PXD055943 is an
original dataset announced via ProteomeXchange.
Dataset Summary
Title | Combining quantitative proteomics and interactomics for a deeper insight into molecular differences between human cell lines |
Description | Cellular functional pathways have evolved through selection based on fitness benefits conferred through protein intra- and inter-molecular interactions that comprise all protein conformational features and protein-protein interactions, collectively referred to as the interactome. While the interactome is regulated by proteome levels, it is also regulated independently by, post translational modification, co-factor, and ligand levels, as well as local protein environmental factors, such as osmolyte concentration, pH, ionic strength, temperature and others. In modern biomedical research, cultivatable cell lines have become an indispensable tool, with selection of optimal cell lines that exhibit specific functional profiles being critical for success in many cases. While it is clear that cell lines derived from different cell types have differential proteome levels, increased understanding of large-scale functional differences requires additional information beyond abundance level measurements, including how protein conformations and interactions are altered in certain cell types to shape functional landscapes. Here, we employed quantitative in vivo protein cross-linking coupled to mass spectrometry to probe large-scale protein conformational and interaction changes among three commonly employed human cell lines, HEK293, MCF-7, and HeLa cells. Isobaric quantitative Protein Interaction Reporter (iqPIR) technologies were used to obtain quantitative values of cross-linked peptides across three cell lines. These data illustrated highly reproducible (R2 values larger than 0.8 for all biological replicates) quantitative interactome levels across multiple biological replicates. We also measured protein abundance levels in these cells using data independent acquisition quantitative proteomics methods. Combining quantitative interactome and proteomics information allowed visualization of cell type-specific interactome changes mediated by proteome level adaptations as well as independently regulated interactome changes to gain deeper insight into possible drivers of these changes. Among the biggest detected alterations in protein interactions and conformations are changes in cytoskeletal proteins, RNA-binding proteins, chromatin remodeling complexes, mitochondrial proteins, and others. Overall, these data demonstrate the utility and reproducibility of quantitative cross-linking to study systems-level interactome variations. Moreover, these results illustrate how combined quantitative interactomics and proteomics can provide unique insight on cellular functional landscapes. |
HostingRepository | PRIDE |
AnnounceDate | 2024-09-17 |
AnnouncementXML | Submission_2024-09-17_09:32:58.113.xml |
DigitalObjectIdentifier | |
ReviewLevel | Peer-reviewed dataset |
DatasetOrigin | Original dataset |
RepositorySupport | Unsupported dataset by repository |
PrimarySubmitter | Anna Bakhtina |
SpeciesList | scientific name: Mus musculus (Mouse); NCBI TaxID: 10090; |
ModificationList | monohydroxylated residue; iodoacetamide derivatized residue |
Instrument | Q Exactive |
Dataset History
Revision | Datetime | Status | ChangeLog Entry |
0 | 2024-09-17 08:45:54 | ID requested | |
⏵ 1 | 2024-09-17 09:32:58 | announced | |
2 | 2024-10-22 06:58:55 | announced | 2024-10-22: Updated project metadata. |
Publication List
Dataset with its publication pending |
Keyword List
submitter keyword: mouse, cross-linking, mitochondria |
Contact List
Jim Bruce |
contact affiliation | Department of Genome Sciences, University of Washington |
contact email | jimbruce@uw.edu |
lab head | |
Anna Bakhtina |
contact affiliation | University of Washington |
contact email | anna.bakhtina91@gmail.com |
dataset submitter | |
Full Dataset Link List
Dataset FTP location
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PRIDE project URI |
Repository Record List
[ + ]
[ - ]
- PRIDE
- PXD055943
- Label: PRIDE project
- Name: Combining quantitative proteomics and interactomics for a deeper insight into molecular differences between human cell lines