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Genomics, Proteomics & Bioinformatics Oct 2021In the past decade, relative proteomic quantification using isobaric labeling technology has developed into a key tool for comparing the expression of proteins in... (Review)
Review
In the past decade, relative proteomic quantification using isobaric labeling technology has developed into a key tool for comparing the expression of proteins in biological samples. Although its multiplexing capacity and flexibility make this a valuable technology for addressing various biological questions, its quantitative accuracy and precision still pose significant challenges to the reliability of its quantification results. Here, we give a detailed overview of the different kinds of isobaric mass tags and the advantages and disadvantages of the isobaric labeling method. We also discuss which precautions should be taken at each step of the isobaric labeling workflow, to obtain reliable quantification results in large-scale quantitative proteomics experiments. In the last section, we discuss the broad applications of the isobaric labeling technology in biological and clinical studies, with an emphasis on thermal proteome profiling and proteogenomics.
Topics: Proteome; Proteomics; Reproducibility of Results; Tandem Mass Spectrometry
PubMed: 35007772
DOI: 10.1016/j.gpb.2021.08.012 -
Molecular Systems Biology Jul 2021While informative, protein amounts and physical protein associations do not provide a full picture of protein function. This Commentary highlights the potential of...
While informative, protein amounts and physical protein associations do not provide a full picture of protein function. This Commentary highlights the potential of structural and stability proteomic technologies to derive new insights in biology and medicine.
Topics: Biophysics; Proteome; Proteomics
PubMed: 34293219
DOI: 10.15252/msb.202110442 -
STAR Protocols Sep 2023Tandem mass tags data-dependent acquisition (TMT-DDA) as well as data-independent acquisition-based label-free quantification (LFQ-DIA) have become the leading workflows...
Tandem mass tags data-dependent acquisition (TMT-DDA) as well as data-independent acquisition-based label-free quantification (LFQ-DIA) have become the leading workflows to achieve deep proteome and phosphoproteome profiles. We present a modular pipeline for TMT-DDA and LFQ-DIA that integrates steps to perform scalable phosphoproteome profiling, including protein lysate extraction, clean-up, digestion, phosphopeptide enrichment, and TMT-labeling. We also detail peptide and/or phosphopeptide fractionation and pre-mass spectrometry desalting and provide researchers guidance on choosing the best workflow based on sample number and input. For complete details on the use and execution of this protocol, please refer to Koenig et al. and Martínez-Val et al..
Topics: Phosphopeptides; Proteome; Proteomics; Mass Spectrometry; Workflow
PubMed: 37659085
DOI: 10.1016/j.xpro.2023.102536 -
Current Protocols in Protein Science Feb 2019Stable isotope labeling by amino acids in cell culture (SILAC) has become very popular as a quantitative proteomic method since it was firstly introduced by Matthias... (Review)
Review
Stable isotope labeling by amino acids in cell culture (SILAC) has become very popular as a quantitative proteomic method since it was firstly introduced by Matthias Mann's group in 2002. It is a metabolic labeling strategy in which isotope-labeled amino acids are metabolically incorporated in vivo into proteins during translation. After natural (light) or heavy amino acid incorporation, differentially labeled samples are mixed immediately after cell lysis and before any further processing, which minimizes quantitative errors caused by handling different samples in parallel. In this unit, we describe protocols for basic duplex SILAC, triplex SILAC for use in nondividing cells such as neurons, and for measuring amounts of newly synthesized proteins. © 2018 by John Wiley & Sons, Inc.
Topics: Animals; Humans; Isotope Labeling; Nerve Tissue Proteins; Neurons; Proteome; Proteomics
PubMed: 30238645
DOI: 10.1002/cpps.74 -
Platelets Dec 2023Multi-omics approaches are being used increasingly to study physiological and pathophysiologic processes. Proteomics specifically focuses on the study of proteins as...
Multi-omics approaches are being used increasingly to study physiological and pathophysiologic processes. Proteomics specifically focuses on the study of proteins as functional elements and key contributors to, and markers of the phenotype, as well as targets for diagnostic and therapeutic approaches. Depending on the condition, the plasma proteome can mirror the platelet proteome, and hence play an important role in elucidating both physiologic and pathologic processes. In fact, both plasma and platelet protein signatures have been shown to be important in the setting of thrombosis-prone disease states such as atherosclerosis and cancer. Plasma and platelet proteomes are increasingly being studied as a part of a single entity, as is the case with patient-centric sample collection approaches such as capillary blood. Future studies should cut across the plasma and platelet proteome silos, taking advantage of the vast knowledge available when they are considered as part of the same studies, rather than studied as distinct entities.
Topics: Blood Platelets; Proteome; Phenotype; Plasma; Proteomics
PubMed: 36894508
DOI: 10.1080/09537104.2023.2186707 -
Nature Methods Nov 2014Proteogenomics is an area of research at the interface of proteomics and genomics. In this approach, customized protein sequence databases generated using genomic and... (Review)
Review
Proteogenomics is an area of research at the interface of proteomics and genomics. In this approach, customized protein sequence databases generated using genomic and transcriptomic information are used to help identify novel peptides (not present in reference protein sequence databases) from mass spectrometry-based proteomic data; in turn, the proteomic data can be used to provide protein-level evidence of gene expression and to help refine gene models. In recent years, owing to the emergence of new sequencing technologies such as RNA-seq and dramatic improvements in the depth and throughput of mass spectrometry-based proteomics, the pace of proteogenomic research has greatly accelerated. Here I review the current state of proteogenomic methods and applications, including computational strategies for building and using customized protein sequence databases. I also draw attention to the challenge of false positive identifications in proteogenomics and provide guidelines for analyzing the data and reporting the results of proteogenomic studies.
Topics: Databases, Nucleic Acid; Databases, Protein; Genetic Variation; Genomics; High-Throughput Nucleotide Sequencing; Mass Spectrometry; Protein Isoforms; Proteome; Proteomics; Sequence Analysis, Protein
PubMed: 25357241
DOI: 10.1038/nmeth.3144 -
Journal of Chromatography. A Nov 2017Microscale separation (e.g., liquid chromatography or capillary electrophoresis) coupled with mass spectrometry (MS) has become the primary tool for advanced proteomics,... (Review)
Review
Microscale separation (e.g., liquid chromatography or capillary electrophoresis) coupled with mass spectrometry (MS) has become the primary tool for advanced proteomics, an indispensable technology for gaining understanding of complex biological processes. In recent decades significant advances have been achieved in MS-based proteomics. However, the current proteomics platforms still face an analytical challenge in overall sensitivity towards nanoproteomics applications for starting materials of less than 1μg total proteins (e.g., cellular heterogeneity in tissue pathologies). Herein, we review recent advances in microscale separation techniques and integrated sample processing strategies that improve the overall sensitivity and proteome coverage of the proteomics workflow, and their contributions towards nanoproteomics applications.
Topics: Chromatography, Liquid; Electrophoresis, Capillary; Mass Spectrometry; Proteome; Proteomics
PubMed: 28765000
DOI: 10.1016/j.chroma.2017.07.055 -
Genome Biology Sep 2023Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of...
BACKGROUND
Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based measurements among different instruments and laboratories.
RESULTS
Here, we develop the Quartet standard as a proteome reference material with built-in truths, and distribute the same aliquots to 15 laboratories with nine conventional LC-MS/MS platforms across six cities in China. Relative abundance of over 12,000 proteins on 816 mass spectrometry files are obtained and compared for reproducibility among the instruments and laboratories to ultimately generate proteomics benchmark datasets. There is a wide dynamic range of proteomes spanning about 7 orders of magnitude, and the injection order has marked effects on quantitative instead of qualitative characteristics.
CONCLUSION
Overall, the Quartet offers valuable standard materials and data resources for improving the quality control of proteomic analyses as well as the reproducibility and reliability of research findings.
Topics: Chromatography, Liquid; Proteomics; Reproducibility of Results; Tandem Mass Spectrometry; Proteome
PubMed: 37674236
DOI: 10.1186/s13059-023-03048-y -
Nucleic Acids Research Jan 2022Proteome-pI 2.0 is an update of an online database containing predicted isoelectric points and pKa dissociation constants of proteins and peptides. The isoelectric...
Proteome-pI 2.0 is an update of an online database containing predicted isoelectric points and pKa dissociation constants of proteins and peptides. The isoelectric point-the pH at which a particular molecule carries no net electrical charge-is an important parameter for many analytical biochemistry and proteomics techniques. Additionally, it can be obtained directly from the pKa values of individual charged residues of the protein. The Proteome-pI 2.0 database includes data for over 61 million protein sequences from 20 115 proteomes (three to four times more than the previous release). The isoelectric point for proteins is predicted by 21 methods, whereas pKa values are inferred by one method. To facilitate bottom-up proteomics analysis, individual proteomes were digested in silico with the five most commonly used proteases (trypsin, chymotrypsin, trypsin + LysC, LysN, ArgC), and the peptides' isoelectric point and molecular weights were calculated. The database enables the retrieval of virtual 2D-PAGE plots and customized fractions of a proteome based on the isoelectric point and molecular weight. In addition, isoelectric points for proteins in NCBI non-redundant (nr), UniProt, SwissProt, and Protein Data Bank are available in both CSV and FASTA formats. The database can be accessed at http://isoelectricpointdb2.org.
Topics: Amino Acid Sequence; Computational Biology; Databases, Protein; Electrophoresis, Gel, Two-Dimensional; Isoelectric Point; Molecular Weight; Peptides; Proteome; Proteomics
PubMed: 34718696
DOI: 10.1093/nar/gkab944 -
Briefings in Bioinformatics Jan 2021Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers... (Review)
Review
Empowered by the advancement of high-throughput bio technologies, recent research on body-fluid proteomes has led to the discoveries of numerous novel disease biomarkers and therapeutic drugs. In the meantime, a tremendous progress in disclosing the body-fluid proteomes was made, resulting in a collection of over 15 000 different proteins detected in major human body fluids. However, common challenges remain with current proteomics technologies about how to effectively handle the large variety of protein modifications in those fluids. To this end, computational effort utilizing statistical and machine-learning approaches has shown early successes in identifying biomarker proteins in specific human diseases. In this article, we first summarized the experimental progresses using a combination of conventional and high-throughput technologies, along with the major discoveries, and focused on current research status of 16 types of body-fluid proteins. Next, the emerging computational work on protein prediction based on support vector machine, ranking algorithm, and protein-protein interaction network were also surveyed, followed by algorithm and application discussion. At last, we discuss additional critical concerns about these topics and close the review by providing future perspectives especially toward the realization of clinical disease biomarker discovery.
Topics: Biomarkers; Body Fluids; Humans; Proteome; Proteomics
PubMed: 32020158
DOI: 10.1093/bib/bbz160