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Multimodal mass spectrometry imaging (MSI) is a critical technique used for deeply investigating biological systems by combining multiple MSI platforms in order to gain the maximum molecular information about a sample that would otherwise be limited by a single analytical technique. The aim of this work was to create a multimodal MSI approach that measures metabolomic and proteomic data from a single biological organ by combining infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) for metabolomic MSI and nanodroplet processing in one pot for trace samples (nanoPOTS) LC-MS/MS for spatially resolved proteome profiling. Adjacent tissue sections of rat brain were analyzed by each platform, and each data set was individually analyzed using previously optimized workflows. IR-MALDESI data sets were annotated by accurate mass and spectral accuracy using HMDB, METLIN, and LipidMaps databases, while nanoPOTS-LC-MS/MS data sets were searched against the rat proteome using the Sequest HT algorithm and filtered with a 1% FDR. The combined data revealed complementary molecular profiles distinguishing the corpus callosum against other sampled regions of the brain. A multiomic pathway integration showed a strong correlation between the two data sets when comparing average abundances of metabolites and corresponding enzymes in each brain region. This work demonstrates the first steps in the creation of a multimodal MSI technique that combines two highly sensitive and complementary imaging platforms. Raw data files are available in METASPACE (https://metaspace2020.eu/project/pace-2021) and MassIVE (identifier: MSV000088211).

Proteomics-based biological research is greatly expanded by high-quality mass spectrometry studies, which are themselves enabled by access to quality mass spectrometry resources, such as high-quality curated proteome data repositories. We present a PeptideAtlas for the domestic chicken, containing an extensive and robust collection of chicken tissue and plasma samples with substantial value for the chicken proteomics community for protein validation and design of downstream targeted proteome quantitation. The chicken PeptideAtlas contains 6646 canonical proteins at a protein FDR of 1.3%, derived from ∼100 000 peptides at a peptide level FDR of 0.1%. The rich collection of readily accessible data is easily mined for the purposes of data validation and experimental planning, particularly in the realm of developing proteome quantitation workflows. Herein we demonstrate the use of the atlas to mine information on common chicken acute-phase proteins and biomarkers for cancer detection research, as well as their localization and polymorphisms. This wealth of information will support future proteome-based research using this highly important agricultural organism in pursuit of both chicken and human health outcomes.

Alexander disease (AxD) is a neurodegenerative disorder characterized by astrocytic protein aggregates called Rosenthal fibers (RFs). We used mouse models of AxD to determine the protein composition of RFs to obtain information about disease mechanisms including the hypothesis that sequestration of proteins in RFs contributes to disease. A method was developed for RF enrichment, and analysis of the resulting fraction using isobaric tags for relative and absolute quantitation mass spectrometry identified 77 proteins not previously associated with RFs. Three of five proteins selected for follow-up were confirmed enriched in the RF fraction by immunobloting of both the AxD mouse models and human patients: receptor for activated protein C kinase 1 (RACK1), G1/S-specific cyclin D2, and ATP-dependent RNA helicase DDX3X. Immunohistochemistry validated cyclin D2 as a new RF component, but results for RACK1 and DDX3X were equivocal. None of these was decreased in the non-RF fractions compared to controls. A similar result was obtained for the previously known RF component, alphaB-crystallin, which had been a candidate for sequestration. Thus, no support was obtained for the sequestration hypothesis for AxD. Providing possible insight into disease progression, the association of several of the RF proteins with stress granules suggests a role for stress granules in the origin of RFs.

Cellulose, the main chemical polymer of wood, is the most abundant polysaccharide in nature.1 The ability to perturb the abundance and structure of cellulose microfibrils is of critical importance to the pulp and paper industry as well as for the textile, wood products, and liquid biofuels industries. Although much has been learned at the transcript level about the biosynthesis of cellulose, a quantitative understanding at the proteome level has yet to be established. The study described herein sought to identify the proteins directly involved in cellulose biosynthesis during wood formation in Populus trichocarpa along with known xylem-specific transcription factors involved in regulating these key proteins. Development of an effective discovery proteomic strategy through a combination of subcellular fractionation of stem differentiating xylem tissue (SDX) with recently optimized FASP digestion protocols, StageTip fractionation, as well as optimized instrument parameters for global proteomic analysis using the quadrupole-orbitrap mass spectrometer resulted in the deepest proteomic coverage of SDX protein from P. trichocarpa with 9,146 protein groups being identified (1% FDR). Of these, 20 cellulosic/hemicellulosic enzymes and 43 xylem-specific transcription factor groups were identified. Finally, selection of surrogate peptides led to an assay for absolute quantification of 14 cellulosic proteins in SDX of P. trichocarpa.

Laser microdissection (LMD) was utilized for the separation of the yolk, follicular wall (granulosa and theca), and surrounding stromal cells of small white follicles (SWF) obtained from reproductively active domestic fowl. Herein, we provide an in situ proteomics-based approach to studying follicular development through the use of LMD and mass spectrometry. This study resulted in a total of 2889 proteins identified from the three specific isolated compartments. White yolk from the smallest avian follicles resulted in the identification of 1984 proteins, while isolated follicular wall and ovarian stroma yielded 2470 and 2456 proteins, respectively. GO annotations highlighted the functional differences between the compartments. Among the three compartments examined, the relative abundance of vitellogenins, steroidogenic enzymes, anti-Mullerian hormone, transcription factors, and proteins involved in retinoic acid receptors/retinoic acid synthesis, transcription factors, and cell surface receptors such as EGFR and their associated signaling pathways reflected known cellular function of the ovary. This study has provided a global proteome for SWF, white yolk, and ovarian stroma of the avian ovary that can be used as a baseline for future studies and verifies that the coupling of LMD with proteomic analysis can be used to evaluate proteins from small, physiologically functional compartments of complex tissue.

Ovarian cancer (OVC) remains the most lethal gynecological malignancy in the world due to the combined lack of early-stage diagnostics and effective therapeutic strategies. The development and application of advanced proteomics technology and new experimental models has created unique opportunities for translational studies. In this study, we investigated the ovarian cancer proteome of the chicken, an emerging experimental model of OVC that develops ovarian tumors spontaneously. Matched plasma, ovary, and oviduct tissue biospecimens derived from healthy, early-stage OVC, and late-stage OVC birds were quantitatively characterized by label-free proteomics. Over 2600 proteins were identified in this study, 348 of which were differentially expressed by more than twofold (p ≤ 0.05) in early- and late-stage ovarian tumor tissue specimens relative to healthy ovarian tissues. Several of the 348 proteins are known to be differentially regulated in human cancers including B2M, CLDN3, EPCAM, PIGR, S100A6, S100A9, S100A11, and TPD52. Of particular interest was ovostatin 2 (OVOS2), a novel 165-kDa protease inhibitor found to be strongly upregulated in chicken ovarian tumors (p = 0.0005) and matched plasma (p = 0.003). Indeed, RT-quantitative PCR and Western blot analysis demonstrated that OVOS2 mRNA and protein were also upregulated in multiple human OVC cell lines compared to normal ovarian epithelia (NOE) cells and immunohistochemical staining confirmed overexpression of OVOS2 in primary human ovarian cancers relative to non-cancerous tissues. Collectively, these data provide the first evidence for involvement of OVOS2 in the pathogenesis of both chicken and human ovarian cancer.

With growing abundance and awareness of endocrine disrupting compounds (EDCs) in the environment, there is a need for accurate and reliable detection of EDC exposure. Our objective in the present study was to observe differences within and between the global plasma proteomes of sexually mature male and female white perch (Morone americana) before (Initial Control, IC) and after 17β-estradiol (E2 ) induction. Semiquantitative nanoLC-MS/MS data were analyzed by machine learning support vector machines (SVMs) and by two-way ANOVA. By ANOVA, the expression levels of 44, 77, and 57 proteins varied significantly by gender, treatment, and the interaction of gender and treatment, respectively. SVMs perfectly classified male and female perch IC and E2 -induced plasma samples using the protein expression data. E2 -induced male and female perch plasma proteomes contained significantly higher levels of the yolk precursors vitellogenin Aa and Ab (VtgAa, VtgAb), as well as latrophilin and seven transmembrane domain-containing protein 1 (Eltd1) and kininogen 1 (Kng1). This is the first report that Eltd1 and Kng1 may be E2 -responsive proteins in fishes and therefore may be useful indicators of estrogen induction., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

We established a predictive kinetic metabolic-flux model for the 21 enzymes and 24 metabolites of the monolignol biosynthetic pathway using Populus trichocarpa secondary differentiating xylem. To establish this model, a comprehensive study was performed to obtain the reaction and inhibition kinetic parameters of all 21 enzymes based on functional recombinant proteins. A total of 104 Michaelis-Menten kinetic parameters and 85 inhibition kinetic parameters were derived from these enzymes. Through mass spectrometry, we obtained the absolute quantities of all 21 pathway enzymes in the secondary differentiating xylem. This extensive experimental data set, generated from a single tissue specialized in wood formation, was used to construct the predictive kinetic metabolic-flux model to provide a comprehensive mathematical description of the monolignol biosynthetic pathway. The model was validated using experimental data from transgenic P. trichocarpa plants. The model predicts how pathway enzymes affect lignin content and composition, explains a long-standing paradox regarding the regulation of monolignol subunit ratios in lignin, and reveals novel mechanisms involved in the regulation of lignin biosynthesis. This model provides an explanation of the effects of genetic and transgenic perturbations of the monolignol biosynthetic pathway in flowering plants.

Instrument parameter values for a quadrupole Orbitrap mass spectrometer were optimized for performing global proteomic analyses. Fourteen factors were evaluated for their influence on data-dependent acquisition with an emphasis on both the rate of sequencing and spectral quality by maximizing two individually tested response variables (unique peptides and protein groups). Of the 14 factors, 12 factors were assigned significant contrast values (P < 0.05) for both response variables. Fundamentally, when optimizing parameters, a balance between spectral quality and duty cycle needs to be reached in order to maximize proteome coverage. This is especially true when using a data-dependent approach for sequencing complex proteomes. For example, maximum ion injection time, automatic gain control settings, and minimum threshold settings for triggering MS/MS isolation and activation all heavily influence ion signal, the number of spectra collected, and spectral quality. To better assess the effect these parameters have on data acquisition, all MS/MS data were parsed according to ion abundance by calculating the percent of the AGC target reached for each MS/MS event and then compared with successful peptide-spectrum matches. This proved to be an effective approach for understanding the effect of ion abundance on successful peptide-spectrum matches and establishing minimum ion abundance thresholds for triggering MS/MS isolation and activation.

The filamentous fungus Magnaporthe oryzae (M. oryzae) is the causative agent of rice blast disease and presents a significant threat to worldwide rice production. To establish the groundwork for future research on the pathogenic development of M. oryzae, a global proteomic study of conidia was performed. The filter aided sample preparation method (FASP) and anion StageTip fractionation combined with long, optimized shallow 210 min nanoLC gradients prior to mass spectrometry analysis on an Orbitrap XL was applied, which resulted in a doubling of protein identifications in comparison to our previous GeLC analysis. Herein, we report the identification of 2912 conidial proteins at a 1% protein false discovery rate (FDR) and we present the most extensive study performed on M. oryzae conidia to date. A similar distribution between identified proteins and the predicted proteome was observed when subcellular localization analysis was performed, suggesting the detected proteins build a representative portion of the predicted proteome. A higher percentage of cytoplasmic proteins (associated with translation, energy, and metabolism) were observed in the conidial proteome relative to the whole predicted proteome. Conversely, nuclear and extracellular proteins were less well represented in the conidial proteome. Further analysis by gene ontology revealed biological insights into identified proteins important for central metabolic processes and the physiology of conidia.

The quantification of intact proteins is a relatively recent development in proteomics. In eukaryotic organisms, proteins are present as multiple isoforms as the result of variations in genetic code, alternative splicing, post-translational modification and other processing events. Understanding the identities and biological functions of these isoforms and how their concentrations vary across different states is the central goal of proteomics. To date, the bulk of proteomics research utilizes a "bottom-up" approach, digesting proteins into their more manageable constitutive peptides, but sacrificing information about the specific isoform and combinations of post-translational modifications present on the protein. Very specific strategies for protein quantification such as the enzyme-linked immunosorbent assay and Western blot are commonplace in laboratories and clinics, but impractical for the study of global biological changes. Herein, we describe strategies for the quantification of intact proteins, their distinct advantages, and challenges to their employment. Techniques contained in this review include the more traditional and widely employed methodology of differential gel electrophoresis and more recently developed mass spectrometry-based techniques including metabolic labeling, chemical labeling, and label-free methodologies.

Fractionation prior to mass spectrometry is an indispensable step in proteomics. In this paper we report the success of performing offline reversed phase high pressure liquid chromatography (HPLC) fractionation on a C18 2.0 mm×150 mm column at the peptide level with microliter per minute flow rates prior to online nano-flow reversed phase liquid chromatography mass spectrometry (nanoLC-MS) using the well-studied fungus Saccharomyces cerevisiae. A C18 75 μm×150 mm column was used online and the online elution gradients for each fraction were adjusted in order to obtain well resolved separation. Comparing this method directly to only performing nanoLC-MS we observed a 61.6% increase in the number of identified proteins. At a 1% false discovery rate 1028 proteins were identified using two dimensions of RPLC versus 636 proteins identified in a single nano-flow separation. The majority of proteins identified by one dimension of nano-LC were present in the proteins identified in our two dimensional strategy. Although increasing analysis time, this non-orthogonal and facile pre-fractionation method affords a more comprehensive examination of the proteome., (Copyright © 2011 Elsevier B.V. All rights reserved.)

The domestic chicken (Gallus domesticus) has emerged as a powerful experimental model for studying the onset and progression of spontaneous epithelial ovarian cancer (EOC) with a disease prevalence that can exceed 35% between 2 and 7 years of age. An experimental strategy for biomarker discovery is reported herein that combines the chicken model of EOC, longitudinal plasma sample collection with matched tissues, advanced mass spectrometry-based proteomics, and concepts derived from the index of individuality (Harris, Clin Chem 20: 1535-1542, 1974). Blood was drawn from 148 age-matched chickens starting at 2.5 years of age every 3 months for 1 year. At the conclusion of the 1 year sample collection period, the 73 birds that remained alive were euthanized, necropsied, and tissues were collected. Pathological assessment of resected tissues from these 73 birds confirmed that five birds (6.8%) developed EOC. A proteomics workflow including in-gel digestion, nanoLC coupled to high-performance mass spectrometry, and label-free (spectral counting) quantification was used to measure the biological intra-individual variability (CV(W)) of the chicken plasma proteome. Longitudinal plasma sample sets from two birds within the 73-bird biorepository were selected for this study; one bird was considered "healthy" and the second bird developed late-stage EOC. A total of 116 proteins from un-depleted plasma were identified with 80 proteins shared among all sample sets. Analytical variability (CV(A)) of the label-free proteomics workflow was measured using a single plasma sample analyzed five times and was found to be ≥CV(W) in both birds for 16 proteins (20%) and in either bird for 25 proteins (31%). Ovomacroglobulin (ovostatin) was found to increase (p < 0.001) over a 6 month period in the late-stage EOC bird providing an initial candidate protein for further investigation.

Human embryonic stem cells (hESCs) are self-renewing pluripotent cells with relevance to treatment of numerous medical conditions. However, a global understanding of the role of the hESC proteome in maintaining pluripotency or triggering differentiation is still largely lacking. The emergence of top-down proteomics has facilitated the identification and characterization of intact protein forms that are not readily apparent in bottom-up studies. Combined with metabolic labeling techniques such as stable isotope labeling by amino acids in cell culture (SILAC), quantitative comparison of intact protein expression under differing experimental conditions is possible. Herein, quantitative top-down proteomics of hESCs is demonstrated using the SILAC method and nano-flow reverse phase chromatography directly coupled to a linear-ion-trap Fourier transform ion cyclotron resonance mass spectrometer (nLC-LTQ-FT-ICR-MS). In this study, which to the best of our knowledge represents the first top-down analysis of hESCs, we have confidently identified 11 proteins by accurate intact mass, MS/MS, and amino acid counting facilitated by SILAC labeling. Although quantification is challenging due to the incorporation of multiple labeled amino acids (i.e., lysine and arginine) and arginine to proline conversion, we are able to quantitatively account for these phenomena using a mathematical model., (Copyright 2010. Published by Elsevier Inc.)

Biomarker discovery and proteomics have become synonymous with mass spectrometry in recent years. Although this conflation is an injustice to the many essential biomolecular techniques widely used in biomarker-discovery platforms, it underscores the power and potential of contemporary mass spectrometry. Numerous novel and powerful technologies have been developed around mass spectrometry, proteomics, and biomarker discovery over the past 20 years to globally study complex proteomes (e.g., plasma). However, very few large-scale longitudinal studies have been carried out using these platforms to establish the analytical variability relative to true biological variability. The purpose of this review is not to cover exhaustively the applications of mass spectrometry to biomarker discovery, but rather to discuss the analytical methods and strategies that have been developed for mass spectrometry-based biomarker-discovery platforms and to place them in the context of the many challenges and opportunities yet to be addressed.

Cerebrospinal fluid (CSF) has become one of the most frequently used biological medium for physiological studies for neurological disorders due to its proximity to the brain and clinical availability; however, before undertaking a rational approach to biomarker discovery or diagnostics, it is crucial to understand the underlying characteristics of CSF proteome in subpopulations. In this study, we examined the differential expression of proteins in pooled male and female CSF utilizing isobaric tags for relative and absolute quantification (iTRAQ) reagents after the depletion of six high abundant proteins using a multiple affinity removal system (MARS). A total of 219 proteins were identified (95% confidence level), and 12 proteins showed difference in expression levels. Eleven out of 12 differentially expressed proteins showed ratios of male/female between 1.15 and 1.29 (duplicate average), indicating a remarkable similarity between male and female CSF. One notable exception was the slightly lower expression level of ceruloplasmin (ferroxidase) in male CSF (0.81), a copper containing protein that catalyzes the conversion of ferrous iron to ferric iron with antioxidant properties. We also examined the levels of ceruloplasmin in each individual patient sample which constituted the pooled CSF using Western blot analysis which confirmed the lower expression levels of ceruloplasmin in male CSF.

16O/18O labeling is one differential proteomics technology among many that promises diagnostic and prognostic biomarkers of disease. Although the incorporation of 18O in the C-terminal carboxyl group during endoproteinase digestion in the presence of H2 18O makes the process of labeling facile, the ease and effectiveness of label incorporation have in some regards been outweighed by the difficulties in interpreting the resulting spectra. Complex isotope patterns result from the composition of unlabeled (18O(0)), singly labeled (18O(1)), and doubly labeled species (18O(2)) as well as contributions from the naturally occurring isotopes (e.g. 13C and 15N). Moreover because labeling is enzymatic, the number of 18O atoms incorporated can vary from peptide to peptide. Finally it is difficult to distinguish highly up-regulated from highly down-regulated or C-terminal peptides. We have developed an algorithm entitled regression analysis applied to mass spectrometry (RAAMS) that automatically, rapidly, and confidently interprets spectra of 18O-labeled peptides without requiring chemical composition information derived from product ion spectra. The algorithm is able to measure the effective 18O incorporation rate due to variable enzyme substrate specificity of the pseudosubstrate during the isotope exchange reaction and corrects for the 18O(0) abundance that remains in the labeled sample when using a two-step digestion/labeling procedure. We have also incorporated a method for distinguishing pure 18O(0) from pure 18O(2) peptides utilizing impure H2 18O. The algorithm operates on centroided peak lists and is therefore very fast: nine chromatograms of, on average, 1,168 spectra and containing, on average, 6,761 isotopic clusters were interpreted in, on average, 45 s per chromatogram. RAAMS is fast enough (average, 38 ms/spectrum) to allow the possibility of performing information-dependent MS/MS on a chromatographic time scale on species exceeding predetermined ratio thresholds. We describe in detail the operation of the algorithm and demonstrate its use on datasets with known and unknown ratios.

Early studies of Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry (MS) explored many of the fundamental issues surrounding the potential of the technique to provide quantitative data. Improvements in instrument technology and the analysis of larger molecules in increasingly complex mixtures warrant not only a revisit to some of these earlier studies, but a more comprehensive examination of the influence of various instrument parameters on quantitative (absolute and relative) measurements in proteomics. We present a detailed examination of the role that acquisition time, excite voltage (i.e. excite radius), trapping voltage, and the type of excitation waveform have on the ability of FT-ICR to accurately quantify biological molecules. The use of a stable-isotope-labeled and unlabeled phenyl isocyanate derivatized peptide allows us to ascribe the effects of FT-ICR-MS on quantification, thus eliminating the contribution of ionization differences to ion abundance. To adequately assess the multiple parameters in the large dataset, we develop a multiplicative quality factor that encompasses the total ion abundance, as well as the accuracy and the precision of abundance ratios. This assessment allows facile determination of optimal instrument parameters for quantitative measurements., (Copyright (c) 2007 John Wiley & Sons, Ltd.)

Relative protein expression levels were compared in leukemic B cells from two patients with chronic lymphocytic leukemia (CLL) having either mutated (M-CLL) or unmutated (UM-CLL) immunoglobulin variable heavy chain genes (IgV(H)). Cells were separated into cytosol and membrane protein fractions then labeled with acid-cleavable ICAT reagents (cICAT). Labeled proteins were digested with trypsin then subjected to SCX and affinity chromatography followed by LC-ESI-MS/MS analysis on a linear ion trap mass spectrometer. A total of 9 proteins from the cytosol fraction and 4 from the membrane fraction showed a 3-fold or greater difference between M-CLL and UM-CLL and a subset of these were examined by Western blot where results concurred with cICAT abundance ratios. The abundance of one of the proteins in particular, the mitochondrial membrane protein cytochrome c oxidase subunit COX G was examined in 6 M-CLL and 6 UM-CLL patients using western blot and results showed significantly greater levels (P < 0.001) in M-CLL patients vs UM-CLL patients. These results demonstrate that stable isotope labeling and mass spectrometry can complement 2D gel electrophoresis and gene microarray technologies for identifying putative and perhaps unique prognostic markers in CLL.

A method is described for the identification and relative quantification of proteomes using accurate mass tags (AMT) generated by nLC-dual ESI-FT-ICR-MS on a 7T instrument in conjunction with stable isotope labeling using 16O/18O ratios. AMTs were used for putative peptide identification, followed by confirmation of peptide identity by tandem mass spectrometry. For a combined set of 58 tryptic peptides from bovine serum albumin (BSA) and human transferrin, a mean mass measurement accuracy of 1.9 ppm +/-0.94 ppm (CIM99%) was obtained. This subset of tryptic peptides was used to measure 16O/18O ratios of 0.36 +/- 0.09 (CIM99%) for BSA (micro = 0.33) and 1.48 +/- 0.47 (CIM99%) for transferrin (micro = 1.0) using a method for calculating 16O/18O ratios from overlapping isotopic multiplets arising from mixtures of 16O, 18O1, and 18O2 labeled C-termini. The model amino acid averagine was used to calculate a representative molecular formula for estimating and subtracting the contributions of naturally occurring isotopes solely as a function of peptide molecular weight. The method was tested against simulated composite 16O/18O spectra where peptide molecular weight, 16O/18O ratio, 18O1/18O2 ratios, and number of sulfur atoms were varied. Relative errors of 20% or less were incurred when the 16O/18O ratios were less than three, even for peptides where the number of sulfur atoms was over- or under-estimated. These data demonstrate that for biomarker discovery, it is advantageous to label the proteome representing the disease state with 18O; and the method is not sensitive to variations in 18O1/18O2 ratio. This approach allows a comprehensive differentiation of expression levels and tentative identification via AMTs, followed by targeted analysis of over- and under-expressed peptides using tandem mass spectrometry, for applications such as the discovery of disease biomarkers.

A universal dual-electrospray (ESI) source is demonstrated on a quadrupole orthogonal-accelerated time-of-flight mass spectrometer (Q-ToF-MS) for both genomic and proteomic applications. This facile source modification enables internal calibration for consistent mass measurements by a mainstream MS platform and requires no mixing of analyte and calibrant prior to ion formation. In this report, the dual-sprayer is demonstrated in the negative-ion mode for internal calibration of polymerase chain reaction (PCR) amplicons generated from synthetic and genomic templates as well as a proteolytic digest of a naturally phosphorylated protein. For all PCR amplicons, experimentally determined average mass measurements are well within the instrument specifications of better than 0.01%. For the proteolytic fragments of the phosphoprotein, average mass errors of the isotopically resolved peptides are better than 10 ppm.