A Global Screen for Assembly State Changes of the Mitotic Proteome by SEC-SWATH-MS

Heusel, Moritz; Frank, M; Köhler, Mario; Amon, Sabine; Frommelt, Fabian; Rosenberger, George; Bludau, Isabell; Aulakh, Simran Kaur; Linder, Monika I.; Liu, Yansheng; Collins, Ben C.; Gstaiger, Matthias; Kutay, Ulrike; Aebersold, Ruedi

doi:10.1016/j.cels.2020.01.001

Cited by 62 publications

(94 citation statements)

References 73 publications

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“…The main input data are the peptide-level PCP profiles, e.g. acquired by SEC-SWATH-MS [3,38], and a set of reference PPI networks ( Fig. 1b, Methods).…”

Section: Resultsmentioning

confidence: 99%

“…To assess SECAT's signal processing and PPI detection modules for parameter selection and validation, we first benchmarked the effect of different peak-picking preprocessing methods. For this, we used a publicly available SEC-SWATH-MS dataset [38] of a HeLa CCL2 cell line that was measured in triplicate in both interphase and mitotic cell state (referred to as HeLa-CC dataset, Methods). Then, we assessed the method's robustness based on its ability to infer bona fide PPIs using reference networks with an increasing ratio of false vs. true PPIs.…”

Section: Parameter Selection and Validation Of Signal Processing And mentioning

confidence: 99%

“…Figure 2. Signal processing and PPI detection performance evaluated using the HeLa-CC [38] dataset. (a) The effect of different peak-picking methods for signal processing on peak-width and PPI quantification within replicates of the same experimental condition is depicted in violin plots (lines representing 25, 50 and 75% quantiles respectively, Methods).…”

Section: Parameter Selection and Validation Of Signal Processing And mentioning

confidence: 99%

“…Building on SECAT's benchmarked ability to detect true PPIs from PCP datasets, we next validated its PPI quantification and network integration modules. For these analyses, we used the previously described HeLa-CC dataset [38]. For this study, we performed independent analyses, using CORUM [37], STRING [19] and PrePPI [24] as reference PPI networks, and used a classifier trained on the full CORUM true/false reference network.…”

Section: Validation Of Ppi Quantification and Network Integration Modulesmentioning

confidence: 99%

“…Using prior knowledge from reference databases like CORUM [37], BioPlex [16,18] or STRING [19], the confidence of protein complex detection was also substantially improved, albeit at the cost of missing potential novel interactions or complexes not included in the query set. Taking advantage of the precise quantitative values generated by SWATH-MS, we have also shown that the CCprofiler strategy is well-suited to detect changes in complex-associated protein abundance across conditions [38].…”

Section: Introductionmentioning

confidence: 99%

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SECAT: Quantifying differential protein-protein interaction states by network-centric analysis

Rosenberger

Heusel

Bludau

et al. 2019

Preprint

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Protein-protein interactions (PPIs) play a critical role in virtually all cellular processes. Their context-dependent characterization is thus a key objective of proteomic research. We and others have previously shown that chromatographic fractionation of native protein complexes (e.g. through size-exclusion chromatography, SEC) can be effectively combined with high-throughput, bottom-up mass-spectrometry-based proteomics (e.g. data-independent acquisition-based SWATH-MS), to support proteome-wide characterization of protein complexes.To enable qualitative and quantitative comparison of the proteome organization encoded in these datasets, across multiple experimental conditions, scalable and robust 1/32 analysis strategies are required. To address this need, we developed the Size-Exclusion Chromatography Algorithmic Toolkit (SECAT), a novel network-centric strategy for the quantitation of protein complex profiles. SECAT elucidates proteins and their context-specific PPIs in terms of both abundance and connectivity. We validate algorithm predictions using publicly available datasets and compare them to established strategies to demonstrate that SECAT represents a more scalable and effective methodology to assess protein-network state, obviating the need to infer individual protein complexes. Further, by comparing PPI-networks in interphase and mitotic HeLa cells, we demonstrate SECAT's ability to provide novel insight about context-specific molecular mechanisms that differentiate cellular states.

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“…The main input data are the peptide-level PCP profiles, e.g. acquired by SEC-SWATH-MS [3,38], and a set of reference PPI networks ( Fig. 1b, Methods).…”

Section: Resultsmentioning

confidence: 99%

Section: Parameter Selection and Validation Of Signal Processing And mentioning

confidence: 99%

Section: Parameter Selection and Validation Of Signal Processing And mentioning

confidence: 99%

Section: Validation Of Ppi Quantification and Network Integration Modulesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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SECAT: Quantifying differential protein-protein interaction states by network-centric analysis

Rosenberger

Heusel

Bludau

et al. 2019

Preprint

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Puzzling out nuclear pore complex assembly

Penzo,

Palancade

2023

FEBS Letters

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Nuclear pore complexes (NPCs) are sophisticated multiprotein assemblies embedded within the nuclear envelope and controlling the exchanges of molecules between the cytoplasm and the nucleus. In this review, we summarize the mechanisms by which these elaborate complexes are built from their subunits, the nucleoporins, based on our ever‐growing knowledge of NPC structural organization and on the recent identification of additional features of this process. We present the constraints faced during the production of nucleoporins, their gathering into oligomeric complexes, and the formation of NPCs within nuclear envelopes, and review the cellular strategies at play, from co‐translational assembly to the enrolment of a panel of cofactors. Remarkably, the study of NPCs can inform our perception of the biogenesis of multiprotein complexes in general – and vice versa.

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Advances in data‐independent acquisition mass spectrometry towards comprehensive digital proteome landscape

Kitata

Yang

Chen

2022

Mass Spectrometry Reviews

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The data‐independent acquisition mass spectrometry (DIA‐MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA‐MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA‐based proteomics profiling. Here, we review the evolution of the DIA‐MS techniques, from earlier proof‐of‐principle of parallel fragmentation of all‐ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH‐MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA‐MS. We further summarize recent applications of DIA‐MS and experimentally‐derived as well as in silico spectra library resources for large‐scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next‐generation DIA‐MS for clinical proteomics, we outline the challenges in processing multi‐dimensional DIA data set and large‐scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

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A Global Screen for Assembly State Changes of the Mitotic Proteome by SEC-SWATH-MS

Cited by 62 publications

References 73 publications

SECAT: Quantifying differential protein-protein interaction states by network-centric analysis

SECAT: Quantifying differential protein-protein interaction states by network-centric analysis

Puzzling out nuclear pore complex assembly

Advances in data‐independent acquisition mass spectrometry towards comprehensive digital proteome landscape

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