This work was supported by a Grand Challenge grant to W.M.O. from the Linda Crnic Institute for Down syndrome and by a DARPA cooperative agreement 13-34-RTA-FP-007. We would like to thank Jesse Kurland and Kira Cozzolino for their contributions in reading and commenting on the manuscript.

<ol>
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The authors have nothing to disclose.

The proteomics workflow outlined here provides an effective method for identifying high-confidence protein interactors for a protein of interest. This approach decreases the sample complexity through subcellular fraction and focuses on increasing the identification interaction partners through robust sample preparation, offline sample clean up, and stringent quality control of the LC-MS system. The downstream data analysis described here allows for a simple statistical evaluation of the proteins identified as copurifying with the bait. However, due to a high number of experimental variables (scale, cell line, antibody choice), each experiment requires different cutoffs and considerations regarding data visualization and enrichment.
The first design consideration in an IP-MS experiment is the selection of antibodies that will be used for copurification of the protein of interest along with its interacting partners. While the availability of commercial antibodies has expanded to cover larger portions of the human proteome over the past several decades, there are still many proteins for which reagents are limited. Furthermore, antibodies that have been validated for applications such as western blot detection may be incapable of selective enrichment of the target protein in an immunoprecipitation experiment. Prior to conducting a large-scale interaction proteomics experiment, it is suggested to complete an IP from a 90% confluent 10 cm dish, or equivalent cell number, and probe for the target protein of interest by western blotting. If more than a single antibody is available for immunoprecipitation, it is additionally suggested to select multiple antibodies recognizing epitopes within different portions of the protein. The binding of an antibody to a bait protein can occlude the necessary binding interface for putative interacting partners. Selection of a secondary epitope for the bait protein will increase the coverage of the interaction profile identified by a mass spectrometry-based experiment.
A second major consideration lies in the selection of the appropriate control for distinguishing high-confidence interactions from low-confidence or nonspecific interactions from those identified as copurifying with the bait. The most stringent control for an IP-MS experiment is to complete the immunoprecipitation from a CRISPR KO cell line of the bait. Such a control enables identification and filtering out of nonspecific proteins that bind directly to the antibody rather than the bait protein. In cases where generating a CRISPR KO cell line of each bait protein is not feasible, an IgG-bead control of the same isotype of the bait antibody can be used. In experiments employing a panel of antibodies representing multiple species, the use of a beads only control can be appropriate but will increase the rate of false positives identified as high-confidence interactors.
Selection of the cell line used in an IP-MS experiment is dependent on several key factors. Protein expression and localization are largely dependent on cell type. While RNA expression estimates can be found for most genes in many commonly used cell lines, protein expression is poorly correlated with RNA expression and must be determined experimentally25. Cell lines in which a bait protein is expressed in very low copy number should be avoided to circumvent problems associated with drastic increases in cell culture scale that may be required. It should be noted, however, that sample preparation can be optimized for the detection of very low abundance proteins. The filter aided sample prep (FASP) method, while robust, can cause a more than 50% loss of peptide in a sample. The Single-Pot Solid-Phase-enhanced Sample Preparation (SP3) is an efficient method of generating samples for mass spectrometry analysis that minimizes sample loss26. The increased recovery enabled by the SP3 method of sample preparation can be a useful alternative in this workflow for quantification of proteins that fall near the limit of detection.
This proteomics workflow has been applied across many nuclear baits, including kinases, E3 ubiquitin ligases, and scaffolding members of multisubunit complexes. Assuming proper validation of antibody reagents, successful execution of this workflow will result in detection of high-confidence protein nuclear interaction partners for a protein of interest.

The human proteome exhibits vast structural and biochemical diversity through the formation of stable multisubunit complexes and transient protein-protein interactions. Accordingly, the identification of interaction partners for a protein of interest is commonly required in investigations to unravel molecular mechanism. Recent advances in affinity purification protocols and the advent of high-resolution fast-scanning mass spectrometry instrumentation have enabled easy mapping of protein interaction landscapes in a single unbiased experiment.
Protein interaction protocols commonly employ ectopic expression systems with affinity-tagged fusion constructs to identify protein interactions without requiring high-quality antibodies recognizing a protein of interest1,2. However, epitope tag-based methods have several drawbacks. Physical interactions with the epitope may lead to the detection of nonspecific copurifying proteins3. Additionally, fusion of these epitope tags to the N- or C-terminal of a protein may block native protein-protein interactions, or disrupt protein folding to promote non-physiological conformations4. Furthermore, ectopic expression systems typically overexpress the bait protein at supraphysiological concentrations, which can result in the identification of artifactual protein interactions, particularly for dosage-sensitive genes5. To circumvent these issues, the endogenous bait protein can be immunoprecipitated along with associated interacting prey proteins, assuming availability of a high-quality antibody that recognizes the native protein.
Provided here is an interaction proteomics workflow for detecting the nuclear interactome of an endogenous protein using the CMGC protein kinase DYRK1A as an example. Disruption of DYRK1A copy number, activity level, or expression can cause severe intellectual disability in humans, and embryonic lethality in mice6,7,8,9. DYRK1A exhibits dynamic spatiotemporal regulation10, and compartmentalized protein interactions11,12, requiring approaches capable of detecting low abundance interaction partners specific to different subcellular compartments.
This protocol employs cellular fractionation of human HeLa cells into cytosol and nucleoplasm fractions, immunoprecipitation, sample preparation for mass spectrometry, and an overview of a bioinformatic pipeline for evaluating data quality and visualizing results, with R scripts provided for analysis and visualization (Figure 1). Proteomics software packages used in this workflow are all freely available for download or can be accessed through a web interface. For additional information on software and computational methods, in-depth tutorials and instruction are available at the links provided.

<table>
	<tbody>
		<tr>
			<td>0.25% Trypsin, 0.1% EDTA</td>
			<td>Thermo Fisher Scientific</td>
			<td>25200056</td>
			<td></td>
		</tr>
		<tr>
			<td>1.5 ml low-rention microcentrifuge tubes</td>
			<td>Fisher Scientific</td>
			<td>02-681-320</td>
			<td></td>
		</tr>
		<tr>
			<td>4-20% Mini PROTEAN TGX Precast Protein Gels</td>
			<td>Bio-Rad</td>
			<td>4561096</td>
			<td></td>
		</tr>
		<tr>
			<td>acetone (HPLC)</td>
			<td>Thermo Fisher Scientific</td>
			<td>A949SK-4</td>
			<td></td>
		</tr>
		<tr>
			<td>Amicon Ultra 0.5 ml 30k filter column</td>
			<td>Millipore Sigma</td>
			<td>UFC503096</td>
			<td></td>
		</tr>
		<tr>
			<td>Benzamidine</td>
			<td>Sigma-Aldrich</td>
			<td>12072</td>
			<td></td>
		</tr>
		<tr>
			<td>benzonase</td>
			<td>Sigma-Aldrich</td>
			<td>E1014</td>
			<td></td>
		</tr>
		<tr>
			<td>Chloroacetamide</td>
			<td>Sigma-Aldrich</td>
			<td>C0267</td>
			<td></td>
		</tr>
		<tr>
			<td>Dialysis tubing closure</td>
			<td>Caroline Biological Supply Company</td>
			<td>684239</td>
			<td></td>
		</tr>
		<tr>
			<td>DTT</td>
			<td>Sigma-Aldrich</td>
			<td>10197777001</td>
			<td></td>
		</tr>
		<tr>
			<td>EDTA</td>
			<td>Sigma-Aldrich</td>
			<td>EDS</td>
			<td></td>
		</tr>
		<tr>
			<td>GAPDH antibody</td>
			<td>Santa Cruz Biotechnology</td>
			<td>Sc-47724</td>
			<td></td>
		</tr>
		<tr>
			<td>Glycerol</td>
			<td>Fisher Scientific</td>
			<td>887845</td>
			<td></td>
		</tr>
		<tr>
			<td>Glycine</td>
			<td>Sigma-Aldrich</td>
			<td>G8898</td>
			<td></td>
		</tr>
		<tr>
			<td>HeLa QC tryptic digest</td>
			<td>Pierce</td>
			<td>88329</td>
			<td></td>
		</tr>
		<tr>
			<td>HEPES</td>
			<td>Fisher Scientific</td>
			<td>AAJ1692630</td>
			<td></td>
		</tr>
		<tr>
			<td>insulin</td>
			<td>Thermo Fisher Scientific</td>
			<td>12585014</td>
			<td></td>
		</tr>
		<tr>
			<td>iodoacetamide</td>
			<td>Sigma-Aldrich</td>
			<td>I1149</td>
			<td></td>
		</tr>
		<tr>
			<td>KONTES Dounce homogenizer 7 ml</td>
			<td>VWR</td>
			<td>KT885300-0007</td>
			<td></td>
		</tr>
		<tr>
			<td>Large Clearance pestle 7ml</td>
			<td>VWR</td>
			<td>KT885301-0007</td>
			<td></td>
		</tr>
		<tr>
			<td>Lysyl endopeptidase C</td>
			<td>VWR</td>
			<td>125-05061</td>
			<td></td>
		</tr>
		<tr>
			<td>Magnesium Chloride</td>
			<td>Sigma-Aldrich</td>
			<td>208337</td>
			<td></td>
		</tr>
		<tr>
			<td>Microcystin</td>
			<td>enzo life sciences</td>
			<td>ALX-350-012-C100</td>
			<td></td>
		</tr>
		<tr>
			<td>Nonidet P 40 Substitute solution</td>
			<td>Sigma-Aldrich</td>
			<td>98379</td>
			<td></td>
		</tr>
		<tr>
			<td>p84 antibody</td>
			<td>GeneTex</td>
			<td>GTX70220</td>
			<td></td>
		</tr>
		<tr>
			<td>Phosphate Buffered Saline</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Pierce BCA Protein Assay Kit</td>
			<td>Thermo Fisher Scientific</td>
			<td>23227</td>
			<td></td>
		</tr>
		<tr>
			<td>Pierce BSA Protein Digest, MS grade</td>
			<td>Thermo Fisher Scientific</td>
			<td>88341</td>
			<td>LCMS QC</td>
		</tr>
		<tr>
			<td>Pierce C18 spin columns</td>
			<td>Thermo Fisher Scientific</td>
			<td>PI-89873</td>
			<td></td>
		</tr>
		<tr>
			<td>Pierce Trypsin Protease, MS Grade</td>
			<td>Thermo Fisher Scientific</td>
			<td>90057</td>
			<td>For mass spectrometry sample prep</td>
		</tr>
		<tr>
			<td>PMSF</td>
			<td>Sigma-Aldrich</td>
			<td>P7626</td>
			<td></td>
		</tr>
		<tr>
			<td>Potassium Chloride</td>
			<td>Sigma-Aldrich</td>
			<td>P9541</td>
			<td></td>
		</tr>
		<tr>
			<td>Protein A Sepharose CL-4B</td>
			<td>GE Healthcare Bio-Sciences</td>
			<td>17-0780-01</td>
			<td></td>
		</tr>
		<tr>
			<td>Protein G Sepharose 4 Fast Flow</td>
			<td>GE Healthcare Bio-Sciences</td>
			<td>17-0618-01</td>
			<td></td>
		</tr>
		<tr>
			<td>SDS</td>
			<td>Sigma-Aldrich</td>
			<td>L3771</td>
			<td></td>
		</tr>
		<tr>
			<td>Silica emitter tip</td>
			<td>Pico TIP</td>
			<td>FS360-20-10</td>
			<td></td>
		</tr>
		<tr>
			<td>Small Clearance pestle 7ml</td>
			<td>VWR</td>
			<td>KT885302-0007</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium Chloride</td>
			<td>Sigma-Aldrich</td>
			<td>S3014</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium Fluoride</td>
			<td>Sigma-Aldrich</td>
			<td>201154</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium metabisulfite</td>
			<td>Sigma-Aldrich</td>
			<td>31448</td>
			<td></td>
		</tr>
		<tr>
			<td>Sodium orthovanadate</td>
			<td>Sigma-Aldrich</td>
			<td>S6508</td>
			<td></td>
		</tr>
		<tr>
			<td>Spectra/ Por 8 kDa 24 mm dialysis tubing</td>
			<td>Thomas Scientific</td>
			<td>3787K17</td>
			<td></td>
		</tr>
		<tr>
			<td>TC Dish 150, Standard</td>
			<td>Sarstedt</td>
			<td>83.3903</td>
			<td>Tissue culture dish for adherent cells</td>
		</tr>
		<tr>
			<td>TCA</td>
			<td>Sigma-Aldrich</td>
			<td>T9159</td>
			<td></td>
		</tr>
		<tr>
			<td>TCEP</td>
			<td>Thermo Scientific</td>
			<td>PG82080</td>
			<td></td>
		</tr>
		<tr>
			<td>TFA</td>
			<td>Thermo Fisher Scientific</td>
			<td>28904</td>
			<td></td>
		</tr>
		<tr>
			<td>Thermo Scientific Orbitrap Fusion MS</td>
			<td>Thermo Fisher Scientific</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Trizma Base</td>
			<td>Sigma-Aldrich</td>
			<td>T6066</td>
			<td></td>
		</tr>
		<tr>
			<td>Urea</td>
			<td>Thermo Fisher Scientific</td>
			<td>29700</td>
			<td></td>
		</tr>
		<tr>
			<td>Waters ACQUITY M-Class UPLC</td>
			<td>Waters</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Waters ACQUITY UPLC M-Class Column Reversed-Phase 1.7&#181;m Spherical Hybrid (1.7 &#181;m, 75 &#181;m x 250 mm)</td>
			<td>Waters</td>
			<td>186007484</td>
			<td>nanoflow C18 column</td>
		</tr>
	</tbody>
</table>

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.

The majority of protein mass identified in an IP-MS experiment consists of nonspecific proteins. Thus, one of the key challenges of an IP-MS experiment is the interpretation of which proteins are high-confidence interactors vs. nonspecific interactors. To demonstrate the crucial parameters used in the evaluation of data quality the study analyzed triplicate immunoprecipitations from 5 mg of HeLa nuclear extract utilizing a bead only control. The first internal check to ensure that an IP-MS experiment is reliable is whether the bait protein ranks as one of the highest enriched proteins identified by both fold-change over control and SAINT probability. In this case, the bait DYRK1A ranked among the top three enriched proteins over the control (Figure 2A,B). In a nuclear interactome study of DYRK1A utilizing four independent antibodies, an FC-A cutoff of &#62;3.00 and SAINT probability cutoff &#62;0.7 provided a stringent cutoff for identification of both novel and previously validated interactors22. When applied to this experiment, a clear separation could be seen between the high-confidence interactors and &#62;95% of copurified proteins identified as nonspecific (Figure 2A,B). Applying both a fold change enrichment and probability threshold increases stringency by requiring a consistently high enrichment of protein IDs across biological replicates.
In addition to statistical scoring, the CRAPome analysis workflow also maps previously reported interactions onto bait-prey data23. While this mapping can be useful for thresholding high and low-confidence interactions, previously reported interactions can score poorly by FC-A and SAINT probabilities, potentially indicating that many known interactions of a given bait may exist only in specific cell types, contexts, or organelles. For the example DYRK1A dataset, iREF interactor FC-A values were as low as 0.45, representing a very low enrichment over control (Figure 2C). To avoid inflation of false positives, statistical thresholding should be performed in a manner that prioritizes stringency over reduction of false negatives. It should be noted that the detection of these interactions was independent of protein abundance (Figure 2C). Calculated absolute copy number of each iREF interaction within HeLa cells showed no correlation to the detection levels of an interaction partner by IP-MS24.
Cytoscape serves as an effective tool for visualizing multiple layers of interaction data19. In the DYRK1A immunoprecipitation experiment described here, the combined use of FC-A &#62; 3.0 and SAINT &#62; 0.9 reduced the list of high-confidence interactors to six proteins (Figure 2D). However, when applying an FC-A cutoff of &#62; 3.0 in isolation, eight additional proteins were added to the network. These additional protein interactors have high connectivity with the interactors already in the network, suggesting they are associated in similar complexes or functional roles. To this end, evidence from the STRING-DB of protein-protein interactions was integrated into this network as blue dashed lines20. While this single-bait, triplicate experiment provides a limited sample of the full DYRK1A interaction network, the use of additional baits, replicates, and integration of large public data sets can be used to expand the network of high-confidence interactions. The statistical cutoffs will thus be specific to each individual experiment and will need to be evaluated thoroughly.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60432/60432fig01.jpg" /> 
Figure 1: Representative proteomics workflow for subcellular IP-MS.&#160;Cells are grown in either 4 L round bottom flasks or 15 cm tissue culture dishes and harvested at the same time for subcellular fractionation. Cells are fractionated into a cytosolic, nuclear, and a nuclear pellet, and immunoprecipitations are done from 1&#8722;10 mg of nuclear lysate using one or multiple antibodies recognizing the same bait. Filter aided sample prep (FASP) and offline sample cleanup are performed prior to single shot mass spectrometry. A downstream computational pipeline is used to process data into interpretable interaction data. <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/60432/60432fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60432/60432fig02.jpg" /> 
Figure 2: Representative data for a single-bait single-antibody IP-MS experiment.&#160;(A) FC-A and SAINT probability output from CRAPome analysis workflow for an optimal experiment using a single antibody for the kinase DYRK1A (n = 3). Beads-only controls were used for comparison. Red solid lines represent cutoffs set at FC-A &#62; 3.00 and SAINT &#62; 0.7. (B) MaxQuant protein abundance estimates (iBAQ) output vs. log2 ratio of protein abundance in DYRK1A IP to control, colored by the adjusted p value range from empirical Bayes analysis of the label-free intensities. (C) FC-A and estimated copy number of proteins listed as interacting proteins in the iRef database23,24. (D) Cytoscape network visualization of DYRK1A interactors. Blue nodes = FC-A &#62; 3.00, SAINT &#62; 0.7. Orange nodes = FC-A &#62; 3.00. Black edges = proteins identified as interactors in IPMS experiment. Blue dashed edge = SAINT interaction between prey protein (confidence &#62; .150). <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/60432/60432fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="2">Protease inhibitor (PI) mixture</td>
		</tr>
		<tr>
			<td>Reagent</td>
			<td>Final Concentration</td>
		</tr>
		<tr>
			<td>Sodium Metabisulfite</td>
			<td>1 mM</td>
		</tr>
		<tr>
			<td>Benzamidine</td>
			<td>1 mM</td>
		</tr>
		<tr>
			<td>Dithiothreitol (DTT)</td>
			<td>1 mM</td>
		</tr>
		<tr>
			<td>Phenylmethanesulfonyl fluoride (PMSF)</td>
			<td>0.25 mM</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">Phosphatase Inhibitor (PhI) mixture</td>
		</tr>
		<tr>
			<td>Reagent</td>
			<td>Final Concentration</td>
		</tr>
		<tr>
			<td>Microcystin LR</td>
			<td>1 &#181;M</td>
		</tr>
		<tr>
			<td>Sodium Orthovanadate</td>
			<td>0.1 mM</td>
		</tr>
		<tr>
			<td>Sodium fluoride</td>
			<td>5 mM</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">Subcellular fractionation Buffers:</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">Buffer A pH 7.9</td>
		</tr>
		<tr>
			<td>Reagent</td>
			<td>Final Concentration</td>
		</tr>
		<tr>
			<td>HEPES</td>
			<td>10 mM</td>
		</tr>
		<tr>
			<td>MgCl2</td>
			<td>1.5 mM</td>
		</tr>
		<tr>
			<td>KCl</td>
			<td>10 mM</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">Buffer B pH 7.9</td>
		</tr>
		<tr>
			<td>Reagent</td>
			<td>Final Concentration</td>
		</tr>
		<tr>
			<td>HEPES</td>
			<td>20 mM</td>
		</tr>
		<tr>
			<td>MgCl2</td>
			<td>1.5 mM</td>
		</tr>
		<tr>
			<td>NaCl</td>
			<td>420 mM</td>
		</tr>
		<tr>
			<td>Ethylenediaminetetraacetic acid (EDTA)</td>
			<td>0.4 mM</td>
		</tr>
		<tr>
			<td>Glycerol</td>
			<td>25% (v/v)</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">Buffer C pH 7.9</td>
		</tr>
		<tr>
			<td>Reagent</td>
			<td>Final Concentration</td>
		</tr>
		<tr>
			<td>HEPES</td>
			<td>20 mM</td>
		</tr>
		<tr>
			<td>MgCl2</td>
			<td>2 mM</td>
		</tr>
		<tr>
			<td>KCl</td>
			<td>100 mM</td>
		</tr>
		<tr>
			<td>Ethylenediaminetetraacetic acid (EDTA)</td>
			<td>0.4 mM</td>
		</tr>
		<tr>
			<td>Glycerol</td>
			<td>20% (v/v)</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">Immunoprecipitation Buffers:</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">IP Buffer 1</td>
		</tr>
		<tr>
			<td>Reagent</td>
			<td>Final Concentration</td>
		</tr>
		<tr>
			<td>HEPES</td>
			<td>20 mM</td>
		</tr>
		<tr>
			<td>KCl</td>
			<td>150 mM</td>
		</tr>
		<tr>
			<td>EDTA</td>
			<td>0.1 mM</td>
		</tr>
		<tr>
			<td>NP-40</td>
			<td>0.1% (v/v)</td>
		</tr>
		<tr>
			<td>Glycerol</td>
			<td>10% (v/v)</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">IP Buffer 2</td>
		</tr>
		<tr>
			<td>Reagent</td>
			<td>Final Concentration</td>
		</tr>
		<tr>
			<td>HEPES</td>
			<td>20 mM</td>
		</tr>
		<tr>
			<td>KCl</td>
			<td>500 mM</td>
		</tr>
		<tr>
			<td>EDTA</td>
			<td>0.1 mM</td>
		</tr>
		<tr>
			<td>NP-40</td>
			<td>0.1% (v/v)</td>
		</tr>
		<tr>
			<td>Glycerol</td>
			<td>10% (v/v)</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">SDS Alkylation Buffer pH 8.5</td>
		</tr>
		<tr>
			<td>Reagent</td>
			<td>Final Concentration</td>
		</tr>
		<tr>
			<td>SDS</td>
			<td>4% (v/v)</td>
		</tr>
		<tr>
			<td>Chloroacetamide</td>
			<td>40 mM</td>
		</tr>
		<tr>
			<td>TCEP</td>
			<td>10 mM</td>
		</tr>
		<tr>
			<td>Tris</td>
			<td>100 mM</td>
		</tr>
		<tr>
			<td colspan="2"></td>
		</tr>
		<tr>
			<td colspan="2">UA pH 8.5</td>
		</tr>
		<tr>
			<td>Reagent</td>
			<td>Final Concentration</td>
		</tr>
		<tr>
			<td>Urea</td>
			<td>8 M</td>
		</tr>
		<tr>
			<td>Tris</td>
			<td>0.1 M</td>
		</tr>
		<tr>
			<td colspan="2">* use HPLC grade H2O</td>
		</tr>
	</tbody>
</table>
Table 1: Buffer compositions
Supplemental Coding Files.&#160;<a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/60432/Supplemental_Coding_Files.zip" target="_blank">Please click here to download this file.</a>

Watch this Scientific Journal Video about Label-Free Immunoprecipitation Mass Spectrometry Workflow for Large-scale Nuclear Interactome Profiling at JoVE.com

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

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 ...

label-free-immunoprecipitation-mass-spectrometry-workflow-for-large

Universidad de Cartagena UDEC

Research

JoVE Journal

Biochemistry

15.9K Views.  University of Colorado. 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.

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

Sheathless Capillary Electrophoresis&#8211;Mass Spectrometry for Metabolic Profiling of Biological Samples

In metabolomics, a wide range of analytical techniques is used for the global profiling of (endogenous) metabolites in complex samples. In this paper, a protocol is presented for the analysis of anionic and cationic metabolites in biological samples by capillary electrophoresis&#8211;mass spectrometry (CE-MS). CE is well-suited for the analysis of highly polar and charged metabolites as compounds are separated on the basis of their charge-to-size ratio. A recently developed sheathless interfacing design, i.e., a porous tip interface, is used for coupling CE to electrospray ionization (ESI) MS. This interfacing approach allows the effective use of the intrinsically low-flow property of CE in combination with MS, resulting in nanomolar detection limits for a broad range of polar metabolite classes. The protocol presented here is based on employing a bare fused-silica capillary with a porous tip emitter at low-pH separation conditions for the analysis of a broad array of metabolite classes in biological samples. It is demonstrated that the same sheathless CE-MS method can be used for the profiling of cationic metabolites, including amino acids, nucleosides and small peptides, or anionic metabolites, including sugar phosphates, nucleotides and organic acids, by only switching the MS detection and separation voltage polarity. Highly information-rich metabolic profiles in various biological samples, such as urine, cerebrospinal fluid and extracts of the glioblastoma cell line, can be obtained by this protocol in less than 1 hr of CE-MS analysis.

A protocol for metabolic profiling of biological samples by capillary electrophoresis&#8211;mass spectrometry using a sheathless porous tip interface design is presented.

In metabolomics, a wide range of analytical techniques is used for the global profiling of (endogenous) metabolites in complex samples. In this paper, a ...

Time-resolved ElectroSpray Ionization Hydrogen-deuterium Exchange Mass Spectrometry for Studying Protein Structure and Dynamics

Intrinsically disordered proteins (IDPs) have long been a challenge to structural biologists due to their lack of stable secondary structure elements. Hydrogen-Deuterium Exchange (HDX) measured at rapid time scales is uniquely suited to detect structures and hydrogen bonding networks that are briefly populated, allowing for the characterization of transient conformers in native ensembles. Coupling of HDX to mass spectrometry offers several key advantages, including high sensitivity, low sample consumption and no restriction on protein size. This technique has advanced greatly in the last several decades, including the ability to monitor HDX labeling times on the millisecond time scale. In addition, by incorporating the HDX workflow onto a microfluidic platform housing an acidic protease microreactor, we are able to localize dynamic properties at the peptide level. In this study, Time-Resolved ElectroSpray Ionization Mass Spectrometry (TRESI-MS) coupled to HDX was used to provide a detailed picture of residual structure in the tau protein, as well as the conformational shifts induced upon hyperphosphorylation.

Conformational flexibility plays a critical role in protein function. Herein, we describe the use of time-resolved electrospray ionization mass spectrometry coupled to hydrogen-deuterium exchange for probing the rapid structural changes that drive function in ordered and disordered proteins.

Intrinsically disordered proteins (IDPs) have long been a challenge to structural biologists due to their lack of stable secondary structure elements. ...

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological processes. Affinity purification coupled to mass spectrometry (AP-MS) has become the method of choice for identifying protein-protein interactions. However, conventional AP-MS studies provide information on protein interactions, but the organizational information is lost. To address this issue, we developed a strategy to unravel the distinct functional assemblies a protein might be involved in, by resolving affinity-purified protein complexes prior to their characterization by mass spectrometry. Protein complexes isolated through affinity purification of a bait protein using an epitope tag and competitive elution are separated through blue native electrophoresis. Comparison of protein migration profiles through correlation profiling using quantitative mass spectrometry allows assignment of interacting proteins to distinct molecular entities. This method is able to resolve protein complexes of close molecular weights that might not be resolved by traditional chromatographic techniques such as gel filtration. With little more work than conventional AP-geLC-MS/MS, we demonstrate this strategy may in many cases be adequate for obtaining protein complex topological information concomitantly to identifying protein interactions.

Here we present protocols for affinity purification of protein complexes and their separation by blue native PAGE, followed by protein correlation profiling using label free quantitative mass spectrometry. This method is useful to resolve interactomes into distinct protein complexes.

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological ...

Lipid Droplet Isolation for Quantitative Mass Spectrometry Analysis

Lipid droplets are vital to the replication of a variety of different pathogens, most prominently the Hepatitis C Virus (HCV), as the putative site of virion morphogenesis. Quantitative lipid droplet proteome analysis can be used to identify proteins that localize to or are displaced from lipid droplets under conditions such as virus infections. Here, we describe a protocol that has been successfully used to characterize the changes in the lipid droplet proteome following infection with HCV. We use Stable Isotope Labeling with Amino Acids in Cell Culture (SILAC) and thus label the complete proteome of one population of cells with &#34;heavy&#34; amino acids to quantitate the proteins by mass spectrometry. For lipid droplet isolation, the two cell populations (i.e.&#160;HCV-infected/&#34;light&#34; amino acids and uninfected control/&#34;heavy&#34; amino acids) are mixed 1:1 and lysed mechanically in hypotonic buffer. After removing the nuclei and cell debris by low speed centrifugation, lipid droplet-associated proteins are enriched by two subsequent ultracentrifugation steps followed by three washing steps in isotonic buffer. The purity of the lipid droplet fractions is analyzed by western blotting with antibodies recognizing different subcellular compartments. Lipid droplet-associated proteins are then separated by SDS-polyacrylamide gel electrophoresis (SDS-PAGE) followed by Coomassie staining. After tryptic digest, the peptides are quantified by liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS). Using this method, we identified proteins recruited to lipid droplets upon HCV infection that might represent pro- or antiviral host factors. Our method can be applied to a variety of different cells and culture conditions, such as infection with pathogens, environmental stress, or drug treatment.

Lipid droplets are important organelles for the replication of several pathogens, including the Hepatitis C Virus (HCV). We describe a method to isolate lipid droplets for quantitative mass spectrometry of associated proteins; it can be used under a variety of conditions, such as virus infection, environmental stress, or drug treatment.

Lipid droplets are vital to the replication of a variety of different pathogens, most prominently the Hepatitis C Virus (HCV), as the putative site of ...

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

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

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

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

Optimal Preparation of Formalin Fixed Samples for Peptide Based Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Workflows

The use of matrix-assisted laser desorption/ionization, mass spectrometry imaging (MALDI MSI) has rapidly expanded, since this technique analyzes a host of biomolecules from drugs and lipids to N-glycans. Although various sample preparation techniques exist, detecting peptides from formaldehyde preserved tissues remains one of the most difficult challenges for this type of mass spectrometric analysis. For this reason, we have created and optimized a robust methodology that preserves the spatial information contained within the sample, while eliciting the greatest number of ionizable peptides. We have also aimed to achieve this in a cost effective and simple way, thereby eliminating potential bias or preparation error, which can occur when using automated instrumentation. The end result is a reproducible and inexpensive protocol.

This protocol describes a reproducible and reliable method for the sublimation-based preparation of formalin fixed tissue destined for imaging mass spectrometry.

The use of matrix-assisted laser desorption/ionization, mass spectrometry imaging (MALDI MSI) has rapidly expanded, since this technique analyzes a host ...

Nitropeptide Profiling and Identification Illustrated by Angiotensin II

Protein nitration is one of the most important post-translational modifications (PTM) on tyrosine residues and it can be induced by chemical actions of reactive oxygen species (ROS) and reactive nitrogen species (RNS) in eukaryotic cells. Precise identification of nitration sites on proteins is crucial for understanding the physiological and pathological processes related to protein nitration, such as inflammation, aging, and cancer. Since the nitrated proteins are of low abundance in cells even under induced conditions, no universal and efficient methods have been developed for the profiling and identification of protein nitration sites. Here we describe a protocol for nitropeptide enrichment by using a chemical reduction reaction and biotin labeling, followed by high resolution mass spectrometry. In our method, nitropeptide derivatives can be identified with high accuracy. Our method exhibits two advantages compared to the previously reported methods. First, dimethyl labeling is used to block the primary amine on nitropeptides, which can be used to generate quantitative results. Second, a disulfide bond containing NHS-biotin reagent is used for the enrichment, which can be further reduced and alkylated to enhance the detection signal on a mass spectrometer. This protocol has been successfully applied to the model peptide Angiotensin II in the current paper.

Proteomic profiling of tyrosine-nitrated proteins has been a challenging technique due to the low abundance of the 3-nitrotyrosine modification. Here we describe a novel approach for nitropeptide enrichment and profiling by using Angiotensin II as the model. This method can be extended for other in vitro or in vivo systems.

Protein nitration is one of the most important post-translational modifications (PTM) on tyrosine residues and it can be induced by chemical actions of ...

Sample Preparation for Probe Electrospray Ionization Mass Spectrometry

Mass spectrometry (MS) is a powerful tool in analytical chemistry because it provides very accurate information about molecules, such as mass-to-charge ratios (m/z), which are useful to deduce molecular weights and structures. While it is essentially a destructive analytical method, recent advancements in the ambient ionization technique have enabled us to acquire data while leaving tissue in a relatively intact state in terms of integrity. Probe electrospray ionization (PESI) is a so-called direct method because it does not require complex and time-consuming pretreatment of samples. A fine needle serves as a sample picker, as well as an ionization emitter. Based on the very sharp and fine property of the probe tip, destruction of the samples is minimal, allowing us to acquire the real-time molecular information from living things in situ. Herein, we introduce three applications of PESI-MS technique that will be useful for biomedical research and development. One involves the application to solid tissue, which is the basic application of this technique for the medical diagnosis. As this technique requires only 10 mg of the sample, it may be very useful in the routine clinical settings. The second application is for in vitro medical diagnostics where human blood serum is measured. The ability to measure fluid samples is also valuable in various biological experiments where a sufficient volume of sample for conventional analytical techniques cannot be provided. The third application leans toward the direct application of probe needles in living animals, where we can obtain real-time dynamics of metabolites or drugs in specific organs. In each application, we can infer the molecules that have been detected by MS or use artificial intelligence to obtain a medical diagnosis.

This article introduces sample preparation methods for a unique real-time analytical method based on the ambient mass spectrometry. This method lets us perform real-time analysis of the biological molecules in vivo without any special pretreatments.

Mass spectrometry (MS) is a powerful tool in analytical chemistry because it provides very accurate information about molecules, such as mass-to-charge ...

Quantitative Analysis of the Cellular Lipidome of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

Lipids are structurally diverse amphipathic molecules that are insoluble in water. Lipids are essential contributors to the organization and function of biological membranes, energy storage and production, cellular signaling, vesicular transport of proteins, organelle biogenesis, and regulated cell death. Because the budding yeast Saccharomyces cerevisiae is a unicellular eukaryote amenable to thorough molecular analyses, its use as a model organism helped uncover mechanisms linking lipid metabolism and intracellular transport to complex biological processes within eukaryotic cells. The availability of a versatile analytical method for the robust, sensitive, and accurate quantitative assessment of major classes of lipids within a yeast cell is crucial for getting deep insights into these mechanisms. Here we present a protocol to use liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) for the quantitative analysis of major cellular lipids of S. cerevisiae. The LC-MS/MS method described is versatile and robust. It enables the identification and quantification of numerous species (including different isobaric or isomeric forms) within each of the 10 lipid classes. This method is sensitive and allows identification and quantitation of some lipid species at concentrations as low as 0.2 pmol/&#181;L. The method has been successfully applied to assessing lipidomes of whole yeast cells and their purified organelles. The use of alternative mobile phase additives for electrospray ionization mass spectrometry in this method can increase the efficiency of ionization for some lipid species and can be therefore used to improve their identification and quantitation.

Quantitative Analysis of the Cellular Lipidome of Saccharomyces Cerevisiae Using Liquid Chromatography Coupled with Tandem Mass Spectrometry

We present a protocol using liquid chromatography coupled with tandem mass spectrometry to identify and quantify major cellular lipids in Saccharomyces cerevisiae. The described method for a quantitative assessment of major lipid classes within a yeast cell is versatile, robust, and sensitive.

Lipids are structurally diverse amphipathic molecules that are insoluble in water. Lipids are essential contributors to the organization and function of ...

Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays

Label-free impedance-based assays are increasingly used to non-invasively study ligand-induced GPCR activation in cell culture experiments. The approach provides real-time cell monitoring with a device-dependent time resolution down to several tens of milliseconds and it is highly automated. However, when sample numbers get high (e.g., dose-response studies for various different ligands), the cost for the disposable electrode arrays as well as the available time resolution for sequential well-by-well recordings may become limiting. Therefore, we here present a serial agonist addition protocol which has the potential to significantly increase the output of label-free GPCR assays. Using the serial agonist addition protocol, a GPCR agonist is added sequentially in increasing concentrations to a single cell layer while continuously monitoring the sample&#39;s impedance (agonist mode). With this serial approach, it is now possible to establish a full dose-response curve for a GPCR agonist from just one single cell layer. The serial agonist addition protocol is applicable to different GPCR coupling types, Gq Gi/0 or Gs and it is compatible with recombinant and endogenous expression levels of the receptor under study. Receptor blocking by GPCR antagonists is assessable as well (antagonist mode).

This protocol demonstrates real-time recording of full dose-response relationships for agonist-induced GPCR activation from a single cell layer grown on a single microelectrode using label-free impedance measurements. The new dosing scheme significantly increases throughput without loss in time resolution.

Label-free impedance-based assays are increasingly used to non-invasively study ligand-induced GPCR activation in cell culture experiments. The approach ...

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Stepwise Dosing Protocol for Increased Throughput in Label-Free Impedance-Based GPCR Assays