Name: quantification of protein interaction network dynamics using multiplexed co-immunoprecipitation
Uploaded: 2019-08-21T14:00:00
Description: Watch this Scientific Journal Video about Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation at JoVE.com

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.

1. Assay design
<ol>
	<li>Candidate antibody preparation
	<ol>
		<li>For each protein of interest, choose 3 to 5 antibodies to screen. When possible, use monoclonal antibodies that recognize different epitopes. Also include one non-specific control antibody.</li>
		<li>To remove Tris, perform buffer exchange by adding the antibody to a 30 kDa spin filter, spinning down to its minimum volume, adding 500 &#181;L of phosphate-buffered saline (PBS), and repeating 3 times. To remove carrier proteins, perform antibody purifi...

<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 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 border="0" cellpadding="0" cellspacing="0" style="width:644px;" width="643">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">96-well flat bottomed plates</td>
			<td style="width:121px;">Bio Rad</td>
			<td style="width:119px;">171025001</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">96-well PCR plates</td>
			<td style="width:121px;">VWR</td>
			<td style="width:119px;">82006-704</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">Bioplex 200 System with HTF</td>
			<td style="width:121px;">Bio Rad</td>
			<td style="width:119px;">171000205</td>
			<td>modiefied to keep partially refrigerated, see Figure S1 for details</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">Bio-Plex Pro Wash Station</td>
			<td style="width:121px;">Bio Rad</td>
			<td style="width:119px;">30034376</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">BSA</td>
			<td style="width:121px;">Sigma</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">CML beads</td>
			<td style="width:121px;">Invitrogen</td>
			<td style="width:119px;">C37481</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">EDTA</td>
			<td style="width:121px;">Sigma</td>
			<td style="width:119px;">E6758</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">EZ-Link Sulfo-NHS-Biotin</td>
			<td style="width:121px;">Thermo Scientific</td>
			<td style="width:119px;">A39256</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">MagPlex Microspheres</td>
			<td style="width:121px;">Luminex</td>
			<td style="width:119px;">MC12xxx-01</td>
			<td>xxx is the 3 digit bead region</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">Melon Gel IgG Spin Purification Kit</td>
			<td style="width:121px;">Thermo Scientific</td>
			<td style="width:119px;">45206</td>
			<td>used for antibody purification</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;width:237px;">MES</td>
			<td style="width:121px;">Sigma</td>
			<td style="width:119px;">M3671</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">Microplate film, non-sterile</td>
			<td style="width:121px;">USA Scientific</td>
			<td style="width:119px;">2920-0000</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">Phosphotase inhibitor cocktail #2</td>
			<td style="width:121px;">Sigma</td>
			<td>P5726</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">Protease inhibitor cocktail</td>
			<td style="width:121px;">Sigma</td>
			<td>P8340</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">Sandwich Prep Refrigerator</td>
			<td style="width:121px;">Norlake</td>
			<td>SMP 36 15</td>
			<td>for custom refrigeration of Bioplex 200</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">Sodium fluoride</td>
			<td>Sigma</td>
			<td style="width:119px;">201154</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">Sodium orthovanadate</td>
			<td style="width:121px;">Sigma</td>
			<td>450243</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">Streptavidin-PE</td>
			<td style="width:121px;">BioLegend</td>
			<td style="width:119px;">405204</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">Sulfo NHS</td>
			<td style="width:121px;">Thermo Scientific</td>
			<td style="width:119px;">A39269</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:237px;">Tris</td>
			<td style="width:121px;">Fisher Scientific</td>
			<td style="width:119px;">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 ...

Quantitative multiplexed co-immunoprecipitation, or QMI, enables users to simultaneously measure hundreds of binary interactions in a predefined protein interaction network. By performing multiple co-IPs simultaneously in the same lysate, bioinformatics techniques can then examine how groups of co-associations change in a coordinated manner. The assay is time consuming to develop, but once the QMI panel is in hand network scale data can be collected about signal transduction systems in a relatively simply overnight assay.QMI requires the accurate pipetting of different samples and reagents into 96 well plates. Careful notes should be taken when setting up and running each assay to prevent errors. For sample preparation and immunoprecipitation, begin by lysing the sample in a suitable detergent with protease and phosphatase inhibitors for a 15 minute incubation on ice.At the end of the incubation, spin down the sample to remove the membranes and debris and perform a BCA assay on the supernatant to determine the protein concentration according to standard protocols. After normalizing the protein concentrations, prepare a magnetic bead master mix that contains approximately 250 magnetic beads of each class per well. Use magnetic separation to wash the magnetic bead mix two times in Fly P buffer before resuspending the beads in 20 microliters of Fly P buffer per sample.After a thorough pipetting, aliquot 20 microliters of beads into one ice cold microcentrifuge tube per sample and add equal volumes of lysate with normalized protein concentrations to each tube for immunoprecipitation. Then divide each lysate sample into one tube for each plate being run and immunoprecipitate the samples on a rotator at four degrees Celsius overnight protected from light. The next morning use a magnetic bead rack to remove the lysate from the first set of tubes and wash the beads two times in 500 microliters of ice cold Fly P buffer per wash.After calculating the resuspension volume, resuspend the immunoprecipitates in the calculated volume of ice cold Fly P buffer. Thoroughly resuspend the magnetic beads by gentle pipetting and distribute 25 microliters of beads per well across a flat bottomed 96 well plate on ice. In a different 96 well plate, dilute by attinilated probe antibodies to a two times working concentration.Use a multichannel pipette to distribute 25 microliters of each probe antibody dilution into the appropriate wells of the magnetic bead containing assay plate and shake at high speed on a horizontal plate shaker to mix and resuspend the magnetic beads. After mixing, reduce the speed for a one hour incubation at four degrees Celsius protected from light. At the end of the incubation, wash the plate three times with fresh Fly P buffer per wash on a magnetic plate washer at four degrees Celsius.After the last wash, resuspend the beads in 50 microliters of streptavidin PE diluted one to 200 in Fly P buffer. Shake the mix and resuspend the beads before incubating the plate at four degrees Celsius for 30 minutes protected from light. At the end of the incubation, wash the plate three times with Fly P buffer on a magnetic plate washer at four degrees Celsius and resuspend the beads in 125 microliters of Fly P buffer.Shake the plate for one minute at 900 rotations to thoroughly resuspend the beads and load the plate into a refridgerated flow cytometer. In the flow cytometer software, select High RP1 Target setting, a stop condition of 1, 000 beads per region, and a sample volume of 80 microliters. Pause the run halfway through and resuspend the beads to prevent settling.At the end of the run, export the data files in the XML format. To perform ANC analysis, fill in the ANC input file to reflect the details of the experimental design and run the program, which will write a CSV file into the active directory. The file ending in hits.csv will report the protein co-associations that are significantly different at a false positive level of 0.05 between the control and experimental conditions in at least three out of four experimental replicates. For weighted correlation network analysis, paste the column titles of the data file output by Matlab ending in mfi. csv into the first column of a new spreadsheet and add the columns Experiment for experiment number and Treatment for experimental treatment and any other variables to be analyzed.Save this file as traits. csv and open R Studio. Set the working directory to a folder containing the mfi.csv and traits. csv files, highlight sections of code, and hit Command Enter to run the R commands as indicated in the commented Command file. The weighted correlation network analysis modules significantly correlated with each experimental trait will be output as a graphic file and the correlation of each interaction with each module will be output as a CSV file.For each interaction in the ANC output file, identify if that interaction is also significant by CNA and create a new column that indicates if each ANC hit is also a CNA hit. Convert the values to log two fold change and calculate the average value for all ANC union CNA hits. Finally, make a new spreadsheet with each ANC union CNA hit listed by its immunoprecipitation target in one column, its probe target in the second column, and its fold change value in the third column.Import this file into Cytoscape as a network to create a node edge diagram. High confidence protein interactions identified by the two independent statistical approaches are visualized as node edge diagrams using the open source software, Cytoscape, or as heatmaps using the R code and analysis instructions included in the supplementary material. Throughout the protocol, users need to take care to pipette accurately, to keep meticulous records, and to keep the samples cold at all times.We have used QMI to understand how signal transduction pathways differentiate between different input types and integrate information from multiple receptors.

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

Research

Education

JoVE Core

JoVE Journal

Molecular Biology

Biochemistry

Unit 1

Protein Function

SCIENTISTS IN ACTION

Method for Identifying Small Molecule Inhibitors of the Protein-protein Interaction Between HCN1 and TRIP8b

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are expressed ubiquitously throughout the brain, where they function to regulate the excitability of neurons. The subcellular distribution of these channels in pyramidal neurons of hippocampal area CA1 is regulated by tetratricopeptide repeat-containing Rab8b interacting protein (TRIP8b), an auxiliary subunit. Genetic knockout of HCN pore forming subunits or TRIP8b, both lead to an increase in antidepressant-like behavior, suggesting that limiting the function of HCN channels may be useful as a treatment for Major Depressive Disorder (MDD). Despite significant therapeutic interest, HCN channels are also expressed in the heart, where they regulate rhythmicity. To circumvent off-target issues associated with blocking cardiac HCN channels, our lab has recently proposed targeting the protein-protein interaction between HCN and TRIP8b in order to specifically disrupt HCN channel function in the brain. TRIP8b binds to HCN pore forming subunits at two distinct interaction sites, although here the focus is on the interaction between the tetratricopeptide repeat (TPR) domains of TRIP8b and the C terminal tail of HCN1. In this protocol, an expanded description of a method for purifying TRIP8b and executing a high throughput screen to identify small molecule inhibitors of the interaction between HCN and TRIP8b, is described. The method for high throughput screening utilizes a Fluorescence Polarization (FP) -based assay to monitor the binding of a large TRIP8b fragment to a fluorophore-tagged eleven amino acid peptide corresponding to the HCN1 C terminal tail. This method allows &#39;hit&#39; compounds to be identified based on the change in the polarization of emitted light. Validation assays are then performed to ensure that &#39;hit&#39; compounds are not artifactual.

The interaction between HCN channels and their auxiliary subunit has been identified as a therapeutic target in Major Depressive Disorder. Here, a fluorescence polarization-based method for identifying small molecule inhibitors of this protein-protein interaction, is presented.

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are expressed ubiquitously throughout the brain, where they function to regulate the ...

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 ...

Investigating Protein Sequence-structure-dynamics Relationships with Bio3D-web

We demonstrate the usage of Bio3D-web for the interactive analysis of biomolecular structure data. The Bio3D-web application provides online functionality for: (1) The identification of related protein structure sets to user specified thresholds of similarity; (2) Their multiple alignment and structure superposition; (3) Sequence and structure conservation analysis; (4) Inter-conformer relationship mapping with principal component analysis, and (5) comparison of predicted internal dynamics via ensemble normal mode analysis. This integrated functionality provides a complete online workflow for investigating sequence-structure-dynamic relationships within protein families and superfamilies.

A protocol for the online investigation of protein sequence-structure-dynamics relationships using Bio3D-web is presented.

We demonstrate the usage of Bio3D-web for the interactive analysis of biomolecular structure data. The Bio3D-web application provides online functionality ...

Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software

Number and brightness is a calibration-free fluorescence fluctuation spectroscopy (FFS) technique for detecting protein homo-oligomerization. It can be employed using a conventional confocal microscope equipped with digital detectors. A protocol for the use of the technique in vitro is shown by means of a use case where number and brightness can be seen to accurately quantify the oligomeric state of mVenus-labelled FKBP12F36V before and after the addition of the dimerizing drug AP20187. The importance of using the correct microscope acquisition parameters and the correct data preprocessing and analysis methods are discussed. In particular, the importance of the choice of photobleaching correction is stressed. This inexpensive method can be employed to study protein-protein interactions in many biological contexts.

Calibration-free In Vitro Quantification of Protein Homo-oligomerization Using Commercial Instrumentation and Free, Open Source Brightness Analysis Software

This protocol describes a calibration-free approach for quantifying protein homo-oligomerization in vitro based on fluorescence fluctuation spectroscopy using commercial light scanning microscopy. The correct acquisition settings and analysis methods are shown.

Number and brightness is a calibration-free fluorescence fluctuation spectroscopy (FFS) technique for detecting protein homo-oligomerization. It can be ...

High Sensitivity Measurement of Transcription Factor-DNA Binding Affinities by Competitive Titration Using Fluorescence Microscopy

Accurate quantification of transcription factor (TF)-DNA interactions is essential for understanding the regulation of gene expression. Since existing approaches suffer from significant limitations, we have developed a new method for determining TF-DNA binding affinities with high sensitivity on a large scale. The assay relies on the established fluorescence anisotropy (FA) principle but introduces important technical improvements. First, we measure a full FA competitive titration curve in a single well by incorporating TF and a fluorescently labeled reference DNA in a porous agarose gel matrix. Unlabeled DNA oligomer is loaded on the top as a competitor and, through diffusion, forms a spatio-temporal gradient. The resulting FA gradient is then read out using a customized epifluorescence microscope setup. This improved setup greatly increases the sensitivity of FA signal detection, allowing both weak and strong binding to be reliably quantified, even for molecules of similar molecular weights. In this fashion, we can measure one titration curve per well of a multi-well plate, and through a fitting procedure, we can extract both the absolute dissociation constant (KD) and active protein concentration. By testing all single-point mutation variants of a given consensus binding sequence, we can survey the entire binding specificity landscape of a TF, typically on a single plate. The resulting position weight matrices (PWMs) outperform those derived from other methods in predicting in vivo TF occupancy. Here, we present a detailed guide for implementing HiP-FA on a conventional automated fluorescent microscope and the data analysis pipeline.

Here we present a novel method for determining binding affinities at equilibrium and in solution with high sensitivity on a large scale. This improves the quantitative analysis of transcription factor-DNA binding. The method is based on automated fluorescence anisotropy measurements in a controlled delivery system.

Accurate quantification of transcription factor (TF)-DNA interactions is essential for understanding the regulation of gene expression. Since existing ...

Proteome-wide Quantification of Labeling Homogeneity at the Single Molecule Level

Cell proteomes are often characterized using electrophoresis assays, where all species of proteins in the cells are non-specifically labeled with a fluorescent dye and are spotted by a photodetector following their separation. Single molecule fluorescence imaging can provide ultrasensitive protein detection with its ability for visualizing individual fluorescent molecules. However, the application of this powerful imaging method to electrophoresis assays is hampered by the lack of ways to characterize the homogeneity of fluorescent labeling of each protein species across the proteome. Here, we developed a method to evaluate the labeling homogeneity across the proteome based on a single molecule fluorescence imaging assay. In our measurement using a HeLa cell sample, the proportion of proteins labeled with at least one dye, which we termed &#8216;labeling occupancy&#8217; (LO), was determined to range from 50% to 90%, supporting the high potential of the application of single molecule imaging to sensitive and precise proteome analysis.

Here, we present a protocol to assess the labeling homogeneity for each protein species in a complex protein sample at the single molecule level.

Cell proteomes are often characterized using electrophoresis assays, where all species of proteins in the cells are non-specifically labeled with a ...

Monitoring Protein-RNA Interaction Dynamics In Vivo at High Temporal Resolution Using &#967;CRAC

The interaction between RNA-binding proteins (RBPs) and their RNA substrates exhibits fluidity and complexity. Within its lifespan, a single RNA can be bound by many different RBPs that will regulate its production, stability, activity, and degradation. As such, much has been done to understand the dynamics that exist between these two types of molecules. A particularly important breakthrough came with the emergence of &#8216;cross-linking and immunoprecipitation&#8217; (CLIP). This technique allowed stringent investigation into which RNAs are bound by a particular RBP. In short, the protein of interest is UV cross-linked to its RNA substrates in vivo, purified under highly stringent conditions, and then the RNAs covalently cross-linked to the protein are converted into cDNA libraries and sequenced. Since its conception, many derivative techniques have been developed in order to make CLIP amenable to particular fields of study. However, cross-linking using ultraviolet light is notoriously inefficient. This results in extended exposure times that make the temporal study of RBP-RNA interactions impossible. To overcome this issue, we recently designed and built much-improved UV irradiation and cell harvesting devices. Using these new tools, we developed a protocol for time-resolved analyses of RBP-RNA interactions in living cells at high temporal resolution: Kinetic CRoss-linking and Analysis of cDNAs (&#967;CRAC). We recently used this technique to study the role of yeast RBPs in nutrient stress adaptation. This manuscript provides a detailed overview of the &#967;CRAC method and presents recent results obtained with the Nrd1 RBP.

Monitoring Protein-RNA Interaction Dynamics In Vivo at High Temporal Resolution Using &amp;#967;CRAC

Kinetic cross-linking and analysis of cDNA is a method that allows investigation of the dynamics of protein-RNA interactions in living cells at high temporal resolution. Here the protocol is described in detail, including the growth of yeast cells, UV cross-linking, harvesting, protein purification, and next generation sequencing library preparation steps.

The interaction between RNA-binding proteins (RBPs) and their RNA substrates exhibits fluidity and complexity. Within its lifespan, a single RNA can be ...

Native Cell Membrane Nanoparticles System for Membrane Protein-Protein Interaction Analysis

Protein-protein interactions in cell membrane systems play crucial roles in a wide range of biological processes- from cell-to-cell interactions to signal transduction; from sensing environmental signals to biological response; from metabolic regulation to developmental control. Accurate structural information of protein-protein interactions is crucial for understanding the molecular mechanisms of membrane protein complexes and for the design of highly specific molecules to modulate these proteins. Many in vivo and in vitro approaches have been developed for the detection and analysis of protein-protein interactions. Among them the structural biology approach is unique in that it can provide direct structural information of protein-protein interactions at the atomic level. However, current membrane protein structural biology is still largely limited to detergent-based methods. The major drawback of detergent-based methods is that they often dissociate or denature membrane protein complexes once their native lipid bilayer environment is removed by detergent molecules. We have been developing a&#160;native cell membrane nanoparticle system for membrane protein structural biology. Here, we demonstrate the use of this system in the analysis of protein-protein interactions on the cell membrane with a case study of the oligomeric state of AcrB.

Presented here is a protocol for the determination of oligomeric state of membrane proteins that utilizes a native cell membrane nanoparticle system in conjunction with electron microscopy.

Protein-protein interactions in cell membrane systems play crucial roles in a wide range of biological processes- from cell-to-cell interactions to signal ...

Resin-Assisted Capture Coupled with Isobaric Tandem Mass Tag Labeling for Multiplexed Quantification of Protein Thiol Oxidation

Reversible oxidative modifications on protein thiols have recently emerged as important mediators of cellular function. Herein we describe the detailed procedure of a quantitative redox proteomics method that utilizes resin-assisted capture (RAC) in combination with tandem mass tag (TMT) isobaric labeling and liquid chromatography-tandem mass spectrometry (LC-MS/MS) to allow multiplexed stochiometric quantification of oxidized protein thiols at the proteome level. The site-specific quantitative information on oxidized cysteine residues provides additional insight into the functional impacts of such modifications. 
The workflow is adaptable across many sample types, including cultured cells (e.g., mammalian, prokaryotic) and whole tissues (e.g., heart, lung, muscle), which are initially lysed/homogenized and with free thiols being alkylated to prevent artificial oxidation. The oxidized protein thiols are then reduced and captured by a thiol-affinity resin, which streamlines and simplifies the workflow steps by allowing the proceeding digestion, labeling, and washing procedures to be performed without additional transfer of proteins/peptides. Finally, the labeled peptides are eluted and analyzed by LC-MS/MS to reveal comprehensive stoichiometric changes related to thiol oxidation across the entire proteome. This method greatly improves the understanding of the role of redox-dependent regulation under physiological and pathophysiological states related to protein thiol oxidation.

Protein thiol oxidation has significant implications under normal physiological and pathophysiological conditions. We describe the details of a quantitative redox proteomics method, which utilizes resin-assisted capture, isobaric labeling, and mass spectrometry, enabling site-specific identification and quantification of reversibly oxidized cysteine residues of proteins.

Reversible oxidative modifications on protein thiols have recently emerged as important mediators of cellular function. Herein we describe the detailed ...

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.