Author Correction: Informed-Proteomics: open-source software package for top-down proteomics.

In the version of this article initially published, the authors erroneously reported the search mode that was used for ProSightPC 3.0 in the Online Methods and in Supplementary Table 3.

Single-Cell Metabolic Profiling: Metabolite Formulas from Isotopic Fine Structures in Heterogeneous Plant Cell Populations.

Characterization of the metabolic heterogeneity in cell populations requires the analysis of single cells. Most current methods in single-cell analysis rely on cell manipulation, potentially altering the abundance of metabolites in individual cells. A small sample volume and the chemical diversity of metabolites are additional challenges in single-cell metabolomics. Here, we describe the combination of fiber-based laser ablation electrospray ionization (f-LAESI) with 21 T Fourier transform ion cyclotron resonance mass spectrometry (21TFTICR-MS) for in situ single-cell metabolic profiling in plant tissue. Single plant cells infected by bacteria were selected and sampled directly from the tissue without cell manipulation through mid-infrared ablation with a fine optical fiber tip for ionization by f-LAESI. Ultrahigh performance 21T-FTICR-MS enabled the simultaneous capture of isotopic fine structures (IFSs) for 47 known and 11 unknown compounds, thus elucidating their elemental compositions from single cells and providing information on metabolic heterogeneity in the cell population.

Informed-Proteomics: open-source software package for top-down proteomics.

Top-down proteomics, the analysis of intact proteins in their endogenous form, preserves valuable information about post-translation modifications, isoforms and proteolytic processing. The quality of top-down liquid chromatography-tandem MS (LC-MS/MS) data sets is rapidly increasing on account of advances in instrumentation and sample-processing protocols. However, top-down mass spectra are substantially more complex than conventional bottom-up data. New algorithms and software tools for confident proteoform identification and quantification are needed. Here we present Informed-Proteomics, an open-source software suite for top-down proteomics analysis that consists of an LC-MS feature-finding algorithm, a database search algorithm, and an interactive results viewer. We compare our tool with several other popular tools using human-in-mouse xenograft luminal and basal breast tumor samples that are known to have significant differences in protein abundance based on bottom-up analysis.

Mutual Interactions between Reduced Fe-Bearing Clay Minerals and Humic Acids under Dark, Oxygenated Conditions: Hydroxyl Radical Generation and Humic Acid Transformation.

Hydroxyl radicals (·OH) exert a strong impact on the carbon cycle due to their nonselective and highly oxidizing nature. Reduced iron-containing clay minerals (RIC) are one of the major contributors to the formation of ·OH in dark environments, but their interactions with humic acids (HA) are poorly known. Here, we investigate the mutual interactions between RIC and HA under dark and oxygenated conditions. HA decreased the oxidation rate of structural Fe(II) in RIC but significantly promoted the ·OH yield. HA dissolved a fraction of Fe(II) from RIC to form an aqueous Fe(II)-HA complex. ·OH were generated through both heterogeneous (through oxidation of structural Fe(II)) and homogeneous pathways (through oxidation of aqueous Fe(II)-HA species). RIC-mediated ·OH production by providing H2O2 to react with Fe(II)-HA and electrons to regenerate Fe(II)-HA. This highly efficient homogeneous pathway was responsible for increased ·OH yield. Abundant ·OH significantly decreased the molecular size, bleached chromophores, and increased the oxygen-containing functional groups of HA. These molecular changes of HA resembled photochemical transformation of HA. The mutual interaction between RIC and HA in dark and redox-fluctuating environments provides a new pathway for fast turnover of recalcitrant organic matters in clay- and HA-rich ecosystems such as tropical forest soils and tidal marsh sediments.

Online supercritical fluid extraction mass spectrometry (SFE-LC-FTMS) for sensitive characterization of soil organic matter.

We report a novel technical approach for subcritical fluid extraction (SFE) for organic matter characterization in complex matrices such as soil. The custom platform combines on-line SFE with micro-solid phase extraction, nano liquid chromatography (LC), electrospray ionization and Fourier transform mass spectrometry (SFE-LC-FTMS). We demonstrated the utility of SFE-LC-FTMS, including results from both Orbitrap and FTICR MS, for analysis of complex mixtures of organic compounds in a solid matrix by characterizing soil organic matter in peat, a high-carbon soil. For example, in a single experiment, >6000 molecular formulas can be assigned based upon FTICR MS data from 1-50 µL of soil samples (roughly 1-50 mg of soil, dependent on soil density), nearly twice that typically obtained from direct infusion liquid solvent extraction (LSE) from an order of magnitude larger volume of the same soil. The detected species consisted predominately of lipid-like, lignin-like and protein-like compounds, based on their O/C and H/C ratios, with predominantly CHO and CHONP molecular compositions. These results clearly demonstrate that SFE has the potential to effectively extract a variety of molecular species and could become an important member of a suite of extraction methods for studying SOM and other natural organic matter. This is especially true when comprehensive coverage, minimal sample volumes, and high sensitivity are required, or when the presence of organic solvent residue in residual soil is problematic. The SFE based extraction protocol could potentially enable spatially resolved characterization of organic matter in soil with a resolution of ∼1 mm3 to facilitate studies probing the spatial heterogeneity of soil.

Evaluation of In Silico Multifeature Libraries for Providing Evidence for the Presence of Small Molecules in Synthetic Blinded Samples.

The current gold standard for unambiguous molecular identification in metabolomics analysis is comparing two or more orthogonal properties from the analysis of authentic reference materials (standards) to experimental data acquired in the same laboratory with the same analytical methods. This represents a significant limitation for comprehensive chemical identification of small molecules in complex samples. The process is time consuming and costly, and the majority of molecules are not yet represented by standards. Thus, there is a need to assemble evidence for the presence of small molecules in complex samples through the use of libraries containing calculated chemical properties. To address this need, we developed a Multi-Attribute Matching Engine (MAME) and a library derived in part from our in silico chemical library engine (ISiCLE). Here, we describe an initial evaluation of these methods in a blinded analysis of synthetic chemical mixtures as part of the U.S. Environmental Protection Agency's (EPA) Non-Targeted Analysis Collaborative Trial (ENTACT, Phase 1). For molecules in all mixtures, the initial blinded false negative rate (FNR), false discovery rate (FDR), and accuracy were 57%, 77%, and 91%, respectively. For high evidence scores, the FDR was 35%. After unblinding of the sample compositions, we optimized the scoring parameters to better exploit the available evidence and increased the accuracy for molecules suspected as present. The final FNR, FDR, and accuracy were 67%, 53%, and 96%, respectively. For high evidence scores, the FDR was 10%. This study demonstrates that multiattribute matching methods in conjunction with in silico libraries may one day enable reduced reliance on experimentally derived libraries for building evidence for the presence of molecules in complex samples.

Moving beyond the van Krevelen Diagram: A New Stoichiometric Approach for Compound Classification in Organisms.

van Krevelen diagrams (O/C vs H/C ratios of elemental formulas) have been widely used in studies to obtain an estimation of the main compound categories present in environmental samples. However, the limits defining a specific compound category based solely on O/C and H/C ratios of elemental formulas have never been accurately listed or proposed to classify metabolites in biological samples. Furthermore, while O/C vs H/C ratios of elemental formulas can provide an overview of the compound categories, such classification is inefficient because of the large overlap among different compound categories along both axes. We propose a more accurate compound classification for biological samples analyzed by high-resolution mass spectrometry based on an assessment of the C/H/O/N/P stoichiometric ratios of over 130 000 elemental formulas of compounds classified in 6 main categories: lipids, peptides, amino sugars, carbohydrates, nucleotides, and phytochemical compounds (oxy-aromatic compounds). Our multidimensional stoichiometric compound classification (MSCC) constraints showed a highly accurate categorization of elemental formulas to the main compound categories in biological samples with over 98% of accuracy representing a substantial improvement over any classification based on the classic van Krevelen diagram. This method represents a signficant step forward in environmental research, especially ecological stoichiometry and eco-metabolomics studies, by providing a novel and robust tool to improve our understanding of the ecosystem structure and function through the chemical characterization of biological samples.

A mass graph-based approach for the identification of modified proteoforms using top-down tandem mass spectra.

Motivation: Although proteomics has rapidly developed in the past decade, researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their biological functions as well as discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a 'bird's eye view' of intact proteoforms. The combinatorial explosion of various alterations on a protein may result in billions of possible proteoforms, making proteoform identification a challenging computational problem. Results: We propose a new data structure, called the mass graph, for efficient representation of proteoforms and design mass graph alignment algorithms. We developed TopMG, a mass graph-based software tool for proteoform identification by top-down mass spectrometry. Experiments on top-down mass spectrometry datasets showed that TopMG outperformed existing methods in identifying complex proteoforms. Availability and implementation: http://proteomics.informatics.iupui.edu/software/topmg/. Contact: xwliu@iupui.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Molecular Insights into Arctic Soil Organic Matter Degradation under Warming.

Molecular composition of the Arctic soil organic carbon (SOC) and its susceptibility to microbial degradation are uncertain due to heterogeneity and unknown SOC compositions. Using ultrahigh-resolution mass spectrometry, we determined the susceptibility and compositional changes of extractable dissolved organic matter (EDOM) in an anoxic warming incubation experiment (up to 122 days) with a tundra soil from Alaska (United States). EDOM was extracted with 10 mM NH4HCO3 from both the organic- and mineral-layer soils during incubation at both -2 and 8 °C. Based on their O:C and H:C ratios, EDOM molecular formulas were qualitatively grouped into nine biochemical classes of compounds, among which lignin-like compounds dominated both the organic and the mineral soils and were the most stable, whereas amino sugars, peptides, and carbohydrate-like compounds were the most biologically labile. These results corresponded with shifts in EDOM elemental composition in which the ratios of O:C and N:C decreased, while the average C content in EDOM, molecular mass, and aromaticity increased after 122 days of incubation. This research demonstrates that certain EDOM components, such as amino sugars, peptides, and carbohydrate-like compounds, are disproportionately more susceptible to microbial degradation than others in the soil, and these results should be considered in SOC degradation models to improve predictions of Arctic climate feedbacks.

De Novo Sequencing of Peptides from High-Resolution Bottom-Up Tandem Mass Spectra using Top-Down Intended Methods.

Despite high-resolution mass spectrometers are becoming accessible for more and more laboratories, tandem (MS/MS) mass spectra are still often collected at a low resolution. And even if acquired at a high resolution, software tools used for their processing do not tend to benefit from that in full, and an ability to specify a relative mass tolerance in this case often remains the only feature the respective algorithms take advantage of. We argue that a more efficient way to analyze high-resolution MS/MS spectra should be with methods more explicitly accounting for the precision level, and sustain this claim through demonstrating that a de novo sequencing framework originally developed for (high-resolution) top-down MS/MS data is perfectly suitable for processing high-resolution bottom-up datasets, even though a top-down like deconvolution performed as the first step will leave in many spectra at most a few peaks.

Formularity: Software for Automated Formula Assignment of Natural and Other Organic Matter from Ultrahigh-Resolution Mass Spectra.

Ultrahigh resolution mass spectrometry, such as Fourier transform ion cyclotron resonance mass spectrometry (FT ICR MS), can resolve thousands of molecular ions in complex organic matrices. A Compound Identification Algorithm (CIA) was previously developed for automated elemental formula assignment for natural organic matter (NOM). In this work, we describe software Formularity with a user-friendly interface for CIA function and newly developed search function Isotopic Pattern Algorithm (IPA). While CIA assigns elemental formulas for compounds containing C, H, O, N, S, and P, IPA is capable of assigning formulas for compounds containing other elements. We used halogenated organic compounds (HOC), a chemical class that is ubiquitous in nature as well as anthropogenic systems, as an example to demonstrate the capability of Formularity with IPA. A HOC standard mix was used to evaluate the identification confidence of IPA. Tap water and HOC spike in Suwannee River NOM were used to assess HOC identification in complex environmental samples. Strategies for reconciliation of CIA and IPA assignments were discussed. Software and sample databases with documentation are freely available.

Top-down analysis of protein samples by de novo sequencing techniques.

MOTIVATION: Recent technological advances have made high-resolution mass spectrometers affordable to many laboratories, thus boosting rapid development of top-down mass spectrometry, and implying a need in efficient methods for analyzing this kind of data. RESULTS: We describe a method for analysis of protein samples from top-down tandem mass spectrometry data, which capitalizes on de novo sequencing of fragments of the proteins present in the sample. Our algorithm takes as input a set of de novo amino acid strings derived from the given mass spectra using the recently proposed Twister approach, and combines them into aggregated strings endowed with offsets. The former typically constitute accurate sequence fragments of sufficiently well-represented proteins from the sample being analyzed, while the latter indicate their location in the protein sequence, and also bear information on post-translational modifications and fragmentation patterns. AVAILABILITY AND IMPLEMENTATION: Freely available on the web at http://bioinf.spbau.ru/en/twister CONTACT: vyatkina@spbau.ru or ppevzner@ucsd.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Molecular Characterization of Organosulfur Compounds in Biodiesel and Diesel Fuel Secondary Organic Aerosol.

Secondary organic aerosol (SOA), formed in the photooxidation of diesel fuel, biodiesel fuel, and 20% biodiesel fuel/80% diesel fuel mixture, are prepared under high-NOx conditions in the presence and absence of sulfur dioxide (SO2), ammonia (NH3), and relative humidity (RH). The composition of condensed-phase organic compounds in SOA is measured using several complementary techniques including aerosol mass spectrometry (AMS), high-resolution nanospray desorption electrospray ionization mass spectrometry (nano-DESI/HRMS), and ultrahigh resolution and mass accuracy 21T Fourier transform ion cyclotron resonance mass spectrometry (21T FT-ICR MS). Results demonstrate that sulfuric acid and condensed organosulfur species formed in photooxidation experiments with SO2 are present in the SOA particles. Fewer organosulfur species are formed in the high humidity experiments, performed at RH 90%, in comparison with experiments done under dry conditions. There is a strong overlap of organosulfur species observed in this study with previous field and chamber studies of SOA. Many MS peaks of organosulfates (R-OS(O)2OH) previously designated as biogenic or of unknown origin in field studies might have originated from anthropogenic sources, such as photooxidation of hydrocarbons present in diesel and biodiesel fuel.

Characterization of Proteoforms with Unknown Post-translational Modifications Using the MIScore.

Various proteoforms may be generated from a single gene due to primary structure alterations (PSAs) such as genetic variations, alternative splicing, and post-translational modifications (PTMs). Top-down mass spectrometry is capable of analyzing intact proteins and identifying patterns of multiple PSAs, making it the method of choice for studying complex proteoforms. In top-down proteomics, proteoform identification is often performed by searching tandem mass spectra against a protein sequence database that contains only one reference protein sequence for each gene or transcript variant in a proteome. Because of the incompleteness of the protein database, an identified proteoform may contain unknown PSAs compared with the reference sequence. Proteoform characterization is to identify and localize PSAs in a proteoform. Although many software tools have been proposed for proteoform identification by top-down mass spectrometry, the characterization of proteoforms in identified proteoform-spectrum matches still relies mainly on manual annotation. We propose to use the Modification Identification Score (MIScore), which is based on Bayesian models, to automatically identify and localize PTMs in proteoforms. Experiments showed that the MIScore is accurate in identifying and localizing one or two modifications.

Unique Organic Matter and Microbial Properties in the Rhizosphere of a Wetland Soil.

Wetlands attenuate the migration of many contaminants through a wide range of biogeochemical reactions. Recent research has shown that the rhizosphere, the zone near plant roots, in wetlands is especially effective at promoting contaminant attenuation. The objective of this study was to compare the soil organic matter (OM) composition and microbial communities of a rhizosphere soil (primarily an oxidized environment) to that of the bulk wetland soil (primarily a reduced environment). The rhizosphere had elevated C, N, Mn, and Fe concentrations and total bacteria, including Anaeromyxobacter, counts (as identified by qPCR). Furthermore, the rhizosphere contained several organic molecules that were not identified in the nonrhizosphere soil (54% of the >2200 ESI-FTICR-MS identified compounds). The rhizosphere OM molecules generally had (1) greater overall molecular weights, (2) less aromaticity, (3) more carboxylate and N-containing COO functional groups, and (4) a greater hydrophilic character. These latter two OM properties typically promote metal binding. This study showed for the first time that not only the amount but also the molecular characteristics of OM in the rhizosphere may in part be responsible for the enhanced immobilization of contaminants in wetlands. These finding have implications on the stewardship and long-term management of contaminated wetlands.

De Novo Sequencing of Peptides from Top-Down Tandem Mass Spectra.

De novo sequencing of proteins and peptides is one of the most important problems in mass spectrometry-driven proteomics. A variety of methods have been developed to accomplish this task from a set of bottom-up tandem (MS/MS) mass spectra. However, a more recently emerged top-down technology, now gaining more and more popularity, opens new perspectives for protein analysis and characterization, implying a need for efficient algorithms to process this kind of MS/MS data. Here, we describe a method that allows for the retrieval, from a set of top-down MS/MS spectra, of long and accurate sequence fragments of the proteins contained in the sample. To this end, we outline a strategy for generating high-quality sequence tags from top-down spectra, and introduce the concept of a T-Bruijn graph by adapting to the case of tags the notion of an A-Bruijn graph widely used in genomics. The output of the proposed approach represents the set of amino acid strings spelled out by optimal paths in the connected components of a T-Bruijn graph. We illustrate its performance on top-down data sets acquired from carbonic anhydrase 2 (CAH2) and the Fab region of alemtuzumab.

Comprehensive quantitative analysis of ovarian and breast cancer tumor peptidomes.

Aberrant degradation of proteins is associated with many pathological states, including cancers. Mass spectrometric analysis of tumor peptidomes, the intracellular and intercellular products of protein degradation, has the potential to provide biological insights on proteolytic processing in cancer. However, attempts to use the information on these smaller protein degradation products from tumors for biomarker discovery and cancer biology studies have been fairly limited to date, largely due to the lack of effective approaches for robust peptidomics identification and quantification and the prevalence of confounding factors and biases associated with sample handling and processing. Herein, we have developed an effective and robust analytical platform for comprehensive analyses of tissue peptidomes, which is suitable for high-throughput quantitative studies. The reproducibility and coverage of the platform, as well as the suitability of clinical ovarian tumor and patient-derived breast tumor xenograft samples with postexcision delay of up to 60 min before freezing for peptidomics analysis, have been demonstrated. Moreover, our data also show that the peptidomics profiles can effectively separate breast cancer subtypes, reflecting tumor-associated protease activities. Peptidomics complements results obtainable from conventional bottom-up proteomics and provides insights not readily obtainable from such approaches.

Advanced solvent based methods for molecular characterization of soil organic matter by high-resolution mass spectrometry.

Soil organic matter (SOM), a complex, heterogeneous mixture of above and belowground plant litter and animal and microbial residues at various degrees of decomposition, is a key reservoir for carbon (C) and nutrient biogeochemical cycling in soil based ecosystems. A limited understanding of the molecular composition of SOM limits the ability to routinely decipher chemical processes within soil and accurately predict how terrestrial carbon fluxes will respond to changing climatic conditions and land use. To elucidate the molecular-level structure of SOM, we selectively extracted a broad range of intact SOM compounds by a combination of different organic solvents from soils with a wide range of C content. Our use of electrospray ionization (ESI) coupled with Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) and a suite of solvents with varying polarity significantly expands the inventory of the types of organic molecules present in soils. Specifically, we found that hexane is selective for lipid-like compounds with very low O/C ratios (<0.1); water (H2O) was selective for carbohydrates with high O/C ratios; acetonitrile (ACN) preferentially extracts lignin, condensed structures, and tannin polyphenolic compounds with O/C > 0.5; methanol (MeOH) has higher selectivity toward compounds characterized with low O/C < 0.5; and hexane, MeOH, ACN, and H2O solvents increase the number and types of organic molecules extracted from soil for a broader range of chemically diverse soil types. Our study of SOM molecules by ESI FTICR MS revealed new insight into the molecular-level complexity of organics contained in soils. We present the first comparative study of the molecular composition of SOM from different ecosystems using ultra high-resolution mass spectrometry.

Quantitative analysis of human salivary gland-derived intact proteome using top-down mass spectrometry.

There are several notable challenges inherent for fully characterizing the entirety of the human saliva proteome using bottom-up approaches, including polymorphic isoforms, PTMs, unique splice variants, deletions, and truncations. To address these challenges, we have developed a top-down based LC-MS/MS approach, which cataloged 20 major human salivary proteins with a total of 83 proteoforms, containing a broad range of PTMs. Among these proteins, several previously reported disease biomarker proteins were identified at the intact protein level, such as beta-2 microglobulin. In addition, intact glycosylated proteoforms of several saliva proteins were also characterized, including intact N-glycosylated protein prolactin inducible protein and O-glycosylated acidic protein rich protein. These characterized proteoforms constitute an intact saliva proteoform database, which was used for quantitative comparison of intact salivary proteoforms among six healthy individuals. Human parotid and submandibular/sublingual gland secretion samples (2 µg of protein each) from six healthy individuals were compared using RPLC coupled with the 12T FT-ICR mass spectrometer. Significantly different proteoform profiles were resolved with high reproducibility between parotid secretion and submandibular/sublingual glands. The results from this study provide further insight into the potential mechanisms of PTM pathways in oral glandular secretion, expanding our knowledge of this complex yet easily accessible fluid. Intact protein LC-MS approach presented herein can potentially be applied for rapid and accurate identification of biomarkers from only a few microliters of human glandular saliva.

The first pilot project of the consortium for top-down proteomics: a status report.

Pilot Project #1--the identification and characterization of human histone H4 proteoforms by top-down MS--is the first project launched by the Consortium for Top-Down Proteomics (CTDP) to refine and validate top-down MS. Within the initial results from seven participating laboratories, all reported the probability-based identification of human histone H4 (UniProt accession P62805) with expectation values ranging from 10(-13) to 10(-105). Regarding characterization, a total of 74 proteoforms were reported, with 21 done so unambiguously; one new PTM, K79ac, was identified. Inter-laboratory comparison reveals aspects of the results that are consistent, such as the localization of individual PTMs and binary combinations, while other aspects are more variable, such as the accurate characterization of low-abundance proteoforms harboring >2 PTMs. An open-access tool and discussion of proteoform scoring are included, along with a description of general challenges that lie ahead including improved proteoform separations prior to mass spectrometric analysis, better instrumentation performance, and software development.