803
Martinez-Gili L, Mullish BH, Correia G, et al., 2021, A DISTINCTIVE SIGNATURE OF FECAL BILE ACIDS AND OTHER NOVEL METABOLITES ACCOMPANYING RECURRENCE AFTER PRIMARY CLOSTRIDIOIDES DIFFICILE INFECTION, Publisher: W B SAUNDERS CO-ELSEVIER INC, Pages: S368-S368, ISSN: 0016-5085
Sands CJ, Gómez-Romero M, Correia G, et al., 2021, Representing the metabolome with high fidelity: range and response as quality control factors in LC-MS-based global profiling., Analytical Chemistry, Vol: 93, Pages: 1924-1933, ISSN: 0003-2700
Liquid chromatography-mass spectrometry (LC-MS) is a powerful and widely used technique for measuring the abundance of chemical species in living systems. Its sensitivity, analytical specificity, and direct applicability to biofluids and tissue extracts impart great promise for the discovery and mechanistic characterization of biomarker panels for disease detection, health monitoring, patient stratification, and treatment personalization. Global metabolic profiling applications yield complex data sets consisting of multiple feature measurements for each chemical species observed. While this multiplicity can be useful in deriving enhanced analytical specificity and chemical identities from LC-MS data, data set inflation and quantitative imprecision among related features is problematic for statistical analyses and interpretation. This Perspective provides a critical evaluation of global profiling data fidelity with respect to measurement linearity and the quantitative response variation observed among components of the spectra. These elements of data quality are widely overlooked in untargeted metabolomics yet essential for the generation of data that accurately reflect the metabolome. Advanced feature filtering informed by linear range estimation and analyte response factor assessment is advocated as an attainable means of controlling LC-MS data quality in global profiling studies and exemplified herein at both the feature and data set level.
Sands C, Wolfer A, DS Correia G, et al., 2019, The nPYc-Toolbox, a Python module for the pre-processing, quality-control, and analysis of metabolic profiling datasets, Bioinformatics, Vol: 35, Pages: 5359-5360, ISSN: 1367-4803
Summary: As large-scale metabolic phenotyping studies become increasingly common, the need forsystemic methods for pre-processing and quality control (QC) of analytical data prior to statistical analysishas become increasingly important, both within a study, and to allow meaningful inter-study comparisons.The nPYc-Toolbox provides software for the import, pre-processing, QC, and visualisation of metabolicphenotyping datasets, either interactively, or in automated pipelines.Availability and Implementation: The nPYc-Toolbox is implemented in Python, and is freelyavailable from the Python package index https://pypi.org/project/nPYc/, source isavailable at https://github.com/phenomecentre/nPYc-Toolbox. Full documentation canbe found at http://npyc-toolbox.readthedocs.io/ and exemplar datasets and tutorials athttps://github.com/phenomecentre/nPYc-toolbox-tutorials
Sabino AR, Tavares SS, Riffel A, et al., 2019, H-1 NMR metabolomic approach reveals chlorogenic acid as a response of sugarcane induced by exposure to Diatraea saccharalis, INDUSTRIAL CROPS AND PRODUCTS, Vol: 140, ISSN: 0926-6690
Inglese P, Correia G, Pruski P, et al., 2019, Colocalization features for classification of tumors using desorption electrospray ionization mass spectrometry imaging, Analytical Chemistry, Vol: 91, Pages: 6530-6540, ISSN: 0003-2700
Supervised modeling of mass spectrometry imaging (MSI) data is a crucial component for the detection of the distinct molecular characteristics of cancerous tissues. Currently, two types of supervised analyses are mainly used on MSI data: pixel-wise segmentation of sample images and whole-sample-based classification. A large number of mass spectra associated with each MSI sample can represent a challenge for designing models that simultaneously preserve the overall molecular content while capturing valuable information contained in the MSI data. Furthermore, intensity-related batch effects can introduce biases in the statistical models. Here we introduce a method based on ion colocalization features that allows the classification of whole tissue specimens using MSI data, which naturally preserves the spatial information associated the with the mass spectra and is less sensitive to possible batch effects. Finally, we propose data visualization strategies for the inspection of the derived networks, which can be used to assess whether the correlation differences are related to coexpression/suppression or disjoint spatial localization patterns and can suggest hypotheses based on the underlying mechanisms associated with the different classes of analyzed samples.
Inglese P, Correia G, Takats Z, et al., 2019, SPUTNIK: an R package for filtering of spatially related peaks in mass spectrometry imaging data, Bioinformatics, Vol: 35, Pages: 178-180, ISSN: 1367-4803
Summary: SPUTNIK is an R package consisting of a series of tools to filter mass spectrometry imaging peaks characterized by a noisy or unlikely spatial distribution. SPUTNIK can produce mass spectrometry imaging datasets characterized by a smaller but more informative set of peaks, reduce the complexity of subsequent multi-variate analysis and increase the interpretability of the statistical results. Availability: SPUTNIK is freely available online from CRAN repository and at https://github.com/paoloinglese/SPUTNIK. The package is distributed under the GNU General Public License version 3 and is accompanied by example files and data. Supplementary information: Supplementary data are available at Bioinformatics online.
Chan Q, Lau C-HE, Gibson R, et al., 2019, Relationships of Dietary and Supplement Magnesium Intake and Its Urinary Metabolomic Biomarkers With Blood Pressure: The INTERMAP Study, Scientific Sessions of the American-Heart-Association on Epidemiology and Prevention/Lifestyle and Cardiometabolic Health, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322
Inglese P, Dos Santos Correia G, Pruski P, et al., 2018, Co-localization features for classification of tumors using mass spectrometry imaging
Statistical modeling of mass spectrometry imaging (MSI) data is a crucial component for the understanding of the molecular characteristics of cancerous tissues. Quantification of the abundances of metabolites or batch effect between multiple spectral acquisitions represents only a few of the challenges associated with this type of data analysis. Here we introduce a method based on ion co-localization features that allows the classification of whole tissue specimens using MSI data, which overcomes the possible batch effect issues and generates data-driven hypotheses on the underlying mechanisms associated with the different classes of analyzed samples.
Chekmeneva E, Dos Santos Correia G, Gomez Romero M, et al., 2018, Ultra performance liquid chromatography-high resolution mass spectrometry and direct infusion-high resolution mass spectrometry for combined exploratory and targeted metabolic profiling of human urine, Journal of Proteome Research, Vol: 17, Pages: 3492-3502, ISSN: 1535-3893
The application of metabolic phenotyping to epidemiological studies involving thousands of biofluid samples presents a challenge for the selection of analytical platforms that meet the requirements of high-throughput precision analysis and cost-effectiveness. Here, direct infusion nanoelectrospray (DI-nESI)- was compared to an ultra-performance (UPLC)-high resolution mass spectrometry (HRMS) method for metabolic profiling of an exemplary set of 132 human urine samples from a large epidemiological cohort. Both methods were developed and optimised to allow simultaneous collection of high resolution urinary metabolic profiles and quantitative data for a selected panel of 35 metabolites. The total run time for measuring the sample set in both polarities by UPLC-HRMS was of 5 days compared to 9 hours by DI-nESI-HRMS. To compare the classification ability of the two MS methods we performed exploratory analysis of the full-scan HRMS profiles to detect sex-related differences in biochemical composition. Although metabolite identification is less specific in DI-nESI-HRMS, the significant features responsible for discrimination between sexes were mostly the same in both MS-based platforms. Using the quantitative data we showed that 10 metabolites have strong correlation (Pearson’s r > 0.9 and Passing-Bablok regression slope 0.8-1.3) and good agreement assessed by Bland-Altman plots between UPLC-HRMS and DI-nESI-HRMS and thus, can be measured using a cheaper and less sample- and time-consuming method. Only five metabolites showed weak correlation (Pearson’s r< 0.4) and poor agreement due to the overestimation of the results by DI-nESI-HRMS, and the rest of metabolites showed acceptable correlation between the two methods.
Turner PJ, Garcia MR, Skypala IJ, et al., 2018, CHANGES IN METABONOMIC PROFILE DURING PEANUT-INDUCED ANAPHYLAXIS AND CORRELATION WITH SYMPTOM, American-Academy-of-Allergy-Asthma-and-Immunology / World-Allergy-Organization Joint Congress, Publisher: MOSBY-ELSEVIER, Pages: AB85-AB85, ISSN: 0091-6749
Chekmeneva E, Correia GDS, Chan Q, et al., 2017, Optimization and Application of Direct Infusion Nanoelectrospray HRMS Method for Large-Scale Urinary Metabolic Phenotyping in Molecular Epidemiology, JOURNAL OF PROTEOME RESEARCH, Vol: 16, Pages: 1646-1658, ISSN: 1535-3893
Large-scale metabolic profiling requires the development of novel economical high-throughput analytical methods to facilitate characterization of systemic metabolic variation in population phenotypes. We report a fit-for-purpose direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) method with time-of-flight detection for rapid targeted parallel analysis of over 40 urinary metabolites. The newly developed 2 min infusion method requires <10 μL of urine sample and generates high-resolution MS profiles in both positive and negative polarities, enabling further data mining and relative quantification of hundreds of metabolites. Here we present optimization of the DI-nESI-HRMS method in a detailed step-by-step guide and provide a workflow with rigorous quality assessment for large-scale studies. We demonstrate for the first time the application of the method for urinary metabolic profiling in human epidemiological investigations. Implementation of the presented DI-nESI-HRMS method enabled cost-efficient analysis of >10 000 24 h urine samples from the INTERMAP study in 12 weeks and >2200 spot urine samples from the ARIC study in <3 weeks with the required sensitivity and accuracy. We illustrate the application of the technique by characterizing the differences in metabolic phenotypes of the USA and Japanese population from the INTERMAP study.
Pruski P, MacIntyre DA, Lewis HV, et al., 2016, Medical swab analysis using desorption electrospray ionization mass spectrometry (DESI-MS) – a non-invasive approach for mucosal diagnostics, Analytical Chemistry, Vol: 89, Pages: 1540-1550, ISSN: 0003-2700
Medical swabs are routinely used worldwide to sample human mucosa for microbiological screening with culture methods. These are usually time-consuming and have a narrow focus on screening for particular microorganism species. As an alternative, direct mass spectrometric profiling of the mucosal metabolome provides a broader window into the mucosal ecosystem. We present for the first time a minimal effort/minimal-disruption technique for augmenting the information obtained from clinical swab analysis with mucosal metabolome profiling using desorption electrospray ionization mass spectrometry (DESI-MS) analysis. Ionization of mucosal biomass occurs directly from a standard rayon swab mounted on a rotating device and analyzed by DESI MS using an optimized protocol considering swab–inlet geometry, tip–sample angles and distances, rotation speeds, and reproducibility. Multivariate modeling of mass spectral fingerprints obtained in this way readily discriminate between different mucosal surfaces and display the ability to characterize biochemical alterations induced by pregnancy and bacterial vaginosis (BV). The method was also applied directly to bacterial biomass to confirm the ability to detect intact bacterial species from a swab. These results highlight the potential of direct swab analysis by DESI-MS for a wide range of clinical applications including rapid mucosal diagnostics for microbiology, immune responses, and biochemistry.
Blaise B, Correia G, Tin A, et al., 2016, A novel method for power analysis and sample size determination in metabolic phenotyping, Analytical Chemistry, Vol: 88, Pages: 5179-5188, ISSN: 1520-6882
Estimation of statistical power and sample size is a key aspect of experimental design. However, in metabolic phenotyping, there is currently no accepted approach for these tasks, in large part due to the unknown nature of the expected effect. In such hypothesis free science, neither the number or class of important analytes nor the effect size are known a priori. We introduce a new approach, based on multivariate simulation, which deals effectively with the highly correlated structure and high-dimensionality of metabolic phenotyping data. First, a large data set is simulated based on the characteristics of a pilot study investigating a given biomedical issue. An effect of a given size, corresponding either to a discrete (classification) or continuous (regression) outcome is then added. Different sample sizes are modeled by randomly selecting data sets of various sizes from the simulated data. We investigate different methods for effect detection, including univariate and multivariate techniques. Our framework allows us to investigate the complex relationship between sample size, power, and effect size for real multivariate data sets. For instance, we demonstrate for an example pilot data set that certain features achieve a power of 0.8 for a sample size of 20 samples or that a cross-validated predictivity QY2 of 0.8 is reached with an effect size of 0.2 and 200 samples. We exemplify the approach for both nuclear magnetic resonance and liquid chromatography–mass spectrometry data from humans and the model organism C. elegans.
Correia GDS, Ng KW, Wijeyesekera A, et al., 2015, Metabolic Profiling of Children Undergoing Surgery for Congenital Heart Disease, Critical Care Medicine, Vol: 43, Pages: 1467-1476, ISSN: 1530-0293
Inflammation and metabolism are closely interlinked.Both undergo significant dysregulation following surgery for congenitalheart disease, contributing to organ failure and morbidity.In this study, we combined cytokine and metabolic profilingto examine the effect of postoperative tight glycemic controlcompared with conventional blood glucose management onmetabolic and inflammatory outcomes in children undergoingcongenital heart surgery. The aim was to evaluate changes in keymetabolites following congenital heart surgery and to examinethe potential of metabolic profiling for stratifying patients in termsof expected clinical outcomes.Design: Laboratory and clinical study.Setting: University Hospital and Laboratory.Patients: Of 28 children undergoing surgery for congenital heartdisease, 15 underwent tight glycemic control postoperatively and13 were treated conventionally.Interventions: Metabolic profiling of blood plasma was undertakenusing proton nuclear magnetic resonance spectroscopy. A panel ofmetabolites was measured using a curve-fitting algorithm. Inflammatorycytokines were measured by enzyme-linked immunosorbentassay. The data were assessed with respect to clinical markers ofdisease severity (Risk Adjusted Congenital heart surgery score-1,Pediatric Logistic Organ Dysfunction, inotrope score, duration ofventilation and pediatric ICU-free days).Measurements and Main Results: Changes in metabolic andinflammatory profiles were seen over the time course from surgeryto recovery, compared with the preoperative state. Tight glycemiccontrol did not significantly alter the response profile. We identifiedeight metabolites (3-d-hydroxybutyrate, acetone, acetoacetate,citrate, lactate, creatine, creatinine, and alanine) associated withsurgical and disease severity. The strength of proinflammatoryresponse, particularly interleukin-8 and interleukin-6 concentrations,inversely correlated with PICU-free days at 28 days. The interleukin-6/interleukin-10ratio directly correlated wi
Chekmeneva E, Correia G, Denes J, et al., 2015, Development of nanoelectrospray high resolution isotope dilution mass spectrometry for targeted quantitative analysis of urinary metabolites: application to population profiling and clinical studies, Analytical Methods, Vol: 7, Pages: 5122-5133, ISSN: 1759-9679
An automated chip-based electrospray platform was used to develop a high-throughput nanoelectrospray high resolution mass spectrometry (nESI-HRMS) method for multiplexed parallel untargeted and targeted quantitative metabolic analysis of urine samples. The method was demonstrated to be suitable for metabolic analysis of large sample numbers and can be applied to large-scale epidemiological and stratified medicine studies. The method requires a small amount of sample (5 μL of injectable volume containing 250 nL of original sample), and the analysis time for each sample is three minutes per sample to acquire data in both negative and positive ion modes. Identification of metabolites was based on the high resolution accurate mass and tandem mass spectrometry using authentic standards. The method was validated for 8 targeted metabolites and was shown to be precise and accurate. The mean accuracy of individual measurements being 106% and the intra- and inter-day precision (expressed as relative standard deviations) were 9% and 14%, respectively. Selected metabolites were quantified by standard addition calibration using the stable isotope labelled internal standards in a pooled urine sample, to account for any matrix effect. The multiple point standard addition calibration curves yielded correlation coefficients greater than 0.99, and the linear dynamic range was more than three orders of magnitude. As a proof-of-concept the developed method was applied for targeted quantitative analysis of a set of 101 urine samples obtained from female participants with different pregnancy outcomes. In addition to the specifically targeted metabolites, several other metabolites were quantified relative to the internal standards. Based on the calculated concentrations, some metabolites showed significant differences according to different pregnancy outcomes. The acquired high resolution full-scan data were used for further untargeted fingerprinting and improved the differentiation of
Virk B, Correia G, Dixon DP, et al., 2012, Excessive folate synthesis limits lifespan in the C. elegans: E. coli aging model, BMC BIOLOGY, Vol: 10, ISSN: 1741-7007
Harris SE, Ritchie SJ, Correia GDS, et al., Plasma lipid and liporotein biomarkers in LBC1936: Do they predict general cognitive ability and brain structure?
<jats:title>Abstract</jats:title><jats:p>Identifying predictors of cognitive ability and brain structure in later life is an important step towards understanding the mechanisms leading to cognitive decline and dementia. This study used ultra-performance liquid chromatography mass spectrometry (UPLC-MS) and nuclear magnetic resonance (NMR) to measure targeted and untargeted metabolites, mainly lipids and lipoproteins, in ∼600 members of the Lothian Birth Cohort 1936 (LBC1936) at aged ∼73 years. Penalized regression models (LASSO) were then used to identify sets of metabolites that predict variation in general cognitive ability and structural brain variables. UPLC-MS-POS measured lipids, together predicted 19% of the variance in total brain volume and 17% of the variance in both grey matter and normal appearing white matter volumes. Multiple subclasses of lipids were included in the predictor, but the best performing lipid was the sphingomyelin SM(d18:2/14:0) which occurred in 100% of iterations of all three significant models. No metabolite set predicted cognitive ability, or white matter hyperintensities or connectivity. Future studies should concentrate on identifying specific lipids as potential cognitive and brain-structural biomarkers in older individuals.</jats:p>
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