Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Quantitative density gradient analysis by mass spectrometry (qDGMS) and complexome profiling analysis (ComPrAn) R package for the study of macromolecular complexes.

ABSTRACT: Many cellular processes involve the participation of large macromolecular assemblies. Understanding their function requires methods allowing to study their dynamic and mechanistic properties. Here we present a method for quantitative analysis of native protein or ribonucleoprotein complexes by mass spectrometry following their separation by density - qDGMS. Mass spectrometric quantitation is enabled through stable isotope labelling with amino acids in cell culture (SILAC). We provide a complete guide, from experimental design to preparation of publication-ready figures, using a purposely-developed R package - ComPrAn. As specific examples, we present the use of sucrose density gradients to inspect the assembly and dynamics of the human mitochondrial ribosome (mitoribosome), its interacting proteins, the small subunit of the cytoplasmic ribosome, cytoplasmic aminoacyl-tRNA synthetase complex and the mitochondrial PDH complex. ComPrAn provides tools for analysis of peptide-level data as well as normalization and clustering tools for protein-level data, dedicated visualization functions and graphical user interface. Although, it has been developed for the analysis of qDGMS samples, it can also be used for other proteomics experiments that involve 2-state labelled samples separated into fractions. We show that qDGMS and ComPrAn can be used to study macromolecular complexes in their native state, accounting for the dynamics inherent to biological systems and benefiting from its proteome-wide quantitative and qualitative capability.

SUBMITTER: Palenikova P

PROVIDER: S-EPMC8047798 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Protocol to study human mitochondrial ribosome using quantitative density gradient analysis by mass spectrometry and complexome profiling analysis.

Project description:Dynamic macromolecular complexes containing a large number of components are often difficult to study using conventional approaches, such as immunoblotting. Here, we present a protocol for the analysis of macromolecular complexes in near-native conditions using a flexible setup to suit different cellular targets. We describe analysis of human mitochondrial ribosome, composed of 82 proteins, in a standardized way using density gradient ultracentrifugation coupled to quantitative mass spectrometry and subsequent analysis of the generated data (ComPrAn). For complete details on the use and execution of this protocol, please refer to Páleníková et al.1 and Rebelo-Guiomar et al.2.

| S-EPMC10692952 | biostudies-literature

RNA aptamer capture of macromolecular complexes for mass spectrometry analysis.

Project description:Specific genomic functions are dictated by macromolecular complexes (MCs) containing multiple proteins. Affinity purification of these complexes, often using antibodies, followed by mass spectrometry (MS) has revolutionized our ability to identify the composition of MCs. However, conventional immunoprecipitations suffer from contaminating antibody/serum-derived peptides that limit the sensitivity of detection for low-abundant interacting partners using MS. Here, we present AptA-MS (aptamer affinity-mass spectrometry), a robust strategy primarily using a specific, high-affinity RNA aptamer against Green Fluorescent Protein (GFP) to identify interactors of a GFP-tagged protein of interest by high-resolution MS. Utilizing this approach, we have identified the known molecular chaperones that interact with human Heat Shock Factor 1 (HSF1), and observed an increased association with several proteins upon heat shock, including translation elongation factors and histones. HSF1 is known to be regulated by multiple post-translational modifications (PTMs), and we observe both known and new sites of modifications on HSF1. We show that AptA-MS provides a dramatic target enrichment and detection sensitivity in evolutionarily diverse organisms and allows identification of PTMs without the need for modification-specific enrichments. In combination with the expanding libraries of GFP-tagged cell lines, this strategy offers a general, inexpensive, and high-resolution alternative to conventional approaches for studying MCs.

| S-EPMC7470977 | biostudies-literature

Analysis of Human Nuclear Protein Complexes by Quantitative Mass Spectrometry Profiling.

Project description:Analysis of protein complexes provides insights into how the ensemble of expressed proteome is organized into functional units. While there have been advances in techniques for proteome-wide profiling of cytoplasmic protein complexes, information about human nuclear protein complexes are very limited. To close this gap, we combined native size exclusion chromatography (SEC) with label-free quantitative MS profiling to characterize hundreds of nuclear protein complexes isolated from human glioblastoma multiforme T98G cells. We identified 1794 proteins that overlapped between two biological replicates of which 1244 proteins were characterized as existing within stably associated putative complexes. co-IP experiments confirmed the interaction of PARP1 with Ku70/Ku80 proteins and HDAC1 (histone deacetylase complex 1) and CHD4. HDAC1/2 also co-migrated with various SIN3A and nucleosome remodeling and deacetylase components in SEC fractionation including SIN3A, SAP30, RBBP4, RBBP7, and NCOR1. Co-elution of HDAC1/2/3 with both the KDM1A and RCOR1 further confirmed that these proteins are integral components of human deacetylase complexes. Our approach also demonstrated the ability to identify potential moonlighting complexes and novel complexes containing uncharacterized proteins. Overall, the results demonstrated the utility of SEC fractionation and LC-MS analysis for system-wide profiling of proteins to predict the existence of distinct forms of nuclear protein complexes.

| S-EPMC6387628 | biostudies-literature

MMP qualitative and quantitative mass spectrometry

Project description:Methanococcus maripaludis is a methanogenic archaeon. Within its genome, there are two operons for membrane associated hydrogenases, eha and ehb. To investigate the regulation of ehb on the cell, an S40 mutant was constructed in such a way that a portion of the ehb operon was replaced by pac cassette in the wild type parental strain S2 (done by Whitman's group at the University of Georgia). The S40 and S2 strains were grown in 14N and 15N media with acetate separately. A biological replicate was made by switching the media. Mass spectrometry based quantitative proteomics were done on the mixtures to investigate the differences in expression patterns between S40 and S2. Keywords: isotope labeling mass spectrometry, quantitative proteomics

2005-09-20 | GSE2744 | GEO

Quantitative mass spectrometry measurements reveal stoichiometry of principal postsynaptic density proteins.

Project description:Quantitative studies are presented of postsynaptic density (PSD) fractions from rat cerebral cortex with the ultimate goal of defining the average copy numbers of proteins in the PSD complex. Highly specific and selective isotope dilution mass spectrometry assays were developed using isotopically labeled polypeptide concatemer internal standards. Interpretation of PSD protein stoichiometry was achieved as a molar ratio with respect to PSD-95 (SAP-90, DLG4), and subsequently, copy numbers were estimated using a consensus literature value for PSD-95. Average copy numbers for several proteins at the PSD were estimated for the first time, including those for AIDA-1, BRAGs, and densin. Major findings include evidence for the high copy number of AIDA-1 in the PSD (144 ± 30)-equivalent to that of the total GKAP family of proteins (150 ± 27)-suggesting that AIDA-1 is an element of the PSD scaffold. The average copy numbers for NMDA receptor sub-units were estimated to be 66 ± 18, 27 ± 9, and 45 ± 15, respectively, for GluN1, GluN2A, and GluN2B, yielding a total of 34 ± 10 NMDA channels. Estimated average copy numbers for AMPA channels and their auxiliary sub-units TARPs were 68 ± 36 and 144 ± 38, respectively, with a stoichiometry of ?1:2, supporting the assertion that most AMPA receptors anchor to the PSD via TARP sub-units. This robust, quantitative analysis of PSD proteins improves upon and extends the list of major PSD components with assigned average copy numbers in the ongoing effort to unravel the complex molecular architecture of the PSD.

| S-EPMC5597335 | biostudies-literature

Quantitative mass spectrometry methods for pharmaceutical analysis.

Project description:Quantitative pharmaceutical analysis is nowadays frequently executed using mass spectrometry. Electrospray ionization coupled to a (hybrid) triple quadrupole mass spectrometer is generally used in combination with solid-phase extraction and liquid chromatography. Furthermore, isotopically labelled standards are often used to correct for ion suppression. The challenges in producing sensitive but reliable quantitative data depend on the instrumentation, sample preparation and hyphenated techniques. In this contribution, different approaches to enhance the ionization efficiencies using modified source geometries and improved ion guidance are provided. Furthermore, possibilities to minimize, assess and correct for matrix interferences caused by co-eluting substances are described. With the focus on pharmaceuticals in the environment and bioanalysis, different separation techniques, trends in liquid chromatography and sample preparation methods to minimize matrix effects and increase sensitivity are discussed. Although highly sensitive methods are generally aimed for to provide automated multi-residue analysis, (less sensitive) miniaturized set-ups have a great potential due to their ability for in-field usage.This article is part of the themed issue 'Quantitative mass spectrometry'.

| S-EPMC5031633 | biostudies-literature

Developing a Multiplexed Quantitative Cross-Linking Mass Spectrometry Platform for Comparative Structural Analysis of Protein Complexes.

Project description:Cross-linking mass spectrometry (XL-MS) represents a recently popularized hybrid methodology for defining protein-protein interactions (PPIs) and analyzing structures of large protein assemblies. In particular, XL-MS strategies have been demonstrated to be effective in elucidating molecular details of PPIs at the peptide resolution, providing a complementary set of structural data that can be utilized to refine existing complex structures or direct de novo modeling of unknown protein structures. To study structural and interaction dynamics of protein complexes, quantitative cross-linking mass spectrometry (QXL-MS) strategies based on isotope-labeled cross-linkers have been developed. Although successful, these approaches are mostly limited to pairwise comparisons. In order to establish a robust workflow enabling comparative analysis of multiple cross-linked samples simultaneously, we have developed a multiplexed QXL-MS strategy, namely, QMIX (Quantitation of Multiplexed, Isobaric-labeled cross (X)-linked peptides) by integrating MS-cleavable cross-linkers with isobaric labeling reagents. This study has established a new analytical platform for quantitative analysis of cross-linked peptides, which can be directly applied for multiplexed comparisons of the conformational dynamics of protein complexes and PPIs at the proteome scale in future studies.

| S-EPMC5361889 | biostudies-literature

NetWeAvers: an R package for integrative biological network analysis with mass spectrometry data.

Project description:SUMMARY: The discovery of functionally related groups in a set of significantly abundant proteins from a mass spectrometry experiment is an important step in a proteomics analysis pipeline. Here we describe NetWeAvers (Network Weighted Averages) for analyzing groups of regulated proteins in a network context, e.g. as defined by clusters of protein-protein interactions. NetWeAvers is an R package that provides a novel method for analyzing proteomics data integrated with biological networks. The method includes an algorithm for finding dense clusters of proteins and a permutation algorithm to calculate cluster P-values. Optional steps include summarizing quantified peptide values to single protein values and testing for differential expression, such that the data input can simply be a list of identified and quantified peaks. AVAILABILITY AND IMPLEMENTATION: ?The NetWeAvers package is written in R, is open source and is freely available on CRAN and from netweavers.erasmusmc.nl under the GPL-v2 license. CONTACT: e.mcclellan@erasmusmc.nl

| S-EPMC3810856 | biostudies-other

Highly Multiplexed Quantitative Mass Spectrometry Analysis of Ubiquitylomes.

Project description:System-wide quantitative analysis of ubiquitylomes has proven to be a valuable tool for elucidating targets and mechanisms of the ubiquitin-driven signaling systems, as well as gaining insights into neurodegenerative diseases and cancer. Current mass spectrometry methods for ubiquitylome detection require large amounts of starting material and rely on stochastic data collection to increase replicate analyses. We describe a method compatible with cell line and tissue samples for large-scale quantification of 5,000-9,000 ubiquitylation forms across ten samples simultaneously. Using this method, we reveal site-specific ubiquitylation in mammalian brain and liver tissues, as well as in cancer cells undergoing proteasome inhibition. To demonstrate the power of the approach for signal-dependent ubiquitylation, we examined protein and ubiquitylation dynamics for mitochondria undergoing PARKIN- and PINK1-dependent mitophagy. This analysis revealed the largest collection of PARKIN- and PINK1-dependent ubiquitylation targets to date in a single experiment, and it also revealed a subset of proteins recruited to the mitochondria during mitophagy.

| S-EPMC5241079 | biostudies-literature

Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis.

Project description:Mass-Suite (MSS) is a Python-based, open-source software package designed to analyze high-resolution mass spectrometry (HRMS)-based non-targeted analysis (NTA) data, particularly for water quality assessment and other environmental applications. MSS provides flexible, user-defined workflows for HRMS data processing and analysis, including both basic functions (e.g., feature extraction, data reduction, feature annotation, data visualization, and statistical analyses) and advanced exploratory data mining and predictive modeling capabilities that are not provided by currently available open-source software (e.g., unsupervised clustering analyses, a machine learning-based source tracking and apportionment tool). As a key advance, most core MSS functions are supported by machine learning algorithms (e.g., clustering algorithms and predictive modeling algorithms) to facilitate function accuracy and/or efficiency. MSS reliability was validated with mixed chemical standards of known composition, with 99.5% feature extraction accuracy and ~ 52% overlap of extracted features relative to other open-source software tools. Example user cases of laboratory data evaluation are provided to illustrate MSS functionalities and demonstrate reliability. MSS expands available HRMS data analysis workflows for water quality evaluation and environmental forensics, and is readily integrated with existing capabilities. As an open-source package, we anticipate further development of improved data analysis capabilities in collaboration with interested users.

| S-EPMC10517472 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data