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      Nanobodies as novel tools to monitor the mitochondrial fission factor Drp1

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          Abstract

          This study reports the first nanobodies against the mitochondrial fission protein Drp1 for application in proteomics, high-resolution microscopy and live cell imaging.

          Abstract

          In cells, mitochondria undergo constant fusion and fission. An essential factor for fission is the mammalian dynamin-related protein 1 (Drp1). Dysregulation of Drp1 is associated with neurodegenerative diseases including Parkinson’s, cardiovascular diseases and cancer, making Drp1 a pivotal biomarker for monitoring mitochondrial status and potential pathophysiological conditions. Here, we developed nanobodies (Nbs) as versatile binding molecules for proteomics, advanced microscopy and live cell imaging of Drp1. To specifically enrich endogenous Drp1 with interacting proteins for proteomics, we functionalized high-affinity Nbs into advanced capture matrices. Furthermore, we detected Drp1 by bivalent Nbs combined with site-directed fluorophore labelling in super-resolution STORM microscopy. For real-time imaging of Drp1, we intracellularly expressed fluorescently labelled Nbs, so-called chromobodies (Cbs). To improve the signal-to-noise ratio, we further converted Cbs into a “turnover-accelerated” format. With these imaging probes, we visualized the dynamics of endogenous Drp1 upon compound-induced mitochondrial fission in living cells. Considering the wide range of research applications, the presented Nb toolset will open up new possibilities for advanced functional studies of Drp1 in disease-relevant models.

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          Fiji: an open-source platform for biological-image analysis.

          Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
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            MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

            Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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              The PRIDE database resources in 2022: a hub for mass spectrometry-based proteomics evidences

              The PRoteomics IDEntifications (PRIDE) database ( https://www.ebi.ac.uk/pride/ ) is the world's largest data repository of mass spectrometry-based proteomics data. PRIDE is one of the founding members of the global ProteomeXchange (PX) consortium and an ELIXIR core data resource. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2019. The number of submitted datasets to PRIDE Archive (the archival component of PRIDE) has reached on average around 500 datasets per month during 2021. In addition to continuous improvements in PRIDE Archive data pipelines and infrastructure, the PRIDE Spectra Archive has been developed to provide direct access to the submitted mass spectra using Universal Spectrum Identifiers. As a key point, the file format MAGE-TAB for proteomics has been developed to enable the improvement of sample metadata annotation. Additionally, the resource PRIDE Peptidome provides access to aggregated peptide/protein evidences across PRIDE Archive. Furthermore, we will describe how PRIDE has increased its efforts to reuse and disseminate high-quality proteomics data into other added-value resources such as UniProt, Ensembl and Expression Atlas.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: ValidationRole: InvestigationRole: VisualizationRole: Writing—original draftRole: Writing—review and editing
                Role: InvestigationRole: VisualizationRole: MethodologyRole: Writing—review and editing
                Role: Data curationRole: Formal analysisRole: Writing—original draftRole: Writing—review and editing
                Role: InvestigationRole: MethodologyRole: Writing—review and editing
                Role: InvestigationRole: MethodologyRole: Writing—original draftRole: Writing—review and editing
                Role: InvestigationRole: MethodologyRole: Writing—review and editing
                Role: InvestigationRole: MethodologyRole: Writing—review and editing
                Role: InvestigationRole: MethodologyRole: Writing—review and editing
                Role: InvestigationRole: MethodologyRole: Writing—review and editing
                Role: SupervisionRole: MethodologyRole: Writing—review and editing
                Role: SupervisionRole: MethodologyRole: Writing—review and editing
                Role: SupervisionRole: MethodologyRole: Writing—review and editing
                Role: ConceptualizationRole: Formal analysisRole: SupervisionRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Writing—original draftRole: Project administrationRole: Writing—review and editing
                Journal
                Life Sci Alliance
                Life Sci Alliance
                lsa
                lsa
                Life Science Alliance
                Life Science Alliance LLC
                2575-1077
                30 May 2024
                August 2024
                30 May 2024
                : 7
                : 8
                : e202402608
                Affiliations
                [1 ] Pharmaceutical Biotechnology, Eberhard Karls University Tübingen, Tübingen, Germany;
                [2 ] Institute for Genetics and Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany;
                [3 ] Quantitative Proteomics, Department of Biology, Institute of Cell Biology, Eberhard Karls University Tübingen, Tübingen, Germany;
                [4 ] Center for Plant Molecular Biology (ZMBP), Eberhard Karls University Tübingen, Tübingen, Germany;
                [5 ] NMI Natural and Medical Sciences Institute at the University of Tübingen ( https://ror.org/03a1kwz48) , Reutlingen, Germany;
                [6 ] Livestock Center of the Faculty of Veterinary Medicine, Ludwig Maximilians University Munich, Munich, Germany;
                [7 ] Max Planck Institute of Biophysics, Frankfurt, Germany;
                [8 ] Cluster of Excellence iFIT (EXC2180) “Image-Guided and Functionally Instructed Tumor Therapies,” University of Tübingen ( https://ror.org/03a1kwz48) , Tübingen, Germany;
                Author notes
                Author information
                https://orcid.org/0009-0001-3822-8858
                https://orcid.org/0000-0002-6388-3053
                https://orcid.org/0009-0006-8026-6694
                https://orcid.org/0000-0001-7075-0067
                https://orcid.org/0000-0001-7876-085X
                https://orcid.org/0000-0001-5923-8986
                Article
                LSA-2024-02608
                10.26508/lsa.202402608
                11140114
                38816213
                1e20f1ac-3efa-4327-b19e-0e029fe74f1f
                © 2024 Froehlich et al.

                This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/).

                History
                : 22 January 2024
                : 14 May 2024
                : 15 May 2024
                Funding
                Funded by: Deutsche Forschungsgemeinschaft (DFG), DOI http://dx.doi.org/10.13039/501100001659;
                Award ID: RTG 2364
                Award Recipient :
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                Methods
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