Isotopic distributions

The isotope distribution: A rose with thorns

The isotope distribution, which reflects the number and probabilities of occurrence of different isotopologues of a molecule, can be theoretically calculated. With the current generation of (ultra)-high-resolution mass spectrometers, the isotope distribution of molecules can be measured with high sensitivity, resolution, and mass accuracy. However, the observed isotope distribution can differ substantially from the expected isotope distribution. Although differences between the observed and expected isotope distribution can complicate the analysis and interpretation of mass spectral data, they can be helpful in a number of specific applications. These applications include, yet are not limited to, the identification of peptides in proteomics, elucidation of the elemental composition of small organic molecules and metabolites, as well as wading through peaks in mass spectra of complex bioorganic mixtures such as petroleum and humus. In this review, we give a nonexhaustive overview of factors that have an impact on the observed isotope distribution, such as elemental isotope deviations, ion sampling, ion interactions, electronic noise and dephasing, centroiding, and apodization. These factors occur at different stages of obtaining the isotope distribution: during the collection of the sample, during the ionization and intake of a molecule in a mass spectrometer, during the mass separation and detection of ionized molecules, and during signal processing.

Analysis of Synthetic Monodisperse Polysaccharides by Wide Mass Range Ultrahigh-Resolution MALDI Mass Spectrometry

Carbohydrates, such as oligo- and polysaccharides, are highly abundant biopolymers that are involved in numerous processes. The study of their structure and functions is commonly based on a material that is isolated from complex natural sources. However, a more precise analysis requires pure compounds with well-defined structures that can be obtained from chemical or enzymatic syntheses. Novel synthetic strategies have increased the accessibility of larger monodisperse polysaccharides, posing a challenge to the analytical methods used for their molecular characterization. Here, we present wide mass range ultrahigh-resolution matrix-assisted laser desorption/ionization (MALDI) Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry (MS) as a powerful platform for the analysis of synthetic oligo- and polysaccharides. Synthetic carbohydrates 16-, 64-, 100-, and 151-mers were mass analyzed and characterized by MALDI in-source decay FT-ICR MS. Detection of fragment ions generated from glycosidic bond cleavage (or cross-ring cleavage) provided information of the monosaccharide content and the linkage type, allowing for the corroboration of the carbohydrate compositions and structures.

A tutorial in small molecule identification via electrospray ionization-mass spectrometry: The practical art of structural elucidation

The identification of unknown molecules has been one of the cornerstone applications of mass spectrometry for decades. This tutorial reviews the basics of the interpretation of electrospray ionization-based MS and MS/MS spectra in order to identify small-molecule analytes (typically below 2000 Da). Most of what is discussed in this tutorial also applies to other atmospheric pressure ionization methods like atmospheric pressure chemical/photoionization. We focus primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds, rather than describing strategies for large-scale identification in complex samples. We critically discuss topics like the detection of protonated and deprotonated ions ([M + H]+ and [M - H]- ) as well as other adduct ions, the determination of the molecular formula, and provide some basic rules on the interpretation of product ion spectra. Our tutorial focuses primarily on the fundamental steps of MS-based structural elucidation of individual unknown compounds (eg, contaminants in chemical production, pharmacological alteration of drugs), rather than describing strategies for large-scale identification in complex samples. This tutorial also discusses strategies to obtain useful orthogonal information (UV/Vis, H/D exchange, chemical derivatization, etc) and offers an overview of the different informatics tools and approaches that can be used for structural elucidation of small molecules. It is primarily intended for beginning mass spectrometrists and researchers from other mass spectrometry sub-disciplines that want to get acquainted with structural elucidation are interested in some practical tips and tricks.

Poisson Model To Generate Isotope Distribution for Biomolecules

We introduce a simplified computational algorithm for computing isotope distributions (relative abundances and masses) of biomolecules. The algorithm is based on Poisson approximation to binomial and multinomial distributions. It leads to a small number of arithmetic operations to compute isotope distributions of molecules. The approach uses three embedded loops to compute the isotope distributions, as compared with the eight embedded loops in exact calculations. The speed improvement is about 3-fold compared to the fast Fourier transformation-based isotope calculations, often termed as ultrafast isotope calculation. The approach naturally incorporates the determination of the masses of each molecular isotopomer. It is applicable to high mass accuracy and resolution mass spectrometry data. The application to tryptic peptides in a UniProt protein database revealed that the mass accuracy of the computed isotopomers is better than 1 ppm. Even better mass accuracy (below 1 ppm) is achievable when the method is paired with the exact calculations, which we term a hybrid approach. The algorithms have been implemented in a freely available C/C++ code.

Determination of Multimodal Isotopic Distributions: The Case of a (15)N Labeled Protein Produced into Hairy Roots

Isotopic labeling is widely used in various fields like proteomics, metabolomics, fluxomics, as well as in NMR structural studies, but it requires an efficient determination of the isotopic enrichment. Mass spectrometry is the method of choice for such analysis. However, when complex expression systems like hairy roots are used for production, multiple populations of labeled proteins may be obtained. If the isotopic incorporation determination is actually well-known for unimodal distributions, the multimodal distributions have scarcely been investigated. Actually, only a few approaches allow the determination of the different labeled population proportions from multimodal distributions. Furthermore, they cannot be used when the number of the populations and their respective isotope ratios are unknown. The present study implements a new strategy to measure the (15)N labeled populations inside a multimodal distribution knowing only the peptide sequence and peak intensities from mass spectrometry analyses. Noteworthy, it could be applied to other elements, like carbon and hydrogen, and extended to a larger range of biomolecules.

Producing Isotopic Distribution Models for Fully Apodized Absorption Mode FT-MS

Isotopic distributions are frequently used as part of the peak assignment process in the processing of mass spectra. The best methods for producing accurate peak assignments must account for the peak shape and resolving power. In other words, the full profile of the isotopic distribution is important. Conventional methods for modeling isotopic distributions generally assume a peak profile that is not applicable to fully apodized absorption mode spectra because the peak shapes in these spectra are distinctly different from those seen in normal (i.e., magnitude mode) spectra. We present results illustrating this problem and describe a method for producing more accurate isotopic distribution models for this class of spectra.

Towards automated discrimination of lipids versus peptides from full scan mass spectra

Although physicochemical fractionation techniques play a crucial role in the analysis of complex mixtures, they are not necessarily the best solution to separate specific molecular classes, such as lipids and peptides. Any physical fractionation step such as, for example, those based on liquid chromatography, will introduce its own variation and noise. In this paper we investigate to what extent the high sensitivity and resolution of contemporary mass spectrometers offers viable opportunities for computational separation of signals in full scan spectra. We introduce an automatic method that can discriminate peptide from lipid peaks in full scan mass spectra, based on their isotopic properties. We systematically evaluate which features maximally contribute to a peptide versus lipid classification. The selected features are subsequently used to build a random forest classifier that enables almost perfect separation between lipid and peptide signals without requiring ion fragmentation and classical tandem MS-based identification approaches. The classifier is trained on in silico data, but is also capable of discriminating signals in real world experiments. We evaluate the influence of typical data inaccuracies of common classes of mass spectrometry instruments on the optimal set of discriminant features. Finally, the method is successfully extended towards the classification of individual lipid classes from full scan mass spectral features, based on input data defined by the Lipid Maps Consortium.

Comparison of the Mahalanobis distance and Pearson's χ² statistic as measures of similarity of isotope patterns

To extract a genuine peptide signal from a mass spectrum, an observed series of peaks at a particular mass can be compared with the isotope distribution expected for a peptide of that mass. To decide whether the observed series of peaks is similar to the isotope distribution, a similarity measure is needed. In this short communication, we investigate whether the Mahalanobis distance could be an alternative measure for the commonly employed Pearson's χ(2) statistic. We evaluate the performance of the two measures by using a controlled MALDI-TOF experiment. The results indicate that Pearson's χ(2) statistic has better discriminatory performance than the Mahalanobis distance and is a more robust measure.