A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC)

Stable isotope labeling by amino acids in cell culture (SILAC) is a simple, robust, yet powerful approach in mass spectrometry (MS)-based quantitative proteomics. SILAC labels cellular proteomes through normal metabolic processes, incorporating non-radioactive, stable isotope-containing amino acids in newly synthesized proteins. Growth medium is prepared where natural ("light") amino acids are replaced by "heavy" SILAC amino acids. Cells grown in this medium incorporate the heavy amino acids after five cell doublings and SILAC amino acids have no effect on cell morphology or growth rates. When light and heavy cell populations are mixed, they remain distinguishable by MS, and protein abundances are determined from the relative MS signal intensities. SILAC provides accurate relative quantification without any chemical derivatization or manipulation and enables development of elegant functional assays in proteomics. In this protocol, we describe how to apply SILAC and the use of nano-scale liquid chromatography coupled to electrospray ionization mass spectrometry for protein identification and quantification. This procedure can be completed in 8 days.     

More than 100,000 detectable peptide species elute in single shotgun proteomics runs but the majority is inaccessible to data-dependent LC-MS/MS

Shotgun proteomics entails the identification of as many peptides as possible from complex mixtures. Here we investigate how many peptides are detectable by high resolution MS in standard LC runs of cell lysate and how many of them are accessible to data-dependent MS/MS. Isotope clusters were determined by MaxQuant and stringently filtered for charge states and retention times typical of peptides. This resulted in more than 100,000 likely peptide features, of which only about 16% had been targeted for MS/MS. Three instrumental attributes determine the proportion of additional peptides that can be identified: sequencing speed, sensitivity, and precursor ion isolation. In our data, an MS/MS scan rate of 25/s would be necessary to target all peptide features, but this drops to less than 17/s for reasonably abundant peptides. Sensitivity is a greater challenge, with many peptide features requiring long MS/MS injection times (>250 ms). The greatest limitation, however, is the generally low proportion of the target peptide ion intensity in the MS/MS selection window (the "precursor ion fraction" or PIF). Median PIF is only 0.14, making the peptides difficult to identify by standard MS/MS methods. Our results aid in developing strategies to further increase coverage in shotgun proteomics.     

ICPLQuant – A software for non‐isobaric isotopic labeling proteomics

The main goal of many proteomics experiments is an accurate and rapid quantification and identification of regulated proteins in complex biological samples. The bottleneck in quantitative proteomics remains the availability of efficient software to evaluate and quantify the tremendous amount of mass spectral data acquired during a proteomics project. A new software suite, ICPLQuant, has been developed to accurately quantify isotope‐coded protein label (ICPL)‐labeled peptides on the MS level during LC‐MALDI and peptide mass fingerprint experiments. The tool is able to generate a list of differentially regulated peptide precursors for subsequent MS/MS experiments, minimizing time‐consuming acquisition and interpretation of MS/MS data. ICPLQuant is based on two independent units. Unit 1 performs ICPL multiplex detection and quantification and proposes peptides to be identified by MS/MS. Unit 2 combines MASCOT MS/MS protein identification with the quantitative data and produces a protein/peptide list with all the relevant information accessible for further data mining. The accuracy of quantification, selection of peptides for MS/MS‐identification and the automated output of a protein list of regulated proteins are demonstrated by the comparative analysis of four different mixtures of three proteins (Ovalbumin, Horseradish Peroxidase and Rabbit Albumin) spiked into the complex protein background of the DGPF Proteome Marker.     

A new algorithm for the evaluation of shotgun peptide sequencing in proteomics: support vector machine classification of peptide MS/MS spectra and SEQUEST scores

Shotgun tandem mass spectrometry-based peptide sequencing using programs such as SEQUEST allows high-throughput identification of peptides, which in turn allows the identification of corresponding proteins. We have applied a machine learning algorithm, called the support vector machine, to discriminate between correctly and incorrectly identified peptides using SEQUEST output. Each peptide was characterized by SEQUEST-calculated features such as delta Cn and Xcorr, measurements such as precursor ion current and mass, and additional calculated parameters such as the fraction of matched MS/MS peaks. The trained SVM classifier performed significantly better than previous cutoff-based methods at separating positive from negative peptides. Positive and negative peptides were more readily distinguished in training set data acquired on a QTOF, compared to an ion trap mass spectrometer. The use of 13 features, including four new parameters, significantly improved the separation between positive and negative peptides. Use of the support vector machine and these additional parameters resulted in a more accurate interpretation of peptide MS/MS spectra and is an important step toward automated interpretation of peptide tandem mass spectrometry data in proteomics.     

Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH) Analysis for Characterization and Quantification of Histone Post-translational Modifications

Histone post-translational modifications (PTMs) have a fundamental function in chromatin biology, as they model chromatin structure and recruit enzymes involved in gene regulation, DNA repair, and chromosome condensation. High throughput characterization of histone PTMs is mostly performed by using nano-liquid chromatography coupled to mass spectrometry. However, limitations in speed and stochastic sampling of data dependent acquisition methods in MS lead to incomplete discrimination of isobaric peptides and loss of low abundant species. In this work, we analyzed histone PTMs with a data-independent acquisition method, namely SWATH™ analysis. This approach allows for MS/MS-based quantification of all analytes without upfront assay development and no issues of biased and incomplete sampling. We purified histone proteins from human embryonic stem cells and mouse trophoblast stem cells before and after differentiation, and prepared them for MS analysis using the propionic anhydride protocol. Results on histone H3 peptides verified that sequential window acquisition of all theoretical mass spectra could accurately quantify peptides (<9% average coefficient of variation, CV) over four orders of magnitude, and we could discriminate isobaric and co-eluting peptides (e.g. H3K18ac and H3K23ac) using MS/MS-based quantification. This method provided high sensitivity and precision, supported by the fact that we could find significant differences for remarkably low abundance PTMs such as H3K9me2S10ph (relative abundance <0.02%). We performed relative quantification for few sample peptides using different fragment ions and observed high consistency (CV <15%) between the fragments. This indicated that different fragment ions can be used independently to achieve the same peptide relative quantification. Taken together, sequential window acquisition of all theoretical mass spectra proved to be an easy-to-use MS acquisition method to perform high quality MS/MS-based quantification of histone-modified peptides.Chromatin is a highly organized and dynamic entity in cell nuclei, mostly composed of DNA and histone proteins. Its structure directly influences gene expression, DNA repair, and cell duplication events such as mitosis and meiosis (1). Histones are assembled in octamers named nucleosomes, wrapped by DNA every ∼200 base pairs. Histones are heavily modified by dynamic post-translational modifications (PTMs)¹, which affect chromatin structure because of their chemical properties and their ability to recruit chromatin modifier enzymes and binding proteins (2). Moreover, histone PTMs can be inherited through cell division and thus are crucial components of epigenetic memory (3). The function of histone PTMs has been extensively studied in the last 15–20 years, and several links have been found between aberrations of histone PTM levels and development of diseases (4, 5). Such discoveries revealed the importance of histone PTMs in fine-tuning cell phenotype. Because of this, technology has been rapidly evolving to investigate histone PTM relative abundance with higher accuracy and throughput.Mass spectrometry (MS)-based strategies have continuously evolved toward higher throughput and flexibility, allowing not only identification and quantification of single histone PTMs, but also their combinatorial patterns and even characterization of the intact proteins (reviewed in (6, 7)). For histone analysis, a widely adopted workflow for nano-liquid chromatography–tandem mass spectrometry (nLC-MS/MS) includes derivatization of lysine residue side chains with propionic anhydride, proteolytic digestion with trypsin, and subsequent derivatization of peptide N termini (8, 9). Such protocol leads to generation of ArgC-like peptides (only cleaved after arginine residues) after digestion. Moreover, propionylation of N termini increases peptide hydrophobicity, thereby improving LC retention of shorter ones, and thus the MS signal. Because of the high mass accuracy, sensitivity, and the possibility to perform label-free quantification MS has become the technique of choice, outperforming antibody-based strategies, to study both known and novel global histone PTMs.Several acquisition methods have been developed for MS analysis to accomplish different needs of identification and quantification (10). The most widely adopted in shotgun or discovery proteomics is the data-dependent acquisition (DDA) mode. Such acquisition method does not require any previous knowledge about the analyte, as it automatically selects precursor ions detectable at the full scan level in a given order (commonly from the most intense) to perform MS/MS fragmentation (11). Label-free quantification is performed at the full MS scan level by integrating the area of the LC peak from an extracted ion chromatogram of the precursor mass corresponding to the given peptide. On the other hand, the selected reaction monitoring (SRM) mode is the most widely used acquisition method in targeted proteomics. Such method performs cyclic precursor ion selection, MS/MS fragmentation, and product ion selection of a list of masses input by the user. Even though the method preparation is intuitively more complex than DDA, SRM is highly popular because of the high selectivity and sensitivity, which leads to more accurate label-free quantification (12). However, both methods have inevitable drawbacks; a DDA approach cannot perform accurate quantification of isobaric and co-eluting peptides, for example, KacQLATKAAR and KQLATKacAAR (histone H3 aa 9–17), as the fragment ions should be monitored through the entire peptide peak elution to define the ratio between the two similar analytes. On the contrary, an SRM experiment prevents future data mining of unpredicted peptides, and thus such method cannot be used for any classical PTM discovery. Therefore, LC-MS/MS analysis of histone peptides is commonly performed by integrating shotgun and targeted acquisition within the same MS method (13). This method requires previous knowledge about retention time and mass of co-eluting isobaric species, and tedious manual peak integration or dedicated software to deconvolute such complex raw data. Although this mixed MS mode is a powerful approach, the targeted sequences in the method reduce the duty cycle and number of DDA MS/MS spectra that can be acquired, making it far from ideal.Data independent acquisition (DIA) modes are a third option that recently gained popularity in proteomics (14, 15). Sequential window acquisition of all theoretical mass spectra (SWATH™)-MS is a data independent workflow that uses a first quadrupole isolation window to step across a mass range, collecting high resolution full scan composite MS/MS at each step and generating an ion map of fragments from all detectable precursor masses (15, 16). From such data set, a virtual SRM, or pseudo-SRM, can be performed by extracting the product ion chromatogram of a given peptide (17) with bioinformatics tools such as Peakview®, Skyline (18), or OpenSWATH™ (19). In order to define which fragment masses should be used to quantify a given peptide, a spectral library of identified peptides can be manually programmed, downloaded (if available), or generated by previous DDA experiments. In terms of quantification power, SWATH™ combines the advantages of both DDA and SRM, as it allows for MS/MS-based label-free quantification, discrimination of isobaric peptides, and subsequent data mining of unpredicted species.Histone proteins are an excellent target sample to test SWATH™, as the peptides are heavily modified by PTMs and often have isobaric proteoforms present. We analyzed with both DDA and SWATH™ two model systems: (1) extracted histones from untreated (pluripotent) and retinoic acid (RA) treated (differentiated) human embryonic stem cells (hESCs, strain H9), and (2) extracted histones from undifferentiated and differentiated mouse trophoblast stem cells (mTSCs). The results from the DDA experiment were used to evaluate the reproducibility of peptide retention time and the variety of species identified. For the SWATH™ analysis we focused on histone H3, as it is the histone with the highest variety of modified peptides (6). Results highlighted that such acquisition method provides sensitive and precise MS/MS-based quantification of both isobaric and nonisobaric peptides. Our data demonstrate that quantification at the MS/MS level is highly reproducible, and identification of the peptide elution profile is assisted by the high mass accuracy and the large number of overlapping elution profiles of the fragment ions. Moreover, we show that by using different fragment ions for MS/MS quantification we achieved similar quantification results. Thus, we used all unique fragment ions for a given species to provide a robust quantification method, where by unique is intended fragment ions that belong to only one of the possible isobaric peptide proteoforms. Taken together, we prove that SWATH™-MS is a reliable and simple-to-use acquisition method to perform epigenetic histone PTM analysis.     

A large-scale assay library for targeted protein quantification in renal cell carcinoma tissues

Renal cell carcinoma (RCC) represents 2.2% of all cancer incidences; however, prognostic or predictive RCC biomarkers at protein level are largely missing. To support proteomics research of localized and metastatic RCC, we introduce a new library of targeted mass spectrometry assays for accurate protein quantification in malignant and normal kidney tissue. Aliquots of 86 initially localized RCC, 75 metastatic RCC and 17 adjacent non-cancerous fresh frozen tissue lysates were trypsin digested, pooled, and fractionated using hydrophilic chromatography. The fractions were analyzed using LC-MS/MS on QExactive HF-X mass spectrometer in data-dependent acquisition (DDA) mode. A resulting spectral library contains 77,817 peptides representing 7960 protein groups (FDR = 1%). Further, we confirm applicability of this library on four RCC datasets measured in data-independent acquisition (DIA) mode, demonstrating a specific quantification of a substantially increased part of RCC proteome, depending on LC-MS/MS instrumentation. Impact of sample specificity of the library on the results of targeted DIA data extraction was demonstrated by parallel analyses of two datasets by two pan human libraries. The new RCC specific library has potential to contribute to better understanding the RCC development at molecular level, leading to new diagnostic and therapeutic targets.     

Relative protein quantification by isobaric SILAC with immonium ion splitting (ISIS)

Metabolic labeling techniques have recently become popular tools for the quantitative profiling of proteomes. Classical stable isotope labeling with amino acids in cell cultures (SILAC) uses pairs of heavy/light isotopic forms of amino acids to introduce predictable mass differences in protein samples to be compared. After proteolysis, pairs of cognate precursor peptides can be correlated, and their intensities can be used for mass spectrometry-based relative protein quantification. We present an alternative SILAC approach by which two cell cultures are grown in media containing isobaric forms of amino acids, labeled either with 13C on the carbonyl (C-1) carbon or 15N on backbone nitrogen. Labeled peptides from both samples have the same nominal mass and nearly identical MS/MS spectra but generate upon fragmentation distinct immonium ions separated by 1 amu. When labeled protein samples are mixed, the intensities of these immonium ions can be used for the relative quantification of the parent proteins. We validated the labeling of cellular proteins with valine, isoleucine, and leucine with coverage of 97% of all tryptic peptides. We improved the sensitivity for the detection of the quantification ions on a pulsing instrument by using a specific fast scan event. The analysis of a protein mixture with a known heavy/light ratio showed reliable quantification. Finally the application of the technique to the analysis of two melanoma cell lines yielded quantitative data consistent with those obtained by a classical two-dimensional DIGE analysis of the same samples. Our method combines the features of the SILAC technique with the advantages of isobaric labeling schemes like iTRAQ. We discuss advantages and disadvantages of isobaric SILAC with immonium ion splitting as well as possible ways to improve it.     

Evaluation of empirical rule of linearly correlated peptide selection (ERLPS) for proteotypic peptide‐based quantitative proteomics

Precise protein quantification is essential in comparative proteomics. Currently, quantification bias is inevitable when using proteotypic peptide‐based quantitative proteomics strategy for the differences in peptides measurability. To improve quantification accuracy, we proposed an “empirical rule for linearly correlated peptide selection (ERLPS)” in quantitative proteomics in our previous work. However, a systematic evaluation on general application of ERLPS in quantitative proteomics under diverse experimental conditions needs to be conducted. In this study, the practice workflow of ERLPS was explicitly illustrated; different experimental variables, such as, different MS systems, sample complexities, sample preparations, elution gradients, matrix effects, loading amounts, and other factors were comprehensively investigated to evaluate the applicability, reproducibility, and transferability of ERPLS. The results demonstrated that ERLPS was highly reproducible and transferable within appropriate loading amounts and linearly correlated response peptides should be selected for each specific experiment. ERLPS was used to proteome samples from yeast to mouse and human, and in quantitative methods from label‐free to O18/O16‐labeled and SILAC analysis, and enabled accurate measurements for all proteotypic peptide‐based quantitative proteomics over a large dynamic range.     

Mass spectrometry at the interface of proteomics and genomics

With the onset of modern DNA sequencing technologies, genomics is experiencing a revolution in terms of quantity and quality of sequencing data. Rapidly growing numbers of sequenced genomes and metagenomes present a tremendous challenge for bioinformatics tools that predict protein-coding regions. Experimental evidence of expressed genomic regions, both at the RNA and protein level, is becoming invaluable for genome annotation and training of gene prediction algorithms. Evidence of gene expression at the protein level using mass spectrometry-based proteomics is increasingly used in refinement of raw genome sequencing data. In a typical "proteogenomics" experiment, the whole proteome of an organism is extracted, digested into peptides and measured by a mass spectrometer. The peptide fragmentation spectra are identified by searching against a six-frame translation of the raw genomic assembly, thus enabling the identification of hitherto unpredicted protein-coding genomic regions. Application of mass spectrometry to genome annotation presents a range of challenges to the standard workflows in proteomics, especially in terms of proteome coverage and database search strategies. Here we provide an overview of the field and argue that the latest mass spectrometry technologies that enable high mass accuracy at high acquisition rates will prove to be especially well suited for proteogenomics applications.     

Andromeda: a peptide search engine integrated into the MaxQuant environment

A key step in mass spectrometry (MS)-based proteomics is the identification of peptides in sequence databases by their fragmentation spectra. Here we describe Andromeda, a novel peptide search engine using a probabilistic scoring model. On proteome data, Andromeda performs as well as Mascot, a widely used commercial search engine, as judged by sensitivity and specificity analysis based on target decoy searches. Furthermore, it can handle data with arbitrarily high fragment mass accuracy, is able to assign and score complex patterns of post-translational modifications, such as highly phosphorylated peptides, and accommodates extremely large databases. The algorithms of Andromeda are provided. Andromeda can function independently or as an integrated search engine of the widely used MaxQuant computational proteomics platform and both are freely available at www.maxquant.org. The combination enables analysis of large data sets in a simple analysis workflow on a desktop computer. For searching individual spectra Andromeda is also accessible via a web server. We demonstrate the flexibility of the system by implementing the capability to identify cofragmented peptides, significantly improving the total number of identified peptides.     

Targeted Proteomic Quantification on Quadrupole-Orbitrap Mass Spectrometer

There is an immediate need for improved methods to systematically and precisely quantify large sets of peptides in complex biological samples. To date protein quantification in biological samples has been routinely performed on triple quadrupole instruments operated in selected reaction monitoring mode (SRM), and two major challenges remain. Firstly, the number of peptides to be included in one survey experiment needs to be increased to routinely reach several hundreds, and secondly, the degree of selectivity should be improved so as to reliably discriminate the targeted analytes from background interferences. High resolution and accurate mass (HR/AM) analysis on the recently developed Q-Exactive mass spectrometer can potentially address these issues. This instrument presents a unique configuration: it is constituted of an orbitrap mass analyzer equipped with a quadrupole mass filter as the front-end for precursor ion mass selection. This configuration enables new quantitative methods based on HR/AM measurements, including targeted analysis in MS mode (single ion monitoring) and in MS/MS mode (parallel reaction monitoring). The ability of the quadrupole to select a restricted m/z range allows one to overcome the dynamic range limitations associated with trapping devices, and the MS/MS mode provides an additional stage of selectivity. When applied to targeted protein quantification in urine samples and benchmarked with the reference SRM technique, the quadrupole-orbitrap instrument exhibits similar or better performance in terms of selectivity, dynamic range, and sensitivity. This high performance is further enhanced by leveraging the multiplexing capability of the instrument to design novel acquisition methods and apply them to large targeted proteomic studies for the first time, as demonstrated on 770 tryptic yeast peptides analyzed in one 60-min experiment. The increased quality of quadrupole-orbitrap data has the potential to improve existing protein quantification methods in complex samples and address the pressing demand of systems biology or biomarker evaluation studies.Shotgun proteomics has emerged over the past decade as the most effective method for the qualitative study of complex proteomes (i.e., the identification of the protein content), as illustrated by a wealth of publications (1, 2). In this approach, after enzymatic digestion of the proteins, the generated peptides are analyzed by means of liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS)¹ in a data dependent mode. However, the complexity of the digested proteomes under investigation and the wide range of protein abundances limit the reproducibility and the sensitivity of this stochastic approach (3), which is critical if one aims at the systematic quantification of the proteins. Thus, alternative MS approaches have emerged for the systematic quantitative study of complex proteomes, the MS-based targeted proteomics (4). In this hypothesis-driven approach, only specific subsets of analytes (a few targeted peptides used as surrogates for the proteins of interest) are selectively measured in predefined m/z ranges and retention time windows, which overcomes the bias toward most abundant compounds commonly observed with shotgun proteomics. When applied to complex biological samples—for example, bodily fluids such as urine or plasma—targeted proteomics requires high performance instruments allowing measurements of a wide dynamic range (many orders of magnitude), with high sensitivity in order to detect peptides in the low amol range and sufficient selectivity to cope with massive biochemical background (5). Selected reaction monitoring (SRM) on triple quadrupole (6) or triple quadrupole-linear ion trap mass spectrometers (7) has emerged as a means to conduct such analyses (8). Initially applied in the MS analysis of small molecules (9, 10), SRM has gradually emerged as the reference quantitative technique for analyzing proteins (or peptides) in biological samples. When coupled with the isotope dilution strategy (11, 12), this very effective technique allows the precise quantification of proteins (13–18). However, despite the increased selectivity provided by the two-stage mass filtering of SRM (at the precursor and fragment ion levels), the low resolution of mass selection does not allow the systematic removal of interferences (19, 20). Moreover, in proteomics, the biochemical background has a composition similar to that of the analytes of interest, which remains a major hurdle limiting the sensitivity of assays, especially in a bodily fluid matrix. High resolution/accurate mass (HR/AM) analysis represents a promising alternative approach that might more efficiently distinguish the compounds of interest from interferences in targeted proteomics. Such analyses can be conducted on orbitrap-based mass spectrometers because of their high sensitivity and high mass accuracy capabilities (21). The introduction of the benchtop standalone orbitrap mass spectrometer (Exactive) (22) further strengthened the attractiveness of the approach, especially in the field of small molecule analysis (23, 24). However, as quantification using trapping devices intrinsically suffers from a limited dynamic range because of the overall ion capacity, the complexity of biological samples remains very challenging even with the HR/AM approach (25). Targeted protein analysis with triple quadrupole mass spectrometers keeps on showing significant superiority for such samples.² The recently developed quadrupole-orbitrap mass spectrometer (Q-Exactive) can potentially address this issue.³ It is constituted of an orbitrap mass analyzer equipped with a quadrupole mass filter as the front-end for precursor ion mass selection (26, 27). This configuration combines advantages of triple quadrupole instruments for mass filtering and orbitrap-based mass spectrometers for HR/AM measurement. The ability of the instrument to select a restricted m/z range or (sequentially) a small number of precursor ions offers new opportunities for quantification in complex samples by selectively enriching low abundant components. The resulting data, acquired in the so-called single ion monitoring (SIM) mode, fully benefit from the trapping capability while keeping a high acquisition rate as a result of the fast switching time between targeted precursor ions of the quadrupole. Although this mode of data acquisition is possible with a configuration combining a linear ion trap with the orbitrap (as in the LTQ-Orbitrap mass spectrometer), its effectiveness is far more limited in this case. The quadrupole-orbitrap configuration presents significant benefits by selectively isolating a narrow population of precursor ions. Other features of the instrument include its multiplexed trapping capability (26) using either the C-trap or the higher energy collisional dissociation (HCD) cell (28, 29), which opens new avenues in the design of innovative acquisition methods for quantification studies. For the first time, a panel of acquisition methods is designed and applied to targeted quantification at the MS and MS/MS levels. In the latter case, the simultaneous monitoring of multiple MS/MS fragmentation channels, also called parallel reaction monitoring⁴ (PRM), is particularly promising for quantifying large sets of peptides with increased selectivity.     

Real-Time Search-Assisted Acquisition on a Tribrid Mass Spectrometer Improves Coverage in Multiplexed Single-Cell Proteomics

In the young field of single-cell proteomics (scMS), there is a great need for improved global proteome characterization, both in terms of proteins quantified per cell and quantitative performance thereof. The recently introduced real-time search (RTS) on the Orbitrap Eclipse Tribrid mass spectrometer in combination with SPS-MS3 acquisition has been shown to be beneficial for the measurement of samples that are multiplexed using isobaric tags. Multiplexed scMS requires high ion injection times and high-resolution spectra to quantify the single-cell signal; however, the carrier channel facilitates peptide identification and thus offers the opportunity for fast on-the-fly precursor filtering before committing to the time-intensive quantification scan. Here, we compared classical MS2 acquisition against RTS-SPS-MS3, both using the Orbitrap Eclipse Tribrid MS with the FAIMS Pro ion mobility interface and present a new acquisition strategy termed RETICLE (RTS enhanced quant of single cell spectra) that makes use of fast real-time searched linear ion trap scans to preselect MS1 peptide precursors for quantitative MS2 Orbitrap acquisition. We show that classical MS2 acquisition is outperformed by both RTS-SPS-MS3 through increased quantitative accuracy at similar proteome coverage, and RETICLE through higher proteome coverage, with the latter enabling the quantification of over 1000 proteins per cell at an MS2 injection time of 750 ms using a 2 h gradient.     

基于数据非依赖采集的蛋白质组质谱数据解析方法研究进展

数据非依赖采集（DIA）是蛋白质组学领域近年来快速发展的质谱采集技术，其通过无偏碎裂隔离窗口内的所有母离子采集二级谱图，理论上可实现蛋白质样品的深度覆盖，同时具有高通量、高重现性和高灵敏度的优点。现有的DIA数据采集方法可以分为全窗口碎裂方法、隔离窗口序列碎裂方法和四维DIA数据采集方法（4D-DIA）3大类。针对DIA数据的不同特点，主要数据解析方法包括谱库搜索方法、蛋白质序列库直接搜索方法、伪二级谱图鉴定方法和从头测序方法4大类。解析得到的肽段鉴定结果需要进行可信度评估，包括使用机器学习方法的重排序和对报告结果集合的假发现率估计两个步骤，实现对数据解析结果的质控。本文对DIA数据的采集方法、数据解析方法及软件和鉴定结果可信度评估方法进行了整理和综述，并展望了未来的发展方向。     

Super-SILAC: current trends and future perspectives

Stable isotope labeling with amino acids in cell culture (SILAC) has risen as a powerful quantification technique in mass spectrometry (MS)–based proteomics in classical and modified forms. Previously, SILAC was limited to cultured cells because of the requirement of active protein synthesis; however, in recent years, it was expanded to model organisms and tissue samples. Specifically, the super-SILAC technique uses a mixture of SILAC-labeled cells as a spike-in standard for accurate quantification of unlabeled samples, thereby enabling quantification of human tissue samples. Here, we highlight the recent developments in super-SILAC and its application to the study of clinical samples, secretomes, post-translational modifications and organelle proteomes. Finally, we propose super-SILAC as a robust and accurate method that can be commercialized and applied to basic and clinical research.     

Monitoring of changes in the membrane proteome during stationary phase adaptation of Bacillus subtilis using in vivo labeling techniques

Bacillus subtilis has been developed as a model system for physiological proteomics. However, thus far these studies have mainly been limited to cytoplasmic, extracellular, and cell-wall attached proteins. Although being certainly important for cell physiology, the membrane protein fraction has not been studied in comparable depth due to inaccessibility by traditional 2-DE-based workflows and limitations in reliable quantification. In this study, we now compare the potential of stable isotope labeling with amino acids (SILAC) and (14)N/(15)N-labeling for the analysis of bacterial membrane fractions in physiology-driven proteomic studies. Using adaptation of B. subtilis to amino acid (lysine) and glucose starvation as proof of principle scenarios, we show that both approaches provide similarly valuable data for the quantification of bacterial membrane proteins. Even if labeling with stable amino acids allows a more straightforward analysis of data, the (14)N/(15)N-labeling has some advantages in general such as labeling of all amino acids and thereby increasing the number of peptides for quantification. Both, SILAC as well as (14)N/(15)N-labeling are compatible with 2-DE, 2-D LC-MS/MS, and GeLC-MS/MS and thus will allow comprehensive simultaneous interrogation of cytoplasmic and enriched membrane proteomes.     

A Fast Workflow for Identification and Quantification of Proteomes

The current in-depth proteomics makes use of long chromatography gradient to get access to more peptides for protein identification, resulting in covering of as many as 8000 mammalian gene products in 3 days of mass spectrometer running time. Here we report a fast sequencing (Fast-seq) workflow of the use of dual reverse phase high performance liquid chromatography - mass spectrometry (HPLC-MS) with a short gradient to achieve the same proteome coverage in 0.5 day. We adapted this workflow to a quantitative version (Fast quantification, Fast-quan) that was compatible to large-scale protein quantification. We subjected two identical samples to the Fast-quan workflow, which allowed us to systematically evaluate different parameters that impact the sensitivity and accuracy of the workflow. Using the statistics of significant test, we unraveled the existence of substantial falsely quantified differential proteins and estimated correlation of false quantification rate and parameters that are applied in label-free quantification. We optimized the setting of parameters that may substantially minimize the rate of falsely quantified differential proteins, and further applied them on a real biological process. With improved efficiency and throughput, we expect that the Fast-seq/Fast-quan workflow, allowing pair wise comparison of two proteomes in 1 day may make MS available to the masses and impact biomedical research in a positive way.The performance of mass spectrometry has been improved tremendously over the last few years (1–3), making mass spectrometry-based proteomics a viable approach for large-scale protein analysis in biological research. Scientists around the world are striving to fulfill the promise of identifying and quantifying almost all gene products expressed in a cell line or tissue. This would make mass spectrometry-based protein analysis an approach that is compatible to the second-generation mRNA deep-seq technique (4, 5).Two liquid chromatography (LC)-MS strategies have been employed to achieve deep proteome coverage. One is a single run with a long chromatography column and gradient to take advantage of the resolving power of HPLC to reduce the complexity of peptide mixtures; the other is a sequential run with two-dimensional separation (typically ion-exchange and reverse phase) to reduce peptide complexity. It was reported by two laboratories that 2761 and 4500 proteins were identified with a 10 h chromatography gradient on a dual pressure linear ion-trap orbitrap mass spectrometer (LTQ Orbitrap Velos)(6–8). Similarly, 3734 proteins were identified using a 8 h gradient on a 2 m long column with a hybrid triple quadrupole - time of flight (Q-TOF, AB sciex 5600 Q-TOF)(9) mass spectrometer. The two-dimensional approach has yielded more identification with longer time. For example, 10,006 proteins (representing over 9000 gene products, GPs)¹ were identified in U2OS cell (10), and 10,255 proteins (representing 9207 GPs) from HeLa cells (11). It took weeks (for example, 2–3 weeks) of machine running time to achieve such proteome coverage, pushing proteome analysis to the level that is comparable to mRNA-seq. With the introduction of faster machines, human proteome coverage now has reached the level of 7000–8500 proteins (representing 7000–8000 GPs) in 3 days (12). Notwithstanding the impressive improvement, the current approach using long column and long gradient suffers from inherent limitations: it takes long machine running time and it is challenging to keep reproducibility among repeated runs. Thus, current throughput and reproducibility have hindered the application of in-depth proteomics to traditional biological researches. A timesaving approach is in urgent need.In this study, we used the first-dimension (1D) short pH 10 RP prefractionation to reduce the complexity of the proteome (13), followed by sequential 30 min second-dimension (2D) short pH 3 reverse phase-(RP)-LC-MS/MS runs for protein identification (14). The results demonstrated that it is possible to identify 8000 gene products from mammalian cells within 12 h of total MS measurement time by applying this dual-short 2D-RPLC-MS/MS strategy (Fast sequencing, Fast-seq). The robustness of the strategy was revealed by parallel testing on different MS systems including quadrupole orbitrap mass spectrometer (Q-Exactive), hybrid Q-TOF (Triple-TOF 5600), and dual pressure linear ion-trap orbitrap mass spectrometer (LTQ-Orbitrap Velos), indicating the inherent strength of the approach as to merely taking advantage of the better MS instruments. This strategy increases the efficiency of MS sequencing in unit time for the identification of proteins. We achieved identification of 2200 proteins/30 mins on LTQ-Orbitrap Velos, 2800 proteins/30 mins on Q-Exactive and Triple-TOF 5600 respectively. We further optimized Fast-seq and worked out a quantitative-version of the Fast-seq workflow: Fast-quantification (Fast-quan) and applied it for protein abundance quantification in HUVEC cell that was treated with a drug candidate MLN4924 (a drug in phase III clinical trial). We were able to quantify > 6700 GPs in 1 day of MS running time and found 99 proteins were up-regulated with high confidence. We expect this efficient alternative approach for in-depth proteome analysis will make the application of MS-based proteomics more accessible to biological applications.     

Evaluation of different peptide fragmentation types and mass analyzers in data‐dependent methods using an Orbitrap Fusion Lumos Tribrid mass spectrometer

One of the major additions in MS technology has been the irruption of the Orbitrap mass analyzer, which has boosted the proteomics analyses of biological complex samples since its introduction. Here, we took advantage of the capabilities of the new Orbitrap Fusion Lumos Tribrid mass spectrometer to assess the performance of different data‐dependent acquisition methods for the identification and quantitation of peptides and phosphopeptides in single‐shot analysis of human whole cell lysates. Our study explored the capabilities of tri‐hibrid mass spectrometers for (phospho‐) peptide identification and quantitation using different gradient lengths, sample amounts, and combinations of different peptide fragmentation types and mass analyzers. Moreover, the acquisition of the same complex sample with different acquisition methods resulted in the generation of a dataset to be used as a reference for further analyses, and a starting point for future optimizations in particular applications.     

Higher-energy C-trap dissociation for peptide modification analysis

Peptide sequencing is the basis of mass spectrometry-driven proteomics. Here we show that in the linear ion trap-orbitrap mass spectrometer (LTQ Orbitrap) peptide ions can be efficiently fragmented by high-accuracy and full-mass-range tandem mass spectrometry (MS/MS) via higher-energy C-trap dissociation (HCD). Immonium ions generated via HCD pinpoint modifications such as phosphotyrosine with very high confidence. Additionally we show that an added octopole collision cell facilitates de novo sequencing.     

Quantitative proteomics using stable isotope labeling with amino acids in cell culture

Stable isotope labeling with amino acids in cell culture (SILAC) is a simple in vivo labeling strategy for mass spectrometry-based quantitative proteomics. It relies on the metabolic incorporation of nonradioactive heavy isotopic forms of amino acids into cellular proteins, which can be readily distinguished in a mass spectrometer. As the samples are mixed before processing in the SILAC methodology, the sample handling errors are also minimized. Here we present protocols for using SILAC in the following types of experiments: (i) studying inducible protein complexes, (ii) identification of Tyr kinase substrates, (iii) differential membrane proteomics and (iv) studying temporal dynamics using SILAC 5-plexing. Although the overall time is largely dependent on the rate of cell growth and various sample processing steps employed, a typical SILAC experiment from start to finish, including data analysis, should take anywhere between 20 and 25 d.     

Incorporating sequence information into the scoring function: a hidden Markov model for improved peptide identification

MOTIVATION: The identification of peptides by tandem mass spectrometry (MS/MS) is a central method of proteomics research, but due to the complexity of MS/MS data and the large databases searched, the accuracy of peptide identification algorithms remains limited. To improve the accuracy of identification we applied a machine-learning approach using a hidden Markov model (HMM) to capture the complex and often subtle links between a peptide sequence and its MS/MS spectrum. Model: Our model, HMM_Score, represents ion types as HMM states and calculates the maximum joint probability for a peptide/spectrum pair using emission probabilities from three factors: the amino acids adjacent to each fragmentation site, the mass dependence of ion types and the intensity dependence of ion types. The Viterbi algorithm is used to calculate the most probable assignment between ion types in a spectrum and a peptide sequence, then a correction factor is added to account for the propensity of the model to favor longer peptides. An expectation value is calculated based on the model score to assess the significance of each peptide/spectrum match. RESULTS: We trained and tested HMM_Score on three data sets generated by two different mass spectrometer types. For a reference data set recently reported in the literature and validated using seven identification algorithms, HMM_Score produced 43% more positive identification results at a 1% false positive rate than the best of two other commonly used algorithms, Mascot and X!Tandem. HMM_Score is a highly accurate platform for peptide identification that works well for a variety of mass spectrometer and biological sample types. AVAILABILITY: The program is freely available on ProteomeCommons via an OpenSource license. See http://bioinfo.unc.edu/downloads/ for the download link.