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.Keywords: absolute quantification; cellulose biosynthesis; PC-IDMS; shotgun discovery proteomics; SRM; targeted mass spectrometry; transcription factor;

Workflows in bottom-up proteomics have traditionally implemented the use of proteolysis during sample preparation; enzymatic digestion is most commonly performed using trypsin. This results in the hydrolysis of peptide bonds forming tryptic peptides, which can then be subjected to LC–MS/MS analysis. While the structure, specificity, and kinetics of trypsin are well characterized, a lack of consensus and understanding has remained regarding fundamental parameters critical to obtaining optimal data from a proteomics experiment. These include the type of trypsin used, pH during digestion, incubation temperature as well as enzyme-to-substrate ratio. Through the use of design of experiments (DOE), we optimized these parameters, resulting in deeper proteome coverage and a greater dynamic range of measurement. The knowledge gained from optimization of a discovery-based proteomics experiment was applied to targeted LC–MS/MS experiments using protein cleavage-isotope dilution mass spectrometry for absolute quantification. We demonstrated the importance of these digest parameters with respect to our limit of detection as well as our ability to acquire more accurate quantitative measurements. Additionally, we were able to quantitatively account for peptide decay observed in previous studies, caused by nonspecific activity of trypsin. The tryptic digest optimization described here has eliminated this previously observed peptide decay as well as provided a greater understanding and standardization for a common but critical sample treatment used across the field of proteomics.Keywords: design of experiments; LC−MS/MS; Populus trichocarpa; proteolytic digestion;

Understanding the rearrangement of gas-phase ions via tandem mass spectrometry is critical to improving manual and automated interpretation of complex datasets. N-glycan analysis may be carried out under collision induced (CID) or higher energy collision dissociation (HCD), which favors cleavage at the glycosidic bond. However, fucose migration has been observed in tandem MS, leading to the formation of new bonds over four saccharide units away. In the following work, we report the second instance of saccharide migration ever to occur for N-glycans. Using horseradish peroxidase as a standard, the beta-1,2 xylose was observed to migrate from a hexose to a glucosamine residue on the (Xyl)Man3GlcNac2 glycan. This investigation was followed up in a complex N-linked glycan mixture derived from stem differentiating xylem tissue, and the rearranged product ion was observed for 75% of the glycans. Rearrangement was not favored in isomeric glycans with a core or antennae fucose and unobserved in glycans predicted to have a permanent core-fucose modification. As the first empirical observation of this rearrangement, this work warrants dissemination so it may be searched in de novo sequencing glycan workflows.

Mass spectrometry (MS) has emerged as a tool that can analyze nearly all classes of molecules, with its scope rapidly expanding in the areas of post-translational modifications, MS instrumentation, and many others. Yet integration of novel analyte preparatory and purification methods with existing or novel mass spectrometers can introduce new challenges for MS sensitivity. The mechanisms that govern detection by MS are particularly complex and interdependent, including ionization efficiency, ion suppression, and transmission. Performance of both off-line and MS methods can be optimized separately or, when appropriate, simultaneously through statistical designs, broadly referred to as “design of experiments” (DOE). The following review provides a tutorial-like guide into the selection of DOE for MS experiments, the practices for modeling and optimization of response variables, and the available software tools that support DOE implementation in any laboratory. This review comes 3 years after the latest DOE review (Hibbert DB, 2012), which provided a comprehensive overview on the types of designs available and their statistical construction. Since that time, new classes of DOE, such as the definitive screening design, have emerged and new calls have been made for mass spectrometrists to adopt the practice. Rather than exhaustively cover all possible designs, we have highlighted the three most practical DOE classes available to mass spectrometrists. This review further differentiates itself by providing expert recommendations for experimental setup and defining DOE entirely in the context of three case-studies that highlight the utility of different designs to achieve different goals. A step-by-step tutorial is also provided.

A high resolving power shotgun lipidomics strategy using gas-phase fractionation and data-dependent acquisition (DDA) was applied toward comprehensive characterization of lipids in a hen ovarian tissue in an untargeted fashion. Using this approach, a total of 822 unique lipids across a diverse range of lipid categories and classes were identified based on their MS/MS fragmentation patterns. Classes of glycerophospholipids and glycerolipids, such as glycerophosphocholines (PC), glycerophosphoethanolamines (PE), and triglycerides (TG), are often the most abundant peaks observed in shotgun lipidomics analyses. These ions suppress the signal from low abundance ions and hinder the chances of characterizing low abundant lipids when DDA is used. These issues were circumvented by utilizing gas-phase fractionation, where DDA was performed on narrow m/z ranges instead of a broad m/z range. Employing gas-phase fractionation resulted in an increase in sensitivity by more than an order of magnitude in both positive- and negative-ion modes. Furthermore, the enhanced sensitivity increased the number of lipids identified by a factor of ≈4, and facilitated identification of low abundant lipids from classes such as cardiolipins that are often difficult to observe in untargeted shotgun analyses and require sample-specific preparation steps prior to analysis. This method serves as a resource for comprehensive profiling of lipids from many different categories and classes in an untargeted manner, as well as for targeted and quantitative analyses of individual lipids. Furthermore, this comprehensive analysis of the lipidome can serve as a species- and tissue-specific database for confident identification of other MS-based datasets, such as mass spectrometry imaging.

Quantitative methods for detection of biological molecules are needed more than ever before in the emerging age of “omics” and “big data.” Here, we provide an integrated approach for systematic analysis of the “lipidome” in tissue. To test our approach in a biological context, we utilized brain tissue selectively deficient for the transcription factor Specificity Protein 2 (Sp2). Conditional deletion of Sp2 in the mouse cerebral cortex results in developmental deficiencies including disruption of lipid metabolism. Silver (Ag) cationization was implemented for infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) to enhance the ion abundances for olefinic lipids, as these have been linked to regulation by Sp2. Combining Ag-doped and conventional IR-MALDESI imaging, this approach was extended to IR-MALDESI imaging of embryonic mouse brains. Further, our imaging technique was combined with bottom-up shotgun proteomic LC-MS/MS analysis and western blot for comparing Sp2 conditional knockout (Sp2-cKO) and wild-type (WT) cortices of tissue sections. This provided an integrated omics dataset which revealed many specific changes to fundamental cellular processes and biosynthetic pathways. In particular, step-specific altered abundances of nucleotides, lipids, and associated proteins were observed in the cerebral cortices of Sp2-cKO embryos.

High-throughput, quantitative processing of N-linked glycans would facilitate large-scale studies correlating the glycome with disease and open the field to basic and applied researchers. We sought to meet these goals by coupling filter-aided-N-glycan separation (FANGS) to the individuality normalization when labeling with glycan hydrazide tags (INLIGHT) for analysis of plasma. A quantitative comparison of this method was conducted against solid phase extraction (SPE), a ubiquitous and trusted method for glycan purification. We demonstrate that FANGS–INLIGHT purification was not significantly different from SPE in terms of glycan abundances, variability, functional classes, or molecular weight distributions. Furthermore, to increase the depth of glycome coverage, we executed a definitive screening design of experiments (DOE) to optimize the MS parameters for glycan analyses. We optimized MS parameters across five N-glycan responses using a standard glycan mixture, translated these to plasma and achieved up to a 3-fold increase in ion abundances.

An early-stage, population-wide biomarker for ovarian cancer (OVC) is essential to reverse its high mortality rate. Aberrant glycosylation by OVC has been reported, but studies have yet to identify an N-glycan with sufficiently high specificity. We curated a human biorepository of 82 case-control plasma samples, with 27%, 12%, 46%, and 15% falling across stages I–IV, respectively. For relative quantitation, glycans were analyzed by the individuality normalization when labeling with glycan hydrazide tags (INLIGHT) strategy for enhanced electrospray ionization, MS/MS analysis. Sixty-three glycan cancer burden ratios (GBRs), defined as the log10 ratio of the case-control extracted ion chromatogram abundances, were calculated above the limit of detection. The final GBR models, built using stepwise forward regression, included three significant terms: OVC stage, normalized mean GBR, and tag chemical purity; glycan class, fucosylation, or sialylation were not significant variables. After Bonferroni correction, seven N-glycans were identified as significant (p < 0.05), and after false discovery rate correction, an additional four glycans were determined to be significant (p < 0.05), with one borderline (p = 0.05). For all N-glycans, the vectors of the effects from stages II–IV were sequentially reversed, suggesting potential biological changes in OVC morphology or in host response.

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.