Understanding biological systems and the roles of their constituents is facilitated by the ability to make quantitative, sensitive, and comprehensive measurements of how their proteome changes, e.g., in response to environmental perturbations. To this end, we have developed a high-throughput methodology to characterize an organism's dynamic proteome based on the combination of global enzymatic digestion, high-resolution liquid chromatographic separations, and analysis by Fourier transform ion cyclotron resonance mass spectrometry. The peptides produced serve as accurate mass tags for the proteins and have been used to identify with high confidence >61% of the predicted proteome for the ionizing radiation-resistant bacterium Deinococcus radiodurans. This fraction represents the broadest proteome coverage for any organism to date and includes 715 proteins previously annotated as either hypothetical or conserved hypothetical.

Top-down proteomics is the analysis of intact proteins in their endogenous form without proteolysis, preserving valuable information about post-translation modifications, isoforms and proteolytic processing. The quality of top-down LC-MS/MS datasets is rapidly increasing due to advances in instrumentation and sample processing protocols. However, the top-down mass spectra are substantially more complex compared to conventional bottom-up data. To take full advantage of the increasing data quality, there is an urgent need to develop algorithms and software tools for confident proteoform identification and quantification. In this study, we present a new open source software suite for top-down proteomics analysis consisting of an LC-MS feature finding algorithm, a database search algorithm, and an interactive results viewer. The presented tool along with several other popular tools were evaluated 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.

Ultrasensitive nanoscale proteomics approaches for characterizing proteins from complex proteomic samples of <50 ng of total mass are described. Protein identifications from 0.5 pg of whole proteome extracts were enabled by ultrahigh sensitivity (<75 zmol for individual proteins) achieved using high-efficiency (peak capacities of approximately 10(3)) 15-microm-i.d. capillary liquid chromatography separations (i.e., using nanoLC, approximately 20 nL/min mobile-phase flow rate at the optimal linear velocity of approximately 0.2 cm/s) coupled on-line with a micro-solid-phase sample extraction and a nanoscale electrospray ionization interface to a 11.4-T Fourier transform ion cyclotron resonance (FTICR) mass spectrometer (MS). Proteome measurement coverage improved as sample size was increased from as little as 0.5 pg of sample. It was found that a 2.5-ng sample provided 14% coverage of all annotated open reading frames for the microorganism Deinococcus radiodurans, consistent with previous results for a specific culture condition. The estimated detection dynamic range for detected proteins was 10(5)-10(6). An improved accurate mass and LC elution time two-dimensional data analysis methodology, used to both speed and increase the confidence of peptide/protein identifications, enabled identification of 872 proteins/run from a single 3-h nanoLC/FTICR MS analysis. The low-zeptomole-level sensitivity provides a basis for extending proteomics studies to smaller cell populations and potentially to a single mammalian cell. Application with ion trap MS/MS instrumentation allowed protein identification from 50 pg (total mass) of proteomic samples (i.e., approximately 100 times larger than FTICR MS), corresponding to a sensitivity of approximately 7 amol for individual proteins. Compared with single-stage FTICR measurements, ion trap MS/MS provided a much lower proteome measurement coverage and dynamic range for a given analysis time and sample quantity.

VIPER may be downloaded free of charge at http://ncrr.pnl.gov/software/

No abstract

We report on the design and application of a high-efficiency multiple-capillary liquid chromatography (LC) system for high-throughput proteome analysis. The multiple-capillary LC system using commercial LC pumps was operated at a pressure of 10,000 psi to deliver mobile phases through a novel passive feedback valve arrangement that permitted mobile-phase flow path switching and efficient sample introduction. The multiple-capillary LC system uses several serially connected dual-capillary column devices. The dual-capillary column approach eliminates the time delays for column regeneration (or equilibration) since one capillary column was used for a separation while the other was being washed. Several serially connected dual-capillary columns and electrospray ionization (ESI) sources were operated independently and can be used either for "backup" operation or for parallel operation with other mass spectrometers. This high-efficiency multiple-capillary LC system utilizes switching valves for all operations, enabling automated operation. The separation efficiency of the dual-capillary column arrangement, optimal capillary dimensions (column length and packed particle size), capillary regeneration conditions, and mobile-phase compositions and their compatibility with electrospray ionization were investigated. A high magnetic field (11.4 T) Fourier transform ion cyclotron resonance (FTICR) mass spectrometer was coupled on-line with this high-efficiency multiple-capillary LC system using an ESI interface. The capillary LC provided a peak capacity of approximately 650, and the 2-D capillary LC-FTICR analysis provided a combined resolving power of > 6 x 10(7) components. For yeast cytosolic tryptic digests > 100,000 polypeptides were detected, and approximately 1,000 proteins could be characterized from a single capillary LC-FTICR analysis using the high mass measurement accuracy (approximately 1 ppm) of FTICR, and likely more if LC retention time information were also exploited for peptide identification.

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.

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.