Single-pot, solid-phase-enhanced sample preparation for proteomics experiments.

In bottom-up proteomics, proteins are typically identified by enzymatic digestion into peptides, tandem mass spectrometry and comparison of the tandem mass spectra with those predicted from a sequence database for peptides within measurement uncertainty from the experimentally obtained mass. Although now decreasingly common, isolated proteins or simple protein mixtures can also be identified by measuring only the masses of the peptides resulting from the enzymatic digest, without any further fragmentation. Separation methods such as liquid chromatography and electrophoresis are often used to fractionate complex protein or peptide mixtures prior to analysis by mass spectrometry. Although the primary reason for this is to avoid ion suppression and improve data quality, these separations are based on physical and chemical properties of the peptides or proteins and therefore also provide information about them. Depending on the separation method, this could be protein molecular weight (SDS-PAGE), isoelectric point (IEF), charge at a known pH (ion exchange chromatography), or hydrophobicity (reversed phase chromatography). These separations produce approximate measurements on properties that to some extent can be predicted from amino acid sequences. In the case of molecular weight of proteins without posttranslational modifications this is straightforward: simply add the molecular weights of the amino acid residues in the protein. For IEF, charge and hydrophobicity, the order of the amino acids, and folding state of the peptide or protein also matter, but it is nevertheless possible to predict the behavior of peptides and proteins in these separation methods to a degree which renders such predictions useful. This chapter reviews the topic of using data from separation methods for identification and validation in proteomics, with special emphasis on predicting retention times of tryptic peptides in reversed-phase chromatography under acidic conditions, as this is one of the most commonly used separation methods in bottom-up proteomics.

The zymogen granule (ZG) is the specialized organelle in pancreatic acinar cells for digestive enzyme storage and regulated secretion and has been a model for studying secretory granule functions. In an initial effort to comprehensively understand the functions of this organelle, we conducted a proteomic study to identify proteins from highly purified ZG membranes. By combining two-dimensional gel electrophoresis and two-dimensional LC with tandem mass spectrometry, 101 proteins were identified from purified ZG membranes including 28 known ZG proteins and 73 previously unknown proteins, including SNAP29, Rab27B, Rab11A, Rab6, Rap1, and myosin Vc. Moreover several hypothetical proteins were identified that represent potential novel proteins. The ZG localization of nine of these proteins was further confirmed by immunocytochemistry. To distinguish intrinsic membrane proteins from soluble and peripheral membrane proteins, a quantitative proteomic strategy was used to measure the enrichment of intrinsic membrane proteins through the purification process. The iTRAQ ratios correlated well with known or Transmembrane Hidden Markov Model-predicted soluble or membrane proteins. By combining subcellular fractionation with high resolution separation and comprehensive identification of proteins, we have begun to elucidate zymogen granule functions through proteomic and subsequent functional analysis of its membrane components.

In bottom-up proteomics, proteins are typically identified by enzymatic digestion into peptides, tandem mass spectrometry and comparison of the tandem mass spectra with those predicted from a sequence database for peptides within measurement uncertainty from the experimentally obtained mass. Although now decreasingly common, isolated proteins or simple protein mixtures can also be identified by measuring only the masses of the peptides resulting from the enzymatic digest, without any further fragmentation. Separation methods such as liquid chromatography and electrophoresis are often used to fractionate complex protein or peptide mixtures prior to analysis by mass spectrometry. Although the primary reason for this is to avoid ion suppression and improve data quality, these separations are based on physical and chemical properties of the peptides or proteins and therefore also provide information about them. Depending on the separation method, this could be protein molecular weight (SDS-PAGE), isoelectric point (IEF), charge at a known pH (ion exchange chromatography), or hydrophobicity (reversed phase chromatography). These separations produce approximate measurements on properties that to some extent can be predicted from amino acid sequences. In the case of molecular weight of proteins without posttranslational modifications this is straightforward: simply add the molecular weights of the amino acid residues in the protein. For IEF, charge and hydrophobicity, the order of the amino acids, and folding state of the peptide or protein also matter, but it is nevertheless possible to predict the behavior of peptides and proteins in these separation methods to a degree which renders such predictions useful. This chapter reviews the topic of using data from separation methods for identification and validation in proteomics, with special emphasis on predicting retention times of tryptic peptides in reversed-phase chromatography under acidic conditions, as this is one of the most commonly used separation methods in proteomics.

Bottom-up proteomics relies on the use of proteases and is the method of choice for identifying thousands of protein groups in complex samples. Top-down proteomics has been shown to be robust for direct analysis of small proteins and offers a solution to the “peptide-to-protein” inference problem inherent with bottom-up approaches. Here, we describe the first large-scale integration of genomic, bottom-up and top-down proteomic data for the comparative analysis of patient-derived mouse xenograft models of basal and luminal B human breast cancer, WHIM2 and WHIM16, respectively. Using these well-characterized xenograft models established by the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium, we compared and contrasted the performance of bottom-up and top-down proteomics to detect cancer-specific aberrations at the peptide and proteoform levels and to measure differential expression of proteins and proteoforms. Bottom-up proteomic analysis of the tumor xenografts detected almost 10 times as many coding nucleotide polymorphisms and peptides resulting from novel splice junctions than top-down. For proteins in the range of 0–30 kDa, where quantitation was performed using both approaches, bottom-up proteomics quantified 3,519 protein groups from 49,185 peptides, while top-down proteomics quantified 982 proteoforms mapping to 358 proteins. Examples of both concordant and discordant quantitation were found in a ∼60:40 ratio, providing a unique opportunity for top-down to fill in missing information. The two techniques showed complementary performance, with bottom-up yielding eight times more identifications of 0–30 kDa proteins in xenograft proteomes, but failing to detect differences in certain posttranslational modifications (PTMs), such as phosphorylation pattern changes of alpha-endosulfine. This work illustrates the potency of a combined bottom-up and top-down proteomics approach to deepen our knowledge of cancer biology, especially when genomic data are available.

In this study, we present a fully automated tool, called IDEAL-Q, for label-free quantitation analysis. It accepts raw data in the standard mzXML format as well as search results from major search engines, including Mascot, SEQUEST, and X!Tandem, as input data. To quantify as many identified peptides as possible, IDEAL-Q uses an efficient algorithm to predict the elution time of a peptide unidentified in a specific LC-MS/MS run but identified in other runs. Then, the predicted elution time is used to detect peak clusters of the assigned peptide. Detected peptide peaks are processed by statistical and computational methods and further validated by signal-to-noise ratio, charge state, and isotopic distribution criteria (SCI validation) to filter out noisy data. The performance of IDEAL-Q has been evaluated by several experiments. First, a serially diluted protein mixed with Escherichia coli lysate showed a high correlation with expected ratios and demonstrated good linearity (R2 = 0.996). Second, in a biological replicate experiment on the THP-1 cell lysate, IDEAL-Q quantified 87% (1,672 peptides) of all identified peptides, surpassing the 45.7% (909 peptides) achieved by the conventional identity-based approach, which only quantifies peptides identified in all LC-MS/MS runs. Manual validation on all 11,940 peptide ions in six replicate LC-MS/MS runs revealed that 97.8% of the peptide ions were correctly aligned, and 93.3% were correctly validated by SCI. Thus, the mean of the protein ratio, 1.00 ± 0.05, demonstrates the high accuracy of IDEAL-Q without human intervention. Finally, IDEAL-Q was applied again to the biological replicate experiment but with an additional SDS-PAGE step to show its compatibility for label-free experiments with fractionation. For flexible workflow design, IDEAL-Q supports different fractionation strategies and various normalization schemes, including multiple spiked internal standards. User-friendly interfaces are provided to facilitate convenient inspection, validation, and modification of quantitation results. In summary, IDEAL-Q is an efficient, user-friendly, and robust quantitation tool. It is available for download.

Mass spectrometry-based proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. Though recent years have seen a tremendous improvement in instrument performance and the computational tools used, significant challenges remain, and there are many opportunities for statisticians to make important contributions. In the most widely used "bottom-up" approach to proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and analyzed using a mass spectrometer. The two fundamental challenges in the analysis of bottom-up MS-based proteomics are: (1) Identifying the proteins that are present in a sample, and (2) Quantifying the abundance levels of the identified proteins. Both of these challenges require knowledge of the biological and technological context that gives rise to observed data, as well as the application of sound statistical principles for estimation and inference. We present an overview of bottom-up proteomics and outline the key statistical issues that arise in protein identification and quantification.

There is intense interest in comprehensive proteomic approaches for analyzing integral membrane proteins and lipoproteins. Key features of mass spectrometric analysis center on enriching biological material for proteins of interest, efficiently digesting them, extracting the resulting peptides, and using fractionation methods to comprehensively sample proteins or peptides by tandem mass spectrometry. However, lipid-associated proteins are generally rich in hydrophobic domains and are often low in abundance. These features, together with the associated lipid, make their mass spectrometric analysis technically challenging. In this article, we review analytical strategies for successful proteomic analysis of lipid-associated proteins.

Cell types with different morphology, and function collaborate to maintain organ function. As such, analyzing proteomic differences and connections between different types of cells forms the foundation for establishing functional connectomes and developing in vitro organoid simulation experiments. However, the efficiency of cell type isolation from organs is limited by time, equipment, and cost. Here, hierarchical dendritic photonic crystal beads (HDPCBs) featuring high-density functional groups via the self-assembly of dendritic mesoporous structure SiO2 nanoparticles (DM-SiO2) and grafting dendrimers onto the surface of dendritic mesoporous photonic crystal beads (DMPCBs) is developed. This platform integrates multitype cell separation with in situ protein cleavage processes. Efficient simultaneous isolation of Kupffer cells and Liver Sinusoidal Endothelial cells (LSECs) from liver, with high specificity and convenient operation in a short separation time are demonstrated. The results reveal 2832 and 3442 unique proteins identified in Kupffer cells and LSECs using only 50 HDPCBs, respectively. 764 and 629 over-expressed proteins associated with the function of Kupffer cells and LSECs are found, respectively. The work offers a new method for efficiently isolating multiple cell types from tissues and downstream proteomic analysis, ultimately facilitating the identification of primary cell compositions and functions.

Background & Objectives Large-scale proteome analysis by mass spectrometry is commonly preceded by enzymatic digestion of proteins. The conventional protocol for in-solution digest of complex protein mixtures includes trypsin and is performed at room temperature for at least 12h. To improve this time-consuming method we assessed the efficiency of focused ultrasound-assisted enzymatic digest. It has previously been shown that ultrasonication can dramatically reduce digestion time. However, it is not known how this affects protein identification and quantification when applied to complex protein mixtures such as whole cell lysates. Here, we explore in-depth the proteome of embryonic stem cells comparing trypsin-digestion methods with and without ultrasound support side-by-side. Methods In-solution protein digest Protein extracts were digested with sequencing-grade trypsin (Promega) for 1, 5, 10, 20, 30, 40min and 1h in the presence or absence of focused ultrasonication (Covaris). A conventional digestion overnight was included. Reactions were stopped by acidification and heat. Peptides were desalted by reverse phase C18 StageTips. Mass spectrometry Peptides were analyzed on a QExactive (Thermo Scientific) coupled to Easy nLC 1000 (Proxeon). Raw files were processed by MaxQuant v1.3.0.3 software. Peptide searches were performed by Andromeda search engine against the UniProt database. Results and Conclusions Focused ultrasonication clearly enhanced the enzymatic digestion and can be implemented in the daily workflow of mass spectrometry-based proteome analysis.

Due to oncoming climate changes, droughts, high salinity, extreme temperatures became quite common stressors universally occurring in the most of terrestrial habitats. Expansions of these changes are often accompanied with strong herbivore attacks. Due to the outstanding impact of these factors on sustainable agriculture, since several last decades, the biochemistry and molecular biology of plant stress response remains in the focus of the research interest worldwide. Thus, bottom-up proteomics became a versatile tool of plant research in general and of stress biology in particular. As plant-derived materials are recognized as an extremely complex matrix, which is rich in polysaccharides, polyphenols and hardly water-soluble proteins, their proteome is typically analyzed by gel-based techniques. However, recent advances in sample preparation techniques (first of all — protein solubilization and digestion) allowed establishment of gel-free methods for plant-derived samples. Implementation of high-throughput nano-flow reversed phase-high performance liquid chromatography coupled on-line to electrospray ionization mass spectrometry (nanoRP-HPLC-ESI-MS) gave access to data-rich datasets giving high protein identification rates. Moreover, high reproducibility of HPLC allows highly sensitive and precise quantification. Therefore, over the recent decade, shotgun proteomics became the method of choice in the study of adaptive stress responses of plant proteome. Here we address the bottom-up shotgun proteomics strategy in plant biology and discuss its application to the study of plant stress response. We also discuss the main steps of the plant proteome analysis pipeline and address emerging problems and future perspectives.

Advances in Proteome Analysis by Mass Spectrometry

The human retina is composed of the sensory neuroretina and the underlying retinal pigmented epithelium (RPE), which is firmly complexed to the vascular choroid layer. Different regions of the retina are anatomically and molecularly distinct, facilitating unique functions and demonstrating differential susceptibility to disease. Proteomic analysis of each of these regions and layers can provide vital insights into the molecular process of many diseases, including Age-Related Macular Degeneration (AMD), diabetes mellitus, and glaucoma. However, separation of retinal regions and layers is essential before quantitative proteomic analysis can be accomplished. Here, we describe a method for dissection and collection of the foveal, macular, and peripheral retinal regions and underlying RPE-choroid complex, involving regional punch biopsies and manual removal of tissue layers from a human eye.One-dimensional SDS-PAGE as well as downstream proteomic analysis, such as liquid chromatography-tandem mass spectrometry (LC-MS/MS), can be used to identify proteins in each dissected retinal layer, revealing molecular biomarkers for retinal disease.

BackgroundCholesterol-rich membrane microdomains known as lipid rafts have been implicated in diverse physiologic processes including lipid transport and signal transduction. Lipid rafts were originally defined as detergent-resistant membranes (DRMs) due to their relative insolubility in cold non-ionic detergents. Recent findings suggest that, although DRMs are not equivalent to lipid rafts, the presence of a given protein within DRMs strongly suggests its potential for raft association in vivo. Therefore, isolation of DRMs represents a useful starting point for biochemical analysis of lipid rafts. The physicochemical properties of DRMs present unique challenges to analysis of their protein composition. Existing methods of isolating DRM-enriched fractions involve flotation of cell extracts in a sucrose density gradient, which, although successful, can be labor intensive, time consuming and results in dilute sucrose-containing fractions with limited utility for direct proteomic analysis. In addition, several studies describing the proteomic characterization of DRMs using this and other approaches have reported the presence of nuclear proteins in such fractions. It is unclear whether these results reflect trafficking of nuclear proteins to DRMs or whether they arise from nuclear contamination during isolation. To address these issues, we have modified a published differential detergent extraction method to enable rapid DRM isolation that minimizes nuclear contamination and yields fractions compatible with mass spectrometry.ResultsDRM-enriched fractions isolated using the conventional or modified extraction methods displayed comparable profiles of known DRM-associated proteins, including flotillins, GPI-anchored proteins and heterotrimeric G-protein subunits. Thus, the modified procedure yielded fractions consistent with those isolated by existing methods. However, we observed a marked reduction in the percentage of nuclear proteins identified in DRM fractions isolated with the modified method (15%) compared to DRMs isolated by conventional means (36%). Furthermore, of the 21 nuclear proteins identified exclusively in modified DRM fractions, 16 have been reported to exist in other subcellular sites, with evidence to suggest shuttling of these species between the nucleus and other organelles.ConclusionWe describe a modified DRM isolation procedure that generates DRMs that are largely free of nuclear contamination and that is compatible with downstream proteomic analyses with minimal additional processing. Our findings also imply that identification of nuclear proteins in DRMs is likely to reflect legitimate movement of proteins between compartments, and is not a result of contamination during extraction.

In Brassica napus, seed yield and quality are related to sulfate availability, but the seed metabolic changes in response to sulfate limitation remain largely unknown. To address this question, proteomics and biochemical studies were carried out on mature seeds obtained from plants grown under low sulfate applied at the bolting (LS32), early flowering (LS53), or start of pod filling (LS70) stage. The protein quality of all low-sulfate seeds was reduced and associated with a reduction of S-rich seed storage protein accumulation (as Cruciferin Cru4) and an increase of S-poor seed storage protein (as Cruciferin BnC1). This compensation allowed the protein content to be maintained in LS70 and LS53 seeds but was not sufficient to maintain the protein content in LS32 seeds. The lipid content and quality of LS53 and LS32 seeds were also affected, and these effects were primarily associated with a reduction of C18-derivative accumulation. Proteomics changes related to lipid storage, carbohydrate metabolism, and energy (reduction of caleosins, phosphoglycerate kinase, malate synthase, ATP-synthase β-subunit, and thiazole biosynthetic enzyme THI1 and accumulation of β-glucosidase and citrate synthase) provide insights into processes that may contribute to decreased oil content and altered lipid composition (in favor of long-chain fatty acids in LS53 and LS32 seeds). These data indicate that metabolic changes associated with S limitation responses affect seed storage protein composition and lipid quality. Proteins involved in plant stress response, such as dehydroascorbate reductase and Cu/Zn-superoxide dismutase, were also accumulated in LS53 and LS32 seeds, and this might be a consequence of reduced glutathione content under low S availability. LS32 treatment also resulted in (i) reduced germination vigor, as evidenced by lower germination indexes, (ii) reduced seed germination capacity, related to a lower seed viability, and (iii) a strong decrease of glyoxysomal malate synthase, which is essential for the use of fatty acids during seedling establishment.

Novel approaches for the qualitative and quantitative proteomics analysis by nanoscale LC-MS applied to the study of protein expression response in depleted and undepleted serum of Gaucher patients undergoing enzyme replacement therapy are presented. Particular emphasis is given to the method reproducibility of these LC-MS experiments without the use of isotopic labels. The level of chitotriosidase, an established Gaucher biomarker, was assessed by means of an absolute concentration determination technique for alternate scanning LC-MS generated data. Disease associated proteins, including fibrinogens, complement cascade proteins, and members of the high density lipoprotein serum content, were recognized by various clustering methods and sorting and intensity profile grouping of identified peptides. Condition-unique LC-MS protein signatures could be generated utilizing the measured serum protein concentrations and are presented for all investigated conditions. The clustering results of the study were also used as input for gene ontology searches to determine the correlation between the molecular functions of the identified peptides and proteins.