Minimizing technical variation during sample preparation prior to label-free quantitative mass spectrometry

Sample preparation is the crucial starting point to obtain high-quality mass spectrometry data and can be divided into two main steps in a bottom-up proteomics approach: cell/tissue lysis with or without detergents and a(n) (in-solution) digest comprising denaturation, reduction, alkylation, and digesting of the proteins. Here, some important considerations, among others, are that the reagents used for sample preparation can inhibit the digestion enzyme (e.g., 0.1% sodium dodecyl sulfate [SDS] and 0.5 M guanidine HCl), give rise to ion suppression (e.g., polyethylene glycol [PEG]), be incompatible with liquid chromatography–tandem mass spectrometry (LC–MS/MS) (e.g., SDS), and can induce additional modifications (e.g., urea). Taken together, all of these irreproducible effects are gradually becoming a problem when label-free quantitation of the samples is envisioned such as during the increasingly popular high-definition mass spectrometry (HDMS^E) and sequential window acquisition of all theoretical fragment ion spectra (SWATH) data-independent acquisition strategies. Here, we describe the detailed validation of a reproducible method with sufficient protein yield for sample preparation without any known LC–MS/MS interfering substances by using 1% sodium deoxycholate (SDC) during both cell lysis and in-solution digest.     

A Probabilistic Framework for Peptide and Protein Quantification from Data-Dependent and Data-Independent LC-MS Proteomics Experiments

Abstract A probability-based quantification framework is presented for the calculation of relative peptide and protein abundance in label-free and label-dependent LC-MS proteomics data. The results are accompanied by credible intervals and regulation probabilities. The algorithm takes into account data uncertainties via Poisson statistics modified by a noise contribution that is determined automatically during an initial normalization stage. Protein quantification relies on assignments of component peptides to the acquired data. These assignments are generally of variable reliability and may not be present across all of the experiments comprising an analysis. It is also possible for a peptide to be identified to more than one protein in a given mixture. For these reasons the algorithm accepts a prior probability of peptide assignment for each intensity measurement. The model is constructed in such a way that outliers of any type can be automatically reweighted. Two discrete normalization methods can be employed. The first method is based on a user-defined subset of peptides, while the second method relies on the presence of a dominant background of endogenous peptides for which the concentration is assumed to be unaffected. Normalization is performed using the same computational and statistical procedures employed by the main quantification algorithm. The performance of the algorithm will be illustrated on example data sets, and its utility demonstrated for typical proteomics applications. The quantification algorithm supports relative protein quantification based on precursor and product ion intensities acquired by means of data-dependent methods, originating from all common isotopically-labeled approaches, as well as label-free ion intensity-based data-independent methods.     

Recent progress in liquid chromatography-based separation and label-free quantitative plant proteomics

Recent innovations in liquid chromatography-mass spectrometry (LC-MS)-based methods have facilitated quantitative and functional proteomic analyses of large numbers of proteins derived from complex samples without any need for protein or peptide labelling. Regardless of its great potential, the application of these proteomics techniques to plant science started only recently. Here we present an overview of label-free quantitative proteomics features and their employment for analysing plants. Recent methods used for quantitative protein analyses by MS techniques are summarized and major challenges associated with label-free LC-MS-based approaches, including sample preparation, peptide separation, quantification and kinetic studies, are discussed. Database search algorithms and specific aspects regarding protein identification of non-sequenced organisms are also addressed. So far, label-free LC-MS in plant science has been used to establish cellular or subcellular proteome maps, characterize plant-pathogen interactions or stress defence reactions, and for profiling protein patterns during developmental processes. Improvements in both, analytical platforms (separation technology and bioinformatics/statistical analysis) and high throughput nucleotide sequencing technologies will enhance the power of this method.     

The use of proteome similarity for the qualitative and quantitative profiling of reperfused myocardium

An LC-MS-based approach is presented for the identification and quantification of proteins from unsequenced organisms. The method relies on the preservation of homology across species and the similarity in detection characteristics of proteomes in general. Species related proteomes share similarity that progresses from the amino acid frequency distribution to the complete amino sequence of matured proteins. Moreover, the comparative analysis between theoretical and experimental proteome distributions can be used as a measure for the correctness of detection and identification obtained through LC-MS-based schemes. Presented are means to the identification and quantification of rabbit myocardium proteins, immediately after inducing cardiac arrest, using a data-independent LC-MS acquisition strategy. The employed method of acquisition affords accurate mass information on both the precursor and associated product ions, whilst preserving and recording the intensities of the ions. The latter facilitates label-free quantification. The experimental ion density observations obtained for the rabbit sub proteome were found to share great similarity with five other mammalian samples, including human heart, human breast tissue, human plasma, rat liver and a mouse cell line. Redundant, species-homologues peptide identifications from other mammalian organisms were used for initial protein identification, which were complemented with peptide identifications of translated gene sequences. The feasibility and accuracy of label-free quantification of the identified peptides and proteins utilizing above mentioned strategy is demonstrated for selected cardiac rabbit proteins.     

Simultaneous quantification of free nucleotides in complex biological samples using ion pair reversed phase liquid chromatography isotope dilution tandem mass spectrometry

A new sensitive and accurate analytical method has been developed for quantification of intracellular nucleotides in complex biological samples from cultured cells of different microorganisms such as Saccharomyces cerevisiae, Escherichia coli, and Penicillium chrysogenum. This method is based on ion pair reversed phase liquid chromatography electrospray ionization isotope dilution tandem mass spectrometry (IP-LC-ESI-ID-MS/MS. A good separation and low detection limits were observed for these compounds using dibutylamine as volatile ion pair reagent in the mobile phase of the LC. Uniformly ¹³C-labeled isotopes of nucleotides were used as internal standards for both extraction and quantification of intracellular nucleotides. The method was validated by determining the linearity, sensitivity, and repeatability.     

The Effect of Peptide Identification Search Algorithms on MS2-Based Label-Free Protein Quantification

Abstract Several approaches exist for the quantification of proteins in complex samples processed by liquid chromatography-mass spectrometry followed by fragmentation analysis (MS2). One of these approaches is label-free MS2-based quantification, which takes advantage of the information computed from MS2 spectrum observations to estimate the abundance of a protein in a sample. As a first step in this approach, fragmentation spectra are typically matched to the peptides that generated them by a search algorithm. Because different search algorithms identify overlapping but non-identical sets of peptides, here we investigate whether these differences in peptide identification have an impact on the quantification of the proteins in the sample. We therefore evaluated the effect of using different search algorithms by examining the reproducibility of protein quantification in technical repeat measurements of the same sample. From our results, it is clear that a search engine effect does exist for MS2-based label-free protein quantification methods. As a general conclusion, it is recommended to address the overall possibility of search engine-induced bias in the protein quantification results of label-free MS2-based methods by performing the analysis with two or more distinct search engines.     

Extending the dynamic range of label-free mass spectrometric quantification of affinity purifications

Affinity purification (AP) of protein complexes combined with LC-MS/MS analysis is the current method of choice for identification of protein-protein interactions. Their interpretation with respect to significance, specificity, and selectivity requires quantification methods coping with enrichment factors of more than 1000-fold, variable amounts of total protein, and low abundant, unlabeled samples. We used standardized samples (0.1-1000 fmol) measured on high resolution hybrid linear ion trap instruments (LTQ-FT/Orbitrap) to characterize and improve linearity and dynamic range of label-free approaches. Quantification based on spectral counts was limited by saturation and ion suppression effects with samples exceeding 100 ng of protein, depending on the instrument setup. In contrast, signal intensities of peptides (peak volumes) selected by a novel correlation-based method (TopCorr-PV) were linear over at least 4 orders of magnitude and allowed for accurate relative quantification of standard proteins spiked into a complex protein background. Application of this procedure to APs of the voltage-gated potassium channel Kv1.1 as a model membrane protein complex unambiguously identified the whole set of known interaction partners together with novel candidates. In addition to discriminating these proteins from background, we could determine efficiency, cross-reactivities, and selection biases of the used purification antibodies. The enhanced dynamic range of the developed quantification procedure appears well suited for sensitive identification of specific protein-protein interactions, detection of antibody-related artifacts, and optimization of AP conditions.     

Generic workflow for quality assessment of quantitative label-free LC-MS analysis

As high-resolution instruments are becoming standard in proteomics laboratories, label-free quantification using precursor measurements is becoming a viable option, and is consequently rapidly gaining popularity. Several software solutions have been presented for label-free analysis, but to our knowledge no conclusive studies regarding the sensitivity and reliability of each step of the analysis procedure has been described. Here, we use real complex samples to assess the reliability of label-free quantification using four different software solutions. A generic approach to quality test quantitative label-free LC-MS is introduced. Measures for evaluation are defined for feature detection, alignment and quantification. All steps of the analysis could be considered adequately performed by the utilized software solutions, although differences and possibilities for improvement could be identified. The described method provides an effective testing procedure, which can help the user to quickly pinpoint where in the workflow changes are needed.     

Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics

Central tendency, linear regression, locally weighted regression, and quantile techniques were investigated for normalization of peptide abundance measurements obtained from high-throughput liquid chromatography-Fourier transform ion cyclotron resonance mass spectrometry (LC-FTICR MS). Arbitrary abundances of peptides were obtained from three sample sets, including a standard protein sample, two Deinococcus radiodurans samples taken from different growth phases, and two mouse striatum samples from control and methamphetamine-stressed mice (strain C57BL/6). The selected normalization techniques were evaluated in both the absence and presence of biological variability by estimating extraneous variability prior to and following normalization. Prior to normalization, replicate runs from each sample set were observed to be statistically different, while following normalization replicate runs were no longer statistically different. Although all techniques reduced systematic bias to some degree, assigned ranks among the techniques revealed that for most LC-FTICR-MS analyses linear regression normalization ranked either first or second. However, the lack of a definitive trend among the techniques suggested the need for additional investigation into adapting normalization approaches for label-free proteomics. Nevertheless, this study serves as an important step for evaluating approaches that address systematic biases related to relative quantification and label-free proteomics.     

Comparative proteomic analysis for the insoluble fractions of colorectal cancer patients

We used label-free quantitative proteomics with the insoluble fractions from colorectal cancer (CRC) patients to gain further insight into the utility of profiling altered protein expression as a potential biomarker for cancer. The insoluble fractions were prepared from paired tumor/normal biopsies from 13 patients diagnosed with CRC (stages I to IV). Fifty-six proteins identified in data pooled from the 13 cases were differentially expressed between the tumor and adjacent normal tissue. The connections between these proteins are involved in reciprocal networks related to tumorigenesis, cancer incidence based on genetic disorder, and skeletal and muscular disorders. To assess their potential utility as biomarkers, the relative expression levels of the proteins were validated using personal proteomics and a heat map to compare five individual CRC samples with five normal tissue samples. Further validation of a panel of proteins (KRT5, JUP, TUBB, and COL6A1) using western blotting confirmed the differential expression. These proteins gave specific network information for CRC, and yielded a panel of novel markers and potential targets for treatment. It is anticipated that the experimental approach described here will increase our understanding of the membrane environment in CRC, which may provide direction for making diagnoses and prognoses through molecular biomarker targeting.     

Yeast Secretes High Amounts of Human Calreticulin without Cellular Stress

The ER chaperone calreticulin (CALR) also has extracellular functions and can exit the mammalian cell in response to various factors, although the mechanism by which this takes place is unknown. The yeast Saccharomyces cerevisiae efficiently secretes human CALR, and the analysis of this process in yeast could help to clarify how it gets out of eukaryotic cells. We have achieved a secretion titer of about 140 mg/L CALR in our S. cerevisiae system. Here, we present a comparative quantitative whole proteome study in CALR-secreting yeast using non-equilibrium pH gradient electrophoresis (NEPHGE)-based two-dimensional gel electrophoresis (2DE) as well as liquid chromatography mass spectrometry in data-independent analysis mode (LC-MS^E). A reconstructed carrier ampholyte (CA) composition of NEPHGE-based first-dimension separation for 2DE could be used instead of formerly commercially available gels. Using LC-MS^E, we identified 1574 proteins, 20 of which exhibited differential expression. The largest group of differentially expressed proteins were structural ribosomal proteins involved in translation. Interestingly, we did not find any signs of cellular stress which is usually observed in recombinant protein-producing yeast, and we did not identify any secretory pathway proteins that exhibited changes in expression. Taken together, high-level secretion of human recombinant CALR protein in S. cerevisiae does not induce cellular stress and does not burden the cellular secretory machinery. There are only small changes in the cellular proteome of yeast secreting CALR at a high level.     

Real-time quantitation of viral replication and inhibitor potency using a label-free optical biosensor

Real-time detection of viral replication inside cells remains a challenge to researchers. The Epic^® System is a high-throughput, label-free optical detection platform capable of measuring molecular interaction in a biochemical assay, as well as integrated cellular response from measurement of cellular dynamic mass redistribution (DMR) in a cell-based assay. DMR has previously been used to measure cell signaling upon receptor stimulation. In this report, we present the first example of Epic^® measurement of viral replication-induced cellular response and demonstrate that this system is extremely powerful not only for the sensitive and quantitative detection of viral replication inside cells but also for screening of viral inhibitors. By comparing with conventional assays used for the measurement of viral replication, we show that the Epic^® response has many advantages including sensitivity, high throughput, real-time quantification and label-free detection. We propose that the Epic^® system for measurement of integrated cellular response will be an excellent method for elucidating steps in viral replication as well as for the high-throughput screening of inhibitors of rhinovirus and other viruses.     

Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments

Liquid chromatography (LC) coupled to electrospray mass spectrometry (MS) is well established in high-throughput proteomics. The technology enables rapid identification of large numbers of proteins in a relatively short time. Comparative quantification of identified proteins from different samples is often regarded as the next step in proteomics experiments enabling the comparison of protein expression in different proteomes. Differential labeling of samples using stable isotope incorporation or conjugation is commonly used to compare protein levels between samples but these procedures are difficult to carry out in the laboratory and for large numbers of samples. Recently, comparative quantification of label-free LC(n)-MS proteomics data has emerged as an alternative approach. In this review, we discuss different computational approaches for extracting comparative quantitative information from label-free LC(n)-MS proteomics data. The procedure for computationally recovering the quantitative information is described. Furthermore, statistical tests used to evaluate the relevance of results will also be discussed.     

Multiplexed complexome profiling using tandem mass tags

Complexome profiling is a rapidly spreading, powerful technique to gain insight into the nature of protein complexes. It identifies and quantifies protein complexes separated into multiple fractions of increasing molecular mass using mass spectrometry-based, label-free bottom-up proteomics. Complexome profiling enables a sophisticated and thorough characterization of the composition, molecular mass, assembly, and interactions of protein complexes. However, in practice, its application is limited by the large number of samples it generates and the related time of mass spectrometry analyses. Here, we report an improved process workflow that implements tandem mass tags for multiplexing complexome profiling. This workflow substantially reduces the number of samples and measuring time without compromising protein identification or quantification reliability. In profiles from mitochondrial fractions of cells recovering from chloramphenicol treatment, tandem mass tags-multiplexed complexome profiling exhibited migration patterns of mature ATP synthase (complex V) and assembly intermediates that were consistent in composition and abundance with profiles obtained by the label-free approach. Reporter ion quantifications of proteins and complexes unaffected by the chloramphenicol treatment presented less variation in comparison to the label-free method. Incorporation of tandem mass tags enabled an efficient and robust complexome profiling analysis and may foster broader application for protein complex profiling in biomedical research and diagnostics.     

Combination of peptide OFFGEL fractionation and label-free quantitation facilitated proteomics profiling of extraocular muscle

Several label-free quantitation strategies have been introduced that obliterate the need for expensive isotopically labeled molecules. However label-free approaches have considerably higher demands in respect of repeatability of sample preparation and fractionation than multiplexing isotope labeling-based strategies. OFFGEL fractionation promises the necessary separation efficiency and repeatability. To test this platform, 12-fraction peptide OFFGEL electrophoresis and online reversed-phase LC connected to a quadrupole TOF mass spectrometer were used to determine differences of the physiological, pathological and biochemical distinct extraocular muscle allotype in comparison to hind-limb muscle. Close to 70% of the peptides separated by OFFGEL electrophoresis were detected only in a single fraction. To determine the separation repeatability of four samples, we compared the ion volumes of multiple peptides deriving from the thick filament-associated protein titin over several fractions and determined a coefficient of variation below 20%. Of the 474 proteins identified, 61 proteins were differently expressed between the two muscle allotypes and were involved in metabolism, muscle contraction, stress response, or gene expression. Several expression differences were validated using immunohistochemistry and Western blot analysis. We therefore consider peptide OFFGEL fractionation an effective and efficient addition to our label-free quantitative proteomics workflow.     

Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics

Background  

Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the sample. While there are numerous approaches to circumvent this problem experimentally (e.g. labeling techniques), reliable prediction of the peak intensities from peptide sequences could provide a peptide-specific correction factor. Thus, it would be a valuable tool towards label-free absolute quantification.     

Characterization of a TiO₂ enrichment method for label-free quantitative phosphoproteomics

Phosphorylation is a protein post-translational modification with key roles in the regulation of cell biochemistry and signaling. In-depth analysis of phosphorylation using mass spectrometry is permitting the investigation of processes controlled by phosphorylation at the system level. A critical step of these phosphoproteomics methods involves the isolation of phosphorylated peptides from the more abundant unmodified peptides produced by the digestion of cell lysates. Although different techniques to enrich for phosphopeptides have been reported, there are limited data on their suitability for direct quantitative analysis by MS. Here we report a TiO₂ based enrichment method compatible with large-scale and label-free quantitative analysis by LC–MS/MS. Starting with just 500 μg of protein, the technique reproducibly isolated hundreds of peptides, >85% of which were phosphorylated. These results were obtained by using relatively short LC–MS/MS gradient runs (45 min) and without any previous separation step. In order to characterize the performance of the method for quantitative analyses, we employed label-free LC–MS/MS using extracted ion chromatograms as the quantitative readout. After normalization, phosphopeptides were quantified with good precision (coefficient of variation was 20% on average, n = 900 phosphopeptides), linearity (correlation coefficients >0.98) and accuracy (deviations <20%). Thus, phosphopeptide ion signals correlated with the concentration of the respective phosphopeptide in samples, making the approach suitable for in-depth relative quantification of phosphorylation by label-free LC–MS/MS.     

Four-dimensional proteomics analysis of human cerebrospinal fluid with trapped ion mobility spectrometry using PASEF

A quadrupole time-of-flight mass spectrometer coupled with a trapped ion mobility spectrometry (timsTOF) operated in parallel accumulation-serial fragmentation (PASEF) mode has recently emerged as a platform capable of providing four-dimensional (4D) features comprising of elution time, collision cross section (CCS), mass-to-charge ratio, and intensity of peptides. The PASEF mode provides ∼100% ion sampling efficiency both in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes without sacrificing sensitivity. In addition, targeted measurements using PASEF integrated parallel reaction monitoring (PRM) mode have also been described. However, only limited number of studies have used timsTOF for analysis of clinical samples. Although Orbitrap mass spectrometers have been used for biomarker discovery from cerebrospinal fluid (CSF) in a variety of neurological diseases, these Orbitrap-derived datasets cannot readily be applied for driving experiments on timsTOF mass spectrometers. We generated a catalog of peptides and proteins in human CSF in DDA mode on a timsTOF mass spectrometer and used these data to build a spectral library. This strategy allowed us to use elution times and ion mobility values from the spectral library to design PRM experiments for quantifying previously discovered biomarkers from CSF samples in Alzheimer's disease. When the same samples were analyzed using a DIA approach combined with a spectral library search, a higher number of proteins were identified than in a library-free approach. Overall, we have established a spectral library of CSF as a resource and demonstrated its utility for PRM and DIA studies, which should facilitate studies of neurological disorders.     

Mass spectrometry–based relative quantification of proteins in precatalytic and catalytically active spliceosomes by metabolic labeling (SILAC), chemical labeling (iTRAQ), and label-free spectral count

The spliceosome undergoes major changes in protein and RNA composition during pre-mRNA splicing. Knowing the proteins—and their respective quantities—at each spliceosomal assembly stage is critical for understanding the molecular mechanisms and regulation of splicing. Here, we applied three independent mass spectrometry (MS)–based approaches for quantification of these proteins: (1) metabolic labeling by SILAC, (2) chemical labeling by iTRAQ, and (3) label-free spectral count for quantification of the protein composition of the human spliceosomal precatalytic B and catalytic C complexes. In total we were able to quantify 157 proteins by at least two of the three approaches. Our quantification shows that only a very small subset of spliceosomal proteins (the U5 and U2 Sm proteins, a subset of U5 snRNP-specific proteins, and the U2 snRNP-specific proteins U2A′ and U2B′′) remains unaltered upon transition from the B to the C complex. The MS-based quantification approaches classify the majority of proteins as dynamically associated specifically with the B or the C complex. In terms of experimental procedure and the methodical aspect of this work, we show that metabolically labeled spliceosomes are functionally active in terms of their assembly and splicing kinetics and can be utilized for quantitative studies. Moreover, we obtain consistent quantification results from all three methods, including the relatively straightforward and inexpensive label-free spectral count technique.     

RIBAR and xRIBAR: Methods for reproducible relative MS/MS-based label-free protein quantification

Mass spectrometry-driven proteomics is increasingly relying on quantitative analyses for biological discoveries. As a result, different methods and algorithms have been developed to perform relative or absolute quantification based on mass spectrometry data. One of the most popular quantification methods are the so-called label-free approaches, which require no special sample processing, and can even be applied retroactively to existing data sets. Of these label-free methods, the MS/MS-based approaches are most often applied, mainly because of their inherent simplicity as compared to MS-based methods. The main application of these approaches is the determination of relative protein amounts between different samples, expressed as protein ratios. However, as we demonstrate here, there are some issues with the reproducibility across replicates of these protein ratio sets obtained from the various MS/MS-based label-free methods, indicating that the existing methods are not optimally robust. We therefore present two new methods (called RIBAR and xRIBAR) that use the available MS/MS data more effectively, achieving increased robustness. Both the accuracy and the precision of our novel methods are analyzed and compared to the existing methods to illustrate the increased robustness of our new methods over existing ones.