The challenge of the proteome dynamic range and its implications for in-depth proteomics

Introduction to Proteomics Technologies

Compared to genomics or transcriptomics, proteomics is often regarded as an "emerging technology," i.e., as not having reached the same level of maturity. While the successful implementation of proteomics workflows and technology still requires significant levels of expertise and specialization, great strides have been made to make the technology more powerful, streamlined and accessible. In 2014, two landmark studies published the first draft versions of the human proteome.We aim to provide an introduction specifically into the background of mass spectrometry (MS)-based proteomics. Within the field, mass spectrometry has emerged as a core technology. Coupled to increasingly powerful separations and data processing and bioinformatics solution, it allows the quantitative analysis of whole proteomes within a matter of days, a timescale that has made global comparative proteome studies feasible at last. We present and discuss the basic concepts behind proteomics mass spectrometry and the accompanying topic of protein and peptide separations, with a focus on the properties of datasets emerging from such studies.

Silicon: Potential to Promote Direct and Indirect Effects on Plant Defense Against Arthropod Pests in Agriculture

Silicon has generally not been considered essential for plant growth, although it is well recognized that many plants, particularly Poaceae, have substantial plant tissue concentrations of this element. Recently, however, the International Plant Nutrition Institute [IPNI] (2015), Georgia, USA has listed it as a "beneficial substance". This reflects that numerous studies have now established that silicon may alleviate both biotic and abiotic stress. This paper explores the existing knowledge and recent advances in elucidating the role of silicon in plant defense against biotic stress, particularly against arthropod pests in agriculture and attraction of beneficial insects. Silicon confers resistance to herbivores via two described mechanisms: physical and biochemical/molecular. Until recently, studies have mainly centered on two trophic levels; the herbivore and plant. However, several studies now describe tri-trophic effects involving silicon that operate by attracting predators or parasitoids to plants under herbivore attack. Indeed, it has been demonstrated that silicon-treated, arthropod-attacked plants display increased attractiveness to natural enemies, an effect that was reflected in elevated biological control in the field. The reported relationships between soluble silicon and the jasmonic acid (JA) defense pathway, and JA and herbivore-induced plant volatiles (HIPVs) suggest that soluble silicon may enhance the production of HIPVs. Further, it is feasible that silicon uptake may affect protein expression (or modify proteins structurally) so that they can produce additional, or modify, the HIPV profile of plants. Ultimately, understanding silicon under plant ecological, physiological, biochemical, and molecular contexts will assist in fully elucidating the mechanisms behind silicon and plant response to biotic stress at both the bi- and tri-trophic levels.

Proteomic analysis of Pteropus alecto kidney cells in response to the viral mimic, Poly I:C

Background

Bats are recognised as an important reservoir for a number of highly pathogenic zoonotic viruses. While many of these viruses cause severe and often fatal disease in humans, bats are able to coexist with these viruses without clinical signs of disease. The mechanism conferring this antiviral response is not fully understood. Here, we investigated the differential protein expression of immortalised Pteropus alecto kidney cells (PaKiT03) following transfection with the viral mimic, Poly I:C. Two complementary proteomic approaches, difference gel electrophoresis (DIGE) and isobaric tagging for relative and absolute quantitation (iTRAQ) were used to quantify changes in protein expression following Poly I:C stimulation at 4, 8 and 20 hr post treatment (hpt).

Results

The expression of ISG54 gene, a known responder to virus infection and Poly I:C treatment, was significantly induced in transfected cells compared with mock-transfected cells. Through iTRAQ analysis we show that Poly I:C up-regulates key glycolytic enzymes at 4 hpt within PaKiT03 cells. In contrast, at 20 hpt PaKiT03 cells down-regulated ribosomal subunit proteins. The analysis with DIGE of Poly I:C transfected PaKiT03 cells showed over 215 individual spots differentially regulated, however only 25 spots could be unambiguously identified by LC-MS/MS. Immunoblotting confirmed the up-regulation of Eno1 and Tpi1 in PaKiT03 cells following Poly I:C transfection. A comparison with human cells (HEK293T and HeLa) and one additional bat cell line (PaLuT02), demonstrated that glycolytic pathways are also induced in these cell types, but at different intensities.

Conclusion

The two techniques, DIGE and iTRAQ identified largely overlapping sets of differentially expressed proteins, however DIGE unambiguously identified significantly less proteins than iTRAQ. Poly I:C induced a rapid metabolic shift towards glycolysis within the PaKiT03 cells at 4 hpt, presumably as a consequence of increased energy requirements. On the other hand ribosomal subunit proteins were seen as down-regulated by iTRAQ, these proteins may be the limiting factors in the translational machinery available for virus replication. This study provides new insight into the antiviral response of bat cells, highlighting the importance of energy metabolism.

Electronic supplementary material

The online version of this article (doi:10.1186/s12953-015-0081-6) contains supplementary material, which is available to authorized users.

C-STrap Sample Preparation Method--In-Situ Cysteinyl Peptide Capture for Bottom-Up Proteomics Analysis in the STrap Format

Recently we introduced the concept of Suspension Trapping (STrap) for bottom-up proteomics sample processing that is based upon SDS-mediated protein extraction, swift detergent removal and rapid reactor-type protein digestion in a quartz depth filter trap. As the depth filter surface is made of silica, it is readily modifiable with various functional groups using the silane coupling chemistries. Thus, during the digest, peptides possessing specific features could be targeted for enrichment by the functionalized depth filter material while non-targeted peptides could be collected as an unbound distinct fraction after the digest. In the example presented here the quartz depth filter surface is functionalized with the pyridyldithiol group therefore enabling reversible in-situ capture of the cysteine-containing peptides generated during the STrap-based digest. The described C-STrap method retains all advantages of the original STrap methodology and provides robust foundation for the conception of the targeted in-situ peptide fractionation in the STrap format for bottom-up proteomics. The presented data support the method’s use in qualitative and semi-quantitative proteomics experiments.

Proteomics in cancer research: Are we ready for clinical practice?

Although genomics has delivered major advances in cancer prognostics, treatment and diagnostics, it still only provides a static image of the situation. To study more dynamic molecular entities, proteomics has been introduced into the cancer research field more than a decade ago. Currently, however, the impact of clinical proteomics on patient management and clinical decision-making is low and the implementations of scientific results in the clinic appear to be scarce. The search for cancer-related biomarkers with proteomics however, has major potential to improve risk assessment, early detection, diagnosis, prognosis, treatment selection and monitoring. In this review, we provide an overview of the transition of oncoproteomics towards translational oncology. We describe which lessons are learned from currently approved protein biomarkers and previous proteomic studies, what the pitfalls and challenges are in clinical proteomics applications, and how proteomic research can be successfully translated into medical practice.

The role of proteomics in progressing insights into plant secondary metabolism

The development of omics has enabled the genome-wide exploration of all kinds of biological processes at the molecular level. Almost every field of plant biology has been analyzed at the genomic, transcriptomic and proteomic level. Here we focus on the particular contribution that proteomic technologies have made in progressing knowledge and characterising plant secondary metabolism (SM) pathways since early expectations were created 15 years ago. We analyzed how three major issues in the proteomic analysis of plant SM have been implemented in various research studies. These issues are: (i) the selection of a suitable plant material rich in secondary metabolites of interest, such as specialized tissues and organs, and in vitro cell cultures; (ii) the proteomic strategy to access target proteins, either a comprehensive or a differential analysis; (iii) the proteomic approach, represented by the hypothesis-free discovery proteomics and the hypothesis-driven targeted proteomics. We also examine to what extent the most-advanced technologies have been incorporated into proteomic research in plant SM and highlight some cutting edge techniques that would strongly benefit the progress made in this field.

Functional Identification of Target by Expression Proteomics (FITExP) reveals protein targets and highlights mechanisms of action of small molecule drugs

Phenomenological screening of small molecule libraries for anticancer activity yields potentially interesting candidate molecules, with a bottleneck in the determination of drug targets and the mechanism of anticancer action. We have found that, for the protein target of a small-molecule drug, the abundance change in late apoptosis is exceptional compared to the expectations based on the abundances of co-regulated proteins. Based on this finding, a novel method to drug target deconvolution is proposed. In a proof of principle experiment, the method yielded known targets of several common anticancer agents among a few (often, just one) likely candidates identified in an unbiased way from cellular proteome comprising more than 4,000 proteins. A validation experiment with a different set of cells and drugs confirmed the findings. As an additional benefit, mapping most specifically regulated proteins on known protein networks highlighted the mechanism of drug action. The new method, if proven to be general, can significantly shorten drug target identification, and thus facilitate the emergence of novel anticancer treatments.

Comparative proteomic analysis of hypertrophic chondrocytes in osteoarthritis

Background

Osteoarthritis (OA) is a multi-factorial disease leading progressively to loss of articular cartilage and subsequently to loss of joint function. While hypertrophy of chondrocytes is a physiological process implicated in the longitudinal growth of long bones, hypertrophy-like alterations in chondrocytes play a major role in OA. We performed a quantitative proteomic analysis in osteoarthritic and normal chondrocytes followed by functional analyses to investigate proteome changes and molecular pathways involved in OA pathogenesis.

Methods

Chondrocytes were isolated from articular cartilage of ten patients with primary OA undergoing knee replacement surgery and six normal donors undergoing fracture repair surgery without history of joint disease and no OA clinical manifestations. We analyzed the proteome of chondrocytes using high resolution mass spectrometry and quantified it by label-free quantification and western blot analysis. We also used WebGestalt, a web-based enrichment tool for the functional annotation and pathway analysis of the differentially synthesized proteins, using the Wikipathways database. ClueGO, a Cytoscape plug-in, is also used to compare groups of proteins and to visualize the functionally organized Gene Ontology (GO) terms and pathways in the form of dynamical network structures.

Results

The proteomic analysis led to the identification of a total of ~2400 proteins. 269 of them showed differential synthesis levels between the two groups. Using functional annotation, we found that proteins belonging to pathways associated with regulation of the actin cytoskeleton, EGF/EGFR, TGF-β, MAPK signaling, integrin-mediated cell adhesion, and lipid metabolism were significantly enriched in the OA samples (p ≤10⁻⁵). We also observed that the proteins GSTP1, PLS3, MYOF, HSD17B12, PRDX2, APCS, PLA2G2A SERPINH1/HSP47 and MVP, show distinct synthesis levels, characteristic for OA or control chondrocytes.

Conclusion

In this study we compared the quantitative changes in proteins synthesized in osteoarthritic compared to normal chondrocytes. We identified several pathways and proteins to be associated with OA chondrocytes. This study provides evidence for further testing on the molecular mechanism of the disease and also propose proteins as candidate markers of OA chondrocyte phenotype.

Electronic supplementary material

The online version of this article (doi:10.1186/s12014-015-9085-6) contains supplementary material, which is available to authorized users.

Bovine sperm plasma membrane proteomics through biotinylation and subcellular enrichment

A significant proportion of mammalian fertilization is mediated through the proteomic composition of the sperm surface. These protein constituents can present as biomarkers to control and regulate breeding of agricultural animals. Previous studies have addressed the bovine sperm cell apical plasma membrane (PM) proteome with nitrogen cavitation enrichment. Alternative workflows would enable to expand the compositional data more globally around the entire sperm's surface. We used a cell surface biotin-labeling in combination with differential centrifugation to enrich sperm surface proteins. Using nano-LC MS/MS, 338 proteins were confidently identified in the PM-enriched proteome. Functional categories of sperm-egg interaction, protein turnover, metabolism as well as molecular transport, spermatogenesis, and signal transduction were represented by proteins with high quantitative signal in our study. A highly significant degree of enrichment was found for transmembrane and PM-targeted proteins. Among them, we also report proteins previously not described on bovine sperm (CPQ, CD58, CKLF, CPVL, GLB1L3, and LPCAT2B) of which CPQ and CPVL cell surface localization was further validated. A descriptive overview of the bovine sperm PM integral and peripheral proteins is provided to complement future studies on animal reproduction and its relation to sperm cell surface. All MS data have been deposited in the ProteomeXchange with identifier PXD001096 (http://proteomecentral.proteomexchange.org/dataset/PXD001096).

Version 4.0 of PaxDb: Protein abundance data, integrated across model organisms, tissues, and cell-lines

Protein quantification at proteome‐wide scale is an important aim, enabling insights into fundamental cellular biology and serving to constrain experiments and theoretical models. While proteome‐wide quantification is not yet fully routine, many datasets approaching proteome‐wide coverage are becoming available through biophysical and MS techniques. Data of this type can be accessed via a variety of sources, including publication supplements and online data repositories. However, access to the data is still fragmentary, and comparisons across experiments and organisms are not straightforward. Here, we describe recent updates to our database resource “PaxDb” (Protein Abundances Across Organisms). PaxDb focuses on protein abundance information at proteome‐wide scope, irrespective of the underlying measurement technique. Quantification data is reprocessed, unified, and quality‐scored, and then integrated to build a meta‐resource. PaxDb also allows evolutionary comparisons through precomputed gene orthology relations. Recently, we have expanded the scope of the database to include cell‐line samples, and more systematically scan the literature for suitable datasets. We report that a significant fraction of published experiments cannot readily be accessed and/or parsed for quantitative information, requiring additional steps and efforts. The current update brings PaxDb to 414 datasets in 53 organisms, with (semi‐) quantitative abundance information covering more than 300 000 proteins.

Comprehensive proteome analysis of fresh frozen and optimal cutting temperature (OCT) embedded primary non-small cell lung carcinoma by LC-MS/MS

Clinical tissue samples provide valuable information for understanding human diseases. One major type of clinical tissue sample that is amenable to various kinds of analysis is fresh frozen and optimal cutting temperature (OCT)-embedded primary patient tissue. Recent advances in mass spectrometry (MS) technologies have been widely applied to study human proteomes by using clinical specimens. However, polymeric compounds such as OCT can interfere with MS analyses. Here we present methods that enable the preparation and analysis of fresh frozen and OCT embedded primary tissue samples by LC-MS/MS. A scraping method was first introduced to reduce the heterogeneity of OCT-embedded non-small cell lung carcinoma tumor sections. OCT compound was reproducibly removed by a series of washing steps involving ethanol and water prior to trypsin digestion. In data-dependent acquisition mode, optimized dynamic exclusion duration settings were established to maximize peptide identifications. These sample preparation conditions and MS parameter settings should be utilized or carefully adjusted in order to achieve optimal comprehensive proteome characterization starting from fresh frozen and OCT embedded clinical tissue specimens.

Bioinformatics approaches for the functional interpretation of protein lists: from ontology term enrichment to network analysis

The main result of a great deal of the published proteomics studies is a list of identified proteins, which then needs to be interpreted in relation to the research question and existing knowledge. In the early days of proteomics this interpretation was only based on expert insights, acquired by digesting a large amount of relevant literature. With the growing size and complexity of the experimental datasets, many computational techniques, databases, and tools have claimed a central role in this task. In this review we discuss commonly and less commonly used methods to functionally interpret experimental proteome lists and compare them with available knowledge. We first address several functional analysis and enrichment techniques based on ontologies and literature. Then we outline how various types of network and pathway information can be used. While the problem of functional interpretation of proteome data is to an extent equivalent to the interpretation of transcriptome or other ''omics'' data, this paper addresses some of the specific challenges and solutions of the proteomics field.

Targeting the proteome of cellular fractions: focus on secreted proteins

The high complexity of the total cellular proteome underscores the need for a more targeted investigation of particular subcellular fractions as a means to detect the changes at the level of low abundance proteins. However, this approach requires the application of an enrichment strategy. In this chapter, we present the protocols, which have been used for the analysis of secretome from cell lines, targeting the investigation of protein expression changes.

Systematic optimization of long gradient chromatography mass spectrometry for deep analysis of brain proteome

The development of high-resolution liquid chromatography (LC) is essential for improving the sensitivity and throughput of mass spectrometry (MS)-based proteomics. Here we present systematic optimization of a long gradient LC-MS/MS platform to enhance protein identification from a complex mixture. The platform employed an in-house fabricated, reverse-phase long column (100 μm × 150 cm, 5 μm C18 beads) coupled to Q Exactive MS. The column was capable of achieving a peak capacity of ∼700 in a 720 min gradient of 10-45% acetonitrile. The optimal loading level was ∼6 μg of peptides, although the column allowed loading as many as 20 μg. Gas-phase fractionation of peptide ions further increased the number of peptide identification by ∼10%. Moreover, the combination of basic pH LC prefractionation with the long gradient LC-MS/MS platform enabled the identification of 96,127 peptides and 10,544 proteins at 1% protein false discovery rate in a post-mortem brain sample of Alzheimer's disease. Because deep RNA sequencing of the same specimen suggested that ∼16,000 genes were expressed, the current analysis covered more than 60% of the expressed proteome. Further improvement strategies of the LC/LC-MS/MS platform were also discussed.

Proteome sequencing goes deep

Advances in mass spectrometry (MS) have transformed the scope and impact of protein characterization efforts. Identifying hundreds of proteins from rather simple biological matrices, such as yeast, was a daunting task just a few decades ago. Now, expression of more than half of the estimated ∼20,000 human protein coding genes can be confirmed in record time and from minute sample quantities. Access to proteomic information at such unprecedented depths has been fueled by strides in every stage of the shotgun proteomics workflow-from sample processing to data analysis-and promises to revolutionize our understanding of the causes and consequences of proteome variation.

DeMix workflow for efficient identification of cofragmented peptides in high resolution data-dependent tandem mass spectrometry

Based on conventional data-dependent acquisition strategy of shotgun proteomics, we present a new workflow DeMix, which significantly increases the efficiency of peptide identification for in-depth shotgun analysis of complex proteomes. Capitalizing on the high resolution and mass accuracy of Orbitrap-based tandem mass spectrometry, we developed a simple deconvolution method of "cloning" chimeric tandem spectra for cofragmented peptides. Additional to a database search, a simple rescoring scheme utilizes mass accuracy and converts the unwanted cofragmenting events into a surprising advantage of multiplexing. With the combination of cloning and rescoring, we obtained on average nine peptide-spectrum matches per second on a Q-Exactive workbench, whereas the actual MS/MS acquisition rate was close to seven spectra per second. This efficiency boost to 1.24 identified peptides per MS/MS spectrum enabled analysis of over 5000 human proteins in single-dimensional LC-MS/MS shotgun experiments with an only two-hour gradient. These findings suggest a change in the dominant "one MS/MS spectrum - one peptide" paradigm for data acquisition and analysis in shotgun data-dependent proteomics. DeMix also demonstrated higher robustness than conventional approaches in terms of lower variation among the results of consecutive LC-MS/MS runs.

Separation methodology to improve proteome coverage depth

So-called 'in-depth proteomics' and its applied separation methodology to improve the proteome coverage depth has become an important issue in mass spectrometric-based proteomics and system-wide cell biology studies. Employing a bottom-up approach and a variety of separation techniques, it allows for identification of proteins with low copy numbers and enables researchers to correlate the number of expressed genes in a cell with the proteome. Here we describe recent advances in this field with emphasis on peptide and protein separation technologies. The discussion is focused both on single injection analyses employing long reversed phase liquid chromatography separations of peptides ('single shot proteomics') and on the combination of orthogonal protein and peptide separation methods to achieve maximum protein coverage. Owing to these improvements, in-depth proteomics has now fully entered the field and is being implemented in an increasing number of laboratories.

Clinical proteomic biomarkers: relevant issues on study design & technical considerations in biomarker development

Biomarker research is continuously expanding in the field of clinical proteomics. A combination of different proteomic-based methodologies can be applied depending on the specific clinical context of use. Moreover, current advancements in proteomic analytical platforms are leading to an expansion of biomarker candidates that can be identified. Specifically, mass spectrometric techniques could provide highly valuable tools for biomarker research. Ideally, these advances could provide with biomarkers that are clinically applicable for disease diagnosis and/ or prognosis. Unfortunately, in general the biomarker candidates fail to be implemented in clinical decision making. To improve on this current situation, a well-defined study design has to be established driven by a clear clinical need, while several checkpoints between the different phases of discovery, verification and validation have to be passed in order to increase the probability of establishing valid biomarkers. In this review, we summarize the technical proteomic platforms that are available along the different stages in the biomarker discovery pipeline, exemplified by clinical applications in the field of bladder cancer biomarker research.

Distinguishing analyte from noise components in mass spectra of complex samples: where to cut the noise?

Fourier transform mass spectrometry (FTMS) enables comprehensive analysis of complex molecular mixtures. Given the broad intensity ranges of components in the mass spectra, it is imperative to accurately determine a noise threshold level above which peak assignments will be made. Conventionally, to find the threshold level, the "N sigma" approach or an equivalent rule is used. However, the "N sigma" approach cannot be applied to mass spectra stored with partially removed noise (reduced-profile mode). It is also not directly applicable to mass spectra acquired in the absorption mode with removed negative spectral amplitudes. Moreover, N value selection is normally made based on a rule of thumb, meaning that the calculated threshold level may be biased. Here, we present a noise thresholding method which addresses these limitations for analysis of mass spectra of complex molecular mixtures. The introduced data-dependent thresholding method involves analysis of the distribution of logarithmic intensity of all peaks, including noise and analyte, for a given mass spectrum. Selected method applications include FTMS analysis of crude oil fractions as well as tandem MS analysis of intact proteins.

Comprehensive tracking of host cell proteins during monoclonal antibody purifications using mass spectrometry

An advanced two-dimensional liquid chromatography/mass spectrometry platform was used to quantify individual host cell proteins (HCPs) present at various purification steps for several therapeutic monoclonal antibodies (mAbs) produced in Chinese hamster ovary cells. The methodology produced reproducible identifications and quantifications among replicate analyses consistent with a previously documented individual limit of quantification of ~13 ppm. We were able to track individual HCPs from cell culture fluid to protein A eluate pool to subsequent viral inactivation pool and, in some cases, further downstream. Approximately 500 HCPs were confidently identified in cell culture fluid and this number declined progressively through the purification scheme until no HCPs could be confidently identified in polishing step cation-exchange eluate pools. The protein A eluate pool of nine different mAbs contained widely differing numbers, and total levels, of HCPs, yet the bulk of the total HCP content in each case consisted of a small subset of normally intracellular HCPs highly abundant in cell culture fluid. These observations hint that minimizing cell lysis during cell culture/harvest may be useful in minimizing downstream HCP content. Clusterin and actin are abundant in the protein A eluate pools of most mAbs studied. HCP profiling by this methodology can provide useful information to process developers and lead to the refinement of existing purification platforms.

Autopilot: an online data acquisition control system for the enhanced high-throughput characterization of intact proteins

The ability to study organisms by direct analysis of their proteomes without digestion via mass spectrometry has benefited greatly from recent advances in separation techniques, instrumentation, and bioinformatics. However, improvements to data acquisition logic have lagged in comparison. Past workflows for Top Down Proteomics (TDPs) have focused on high throughput at the expense of maximal protein coverage and characterization. This mode of data acquisition has led to enormous overlap in the identification of highly abundant proteins in subsequent LC-MS injections. Furthermore, a wealth of data is left underutilized by analyzing each newly targeted species as unique, rather than as part of a collection of fragmentation events on a distinct proteoform. Here, we present a major advance in software for acquisition of TDP data that incorporates a fully automated workflow able to detect intact masses, guide fragmentation to achieve maximal identification and characterization of intact protein species, and perform database search online to yield real-time protein identifications. On Pseudomonas aeruginosa, the software combines fragmentation events of the same precursor with previously obtained fragments to achieve improved characterization of the target form by an average of 42 orders of magnitude in confidence. When HCD fragmentation optimization was applied to intact proteins ions, there was an 18.5 order of magnitude gain in confidence. These improved metrics set the stage for increased proteome coverage and characterization of higher order organisms in the future for sharply improved control over MS instruments in a project- and lab-wide context.

Post-genomics nanotechnology is gaining momentum: nanoproteomics and applications in life sciences

The post-genomics era has brought about new Omics biotechnologies, such as proteomics and metabolomics, as well as their novel applications to personal genomics and the quantified self. These advances are now also catalyzing other and newer post-genomics innovations, leading to convergences between Omics and nanotechnology. In this work, we systematically contextualize and exemplify an emerging strand of post-genomics life sciences, namely, nanoproteomics and its applications in health and integrative biological systems. Nanotechnology has been utilized as a complementary component to revolutionize proteomics through different kinds of nanotechnology applications, including nanoporous structures, functionalized nanoparticles, quantum dots, and polymeric nanostructures. Those applications, though still in their infancy, have led to several highly sensitive diagnostics and new methods of drug delivery and targeted therapy for clinical use. The present article differs from previous analyses of nanoproteomics in that it offers an in-depth and comparative evaluation of the attendant biotechnology portfolio and their applications as seen through the lens of post-genomics life sciences and biomedicine. These include: (1) immunosensors for inflammatory, pathogenic, and autoimmune markers for infectious and autoimmune diseases, (2) amplified immunoassays for detection of cancer biomarkers, and (3) methods for targeted therapy and automatically adjusted drug delivery such as in experimental stroke and brain injury studies. As nanoproteomics becomes available both to the clinician at the bedside and the citizens who are increasingly interested in access to novel post-genomics diagnostics through initiatives such as the quantified self, we anticipate further breakthroughs in personalized and targeted medicine.

GeLC-MS/MS analysis of complex protein mixtures

Discovery-based proteomics has found its place in nearly every facet of biological research. A key objective of this approach is to maximize sequence coverage for proteins across a wide concentration range. Fractionating samples at the protein level is one of the most common ways to circumvent challenges due to sample complexity and improve proteome coverage. Of the available methods, one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis followed by liquid chromatography-tandem mass spectrometry (GeLC-MS/MS) is a robust and reproducible method for qualitative and quantitative proteomic analysis. Here we describe a general GeLC-MS/MS protocol and include technical advice and outline caveats to increase the probability of a successful analysis.

Rapid and deep human proteome analysis by single-dimension shotgun proteomics

Multiparameter optimization of an LC-MS/MS shotgun proteomics experiment was performed without any hardware or software modification of the commercial instrument. Under the optimized experimental conditions, with a 50-cm-long separation column and a 4-h LC-MS run (including a 3-h optimized gradient), 4,825 protein groups and 37,550 peptides were identified in a single run and 5,354 protein groups and 56,390 peptides in a triplicate analysis of the A375 human cell line, for approximately 50% coverage of the expressed proteome. The major steps enabling such performance included optimization of the cell lysis and protein extraction, digestion of even insoluble cell debris, tailoring the LC gradient profile, and choosing the optimal dynamic exclusion window in data-dependent MS/MS, as well as the optimal m/z scan window.

Orbitrap mass spectrometry

Orbitrap is the newest addition to the family of high-resolution mass spectrometry analyzers. With its revolutionarily new, miniature design, Orbitrap combines high speed with excellent quantification properties, ranking favorably in many analytical applications.