Calculation of the isotope cluster for polypeptides by probability grouping

An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies

The mass accuracy and peak intensity of ions detected by mass spectrometry (MS) measurements are essential to facilitate compound identification and quantitation. However, high concentration species can yield erroneous results if their ion intensities reach beyond the limits of the detection system, leading to distorted and non-ideal detector response (e.g. saturation), and largely precluding the calculation of accurate m/z and intensity values. Here we present an open source computational method to correct peaks above a defined intensity (saturated) threshold determined by the MS instrumentation such as the analog-to-digital converters or time-to-digital converters used in conjunction with time-of-flight MS. In this method, the isotopic envelope for each observed ion above the saturation threshold is compared to its expected theoretical isotopic distribution. The most intense isotopic peak for which saturation does not occur is then utilized to re-calculate the precursor m/z and correct the intensity, resulting in both higher mass accuracy and greater dynamic range. The benefits of this approach were evaluated with proteomic and lipidomic datasets of varying complexities. After correcting the high concentration species, reduced mass errors and enhanced dynamic range were observed for both simple and complex omic samples. Specifically, the mass error dropped by more than 50% in most cases for highly saturated species and dynamic range increased by 1-2 orders of magnitude for peptides in a blood serum sample.

Product ion isotopologue pattern: A tool to improve the reliability of elemental composition elucidations of unknown compounds in complex matrices

RATIONALE

Elucidation of the elemental compositions of unknown compounds (e.g., in metabolomics) generally relies on the availability of accurate masses and isotopic ratios. This study focuses on the information provided by the abundance ratio within a product ion pair (monoisotopic versus the first isotopic peak) when isolating and fragmenting the first isotopic ion (first isotopic mass spectrum) of the precursor.

METHODS

This process relies on the capability of the quadrupole within the Q Orbitrap instrument to isolate a very narrow mass window. Selecting only the first isotopic peak (first isotopic mass spectrum) leads to the observation of a unique product ion pair. The lighter ion within such an isotopologue pair is monoisotopic, while the heavier ion contains a single carbon isotope. The observed abundance ratio is governed by the percentage of carbon atoms lost during the fragmentation and can be described by a hypergeometric distribution.

RESULTS

The observed carbon isotopologue abundance ratio (product ion isotopologue pattern) gives reliable information regarding the percentage of carbon atoms lost in the fragmentation process. It therefore facilitates the elucidation of the involved precursor and product ions. Unlike conventional isotopic abundances, the product ion isotopologue pattern is hardly affected by isobaric interferences. Furthermore, the appearance of these pairs greatly aids in cleaning up a 'matrix-contaminated' product ion spectrum.

CONCLUSIONS

The product ion isotopologue pattern is a valuable tool for structural elucidation. It increases confidence in results and permits structural elucidations for heavier ions. This tool is also very useful in elucidating the elemental composition of product ions. Such information is highly valued in the field of multi-residue analysis, where the accurate mass of product ions is required for the confirmation process. Copyright © 2016 John Wiley & Sons, Ltd.

Morphologically compatible mass spectrometric analysis of lipids in cytological specimens

INTRODUCTION

Modern lipid analysis requires mass spectrometric techniques, though to date these have been developed and applied primarily to histological serial sections. As such, there has been little emphasis on using mass spectrometry in such a way that the same specimen can yield both chemical and morphological information. Here, we present a mass spectrometric method that enables measurement of lipids from cells on cytospin slides in a way that preserves the cells for subsequent cytomorphologic evaluation.

MATERIALS AND METHODS

Standardized cultures of MDA-MB-231, a breast cancer cell line, were prepared as cytospins and subjected to analysis using a Prosolia Flowprobe sampling and ionization source attached to a Thermo Scientific Quadrupole-Orbitrap mass spectrometer. Chemical compositions were deduced with accurate mass measurements and fragmentation of high intensity peaks to further determine chemical structure. After mass spectrometry, the slides were stained and cover-slipped, and the cells were reviewed for cytomorphologic features of breast cancer. These were compared to control slides of the same cellular concentration that had not been subjected to this analysis.

RESULTS

Spectra from samples of all cellular concentrations demonstrated characteristic qualitative features that were discovered to represent phosphatidylcholines, phosphatidylglycerols, and phosphatidylserines with fragmentation and accurate mass matching. Cytomorphologic analysis demonstrated excellent preservation of the cells subjected to the Flowprobe analysis.

CONCLUSION

Direct extraction, ionization, and identification of lipids is possible from cytologic preparations in such a way that the analyzed material is preserved and useful for subsequent microscopic analysis.

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.

BRAF pyrosequencing analysis aided by a lookup table

OBJECTIVES

BRAF mutations have substantial therapeutic, diagnostic, and prognostic significance, so detecting and specifying them is an important part of the workload of molecular pathology laboratories. Pyrosequencing assays are well suited for this analysis but can produce complex results. Therefore, we introduce a pyrosequencing lookup table based on Pyromaker that assists the user in generating hypotheses for solving complex pyrosequencing results.

METHODS

The lookup table contains all known mutations in the sequenced region and the positions in the dispensation sequence at which changes would occur with those mutations. We demonstrate the lookup table using a homebrew dispensation sequence for BRAF codons 596 to 605 as well as a commercially available kit-based dispensation sequence for codons 599 to 600.

RESULTS

These results demonstrate that the homebrew dispensation sequence unambiguously identifies all known BRAF mutations in this region, whereas the kit-based dispensation sequence has one unresolvable degeneracy that could be solved with the addition of two injections.

CONCLUSIONS

Using the lookup table and confirmatory virtual pyrogram, we unambiguously solved clinical pyrograms of the complex mutations V600K (c.1798_1799delGTinsAA), V600R (c.1798_1799delGTinsAG), V600D (c.1799_1800delTGinsAT), V600E (c.1799_1800delTGinsAA), and V600_K601delinsE (c.1799_1801delTGA). In addition, we used the approach to hypothesize and confirm a new mutation in human melanoma, V600_K601delinsEI (c.1799_1802delTGAAinsAAAT).

BRAIN 2.0: time and memory complexity improvements in the algorithm for calculating the isotope distribution

Recently, an elegant iterative algorithm called BRAIN (Baffling Recursive Algorithm for Isotopic distributioN calculations) was presented. The algorithm is based on the classic polynomial method for calculating aggregated isotope distributions, and it introduces algebraic identities using Newton-Girard and Viète's formulae to solve the problem of polynomial expansion. Due to the iterative nature of the BRAIN method, it is a requirement that the calculations start from the lightest isotope variant. As such, the complexity of BRAIN scales quadratically with the mass of the putative molecule, since it depends on the number of aggregated peaks that need to be calculated. In this manuscript, we suggest two improvements of the algorithm to decrease both time and memory complexity in obtaining the aggregated isotope distribution. We also illustrate a concept to represent the element isotope distribution in a generic manner. This representation allows for omitting the root calculation of the element polynomial required in the original BRAIN method. A generic formulation for the roots is of special interest for higher order element polynomials such that root finding algorithms and its inaccuracies can be avoided.

Molecular Isotopic Distribution Analysis (MIDAs) with adjustable mass accuracy

In this paper, we present Molecular Isotopic Distribution Analysis (MIDAs), a new software tool designed to compute molecular isotopic distributions with adjustable accuracies. MIDAs offers two algorithms, one polynomial-based and one Fourier-transform-based, both of which compute molecular isotopic distributions accurately and efficiently. The polynomial-based algorithm contains few novel aspects, whereas the Fourier-transform-based algorithm consists mainly of improvements to other existing Fourier-transform-based algorithms. We have benchmarked the performance of the two algorithms implemented in MIDAs with that of eight software packages (BRAIN, Emass, Mercury, Mercury5, NeutronCluster, Qmass, JFC, IC) using a consensus set of benchmark molecules. Under the proposed evaluation criteria, MIDAs's algorithms, JFC, and Emass compute with comparable accuracy the coarse-grained (low-resolution) isotopic distributions and are more accurate than the other software packages. For fine-grained isotopic distributions, we compared IC, MIDAs's polynomial algorithm, and MIDAs's Fourier transform algorithm. Among the three, IC and MIDAs's polynomial algorithm compute isotopic distributions that better resemble their corresponding exact fine-grained (high-resolution) isotopic distributions. MIDAs can be accessed freely through a user-friendly web-interface at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/midas/index.html.

Computational mass spectrometry for small molecules

: The identification of small molecules from mass spectrometry (MS) data remains a major challenge in the interpretation of MS data. This review covers the computational aspects of identifying small molecules, from the identification of a compound searching a reference spectral library, to the structural elucidation of unknowns. In detail, we describe the basic principles and pitfalls of searching mass spectral reference libraries. Determining the molecular formula of the compound can serve as a basis for subsequent structural elucidation; consequently, we cover different methods for molecular formula identification, focussing on isotope pattern analysis. We then discuss automated methods to deal with mass spectra of compounds that are not present in spectral libraries, and provide an insight into de novo analysis of fragmentation spectra using fragmentation trees. In addition, this review shortly covers the reconstruction of metabolic networks using MS data. Finally, we list available software for different steps of the analysis pipeline.

Computation of isotopic peak center-mass distribution by Fourier transform

We derive a new efficient algorithm for the computation of the isotopic peak center-mass distribution of a molecule. With the use of Fourier transform techniques, the algorithm accurately computes the total abundance and average mass of all the isotopic species with the same number of nucleons. We evaluate the performance of the method with 10 benchmark proteins and other molecules; results are compared with BRAIN, a recently reported polynomial method. The new algorithm is comparable to BRAIN in accuracy and superior in terms of speed and memory, particularly for large molecules. An implementation of the algorithm is available for download.

An efficient method to calculate the aggregated isotopic distribution and exact center-masses

In this article, we present a computation- and memory-efficient method to calculate the probabilities of occurrence and exact center-masses of the aggregated isotopic distribution of a molecule. The method uses fundamental mathematical properties of polynomials given by the Newton-Girard theorem and Viete's formulae. The calculation is based on the atomic composition of the molecule and the natural abundances of the elemental isotopes in normal terrestrial matter. To evaluate the performance of the proposed method, which we named BRAIN, we compare it with the results obtained from five existing software packages (IsoPro, Mercury, Emass, NeutronCluster, and IsoDalton) for 10 biomolecules. Additionally, we compare the computed mass centers with the results obtained by calculating, and subsequently aggregating, the fine isotopic distribution for two of the exemplary biomolecules. The algorithm will be made available as a Bioconductor package in R, and is also available upon request.

The isotopic distribution conundrum

Although access to high-resolution mass spectrometry (MS), especially in the field of biomolecular MS, is becoming readily available due to recent advances in MS technology, the accompanied information on isotopic distribution in high-resolution spectra is not used at its full potential, mainly because of lack of knowledge and/or awareness. In this review, we give an insight into the practical problems related to calculating the isotopic distribution for large biomolecules, and present an overview of methods for the calculation of the isotopic distribution. We discuss the key events that triggered the development of various algorithms and explain the rationale of how and why the various isotopic-distribution calculations were performed. The review is focused around the developmental stages as briefly outlined below, starting with the first observation of an isotopic distribution. The observations of Beynon in the field of organic MS that chlorine appeared in a mass spectrum as two variants with odds 3:1 lie at the basis of the first wave of algorithms for the calculation of the isotopic distribution, based on the atomic composition of a molecule. From here on, we explain why more complex biomolecules such as peptides exhibit a highly complex isotope pattern when assayed by MS, and we discuss how combinatorial difficulties complicate the calculation of the isotopic distribution on computers. For this purpose, we highlight three methods, which were introduced in the 1980s. These are the stepwise procedure introduced by Kubinyi, the polynomial expansion from Brownawell and Fillippo, and the multinomial expansion from Yergey. The next development was instigated by Rockwood, who suggested to decompose the isotopic distribution in terms of their nucleon count instead of the exact mass. In this respect, we could claim that the term "aggregated" isotopic distribution is more appropriate. Due to the simplification of the isotopic distribution to its aggregated counterpart, Rockwood was able to use the convolution for the calculation of the "aggregated" isotopic distribution. Convolution methods are computationally efficient and economic in their memory usage. We spend a section on the work introduced by Rockwood during the 1990s. Due to recent breakthroughs in mass spectrometric technology and the widespread high-resolution instruments (e.g., FTICR-MS, FTOrbitrap-MS, and TOF-MS) that provide high-resolution, isotope-resolved, accurate mass data, there is an emerging need for algorithms that can calculate isotopic distributions for large biomolecules. The number of recent publications on this topic does witness this trend. The new methods are mostly based on complex mathematical developments such as, for example, cellular automata (Meija and Caruso [2004]. J Am Soc Mass Spectrom, 15(5):654-658), dynamic programming (Snider [2007]. J Am Soc Mass Spectrom, 18:1511-1515), and hierarchical models (Li et al. [2008] J Am Soc Mass Spectrom, 19:1867-1874). We also comment on the ideas to use Punnet squares and Pascal's triangle to introduce the concept of the isotopic distribution for educational and didactic purposes.

Advances in structure elucidation of small molecules using mass spectrometry

The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12566-010-0015-9) contains supplementary material, which is available to authorized users.

Memory-efficient calculation of the isotopic mass states of a molecule

Our previous work postulated a transition concept among different isotopic mass states (i.e., isotopic species) of a molecule, and developed a hierarchical algorithm for accurately calculating their masses and abundances. A theoretical mass spectrum can be generated by convoluting a peak shape function to these discrete mass states. This approach suffers from limited memory if a level in the hierarchical structure has too many mass states. Here we present a memory efficient divide-and-recursively-combine algorithm to do the calculation, which also improves the truncation method used in the previous hierarchical algorithm. Instead of treating all of the elements in a molecule as a whole, the new algorithm first 'strips' each element one by one. For the mass states of each element, a hierarchical structure is established and kept in the memory. This process reduces the memory usage by orders of magnitude (e.g., for bovine insulin, memory can be reduced from gigabytes to kilobytes). Next, a recursive algorithm is applied to combine mass states of elements to mass states of the whole molecule. The algorithm described above has been implemented as a computer program called Isotope Calculator, which was written in C++. It is freely available under the GNU Lesser General Public License from http://www.cs.brandeis.edu/~hong/software.html or http://people.brandeis.edu/~agar.