Name: jumpn: a streamlined application for protein co-expression clustering and network analysis in proteomics
Uploaded: 2021-10-19T13:30:00
Description: Watch this Scientific Journal Video about JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics at JoVE.com

Funding support was provided by the National Institutes of Health (NIH) (R01AG047928, R01AG053987, RF1AG064909, RF1AG068581, and U54NS110435) and ALSAC (American Lebanese Syrian Associated Charities). The MS analysis was carried out in St. Jude Children&#39;s Research Hospital&#39;s Center of Proteomics and Metabolomics, which was partially supported by NIH Cancer Center Support Grant (P30CA021765). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

NOTE: In this protocol, the usage of JUMPn is illustrated by utilizing a published dataset of whole proteome profiling during B cell differentiation quantified by TMT isobaric label reagent27.
1. Setup of JUMPn software
NOTE: Two options are provided for setting up the JUMPn software: (i) installation on a local computer for personal use; and (ii) deployment of JUMPn on a remote Shiny Server for multiple users. For local installation, a persona...

Here we introduced our JUMPn software and its protocol, which have been applied in multiple projects for dissecting molecular mechanisms using deep quantitative proteomics data25,26,27,30,64. The JUMPn software and protocol have been fully optimized, including consideration of DE proteins for co-expression network analysis, a compilation of comprehensive and h...

Mass spectrometry-based shotgun proteomics has become the key approach for analyzing proteome diversity of complex samples1. With recent advances in mass spectrometry instrumentation2,3, chromatography4,5, ion mobility detection6, acquisition methods (data-independent7 and data-dependent acquisition8), quantification approaches (multi-plex isobaric peptide labeling method, e.g., TMT9,10, and label-free quantification11,12) and data analysis strategies/software development13,14,15,16,17,18, quantification of the whole proteome (e.g., over 10,000 proteins) is now routine19,20,21. However, how to gain mechanistic insights from such deep quantitative datasets is still challenging22. Initial attempts for investigating these datasets relied predominantly upon the annotation of individual elements of the data, treating each component (protein) independently. However, biological systems and their behavior cannot be solely explained by examining individual components23. Therefore, a systems approach that places the quantified biomolecules in the context of interaction networks is essential for the understanding of complex systems and the associated processes such as embryogenesis, immune response, and pathogenesis of human diseases24.
Network-based systems biology has emerged as a powerful paradigm for analyzing large-scale quantitative proteomics data25,26,27,28,29,30,31,32,33. Conceptually, complex systems such as mammalian cells could be modeled as a hierarchical network34,35, in which the whole system is represented in tiers: first by a number of large components, each of which then iteratively modeled by smaller subsystems. Technically, the structure of proteome dynamics can be presented by inter-connected networks of co-expressed protein clusters (because co-expressed genes/proteins often share similar biological functions or mechanisms of regulation36) and physically interacting PPI modules37. As a recent example25, we generated temporal profiles of whole proteome and phosphoproteome during T cell activation and used integrative co-expression networks with PPIs to identify functional modules that mediate T-cell quiescence exit. Multiple bioenergetic-related modules were highlighted and experimentally validated (e.g., the mitoribosome and complex IV modules25, and the one-carbon module38). In another example26, we further extended our approach to study the pathogenesis of Alzheimer&#39;s disease, and successfully prioritized disease progression associated protein modules and molecules. Importantly, many of our unbiased discoveries were validated by independent patient cohorts26,29 and/or disease mouse models26. These examples illustrated the power of the systems biology approach for dissecting molecular mechanisms with quantitative proteomics and other omics integrations.
Here we introduce JUMPn, a streamlined software that explores quantitative proteomics data using network-based systems biology approaches. JUMPn serves as the downstream component of the established JUMP proteomics software suite13,14,39, and aims to fill the gap from individual protein quantifications to biologically meaningful pathways and protein modules using the systems biology approach. By taking the quantification matrix of differentially expressed (or the most variable) proteins as input, JUMPn aims to organize the proteome into a tiered hierarchy of protein clusters co-expressed across samples and densely connected PPI modules (e.g., protein complexes), which are further annotated with public pathway databases by over-representation (or enrichment) analysis (Figure 1). JUMPn is developed with the R/Shiny platform40 for a user-friendly interface and integrates three major functional modules: co-expression clustering analysis, pathway enrichment analysis, and PPI network analysis (Figure 1). After each analysis, results are automatically visualized and are adjustable via the R/shiny widget functions and readily downloadable as publication tables in Microsoft Excel format. In the following protocol, we use quantitative whole proteome data as an example and describe the major steps of using JUMPn, including installation of the JUMPn software, the definition of differentially expressed proteins or the (dys)regulated proteome, co-expression network analysis, and PPI module analysis, result visualization and interpretation, and trouble shootings. JUMPn software is freely available on GitHub41.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>MacBook Pro with a 2.3 GHz Quad-Core Processor running OS 10.15.7.</td>
			<td>Apple Inc.</td>
			<td>MacBook Pro 13&#39;&#39;</td>
			<td>Hardware used for software development and testing</td>
		</tr>
		<tr>
			<td>Anoconda</td>
			<td>Anaconda, Inc.</td>
			<td>version 4.9.2</td>
			<td>https://docs.anaconda.com/anaconda/install/</td>
		</tr>
		<tr>
			<td>miniconda</td>
			<td>Anaconda, Inc.</td>
			<td>version 4.9.2</td>
			<td>https://docs.conda.io/en/latest/miniconda.html</td>
		</tr>
		<tr>
			<td>RStudio</td>
			<td>RStudio Public-benefit corporation</td>
			<td>version 4.0.3</td>
			<td>https://www.rstudio.com/products/rstudio/download/</td>
		</tr>
		<tr>
			<td>Shiny Server</td>
			<td>RStudio Public-benefit corporation</td>
			<td></td>
			<td>https://shiny.rstudio.com/articles/shinyapps.html</td>
		</tr>
	</tbody>
</table>

jumpn: a streamlined application for protein co-expression clustering and network analysis in proteomics

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. However, deriving biological insights from such valuable datasets is challenging. Here we introduce a systems biology-based software JUMPn, and its associated protocol to organize the proteome into protein co-expression clusters across samples and protein-protein interaction (PPI) networks connected by modules (e.g., protein complexes). Using the R/Shiny platform, the JUMPn software streamlines the analysis of co-expression clustering, pathway enrichment, and PPI module detection, with integrated data visualization and a user-friendly interface. The main steps of the protocol include installation of the JUMPn software, the definition of differentially expressed proteins or the (dys)regulated proteome, determination of meaningful co-expression clusters and PPI modules, and result visualization. While the protocol is demonstrated using an isobaric labeling-based proteome profile, JUMPn is generally applicable to a wide range of quantitative datasets (e.g., label-free proteomics). The JUMPn software and protocol thus provide a powerful tool to facilitate biological interpretation in quantitative proteomics.

We used our published deep proteomics datasets25,26,27,30 (Figures 5 and Figure 6) as well as data simulations57 (Table 1) to optimize and evaluate JUMPn performance. For co-expression protein clustering analysis via WGCNA, we recommend utilizing proteins significantly changed across samples as ...

Watch this Scientific Journal Video about JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics at JoVE.com

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

We present a systems biology tool JUMPn to perform and visualize network analysis for quantitative proteomics data, with a detailed protocol including data pre-processing, co-expression clustering, pathway enrichment, and protein-protein interaction network analysis.

With recent advances in mass spectrometry-based proteomics technologies, deep profiling of hundreds of proteomes has become increasingly feasible. ...

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