MM and EM are PhD students within the European School of Molecular Medicine (SEMM). EM is the recipient of a 3-years FIRC-AIRC bursary (Project Code: 22506). Global analyses of R-methyl-proteomes in the TB group are supported by the AIRC IG Grant (Project Code: 21834).

<ol>
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F., 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=LysargiNase+mirrors+trypsin+for+protein+C-terminal+and+methylation-site+identification.">LysargiNase mirrors trypsin for protein C-terminal and methylation-site identification.</a> Nature Methods. 12 (1), 55-58 (2015).</li><li>Rappsilber, J., Mann, M., Ishihama, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protocol+for+micro-purification,+enrichment,+pre-fractionation+and+storage+of+peptides+for+proteomics+using+StageTips.">Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips.</a> Nature Protocols. 2 (8), 1896 (2007).</li><li>Hartel, N. G., Chew, B., Qin, J., Xu, J., Graham, N. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+protein+methylation+profiling+by+combined+chemical+and+immunoaffinity+approaches+reveals+novel+PRMT1+targets.">Deep protein methylation profiling by combined chemical and immunoaffinity approaches reveals novel PRMT1 targets.</a> Molecular &amp; Cellular Proteomics. 18 (11), 2149-2164 (2019).</li><li>Bremang, M., 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=Mass+spectrometry-based+identification+and+characterisation+of+lysine+and+arginine+methylation+in+the+human+proteome.">Mass spectrometry-based identification and characterisation of lysine and arginine methylation in the human proteome.</a> Molecular bioSystems. 9 (9), 2231-2247 (2013).</li><li>Geoghegan, V., Guo, A., Trudgian, D., Thomas, B., Acuto, O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comprehensive+identification+of+arginine+methylation+in+primary+T+cells+reveals+regulatory+roles+in+cell+signalling.">Comprehensive identification of arginine methylation in primary T cells reveals regulatory roles in cell signalling.</a> Nature Communications. 6 (1), 1-8 (2015).</li><li>Tabb, D. L., Huang, Y., Wysocki, V. H., Yates, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Influence+of+basic+residue+content+on+fragment+ion+peak+intensities+in+low-energy+collision-induced+dissociation+spectra+of+peptides.">Influence of basic residue content on fragment ion peak intensities in low-energy collision-induced dissociation spectra of peptides.</a> Analytical Chemistry. 76 (5), 1243-1248 (2004).</li><li>Guthals, A., Bandeira, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Peptide+identification+by+tandem+mass+spectrometry+with+alternate+fragmentation+modes.">Peptide identification by tandem mass spectrometry with alternate fragmentation modes.</a> Molecular &amp; Cellular Proteomics. 11 (9), 550-557 (2012).</li><li>Swaney, D. 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A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Deep+protein+methylation+profiling+by+combined+chemical+and+immunoaffinity+approaches+reveals+novel+PRMT1+targets.">Deep protein methylation profiling by combined chemical and immunoaffinity approaches reveals novel PRMT1 targets.</a> Molecular &amp; Cellular Proteomics. 18 (11), 2149-2164 (2019).</li><li>Uhlmann, T., 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=A+method+for+large-scale+identification+of+protein+arginine+methylation.">A method for large-scale identification of protein arginine methylation.</a> Molecular &amp; Cellular Proteomics. 11 (11), 1489-1499 (2012).</li><li>Wang, K., 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=Antibody-free+approach+for+the+global+analysis+of+protein+methylation.">Antibody-free approach for the global analysis of protein methylation.</a> Analytical chemistry. 88 (23), 11319-11327 (2016).</li><li>Moore, K. 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=A+general+molecular+affinity+strategy+for+global+detection+and+proteomic+analysis+of+lysine+methylation.">A general molecular affinity strategy for global detection and proteomic analysis of lysine methylation.</a> Molecular cell. 50 (3), 444-456 (2013).</li><li>Wu, Z., 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=A+chemical+proteomics+approach+for+global+analysis+of+lysine+monomethylome+profiling.">A chemical proteomics approach for global analysis of lysine monomethylome profiling.</a> Molecular &amp; Cellular Proteomics. 14 (2), 329-339 (2015).</li><li>Sylvestersen, K. B., Horn, H., Jungmichel, S., Jensen, L. J., Nielsen, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proteomic+analysis+of+arginine+methylation+sites+in+human+cells+reveals+dynamic+regulation+during+transcriptional+arrest.">Proteomic analysis of arginine methylation sites in human cells reveals dynamic regulation during transcriptional arrest.</a> Molecular &amp; Cellular Proteomics. 13 (8), 2072-2088 (2014).</li><li>Tay, A. P., Geoghegan, V., Yagoub, D., Wilkins, M. R., Hart-Smith, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MethylQuant:+A+Tool+for+Sensitive+Validation+of+Enzyme-Mediated+Protein+Methylation+Sites+from+Heavy-Methyl+SILAC+Data.">MethylQuant: A Tool for Sensitive Validation of Enzyme-Mediated Protein Methylation Sites from Heavy-Methyl SILAC Data.</a> Journal of Proteome Research. 17 (1), 359-373 (2018).</li></ol>

The authors have nothing to disclose.

The high confidence identification of in vivo protein/peptide methylation by global MS-based proteomics is challenging, due to the risk of high FDR, with several amino acid substitutions and methyl-esterification occurring during sample preparation that are isobaric to methylation and can cause wrong assignments in the absence of orthogonal MS validation strategies. The substoichiometric nature of this PTM further complicates the task of global methyl-proteomics, but can be overcome with the selective enrichment of modified peptides10.
Here, a biochemical and analytical workflow is presented, which is designed to increase the efficiency and reliability of global MS-analysis of R-methyl-peptides through the application of hmSILAC strategy coupled to HpH-RP chromatography peptide fractionation and affinity-enrichment with anti-pan-R-methyl-peptides antibody kits. The former strategy allows orthogonal validation of methyl-peptides and strongly reduces the FDR of identification, while the latter protocol increases their detectability from the background of unmodified peptides22. However, a limitation of this protocol is the requirement of very large amount of starting protein extract (in the range of 20-40 mg) as input for the subsequent peptide fractionation and affinity enrichment, which limits the application of the method to immortalized, fast growing cell lines which can be expanded extensively. Instead, the current setup is not applicable to patient-derived primary cells or tissues. Future investigations should be directed to improve&#160;the protocol in this direction: additional strategies for the biochemical enrichment of methylated peptide over unmodified ones could allow circumventing the use of antibodies, enabling the scaling down of the experiments. Another interesting development could be represented by the combination of the current methods with the chemical modification of proteolytic peptides with isobaric or tandem mass tags, with two-fold potential advantages: on the one hand, the possibility of combining multiple conditions in one single experiment, thus multiplexing the relative quantification of methyl-proteomic changes upon different perturbations; on the other hand, pooling different samples into one prior to chromatographic fractionation and affinity enrichment may allow to reduce the scale of individual experiments.
This protocol relies on two separate digestions of the whole cell extract in parallel with Trypsin and LysargiNase. Trypsin cleaves the peptide bond at the C-terminal side of K and R residues, generating peptides that present a positively charged residue at the C-terminus, in addition to the N-terminal positive charge from the &#945;-amine23. The LysargiNase enzyme selectively hydrolyzes peptidyl-K and -R bonds, generating peptides that bear a K or R at the N-terminal site, which can include K-methylated forms. The use of both proteases increases the overall proteome coverage in large scale MS-analysis, leading to the identification of peptides eventually missed upon a single tryptic digestion18. The double enzymatic digestion, instead, is carried out to reduce the number of possible missed enzymatic cleavages. In fact, methylation of K and R strongly reduce the efficiency of protein cleavage by trypsin. In spite of this precaution, it is still common for methylated peptides to be longer and contain missed cleavages,&#160;which lead to poor CID fragmentation.
The use of another type of fragmentation, such as Electron Transfer Dissociation (ETD), could solve this issue. As a matter of fact, ETD usually does not fragment doubly charged peptide ions efficiently like CID does, but it provides fairly uniform cleavage of peptide precursors of higher charge states (&#8805;3). This could be an advantage in the case of R-methylation, since it frequently occurs in Arginine-rich domains that contain multiple and neighboring R residues. However, ETD has a lower scan rate than CID, so the total number of peptide identifications is reduced24,25,26.
Recently, several protocols that involve the enrichment of post-translationally modified peptides have been coupled with different chromatography separation strategies that help reducing the complexity of the peptide mixture, thus increasing the overall efficiency of modified peptides detection in MS. Here, HpH-RP chromatographic fractionation coupled with non-contiguous concatenation of the fractions is applied. The off-line peptide fractionation based on a high pH reversed phase chromatography displays a high resolving power separation that is orthogonal to the on-line low pH RP-separation carried out downstream during the LC-MS/MS run27. Moreover, the non-contiguous concatenation strategy has two main advantages: first, it increases the protein coverage by pooling early-, middle-, and late-eluting fractions into individual concatenated fractions, preserving the heterogeneity of peptide mixture. Second, the concatenation reduces the subsequent MS run-time analysis, by acquiring a lower number of sample fractions28.
Due to the substoichiometric nature of R-methylation, an enrichment step is necessary in order to facilitate the detection of methyl-peptides in global MS-analysis of modification proteomes. In this protocol, the methyl-peptides are enriched by immuno-affinity precipitation (IAP) using the antibodies anti-SDMA and anti-ADMA in parallel, while the immuno-precipitation of mono-methyl-peptides using anti-MMA antibody is carried out on the FTs from the previous IAP experiments. This order reflects the different efficiency of these antibodies: anti-SDMA and anti-ADMA antibodies have lower binding efficiency compared to&#160;anti-MMA antibody. Noteworthy, this different efficiency may also cause biases in the representation of the different degrees of R-methylations in modification-proteomes experimentally annotated29.
Before the commercial availability of anti-pan-R-methylation antibodies, other separation strategies were applied to boost&#160;R-methylated peptide detection by MS, such as strong cation exchange (SCX) and hydrophilic interaction (HILIC) chromatography. Despite&#160;these techniques could reduce&#160;the complexity of the peptide mixture analyzed in MS, they did not significantly improve the identification of methyl-peptides30,31,32,33.
In spite of all these technical and analytical solutions aiming at increasing the methyl-peptide separation, detection, fragmentation, and sequence annotation, the methyl-proteome coverage is still limited and biased toward the more abundant methylated proteins, such as ribonucleoprotein, RNA-binding helicases, while several known low-abundant modified proteins (e.g., TP53BP1, CHTF8, MCM2) are only detected serendipitously and not reliably over multiple global experiments34. Subcellular fractionation applied prior to the current workflow could improve the detection of such proteins; however, the current experimental scale required do not make this a viable alternative.
Upon MS, the raw data are analyzed through the MaxQuant algorithm for peptide and PTM identification. The analysis of data from hmSILAC experiments is, however, not straightforward with standard search algorithms. For instance, while MaxQuant can efficiently analyze standard SILAC experiments based on the metabolic labeling with isotopically encoded K and R, it does not work efficiently when the isotope-labeling is encoded into a variable PTM, as in the case of heavy-methyl labeling that leads to heavy-methylation. Therefore, the strategy adopted here consists in first analyzing the hmSILAC data with MaxQuant without using its built-in doublet-searching functionality so that the light and heavy peptides can be identified independently; then they are matched with a post-processing software. This bioinformatic workflow also has its own pitfalls, as one has to specify methylations in both heavy and light forms in the Variable Modifications panel of MaxQuant, ending up with a total of eight variable modifications when Methionine oxidation (heavy and light) is also included. Searching too many PTMs with a database search engine such as MaxQuant/Andromeda is impractical, because it leads to an exponential increase of the theoretical peptides the algorithm has to test: our solution was to analyze each MS raw data twice, with different sets of variable PTMs (through the parameters groups function of MaxQuant). After&#160;peptide search, the in-house developed tool&#160;hmSEEKER is employed to support the assignment of heavy-light peptide pairs from the output tables produced by MaxQuant. The first release of the hmSEEKER algorithm has been recently published13, where it was shown that hmSEEKER can identify hmSILAC doublets with FDR &#60; 1%. False positives can still arise from pairs of peaks that by chance have a mass difference&#160;multiple of 4.02 Da, but this is very unlikely&#160;for the doublets&#160;classified as Matched or Mismatched, in light of the following facts: for a Matched or Mismatched doublet to be false, Andromeda has to incorrectly determine the sequence of both the heavy and the light counterpart. Assuming that the search engine has been run with its default parameters, each identification has a 1% probability of being incorrect. Thus, the probability of the hmSILAC counterpart also being incorrect is 0.01%.
One pitfall of hmSILAC is that peptides&#160;containing Methionine in their backbone also generate doublets that are indistinguishable from those generated by methyl-peptides. Nevertheless, from our experience, this should not represent a major issue, first because peptides without methylations can be simply discarded from the MaxQuant output and, second, because hmSEEKER automatically takes into account any Methionine residue in a methyl-peptide when calculating the expected mass difference; last, this risk is also excluded by the fact that the heavy and light modifications are searched in separate parameters groups, so that the search engine cannot split a heavy mono-methylation (+18.03 Da) into a light mono-methylation plus a heavy Methionine (14.01 + 4.02 Da).
A more formal and experimental solution to this problem was proposed by Oreste Acuto and his collaborators, who developed a variant of hmSILAC, named isomethionine methyl-SILAC (iMethyl-SILAC)22. In this alternative metabolic labeling protocol, natural light Methionine is replaced by [13C4]-Methionine, which has the same mass as [13CD3]-Methionine (Met-4), yet it does not produce stable isotopically-encoded methyl-groups, due to the different distribution of the heavy isotopes within the molecular tag. Thus, in iMethyl-SILAC experiments, unmodified Methionine-containing peptides do not generate doublets. However, it should be noted that when Acuto and co-workers compared the performance of iMethyl-SILAC and traditional hmSILAC, the two methods still displayed very similar FDRs.
A possible limitation of hmSEEKER is that it is designed to work directly on MaxQuant output tables so that its source code is not compatible with other search engines, whose output files are structured differently; in this sense, MethylQuant35&#160;provides a good alternative bioinformatic tool that is tailored ad hoc for the direct analysis of MS raw data from hmSILAC-type of experiments and is more flexible in terms of the input files provided. A machine learning model is under development in order to distinguish true and false methyl-peptide H/L doublets without relying on user-defined thresholds.

Arginine (R)-methylation is a post translational modification (PTM) that decorates around 1% of the mammalian proteome1. Protein Arginine Methyltransferases (PRMTs) are the enzymes catalyzing R-methylation reaction by the deposition of one or two methyl groups to the nitrogen (N) atoms of the guanidino group of the side chain of R in a symmetric or asymmetric manner. In mammals, PRMTs can be grouped into three classes-type I, type II, and type III-depending on their capability to deposit both mono-methylation (MMA) and asymmetric di-methylation (ADMA), MMA and symmetric di-methylation (SDMA) or only MMA, respectively2,3. PRMTs mainly target R residues located within glycine- and arginine-rich regions, known as GAR motifs, but some PRMTs, such as PRMT5 and CARM1, can methylate proline-glycine-methionine-rich (PGM) motifs4. R-methylation has emerged as a protein modulator of several biological processes, such as RNA splicing5, DNA repair6, miRNA biogenesis7, and translation2, fostering the research on this PTM.
Mass Spectrometry (MS) is recognized as the most effective technology to systematically study global R-methylation at protein-, peptide-, and site-resolution. However, this PTM requires some particular precautions for its high-confidence identification by MS. First, R-methylation is substoichiometric, with the unmodified form of the peptides being much more abundant than the modified ones, so that mass spectrometers operating in the Data Dependent Acquisition (DDA) mode will fragment high-intensity unmodified peptides more often than their lower-intensity methylated counterparts8. Moreover, most MS-based workflows for R-methylated site identification suffer from limitations at the bioinformatic analysis level. Indeed, the computational identification of methyl-peptides is prone to high False Discovery Rates (FDR), because this PTM is isobaric to various amino acid substitutions (e.g., glycine into alanine) and chemical modification, such as methyl-esterification of aspartate and glutamate9. Hence, methods based on the isotope labeling of methyl groups, such as Heavy Methyl Stable Isotope Labeling with Amino Acids in Cell culture (hmSILAC), have been implemented as orthogonal strategies for confident MS-identification of in vivo methylations, significantly reducing the rate of false positive annotations10.
Recently, various proteome-wide protocols to study R-methylated proteins have been optimized. The development of antibody-based strategies for the immuno-affinity enrichment of R-methyl-peptides has led to the annotation of several hundreds of R-methylated sites in human cells11,12. Furthermore, many studies3,13 reported that coupling antibody-based enrichment with peptide separation techniques such as HpH-RP chromatography fractionation can boost the overall number of methyl-peptides identified.
This article describes an experimental strategy designed for the systematic and high-confidence identification of R-methylated sites in human cells, based on various biochemical and analytical steps: protein extraction from hmSILAC-labeled cells, parallel double enzymatic digestion with Trypsin and LysargiNase proteases, followed by HpH-RP chromatographic fractionation of digested peptides, coupled with antibody-based immuno-affinity enrichment of MMA-, SDMA-, and ADMA-containing peptides. All affinity-enriched peptides are then analyzed by high-resolution Liquid Chromatography (LC)-MS/MS in DDA mode, and raw MS data are processed by MaxQuant algorithm for identificationof R-methyl-peptides. Finally, the MaxQuant output results are processed with hmSEEKER, an in-house developed bioinformatics tool to search pairs of heavy and light methyl-peptides. Briefly, hmSEEKER reads and filters methyl-peptides identifications from the msms file, then matches each methyl-peptide to its corresponding MS1 peak in the allPeptides file, and, finally, searches the peak of the heavy/light peptide counterpart. For each putative heavy-light pair, the Log2 H/L ratio (LogRatio), Retention Time difference (dRT), and Mass Error (ME) parameters are calculated, and doublets that are located within user-defined cut-offs are labeled as true positives. The workflow of the biochemical protocol is described in Figure 1.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Ammonium Bicarbonate (AMBIC)</td>
			<td>Sigma-Aldrich</td>
			<td>09830</td>
			<td></td>
		</tr>
		<tr>
			<td>Ammonium Persulfate (APS)</td>
			<td>Sigma-Aldrich</td>
			<td>497363</td>
			<td></td>
		</tr>
		<tr>
			<td>C18 Sep-Pak columns vacc 6cc (1g)</td>
			<td>Waters</td>
			<td>WAT036905</td>
			<td></td>
		</tr>
		<tr>
			<td>Colloidal Coomassie staining Instant</td>
			<td>Sigma-Aldrich</td>
			<td>ISB1L-1L</td>
			<td></td>
		</tr>
		<tr>
			<td>cOmplete Mini, EDTA-free</td>
			<td>Roche-Sigma Aldrich</td>
			<td>11836170001</td>
			<td>Protease Inhibitor</td>
		</tr>
		<tr>
			<td>Dialyzed Fetal Bovine Serum (FBS)</td>
			<td>GIBCO ThermoFisher</td>
			<td>26400-044</td>
			<td></td>
		</tr>
		<tr>
			<td>DL-Dithiothreitol (DTT)</td>
			<td>Sigma-Aldrich</td>
			<td>3483-12-3</td>
			<td></td>
		</tr>
		<tr>
			<td>DMEM Medium</td>
			<td>GIBCO ThermoFisher</td>
			<td>requested</td>
			<td>&#160;with stabile glutamine and without methionine</td>
		</tr>
		<tr>
			<td>EASY-nano LC 1200 chromatography system</td>
			<td>ThermoFisher</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>EASY-Spray HPLC Columns</td>
			<td>ThermoFisher</td>
			<td>ES907</td>
			<td></td>
		</tr>
		<tr>
			<td>Glycerolo</td>
			<td>Sigma-Aldrich</td>
			<td>G5516</td>
			<td></td>
		</tr>
		<tr>
			<td>HeLa cells</td>
			<td>ATCC</td>
			<td>ATCC CCL-2</td>
			<td></td>
		</tr>
		<tr>
			<td>HEPES</td>
			<td>Sigma-Aldrich</td>
			<td>H3375</td>
			<td></td>
		</tr>
		<tr>
			<td>Iodoacetamide (IAA)</td>
			<td>Sigma-Aldrich</td>
			<td>144-48-9</td>
			<td></td>
		</tr>
		<tr>
			<td>Jupiter C12-RP column</td>
			<td>Phenomenex</td>
			<td>00G-4396-E0</td>
			<td></td>
		</tr>
		<tr>
			<td>L-Methionine</td>
			<td>Sigma-Aldrich</td>
			<td>M5308</td>
			<td>Light (L) Methionine</td>
		</tr>
		<tr>
			<td>L-Methionine-(methyl-13C,d3)</td>
			<td>Sigma-Aldrich</td>
			<td>299154</td>
			<td>Heavy (H) Methionine</td>
		</tr>
		<tr>
			<td>LysargiNase</td>
			<td>Merck Millipore</td>
			<td>EMS0008</td>
			<td></td>
		</tr>
		<tr>
			<td>Microtip Cell Disruptor Sonifier 250</td>
			<td>Branson</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>N,N,N&#8242;,N&#8242;-Tetramethylethylenediamine (TEMED)</td>
			<td>Sigma-Aldrich</td>
			<td>T9281</td>
			<td></td>
		</tr>
		<tr>
			<td>Penicillin-Streptomycin</td>
			<td>GIBCO ThermoFisher</td>
			<td>15140122</td>
			<td></td>
		</tr>
		<tr>
			<td>PhosSTOP</td>
			<td>Roche-Sigma Aldrich</td>
			<td>4906837001</td>
			<td>Phosphatase Inhibitor</td>
		</tr>
		<tr>
			<td>Pierce C18 Tips</td>
			<td>ThermoFisher</td>
			<td>87782</td>
			<td></td>
		</tr>
		<tr>
			<td>Pierce&#160; 0.1% Formic Acid (v/v) in Acetonitrile, LC-MS Grade</td>
			<td>ThermoFisher</td>
			<td>85175</td>
			<td>LC-MS Solvent B</td>
		</tr>
		<tr>
			<td>Pierce&#160; 0.1% Formic Acid (v/v) in Water, LC-MS Grade</td>
			<td>ThermoFisher</td>
			<td>85170</td>
			<td>LC-MS Solvent A</td>
		</tr>
		<tr>
			<td>Pierce&#160; Acetonitrile (ACN), LC-MS Grade</td>
			<td>ThermoFisher</td>
			<td>51101</td>
			<td></td>
		</tr>
		<tr>
			<td>Pierce&#160; Water, LC-MS Grade</td>
			<td>ThermoFisher</td>
			<td>51140</td>
			<td></td>
		</tr>
		<tr>
			<td>Polyacrylamide</td>
			<td>Sigma-Aldrich</td>
			<td>92560</td>
			<td></td>
		</tr>
		<tr>
			<td>Precision Plus Protein&#160; All Blue Prestained Protein Standards</td>
			<td>Bio-Rad</td>
			<td>1610373</td>
			<td></td>
		</tr>
		<tr>
			<td>PTMScan antibodies &#945;-ADMA</td>
			<td>Cell Signaling Technology</td>
			<td>13474</td>
			<td></td>
		</tr>
		<tr>
			<td>PTMScan antibodies &#945;-MMA</td>
			<td>Cell Signaling Technology</td>
			<td>12235</td>
			<td></td>
		</tr>
		<tr>
			<td>PTMScan antibodies &#945;-SDMA</td>
			<td>Cell Signaling Technology</td>
			<td>13563</td>
			<td></td>
		</tr>
		<tr>
			<td>Q Exactive HF Hybrid Quadrupole-Orbitrap Mass Spectrometer</td>
			<td>ThermoFisher</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Sequencing Grade Modified Trypsin</td>
			<td>Promega</td>
			<td>V5113</td>
			<td></td>
		</tr>
		<tr>
			<td>Trifluoroacetic acid</td>
			<td>Sigma-Aldrich</td>
			<td>T6508</td>
			<td></td>
		</tr>
		<tr>
			<td>Ultimate 3000 HPLC</td>
			<td>Dionex</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Urea</td>
			<td>Sigma-Aldrich</td>
			<td>U5378</td>
			<td></td>
		</tr>
		<tr>
			<td>Vacuum Concentrator 5301</td>
			<td>Eppendorf</td>
			<td></td>
			<td>Speed vac</td>
		</tr>
	</tbody>
</table>

a mass spectrometry-based proteomics approach for global and high-confidence protein r-methylation analysis

Protein Arginine (R)-methylation is a widespread protein post-translational modification (PTM) involved in the regulation of several cellular pathways, including RNA processing, signal transduction, DNA damage response, miRNA biogenesis, and translation.
In recent years, thanks to biochemical and analytical developments, mass spectrometry (MS)-based proteomics has emerged as the most effective strategy to characterize the cellular methyl-proteome with single-site resolution. However, identifying and profiling in vivo protein R-methylation by MS remains challenging and error-prone, mainly due to the substoichiometric nature of this modification and the presence of various amino acid substitutions and chemical methyl-esterification of acidic residues that are isobaric to methylation. Thus, enrichment methods to enhance the identification of R-methyl-peptides and orthogonal validation strategies to reduce False Discovery Rates (FDR) in methyl-proteomics studies are required.
Here, a protocol specifically designed for high-confidence R-methyl-peptides identification and quantitation from cellular samples is described, which couples metabolic labeling of cells with heavy isotope-encoded Methionine (hmSILAC) and dual protease in-solution digestion of whole cell extract, followed by off-line High-pH Reversed Phase (HpH-RP) chromatography fractionation and affinity enrichment of R-methyl-peptides using anti-pan-R-methyl antibodies. Upon high-resolution MS analysis, raw data are first processed with the MaxQuant software package and the results are then analyzed by hmSEEKER, a software designed for the in-depth search of MS peak pairs corresponding to light and heavy methyl-peptide within the MaxQuant output files.

Watch this Scientific Journal Video about A Mass Spectrometry-Based Proteomics Approach for Global and High-Confidence Protein R-Methylation Analysis at JoVE.com

A Mass Spectrometry-Based Proteomics Approach for Global and High-Confidence Protein R-Methylation Analysis

Protein Arginine (R)-methylation is a wide-spread post-translational modification regulating multiple biological pathways. Mass spectrometry is the best technology to globally profile the R-methyl-proteome, when coupled to biochemical approaches for modified peptide enrichment. The workflow designed for the high confidence identification of global R-methylation in human cells is described here.

Protein Arginine (R)-methylation is a widespread protein post-translational modification (PTM) involved in the regulation of several cellular pathways, ...

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Nanyang Technological University

Research

JoVE Journal

Biochemistry

2.2K Views.  European Institute of Oncology IRCCS (IEO). Protein Arginine (R)-methylation is a wide-spread post-translational modification regulating multiple biological pathways. Mass spectrometry is the best technology to globally profile the R-methyl-proteome, when coupled to biochemical approaches for modified peptide enrichment. The workflow designed for the high confidence identification of global R-methylation in human cells is described here.

Video: A Mass Spectrometry-Based Proteomics Approach for Global and High-Confidence Protein R-Methylation Analysis

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 ...

A Tandem Liquid Chromatography&#8211;Mass Spectrometry-based Approach for Metabolite Analysis of Staphylococcus aureus

In an effort to thwart bacterial pathogens, hosts often limit the availability of nutrients at the site of infection. This limitation can alter the abundances of key metabolites to which regulatory factors respond, adjusting cellular metabolism. In recent years, a number of proteins and RNA have emerged as important regulators of virulence gene expression. For example, the CodY protein responds to levels of branched-chain amino acids and GTP and is widely conserved in low G+C Gram-positive bacteria. As a global regulator in Staphylococcus aureus, CodY controls the expression of dozens of virulence and metabolic genes. We hypothesize that S. aureus uses CodY, in part, to alter its metabolic state in an effort to adapt to nutrient-limiting conditions potentially encountered in the host environment. This manuscript describes a method for extracting and analyzing metabolites from S. aureus using liquid chromatography coupled with mass spectrometry, a protocol that was developed to test this hypothesis. The method also highlights best practices that will ensure rigor and reproducibility, such as maintaining biological steady state and constant aeration without the use of continuous chemostat cultures. Relative to the USA200 methicillin-susceptible S. aureus isolate UAMS-1 parental strain, the isogenic codY mutant exhibited significant increases in amino acids derived from aspartate (e.g., threonine and isoleucine) and decreases in their precursors (e.g., aspartate and O-acetylhomoserine). These findings correlate well with transcriptional data obtained with RNA-seq analysis: genes in these pathways were up-regulated between 10- and 800-fold in the codY null mutant. Coupling global analyses of the transcriptome and the metabolome can reveal how bacteria alter their metabolism when faced with environmental or nutritional stress, providing potential insight into the physiological changes associated with nutrient depletion experienced during infection. Such discoveries may pave the way for the development of novel anti-infectives and therapeutics.

Here we describe a protocol for the extraction of metabolites from Staphylococcus aureus and their subsequent analysis via liquid chromatography and mass spectrometry.

In an effort to thwart bacterial pathogens, hosts often limit the availability of nutrients at the site of infection. This limitation can alter the ...

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 ...

Sample Preparation for Mass Spectrometry-based Identification of RNA-binding Regions

Noncoding RNAs play important roles in several nuclear processes, including regulating gene expression, chromatin structure, and DNA repair. In most cases, the action of noncoding RNAs is mediated by proteins whose functions are in turn regulated by these interactions with noncoding RNAs. Consistent with this, a growing number of proteins involved in nuclear functions have been reported to bind RNA and in a few cases the RNA-binding regions of these proteins have been mapped, often through laborious, candidate-based methods.
Here, we report a detailed protocol to perform a high-throughput, proteome-wide unbiased identification of RNA-binding proteins and their RNA-binding regions. The methodology relies on the incorporation of a photoreactive uridine analog in the cellular RNA, followed by UV-mediated protein-RNA crosslinking, and mass spectrometry analyses to reveal RNA-crosslinked peptides within the proteome. Although we describe the procedure for mouse embryonic stem cells, the protocol should be easily adapted to a variety of cultured cells.

We describe a protocol to identify RNA-binding proteins and map their RNA-binding regions in live cells using UV-mediated photocrosslinking and mass spectrometry.

Noncoding RNAs play important roles in several nuclear processes, including regulating gene expression, chromatin structure, and DNA repair. In most ...

Protein Digestion, Ultrafiltration, and Size Exclusion Chromatography to Optimize the Isolation of Exosomes from Human Blood Plasma and Serum

Exosomes, a type of nanovesicle released from all cell types, can be isolated from any bodily fluid. The contents of exosomes, including proteins and RNAs, are unique to the cells from which they are derived and can be used as indicators of disease. Several common enrichment protocols, including ultracentrifugation, yield exosomes laden with soluble protein contaminants. Specifically, we have found that the most abundant proteins within blood often co-purify with exosomes and can confound downstream proteomic studies, thwarting the identification of low abundance biomarker candidates. Of additional concern is irreproducibility of exosome protein quantification due to inconsistent representation of non-exosomal protein levels. The protocol detailed here was developed to remove non-exosomal proteins that co-purify along with exosomes, adding rigor to the exosome purification process. Five methods were compared using paired blood plasma and serum from five donors. Analysis using nanoparticle tracking analysis and micro bicinchoninic acid protein assay revealed that a combined protocol utilizing ultrafiltration and size exclusion chromatography yielded the optimal vesicle enrichment and soluble protein removal. Western blotting was used to verify that the expected abundant blood proteins, including albumin and apolipoproteins, were depleted.

Here, we present a protocol to purify exosomes from both plasma and serum with reduced co-purification of non-exosomal blood proteins. The optimized protocol includes ultrafiltration, protease treatment, and size exclusion chromatography. Enhanced purification of exosomes benefits downstream analyses, including more accurate quantification of vesicles and proteomic characterization.

Exosomes, a type of nanovesicle released from all cell types, can be isolated from any bodily fluid. The contents of exosomes, including proteins and ...

Extracellular Protein Microarray Technology for High Throughput Detection of Low Affinity Receptor-Ligand Interactions

Secreted factors, membrane-tethered receptors, and their interacting partners are main regulators of cellular communication and initiation of signaling cascades during homeostasis and disease, and as such represent prime therapeutic targets. Despite their relevance, these interaction networks remain significantly underrepresented in current databases; therefore, most extracellular proteins have no documented binding partner. This discrepancy is primarily due to the challenges associated with the study of the extracellular proteins, including expression of functional proteins, and the weak, low affinity, protein interactions often established between cell surface receptors. The purpose of this method is to describe the printing of a library of extracellular proteins in a microarray format for screening of protein-protein interactions. To enable detection of weak interactions, a method based on multimerization of the query protein under study is described. Coupled to this microbead-based multimerization approach for increased multivalency, the protein microarray allows robust detection of transient protein-protein interactions in high throughput. This method offers a rapid and low sample consuming-approach for identification of new interactions applicable to any extracellular protein. Protein microarray printing and screening protocol are described. This technology will be useful for investigators seeking a robust method for discovery of protein interactions in the extracellular space.

Here, we present a protocol to screen extracellular protein microarrays for identification of novel receptor-ligand interactions in high throughput. We also describe a method to enhance detection of transient protein-protein interactions by using protein-microbead complexes.

Secreted factors, membrane-tethered receptors, and their interacting partners are main regulators of cellular communication and initiation of signaling ...

Preparation of Chloroplast Sub-compartments from Arabidopsis for the Analysis of Protein Localization by Immunoblotting or Proteomics

Chloroplasts are major components of plant cells. Such plastids fulfill many crucial functions, such as assimilation of carbon, sulfur and nitrogen as well as synthesis of essential metabolites. These organelles consist of the following three key sub-compartments. The envelope, characterized by two membranes, surrounds the organelle and controls the communication of the plastid with other cell compartments. The stroma is the soluble phase of the chloroplast and the main site where carbon dioxide is converted into carbohydrates. The thylakoid membrane is the internal membrane network consisting of grana (flat compressed sacs) and lamellae (less dense structures), where oxygenic photosynthesis takes place. The present protocol describes step by step procedures required for the purification, using differential centrifugations and Percoll gradients, of intact chloroplasts from Arabidopsis, and their fractionation, using sucrose gradients, in three sub-compartments (i.e., envelope, stroma, and thylakoids). This protocol also provides instructions on how to assess the purity of these fractions using markers associated to the various chloroplast sub-compartments. The method described here is valuable for subplastidial localization of proteins using immunoblotting, but also for subcellular and subplastidial proteomics and other studies.

Here, we describe a method to purify intact chloroplasts from Arabidopsis leaves and their three main sub-compartments (envelope, stroma, and thylakoids), using a combination of differential centrifugations, continuous Percoll gradients, and discontinuous sucrose gradients. The method is valuable for subplastidial and subcellular localization of proteins by immunoblotting and proteomics.

Chloroplasts are major components of plant cells. Such plastids fulfill many crucial functions, such as assimilation of carbon, sulfur and nitrogen as ...

Detection of Protein Ubiquitination Sites by Peptide Enrichment and Mass Spectrometry

The posttranslational modification of proteins by the small protein ubiquitin is involved in many cellular events. After tryptic digestion of ubiquitinated proteins, peptides with a diglycine remnant conjugated to the epsilon amino group of lysine (&#39;K-&#949;-diglycine&#39; or simply &#39;diGly&#39;) can be used to track back the original modification site. Efficient immunopurification of diGly peptides combined with sensitive detection by mass spectrometry has resulted in a huge increase in the number of ubiquitination sites identified up to date. We have made several improvements to this workflow, including offline high pH reverse-phase fractionation of peptides prior to the enrichment procedure, and the inclusion of more advanced peptide fragmentation settings in the ion routing multipole. Also, more efficient cleanup of the sample using a filter-based plug in order to retain the antibody beads results in a greater specificity for diGly peptides. These improvements result in the routine detection of more than 23,000 diGly peptides from human cervical cancer cells (HeLa) cell lysates upon proteasome inhibition in the cell. We show the efficacy of this strategy for in-depth analysis of the ubiquitinome profiles of several different cell types and of in vivo samples, such as brain tissue. This study presents an original addition to the toolbox for protein ubiquitination analysis to uncover the deep cellular ubiquitinome.

We present a method for the purification, detection, and identification of diGly peptides that originate from ubiquitinated proteins from complex biological samples. The presented method is reproducible, robust, and outperforms published methods with respect to the level of depth of the ubiquitinome analysis.

The posttranslational modification of proteins by the small protein ubiquitin is involved in many cellular events. After tryptic digestion of ...

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Understanding the interactions between genes, the environment and management in agricultural practice could allow more accurate prediction and management of product yield and quality. Metabolomics data provides a read-out of these interactions at a given moment in time and is informative of an organism&#39;s biochemical status. Further, individual metabolites or panels of metabolites can be used as precise biomarkers for yield and quality prediction and management. The plant metabolome is predicted to contain thousands of small molecules with varied physicochemical properties that provide an opportunity for a biochemical insight into physiological traits and biomarker discovery. To exploit this, a key aim for metabolomics researchers is to capture as much of the physicochemical diversity as possible within a single analysis. Here we present a liquid chromatography-mass spectrometry-based untargeted metabolomics method for the analysis of field-grown wheat grain. The method uses the liquid chromatograph quaternary solvent manager to introduce a third mobile phase and combines a traditional reversed-phase gradient with a lipid-amenable gradient. Grain preparation, metabolite extraction, instrumental analysis and data processing workflows are described in detail. Good mass accuracy and signal reproducibility were observed, and the method yielded approximately 500 biologically relevant features per ionization mode. Further, significantly different metabolite and lipid feature signals between wheat varieties were determined.

A method for the untargeted analysis of wheat grain metabolites and lipids is presented. The protocol includes an acetonitrile metabolite extraction method and reversed phase liquid chromatography-mass spectrometry methodology, with acquisition in positive and negative electrospray ionization modes.

Understanding the interactions between genes, the environment and management in agricultural practice could allow more accurate prediction and management ...