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Khan S, Conover R, Asthagiri AR, Slavov N. Dynamics of Single-Cell Protein Covariation during Epithelial-Mesenchymal Transition. J Proteome Res 2024. [PMID: 38663020 DOI: 10.1021/acs.jproteome.4c00277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Physiological processes, such as the epithelial-mesenchymal transition (EMT), are mediated by changes in protein interactions. These changes may be better reflected in protein covariation within a cellular cluster than in the temporal dynamics of cluster-average protein abundance. To explore this possibility, we quantified proteins in single human cells undergoing EMT. Covariation analysis of the data revealed that functionally coherent protein clusters dynamically changed their protein-protein correlations without concomitant changes in the cluster-average protein abundance. These dynamics of protein-protein correlations were monotonic in time and delineated protein modules functioning in actin cytoskeleton organization, energy metabolism, and protein transport. These protein modules are defined by protein covariation within the same time point and cluster and, thus, reflect biological regulation masked by the cluster-average protein dynamics. Thus, protein correlation dynamics across single cells offers a window into protein regulation during physiological transitions.
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Affiliation(s)
- Saad Khan
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Rachel Conover
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Anand R Asthagiri
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Biology, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Biology, Northeastern University, Boston, Massachusetts 02115, United States
- Parallel Squared Technology Institute, Watertown, Massachusetts 02472, United States
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Khan S, Conover R, Asthagiri AR, Slavov N. Dynamics of single-cell protein covariation during epithelial-mesenchymal transition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.21.572913. [PMID: 38187715 PMCID: PMC10769332 DOI: 10.1101/2023.12.21.572913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Physiological processes, such as epithelial-mesenchymal transition (EMT), are mediated by changes in protein interactions. These changes may be better reflected in protein covariation within cellular cluster than in the temporal dynamics of cluster-average protein abundance. To explore this possibility, we quantified proteins in single human cells undergoing EMT. Covariation analysis of the data revealed that functionally coherent protein clusters dynamically changed their protein-protein correlations without concomitant changes in cluster-average protein abundance. These dynamics of protein-protein correlations were monotonic in time and delineated protein modules functioning in actin cytoskeleton organization, energy metabolism and protein transport. These protein modules are defined by protein covariation within the same time point and cluster and thus reflect biological regulation masked by the cluster-average protein dynamics. Thus, protein correlation dynamics across single cells offer a window into protein regulation during physiological transitions.
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Affiliation(s)
- Saad Khan
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Biology, Northeastern University, Boston, MA, USA
| | - Rachel Conover
- Department of Bioengineering, Northeastern University, Boston, MA, USA
| | - Anand R. Asthagiri
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Biology, Northeastern University, Boston, MA, USA
- Department of Chemical Engineering, Northeastern University, Boston, MA, USA
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Biology, Northeastern University, Boston, MA, USA
- Parallel Squared Technology Institute, Watertown, MA 02472, USA
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Huang J, Staes A, Impens F, Demichev V, Van Breusegem F, Gevaert K, Willems P. CysQuant: Simultaneous quantification of cysteine oxidation and protein abundance using data dependent or independent acquisition mass spectrometry. Redox Biol 2023; 67:102908. [PMID: 37793239 PMCID: PMC10562924 DOI: 10.1016/j.redox.2023.102908] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 09/20/2023] [Accepted: 09/26/2023] [Indexed: 10/06/2023] Open
Abstract
Protein cysteinyl thiols are susceptible to reduction-oxidation reactions that can influence protein function. Accurate quantification of cysteine oxidation is therefore crucial for decoding protein redox regulation. Here, we present CysQuant, a novel approach for simultaneous quantification of cysteine oxidation degrees and protein abundancies. CysQuant involves light/heavy iodoacetamide isotopologues for differential labeling of reduced and reversibly oxidized cysteines analyzed by data-dependent acquisition (DDA) or data-independent acquisition mass spectrometry (DIA-MS). Using plexDIA with in silico predicted spectral libraries, we quantified an average of 18% cysteine oxidation in Arabidopsis thaliana by DIA-MS, including a subset of highly oxidized cysteines forming disulfide bridges in AlphaFold2 predicted structures. Applying CysQuant to Arabidopsis seedlings exposed to excessive light, we successfully quantified the well-established increased reduction of Calvin-Benson cycle enzymes and discovered yet uncharacterized redox-sensitive disulfides in chloroplastic enzymes. Overall, CysQuant is a highly versatile tool for assessing the cysteine modification status that can be widely applied across various mass spectrometry platforms and organisms.
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Affiliation(s)
- Jingjing Huang
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052, Ghent, Belgium; Center for Plant Systems Biology, VIB, 9052, Ghent, Belgium
| | - An Staes
- Department of Biomolecular Medicine, Ghent University, 9052, Ghent, Belgium; Center for Medical Biotechnology, VIB, 9052, Ghent, Belgium; VIB Proteomics Core, 9052, Ghent, Belgium
| | - Francis Impens
- Department of Biomolecular Medicine, Ghent University, 9052, Ghent, Belgium; Center for Medical Biotechnology, VIB, 9052, Ghent, Belgium; VIB Proteomics Core, 9052, Ghent, Belgium
| | - Vadim Demichev
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, 10117, Berlin, Germany
| | - Frank Van Breusegem
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052, Ghent, Belgium; Center for Plant Systems Biology, VIB, 9052, Ghent, Belgium
| | - Kris Gevaert
- Department of Biomolecular Medicine, Ghent University, 9052, Ghent, Belgium; Center for Medical Biotechnology, VIB, 9052, Ghent, Belgium.
| | - Patrick Willems
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052, Ghent, Belgium; Center for Plant Systems Biology, VIB, 9052, Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, 9052, Ghent, Belgium; Center for Medical Biotechnology, VIB, 9052, Ghent, Belgium.
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Wallmann G, Leduc A, Slavov N. Data-Driven Optimization of DIA Mass Spectrometry by DO-MS. J Proteome Res 2023; 22:3149-3158. [PMID: 37695820 PMCID: PMC10591957 DOI: 10.1021/acs.jproteome.3c00177] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Indexed: 09/13/2023]
Abstract
Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever-increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data-independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of MS methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows one to optimize and evaluate results from both label-free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant to single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication of quality figures that can be easily shared. The source code is available at github.com/SlavovLab/DO-MS.
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Affiliation(s)
- Georg Wallmann
- Departments
of Bioengineering, Biology, Chemistry and Chemical Biology, Single
Cell Proteomics Center, Northeastern University, Boston, Massachusetts 02115, United States
| | - Andrew Leduc
- Departments
of Bioengineering, Biology, Chemistry and Chemical Biology, Single
Cell Proteomics Center, Northeastern University, Boston, Massachusetts 02115, United States
| | - Nikolai Slavov
- Departments
of Bioengineering, Biology, Chemistry and Chemical Biology, Single
Cell Proteomics Center, Northeastern University, Boston, Massachusetts 02115, United States
- Parallel
Squared Technology Institute, Watertown, Massachusetts 02472, United States
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