“Lossless” compression of high resolution mass spectra of small molecules

Fourier transform ion cyclotron resonance (FTICR) provides the highest resolving power of any commercially available mass spectrometer. This advantage is most significant for species of low mass-to-charge ratio (m/z), such as metabolites. Unfortunately, FTICR spectra contain a very large number of data points, most of which are noise. This is most pronounced at the low m/z end of spectra, where data point density is the highest but peak density low. We therefore developed a filter that offers lossless compression of FTICR mass spectra from singly charged metabolites. The filter relies on the high resolving power and mass measurement precision of FTICR and removes only those m/z channels that cannot contain signal from singly charged organic species. The resulting pseudospectra still contain the same signal as the original spectra but less uninformative background. The filter does not affect the outcome of standard downstream chemometric analysis methods, such as principal component analysis, but use of the filter significantly reduces memory requirements and CPU time for such analyses. We demonstrate the utility of the filter for urinary metabolite profiling using direct infusion electrospray ionization and a 15 tesla FTICR mass spectrometer

Extraction of pure components from overlapped signals in gas chromatography-mass spectrometry (GC-MS)

Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical technique for the identification and quantification of trace chemicals in complex mixtures. When complex samples are analyzed by GC-MS it is common to observe co-elution of two or more components, resulting in an overlap of signal peaks observed in the total ion chromatogram. In such situations manual signal analysis is often the most reliable means for the extraction of pure component signals; however, a systematic manual analysis over a number of samples is both tedious and prone to error. In the past 30 years a number of computational approaches were proposed to assist in the process of the extraction of pure signals from co-eluting GC-MS components. This includes empirical methods, comparison with library spectra, eigenvalue analysis, regression and others. However, to date no approach has been recognized as best, nor accepted as standard. This situation hampers general GC-MS capabilities, and in particular has implications for the development of robust, high-throughput GC-MS analytical protocols required in metabolic profiling and biomarker discovery. Here we first discuss the nature of GC-MS data, and then review some of the approaches proposed for the extraction of pure signals from co-eluting components. We summarize and classify different approaches to this problem, and examine why so many approaches proposed in the past have failed to live up to their full promise. Finally, we give some thoughts on the future developments in this field, and suggest that the progress in general computing capabilities attained in the past two decades has opened new horizons for tackling this important problem

Quantitative metabolomics based on gas chromatography mass spectrometry: status and perspectives

Metabolomics involves the unbiased quantitative and qualitative analysis of the complete set of metabolites present in cells, body fluids and tissues (the metabolome). By analyzing differences between metabolomes using biostatistics (multivariate data analysis; pattern recognition), metabolites relevant to a specific phenotypic characteristic can be identified. However, the reliability of the analytical data is a prerequisite for correct biological interpretation in metabolomics analysis. In this review the challenges in quantitative metabolomics analysis with regards to analytical as well as data preprocessing steps are discussed. Recommendations are given on how to optimize and validate comprehensive silylation-based methods from sample extraction and derivatization up to data preprocessing and how to perform quality control during metabolomics studies. The current state of method validation and data preprocessing methods used in published literature are discussed and a perspective on the future research necessary to obtain accurate quantitative data from comprehensive GC-MS data is provided

Analyses of Cholesterol Metabolites of Optic Nerve Using GC-MS Methods

Gas chromatography-mass spectrometry (GC-MS) is considered the gold standard for analyzing and quantifying the presence of biological compounds in tissue samples due to its high sensitivity, peak resolution, and reproducibility. In this chapter, we describe a step-by-step modified Bligh and Dyer protocol for lipid extraction from the optic nerve tissue and a procedure for GC-MS analyses of the lipid extract. These protocols are based on our experience and can be modified depending on samples and compounds of interest

Global urinary metabolic profiling procedures using gas chromatography-mass spectrometry

10.1038/nprot.2011.375Nature Protocols6101483-149

Exploratory GC/MS-Based Metabolomics of Body Fluids

Part of the Methods in Molecular Biology book series (MIMB, volume 1730)GC/MS-based metabolomics is a powerful tool for metabolic phenotyping and biomarker discovery from body biofluids. In this chapter, we describe an untargeted metabolomic approach for plasma/serum and fecal water sample profiling. It describes a multistep procedure, from sample preparation, oximation/silylation derivatization, and data acquisition using GC/QToF to data processing consisting in data extraction and identification of metabolites

Application of gas chromatography mass spectrometry (GC–MS) in conjunction with multivariate classification for the diagnosis of gastrointestinal diseases

Gastrointestinal diseases such as irritable bowel syndrome, Crohn’s disease (CD) and ulcerative colitis are a growing concern in the developed world. Current techniques for diagnosis are often costly, time consuming, inefficient, of great discomfort to the patient, and offer poor sensitivities and specificities. This paper describes the development and evaluation of a new methodology for the non-invasive diagnosis of such diseases using a combination of gas chromatography mass spectrometry (GC–MS) and chemometrics. Several potential sample matrices were tested: blood, breath, faeces and urine. Faecal samples provided the only statistically significant results, providing discrimination between CD and healthy controls with an overall classification accuracy of 85 %(78 %specificity; 93 %sensitivity). Differentiating CD from other diseases proved more challenging, with overall classification accuracy dropping to 79 % (83 % specificity; 68 % sensitivity). This diagnostic performance compares well with the gold standard technique of colonoscopy, suggesting that GC–MS may have potential as a non-invasive screening tool