218,052 research outputs found

    Pathway analysis and transcriptomics improve protein identification by shotgun proteomics from samples comprising small number of cells - a benchmarking study

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    BACKGROUND: Proteomics research is enabled with the high-throughput technologies, but our ability to identify expressed proteome is limited in small samples. The coverage and consistency of proteome expression are critical problems in proteomics. Here, we propose pathway analysis and combination of microproteomics and transcriptomics analyses to improve mass-spectrometry protein identification from small size samples. RESULTS: Multiple proteomics runs using MCF-7 cell line detected 4,957 expressed proteins. About 80% of expressed proteins were present in MCF-7 transcripts data; highly expressed transcripts are more likely to have expressed proteins. Approximately 1,000 proteins were detected in each run of the small sample proteomics. These proteins were mapped to gene symbols and compared with gene sets representing canonical pathways, more than 4,000 genes were extracted from the enriched gene sets. The identified canonical pathways were largely overlapping between individual runs. Of identified pathways 182 were shared between three individual small sample runs. CONCLUSIONS: Current technologies enable us to directly detect 10% of expressed proteomes from small sample comprising as few as 50 cells. We used knowledge-based approaches to elucidate the missing proteome that can be verified by targeted proteomics. This knowledge-based approach includes pathway analysis and combination of gene expression and protein expression data for target prioritization. Genes present in both the enriched gene sets (canonical pathways collection) and in small sample proteomics data correspond to approximately 50% of expressed proteomes in larger sample proteomics data. In addition, 90% of targets from canonical pathways were estimated to be expressed. The comparison of proteomics and transcriptomics data, suggests that highly expressed transcripts have high probability of protein expression. However, approximately 10% of expressed proteins could not be matched with the expressed transcripts.The cost of this publication was funded by Vladimir Brusic. (Vladimir Brusic)Published versio

    Two-dimensional gel electrophoresis in proteomics: A tutorial

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    Two-dimensional electrophoresis of proteins has preceded, and accompanied, the birth of proteomics. Although it is no longer the only experimental scheme used in modern proteomics, it still has distinct features and advantages. The purpose of this tutorial paper is to guide the reader through the history of the field, then through the main steps of the process, from sample preparation to in-gel detection of proteins, commenting the constraints and caveats of the technique. Then the limitations and positive features of two-dimensional electrophoresis are discussed (e.g. its unique ability to separate complete proteins and its easy interfacing with immunoblotting techniques), so that the optimal type of applications of this technique in current and future proteomics can be perceived. This is illustrated by a detailed example taken from the literature and commented in detail. This Tutorial is part of the International Proteomics Tutorial Programme (IPTP 2)

    Clinical proteomics for precision medicine: the bladder cancer case

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    Precision medicine can improve patient management by guiding therapeutic decision based on molecular characteristics. The concept has been extensively addressed through the application of –omics based approaches. Proteomics attract high interest, as proteins reflect a “real-time” dynamic molecular phenotype. Focusing on proteomics applications for personalized medicine, a literature search was conducted to cover: a) disease prevention, b) monitoring/ prediction of treatment response, c) stratification to guide intervention and d) identification of drug targets. The review indicates the potential of proteomics for personalized medicine by also highlighting multiple challenges to be addressed prior to actual implementation. In oncology, particularly bladder cancer, application of precision medicine appears especially promising. The high heterogeneity and recurrence rates together with the limited treatment options, suggests that earlier and more efficient intervention, continuous monitoring and the development of alternative therapies could be accomplished by applying proteomics-guided personalized approaches. This notion is backed by studies presenting biomarkers that are of value in patient stratification and prognosis, and by recent studies demonstrating the identification of promising therapeutic targets. Herein, we aim to present an approach whereby combining the knowledge on biomarkers and therapeutic targets in bladder cancer could serve as basis towards proteomics- guided personalized patient management

    Functional proteomics.

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    Background: With the increase in the number of genome sequencing projects, there is a concomitant exponential growth in the number of protein sequences whose function is still unknown. Functional proteomics constitutes an emerging research area in the proteomic field whose approaches are addressed towards two major targets: the elucidation of the biological function of unknown proteins and the definition of cellular mechanisms at the molecular level. Methods: The identification of interacting proteins in stable complexes in vivo is essentially achieved by affinity-based procedures. The basic idea is to express the protein of interest with a suitable tag to be used as a bait to fish its specific partners out from a cellular extract. Individual components within the multi-protein complex can then be identified by mass spectrometric methodologies. Results and conclusions: The association of an unknown protein with partners belonging to a specific protein complex involved in a particular mechanism is strongly suggestive of the biological function of the protein. Moreover, the identification of protein partners interacting with a given protein will lead to the description of cellular mechanisms at the molecular level. The next goal will be to generate animal models bearing a tagged form of the bait protein

    Proteomics-on-a-chip for Biomarker discovery

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    In proteomics research still two-dimensional gel electrophoresis (2D-GE) is currently used for biomarker discovery. We applied free flow electrophoresis (FFE) separation technology combined with biomolecular interaction sensing using Surface Plasmon Resonance (SPR) imaging in an integrated proteomics-on-a-chip device as a proof of concept for biomarker discovery

    Is a gene-centric human proteome project the best way for proteomics to serve biology?

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    With the recent developments in proteomic technologies, a complete human proteome project (HPP) appears feasible for the first time. However, there is still debate as to how it should be designed and what it should encompass. In "proteomics speak", the debate revolves around the central question as to whether a gene-centric or a protein-centric proteomics approach is the most appropriate way forward. In this paper, we try to shed light on what these definitions mean, how large-scale proteomics such as a HPP can insert into the larger omics chorus, and what we can reasonably expect from a HPP in the way it has been proposed so far

    A golden age for working with public proteomics data

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    Data sharing in mass spectrometry (MS)-based proteomics is becoming a common scientific practice, as is now common in the case of other, more mature 'omics' disciplines like genomics and transcriptomics. We want to highlight that this situation, unprecedented in the field, opens a plethora of opportunities for data scientists. First, we explain in some detail some of the work already achieved, such as systematic reanalysis efforts. We also explain existing applications of public proteomics data, such as proteogenomics and the creation of spectral libraries and spectral archives. Finally, we discuss the main existing challenges and mention the first attempts to combine public proteomics data with other types of omics data sets

    Urinary proteomics using capillary electrophoresis coupled to mass spectrometry for diagnosis and prognosis in kidney diseases

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    Purpose of review: Urine is the most useful of body fluids for biomarker research. Therefore, we have focused on urinary proteomics, using capillary electrophoresis coupled to mass spectrometry, to investigate kidney diseases in recent years. Recent findings: Several urinary proteomics studies for the detection of various kidney diseases have indicated the potential of this approach aimed at diagnostic and prognostic assessment. Urinary protein biomarkers such as collagen fragments, serum albumin, [alpha]-1-antitrypsin, and uromodulin can help to explain the processes involved during disease progression. Summary: Urinary proteomics has been used in several studies in order to identify and validate biomarkers associated with different kidney diseases. These biomarkers, with improved sensitivity and specificity when compared with the current gold standards, provide a significant alternative for diagnosis and prognosis, as well as improving clinical decision-making

    Exploitation of proteomics strategies in protein structure-function studies

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    Mass spectrometry plays a central role in structural proteomics, particularly in highly intensive structural genomics projects. This review paper reports some examples taken from recent work from the authors' laboratory and is aimed at showing that modem proteomics strategies are instrumental in the integration of structural genomic projects in fields such as: (i) protein-protein interactions, (ii) protein-DNA interactions, (iii) protein-ligand interactions, and (iv) protein-folding intermediates

    Mol. Cell. Proteomics

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    Chemical cross-linking in combination with mass spectrometric analysis offers the potential to obtain low-resolution structural information from proteins and protein complexes. Identification of peptides connected by a cross-link provides direct evidence for the physical interaction of amino acid side chains, information that can be used for computational modeling purposes. Despite impressive advances that were made in recent years, the number of experimentally observed cross-links still falls below the number of possible contacts of cross-linkable side chains within the span of the cross-linker. Here, we propose two complementary experimental strategies to expand cross-linking data sets. First, enrichment of cross-linked peptides by size exclusion chromatography selects cross-linked peptides based on their higher molecular mass, thereby depleting the majority of unmodified peptides present in proteolytic digests of cross-linked samples. Second, we demonstrate that the use of proteases in addition to trypsin, such as Asp-N, can additionally boost the number of observable cross-linking sites. The benefits of both SEC enrichment and multiprotease digests are demonstrated on a set of model proteins and the improved workflow is applied to the characterization of the 20S proteasome from rabbit and Schizosaccharomyces pombe
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