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

Summary

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.

Abstract

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.

Introduction

Mass spectrometry-based shotgun proteomics has become the key approach for analyzing proteome diversity of complex samples¹. With recent advances in mass spectrometry instrumentation^2,3, chromatography^4,5, ion mobility detection⁶, acquisition methods (data-independent⁷ and data-dependent acquisition⁸), quantification approaches (multi-plex isobaric peptide labeling method, e.g., TMT^9,10, and label-fr....

Protocol

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 personal computer with Internet access and ≥4 Gb of RAM is sufficient to run JUMPn analysis for a dataset with a small sample size (n < 30); larger....

Results

We used our published deep proteomics datasets^25,26,27,30 (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 .......

Discussion

Here we introduced our JUMPn software and its protocol, which have been applied in multiple projects for dissecting molecular mechanisms using deep quantitative proteomics data^{25,26,27,30,64}. 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.......

Materials

Name	Company	Catalog Number	Comments
MacBook Pro with a 2.3 GHz Quad-Core Processor running OS 10.15.7.	Apple Inc.	MacBook Pro 13''	Hardware used for software development and testing
Anoconda	Anaconda, Inc.	version 4.9.2	https://docs.anaconda.com/anaconda/install/
miniconda	Anaconda, Inc.	version 4.9.2	https://docs.conda.io/en/latest/miniconda.html
RStudio	RStudio Public-benefit corporation	version 4.0.3	https://www.rstudio.com/products/rstudio/download/
Shiny Server	RStudio Public-benefit corporation		https://shiny.rstudio.com/articles/shinyapps.html