Improving proteome coverage on a LTQ-Orbitrap using design of experiments

Establishing Quality Control Procedures for Large-Scale Plasma Proteomics Analyses

Proteomics research has been transformed due to high-throughput liquid chromatography (LC-MS/MS) tandem mass spectrometry instruments combined with highly sophisticated automated sample preparation and multiplexing workflows. However, scaling proteomics experiments to large sample cohorts (hundreds to thousands) requires thoughtful quality control (QC) protocols. Robust QC protocols can help with reproducibility, quantitative accuracy, and provide opportunities for more decisive troubleshooting. Our laboratory conducted a plasma proteomics study of a cohort of N = 335 patient samples using tandem mass tag (TMT_pro) 16-plex batches. Over the course of a 10-month data acquisition period for this cohort we collected 271 pooled QC LC-MS/MS result files obtained from MS/MS analysis of a patient-derived pooled plasma sample, representative of the entire cohort population. This sample was tagged with TMT_zero or TMT_pro reagents and used to inform the daily performance of the LC-MS/MS instruments and to allow within and across sample batch normalization. Analytical variability of a number of instrumental and data analysis metrics including protein and peptide identifications, peptide spectral matches (PSMs), number of obtained MS/MS spectra, average peptide abundance, percent of peptides with a Δ m/z between ±0.003 Da, percent of MS/MS spectra obtained at the maximum injection time, and the retention time of selected tracking peptides were evaluated to help inform the design of a robust LC-MS/MS QC workflow for use in future cohort studies. This study also led to general tips for using selected metrics to inform real-time troubleshooting of LC-MS/MS performance issues with daily QC checks.

Discovery Proteomics and Absolute Protein Quantification Can Be Performed Simultaneously on an Orbitrap-Based Mass Spectrometer

Mass spectrometry (MS) has steadily moved into the forefront of quantification-centered protein research. Protein cleavage isotope dilution MS is a proven way for quantifying proteins by using an isotope-labeled analogue of a peptide fragment of the parent protein as an internal standard. Parallel reaction monitoring (PRM) has become the go-to approach for such quantification on an Orbitrap-based instrument as it is assumed that the instrument sensitivity is enhanced. We performed a comparative study on data-dependent acquisition (DDA) and PRM-based workflows to quantify egg yolk protein precursors or vitellogenins (VTGs) Aa, Ab, and C in striped bass (Morone saxatilis). VTG proportions serve as a developmental measure of egg quality, possibly changing with the environment, and have been studied as an indicator of the health of North Carolina stocks. Based on single-factor analysis of variance comparisons of mean VTG amounts across fish from the same sample groupings, our results indicate that there is no statistical difference between MS1-based and MS2-based VTG quantification. We further conclude that DDA is able to deliver both discovery data and absolute quantification data in the same experiment.

Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides

Method optimization is crucial for successful mass spectrometry (MS) analysis. However, extensive method assessments, altering various parameters individually, are rarely performed due to practical limitations regarding time and sample quantity. To maximize sample space for optimization while maintaining reasonable instrumentation requirements, a definitive screening design (DSD) is leveraged for systematic optimization of data-independent acquisition (DIA) parameters to maximize crustacean neuropeptide identifications. While DSDs require several injections, a library-free methodology enables surrogate sample usage for comprehensive optimization of MS parameters to assess biomolecules from limited samples. We identified several parameters contributing significant first- or second-order effects to method performance, and the DSD model predicted ideal values to implement. These increased reproducibility and detection capabilities enabled the identification of 461 peptides, compared to 375 and 262 peptides identified through data-dependent acquisition (DDA) and a published DIA method for crustacean neuropeptides, respectively. Herein, we demonstrate a DSD optimization workflow, using standard material, not reliant on spectral libraries for the analysis of any low abundance molecules from previous samples of limited availability. This extends the DIA method to low abundance isoforms dysregulated or only detectable in disease samples, thus improving characterization of previously inaccessible biomolecules, such as neuropeptides. Data are available via ProteomeXchange with identifier PXD038520.

Non-target molecular network and putative genes of flavonoid biosynthesis in Erythrina velutina Willd., a Brazilian semiarid native woody plant

Erythrina velutina is a Brazilian native tree of the Caatinga (a unique semiarid biome). It is widely used in traditional medicine showing anti-inflammatory and central nervous system modulating activities. The species is a rich source of specialized metabolites, mostly alkaloids and flavonoids. To date, genomic information, biosynthesis, and regulation of flavonoids remain unknown in this woody plant. As part of a larger ongoing research goal to better understand specialized metabolism in plants inhabiting the harsh conditions of the Caatinga, the present study focused on this important class of bioactive phenolics. Leaves and seeds of plants growing in their natural habitat had their metabolic and proteomic profiles analyzed and integrated with transcriptome data. As a result, 96 metabolites (including 43 flavonoids) were annotated. Transcripts of the flavonoid pathway totaled 27, of which EvCHI, EvCHR, EvCHS, EvCYP75A and EvCYP75B1 were identified as putative main targets for modulating the accumulation of these metabolites. The highest correspondence of mRNA vs. protein was observed in the differentially expressed transcripts. In addition, 394 candidate transcripts encoding for transcription factors distributed among the bHLH, ERF, and MYB families were annotated. Based on interaction network analyses, several putative genes of the flavonoid pathway and transcription factors were related, particularly TFs of the MYB family. Expression patterns of transcripts involved in flavonoid biosynthesis and those involved in responses to biotic and abiotic stresses were discussed in detail. Overall, these findings provide a base for the understanding of molecular and metabolic responses in this medicinally important species. Moreover, the identification of key regulatory targets for future studies aiming at bioactive metabolite production will be facilitated.

Developing mass spectrometry for the quantitative analysis of neuropeptides

INTRODUCTION

Neuropeptides are signaling molecules originating in the neuroendocrine system that can act as neurotransmitters and hormones in many biochemical processes. Their exact function is difficult to characterize, however, due to dependence on concentration, post-translational modifications, and the presence of other comodulating neuropeptides. Mass spectrometry enables sensitive, accurate, and global peptidomic analyses that can profile neuropeptide expression changes to understand their roles in many biological problems, such as neurodegenerative disorders and metabolic function.

AREAS COVERED

We provide a brief overview of the fundamentals of neuropeptidomic research, limitations of existing methods, and recent progress in the field. This review is focused on developments in mass spectrometry and encompasses labeling strategies, post-translational modification analysis, mass spectrometry imaging, and integrated multi-omic workflows, with discussion emphasizing quantitative advancements.

EXPERT OPINION

Neuropeptidomics is critical for future clinical research with impacts in biomarker discovery, receptor identification, and drug design. While advancements are being made to improve sensitivity and accuracy, there is still room for improvement. Better quantitative strategies are required for clinical analyses, and these methods also need to be amenable to mass spectrometry imaging, post-translational modification analysis, and multi-omics to facilitate understanding and future treatment of many diseases.

Identification, characterization and control of a sequence variant in monoclonal antibody drug product: a case study

Sequence variants (SV) in protein bio therapeutics can be categorized as unwanted impurities and may raise serious concerns in efficacy and safety of the product. Early detection of specific sequence modifications, that can result in altered physicochemical and or biological properties, is therefore desirable in product manufacturing. Because of their low abundance, and finite resolving power of conventional analytical techniques, they are often overlooked in early drug development. Here, we present a case study where trace amount of a sequence variant is identified in a monoclonal antibody (mAb) based therapeutic protein by LC-MS/MS and the structural and functional features of the SV containing mAb is assessed using appropriate analytical techniques. Further, a very sensitive selected reaction monitoring (SRM) technique is developed to quantify the SV which revealed both prominent and inconspicuous nature of the variant in process chromatography. We present the extensive characterization of a sequence variant in protein biopharmaceutical and first report on control of sequence variants to < 0.05% in final drug product by utilizing SRM based mass spectrometry method during the purification steps.

Identification of 7 000-9 000 Proteins from Cell Lines and Tissues by Single-Shot Microflow LC-MS/MS

A current trend in proteomics is to acquire data in a "single-shot" by LC-MS/MS because it simplifies workflows and promises better throughput and quantitative accuracy than schemes that involve extensive sample fractionation. However, single-shot approaches can suffer from limited proteome coverage when performed by data dependent acquisition (ssDDA) on nanoflow LC systems. For applications where sample quantities are not scarce, this study shows that high proteome coverage can be obtained using a microflow LC-MS/MS system operating a 1 mm i.d. × 150 mm column, at a flow-rate of 50 μL/min and coupled to an Orbitrap HF-X mass spectrometer. The results demonstrate the identification of ∼9 000 proteins from 50 μg of protein digest from Arabidopsis roots, 7 500 from mouse thymus, and 7 300 from human breast cancer cells in 3 h of analysis time in a single run. The dynamic range of protein quantification measured by the iBAQ approach spanned 5 orders of magnitude and replicate analysis showed that the median coefficient of variation was below 20%. Together, this study shows that ssDDA by μLC-MS/MS is a robust method for comprehensive and large-scale proteome analysis and which may be further extended to more rapid chromatography and data independent acquisition approaches in the future.̀.

Mass Spectrometric Profiling of Neuropeptides in Response to Copper Toxicity via Isobaric Tagging

Copper is a necessary nutrient but quickly becomes toxic at elevated levels. To properly handle environmental copper influxes and maintain metal homeostasis, organisms utilize various methods to chelate, excrete, and metabolize heavy metals. These mechanisms are believed to involve complex signaling pathways mediated by neuropeptides. This study incorporates custom N,N-dimethyl leucine isobaric tags to characterize the neuropeptidomic changes after different time points (1, 2, and 4 h) of copper exposure in a model organism, blue crab, Callinectes sapidus. Using a modified simplex optimization strategy, the number of identifiable and quantifiable neuropeptides was increased 3-fold to facilitate a deeper understanding of the signaling pathways involved in responding to heavy metal exposure. The time course exposure showed many interesting findings, including upregulation of inhibitory allatostatin peptides in the pericardial organs. Additionally, there was evidence of transport of a pigment dispersing hormone from the sinus glands to the brain. Overall, this study improves the multiplexing capabilities of neuropeptidomic studies to understand the temporal changes associated with copper toxicity.

Proteomic profile of pre-implantational ovine embryos produced in vivo

The present study was conducted to decipher the proteome of in vivo-produced pre-implantation ovine embryos. Ten locally adapted Morana Nova ewes received hormonal treatment and were inseminated 12 hr after ovulation. Six days later, 54 embryos (morula and blastocyst developmental state) were recovered from eight ewes and pooled to obtain sufficient protein for proteomic analysis. Extracted embryo proteins were analysed by LC-MS/MS, followed by identification based on four database searches (PEAKS, Proteome Discoverer software, SearchGUI software, PepExplorer). Identified proteins were analysed for gene ontology terms, protein clusters and interactions. Genes associated with the ovine embryo proteome were screened for miRNA targets using data sets of TargetScan (http://www.targetscan.org) and mIRBase (http://www.mirbase.org) servers. There were 667 proteins identified in the ovine embryos. Biological processes of such proteins were mainly related to cellular process and regulation, and molecular functions, to binding and catalytic activity. Analysis of the embryo proteins revealed 49 enriched functional clusters, linked to energy metabolism (TCA cycle, pyruvate and glycolysis metabolism), zona pellucida (ZP), MAPK signalling pathway, tight junction, binding of sperm to ZP, translation, proteasome, cell cycle and calcium/phospholipid binding. Sixteen miRNAs were related to 25 pre-implantation ovine embryo genes, all conserved in human, bovine and ovine species. The interaction network generated by miRNet showed four key miRNAs (hsa-mir-106b-5p; hsa-mir-30-5p; hsa-mir-103a-5p and hsa-mir-106a-5p) with potential interactions with embryo-expressed genes. Functional analysis of the network indicated that miRNAs modulate genes related to cell cycle, regulation of stem cell and embryonic cell differentiation, among others. Retrieved miRNAs also modulate the expression of genes involved in cell signalling pathways, such as MAPK, Wnt, TGF-beta, p53 and Toll-like receptor. The current study describes the first major proteomic profile of 6-day-old ovine embryos produced in vivo, setting a comprehensive foundation for our understanding of embryo physiology in the ovine species.

A classification of liquid chromatography mass spectrometry techniques for evaluation of chemical composition and quality control of traditional medicines

Natural products (NPs) and traditional medicines (TMs) are used for treatment of various diseases and also to develop new drugs. However, identification of drug leads within the immense biodiversity of living organisms is a challenging task that requires considerable time, labor, and computational resources as well as the application of modern analytical instruments. LC-MS platforms are widely used for both drug discovery and quality control of TMs and food supplements. Moreover, a large dataset generated during LC-MS analysis contains valuable information that could be extracted and handled by means of various data mining and statistical tools. Novel sophisticated LC-MS based approaches are being introduced every year. Therefore, this review is prepared for the scientists specialized in pharmacognosy and analytical chemistry of NPs as well as working in related areas, in order to navigate them in the world of diverse LC-MS based techniques and strategies currently employed for NP discovery and dereplication, quality control, pattern recognition and sample comparison, and also in targeted and untargeted metabolomic studies. The suggested classification system includes the following LC-MS based procedures: elemental composition determination, isotopic fine structure analysis, mass defect filtering, de novo identification, clustering of the compounds in Molecular Networking (MN), diagnostic fragment ion (or neutral loss) filtering, manual dereplication using MS/MS data, database-assisted peak annotation, annotation of spectral trees, MS fingerprinting, feature extraction, bucketing of LC-MS data, peak profiling, predicted metabolite screening, targeted quantification of biomarkers, quantitative analysis of multi-component system, construction of chemical fingerprints, multi-targeted and untargeted metabolite profiling.

Chemometrics-assisted optimization of liquid chromatography-quadrupole-time-of-flight mass spectrometry analysis for targeted metabolomics

Mass spectrometry-based metabolomics is characterized by a vast number of variables leading to a great degree of complexity. In this work, we aimed to simplify this process with a stepped chemometric optimization of the both funnel technology (funnel exit DC, FDC; funnel RF LP, FLC; funnel RF HP, FRP) and ion source parameters (Octopolo, Oct; and Fragmentor, Frag) of a quadrupole-time of flight (qTOF) for a human urinary metabolites. The workflow comprised a Box-Behnken experimental design with 47 experiments followed by the identification and quantification of a set of metabolites using high-resolution full-scan MS mode and feature extraction with an inclusion list. Metabolite peak areas were grouped according to abundance (high and low) and modeled by Random Forest regression (variance explained >85%). The full three-level factorial design consisting in 243 experiments was predicted and top 10 solutions for desirability function and those comprising the Pareto front were extracted and investigated. To guarantee the quality of results, we compared the Pareto front solutions with those achieved by standard instrumental parameters suggested by the manufacturer. A set of five solutions were identified that increased the mean peak area by 56-59% and 17%, for high- and low-abundance metabolites, respectively. The optimal parameters were determined to be: FLP, 100 V; FDC, 40 and 30 V; Frag, 275 and 400 V; and Oct, 600 and 800 V. The methodology applied throughout this work represents a flexible strategy to optimize instrumental parameters and exploit the performance of a qTOF MS detector.

Comparative proteome analysis of the capsule from patients with frozen shoulder

BACKGROUND

The etiology of frozen shoulder (FS) is unclear. Accordingly, this study used a label-free quantitative shotgun proteomic approach to elucidate the pathogenesis of FS based on protein expression levels.

METHODS

Tissue samples from the rotator interval (RI), middle glenohumeral ligament (MGHL), and anterior-inferior glenohumeral ligament (IGHL) were collected from 12 FSs with severe stiffness and 7 shoulders with a rotator cuff tear (RCT) as controls. Protein mixtures were digested and analyzed by nano-liquid chromatography/electrospray ionization-tandem mass spectrometry. Relative protein expression levels were calculated by the signal intensity of identified peptide ions on mass spectra. Differentially expressed proteins between FS and RCT samples were evaluated by a gene enrichment analysis using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes.

RESULTS

We identified 1594 proteins, 1358 of which were expressed in all 6 tissue groups. We detected more upregulated proteins in the upper (RI and MGHL) FS groups and the lower (IGHL) RCT group than in the comparative groups, respectively. Various proteins with functions in tissue repair, collagen metabolism and fibrillation, cell-cell and cell-matrix adhesion, blood coagulation, and the immune response were expressed more highly in the RI and MGHL FS groups than in the RCT group. Proteins with functions in phagocytosis, glutathione metabolism, retinoid metabolism, and cholesterol metabolism were expressed more highly in the IGHL RCT group than in the FS group.

CONCLUSIONS

The pathophysiology of FS differs between the upper and lower parts of the joint capsule. Different treatment strategies for FS may be appropriate, depending on the location.

Quality control in mass spectrometry-based proteomics

Mass spectrometry is a highly complex analytical technique and mass spectrometry-based proteomics experiments can be subject to a large variability, which forms an obstacle to obtaining accurate and reproducible results. Therefore, a comprehensive and systematic approach to quality control is an essential requirement to inspire confidence in the generated results. A typical mass spectrometry experiment consists of multiple different phases including the sample preparation, liquid chromatography, mass spectrometry, and bioinformatics stages. We review potential sources of variability that can impact the results of a mass spectrometry experiment occurring in all of these steps, and we discuss how to monitor and remedy the negative influences on the experimental results. Furthermore, we describe how specialized quality control samples of varying sample complexity can be incorporated into the experimental workflow and how they can be used to rigorously assess detailed aspects of the instrument performance.

Deducing the presence of proteins and proteoforms in quantitative proteomics

The human genome harbors just 20,000 genes suggesting that the variety of possible protein products per gene plays a significant role in generating functional diversity. In bottom-up proteomics peptides are mapped back to proteins and proteoforms to describe a proteome; however, accurate quantitation of proteoforms is challenging due to incomplete protein sequence coverage and mapping ambiguities. Here, we demonstrate that a new software tool called ProteinClusterQuant (PCQ) can be used to deduce the presence of proteoforms that would have otherwise been missed, as exemplified in a proteomic comparison of two fly species, Drosophilamelanogaster and D. virilis. PCQ was used to identify reduced levels of serine/threonine protein kinases PKN1 and PKN4 in CFBE41o⁻ cells compared to HBE41o⁻ cells and to elucidate that shorter proteoforms of full-length caspase-4 and ephrin B receptor are differentially expressed. Thus, PCQ extends current analyses in quantitative proteomics and facilitates finding differentially regulated proteins and proteoforms.

An accurate quantitation of different proteoforms remains challenging due to incomplete protein sequence coverage in mass spectrometry datasets. Here the authors describe a method to facilitate the discovery of proteoforms that may otherwise not be considered due to incomplete protein coverage or ambiguities in mapping peptides back to proteins.

Optimization of mass spectrometric parameters improve the identification performance of capillary zone electrophoresis for single-shot bottom-up proteomics analysis

The effects of MS1 injection time, MS2 injection time, dynamic exclusion time, intensity threshold, and isolation width were investigated on the numbers of peptide and protein identifications for single-shot bottom-up proteomics analysis using CZE-MS/MS analysis of a Xenopus laevis tryptic digest. An electrokinetically pumped nanospray interface was used to couple a linear-polyacrylamide coated capillary to a Q Exactive HF mass spectrometer. A sensitive method that used a 1.4 Th isolation width, 60,000 MS2 resolution, 110 ms MS2 injection time, and a top 7 fragmentation produced the largest number of identifications when the CZE loading amount was less than 100 ng. A programmable autogain control method (pAGC) that used a 1.4 Th isolation width, 15,000 MS2 resolution, 110 ms MS2 injection time, and top 10 fragmentation produced the largest number of identifications for CZE loading amounts greater than 100 ng; 7218 unique peptides and 1653 protein groups were identified from 200 ng by using the pAGC method. The effect of mass spectrometer conditions on the performance of UPLC-MS/MS was also investigated. A fast method that used a 1.4 Th isolation width, 30,000 MS2 resolution, 45 ms MS2 injection time, and top 12 fragmentation produced the largest number of identifications for 200 ng UPLC loading amount (6025 unique peptides and 1501 protein groups). This is the first report where the identification number for CZE surpasses that of the UPLC at the 200 ng loading level. However, more peptides (11476) and protein groups (2378) were identified by using UPLC-MS/MS when the sample loading amount was increased to 2 μg with the fast method. To exploit the fast scan speed of the Q-Exactive HF mass spectrometer, higher sample loading amounts are required for single-shot bottom-up proteomics analysis using CZE-MS/MS.

Definitive screening design enables optimization of LC-ESI-MS/MS parameters in proteomics

In proteomics, more than 100,000 peptides are generated from the digestion of human cell lysates. Proteome samples have a broad dynamic range in protein abundance; therefore, it is critical to optimize various parameters of LC-ESI-MS/MS to comprehensively identify these peptides. However, there are many parameters for LC-ESI-MS/MS analysis. In this study, we applied definitive screening design to simultaneously optimize 14 parameters in the operation of monolithic capillary LC-ESI-MS/MS to increase the number of identified proteins and/or the average peak area of MS1. The simultaneous optimization enabled the determination of two-factor interactions between LC and MS. Finally, we found two parameter sets of monolithic capillary LC-ESI-MS/MS that increased the number of identified proteins by 8.1% or the average peak area of MS1 by 67%. The definitive screening design would be highly useful for high-throughput analysis of the best parameter set in LC-ESI-MS/MS systems.

Optimization and Modeling of Quadrupole Orbitrap Parameters for Sensitive Analysis toward Single-Cell Proteomics

Single-cell proteomics represents a field of extremely sensitive proteomic analysis, owing to the minute amount of yet complex proteins in a single cell. Without amplification potential as of nucleic acids, single-cell mass spectrometry (MS) analysis demands special instrumentation running with optimized parameters to maximize the sensitivity and throughput for comprehensive proteomic discovery. To facilitate such analysis, we here investigated two factors critical to peptide sequencing and protein detection in shotgun proteomics, i.e. precursor ion isolation window (IW) and maximum precursor ion injection time (ITmax), on an ultrahigh-field quadrupole Orbitrap (Q-Exactive HF). Counterintuitive to the frequently used proteomic parameters for bulk samples (>100 ng), our experimental data and subsequent modeling suggested a universally optimal IW of 4.0 Th for sample quantity ranging from 100 ng to 1 ng, and a sample-quantity dependent ITmax of more than 250 ms for 1-ng samples. Compared with the benchmark condition of IW = 2.0 Th and ITmax = 50 ms, our optimization generated up to 300% increase to the detected protein groups for 1-ng samples. The additionally identified proteins allowed deeper penetration of proteome for better revealing crucial cellular functions such as signaling and cell adhesion. We hope this effort can prompt single-cell and trace proteomic analysis and enable a rational selection of MS parameters.

Expanding Lipidome Coverage Using LC-MS/MS Data-Dependent Acquisition with Automated Exclusion List Generation

Untargeted omics analyses aim to comprehensively characterize biomolecules within a biological system. Changes in the presence or quantity of these biomolecules can indicate important biological perturbations, such as those caused by disease. With current technological advancements, the entire genome can now be sequenced; however, in the burgeoning fields of lipidomics, only a subset of lipids can be identified. The recent emergence of high resolution tandem mass spectrometry (HR-MS/MS), in combination with ultra-high performance liquid chromatography, has resulted in an increased coverage of the lipidome. Nevertheless, identifications from MS/MS are generally limited by the number of precursors that can be selected for fragmentation during chromatographic elution. Therefore, we developed the software IE-Omics to automate iterative exclusion (IE), where selected precursors using data-dependent topN analyses are excluded in sequential injections. In each sequential injection, unique precursors are fragmented until HR-MS/MS spectra of all ions above a user-defined intensity threshold are acquired. IE-Omics was applied to lipidomic analyses in Red Cross plasma and substantia nigra tissue. Coverage of the lipidome was drastically improved using IE. When applying IE-Omics to Red Cross plasma and substantia nigra lipid extracts in positive ion mode, 69% and 40% more molecular identifications were obtained, respectively. In addition, applying IE-Omics to a lipidomics workflow increased the coverage of trace species, including odd-chained and short-chained diacylglycerides and oxidized lipid species. By increasing the coverage of the lipidome, applying IE to a lipidomics workflow increases the probability of finding biomarkers and provides additional information for determining etiology of disease. Graphical Abstract ᅟ.

Improved Quantitative Plant Proteomics via the Combination of Targeted and Untargeted Data Acquisition

Quantitative proteomics strategies - which are playing important roles in the expanding field of plant molecular systems biology - are traditionally designated as either hypothesis driven or non-hypothesis driven. Many of these strategies aim to select individual peptide ions for tandem mass spectrometry (MS/MS), and to do this mixed hypothesis driven and non-hypothesis driven approaches are theoretically simple to implement. In-depth investigations into the efficacies of such approaches have, however, yet to be described. In this study, using combined samples of unlabeled and metabolically ¹⁵N-labeled Arabidopsis thaliana proteins, we investigate the mixed use of targeted data acquisition (TDA) and data dependent acquisition (DDA) - referred to as TDA/DDA - to facilitate both hypothesis driven and non-hypothesis driven quantitative data collection in individual LC-MS/MS experiments. To investigate TDA/DDA for hypothesis driven data collection, 7 miRNA target proteins of differing size and abundance were targeted using inclusion lists comprised of 1558 m/z values, using 3 different TDA/DDA experimental designs. In samples in which targeted peptide ions were of particularly low abundance (i.e., predominantly only marginally above mass analyser detection limits), TDA/DDA produced statistically significant increases in the number of targeted peptides identified (230 ± 8 versus 80 ± 3 for DDA; p = 1.1 × 10^-3) and quantified (35 ± 3 versus 21 ± 2 for DDA; p = 0.038) per experiment relative to the use of DDA only. These expected improvements in hypothesis driven data collection were observed alongside unexpected improvements in non-hypothesis driven data collection. Untargeted peptide ions with m/z values matching those in inclusion lists were repeatedly identified and quantified across technical replicate TDA/DDA experiments, resulting in significant increases in the percentages of proteins repeatedly quantified in TDA/DDA experiments only relative to DDA experiments only (33.0 ± 2.6% versus 8.0 ± 2.7%, respectively; p = 0.011). These results were observed together with uncompromised broad-scale MS/MS data collection in TDA/DDA experiments relative to DDA experiments. Using our observations we provide guidelines for TDA/DDA method design for quantitative plant proteomics studies, and suggest that TDA/DDA is a broadly underutilized proteomics data acquisition strategy.

Shotgun Proteomics of Tomato Fruits: Evaluation, Optimization and Validation of Sample Preparation Methods and Mass Spectrometric Parameters

An optimized protocol was developed for shotgun proteomics of tomato fruit, which is a recalcitrant tissue due to a high percentage of sugars and secondary metabolites. A number of protein extraction and fractionation techniques were examined for optimal protein extraction from tomato fruits followed by peptide separation on nanoLCMS. Of all evaluated extraction agents, buffer saturated phenol was the most efficient. In-gel digestion [SDS-PAGE followed by separation on LCMS (GeLCMS)] of phenol-extracted sample yielded a maximal number of proteins. For in-solution digested samples, fractionation by strong anion exchange chromatography (SAX) also gave similar high proteome coverage. For shotgun proteomic profiling, optimization of mass spectrometry parameters such as automatic gain control targets (5E+05 for MS, 1E+04 for MS/MS); ion injection times (500 ms for MS, 100 ms for MS/MS); resolution of 30,000; signal threshold of 500; top N-value of 20 and fragmentation by collision-induced dissociation yielded the highest number of proteins. Validation of the above protocol in two tomato cultivars demonstrated its reproducibility, consistency, and robustness with a CV of < 10%. The protocol facilitated the detection of five-fold higher number of proteins compared to published reports in tomato fruits. The protocol outlined would be useful for high-throughput proteome analysis from tomato fruits and can be applied to other recalcitrant tissues.

Optimizing Mass Spectrometry Analyses: A Tailored Review on the Utility of Design of Experiments

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.

Designing biomedical proteomics experiments: state-of-the-art and future perspectives

With the current expanded technical capabilities to perform mass spectrometry-based biomedical proteomics experiments, an improved focus on the design of experiments is crucial. As it is clear that ignoring the importance of a good design leads to an unprecedented rate of false discoveries which would poison our results, more and more tools are developed to help researchers designing proteomic experiments. In this review, we apply statistical thinking to go through the entire proteomics workflow for biomarker discovery and validation and relate the considerations that should be made at the level of hypothesis building, technology selection, experimental design and the optimization of the experimental parameters.

Identification of Analytical Factors Affecting Complex Proteomics Profiles Acquired in a Factorial Design Study with Analysis of Variance: Simultaneous Component Analysis

Complex shotgun proteomics peptide profiles obtained in quantitative differential protein expression studies, such as in biomarker discovery, may be affected by multiple experimental factors. These preanalytical factors may affect the measured protein abundances which in turn influence the outcome of the associated statistical analysis and validation. It is therefore important to determine which factors influence the abundance of peptides in a complex proteomics experiment and to identify those peptides that are most influenced by these factors. In the current study we analyzed depleted human serum samples to evaluate experimental factors that may influence the resulting peptide profile such as the residence time in the autosampler at 4 °C, stopping or not stopping the trypsin digestion with acid, the type of blood collection tube, different hemolysis levels, differences in clotting times, the number of freeze-thaw cycles, and different trypsin/protein ratios. To this end we used a two-level fractional factorial design of resolution IV (2(IV)(7-3)). The design required analysis of 16 samples in which the main effects were not confounded by two-factor interactions. Data preprocessing using the Threshold Avoiding Proteomics Pipeline (Suits, F.; Hoekman, B.; Rosenling, T.; Bischoff, R.; Horvatovich, P. Anal. Chem. 2011, 83, 7786-7794, ref 1) produced a data-matrix containing quantitative information on 2,559 peaks. The intensity of the peaks was log-transformed, and peaks having intensities of a low t-test significance (p-value > 0.05) and a low absolute fold ratio (<2) between the two levels of each factor were removed. The remaining peaks were subjected to analysis of variance (ANOVA)-simultaneous component analysis (ASCA). Permutation tests were used to identify which of the preanalytical factors influenced the abundance of the measured peptides most significantly. The most important preanalytical factors affecting peptide intensity were (1) the hemolysis level, (2) stopping trypsin digestion with acid, and (3) the trypsin/protein ratio. This provides guidelines for the experimentalist to keep the ratio of trypsin/protein constant and to control the trypsin reaction by stopping it with acid at an accurately set pH. The hemolysis level cannot be controlled tightly as it depends on the status of a patient's blood (e.g., red blood cells are more fragile in patients undergoing chemotherapy) and the care with which blood was sampled (e.g., by avoiding shear stress). However, its level can be determined with a simple UV spectrophotometric measurement and samples with extreme levels or the peaks affected by hemolysis can be discarded from further analysis. The loadings of the ASCA model led to peptide peaks that were most affected by a given factor, for example, to hemoglobin-derived peptides in the case of the hemolysis level. Peak intensity differences for these peptides were assessed by means of extracted ion chromatograms confirming the results of the ASCA model.

Optimization of parameters affecting signal intensity in an LTQ-orbitrap in negative ion mode: A design of experiments approach

A multistage optimization of all the parameters affecting detection/response in an LTQ-orbitrap analyzer was performed, using a design of experiments methodology. The signal intensity, a critical issue for mass analysis, was investigated and the optimization process was completed in three successive steps, taking into account the three main regions of an orbitrap, the ion generation, the ion transmission and the ion detection regions. Oleuropein and hydroxytyrosol were selected as the model compounds. Overall, applying this methodology the sensitivity was increased more than 24%, the resolution more than 6.5%, whereas the elapsed scan time was reduced nearly to its half. A high-resolution LTQ Orbitrap Discovery mass spectrometer was used for the determination of the analytes of interest. Thus, oleuropein and hydroxytyrosol were infused via the instruments syringe pump and they were analyzed employing electrospray ionization (ESI) in the negative high-resolution full-scan ion mode. The parameters of the three main regions of the LTQ-orbitrap were independently optimized in terms of maximum sensitivity. In this context, factorial design, response surface model and Plackett-Burman experiments were performed and analysis of variance was carried out to evaluate the validity of the statistical model and to determine the most significant parameters for signal intensity. The optimum MS conditions for each analyte were summarized and the method optimum condition was achieved by maximizing the desirability function. Our observation showed good agreement between the predicted optimum response and the responses collected at the predicted optimum conditions.

Elucidation of Xylem-Specific Transcription Factors and Absolute Quantification of Enzymes Regulating Cellulose Biosynthesis in Populus trichocarpa

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.

Ion tree-based structure elucidation of acetophenone dimers (AtA) from Acronychia pedunculata and their identification in extracts by liquid chromatography electrospray ionization LTQ-Orbitrap mass spectrometry

Acronychia-type acetophenones (AtA) is a chemical group of compounds of important structural and biological interest, abundant in Acronychia species. However, there are no data available for their characterization using mass spectrometry. In the current work, AtA have been investigated by multistage high resolution mass spectrometry and both electrospray ionization and atmospheric pressure chemical ionization, in positive and negative mode, were utilized for their structure elucidation and identification. The analysis of AtA using a linear ion trap-Orbitrap analyzer enabled the structural determination of key fragment ions and cleavages, which can be used for the structural characterization thereof. A systematic nomenclature based on protonated and deprotonated fragment ions under collision-induced dissociation conditions and decision trees for the structural determination of AtA are proposed. Furthermore, taking advantage of the characteristic fragmentation patterns, a selective Ultra High Performance Liquid Chromatography Electrospray Ionization multistage Mass Spectrometry (UHPLC-ESI(-)-MS(n)) method was developed and successfully applied for the dereplication of known AtA and the identification of potentially new ones in Acronychia extracts. Despite the structure similarity and the presence of isomers, accurate characterization of known and unknown AtA derivatives was possible.

Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications

In medicine, there is an urgent need for protein biomarkers in a range of applications that includes diagnostics, disease stratification, and therapeutic decisions. One of the main technologies to address this need is MS, used for protein biomarker discovery and, increasingly, also for protein biomarker validation. Currently, data-dependent analysis (also referred to as shotgun proteomics) and targeted MS, exemplified by SRM, are the most frequently used mass spectrometric methods. Recently developed data-independent acquisition techniques combine the strength of shotgun and targeted proteomics, while avoiding some of the limitations of the respective methods. They provide high-throughput, accurate quantification, and reproducible measurements within a single experimental setup. Here, we describe and review data-independent acquisition strategies and their recent use in clinically oriented studies. In addition, we also provide a detailed guide for the implementation of SWATH-MS (where SWATH is sequential window acquisition of all theoretical mass spectra)-one of the data-independent strategies that have gained wide application of late.

Optimization of data-dependent acquisition parameters for coupling high-speed separations with LC-MS/MS for protein identifications

Recent developments in chromatography, such as ultra-HPLC and superficially porous particles, offer significantly improved peptide separation. The narrow peak widths, often only several seconds, can permit a 15-min liquid chromatography run to have a similar peak capacity as a 60-min run using traditional HPLC approaches. In theory, these larger peak capacities should provide higher protein coverage and/or more protein identifications when incorporated into a proteomic workflow. We initially observed a decrease in protein coverage when implementing these faster chromatographic approaches, due to data-dependent acquisition (DDA) settings that were not properly set to match the narrow peak widths resulting from newly implemented, fast separation techniques. Oversampling of high-intensity peptides lead to low protein-sequence coverage, and tandem mass spectra (MS/MS) from lower-intensity peptides were of poor quality, as automated MS/MS events were occurring late on chromatographic peaks. These observations led us to optimize DDA settings to use these fast separations. Optimized DDA settings were applied to the analysis of Trypanosome brucei peptides, yielding peptide identifications at a rate almost five times faster than previously used methodologies. The described approach significantly improves protein identification workflows that use typical available instrumentation.

Practical Considerations and Current Limitations in Quantitative Mass Spectrometry-based Proteomics

Quantitative mass spectrometry (MS)-based proteomics continues to evolve through advances in sample preparation, chemical and biochemical reagents, instrumentation, and software. The breadth of proteomes and biological applications combined with unique experimental goals makes optimizing MS-based proteomics workflows a daunting task. Several MS-based instrument platforms are commercially available with LC-MS/MS being the most common for quantitative proteomics studies. Although the direction of LC-MS/MS instrumentation development is toward more user-friendly interfaces, there remain fundamental aspects of the technology that can be optimized for improving data quality. The intent of this chapter is to provide an introductory framework for understanding some of the more significant LC-MS/MS experimental conditions that can influence quantitative MS-based proteomics measurements, including electrospray ionization (ESI) bias and ion transmission efficiency. Because each commercial LC-MS/MS system is unique with regard to ESI source, transmission optics, ion isolation and trapping, ion fragmentation, and mass analysis, the use of design of experiments (DoE) is discussed as a potential approach for efficiently optimizing multiple inter-related factors.

Factorial experimental designs elucidate significant variables affecting data acquisition on a quadrupole Orbitrap mass spectrometer

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.

A tool to evaluate correspondence between extraction ion chromatographic peaks and peptide-spectrum matches in shotgun proteomics experiments

Chromatographed peptide signals form the basis of further data processing that eventually results in functional information derived from data-dependent bottom-up proteomics assays. We seek to rank LC/MS parent ions by the quality of their extracted ion chromatograms. Ranked extracted ion chromatograms act as an intuitive physical/chemical preselection filter to improve the quality of MS/MS fragment scans submitted for database search. We identify more than 4900 proteins when considering detector shifts of less than 7 ppm. High quality parent ions for which the database search yields no hits become candidates for subsequent unrestricted analysis for PTMs. Following this rational approach, we prioritize identification of more than 5000 spectrum matches from modified peptides and confirmed the presence of acetylaldehyde-modified His/Lys. We present a logical workflow that scores data-dependent selected ion chromatograms and leverage information about semianalytical LC/LC dimension prior to MS. Our method can be successfully used to identify unexpected modifications in peptides with excellent chromatography characteristics, independent of fragmentation pattern and activation methods. We illustrate analysis of ion chromatograms detected in two different modes by RF linear ion trap and electrostatic field orbitrap.

Evaluation and optimization of mass spectrometric settings during data-dependent acquisition mode: focus on LTQ-Orbitrap mass analyzers

Mass-spectrometry-based proteomics has evolved as the preferred method for the analysis of complex proteomes. Undoubtedly, recent advances in mass spectrometry instrumentation have greatly enhanced proteomic analysis. A popular instrument platform in proteomics research is the LTQ-Orbitrap mass analyzer. In this tutorial, we discuss the significance of evaluating and optimizing mass spectrometric settings on the LTQ-Orbitrap during CID data-dependent acquisition (DDA) mode to improve protein and peptide identification rates. We focus on those MS and MS/MS parameters that have been systematically examined and evaluated by several researchers and are commonly used during DDA. More specifically, we discuss the effect of mass resolving power, preview mode for FTMS scan, monoisotopic precursor selection, signal threshold for triggering MS/MS events, number of microscans per MS/MS scan, number of MS/MS events, automatic gain control target value (ion population) for MS and MS/MS, maximum ion injection time for MS/MS, rapid and normal scan rate, and prediction of ion injection time. We furthermore present data from the latest generation LTQ-Orbitrap system, the Orbitrap Elite, along with recommended MS and MS/MS parameters. The Orbitrap Elite outperforms the Orbitrap Classic in terms of scan speed, sensitivity, dynamic range, and resolving power and results in higher identification rates. Several of the optimized MS parameters determined on the LTQ-Orbitrap Classic and XL were easily transferable to the Orbitrap Elite, whereas others needed to be reevaluated. Finally, the Q Exactive and HCD are briefly discussed, as well as sample preparation, LC-optimization, and bioinformatics analysis. We hope this tutorial will serve as guidance for researchers new to the field of proteomics and assist in achieving optimal results.

Comparative proteomic analysis and IgE binding properties of peanut seed and testa (skin)

To investigate the protein composition and potential allergenicity of peanut testae or skins, proteome analysis was conducted using nanoLC-MS/MS sequencing. Initial amino acid analysis suggested differences in protein compositions between the blanched seed (skins removed) and skin. Phenolic compounds hindered analysis of proteins in skins when the conventional extraction method was used; therefore, phenol extraction of proteins was necessary. A total of 123 proteins were identified in blanched seed and skins, and 83 of the proteins were common between the two structures. The skins contained all of the known peanut allergens in addition to 38 proteins not identified in the seed. Multiple defense proteins with antifungal activity were identified in the skins. Western blotting using sera from peanut-allergic patients revealed that proteins extracted from both the blanched seed and skin bound significant levels of IgE. However, when phenolic compounds were present in the skin protein extract, no IgE binding was observed. These findings indicate that peanut skins contain potentially allergenic proteins; however, the presence of phenolic compounds may attenuate this effect.

Yeast proteome variations reveal different adaptive responses to grape must fermentation

Saccharomyces cerevisiae and S. uvarum are two domesticated species of the Saccharomyces sensu stricto clade that diverged around 100 Ma after whole-genome duplication. Both have retained many duplicated genes associated with glucose fermentation and are characterized by the ability to achieve grape must fermentation. Nevertheless, these two species differ for many other traits, indicating that they underwent different evolutionary histories. To determine how the evolutionary histories of S. cerevisiae and S. uvarum are mirrored on the proteome, we analyzed the genetic variability of the proteomes of domesticated strains of these two species by quantitative mass spectrometry. Overall, 445 proteins were quantified. Massive variations of protein abundances were found, that clearly differentiated the two species. Abundance variations in specific metabolic pathways could be related to phenotypic traits known to discriminate the two species. In addition, proteins encoded by duplicated genes were shown to be differently recruited in each species. Comparing the strain differentiation based on the proteome variability to those based on the phenotypic and genetic variations further revealed that the strains of S. uvarum and some strains of S. cerevisiae displayed similar fermentative performances despite strong proteomic and genomic differences. Altogether, these results indicate that the ability of S. cerevisae and S. uvarum to complete grape must fermentation arose through different evolutionary roads, involving different metabolic pathways and duplicated genes.

Performance comparisons of nano-LC systems, electrospray sources and LC-MS-MS platforms

Selecting a suitable nano-liquid chromatography system (LC), ionization source and mass spectrometer for LC-tandem mass spectrometry (MS-MS) studies is complicated by numerous competing technologies. This study compares four popular nano-LC systems, four ionization sources and three MS facilities that use completely different LC-MS-MS systems. Statistically significant differences in LC performance were identified with similarly performing Proxeon, Waters and Eksigent nanoLC-Ultra systems [retention time routinely at 0.7-0.9% relative standard deviation (RSD)], and all outperformed the Eksigent nanoLC-2D (RSD ∼2%). In addition, compatibility issues were identified between the Bruker HCT ion trap mass spectrometer and both the Eksigent nanoLC-2D and the Bruker nanoelectrospray source. The electrospray source itself had an unexpected and striking effect on chromatographic reproducibility on the Bruker HCT ion trap. The New Objective nanospray source significantly outperformed the Bruker nanospray source in retention time RSD (1% RSD versus 14% RSD, respectively); and the Bruker nebulized nanospray source outperformed both of these traditional, non-nebulized sources (0.5% RSD in retention time). Finally, to provide useful benchmarks for overall proteomics sensitivity, different LC-MS-MS platforms were compared by analyzing a range of concentrations of tryptic digests of bovine serum albumin at three MS facilities. The results indicate that similar sensitivity can be realized with a Bruker HCT-Ultra ion trap, a Thermo LTQ-Velos Linear ion trap and a Thermo LTQ-Orbitrap XL-ETD.

In-depth analysis of the Magnaporthe oryzae conidial proteome

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.

Targeted proteomics of the secretory pathway reveals the secretome of mouse embryonic fibroblasts and human embryonic stem cells

Proteins endogenously secreted by human embryonic stem cells (hESCs) and those present in hESC culture medium are critical regulators of hESC self-renewal and differentiation. Current MS-based approaches for identifying secreted proteins rely predominantly on MS analysis of cell culture supernatants. Here we show that targeted proteomics of secretory pathway organelles is a powerful alternate approach for interrogating the cellular secretome. We have developed procedures to obtain subcellular fractions from mouse embryonic fibroblasts (MEFs) and hESCs that are enriched in secretory pathway organelles while ensuring retention of the secretory cargo. MS analysis of these fractions from hESCs cultured in MEF conditioned medium (MEF-CM) or MEFs exposed to hESC medium revealed 99 and 129 proteins putatively secreted by hESCs and MEFs, respectively. Of these, 53 and 62 proteins have been previously identified in cell culture supernatants of MEFs and hESCs, respectively, thus establishing the validity of our approach. Furthermore, 76 and 37 putatively secreted proteins identified in this study in MEFs and hESCs, respectively, have not been reported in previous MS analyses. The identification of low abundance secreted proteins via MS analysis of cell culture supernatants typically necessitates the use of altered culture conditions such as serum-free medium. However, an altered medium formulation might directly influence the cellular secretome. Indeed, we observed significant differences between the abundances of several secreted proteins in subcellular fractions isolated from hESCs cultured in MEF-CM and those exposed to unconditioned hESC medium for 24 h. In contrast, targeted proteomics of secretory pathway organelles does not require the use of customized media. We expect that our approach will be particularly valuable in two contexts highly relevant to hESC biology: obtaining a temporal snapshot of proteins secreted in response to a differentiation trigger, and identifying proteins secreted by cells that are isolated from a heterogeneous population.

Comprehensive quantification of monolignol-pathway enzymes in Populus trichocarpa by protein cleavage isotope dilution mass spectrometry

The economic value of wood/pulp from many tree species is largely dictated by the quantity and chemical properties of lignin, which is directly related to the composition and linkages of monolignols comprising the polymer. Although much is known regarding the monolignol biosynthetic pathway, our understanding is still deficient due to the lack of quantitative information at the proteomic level. We developed an assay based on protein cleavage isotope dilution mass spectrometry (PC-IDMS) for the determination of all potential, primary enzymes involved in the biosynthesis of monolignols and the peroxidases responsible for their polymerization to form lignin in the model tree species, Populus trichocarpa. Described is the identification of quantitative surrogate peptides through shotgun analysis of native and recombinant proteins, optimization of trypsin proteolysis using fractional factorial design of experiments, and development of a liquid chromatography-selected reaction monitoring method for specific detection of all targeted peptides. Of the 25 targeted enzymes, three were undetected in the normal xylem tissues, and all but two of the detectable species showed good day-to-day precision (CV < 10%). This represents the most comprehensive assay for quantification of proteins regulating monolignol biosynthesis and will lead to a better understanding of lignin formation at a systems level.

The spectra count label-free quantitation in cancer proteomics

Mass spectrometry is used routinely for large-scale protein identification from complex biological mixtures. Recently, relative quantitation approach on the basis of spectra count has been applied in several cancer proteomic studies. In this review, we examine the mechanism of this technique and highlight several important parameters associated with its application.

Global optimization of the infrared matrix-assisted laser desorption electrospray ionization (IR MALDESI) source for mass spectrometry using statistical design of experiments

Design of experiments (DOE) is a systematic and cost-effective approach to system optimization by which the effects of multiple parameters and parameter interactions on a given response can be measured in few experiments. Herein, we describe the use of statistical DOE to improve a few of the analytical figures of merit of the infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) source for mass spectrometry. In a typical experiment, bovine cytochrome c was ionized via electrospray, and equine cytochrome c was desorbed and ionized by IR-MALDESI such that the ratio of equine:bovine was used as a measure of the ionization efficiency of IR-MALDESI. This response was used to rank the importance of seven source parameters including flow rate, laser fluence, laser repetition rate, ESI emitter to mass spectrometer inlet distance, sample stage height, sample plate voltage, and the sample to mass spectrometer inlet distance. A screening fractional factorial DOE was conducted to designate which of the seven parameters induced the greatest amount of change in the response. These important parameters (flow rate, stage height, sample to mass spectrometer inlet distance, and laser fluence) were then studied at higher resolution using a full factorial DOE to obtain the globally optimized combination of parameter settings. The optimum combination of settings was then compared with our previously determined settings to quantify the degree of improvement in detection limit. The limit of detection for the optimized conditions was approximately 10 attomoles compared with 100 femtomoles for the previous settings, which corresponds to a four orders of magnitude improvement in the detection limit of equine cytochrome c.

Evaluation of normalization methods on GeLC-MS/MS label-free spectral counting data to correct for variation during proteomic workflows

Normalization of spectral counts (SpCs) in label-free shotgun proteomic approaches is important to achieve reliable relative quantification. Three different SpC normalization methods, total spectral count (TSpC) normalization, normalized spectral abundance factor (NSAF) normalization, and normalization to selected proteins (NSP) were evaluated based on their ability to correct for day-to-day variation between gel-based sample preparation and chromatographic performance. Three spectral counting data sets obtained from the same biological conidia sample of the rice blast fungus Magnaporthe oryzae were analyzed by 1D gel and liquid chromatography-tandem mass spectrometry (GeLC-MS/MS). Equine myoglobin and chicken ovalbumin were spiked into the protein extracts prior to 1D-SDS- PAGE as internal protein standards for NSP. The correlation between SpCs of the same proteins across the different data sets was investigated. We report that TSpC normalization and NSAF normalization yielded almost ideal slopes of unity for normalized SpC versus average normalized SpC plots, while NSP did not afford effective corrections of the unnormalized data. Furthermore, when utilizing TSpC normalization prior to relative protein quantification, t-testing and fold-change revealed the cutoff limits for determining real biological change to be a function of the absolute number of SpCs. For instance, we observed the variance decreased as the number of SpCs increased, which resulted in a higher propensity for detecting statistically significant, yet artificial, change for highly abundant proteins. Thus, we suggest applying higher confidence level and lower fold-change cutoffs for proteins with higher SpCs, rather than using a single criterion for the entire data set. By choosing appropriate cutoff values to maintain a constant false positive rate across different protein levels (i.e., SpC levels), it is expected this will reduce the overall false negative rate, particularly for proteins with higher SpCs.

Effect of mass spectrometric parameters on peptide and protein identification rates for shotgun proteomic experiments on an LTQ-orbitrap mass analyzer

The success of a shotgun proteomic experiment relies heavily on the performance and optimization of both the LC and the MS systems. Despite this, little consideration has, so far, been given to the importance of evaluating and optimizing the MS instrument settings during data-dependent acquisition mode. Moreover, during data-dependent acquisition, the users have to decide and choose among various MS parameters and settings, making a successful analysis even more challenging. We have systematically investigated and evaluated the effect of enabling and disabling the preview mode for FTMS scan, the number of microscans per MS/MS scan, the number of MS/MS events, the maximum ion injection time for MS/MS, and the automatic gain control target value for MS and MS/MS events on protein and peptide identification rates on an LTQ-Orbitrap using the Saccharomyces cerevisiae proteome. Our investigations aimed to assess the significance of each MS parameter to improve proteome analysis and coverage. We observed that higher identification rates were obtained at lower ion injection times i.e. 50-150 ms, by performing one microscan and 12-15 MS/MS events. In terms of ion population, optimal automatic gain control target values were at 5×10(5) -1×10(6) ions for MS and 3×10(3) -1×10(4) ions for MS/MS. The preview mode scan had a minimal effect on identification rates. Using optimized MS settings, we identified 1038 (±2.3%) protein groups with a minimum of two peptide identifications and an estimated false discovery rate of ∼1% at both peptide and protein level in a 160-min LC-MS/MS analysis.