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.

The authors have nothing to disclose.

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 high-quality PPI network, stringent statistical analysis (e.g., by consideration of multiple hypothesis testing) with a streamlined and user-friendly interface. Multiple protein modules identified by JUMPn have been validated by functional experiment studies25,27 or independent patient cohorts26, exemplifying JUMPn as an effective tool for identifying key molecules and pathways underlying diverse biological processes.
Critical steps of this protocol include the generation of optimal results of co-expression clusters and PPI modules, which may require multiple iterations of parameter tuning, as well as upload of customized PPI network. In our protocol, we discussed common practical scenarios, including how to handle missing of important clusters, a high percentage of unassigned proteins, merging of two redundant clusters, and missing of important proteins within PPI modules. We recommend the user to prepare several positive control proteins and confirm their presence in the final co-expression clusters. Sometimes a positive control will never be included in the final PPI modules due to an incomplete PPI network database. To partially alleviate this, we have updated our PPI network with the latest versions of BioPlex V362 and STRING V1160. In addition, JUMPn allows users to upload customized PPI networks. For example, novel interactions derived from affinity purification-mass spectrometry (AP-MS) experiments using an important positive control protein as bait may be integrated with the current composite PPI network for more customized analysis.
By using the framework of pathway enrichment analysis for each co-expression protein cluster, JUMPn can be extended for inferring transcription factor (TF) activity. The assumption is that if there exists an over-representation of target genes of a specific TF in a co-expression cluster (i.e., these targets are differentially expressed and follow the same expression pattern), the activity of that TF is potentially altered across experimental conditions because its target protein abundance is changed consistently. Technically, this can be simply achieved via JUMPn by replacing the current pathway database with the TF-target database (e.g., from the ENCODE project65). Similarly, kinase activity may also be inferred by leveraging the kinase-substrate database, taking deep phosphoproteomics as input. As an example, we successfully identified dysregulated TFs and kinases underlying brain tumor pathogenesis64. Indeed, using the network approach for activity inference has emerged as a powerful approach for identifying dysregulated drivers for human diseases66,67.
The JUMPn software is readily applied to a wide range of data types. Even though isobaric labeling quantified proteome was used as an illustrative example, the same protocol is applicable also for label-free quantified proteomics data, as well as genome-wide expression profiles (e.g., quantified by RNA-seq or microarray; see our recent example of applying JUMPn for both gene and protein expression profiles27). Phosphoproteomics data could also be taken by JUMPn to identify co-expressed phosphosites, followed by kinase activity inference25. In addition, interactome data generated by the AP-MS approach will also be appropriate, by which prey proteins that follow similar bait interaction strength and stoichiometry will form co-expression clusters and further overlapped with known PPIs for data interpretation68.
Limitations exist for the current version of JUMPn. First, the installation procedure is command line-based and requires basic knowledge of computer science. This hinders wider usage of JUMPn, especially from biologists without computational background. A more ideal implementation is to publish JUMPn on an online server. Second, the current databases are human-centric because of our focus on human disease studies. Note that proteomics data generated by mice has also been analyzed by JUMPn using such human-centric databases25,27, assuming that most PPIs are conserved across both species69,70. Mouse-specific signaling will not be captured by this approach but is not of interest in those human studies. However, for non-mammalian model systems (e.g., zebrafish, fly, or yeast), species-specific databases should be prepared and uploaded to JUMPn using the advanced options. Resources of additional species may be provided via future JUMPn release. Third, the current step of ontology/pathway analysis takes significant time, which can be further optimized by parallel computing.
In conclusion, we present the JUMPn software and protocol for exploring quantitative proteomics data to identify and visualize co-expressed and potentially physically interacting protein modules by systems biology approach. The key features that distinguish JUMPn from others53,71,72 include: (i) JUMPn integrates and streamlines four major components of the pathway and network analysis (Figure 1); (ii) Different from most pathway analysis software that takes a simple gene list as input, JUMPn starts from quantification matrix, by which quantitative information can be seamlessly integrated with literature documented pathways and networks; (iii) Both co-expression protein clusters and interaction modules are automatically annotated by known pathways, and visualized via the R/shiny interacting platform using a user-friendly web browser; (iv) Final results are organized into three tables that are readily publishable in Excel format. Thus, we expect the JUMPn and this protocol will be widely applicable to many studies for dissecting mechanisms using quantitative proteomics data.

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><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&#039;&#039;</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 the input (e.g., differentially expressed (DE) proteins detected by statistical analysis). While including non-DE proteins for the analysis may result in more co-expression clusters returned by the program (due to larger input size), we hypothesize that mixing the real signal (e.g., the DE proteins) with the background (the remaining non-DE) for systems-level analysis may dilute the signal and mask the underlying network structure. To test this, simulation analysis was performed under two different conditions: i) highly dynamic proteome (e.g., 50% altered in T cell activation25) and ii) relatively stable proteome (e.g., 2% proteome changed in AD26). For the highly dynamic proteome, six co-expression clusters were simulated from 50% proteome following the same cluster size and expression patterns (i.e., eigengenes) of our published results25. Similarly, for a relatively stable proteome, we simulated three clusters from 2% proteome following our recent AD proteomics study26. As expected, increasing the input number of proteins increases the number of detected clusters (Table 1). For the highly dynamic proteome, using all proteins as input can capture most of the true clusters (5 out of the 6 simulated bona fide clusters; 83% recall) with 63% precision (5 out of the 8 returned clusters are true positives; i.e., the remaining 3 clusters are false positives). However, for the relatively stable proteome, increasing the input size with non-DE proteins dramatically reduces precision (Table 1). For instance, using the whole proteome as input, 169 modules are detected, of which only 2 are correct (1.2% precision; the remaining 98.8% detected modules are false positives). These results thus indicate that choosing only the changed proteome as input will increase the precision of co-expression analysis, especially for relatively stable proteome.
Following the detection of co-expression protein clusters, each cluster will be annotated by JUMPn using the pathway enrichment analysis (Figure 1). The current version includes four commonly used pathway databases, including Gene Ontology (GO), KEGG, Hallmark, and Reactome. Users may also compile their own database in GMT format54, which can be uploaded into JUMPn. Integrating multiple databases for pathway enrichment analysis may provide more comprehensive views; however, the sizes of different pathway databases vary significantly, which may induce unwanted bias to certain (especially large) databases. Two solutions are provided within JUMPn. First, using a statistical approach, nominal p values are adjusted (or penalized) for multiple-hypothesis testing by the Benjamini-Hochberg method58, with a larger database requiring a more significant nominal p-value to reach the same adjusted p level than that from a small database. Second, JUMPn highlights the top significantly enriched pathway for each database separately, thus database-specific top enriched pathways are always displayed.
Similar to pathway enrichment analysis, a composite PPI network was compiled by combining STRING59,60, BioPlex61,62, and InWeb_IM63 databases. The BioPlex database was created using affinity purification followed by mass spectrometry in human cell lines, whereas the STRING and InWeb contain information from various sources. Therefore the STRING and InWeb databases were further filtered by the edge score to ensure high quality, with the cutoff determined by best fitting the scale-free criteria24. The final merged PPI network covers more than 20,000 human genes with ~1,100,000 edges (Table 2). This comprehensive interactome is included and published in a bundle with our JUMPn software for sensitive PPI analysis.
After the analysis is finished, JUMPn generates the publication table spreadsheet file ComprehensiveSummaryTables.xlsx, consisting of three individual sheets. The first sheet contains results of co-expression protein clusterswith one protein per row: the first column indicates the cluster membership of each input protein, and the remaining columns are copied from the user-input file, which contains the protein accession, gene names, protein description, and quantification of individual samples. The second sheet contains results of pathway enrichment analysis, displaying significant pathways enriched in each co-expression cluster. This table is first organized by different pathway databases, then sorted by co-expression clusters, functional pathways, the total number of pathway genes, the total number of genes in the individual cluster, the overlapped gene numbers and names, enrichment fold, Fisher exact test derived P-values and Benjamini-Hochberg false discovery rate. The third sheet contains results of PPI module analysis with one PPI module per row; its columns include the module name (defined by its co-expression membership and module ID, for example, Cluster1_Module1), the mapped proteins and numbers, as well as functional pathways that are defined by searching the module proteins against the pathway databases.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/62796/62796fig01.jpg" /> 
Figure 1: Workflow of JUMPn. Quantification matrix of the top variable of differentially expressed (DE) proteins are taken as input, and proteins are grouped into co-expression clusters by the WGCNA algorithm. Each co-expression is then annotated by pathway enrichment analysis and further superimposed onto the protein-protein interaction (PPI) network for densely connected protein module identifications. <a href="https://www.jove.com/files/ftp_upload/62796/62796fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/62796/62796fig02.jpg" /> 
Figure 2: JUMPn welcome page. <a href="https://www.jove.com/files/ftp_upload/62796/62796fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/62796/62796fig03.jpg" /> 
Figure 3: Input page of JUMPn. The page includes the input file upload panel and parameter configuration panels for co-expression clustering and PPI network analysis, respectively. <a href="https://www.jove.com/files/ftp_upload/62796/62796fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/62796/62796fig04.jpg" /> 
Figure 4: Example input file of quantification matrix. Columns include protein accession (or any unique IDs), GN (official gene symbols), protein description (or any user-provided information), followed by protein quantification of individual samples. <a href="https://www.jove.com/files/ftp_upload/62796/62796fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/62796/62796fig05.jpg" /> 
Figure 5: Co-expression cluster results reported by JUMPn. The co-expression clustering patterns (A), top enriched pathway heatmap across clusters (B), and detailed protein abundance for each cluster are shown (C). Users may select various display options and navigate between different clusters via the selection box. <a href="https://www.jove.com/files/ftp_upload/62796/62796fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/62796/62796fig06.jpg" /> 
Figure 6: PPI network analysis results reported by JUMPn. The global inter-module network is shown (A), followed by a subnetwork of individual modules (B) and its significantly enriched pathways (C). Users may select various display options and navigate between different clusters and modules via the selection box. <a href="https://www.jove.com/files/ftp_upload/62796/62796fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>% top proteins for analysis</td>
			<td># simulated modules</td>
			<td># detected modules</td>
			<td># recaptured modules1</td>
			<td>precision2</td>
			<td>recall3</td>
		</tr>
		<tr>
			<td colspan="6">Highly dynamic proteome (e.g., during T cell activation): 6 simulated modules from 50% proteome</td>
		</tr>
		<tr>
			<td>2</td>
			<td>6</td>
			<td>2</td>
			<td>2</td>
			<td>1</td>
			<td>0.33</td>
		</tr>
		<tr>
			<td>5</td>
			<td>6</td>
			<td>2</td>
			<td>2</td>
			<td>1</td>
			<td>0.33</td>
		</tr>
		<tr>
			<td>10</td>
			<td>6</td>
			<td>3</td>
			<td>3</td>
			<td>1</td>
			<td>0.5</td>
		</tr>
		<tr>
			<td>20</td>
			<td>6</td>
			<td>4</td>
			<td>4</td>
			<td>1</td>
			<td>0.67</td>
		</tr>
		<tr>
			<td>50</td>
			<td>6</td>
			<td>6</td>
			<td>6</td>
			<td>1</td>
			<td>1</td>
		</tr>
		<tr>
			<td>100</td>
			<td>6</td>
			<td>8</td>
			<td>5</td>
			<td>0.63</td>
			<td>0.83</td>
		</tr>
		<tr>
			<td colspan="6">Relatively stable proteome (e.g., during pathogenesis of AD): 3 simulated modules from 2% proteome</td>
		</tr>
		<tr>
			<td>1</td>
			<td>3</td>
			<td>1</td>
			<td>1</td>
			<td>1</td>
			<td>0.33</td>
		</tr>
		<tr>
			<td>2</td>
			<td>3</td>
			<td>3</td>
			<td>3</td>
			<td>1</td>
			<td>1</td>
		</tr>
		<tr>
			<td>5</td>
			<td>3</td>
			<td>8</td>
			<td>3</td>
			<td>0.38</td>
			<td>1</td>
		</tr>
		<tr>
			<td>10</td>
			<td>3</td>
			<td>13</td>
			<td>3</td>
			<td>0.23</td>
			<td>1</td>
		</tr>
		<tr>
			<td>20</td>
			<td>3</td>
			<td>19</td>
			<td>3</td>
			<td>0.16</td>
			<td>1</td>
		</tr>
		<tr>
			<td>50</td>
			<td>3</td>
			<td>71</td>
			<td>2</td>
			<td>0.03</td>
			<td>0.67</td>
		</tr>
		<tr>
			<td>100</td>
			<td>3</td>
			<td>169</td>
			<td>2</td>
			<td>0.01</td>
			<td>0.67</td>
		</tr>
		<tr>
			<td colspan="6">1A recaptured module is a detected module whose eigengene highly correlates (Pearson R &#62; 0.95) with one of the simulated eigengenes.</td>
		</tr>
		<tr>
			<td colspan="6">2precision = # recaptured modules / # detected modules</td>
		</tr>
		<tr>
			<td colspan="6">3recall = # recaptured modules / # simulated modules</td>
		</tr>
	</tbody>
</table>
Table 1: Simulation studies of co-expression cluster detection.&#160;
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>PPI networks</td>
			<td>No. of Nodes</td>
			<td>No. of Edges</td>
		</tr>
		<tr>
			<td>BioPlex 3.0 combined (293T+HCT116)</td>
			<td>14,551</td>
			<td>1,67,399</td>
		</tr>
		<tr>
			<td>InBio_Map_core_2016_09_12</td>
			<td>17,429</td>
			<td>6,08,166</td>
		</tr>
		<tr>
			<td>STRING (v11.0)</td>
			<td>18,954</td>
			<td>5,87,482</td>
		</tr>
		<tr>
			<td>Composite PPI network</td>
			<td>20,485</td>
			<td>11,52,607</td>
		</tr>
	</tbody>
</table>
Table 2: Statistics of human protein-protein interaction (PPI) networks. PPI networks are filtered by edge score to ensure high quality, with the score cutoff determined by best fitting the scale-free criteria.

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. ...

jumpn-streamlined-application-for-protein-co-expression-clustering

Universidad Científica del Sur

Research

JoVE Journal

Biochemistry

3.1K Views.  St. Jude Children’s Research Hospital. 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.

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

mRNA Interactome Capture from Plant Protoplasts

RNA-binding proteins (RBPs) determine the fates of RNAs. They participate in all RNA biogenesis pathways and especially contribute to post-transcriptional gene regulation (PTGR) of messenger RNAs (mRNAs). In the past few years, a number of mRNA-bound proteomes from yeast and mammalian cell lines have been successfully isolated through the use of a novel method called &#34;mRNA interactome capture,&#34; which allows for the identification of mRNA-binding proteins (mRBPs) directly from a physiological environment. The method is composed of in vivo ultraviolet (UV) crosslinking, pull-down and purification of messenger ribonucleoprotein complexes (mRNPs) by oligo(dT) beads, and the subsequent identification of the crosslinked proteins by mass spectrometry (MS). Very recently, by applying the same method, several plant mRNA-bound proteomes have been reported simultaneously from different Arabidopsis tissue sources: etiolated seedlings, leaf tissue, leaf mesophyll protoplasts, and cultured root cells. Here, we present the optimized mRNA interactome capture method for Arabidopsis thaliana leaf mesophyll protoplasts, a cell type that serves as a versatile tool for experiments that include various cellular assays. The conditions for optimal protein yield include the amount of starting tissue and the duration of UV irradiation. In the mRNA-bound proteome obtained from a medium-scale experiment (107 cells), RBPs noted to have RNA-binding capacity were found to be overrepresented, and many novel RBPs were identified. The experiment can be scaled up (109 cells), and the optimized method can be applied to other plant cell types and species to broadly isolate, catalog, and compare mRNA-bound proteomes in plants.

Here, we present an interactome capture protocol applied to Arabidopsis thaliana leaf mesophyll protoplasts. This method critically relies on in vivo UV crosslinking and allows for the isolation and identification of plant mRNA-binding proteins from a physiological environment.

RNA-binding proteins (RBPs) determine the fates of RNAs. They participate in all RNA biogenesis pathways and especially contribute to post-transcriptional ...

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Many exceptional advances have been made in mass spectrometry (MS)-based proteomics, with particular technical progress in liquid chromatography (LC) coupled to tandem mass spectrometry (LC-MS/MS) and isobaric labeling multiplexing capacity. Here, we introduce a deep-proteomics profiling protocol that combines 10-plex tandem mass tag (TMT) labeling with an extensive LC/LC-MS/MS platform, and post-MS computational interference correction to accurately quantitate whole proteomes. This protocol includes the following main steps: protein extraction and digestion, TMT labeling, 2-dimensional (2D) LC, high-resolution mass spectrometry, and computational data processing. Quality control steps are included for troubleshooting and evaluating experimental variation. More than 10,000 proteins in mammalian samples can be confidently quantitated with this protocol. This protocol can also be applied to the quantitation of post translational modifications with minor changes. This multiplexed, robust method provides a powerful tool for proteomic analysis in a variety of complex samples, including cell culture, animal tissues, and human clinical specimens.

We present a protocol to accurately quantitate proteins with isobaric labelling, extensive fractionation, bioinformatics tools, and quality control steps in combination with liquid chromatography interfaced to a high-resolution mass spectrometer.

Many exceptional advances have been made in mass spectrometry (MS)-based proteomics, with particular technical progress in liquid chromatography (LC) ...

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic interactions among multiple proteins in a protein interaction network is technically difficult. Here, we present a protocol for Quantitative Multiplex Immunoprecipitation (QMI), which allows quantitative assessment of fold changes in protein interactions based on relative fluorescence measurements of Proteins in Shared Complexes detected by Exposed Surface epitopes (PiSCES). In QMI, protein complexes from cell lysates are immunoprecipitated onto microspheres, and then probed with a labeled antibody for a different protein in order to quantify the abundance of PiSCES. Immunoprecipitation antibodies are conjugated to different MagBead spectral regions, which allows a flow cytometer to differentiate multiple parallel immunoprecipitations and simultaneously quantify the amount of probe antibody associated with each. QMI does not require genetic tagging and can be performed using minimal biomaterial compared to other immunoprecipitation methods. QMI can be adapted for any defined group of interacting proteins, and has thus far been used to characterize signaling networks in T cells and neuronal glutamate synapses. Results have led to new hypothesis generation with potential diagnostic and therapeutic applications. This protocol includes instructions to perform QMI, from the initial antibody panel selection through to running assays and analyzing data. The initial assembly of a QMI assay involves screening antibodies to generate a panel, and empirically determining an appropriate lysis buffer. The subsequent reagent preparation includes covalently coupling immunoprecipitation antibodies to MagBeads, and biotinylating probe antibodies so they can be labeled by a streptavidin-conjugated fluorophore. To run the assay, lysate is mixed with MagBeads overnight, and then beads are divided and incubated with different probe antibodies, and then a fluorophore label, and read by flow cytometry. Two statistical tests are performed to identify PiSCES that differ significantly between experimental conditions, and results are visualized using heatmaps or node-edge diagrams.

Quantitative Multiplex Immunoprecipitation (QMI) uses flow cytometry for sensitive detection of differences in the abundance of targeted protein-protein interactions between two samples. QMI can be performed using a small amount of biomaterial, does not require genetically engineered tags, and can be adapted for any previously defined protein interaction network.

Dynamic protein-protein interactions control cellular behavior, from motility to DNA replication to signal transduction. However, monitoring dynamic ...

Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling

Immunoaffinity purification mass spectrometry (IP-MS) has emerged as a robust quantitative method of identifying protein-protein interactions. This publication presents a complete interaction proteomics workflow designed for identifying low abundance protein-protein interactions from the nucleus that could also be applied to other subcellular compartments. This workflow includes subcellular fractionation, immunoprecipitation, sample preparation, offline cleanup, single-shot label-free mass spectrometry, and downstream computational analysis and data visualization. Our protocol is optimized for detecting compartmentalized, low abundance interactions that are difficult to identify from whole cell lysates (e.g., transcription factor interactions in the nucleus) by immunoprecipitation of endogenous proteins from fractionated subcellular compartments. The sample preparation pipeline outlined here provides detailed instructions for the preparation of HeLa cell nuclear extract, immunoaffinity purification of endogenous bait protein, and quantitative mass spectrometry analysis. We also discuss methodological considerations for performing large-scale immunoprecipitation in mass spectrometry-based interaction profiling experiments and provide guidelines for evaluating data quality to distinguish true positive protein interactions from nonspecific interactions. This approach is demonstrated here by investigating the nuclear interactome of the CMGC kinase, DYRK1A, a low abundance protein kinase with poorly defined interactions within the nucleus.

Described is a proteomics workflow for identifying protein interaction partners from a nuclear subcellular fraction using immunoaffinity enrichment of a given protein of interest and label-free mass spectrometry. The workflow includes subcellular fractionation, immunoprecipitation, filter aided sample preparation, offline cleanup, mass spectrometry, and a downstream bioinformatics pipeline.

Immunoaffinity purification mass spectrometry (IP-MS) has emerged as a robust quantitative method of identifying protein-protein interactions. This ...

Pulldown Assay Coupled with Co-Expression in Bacteria Cells as a Time-Efficient Tool for Testing Challenging Protein-Protein Interactions

Pulldown is an easy and widely used protein-protein interaction assay. However, it has limitations in studying protein complexes that do not assemble effectively in vitro. Such complexes may require co-translational assembly and the presence of molecular chaperones; either they form stable oligomers which cannot dissociate and re-associate in vitro or are unstable without a binding partner. To overcome these problems, it is possible to use a method based on the bacterial co-expression of differentially tagged proteins using a set of compatible vectors followed by the conventional pulldown techniques. The workflow is more time-efficient compared to traditional pulldown because it lacks the time-consuming steps of separate purification of interacting proteins and their following incubation. Another advantage is a higher reproducibility due to a significantly smaller number of steps and a shorter period of time in which proteins that exist within the in vitro environment are exposed to proteolysis and oxidation. The method was successfully applied for studying a number of protein-protein interactions when other in vitro techniques were found to be unsuitable. The method can be used for batch testing protein-protein interactions. Representative results are shown for studies of interactions between BTB domain and intrinsically disordered proteins, and of heterodimers of zinc-finger-associated domains.

Here, we describe a method for the bacterial co-expression of differentially tagged proteins using a set of compatible vectors, followed by the conventional pulldown techniques to study protein complexes that cannot assemble in vitro.

Pulldown is an easy and widely used protein-protein interaction assay. However, it has limitations in studying protein complexes that do not assemble ...

Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification (BiCAP)

The assembly of protein complexes is a central mechanism underlying the regulation of many cell signaling pathways. A major focus of biomedical research is deciphering how these dynamic protein complexes act to integrate signals from multiple sources in order to direct a specific biological response, and how this becomes deregulated in many disease settings. Despite the importance of this key biochemical mechanism, there is a lack of experimental techniques that can facilitate the specific and sensitive deconvolution of these multi-molecular signaling complexes.
Here this shortcoming is addressed through the combination of a protein complementation assay with a conformation-specific nanobody, which we have termed Bimolecular Complementation Affinity Purification (BiCAP). This novel technique facilitates the specific isolation and downstream proteomic characterization of any pair of interacting proteins, to the exclusion of un-complexed individual proteins and complexes formed with competing binding partners. 
The BiCAP technique is adaptable to a wide array of downstream experimental assays, and the high degree of specificity afforded by this technique allows more nuanced investigations into the mechanics of protein complex assembly than is currently possible using standard affinity purification techniques.

Dissecting Multi-protein Signaling Complexes by Bimolecular Complementation Affinity Purification BiCAP

This manuscript describes the protocol for Bimolecular Complementation Affinity Purification (BiCAP). This novel method facilitates the specific isolation and downstream proteomic characterization of any two interacting proteins, while excluding un-complexed individual proteins as well as complexes formed with competing binding partners.

The assembly of protein complexes is a central mechanism underlying the regulation of many cell signaling pathways. A major focus of biomedical research ...

Immunology and Infection

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Mass spectrometry (MS)-based proteomics enables comprehensive proteome analysis across a wide range of biological samples, including cells, tissues, and body fluids. Formalin-fixed, paraffin-embedded (FFPE) tissue sections, commonly used for long-term archiving, have emerged as valuable resources for proteomic studies. Beyond their storage benefits, researchers can isolate regions of interest (ROIs) from normal tissue regions through collaborative efforts with pathologists. Despite this potential, a streamlined approach for proteomic experiments encompassing ROI isolation, proteomic sample preparation, and MS analysis remains lacking. In this protocol, an integrated workflow that combines macrodissection of ROIs, suspension trapping-based sample preparation, and high-throughput MS analysis is presented. Through this approach, the ROIs of patients' FFPE tissues, consisting of benign serous cystic neoplasms (SCN) and precancerous intraductal papillary mucinous neoplasms (IPMN) diagnosed by pathologists, were macrodissected, collected, and analyzed, resulting in high proteome coverage. Furthermore, molecular differences between the two distinct pancreatic cystic neoplasms were successfully identified, thus demonstrating the applicability of this approach for advancing proteomic research with FFPE tissues.

Here, we establish a mass spectrometry-based proteomic method using isolated regions of interest in formalin-fixed, paraffin-embedded tissue sections. This protocol is used to analyze proteome from specific tissue areas in archived formalin-fixed, paraffin-embedded tissue sections.

Mass spectrometry (MS)-based proteomics enables comprehensive proteome analysis across a wide range of biological samples, including cells, tissues, and ...

Probing High-density Functional Protein Microarrays to Detect Protein-protein Interactions

High-density functional protein microarrays containing ~4,200 recombinant yeast proteins are examined for kinase protein-protein interactions using an affinity purified yeast kinase fusion protein containing a V5-epitope tag for read-out. Purified kinase is obtained through culture of a yeast strain optimized for high copy protein production harboring a plasmid containing a Kinase-V5 fusion construct under a GAL inducible promoter. The yeast is grown in restrictive media with a neutral carbon source for 6 hr followed by induction with 2% galactose. Next, the culture is harvested and kinase is purified using standard affinity chromatographic techniques to obtain a highly purified protein kinase for use in the assay. The purified kinase is diluted with kinase buffer to an appropriate range for the assay and the protein microarrays are blocked prior to hybridization with the protein microarray. After the hybridization, the arrays are probed with monoclonal V5 antibody to identify proteins bound by the kinase-V5 protein. Finally, the arrays are scanned using a standard microarray scanner, and data is extracted for downstream informatics analysis1,2 to determine a high confidence set of protein interactions for downstream validation in vivo.

Using protein microarrays containing nearly the entire S. cerevisiae proteome is probed for rapid unbiased interrogation of thousands of protein-protein interactions in parallel. This method can be utilized for protein-small molecule, posttranslational modification, and other assays in high-throughput.

High-density functional protein microarrays containing ~4,200 recombinant yeast proteins are examined for kinase protein-protein interactions using an ...

Biology

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often depend on its interactions with other proteins. Here, we describe a protocol for the yeast protein-fragment complementation assay (PCA), a powerful method to detect direct and proximal associations between proteins in living cells. The interaction between two proteins, each fused to a dihydrofolate reductase (DHFR) protein fragment, translates into growth of yeast strains in presence of the drug methotrexate (MTX). Differential fitness, resulting from different amounts of reconstituted DHFR enzyme, can be quantified on high-density colony arrays, allowing to differentiate interacting from non-interacting bait-prey pairs. The high-throughput protocol presented here is performed using a robotic platform that parallelizes mating of bait and prey strains carrying complementary DHFR-fragment fusion proteins and the survival assay on MTX. This protocol allows to systematically test for thousands of protein-protein interactions (PPIs) involving bait proteins of interest and offers several advantages over other PPI detection assays, including the study of proteins expressed from their endogenous promoters without the need for modifying protein localization and for the assembly of complex reporter constructs.

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

Proteins interact with each other and these interactions determine in a large part their functions. Protein interaction partners can be identified at high-throughput in vivo using a yeast fitness assay based on the dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA).

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often ...

Characterization of Neuronal Lysosome Interactome with Proximity Labeling Proteomics

Lysosomes frequently communicate with a variety of biomolecules to achieve the degradation and other diverse cellular functions. Lysosomes are critical to human brain function, as neurons are postmitotic and rely heavily on the autophagy-lysosome pathway to maintain cellular homeostasis. Despite advancements in the understanding of various lysosomal functions, capturing the highly dynamic communications between lysosomes and other cellular components is technically challenging, particularly in a high-throughput fashion. Here, a detailed protocol is provided for the recently published endogenous (knock-in) lysosome proximity labeling proteomic method in human induced pluripotent stem cell (hiPSC)-derived neurons. 
Both lysosomal membrane proteins and proteins surrounding lysosomes within a 10-20 nm radius can be confidently identified and accurately quantified in live human neurons. Each step of the protocol is described in detail, i.e., hiPSC-neuron culture, proximity labeling, neuron harvest, fluorescence microscopy, biotinylated protein enrichment, protein digestion, LC-MS analysis, and data analysis. In summary, this unique endogenous lysosomal proximity labeling proteomics method provides a high-throughput and robust analytical tool to study the highly dynamic lysosomal activities in live human neurons.

A neuronal lysosome proximity labeling proteomics protocol is described here to characterize the dynamic lysosomal microenvironment in human induced pluripotent stem cell-derived neurons. Lysosomal membrane proteins and proteins that interact with lysosomes (stably or transiently) can be accurately quantified in this method with excellent intracellular spatial resolution in live human neurons.

Lysosomes frequently communicate with a variety of biomolecules to achieve the degradation and other diverse cellular functions. Lysosomes are critical to ...