Structured intervention plan including component-resolved diagnostics helps reducing the burden of food allergy among school-aged children.

BACKGROUND: Food allergies can substantially burden patients and families by negatively affecting finances, social relationships, and personal perceptions of health. This study was performed under the Finnish Allergy Programme aimed at reducing avoidance diets to foods in schoolchildren by 50%. The main goal of this study was to investigate how many children could be freed from diet restrictions in a Finnish school district through a diagnostic algorithm including component-resolved diagnostics and food challenge. The secondary aim was to provide a crude estimate of the burden of the elimination food diets in the region, and the savings associated with the proposed intervention. METHODS: A total of 205 children on a food avoidance diet according to the school register because of food allergy were invited into the study. One hundred and fifty-seven children were interviewed, tested for IgE to extracts and allergen components and food challenged in respective order. RESULTS: After two years, 12 children still had an avoidance diet and three of them were treated successfully with sOTI; the rest suspended their avoidance diet (n = 134) or dropped out of the study (n = 11). The cost of the elimination diets was estimated in 172 700€ per year at start and 13 200€ per year at the end of the study; total savings were 128 400€ yearly. CONCLUSIONS: The results demonstrate a 65% reduction of avoidance diets to foods in school-aged children, exceeding the 50% aim of the Finnish Allergy Programme. Therefore, it is possible to actively reduce the number of food allergy diagnoses that remain unmonitored in the society through a tailored diagnostic work-up.

Elucidation of chromatographic peak shifts in complex samples using a chemometrical approach.

Chromatographic retention time peak shifts between consecutive analyses is a well-known fact yet not fully understood. Algorithms have been developed to align peaks between runs, but with no specific studies considering the causes of peak shifts. Here, designed experiments reveal chromatographic shift patterns for a complex peptide mixture that are attributable to the temperature and pH of the mobile phase. These results demonstrate that peak shifts are highly structured and are to a high degree explained by underlying differences in physico-chemical parameters of the chromatographic system and also provide experimental support for the alignment algorithm called the generalized fuzzy Hough transform which exploits this fact. It can be expected that the development of alignment algorithms enters a new phase resulting in increasingly accurate alignment by considering the latent structure of the peak shifts.

Non-target time trend screening: a data reduction strategy for detecting emerging contaminants in biological samples.

Non-targeted mass spectrometry-based approaches for detecting novel xenobiotics in biological samples are hampered by the occurrence of naturally fluctuating endogenous substances, which are difficult to distinguish from environmental contaminants. Here, we investigate a data reduction strategy for datasets derived from a biological time series. The objective is to flag reoccurring peaks in the time series based on increasing peak intensities, thereby reducing peak lists to only those which may be associated with emerging bioaccumulative contaminants. As a result, compounds with increasing concentrations are flagged while compounds displaying random, decreasing, or steady-state time trends are removed. As an initial proof of concept, we created artificial time trends by fortifying human whole blood samples with isotopically labelled standards. Different scenarios were investigated: eight model compounds had a continuously increasing trend in the last two to nine time points, and four model compounds had a trend that reached steady state after an initial increase. Each time series was investigated at three fortification levels and one unfortified series. Following extraction, analysis by ultra performance liquid chromatography high-resolution mass spectrometry, and data processing, a total of 21,700 aligned peaks were obtained. Peaks displaying an increasing trend were filtered from randomly fluctuating peaks using time trend ratios and Spearman's rank correlation coefficients. The first approach was successful in flagging model compounds spiked at only two to three time points, while the latter approach resulted in all model compounds ranking in the top 11 % of the peak lists. Compared to initial peak lists, a combination of both approaches reduced the size of datasets by 80-85 %. Overall, non-target time trend screening represents a promising data reduction strategy for identifying emerging bioaccumulative contaminants in biological samples. Graphical abstract Using time trends to filter out emerging contaminants from large peak lists.

TracMass 2--a modular suite of tools for processing chromatography-full scan mass spectrometry data.

In untargeted proteomics and metabolomics, raw data obtained with an LC/MS instrument are processed into a format that can be used for statistical analysis. Full scan MS data from chromatographic separation of biological samples are complex and analyte concentrations need to be extracted and aligned so that they can be compared across the samples. Several computer programs and methods have been developed for this purpose. There is still a need to improve the ease of use and feedback to the user because of the advanced multiparametric algorithms used. Here, we present and make publicly available, TracMass 2, a suite of computer programs that gives immediate graphical feedback to the data analyst on parameter settings and processing results, as well as producing state-of-the-art results. The main advantage of TracMass 2 is that the feedback and transparency of the processing steps generate confidence in the end result, which is a table of peak intensities. The data analyst can easily validate every step of the processing pipeline. Because the user receives feedback on how all parameter values affect the result before starting a lengthy computation, the user's learning curve is enhanced and the total time used for data processing can be reduced. TracMass 2 has been released as open source and is included in the Supporting Information . We anticipate that TracMass 2 will set a new standard for how chemometrical algorithms are implemented in computer programs.

A quantitative LC/MS method targeting urinary 1-methyl-4-imidazoleacetic acid for safety monitoring of the global histamine turnover in clinical studies.

Anaphylaxis is a potentially life-threatening condition triggered mainly by the release of inflammatory mediators, notably histamine. In pharmaceutical research, drug discovery, and clinical evaluation, it may be necessary to accurately assess the potential of a compound, event, or disorder to promote the release of histamine. In contrast to the measurement of plasma histamine, determination of the stable metabolite 1-methyl-4-imidazoleacetic acid (tele-MIAA) in urine provides a noninvasive and more reliable methodology to monitor histamine release. This study presents a repeatable high-performance liquid chromatography coupled to electrospray mass spectrometry (LC-ESI-MS) method where tele-MIAA is baseline separated from its structural isomer 1-methyl-5-imidazoleacetic acid (pi-MIAA) and an unknown in human urine. The ion-pairing chromatography method, in reversed-phase mode, based on 0.5 mM tridecafluoroheptanoic acid demonstrated high repeatability and was applied in a clinical development program that comprised a large number of clinical samples from different cohorts. The inter- and intra-run precision of the method for tele-MIAA were 8.4 and 4.3%, respectively, at the mean urinary concentration level, while method accuracy was between -16.2 and 8.0% across the linear concentration range of 22-1,111 ng mL(-1). Overall, method precision was greater than that reported in previously published methods and enabled the identification of gender differences that were independent of age or demography. The median concentration measured in female subjects was 3.0 µmol mmol(-1) of creatinine, and for male subjects, it was 2.1 µmol mmol(-1) of creatinine. The results demonstrate that the method provides unprecedented accuracy, precision, and practicality for the measurement of tele-MIAA in large clinical settings.

Discrimination of conventional and organic white cabbage from a long-term field trial study using untargeted LC-MS-based metabolomics.

The influence of organic and conventional farming practices on the content of single nutrients in plants is disputed in the scientific literature. Here, large-scale untargeted LC-MS-based metabolomics was used to compare the composition of white cabbage from organic and conventional agriculture, measuring 1,600 compounds. Cabbage was sampled in 2 years from one conventional and two organic farming systems in a rigidly controlled long-term field trial in Denmark. Using Orthogonal Projection to Latent Structures-Discriminant Analysis (OPLS-DA), we found that the production system leaves a significant (p = 0.013) imprint in the white cabbage metabolome that is retained between production years. We externally validated this finding by predicting the production system of samples from one year using a classification model built on samples from the other year, with a correct classification in 83 % of cases. Thus, it was concluded that the investigated conventional and organic management practices have a systematic impact on the metabolome of white cabbage. This emphasizes the potential of untargeted metabolomics for authenticity testing of organic plant products.

Antidepressant Exposure and DNA Methylation: Insights from a Methylome-Wide Association Study.

Importance: Understanding antidepressant mechanisms could help design more effective and tolerated treatments. Objective: Identify DNA methylation (DNAm) changes associated with antidepressant exposure. Design: Case-control methylome-wide association studies (MWAS) of antidepressant exposure were performed from blood samples collected between 2006-2011 in Generation Scotland (GS). The summary statistics were tested for enrichment in specific tissues, gene ontologies and an independent MWAS in the Netherlands Study of Depression and Anxiety (NESDA). A methylation profile score (MPS) was derived and tested for its association with antidepressant exposure in eight independent cohorts, alongside prospective data from GS. Setting: Cohorts; GS, NESDA, FTC, SHIP-Trend, FOR2107, LBC1936, MARS-UniDep, ALSPAC, E-Risk, and NTR. Participants: Participants with DNAm data and self-report/prescription derived antidepressant exposure. Main Outcomes and Measures: Whole-blood DNAm levels were assayed by the EPIC/450K Illumina array (9 studies, N exposed = 661, N unexposed = 9,575) alongside MBD-Seq in NESDA (N exposed = 398, N unexposed = 414). Antidepressant exposure was measured by self- report and/or antidepressant prescriptions. Results: The self-report MWAS (N = 16,536, N exposed = 1,508, mean age = 48, 59% female) and the prescription-derived MWAS (N = 7,951, N exposed = 861, mean age = 47, 59% female), found hypermethylation at seven and four DNAm sites (p < 9.42x10 -8 ), respectively. The top locus was cg26277237 ( KANK1, p self-report = 9.3x10 -13 , p prescription = 6.1x10 -3 ). The self-report MWAS found a differentially methylated region, mapping to DGUOK-AS1 ( p adj = 5.0x10 -3 ) alongside significant enrichment for genes expressed in the amygdala, the "synaptic vesicle membrane" gene ontology and the top 1% of CpGs from the NESDA MWAS (OR = 1.39, p < 0.042). The MPS was associated with antidepressant exposure in meta-analysed data from external cohorts (N studies = 9, N = 10,236, N exposed = 661, f3 = 0.196, p < 1x10 -4 ). Conclusions and Relevance: Antidepressant exposure is associated with changes in DNAm across different cohorts. Further investigation into these changes could inform on new targets for antidepressant treatments. 3 Key Points: Question: Is antidepressant exposure associated with differential whole blood DNA methylation?Findings: In this methylome-wide association study of 16,536 adults across Scotland, antidepressant exposure was significantly associated with hypermethylation at CpGs mapping to KANK1 and DGUOK-AS1. A methylation profile score trained on this sample was significantly associated with antidepressant exposure (pooled f3 [95%CI]=0.196 [0.105, 0.288], p < 1x10 -4 ) in a meta-analysis of external datasets. Meaning: Antidepressant exposure is associated with hypermethylation at KANK1 and DGUOK-AS1 , which have roles in mitochondrial metabolism and neurite outgrowth. If replicated in future studies, targeting these genes could inform the design of more effective and better tolerated treatments for depression.

Strategy for quantifying trace levels of BMAA in cyanobacteria by LC/MS/MS.

The cyanobacterial neurotoxin ß-N-methylamino-L-alanine (BMAA) is an amino acid that is putatively associated with the pathology of amyotrophic lateral sclerosis/Parkinsonism-dementia complex (ALS-PDC) disease. It raises serious health risk concerns since cyanobacteria are ubiquitous thus making human exposure almost inevitable. The identification and quantification of BMAA in cyanobacteria is challenging because it is present only in trace amounts and occurs alongside structurally similar compounds such as BMAA isomers. This work describes an enhanced liquid chromatography/tandem mass spectrometry platform that can distinguish BMAA from its isomers ß-amino-N-methyl-alanine, N-(2-aminoethyl) glycine (AEG), and 2,4-diaminobutyric acid, thus ensuring confident identification of BMAA. The method's sensitivity was improved fourfold by a post-column addition of acetonitrile. The instrument and method limits of detection were shown to be 4.2 fmol/injection (or 0.5 pg/one column) and 0.1 µg/g dry weight of cyanobacteria, respectively. The quantification method uses synthesized deuterated BMAA as an internal standard and exhibits good linearity, accuracy, and precision. Matrix effects were also investigated, revealing an ion enhancement of around 18 %. A lab-cultured cyanobacterial sample (Leptolyngbya PCC73110) was analyzed and shown to contain about 0.73 µg/g dry weight BMAA. The isomer AEG, whose chromatographic properties closely resemble those of BMAA, was also detected. These results highlight the importance of distinguishing BMAA from its isomers for reliable identification as well as providing a sensitive and accurate quantification method for measuring trace levels of BMAA in cyanobacterial samples.

A concept study on non-targeted screening for chemical contaminants in food using liquid chromatography-mass spectrometry in combination with a metabolomics approach.

A generic method to screen for new or unexpected contaminants at ppm levels in food has been developed. The method comprises an acidic acetonitrile extraction, detection with ultra-high-pressure liquid chromatography coupled to electrospray ionisation time-of-flight mass spectrometry and statistical evaluation using a metabolomics approach comparing suspected contaminated food with uncontaminated foods. The method was tested for 26 model contaminants from 100 µg/g down to 0.4 µg/g in three brands of fresh orange juice. Blinded statistical evaluation revealed signals from all added contaminants detectable by liquid chromatography-electrospray ionisation using positive ionisation mode, while only two false-positive signals were reported. The method is primarily intended to be used for investigation of food samples suspected to be contaminated with unknown substances. Additionally it could be used to continuously monitor for appearance of new food contaminants as a complement to the specific targeted analysis that is today's foundation of food safety analysis.

Genome-wide association study of antipsychotic-induced QTc interval prolongation.

QT prolongation is associated with increased risk of cardiac arrhythmias. Identifying the genetic variants that mediate antipsychotic-induced prolongation may help to minimize this risk, which might prevent the removal of efficacious drugs from the market. We performed candidate gene analysis and five drug-specific genome-wide association studies (GWASs) with 492K single-nucleotide polymorphisms to search for genetic variation mediating antipsychotic-induced QT prolongation in 738 schizophrenia patients from the Clinical Antipsychotic Trial of Intervention Effectiveness study. Our candidate gene study suggests the involvement of NOS1AP and NUBPL (P-values=1.45 × 10(-05) and 2.66 × 10(-13), respectively). Furthermore, our top GWAS hit achieving genome-wide significance, defined as a Q-value <0.10 (P-value=1.54 × 10(-7), Q-value=0.07), located in SLC22A23, mediated the effects of quetiapine on prolongation. SLC22A23 belongs to a family of organic ion transporters that shuttle a variety of compounds, including drugs, environmental toxins and endogenous metabolites, across the cell membrane. This gene is expressed in the heart and is integral in mouse heart development. The genes mediating antipsychotic-induced QT prolongation partially overlap with the genes affecting normal QT interval variation. However, some genes may also be unique for drug-induced prolongation. This study demonstrates the potential of GWAS to discover genes and pathways that mediate antipsychotic-induced QT prolongation.

Pharmacogenomic study of side-effects for antidepressant treatment options in STAR*D.

BACKGROUND: Understanding individual differences in susceptibility to antidepressant therapy side-effects is essential to optimize the treatment of depression. METHOD: We performed genome-wide association studies (GWAS) to search for genetic variation affecting the susceptibility to side-effects. The analysis sample consisted of 1439 depression patients, successfully genotyped for 421K single nucleotide polymorphisms (SNPs), from the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study. Outcomes included four indicators of side-effects: general side-effect burden, sexual side-effects, dizziness and vision/hearing-related side-effects. Our criterion for genome-wide significance was a prespecified threshold ensuring that, on average, only 10% of the significant findings are false discoveries. RESULTS: Thirty-four SNPs satisfied this criterion. The top finding indicated that 10 SNPs in SACM1L mediated the effects of bupropion on sexual side-effects (p = 4.98 × 10(-7), q = 0.023). Suggestive findings were also found for SNPs in MAGI2, DTWD1, WDFY4 and CHL1. CONCLUSIONS: Although our findings require replication and functional validation, this study demonstrates the potential of GWAS to discover genes and pathways that could mediate adverse effects of antidepressant medication.

Genome-wide pharmacogenomic analysis of response to treatment with antipsychotics.

Schizophrenia is an often devastating neuropsychiatric illness. Understanding the genetic variation affecting response to antipsychotics is important to develop novel diagnostic tests to match individual schizophrenia patients to the most effective and safe medication. In this study, we use a genome-wide approach to detect genetic variation underlying individual differences in response to treatment with the antipsychotics olanzapine, quetiapine, risperidone, ziprasidone and perphenazine. Our sample consisted of 738 subjects with DSM-IV schizophrenia who took part in the Clinical Antipsychotic Trials of Intervention Effectiveness. Subjects were genotyped using the Affymetrix 500 K genotyping platform plus a custom 164 K chip to improve genome-wide coverage. Treatment outcome was measured using the Positive and Negative Syndrome Scale. Our criterion for genome-wide significance was a prespecified threshold that ensures that, on an average, only 10% of the significant findings are false discoveries. The top statistical result reached significance at our prespecified threshold and involved a single-nucleotide polymorphism (SNP) in an intergenic region on chromosome 4p15. In addition, SNPs in Ankyrin Repeat and Sterile Alpha Motif Domain-Containing Protein 1B (ANKS1B) and in the Contactin-Associated Protein-Like 5 gene (CNTNAP5), which mediated the effects of olanzapine and risperidone on Negative symptoms, were very close to our threshold for declaring significance. The most significant SNP in CNTNAP5 is nonsynonymous, giving rise to an amino-acid substitution. In addition to highlighting our top results, we provide all P-values for download as a resource for investigators with the requisite samples to carry out replication. This study demonstrates the potential of genome-wide association studies to discover novel genes that mediate the effects of antipsychotics, which could eventually help to tailor drug treatment to schizophrenic patients.

Genomewide pharmacogenomic study of metabolic side effects to antipsychotic drugs.

Understanding individual differences in the susceptibility to metabolic side effects as a response to antipsychotic therapy is essential to optimize the treatment of schizophrenia. Here, we perform genomewide association studies (GWAS) to search for genetic variation affecting the susceptibility to metabolic side effects. The analysis sample consisted of 738 schizophrenia patients, successfully genotyped for 492K single nucleotide polymorphisms (SNPs), from the genomic subsample of the Clinical Antipsychotic Trial of Intervention Effectiveness study. Outcomes included 12 indicators of metabolic side effects, quantifying antipsychotic-induced change in weight, blood lipids, glucose and hemoglobin A1c, blood pressure and heart rate. Our criterion for genomewide significance was a pre-specified threshold that ensures, on average, only 10% of the significant findings are false discoveries. A total of 21 SNPs satisfied this criterion. The top finding indicated that a SNP in Meis homeobox 2 (MEIS2) mediated the effects of risperidone on hip circumference (q=0.004). The same SNP was also found to mediate risperidone's effect on waist circumference (q=0.055). Genomewide significant finding were also found for SNPs in PRKAR2B, GPR98, FHOD3, RNF144A, ASTN2, SOX5 and ATF7IP2, as well as in several intergenic markers. PRKAR2B and MEIS2 both have previous research indicating metabolic involvement, and PRKAR2B has previously been shown to mediate antipsychotic response. Although our findings require replication and functional validation, this study shows the potential of GWAS to discover genes and pathways that potentially mediate adverse effects of antipsychotic medication.

Automated annotation and quantification of metabolites in 1H NMR data of biological origin.

In (1)H NMR metabolomic datasets, there are often over a thousand peaks per spectrum, many of which change position drastically between samples. Automatic alignment, annotation, and quantification of all the metabolites of interest in such datasets have not been feasible. In this work we propose a fully automated annotation and quantification procedure which requires annotation of metabolites only in a single spectrum. The reference database built from that single spectrum can be used for any number of (1)H NMR datasets with a similar matrix. The procedure is based on the generalized fuzzy Hough transform (GFHT) for alignment and on Principal-components analysis (PCA) for peak selection and quantification. We show that we can establish quantities of 21 metabolites in several (1)H NMR datasets and that the procedure is extendable to include any number of metabolites that can be identified in a single spectrum. The procedure speeds up the quantification of previously known metabolites and also returns a table containing the intensities and locations of all the peaks that were found and aligned but not assigned to a known metabolite. This enables both biopattern analysis of known metabolites and data mining for new potential biomarkers among the unknowns.

Organophosphate and phthalate esters in indoor air: a comparison between multi-storey buildings with high and low prevalence of sick building symptoms.

An extensive study has been conducted on the prevalence of organophosphorous flame retardants/plasticizers and phthalate ester plasticizers in indoor air. The targeted substances were measured in 45 multi-storey apartment buildings in Stockholm, Sweden. The apartment buildings were classified as high or low risk with regard to the reporting of sick building symptoms (SBS) within the project Healthy Sustainable Houses in Stockholm (3H). Air samples were taken from two to four apartments per building (in total 169 apartments) to facilitate comparison within and between buildings. Association with building characteristics has been examined as well as association with specific sources by combining chemical analysis and exploratory uni- and multivariate data analysis. The study contributes to the overall perspective of levels of organophosphate and phthalate ester in indoor air enabling comparison with other studies. The results indicated little or no difference in the concentrations of the target substances between the two risk classifications of the buildings. The differences between the apartments sampled within (intra) buildings were greater than the differences between (inter) buildings. The concentrations measured in air ranged up to 1200 ng m(-3) for organophosphate esters and up to 11 000 ng m(-3) for phthalate esters. Results in terms of sources were discerned e.g. PVC flooring is a major source of benzylbutyl phthalate in indoor air.

nLossFinder-A Graphical User Interface Program for the Nontargeted Detection of DNA Adducts.

DNA adductomics is a relatively new omics approach aiming to measure known and unknown DNA modifications, called DNA adducts. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become the most common method for analyzing DNA adducts. Recent advances in the field of mass spectrometry have allowed the possibility to perform a comprehensive analysis of adducts, for instance, by using a nontargeted data-independent acquisition method, with multiple precursor m/z windows as an inclusion list. However, the generated data are large and complex, and there is a need to develop algorithms to simplify and automate the time-consuming manual analysis that has hitherto been used. Here, a graphical user interface (GUI) program was developed, with the purpose of tracking a characteristic neutral loss reaction from tandem mass spectrometry of the nucleoside adducts. This program, called nLossFinder, was developed in the MATLAB platform, available as open-source code. Calf thymus DNA was used as a model for method optimization, and the overall adductomics approach was applied to DNA from amphipods (Monoporeia affinis) collected within the Swedish National Marine Monitoring Program. In the amphipod DNA, over 150 putative adducts were found in comparison to 18 using a manual approach in a previous study. The developed program can improve the processing time for large MS data, as it processes each sample in a few seconds, and hence can be applicable for high-throughput screening of adducts.

Time-resolved biomarker discovery in 1H-NMR data using generalized fuzzy Hough transform alignment and parallel factor analysis.

This work addresses the subject of time-series analysis of comprehensive (1)H-NMR data of biological origin. One of the problems with toxicological and efficacy studies is the confounding of correlation between the administered drug, its metabolites and the systemic changes in molecular dynamics, i.e., the flux of drug-related molecules correlates with the molecules of system regulation. This correlation poses a problem for biomarker mining since this confounding must be untangled in order to separate true biomarker molecules from dose-related molecules. One way of achieving this goal is to perform pharmacokinetic analysis. The difference in pharmacokinetic time profiles of different molecules can aid in the elucidation of the origin of the dynamics, this can even be achieved regardless of whether the identity of the molecule is known or not. This mode of analysis is the basis for metabonomic studies of toxicology and efficacy. One major problem concerning the analysis of (1)H-NMR data generated from metabonomic studies is that of the peak positional variation and of peak overlap. These phenomena induce variance in the data, obscuring the true information content and are hence unwanted but hard to avoid. Here, we show that by using the generalized fuzzy Hough transform spectral alignment, variable selection, and parallel factor analysis, we can solve both the alignment and the confounding problem stated above. Using the outlined method, several different temporal concentration profiles can be resolved and the majority of the studied molecules and their respective fluxes can be attributed to these resolved kinetic profiles. The resolved time profiles hereby simplifies finding true biomarkers and bio-patterns for early detection of biological conditions as well as providing more detailed information about the studied biological system. The presented method represents a significant step forward in time-series analysis of biological (1)H-NMR data as it provides almost full automation of the whole data analysis process and is able to analyze over 800 unique features per sample. The method is demonstrated using a (1)H-NMR rat urine dataset from a toxicology study and is compared with a classical approach: COW alignment followed by bucketing.

The correspondence problem for metabonomics datasets.

In metabonomics it is difficult to tell which peak is which in datasets with many samples. This is known as the correspondence problem. Data from different samples are not synchronised, i.e., the peak from one metabolite does not appear in exactly the same place in all samples. For datasets with many samples, this problem is nontrivial, because each sample contains hundreds to thousands of peaks that shift and are identified ambiguously. Statistical analysis of the data assumes that peaks from one metabolite are found in one column of a data table. For every error in the data table, the statistical analysis loses power and the risk of missing a biomarker increases. It is therefore important to solve the correspondence problem by synchronising samples and there is no method that solves it once and for all. In this review, we analyse the correspondence problem, discuss current state-of-the-art methods for synchronising samples, and predict the properties of future methods.

A solution to the 1D NMR alignment problem using an extended generalized fuzzy Hough transform and mode support.

This paper approaches the problem of intersample peak correspondence in the context of later applying statistical data analysis techniques to 1D 1H-nuclear magnetic resonance (NMR) data. Any data analysis methodology will fail to produce meaningful results if the analyzed data table is not synchronized, i.e., each analyzed variable frequency (Hz) does not originate from the same chemical source throughout the entire dataset. This is typically the case when dealing with NMR data from biological samples. In this paper, we present a new state of the art for solving this problem using the generalized fuzzy Hough transform (GFHT). This paper describes significant improvements since the method was introduced for NMR datasets of plasma in Csenki et al. (Anal Bioanal Chem 389:875-885, 15) and is now capable of synchronizing peaks from more complex datasets such as urine as well as plasma data. We present a novel way of globally modeling peak shifts using principal component analysis, a new algorithm for calculating the transform and an effective peak detection algorithm. The algorithm is applied to two real metabonomic 1H-NMR datasets and the properties of the method are compared to bucketing. We implicitly prove that GFHT establishes the objectively true correspondence. Desirable features of the GFHT are: (1) intersample peak correspondence even if peaks change order on the frequency axis and (2) the method is symmetric with respect to the samples.