| 1. |
- Bludau, Isabell, et al.
(författare)
-
Complex-centric proteome profiling by SEC-SWATH-MS for the parallel detection of hundreds of protein complexes
- 2020
-
Ingår i: Nature Protocols. - : Springer Science and Business Media LLC. - 1754-2189 .- 1750-2799. ; 15:8, s. 2341-2386
-
Tidskriftsartikel (refereegranskat)abstract
- Most catalytic, structural and regulatory functions of the cell are carried out by functional modules, typically complexes containing or consisting of proteins. The composition and abundance of these complexes and the quantitative distribution of specific proteins across different modules are therefore of major significance in basic and translational biology. However, detection and quantification of protein complexes on a proteome-wide scale is technically challenging. We have recently extended the targeted proteomics rationale to the level of native protein complex analysis (complex-centric proteome profiling). The complex-centric workflow described herein consists of size exclusion chromatography (SEC) to fractionate native protein complexes, data-independent acquisition mass spectrometry to precisely quantify the proteins in each SEC fraction based on a set of proteotypic peptides and targeted, complex-centric analysis where prior information from generic protein interaction maps is used to detect and quantify protein complexes with high selectivity and statistical error control via the computational framework CCprofiler (https://github.com/CCprofiler/CCprofiler). Complex-centric proteome profiling captures most proteins in complex-assembled state and reveals their organization into hundreds of complexes and complex variants observable in a given cellular state. The protocol is applicable to cultured cells and can potentially also be adapted to primary tissue and does not require any genetic engineering of the respective sample sources. At present, it requires ~8 d of wet-laboratory work, 15 d of mass spectrometry measurement time and 7 d of computational analysis.
|
|
| 2. |
- Bludau, Isabell, et al.
(författare)
-
Rapid Profiling of Protein Complex Reorganization in Perturbed Systems
- 2023
-
Ingår i: Journal of Proteome Research. - 1535-3893. ; 22:5, s. 1520-1536
-
Tidskriftsartikel (refereegranskat)abstract
- Protein complexes constitute the primary functional modules of cellular activity. To respond to perturbations, complexes undergo changes in their abundance, subunit composition, or state of modification. Understanding the function of biological systems requires global strategies to capture this contextual state information. Methods based on cofractionation paired with mass spectrometry have demonstrated the capability for deep biological insight, but the scope of studies using this approach has been limited by the large measurement time per biological sample and challenges with data analysis. There has been little uptake of this strategy into the broader life science community despite its rich biological information content. We present a rapid integrated experimental and computational workflow to assess the reorganization of protein complexes across multiple cellular states. The workflow combines short gradient chromatography and DIA/SWATH mass spectrometry with a data analysis toolset to quantify changes in a complex organization. We applied the workflow to study the global protein complex rearrangements of THP-1 cells undergoing monocyte to macrophage differentiation and subsequent stimulation of macrophage cells with lipopolysaccharide. We observed substantial proteome reorganization on differentiation and less pronounced changes in macrophage stimulation. We establish our integrated differential pipeline for rapid and state-specific profiling of protein complex organization.
|
|
| 3. |
- Bludau, Isabell, et al.
(författare)
-
Systematic detection of functional proteoform groups from bottom-up proteomic datasets
- 2021
-
Ingår i: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 12:1
-
Tidskriftsartikel (refereegranskat)abstract
- To a large extent functional diversity in cells is achieved by the expansion of molecular complexity beyond that of the coding genome. Various processes create multiple distinct but related proteins per coding gene – so-called proteoforms – that expand the functional capacity of a cell. Evaluating proteoforms from classical bottom-up proteomics datasets, where peptides instead of intact proteoforms are measured, has remained difficult. Here we present COPF, a tool for COrrelation-based functional ProteoForm assessment in bottom-up proteomics data. It leverages the concept of peptide correlation analysis to systematically assign peptides to co-varying proteoform groups. We show applications of COPF to protein complex co-fractionation data as well as to more typical protein abundance vs. sample data matrices, demonstrating the systematic detection of assembly- and tissue-specific proteoform groups, respectively, in either dataset. We envision that the presented approach lays the foundation for a systematic assessment of proteoforms and their functional implications directly from bottom-up proteomic datasets.
|
|
| 4. |
- Fossati, Andrea, et al.
(författare)
-
PCprophet : a framework for protein complex prediction and differential analysis using proteomic data
- 2021
-
Ingår i: Nature Methods. - : Springer Science and Business Media LLC. - 1548-7091 .- 1548-7105. ; 18:5, s. 520-527
-
Tidskriftsartikel (refereegranskat)abstract
- Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein profiles have shown promise. However, most approaches for interpreting cofractionation datasets to yield complex composition and rearrangements between samples depend considerably on protein–protein interaction inference. We introduce PCprophet, a toolkit built on size exclusion chromatography–sequential window acquisition of all theoretical mass spectrometry (SEC-SWATH-MS) data to predict protein complexes and characterize their changes across experimental conditions. We demonstrate improved performance of PCprophet over state-of-the-art approaches and introduce a Bayesian approach to analyze altered protein–protein interactions across conditions. We provide both command-line and graphical interfaces to support the application of PCprophet to any cofractionation MS dataset, independent of separation or quantitative liquid chromatography–MS workflow, for the detection and quantitative tracking of protein complexes and their physiological dynamics.
|
|
| 5. |
- Heusel, Moritz, et al.
(författare)
-
A Global Screen for Assembly State Changes of the Mitotic Proteome by SEC-SWATH-MS
- 2020
-
Ingår i: Cell systems. - : Elsevier BV. - 2405-4712. ; 10:2, s. 6-155
-
Tidskriftsartikel (refereegranskat)abstract
- Living systems integrate biochemical reactions that determine the functional state of each cell. Reactions are primarily mediated by proteins. In proteomic studies, these have been treated as independent entities, disregarding their higher-level organization into complexes that affects their activity and/or function and is thus of great interest for biological research. Here, we describe the implementation of an integrated technique to quantify cell-state-specific changes in the physical arrangement of protein complexes concurrently for thousands of proteins and hundreds of complexes. Applying this technique to a comparison of human cells in interphase and mitosis, we provide a systematic overview of mitotic proteome reorganization. The results recall key hallmarks of mitotic complex remodeling and suggest a model of nuclear pore complex disassembly, which we validate by orthogonal methods. To support the interpretation of quantitative SEC-SWATH-MS datasets, we extend the software CCprofiler and provide an interactive exploration tool, SECexplorer-cc.
|
|
| 6. |
- Neely, Benjamin A., et al.
(författare)
-
Toward an Integrated Machine Learning Model of a Proteomics Experiment
- 2023
-
Ingår i: Journal of Proteome Research. - : American Chemical Society (ACS). - 1535-3893 .- 1535-3907. ; 22:3, s. 681-696
-
Forskningsöversikt (refereegranskat)abstract
- In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.
|
|
