Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation
Summary
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.
Abstract
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.
Introduction
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'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.
Protocol
Representative Results
Discussion
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…
Divulgaciones
The authors have nothing to disclose.
Acknowledgements
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.
Materials
| 96-well flat bottomed plates | Bio Rad | 171025001 | |
| 96-well PCR plates | VWR | 82006-704 | |
| Bioplex 200 System with HTF | Bio Rad | 171000205 | modiefied to keep partially refrigerated, see Figure S1 for details |
| Bio-Plex Pro Wash Station | Bio Rad | 30034376 | |
| BSA | Sigma | ||
| CML beads | Invitrogen | C37481 | |
| EDTA | Sigma | E6758 | |
| EZ-Link Sulfo-NHS-Biotin | Thermo Scientific | A39256 | |
| MagPlex Microspheres | Luminex | MC12xxx-01 | xxx is the 3 digit bead region |
| Melon Gel IgG Spin Purification Kit | Thermo Scientific | 45206 | used for antibody purification |
| MES | Sigma | M3671 | |
| Microplate film, non-sterile | USA Scientific | 2920-0000 | |
| Phosphotase inhibitor cocktail #2 | Sigma | P5726 | |
| Protease inhibitor cocktail | Sigma | P8340 | |
| Sandwich Prep Refrigerator | Norlake | SMP 36 15 | for custom refrigeration of Bioplex 200 |
| Sodium fluoride | Sigma | 201154 | |
| Sodium orthovanadate | Sigma | 450243 | |
| Streptavidin-PE | BioLegend | 405204 | |
| Sulfo NHS | Thermo Scientific | A39269 | |
| Tris | Fisher Scientific | BP152 |
Referencias
- Pawson, T., Nash, P. Protein-protein interactions define specificity in signal transduction. Genes and Development. 14 (9), 1027-1047 (2000).
- Rodríguez-Jorge, O., et al. Cooperation between T cell receptor and Toll-like receptor 5 signaling for CD4. Science Signaling. 12 (577), (2019).
- Bugaj, L. J., et al. Cancer mutations and targeted drugs can disrupt dynamic signal encoding by the Ras-Erk pathway. Science. 361 (6405), (2018).
- Pawson, T. Dynamic control of signaling by modular adaptor proteins. Current Opinion in Cell Biology. 19 (2), 112-116 (2007).
- Hamm, H. E. The many faces of G protein signaling. Journal of Biological Chemistry. 273 (2), 669-672 (1998).
- Lohse, M. J., Hofmann, K. P. Spatial and Temporal Aspects of Signaling by G-Protein-Coupled Receptors. Molecular Pharmacology. 88 (3), 572-578 (2015).
- Eltschinger, S., Loewith, R. TOR Complexes and the Maintenance of Cellular Homeostasis. Trends in Cell Biology. 26 (2), 148-159 (2016).
- Papin, J. A., Hunter, T., Palsson, B. O., Subramaniam, S. Reconstruction of cellular signalling networks and analysis of their properties. Nature Reviews Molecular Cell Biology. 6 (2), 99-111 (2005).
- Komarova, N. L., Zou, X., Nie, Q., Bardwell, L. A theoretical framework for specificity in cell signaling. Molecular Systems Biology. 1, 23 (2005).
- Schrum, A. G., Gil, D. Robustness and Specificity in Signal Transduction via Physiologic Protein Interaction Networks. Clinical and Experimental Pharmacology. 2 (3), (2012).
- De Las Rivas, J., Fontanillo, C. Protein-protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Computational Biology. 6 (6), 1000807 (2010).
- Ron, D., Walter, P. Signal integration in the endoplasmic reticulum unfolded protein response. Nature Reviews Molecular Cell Biology. 8 (7), 519-529 (2007).
- Bogdan, A. R., Miyazawa, M., Hashimoto, K., Tsuji, Y. Regulators of Iron Homeostasis: New Players in Metabolism, Cell Death, and Disease. Trends in Biochemical Sciences. 41 (3), 274-286 (2016).
- Smith, S. E., et al. Multiplex matrix network analysis of protein complexes in the human TCR signalosome. Science Signaling. 9 (439), 7 (2016).
- Schrum, A. G., et al. High-sensitivity detection and quantitative analysis of native protein-protein interactions and multiprotein complexes by flow cytometry. Science’s STKE. 2007 (389), (2007).
- Brown, E. A., et al. Clustering the autisms using glutamate synapse protein interaction networks from cortical and hippocampal tissue of seven mouse models. Molecular Autism. 9 (1), 48 (2018).
- Lautz, J. D., Brown, E. A., Williams VanSchoiack, A. A., Smith, S. E. P. Synaptic activity induces input-specific rearrangements in a targeted synaptic protein interaction network. Journal of Neurochemistry. 146 (5), 540-559 (2018).
- Smith, S. E., et al. Signalling protein complexes isolated from primary human skin-resident T cells can be analysed by Multiplex IP-FCM. Experimental Dermatology. 23 (4), 272-273 (2014).
- Neier, S. C., et al. The early proximal αβ TCR signalosome specifies thymic selection outcome through a quantitative protein interaction network. Science Immunology. 4 (32), (2019).
- Davis, T. R., Schrum, A. G. IP-FCM: immunoprecipitation detected by flow cytometry. Journal of Visualized Expiriments. (46), (2010).
- Smith, S. E., et al. IP-FCM measures physiologic protein-protein interactions modulated by signal transduction and small-molecule drug inhibition. PLoS One. 7 (9), 45722 (2012).
- Lautz, J., et al. Activity-dependent changes in synaptic protein complex composition are consistent in different detergents despite differential solubility. BioRXiv. , (2019).
- Langfelder, P., Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 9, 559 (2008).
- Schrum, A. G., Gil, D., Turka, L. A., Palmer, E. Physical and functional bivalency observed among TCR/CD3 complexes isolated from primary T cells. Journal of Immunology. 187 (2), 870-878 (2011).
