The authors wish to acknowledge Tessa Davis for important contributions to QMI assay development, and current and former members of the Smith and Schrum labs for technical guidance and intellectual input. This work was funded by NIMH grants R01 MH113545 and R00 MH 102244.

<ol>
	<li>Pawson, T., Nash, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein-protein+interactions+define+specificity+in+signal+transduction.">Protein-protein interactions define specificity in signal transduction.</a> Genes and Development. 14 (9), 1027-1047 (2000).</li><li>Rodr&#237;guez-Jorge, O., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cooperation+between+T+cell+receptor+and+Toll-like+receptor+5+signaling+for+CD4.">Cooperation between T cell receptor and Toll-like receptor 5 signaling for CD4.</a> Science Signaling. 12 (577), (2019).</li><li>Bugaj, L. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cancer+mutations+and+targeted+drugs+can+disrupt+dynamic+signal+encoding+by+the+Ras-Erk+pathway.">Cancer mutations and targeted drugs can disrupt dynamic signal encoding by the Ras-Erk pathway.</a> Science. 361 (6405), (2018).</li><li>Pawson, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Dynamic+control+of+signaling+by+modular+adaptor+proteins.">Dynamic control of signaling by modular adaptor proteins.</a> Current Opinion in Cell Biology. 19 (2), 112-116 (2007).</li><li>Hamm, H. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+many+faces+of+G+protein+signaling.">The many faces of G protein signaling.</a> Journal of Biological Chemistry. 273 (2), 669-672 (1998).</li><li>Lohse, M. J., Hofmann, K. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Spatial+and+Temporal+Aspects+of+Signaling+by+G-Protein-Coupled+Receptors.">Spatial and Temporal Aspects of Signaling by G-Protein-Coupled Receptors.</a> Molecular Pharmacology. 88 (3), 572-578 (2015).</li><li>Eltschinger, S., Loewith, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TOR+Complexes+and+the+Maintenance+of+Cellular+Homeostasis.">TOR Complexes and the Maintenance of Cellular Homeostasis.</a> Trends in Cell Biology. 26 (2), 148-159 (2016).</li><li>Papin, J. A., Hunter, T., Palsson, B. O., Subramaniam, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reconstruction+of+cellular+signalling+networks+and+analysis+of+their+properties.">Reconstruction of cellular signalling networks and analysis of their properties.</a> Nature Reviews Molecular Cell Biology. 6 (2), 99-111 (2005).</li><li>Komarova, N. L., Zou, X., Nie, Q., Bardwell, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+theoretical+framework+for+specificity+in+cell+signaling.">A theoretical framework for specificity in cell signaling.</a> Molecular Systems Biology. 1, 23 (2005).</li><li>Schrum, A. G., Gil, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Robustness+and+Specificity+in+Signal+Transduction+via+Physiologic+Protein+Interaction+Networks.">Robustness and Specificity in Signal Transduction via Physiologic Protein Interaction Networks.</a> Clinical and Experimental Pharmacology. 2 (3), (2012).</li><li>De Las Rivas, J., Fontanillo, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein-protein+interactions+essentials:+key+concepts+to+building+and+analyzing+interactome+networks.">Protein-protein interactions essentials: key concepts to building and analyzing interactome networks.</a> PLoS Computational Biology. 6 (6), 1000807 (2010).</li><li>Ron, D., Walter, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Signal+integration+in+the+endoplasmic+reticulum+unfolded+protein+response.">Signal integration in the endoplasmic reticulum unfolded protein response.</a> Nature Reviews Molecular Cell Biology. 8 (7), 519-529 (2007).</li><li>Bogdan, A. R., Miyazawa, M., Hashimoto, K., Tsuji, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regulators+of+Iron+Homeostasis:+New+Players+in+Metabolism,+Cell+Death,+and+Disease.">Regulators of Iron Homeostasis: New Players in Metabolism, Cell Death, and Disease.</a> Trends in Biochemical Sciences. 41 (3), 274-286 (2016).</li><li>Smith, S. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiplex+matrix+network+analysis+of+protein+complexes+in+the+human+TCR+signalosome.">Multiplex matrix network analysis of protein complexes in the human TCR signalosome.</a> Science Signaling. 9 (439), 7 (2016).</li><li>Schrum, A. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-sensitivity+detection+and+quantitative+analysis+of+native+protein-protein+interactions+and+multiprotein+complexes+by+flow+cytometry.">High-sensitivity detection and quantitative analysis of native protein-protein interactions and multiprotein complexes by flow cytometry.</a> Science's STKE. 2007 (389), (2007).</li><li>Brown, E. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clustering+the+autisms+using+glutamate+synapse+protein+interaction+networks+from+cortical+and+hippocampal+tissue+of+seven+mouse+models.">Clustering the autisms using glutamate synapse protein interaction networks from cortical and hippocampal tissue of seven mouse models.</a> Molecular Autism. 9 (1), 48 (2018).</li><li>Lautz, J. D., Brown, E. A., Williams VanSchoiack, A. A., Smith, S. E. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Synaptic+activity+induces+input-specific+rearrangements+in+a+targeted+synaptic+protein+interaction+network.">Synaptic activity induces input-specific rearrangements in a targeted synaptic protein interaction network.</a> Journal of Neurochemistry. 146 (5), 540-559 (2018).</li><li>Smith, S. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Signalling+protein+complexes+isolated+from+primary+human+skin-resident+T+cells+can+be+analysed+by+Multiplex+IP-FCM.">Signalling protein complexes isolated from primary human skin-resident T cells can be analysed by Multiplex IP-FCM.</a> Experimental Dermatology. 23 (4), 272-273 (2014).</li><li>Neier, S. C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+early+proximal+&#945;&#946;+TCR+signalosome+specifies+thymic+selection+outcome+through+a+quantitative+protein+interaction+network.">The early proximal &#945;&#946; TCR signalosome specifies thymic selection outcome through a quantitative protein interaction network.</a> Science Immunology. 4 (32), (2019).</li><li>Davis, T. R., Schrum, A. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=IP-FCM:+immunoprecipitation+detected+by+flow+cytometry.">IP-FCM: immunoprecipitation detected by flow cytometry.</a> Journal of Visualized Expiriments. (46), (2010).</li><li>Smith, S. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=IP-FCM+measures+physiologic+protein-protein+interactions+modulated+by+signal+transduction+and+small-molecule+drug+inhibition.">IP-FCM measures physiologic protein-protein interactions modulated by signal transduction and small-molecule drug inhibition.</a> PLoS One. 7 (9), 45722 (2012).</li><li>Lautz, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Activity-dependent+changes+in+synaptic+protein+complex+composition+are+consistent+in+different+detergents+despite+differential+solubility.">Activity-dependent changes in synaptic protein complex composition are consistent in different detergents despite differential solubility.</a> BioRXiv. , (2019).</li><li>Langfelder, P., Horvath, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=WGCNA:+an+R+package+for+weighted+correlation+network+analysis.">WGCNA: an R package for weighted correlation network analysis.</a> BMC Bioinformatics. 9, 559 (2008).</li><li>Schrum, A. G., Gil, D., Turka, L. A., Palmer, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Physical+and+functional+bivalency+observed+among+TCR/CD3+complexes+isolated+from+primary+T+cells.">Physical and functional bivalency observed among TCR/CD3 complexes isolated from primary T cells.</a> Journal of Immunology. 187 (2), 870-878 (2011).</li></ol>

The authors have no conflicts of interest to disclose.

The QMI assay requires substantial investment in antibody panel development, equipment and reagents, but once the assay is established, one can collect high-dimensional data observing protein interaction networks as they respond to experimentally-controlled stimuli. Technically, QMI requires careful pipetting and tracking of sample and antibody well locations. Carefully labeling the assay plates is useful, as is making a detailed template of well locations on paper, which is then saved for data analysis. The importance o...

Dynamic protein-protein interactions constitute the molecular signaling cascades and motile structures that are the functional basis of most cellular physiology1. These processes are often depicted as linear signaling pathways that switch between steady states based on single inputs, but experimental and modeling data clearly show that they function as integrated networks2,3,4. In the case of G proteins, different receptors often have the ability to activate the same G protein, and a single receptor can also activate more than one type of G protein5,6. In order for the relatively small number of G protein classes to specifically modulate a vast array of cellular functions such as synaptic transmission, hormone regulation, and cell migration, cells must both integrate and differentiate these signals4,5. Evidence has shown that this signal specificity, for G proteins as well as others, is primarily derived on the basis of finely tuned protein-protein interactions and their temporal dynamics1,3,4,5,6,7. Because signaling networks are comprised of dynamic protein complexes with multiple inputs, outputs, and feedback loops, a single perturbation has the opportunity to alter the overall homeostatic balance of a cell&#39;s physiology4,7. It is now widely agreed that signaling should be examined from a network perspective in order to better understand how the integration of multiple inputs controls discrete cellular functions in health and disease7,8,9,10,11,12,13. In light of this, Quantitative Multiplex Immunoprecipitation (QMI) was developed to gather medium-throughput, quantitative data about fold changes in dynamic protein interaction networks.
QMI is an antibody-based assay in which cell lysate is incubated with a panel of immunoprecipitation antibodies that are covalently coupled to magnetic beads containing distinct ratios of fluorescent dyes. Having specific antibodies coupled to distinct magnetic bead classes allows for simultaneous co-immunoprecipitation of multiple target proteins from the same lysate. Following immunoprecipitation (IP), magnetic beads are incubated with a second, fluorophore-conjugated probe antibody (or biotinylated antibody in conjunction with fluorophore-conjugated streptavidin). Co-associations between the proteins recognized by each IP antibody-probe antibody pair, or PiSCES (proteins in shared complexes detected by exposed surface epitopes), are then detected by flow cytometry and can be quantitatively compared between different sample conditions14. Illustrations in Figure 1 show the steps involved in running a QMI assay, including a diagram of magnetic beads with immunoprecipitated protein complexes labeled by fluorescently conjugated probe antibodies (Figure 1C).
The sensitivity of QMI depends on the protein concentration of the lysate relative to the number of magnetic beads used for immunoprecipitation, and achieving a resolution to detect 10% fold changes requires only a small amount of starting material compared to other co-IP methods14,15. For example, the amount of starting material used in QMI is similar to that required for a sandwich Enzyme-Linked ImmunoSorbent Assay (ELISA), but multiple interactions are detected in a single QMI assay. QMI assays using 20 IPs and 20 probe targets have been performed using 1-5 x 105 primary T cells isolated from a 4 mm skin biopsy, P2 synaptosomal preparations from a 3 mm coronal section of mouse prefrontal cortex, or 3 x 106 cultured mouse primary cortical neurons14,16,17. This sensitivity makes QMI useful for analysis of cells or tissue with limited availability, such as clinical samples.
QMI can be adapted for any previously defined protein interaction network (provided that antibodies are available), and to date has been developed to analyze the T cell antigen receptor (TCR) signalosome and a subset of proteins at glutamatergic synapses in neurons17,18. In studies of T cell receptor signaling, QMI was first used to identify stimulation-induced changes in PiSCES, and then to distinguish autoimmune patients from a control group, detect endogenous autoimmune signaling, and finally to generate a hypothesis involving an unbalanced disease-associated subnetwork of interactions14. More recently, the same QMI panel was used to determine that thymocyte selection is determined by quantitative rather than qualitative differences in TCR-associated protein signaling19. In neurons, QMI was used to describe input-specific rearrangement of a protein interaction network for distinct types of input signals in a manner which supports newly emerging models of synaptic plasticity17. Additionally, this synaptic QMI panel was used to identify differences in seven mouse models of autism, cluster the models into subgroups based on their PiSCES biosignatures, and accurately hypothesize a shared molecular deficit that was previously unrecognized in one of the models16. A similar approach could be used to screen for other subgroups that might respond to different drug treatments, or assign drugs to specific responsive subgroups. QMI has potential applications in diagnostics, patient sub-typing, and drug development, in addition to basic science.
To assemble a QMI antibody panel, initial antibody screening and selection protocols are described in Section 1, below. Once antibody panels are identified, protocols for conjugation of the selected antibodies to magnetic beads for IP, and for biotinylation of the selected probe antibodies, are described in Section 2. The protocol for running the QMI assay on cell or tissue lysates is described in Section 3. Finally, since a single experiment can generate ~5 x 105 individual datapoints, instructions and computer codes to assist in data processing, analysis, and visualization are provided in Section 4. An overview of the workflow described in sections 2-4 is shown in Figure 1.

<table><tbody><tr><td>96-well flat bottomed plates</td><td>Bio Rad</td><td>171025001</td><td></td></tr><tr><td>96-well PCR plates</td><td>VWR</td><td>82006-704</td><td></td></tr><tr><td>Bioplex 200 System with HTF</td><td>Bio Rad</td><td>171000205</td><td>modiefied to keep partially refrigerated, see Figure S1 for details</td></tr><tr><td>Bio-Plex Pro Wash Station</td><td>Bio Rad</td><td>30034376</td><td></td></tr><tr><td>BSA</td><td>Sigma</td><td></td><td></td></tr><tr><td>CML beads</td><td>Invitrogen</td><td>C37481</td><td></td></tr><tr><td>EDTA</td><td>Sigma</td><td>E6758</td><td></td></tr><tr><td>EZ-Link Sulfo-NHS-Biotin</td><td>Thermo Scientific</td><td>A39256</td><td></td></tr><tr><td>MagPlex Microspheres</td><td>Luminex</td><td>MC12xxx-01</td><td>xxx is the 3 digit bead region</td></tr><tr><td>Melon Gel IgG Spin Purification Kit</td><td>Thermo Scientific</td><td>45206</td><td>used for antibody purification</td></tr><tr><td>MES</td><td>Sigma</td><td>M3671</td><td></td></tr><tr><td>Microplate film, non-sterile</td><td>USA Scientific</td><td>2920-0000</td><td></td></tr><tr><td>Phosphotase inhibitor cocktail #2</td><td>Sigma</td><td>P5726</td><td></td></tr><tr><td>Protease inhibitor cocktail</td><td>Sigma</td><td>P8340</td><td></td></tr><tr><td>Sandwich Prep Refrigerator</td><td>Norlake</td><td>SMP 36 15</td><td>for custom refrigeration of Bioplex 200</td></tr><tr><td>Sodium fluoride</td><td>Sigma</td><td>201154</td><td></td></tr><tr><td>Sodium orthovanadate</td><td>Sigma</td><td>450243</td><td></td></tr><tr><td>Streptavidin-PE</td><td>BioLegend</td><td>405204</td><td></td></tr><tr><td>Sulfo NHS</td><td>Thermo Scientific</td><td>A39269</td><td></td></tr><tr><td>Tris</td><td>Fisher Scientific</td><td>BP152</td><td></td></tr></tbody></table>

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.

Antibody Screening 
Figure 2B shows the results of a screen for the protein Connexin36. Most IP_probe combinations produce no signal over IgG controls. IP with the monoclonal antibody 1E5 and probe with either 1E5 or the polyclonal antibody 6200 produces a rightward shift in the bead distribution compared to IgG controls. Here, IP 1E5 and probe 6200poly were selected to avoid using the same antibody as IP and probe, both to reduce the probability of a n...

Watch this Scientific Journal Video about Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation at JoVE.com

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

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 ...

quantification-protein-interaction-network-dynamics-using-multiplexed

Research

Education

JoVE Core

JoVE Journal

Molecular Biology

Biochemistry

Unit 1

Protein Function

SCIENTISTS IN ACTION

8.3K Views.  Seattle Children's Research Institute. Kwantitatieve multiplexed co-immunoprecipitatie, of QMI, stelt gebruikers in staat om tegelijkertijd honderden binaire interacties te meten in een vooraf gedefinieerd eiwitinteractienetwerk. Door meerdere co-IP's tegelijk in hetzelfde lysaat uit te voeren, kunnen bioinformaticatechnieken vervolgens op gecoördineerde wijze onderzoeken hoe groepen co-associaties veranderen. De test is tijdrovend om te ontwikkelen, maar zodra het QMI-paneel in de hand netwerk schaal gegevens kunnen worden verza...

Video: Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Visualization of Protein-protein Interaction in Nuclear and Cytoplasmic Fractions by Co-immunoprecipitation and In Situ Proximity Ligation Assay

Protein-protein interactions are involved in thousands of cellular processes and occur in distinct spatial context. Traditionally, co-immunoprecipitation is a popular technique to detect protein-protein interactions. Subsequent Western blot analysis is the most common method to visualize co-immunoprecipitated proteins. Recently, the proximity ligation assay has become a powerful tool to visualize protein-protein interactions in situ and provides the possibility to quantify protein-protein interactions by this method. Similar to conventional immunocytochemistry, the proximity ligation assay technique is also based on the accessibility of primary antibodies to the antigens, but in contrast, proximity ligation assay detects protein-protein interactions with a unique technique involving rolling-circle PCR, while conventional immunocytochemistry only shows co-localization of proteins.
Nuclear factor 90 (NF90) and RNA-binding motif protein 3 (RBM3) have been previously demonstrated as interacting partners. They are predominantly localized in the nucleus, but also migrate into the cytoplasm and regulate signaling pathways in the cytoplasmic compartment. Here, we compared NF90-RBM3 interaction in both the nucleus and the cytoplasm by co-immunoprecipitation and proximity ligation assay. In addition, we discussed the advantages and limitations of these two techniques in visualizing protein-protein interactions in respect to spatial distribution and the properties of protein-protein interactions.

Visualization of Protein-protein Interaction in Nuclear and Cytoplasmic Fractions by Co-immunoprecipitation and In Situ Proximity Ligation Assay

Protein-protein interactions can occur in both the nucleus and the cytoplasm of a cell. To investigate these interactions, traditional co-immunoprecipitation and modern proximity ligation assay are applied. In this study, we compare these two methods to visualize the distribution of NF90-RBM3 interactions in the nucleus and the cytoplasm.

Protein-protein interactions are involved in thousands of cellular processes and occur in distinct spatial context. Traditionally, co-immunoprecipitation ...

Analysis of Protein-protein Interactions and Co-localization Between Components of Gap, Tight, and Adherens Junctions in Murine Mammary Glands

Cell-cell interactions play a pivotal role in preserving tissue integrity and the barrier between the different compartments of the mammary gland. These interactions are provided by junctional proteins that form nexuses between adjacent cells. Junctional protein mislocalization and reduced physical associations with their binding partners can result in the loss of function and, consequently, to organ dysfunction. Thus, identifying protein localization and protein-protein interactions (PPIs) in normal and disease-related tissues is essential to finding new evidences and mechanisms leading to the progression of diseases or alterations in developmental status. This manuscript presents a two-step method to evaluate PPIs in murine mammary glands. In protocol section 1, a method to perform co-immunofluorescence (co-IF) using antibodies raised against the proteins of interest, followed by secondary antibodies labeled with fluorochromes, is described. Although co-IF allows for the demonstration of the proximity of the proteins, it does make it possible to study their physical interactions. Therefore, a detailed protocol for co-immunoprecipitation (co-IP) is provided in protocol section 2. This method is used to determine the physical interactions between proteins, without confirming whether these interactions are direct or indirect. In the last few years, co-IF and co-IP techniques have demonstrated that certain components of intercellular junctions co-localize and interact together, creating stage-dependent junctional nexuses that vary during mammary gland development.

Intercellular junctions are requisites for mammary gland stage-specific functions and development. This manuscript provides a detailed protocol for the study of protein-protein interactions (PPIs) and co-localization using murine mammary glands. These techniques allow for the investigation of the dynamics of the physical association between intercellular junctions at different developmental stages.

Cell-cell interactions play a pivotal role in preserving tissue integrity and the barrier between the different compartments of the mammary gland. These ...

Developmental Biology

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 ...

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 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. ...

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 ...

Expanding the Mass Photometry Analysis Concentration Range with Microfluidics

Analysis of Protein Complex Formation at Micromolar Concentrations by Coupling Microfluidics with Mass Photometry

Mass photometry is a versatile mass measurement technology that enables the study of biomolecular interactions and complex formation in solution without labels. Mass photometry is generally suited to analyzing samples in the 100 pM-100 nM concentration range. However, in many biological systems, it is necessary to measure more concentrated samples to study low-affinity or transient interactions. Here, we demonstrate a method that effectively expands the range of sample concentrations that can be analyzed by mass photometry from nanomolar to tens of micromolar. 
In this protocol, mass photometry is combined with a novel microfluidics system to investigate the formation of protein complexes in solution in the micromolar concentration range. With the microfluidics system, users can maintain a sample at a desired higher concentration followed by dilution to the nanomolar range - several milliseconds prior to the mass photometry measurement. Due to the speed of the dilution, data is obtained before the equilibrium of the sample has shifted (i.e., dissociation of the complex). 
The technique is applied to measure interactions between an immunoglobulin G (IgG) antibody and the neonatal Fc receptor, showing the formation of high-order complexes that were not quantifiable with static mass photometry measurements. 
In conclusion, the combination of mass photometry and microfluidics makes it possible to characterize samples in the micromolar concentration range and is proficient in measuring biomolecular interactions with weaker affinities. These capabilities can be applied in a range of contexts - including the development and design of biotherapeutics - enabling thorough characterization of diverse protein-protein interactions.

Author Spotlight: Characterization of Low-Affinity Protein Interactions in Solution Using MassFluidix Technology

This protocol combines mass photometry with a novel microfluidics system to investigate low-affinity protein-protein interactions. This approach is based on the rapid dilution of highly concentrated complexes in solution, which enables low-affinity measurements and broadens the applicability of mass photometry.

Mass photometry is a versatile mass measurement technology that enables the study of biomolecular interactions and complex formation in solution without ...

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

Protein-protein interactions are fundamental for many biological processes in the cell. Therefore, their characterization plays an important role in current research and a plethora of methods for their investigation is available1. Protein-protein interactions often are highly dynamic and may depend on subcellular localization, post-translational modifications and the local protein environment2. Therefore, they should be investigated in their natural environment, for which co-immunoprecipitation approaches are the method of choice3. Co-precipitated interaction partners are identified either by immunoblotting in a targeted approach, or by mass spectrometry (LC-MS/MS) in an untargeted way. The latter strategy often is adversely affected by a large number of false positive discoveries, mainly derived from the high sensitivity of modern mass spectrometers that confidently detect traces of unspecifically precipitating proteins. A recent approach to overcome this problem is based on the idea that reduced amounts of specific interaction partners will co-precipitate with a given target protein whose cellular concentration is reduced by RNAi, while the amounts of unspecifically precipitating proteins should be unaffected. This approach, termed QUICK for QUantitative Immunoprecipitation Combined with Knockdown4, employs Stable Isotope Labeling of Amino acids in Cell culture (SILAC)5 and MS to quantify the amounts of proteins immunoprecipitated from wild-type and knock-down strains. Proteins found in a 1:1 ratio can be considered as contaminants, those enriched in precipitates from the wild type as specific interaction partners of the target protein. Although innovative, QUICK bears some limitations: first, SILAC is cost-intensive and limited to organisms that ideally are auxotrophic for arginine and/or lysine. Moreover, when heavy arginine is fed, arginine-to-proline interconversion results in additional mass shifts for each proline in a peptide and slightly dilutes heavy with light arginine, which makes quantification more tedious and less accurate5,6. Second, QUICK requires that antibodies are titrated such that they do not become saturated with target protein in extracts from knock-down mutants.
Here we introduce a modified QUICK protocol which overcomes the abovementioned limitations of QUICK by replacing SILAC for 15N metabolic labeling and by replacing RNAi-mediated knock-down for affinity modulation of protein-protein interactions. We demonstrate the applicability of this protocol using the unicellular green alga Chlamydomonas reinhardtii as model organism and the chloroplast HSP70B chaperone as target protein7 (Figure 1). HSP70s are known to interact with specific co-chaperones and substrates only in the ADP state8. We exploit this property as a means to verify the specific interaction of HSP70B with its nucleotide exchange factor CGE19.

A Protocol for the Identification of Protein-protein Interactions Based on 15N Metabolic Labeling, Immunoprecipitation, Quantitative Mass Spectrometry and Affinity Modulation

We present a variation of the QUICK (QUantitative Immunoprecipitation Combined with Knockdown) approach that was introduced previously to distinguish between true and false protein-protein interactions. Our approach is based on 15N metabolic labeling, the modulation of affinities of protein-protein interactions by the presence/absence of ATP, immunoprecipitation, and quantitative mass spectrometry.

Protein-protein interactions are fundamental for many biological processes in the cell. Therefore, their characterization plays an important role in ...

Biology

Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics

Quantitative proteomics combined with immuno-affinity purification, SILAC immunoprecipitation, represent a powerful means for the discovery of novel protein:protein interactions. By allowing the accurate relative quantification of protein abundance in both control and test samples, true interactions may be easily distinguished from experimental contaminants. Low affinity interactions can be preserved through the use of less-stringent buffer conditions and remain readily identifiable. This protocol discusses the labeling of tissue culture cells with stable isotope labeled amino acids, transfection and immunoprecipitation of an affinity tagged protein of interest, followed by the preparation for submission to a mass spectrometry facility. This protocol then discusses how to analyze and interpret the data returned from the mass spectrometer in order to identify cellular partners interacting with a protein of interest. As an example this technique is applied to identify proteins binding to the eukaryotic translation initiation factors: eIF4AI and eIF4AII.

SILAC immunoprecipitation experiments represent a powerful means for discovering novel protein:protein interactions. By allowing the accurate relative quantification of protein abundance in both control and test samples, true interactions may be easily distinguished from experimental contaminants, and low affinity interactions preserved through use of less-stringent buffer conditions.

Quantitative proteomics combined with immuno-affinity purification, SILAC immunoprecipitation, represent a powerful means for the discovery of novel ...

Protein Interaction Analysis in HEK-293 Cells Using Co-Immunoprecipitation

Immunoprecipitation with an Anti-Epitope Tag Affinity Gel to Study Protein-Protein Interactions

Protein-protein interactions (PPIs) play a pivotal role in biological phenomena, such as cellular organization, intracellular signal transduction, and transcriptional regulation. Therefore, understanding PPIs is an important starting point for further investigation of the function of the target protein. In this study, we propose a simple method to determine the binding of two target proteins by introducing mammalian expression vectors into HEK-293 cells using the polyethylenimine method, lysing the cells in homemade protein lysis buffer, and pulling down the target proteins on an epitope tag affinity gel. In addition, the PPI between the various epitope tag fused proteins can be confirmed by using affinity antibodies against each tag instead of the epitope tag affinity gel. This protocol could also be used to verify various PPIs, including nuclear extracts, from other cell lines. Therefore, it can be used as a basic method in a variety of PPI experiments. Proteins degrade by extended time course and repeated freeze-thaw cycles. Therefore, cell lysis, immunoprecipitation, and immunoblotting should be performed as seamlessly as possible.

Author Spotlight: Unraveling the Molecular Mechanisms of Brown and Beige Adipocyte Regulation

Protein-protein interactions are important for elucidating the function of target proteins, and co-immunoprecipitation (co-IP) can easily confirm PPIs. We transiently transfected a plasmid encoding an epitope-tagged protein into HEK-293 cells and developed an immunoprecipitation method to easily confirm the binding of two target proteins.

Protein-protein interactions (PPIs) play a pivotal role in biological phenomena, such as cellular organization, intracellular signal transduction, and ...

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein, and edges are lines that connect two interacting proteins. These networks provide a way to visualize the complexity of the protein-protein interactions in a system. These maps can include both stable interactions, like the ones formed in protein complexes, as well as transient interactions. The protein interactions occurring in a cell, an organism, or a specific biological context can be collectively called an &#8216;interactome&#8217;.
Protein networks can be studied using various biochemical and computational methods. One of the first steps in studying protein interactions is to isolate a protein of interest along with the other associated proteins. This can be carried out by tagging the protein of interest with an affinity tag, such as a histidine tag. This tag can then be used to separate the protein along with the other proteins using affinity chromatography. Isolated proteins are then digested with a protease, such as trypsin,&#160; and then analyzed using liquid chromatography-mass spectrometry (LC-MS). The peptide mass can then be compared to a database with known protein sequences to determined its identity.
Computationally, protein-protein interactions can be analyzed using databases as well as prediction tools. There are various databases, such as IntAct managed by EMBL-EBI, that consist of experimentally validated and predicted protein interactions. Other tools like STRING by the Swiss Institute of Bioinformatics can be used to predict these interaction networks.
The study of protein networks can lead to scientific discoveries, such as determining the function of an unknown protein. Examining the changes in these networks can help elucidate the differences between healthy and diseased cells. This information can also be used for crucial applications such as designing drugs for the treatment of diseases.&#160; Analysis of protein networks can identify highly connected nodes that may be crucial for cellular survival, which can be targeted in cancer and diseases where cell death is desired but would be unsuitable for most diseases. On the other hand, less connected nodes that only interact with a few specific pathways may be targeted if a specific cell function is affected, and designing drugs that interact with these less connected nodes may lead to fewer side effects.

Protein Networks and PredictionTools: IntAct and STRING

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other ...

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Samenvatting

Meer video's verkennen