We acknowledge financial support from Institute of Food research BBSRC Core Strategic Grant funds, BBSRC Project BB/J004545/1.

1. Proteolysis and Analysis of Reference Myoglobins
<ol>
	<li>Proteolysis of reference myoglobins
	<ol>
		<li>Prepare solutions of the purified reference myoglobins (range 0.2 - 0.5 mg/ml in 25 mM ammonium bicarbonate)3.</li>
		<li>Transfer 1 ml aliquots of each sample to 2 ml centrifuge tubes.</li>
		<li>Thermally denature the extracted proteins by heating the sample in a hot block at 95 &#176;C for 30 min. Cool the sample for approximately 15 min until it reaches room temperature. Add 30 mg of urea (final concentration 0.5 M) to enhance the digestion, then mix.</li>
	</ol>
	</li>
	<li>Tryptic proteolysis
	<ol>
		<li>Prepare a 1 mg/ml solution of trypsin in 25 mM ammonium bicarbonate and store on ice as required. Add a sufficient volume of trypsin such that the final enzyme activity is 420 BAEE (N-benzoyl-L-arginine ethyl ester hydrochloride) units/mg of extracted protein, then mix by gentle vortexing and allow to proteolyze overnight at 37 &#176;C.</li>
		<li>Carry out sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE)17 to demonstrate the completeness of the proteolysis.</li>
	</ol>
	</li>
	<li>Desalting of the post-proteolysis sample
	<ol>
		<li>Dilute the sample 1:2 v:v with water.</li>
		<li>Activate a polymeric reversed-phase (RP) cartridge filled with 30 mg RP material by adding 1 ml of methanol, then equilibrate the cartridge by adding 1 ml of 1% formic acid.</li>
		<li>Load the sample onto the cartridge under gravity.</li>
		<li>Wash with 1 ml of 5% methanol/1% formic acid under gravity.</li>
		<li>Elute the peptides with 1 ml of acetonitrile/water (90:10 v:v; 0.1% formic acid) under gravity into 2 ml microcentrifuge tubes prefilled with 5 &#181;l dimethylsulphoxide (DMSO).</li>
		<li>Remove the solvent under vacuum at 50 &#176;C using a centrifugal evaporator for 120 min, then redissolve the residue in 250 &#181;l acetonitrile/water (3:97 v:v; 0.1% formic acid).</li>
		<li>Transfer the solution to a low volume auto sampler vial. 
		Note: Samples can be stored at 4 &#176;C until ready for liquid chromatography mass spectrometry (LC/MS) analysis.</li>
	</ol>
	</li>
	<li>Generation of transition lists for MRM
	<ol>
		<li>Locate the myoglobin sequences for the different meats from the UniProt database.</li>
		<li>Enter the myoglobin sequences into the &#39;Target&#39; box of the peptide and transition prediction software (e.g., Skyline). If required, hover over a peptide to reveal its fragment list.</li>
		<li>Click on &#39;Settings&#39; and select &#39;Peptide Settings&#39;. Input the preferences for the digestion (i.e., trypsin) and the number of missed cleavages (0). Enter the required selection for additional parameters, in particular, the peptide length (6 - 25), N-terminal exclusions (0) and assumed amino acid modifications (none).</li>
		<li>Click on &#39;Settings&#39; and select &#39;Transition Settings&#39;. Select the preferences for the instrument type used for the LC/MS analysis.</li>
		<li>Click on &#39;Export&#39; and select &#39;Transition List&#39; to create a spreadsheet containing the generated MRM transitions and parameters.</li>
	</ol>
	</li>
	<li>Analysis by LC/MS
	<ol>
		<li>Set up a system of binary gradient (water (A) and acetonitrile (B), each with 0.1% formic acid v:v) high performance liquid chromatograph (HPLC) with auto sampler, C18 core shell HPLC column (10 cm x 2.1 mm, 2.6 &#181;m particle size) connected to a triple quadrupole mass spectrometer operated in positive electrospray mode with MRM detection.</li>
		<li>In the data collection software (e.g., Analyst), select &#39;File&#39; and &#39;New&#39; and click on &#39;Acquisition Method&#39; in the pop-up box then click on &#39;OK&#39;. 
		Note: This opens the instrument method editor, which contains a list of the connected devices that will enable the setup of a new LC/MS method.</li>
		<li>Click on &#39;Binary Pump&#39; and input the flow rate value (300 &#181;l/min) and the gradient times in the table, setting a binary gradient profile of 3% B to 30% B over 22 min, increasing to 100% B at 23 min for a 5 min wash out before returning to initial conditions and re-equilibration for a further 6 min.</li>
		<li>Click on &#39;Autosampler&#39; and insert the injection volume (5 &#181;l). Enable the &#39;Needle Wash Cycle&#39; and enter the &#39;Wash Time&#39; (30 sec) and select &#39;Flush Port&#39;.</li>
		<li>Click on &#39;Thermostatted Column Controller&#39; and in &#39;Column Oven Properties&#39; set the &#39;Left Temperature&#39; and &#39;Right Temperature&#39; (40 &#176;C).</li>
		<li>Click on &#39;Mass Spectrometer&#39; and then click on &#39;Edit Parameters&#39; to enter the source gas conditions. Select the &#39;Scan Type&#39; as &#39;MRM(MRM)&#39; and the &#39;Polarity&#39; as &#39;Positive&#39;. Go to &#39;Period Summary&#39; and enter the &#39;Duration Time&#39;, the total time for the LC analysis and equilibration (35 min).</li>
		<li>In the table right click and select &#39;Declustering Potential (DP)&#39; and &#39;Collision Energy (CP)&#39; to add these columns to the table. Enter the Q1, Q3, Time (msec), ID, DP and CE values for all of the transitions, for a single meat species, created in the transition list (see step 1.4.5). 
		Note: Time (msec) refers to the dwell time, the time the mass spectrometer spends scanning each transition, the summation of which should not exceed 3 sec.</li>
		<li>Save the Acquisition Method file (file extension .dam). 
		Note: Steps 1.5.2 - 1.5.8 need to be repeated for each meat species. This will create a single method file for each meat species in screen mode in preparation for analysis below.</li>
		<li>In the data collection software, click on &#39;Acquire&#39; and select &#39;Equilibrate&#39;. In the box that opens, select the required Acquisition Method to begin the instrument equilibration.</li>
		<li>Put the sample vials in a rack in the auto sampler.</li>
		<li>Click on &#39;File&#39; and select &#39;New&#39; then &#39;Acquisition Batch&#39;. In the &#39;Sample&#39; tab select &#39;Add Set&#39; then &#39;Add Samples&#39;. Insert the number of samples to be analyzed and click on &#39;OK&#39;. In the &#39;Acquisition&#39; box select the method file that will be used for the analysis from the drop down menu.</li>
		<li>In the table, select &#39;Plate Code&#39; and select the appropriate tray configuration from the drop down menu. Left click in the &#39;Plate Code&#39; column header then right click and select &#39;Fill Down&#39;. In &#39;Vial Position&#39; enter the position of each sample in the auto sampler in the rows.</li>
		<li>In &#39;Data File&#39; enter the file name for the acquisition, then left click in the column header followed by right click and select &#39;Fill Down&#39;. In &#39;Sample Name&#39; insert the identity of each of the samples to be analyzed. Save as an acquisition batch file (file extension .dab).</li>
		<li>Click on the &#39;Submit&#39; tab then highlight the samples that need to be analyzed on the LC/MS. Click on &#39;Submit&#39;. Click on &#39;Acquire&#39; and &#39;Start Sample&#39; to begin the analysis. 
		Note: Each acquisition method will scan for the MRM transitions across the entire length of the chromatograph for a single meat species. Mass spectrometer settings for an MRM acquisition vary according to instrument type and peptide.</li>
		<li>View the generated data files using data viewing software. Click on XIC (extracted ions) and in the drop down list highlight all the fragments (Q3 values) for a single precursor (Q1). A new pane will open that shows only the selected transitions.</li>
		<li>Record the retention time (Rt) for groups of concurrent transitions since these correspond to a single peptide.</li>
		<li>Repeat the previous two steps for each set of transitions in order to assign the peaks to their respective peptides for each of the meat species.</li>
		<li>Record the marker peptides which are suitable for providing species identification (e.g., peptide HPGDFGADAQGAMTK, precursor m/z = 752, Rt = 12.0 min, for horse), together with their retention times, and note which form corresponding pairs suitable for relative quantitation. 
		Note: For example, the horse marker peptide (precursor m/z = 752) has a corresponding beef peptide, HPSDFGADAQAAMSK (precursor m/z = 767, Rt = 13.2 min).</li>
		<li>In order to create a single dynamic method embracing all of the meat species, in the data viewing software, for each meat species in turn, open the XIC transition data for each precursor (assigned to a particular peptide in 1.5.8).</li>
		<li>Zoom in on the peak cluster at the selected retention time by left-clicking and dragging the cursor underneath the cluster. Identify the most intense transitions (by right-clicking on the peak label).</li>
		<li>Manually record the transitions and retention times in a spreadsheet.</li>
		<li>To enter the parameters as a new dynamic method on the LC/MS software, click on &#39;Mass Spectrometer&#39; and then click on &#39;Edit Parameters&#39; to enter the source gas conditions. Select the &#39;Scan Type&#39; as &#39;MRM(MRM)&#39; and the &#39;Polarity&#39; as &#39;Positive&#39;.</li>
		<li>Go to &#39;Period Summary&#39; and enter the duration time (set as the total time for the LC analysis and equilibration). In the table right click and select &#39;Declustering Potential (DP)&#39; and &#39;Collision Energy (CP)&#39; to add these columns to the table. 
		Note: The &#39;Time&#39; column now refers to the expected retention time (min) for each transition.</li>
		<li>In the &#39;Edit Parameters&#39; section of the LC/MS data collection software, check the &#39;Scheduled MRM&#39; box. Input the Q1, Q3, Time (min), ID, DP and CE values for the transitions created in the spreadsheet (1.5.21) and save the Acquisition Method (file extension .dam). 
		Note: This method typically reduces the number of MRM transitions to the 4 most intense for each peptide and scans only across the retention time window for each peptide peak, giving improved sensitivity and quality of the data. A &#39;dynamic&#39; method is a &#39;guided retention time windowing&#39; method, sometimes called scheduling.</li>
	</ol>
	</li>
</ol>
2. Preparation and Analysis of Calibration Samples
<ol>
	<li>Extraction of meat mixtures
	<ol>
		<li>Using meat previously frozen then ground into a powder, prepare a range of meat mixtures by weighing respective amounts of meat (total mass of about 300 mg) into 15 ml plastic centrifuge tubes.</li>
		<li>Add 4 ml of extraction buffer (0.15 M potassium chloride + 0.15 M phosphate buffer at pH 6.5). Vortex for 30 sec. Extract on a lab shaker at room temperature for 2 hr at 250 cycles/min. 
		Note: Cycles/min refers to an oscillatory motion.</li>
		<li>Transfer 2 ml of the extract into a 2 ml microcentrifuge tube. Centrifuge for 5 min at 4 &#176;C at 17,000 x g.</li>
		<li>Transfer 200 &#181;l aliquots of the supernatant (reserving a small amount for protein assay, see 2.2) into 2 ml centrifuge tubes and dry using a centrifugal evaporator (pre-set program: 50 &#176;C, with no venting and 120 min duration).</li>
	</ol>
	</li>
	<li>Protein assay
	<ol>
		<li>Transfer 7 &#181;l aliquots of the reserved supernatant (see 2.1.4) in triplicate into the wells of a 96 well plate.</li>
		<li>Transfer 7 &#181;l aliquots of a series of protein standards in triplicate, range 0 - 1.0 mg/ml bovine serum albumin (BSA), to the same 96 well plate.</li>
		<li>Add 200 &#181;l of Coomassie plus protein assay reagent to each well.</li>
		<li>Visually compare the color of the sample wells with the protein standards to check the samples are in the range of the calibration standards. If necessary, repeat with diluted sample so it becomes in range.</li>
		<li>Leave the plate to stand for 3 min.</li>
		<li>Burst any bubbles that have formed with a hypodermic needle.</li>
		<li>Analyze the plate on the plate reader using a standard endpoint protocol at a wavelength of 595 nm.</li>
		<li>Determine the protein concentration of the samples using calibration data from the protein standards. 
		Note: This is required for calculation of the amount of trypsin used in the tryptic digest.</li>
	</ol>
	</li>
	<li>Proteolysis of meat mixtures
	<ol>
		<li>Redissolve the dried residue from step 2.1.4 in 1 ml of 25 mM ammonium bicarbonate solution. Mix well on a rotamixer.</li>
		<li>Follow the protocol from step 1.1.3 to 1.3.7.</li>
	</ol>
	</li>
	<li>Analysis by LC/MS
	<ol>
		<li>Set up the LC/MS as previously (step 1.5.1).</li>
		<li>Create a new acquisition batch as outlined previously (steps 1.5.9 - 1.5.14), selecting the acquisition method created at step at 1.5.24 that uses a dynamic LC/MS method combining all of the meat species, and acquire the data for the digested meat samples.</li>
		<li>Display the full chromatogram in the data viewing software. Display the XIC for each transition set in turn. Visually confirm each cluster contains the requisite number of bell-shaped peaks at the expected retention time, thereby confirming the existence of the selected peptide.</li>
		<li>Perform quantitation using the data viewing software to integrate peak areas for each of the transitions of interest by double-clicking on &#39;Build Quantitation Method&#39; in the Navigation bar.</li>
		<li>In the &#39;Select Sample&#39; pane select the &#39;Data File&#39; and the &#39;Sample&#39; to be analyzed to generate an &#39;Analytes&#39; table.</li>
		<li>Click on the &#39;Integration&#39; tab to display the first of the transitions (Analytes) to be integrated.</li>
		<li>Click on &#39;Analyte&#39; box to display the drop down list of transitions. Select each transition in turn to display it and visually confirm the correct peak is selected for integration. To modify or force the integration, left click and drag the cursor over the target peak (this will be highlighted in green). Click on the &#39;Select Peak&#39; button and click &#39;Apply&#39;.</li>
		<li>Save the workspace as a method file (.qmf). 
		Note: This creates a Quantitation Method file for subsequent calculation of sample peak areas.</li>
		<li>Double click &#39;Quantitation Wizard&#39; in the Navigation bar. In the &#39;Select Samples&#39; window create &#39;Quantitation Set&#39; by selecting a single &#39;Data File&#39;, then one or more &#39;Available Samples&#39;. Select &#39;Next&#39; to display &#39;Select Settings and Query&#39; box. Leave with defaults, select &#39;Next&#39; to display &#39;Select Method&#39;. From the drop down &#39;Method&#39; box select the &#39;Integration Method&#39; file created in step 2.4.8, then select &#39;Finish&#39;. 
		Note: This creates a &#39;Results Table&#39;, including transition peak areas arising from meat mixtures.</li>
		<li>Save the &#39;Results Table&#39; (file extension .rdb), export as a text file (.txt) and open it in spreadsheet to review the data.</li>
		<li>Plot graphs of the percentage (by transition peak area) of one meat in another versus the measured percentage (w/w) of the two meats for the selected MRM for selected transitions from corresponding peptides, focusing on those cases where the two fragments contain the same number of amino acids as counted from the C-terminal end. 
		Note: Identical fragments with identical fragmentation sites give optimal results.</li>
		<li>Examine the plots from 2.4.11 above. Either visually, or using a trend line tool in the plotting package, identify a group of plots which are both linear and of similar gradient. Use any one or more of these CPCP plus fragment combinations for calibration in real meat samples. 
		Note: A plot showing an unusual gradient may indicate either peptide or fragment suppression with a consequent reduction in signal strength. Non-linear plots may indicate poor peak detection or other problems.</li>
	</ol>
	</li>
</ol>
3. Meat Samples
<ol>
	<li>Extraction of proteins from target meat samples
	<ol>
		<li>Where applicable, excise extraneous non-meat material from the sample using a spatula. For example, scrape away sauce and pasta from a chilled lasagna.</li>
		<li>Weigh 20 g of the meat into a metal beaker.</li>
		<li>Add 100 ml of 0.15 M potassium chloride/0.15 M potassium monophosphate buffer at pH 6.5.</li>
		<li>Extract the proteins by blending the meat in a high speed homogenizer for 1 min.</li>
		<li>Follow the protocol from step 2.1.4 - 2.3.2.</li>
	</ol>
	</li>
	<li>Analysis of samples by LC/MS
	<ol>
		<li>Repeat step 2.4.2 to acquire data using the dynamic LC/MS method.</li>
		<li>Identify the peptides from each meat myoglobin as performed in step 2.4.3.</li>
		<li>For quantitation, use quantitation software to integrate the peak areas for each transition of interest, as outlined in step 2.4.9.</li>
		<li>For identification of species in a mixture, record those marker peptides satisfying agreed criteria for numbers of transitions and signal to noise for those transitions.</li>
		<li>For quantitation, use integrated transition peak areas as agreed from step 2.4.12 and, using percentage by transition peak area, calculate the percentage of myoglobin from the two species in the mixture.</li>
		<li>Use prior knowledge from the literature18 of likely myoglobin levels in the meats to estimate the relative w/w amounts of two meats present in the sample.</li>
	</ol>
	</li>
</ol>

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The authors have nothing to disclose.

The selection of a suitable target protein is important. A good target protein needs to have corresponding forms in species of interest, sufficient species-dependent sequence variation, species specificity, and exist in accessible quantities within the organisms. For assessing mixtures that have undergone processing (for example, heat treatment), a protein having a sequence relatively immune to that processing is desirable. Myoglobin is a good candidate for red meats, including cooked red meats, but is not the only possibility. Once the target protein is decided, the most critical part of the protocol is the protein proteolysis. A protein different from myoglobin may well demand an alternative proteolysis protocol.
The protocol as described includes a segment based on reference purified protein. This aims to discover retention time windows and suitable precursor and fragment ions. This segment is very helpful but not essential.
Although corresponding peptide pairs from two species of interest can be listed even without experiment, it is sometimes the case that a sequence difference has dramatic consequences on the digestion profile. For example, the peptide pair VLGFHG (beef) and ELGFQG (horse) give an anomalous quantitation result (manifest as a gradient less than one in Figure 2). This is because the latter peptide arises from a relatively suppressed K-E cleavage, causing an under-estimate of the level of horse in the mixture. Corresponding peptides starting with different amino acids are therefore best avoided. Often the fragments from two corresponding peptides have identical amino acid sequences and are well-behaved, but this is not always the case and needs to be checked during method development. Species identification is much less sensitive to these issues than relative quantitation.
The protocol has been demonstrated for four red meats3. Additional meat species can be included, though the quality of the transition peak shape may deteriorate if too many marker peptides co-elute, effectively reducing the dwell time and ultimately degrading relative quantitation estimates. Improved instrumentation, already available, will improve this. A related issue is that not all meats have different myoglobins. For example, horse, donkey and zebra myoglobins are identical and thus strictly speaking the method is only capable of detecting horse or donkey or zebra in beef. In some cases, even though myoglobins are not identical, some key peptides can be. For example, some lamb myoglobin-derived marker peptides also appear in goat.
A complication facing this and any other protein-based quantitation method is that the protein level must be assumed constant across all species if the protein or peptide levels are to equate trivially to levels of meats in a mixture. For myoglobin and the four red meats this is not universally true. The levels in general are species dependent, with pork exhibiting the lowest level of the four. In addition, the myoglobin level varies with meat cut and animal age. So although ratios of transition peak areas map reliably to ratios of myoglobin, the mapping to ratio of actual meats is an estimate drawing on assumptions regarding likely sources of the meats in the mixture.
The approach outlined in this work differs in a number of ways from other published contributions. A more typical route is to use proteomic methods to identify various disparate species-dependent marker peptides, in which case the markers for different species possess no particular relationship with one another8-12,14,19. By contrast, we have selected proteins common to all species of interest up to species-dependent sequence variants3. Apart from being central to our relative quantitation strategy, this has the advantage that sample preparation strategies can be optimized. In addition, such corresponding proteins might be expected to behave similarly, for example, in extraction or in commercial processing of samples such as cooking or canning. Species identification then normally proceeds via detection of disparate marker peptides, whereas in the CPCP approach species identification proceeds via detection of closely related peptides possessing typically one or two sequence differences. Finally, quantitation of proteins to estimate the percent by weight of one species in another might conventionally proceed via absolute quantitation of each protein separately based on known standards7,14,15. However using the CPCP method there is no need for calibration methods. Instead, relative levels are estimated by comparing signal strengths of two corresponding peptides from the two species, bypassing the absolute measurement stage altogether. Since the ultimate goal is a percentage by weight of one species in another, a relative quantitation, then the CPCP is both more direct and simpler than comparing two absolute quantitation measurements. These features translate into short experimental times, anticipated to be approximately two hr using refined protocols, making the technique useful as a rapid surveillance tool in the realm of food fraud detection.

The European horse meat scandal of 2013, in which undeclared horse meat was found in a number of supermarket beef products1, highlights the need for testing methods capable of detecting and measuring food fraud in meat. Several technologies have been explored, especially enzyme-linked immunosorbent assay (ELISA) and DNA-based methods2. An alternative route, based on mass spectrometry, targets species-specific peptides which in turn arise from species-specific proteins. Here we outline one such peptide-based approach that offers both identification and relative quantitation of the adulterant species in a meat mixture3. 
The protocol is framed in the context of red meats and the desire to determine the presence of one in another at the level of 1% by weight, the level considered by some to represent fraudulent food adulteration as opposed to contamination4. The method relies in the first instance on identifying a protein which is nominally 'the same' in all target meats. Myoglobin, the protein responsible for the red color of meat, is a good candidate since it is abundant, relatively heat tolerant and water soluble, and has been used for species determination of meat previously5,6. The myoglobins for beef (Bos Taurus), pork (Sus scrofa), horse (Equus caballus) and lamb (Ovis aries)3, for instance, are nominally the same, as required, but their sequences are not identical. Such groups of 'similar but different' proteins, like these four myoglobins, can conveniently be described as 'corresponding proteins'. The sequence differences in these four myoglobins are species-specific: for example, the full myoglobin proteins for beef and horse, P02192 and P68082 respectively, each comprise 154 amino acids with 18 sequence differences between the two. Subject to proteolysis using trypsin these proteins produce two sets of peptides, some of which are identical, and some which show one or more species-specific amino acid differences: corresponding proteins therefore give rise to corresponding peptides. 
The CPCP approach, therefore, seeks first to identify proteins from two or more species where these proteins exhibit limited species-specific sequence variants. These are corresponding proteins. Following proteolysis, corresponding proteins give rise to peptides, some of which likewise display species-specific sequence variants inherited from the parent protein. These are corresponding peptides. The CPCP approach can be used to compare levels of two corresponding proteins in a mixed species sample by monitoring the levels of corresponding peptides.
The natural technology for the detection of known peptides is multiple reaction monitoring mass spectrometry, or MRM-MS7. Species-specific peptides yield precursor ions, which along with their mass spectrometry fragment ions, are easily itemized in advance by software tools. These lists are then used to instruct the mass spectrometer to record only specific precursor plus fragment ion pairs, called transitions. A particular target peptide is therefore identified not only by its retention time in the chromatography preceding the mass spectrometer, but also by a set of transitions sharing a common precursor ion. This is a highly selective means of detecting known peptides that makes efficient use of the mass spectrometer resource.
Other authors have used mass spectrometry to test for meat adulteration via peptide markers but from disparate proteins8-14. Using the corresponding proteins, corresponding peptides (CPCP) scheme, however, means experimental conditions can be optimized, aiding identification of the species in the mixture from known species-specific transitions. In addition, corresponding proteins and peptides will generally behave similarly in the extraction, proteolysis and detection stages. Since transition peak areas are quantitative and reproducible, ratios of peak areas arising from pairs of corresponding peptides from different species provide a direct estimate of the relative quantities of two meats in a mixture. In contrast, more traditional quantitation routes exploit calibrations based on reference materials to establish absolute quantitation14,15.
Though the protocol is outlined in the context of myoglobin and meat, proteins other than myoglobin could be used for identification and relative quantitation via the CPCP strategy in meat mixtures, though potentially with modifications to the protocol. In addition the strategy is also applicable to binary mixtures of other species sharing one or more corresponding proteins.
The starting point for the protocol is purified 'reference' myoglobin, which for some species can be purchased but which for others must be prepared by conventional size-exclusion chromatography. The procedure for preparing reference myoglobin is not included in the protocol, but is described elsewhere3. Software tools16 are used to list candidate peptides and transitions arising from myoglobins of interest. Each reference myoglobin is subjected to proteolysis and the resultant peptides analyzed by liquid chromatography electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) to discover which of the candidate precursor ions and transitions are most useful, and to determine the matching peptide retention times. The outcome of this stage is a revised list of target peptides with their transitions, suitable for species determination, and a list of CPCP pairs, suitable for relative quantitation. To test real meats, sample extractions are prepared then subjected to proteolysis to generate peptides both from myoglobin and other extraneous proteins. The myoglobin-based peptides are then monitored by LC-ESI-MS/MS based on their listed transitions. The species present in a mixture are identified by the transition peaks associated with marker peptides. Estimates of the relative amounts of two meats in a binary mixture are calculated using ratios of transition peak areas. A set of test mixtures of pairs of meats will allow the ratio of peak areas for a given pair of transitions to be checked and calibrated against actual mixtures.

<table border="0" cellpadding="0" cellspacing="0" width="1076">
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		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Uniprot database</td>
			<td style="width:243px;">www.uniprot.org</td>
			<td style="width:119px;"></td>
			<td style="width:499px;">Freely accessible database of protein sequences</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;">Skyline software</td>
			<td style="width:243px;">www.skyline.gs.washington.edu</td>
			<td style="width:119px;"></td>
			<td style="width:499px;">Free software to download that enables the creation of targeted methods for proteomic studies, peptide and fragment prediction&#160;</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Ammonium bicarbonate</td>
			<td style="width:243px;">Sigma-Aldrich Co Ltd, Gillingham, UK www.sigmaaldrich.com</td>
			<td style="width:119px;">O9830</td>
			<td></td>
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		<tr height="44">
			<td height="44" style="height:44px;width:216px;">Methanol, HPLC grade</td>
			<td style="width:243px;">Fisher Scientific, Loughoborough, UK www. fisher.co.uk</td>
			<td style="width:119px;">10674922</td>
			<td></td>
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			<td height="42" style="height:42px;width:216px;">Acetonitrile, HPLC grade</td>
			<td style="width:243px;">Fisher Scientific, Loughoborough, UK www. fisher.co.uk</td>
			<td style="width:119px;">10010010</td>
			<td></td>
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		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Urea</td>
			<td style="width:243px;">Sigma-Aldrich Co Ltd, Gillingham, UK www.sigmaaldrich.com</td>
			<td style="width:119px;">U5378</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Trypsin(from bovine pancreas treated with TPCK)</td>
			<td style="width:243px;">Sigma-Aldrich Co Ltd, Gillingham, UK www.sigmaaldrich.com</td>
			<td style="width:119px;">T1426</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Formic acid&#160;</td>
			<td style="width:243px;">Sigma-Aldrich Co Ltd, Gillingham, UK www.sigmaaldrich.com</td>
			<td style="width:119px;">F0507</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Coomassie Plus Protein Assay Reagent</td>
			<td style="width:243px;">Thermo Fisher Scientific www.thermofisher.com</td>
			<td style="width:119px;">1856210</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Protein standard</td>
			<td style="width:243px;">Sigma-Aldrich Co Ltd, Gillingham, UK www.sigmaaldrich.com</td>
			<td style="width:119px;">P0914</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Ultra Turrax homogeniser T25</td>
			<td style="width:243px;">Fisher Scientific, Loughoborough, UK www. fisher.co.uk</td>
			<td style="width:119px;">13190693</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Edmund and Buhler KS10 lab shaker</td>
			<td style="width:243px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Heraeus Fresco 17 Centrifuge</td>
			<td style="width:243px;">Thermo Fisher Scientific www.thermoscientific.com</td>
			<td style="width:119px;">75002420</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Vacuum centrifuge RC 1022</td>
			<td style="width:243px;">Jouan</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Plate Reader</td>
			<td style="width:243px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">Strata-X 33u polymeric reversed-phase cartridges 60 mg/3 ml tubes</td>
			<td style="width:243px;">Phenomenex, Macclesfield, UK</td>
			<td style="width:119px;">8B-S100-UBJ</td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">4000 QTrap triple-quadrupole mass spectrometer</td>
			<td style="width:243px;">AB Sciex, Warrington, UK www.sciex.com</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">1200 rapid resolution LC system</td>
			<td style="width:243px;">Agilent, Stockport, UK</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">XB C18 reversed-phase capillary column (100 x 2.1mm, 2.6&#181; particle size)</td>
			<td style="width:243px;">Phenomenex, Macclesfield, UK www.phenomenex.com</td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Analyst 1.6.2 software</td>
			<td style="width:243px;">AB Sciex, Warrington, UK www.sciex.com</td>
			<td style="width:119px;"></td>
			<td>QTrap data acquisition and analysis, including peak area integration</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Autosampler vials</td>
			<td style="width:243px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
	</tbody>
</table>

species determination and quantitation in mixtures using mrm mass spectrometry of peptides applied to meat authentication

We describe a simple protocol for identifying and quantifying the two components in binary mixtures of species possessing one or more similar proteins. Central to the method is the identification of 'corresponding proteins' in the species of interest, in other words proteins that are nominally the same but possess species-specific sequence differences. When subject to proteolysis, corresponding proteins will give rise to some peptides which are likewise similar but with species-specific variants. These are 'corresponding peptides'. Species-specific peptides can be used as markers for species determination, while pairs of corresponding peptides permit relative quantitation of two species in a mixture. The peptides are detected using multiple reaction monitoring (MRM) mass spectrometry, a highly specific technique that enables peptide-based species determination even in complex systems. In addition, the ratio of MRM peak areas deriving from corresponding peptides supports relative quantitation. Since corresponding proteins and peptides will, in the main, behave similarly in both processing and in experimental extraction and sample preparation, the relative quantitation should remain comparatively robust. In addition, this approach does not need the standards and calibrations required by absolute quantitation methods. The protocol is described in the context of red meats, which have convenient corresponding proteins in the form of their respective myoglobins. This application is relevant to food fraud detection: the method can detect 1% weight for weight of horse meat in beef. The corresponding protein, corresponding peptide (CPCP) relative quantitation using MRM peak area ratios gives good estimates of the weight for weight composition of a horse plus beef mixture.

In a single dynamic-mode MRM experiment each programmed transition is recorded separately (as detector counts per sec, cps) over a specified retention time window. Therefore, from all the data collected in one experiment, the peak intensity for each transition can be individually extracted. Then the only finite signal is for the retention time window set for that transition. Outside of the window, the signal is zero by definition. The signal for any one transition, for example, 752 &#8594; 1269 from horse (peptide monoisotopic mass 1,501.66 daltons, precursor ion m/z 751.84 daltons, charge state = 2, fragment ion y13) typically has to compete only with measurement noise and not from other transition peaks that might perhaps be from other species. The output is therefore a set of clean peaks, one per transition, at a common retention time for those transitions sharing a common precursor ion.
Figure 1 shows the output for the set of four transitions 752 &#8594; (1269, 706, 248, 1366) for a mixture of 1% w/w horse in beef. Since the four transitions displayed are associated with horse, and are absent in samples of pure beef, lamb or pork, these peaks signify the presence of horse. Depending on robustness criteria, a set of two or more transitions each exceeding some specified signal to noise level establishes identification. This figure therefore establishes the presence of horse in the mixture of 1% w/w horse in beef.
Occasionally, a single isolated transition is detected. This indicates a chance match of precursor ion and a single fragment, possibly from an extraneous protein, with those expected from the system and programed into the mass spectrometer. The singular nature of the peak, and its occurrence at an unexpected retention time, is the signature of an accidental transition that can be ignored.
The area under each transition peak can be calculated individually. Based on a suitable fragment, the ratio of horse to beef transition peak areas, for example, 752 &#8594; 1269 (horse) to 767 &#8594; 1299 (beef), will be proportional to the ratio of actual meats in the mixture. Figure 2 shows a plot of percentage by peak area for these two transitions versus the percentage weight for weight of horse in a mixture of horse with beef. If the percentage transition peak areas match the percentage weight for weight of meat then the slope is 1. The slope in this plot is 1.03, indicating that, for these transitions and CPCP pair, the transition peak areas give a reliable measure of the relative amounts of the two meats in the mixture. If the horse meat in the sample was twice as rich in myoglobin as the beef then, with other factors unchanged, the slope of the line would be greater than one.
<img alt="Figure 1" src="/files/ftp_upload/54420/54420fig1.jpg" /> 
Figure 1. MRM transition intensities versus retention time for 1% w/w horse in beef. The transitions are 752 &#8594; (1269, 706, 248, 1366), shown in orange, black, blue and green, respectively. The marker peptide is HPGDFGADAQGAMTK. The four transition fragments can be denoted y13, y7, y2 and y14, respectively, where yn denotes counting in n amino acids from the peptide C-terminal end. The signal to noise varies from 23 to 53 over the four transitions. An additional red line denotes the 752 &#8594; 1269 transition for 0% horse, 100% beef for comparison. Only the non-zero region of the retention time is displayed. This figure has been modified from Watson et al.3. <a href="https://www.jove.com/files/ftp_upload/54420/54420fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/54420/54420fig2.jpg" /> 
Figure 2. Plot of horse in beef, as percent weight for weight, versus horse in beef as percent transition peak area. The plot uses the pair of peptides beef (767) and horse (752) and the y13 fragment ion for both. If A denotes peak area then the ordinate is 100AH / (AH + AB). The slope of the best fit line (R2 = 0.99) is 1.03. This figure has been modified from Watson et al.3. <a href="https://www.jove.com/files/ftp_upload/54420/54420fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Species Determination and Quantitation in Mixtures Using MRM Mass Spectrometry of Peptides Applied to Meat Authentication at JoVE.com

Species Determination and Quantitation in Mixtures Using MRM Mass Spectrometry of Peptides Applied to Meat Authentication

We present a protocol for identifying and quantifying the components in mixtures of species possessing similar proteins. Mass spectrometry detects peptides for identification, and gives relative quantitation by ratios of peak areas. As a tool food for fraud detection, the method can detect 1% horse in beef.

We describe a simple protocol for identifying and quantifying the two components in binary mixtures of species possessing one or more similar proteins. ...

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Biochemistry

11.5K Views.  Institute of Food Research. We present a protocol for identifying and quantifying the components in mixtures of species possessing similar proteins. Mass spectrometry detects peptides for identification, and gives relative quantitation by ratios of peak areas. As a tool food for fraud detection, the method can detect 1% horse in beef.

Video: Species Determination and Quantitation in Mixtures Using MRM Mass Spectrometry of Peptides Applied to Meat Authentication

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 ...

Determination of High-affinity Antibody-antigen Binding Kinetics Using Four Biosensor Platforms

Label-free optical biosensors are powerful tools in drug discovery for the characterization of biomolecular interactions. In this study, we describe the use of four routinely used biosensor platforms in our laboratory to evaluate the binding affinity and kinetics of ten high-affinity monoclonal antibodies (mAbs) against human proprotein convertase subtilisin kexin type 9 (PCSK9). While both Biacore T100 and ProteOn XPR36 are derived from the well-established Surface Plasmon Resonance (SPR) technology, the former has four flow cells connected by serial flow configuration, whereas the latter presents 36 reaction spots in parallel through an improvised 6 x 6 crisscross microfluidic channel configuration. The IBIS MX96 also operates based on the SPR sensor technology, with an additional imaging feature that provides detection in spatial orientation. This detection technique coupled with the Continuous Flow Microspotter (CFM) expands the throughput significantly by enabling multiplex array printing and detection of 96 reaction sports simultaneously. In contrast, the Octet RED384 is based on the BioLayer Interferometry (BLI) optical principle, with fiber-optic probes acting as the biosensor to detect interference pattern changes upon binding interactions at the tip surface. Unlike the SPR-based platforms, the BLI system does not rely on continuous flow fluidics; instead, the sensor tips collect readings while they are immersed in analyte solutions of a 384-well microplate during orbital agitation.
Each of these biosensor platforms has its own advantages and disadvantages. To provide a direct comparison of these instruments&#39; ability to provide quality kinetic data, the described protocols illustrate experiments that use the same assay format and the same high-quality reagents to characterize antibody-antigen kinetics that fit the simple 1:1 molecular interaction model.

We describe here protocols for the measurement of antibody-antigen binding affinity and kinetics using four commonly used biosensor platforms.

Label-free optical biosensors are powerful tools in drug discovery for the characterization of biomolecular interactions. In this study, we describe the ...

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 ...

The Determination of Protease Specificity in Mouse Tissue Extracts by MALDI-TOF Mass Spectrometry: Manipulating PH to Cause Specificity Changes

Proteases have several biological functions, including protein activation/inactivation and food digestion. Identifying protease specificity is important for revealing protease function. The method proposed in this study determines protease specificity by measuring the molecular weight of unique substrates using Matrix-assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) mass spectrometry. The substrates contain iminobiotin, while the cleaved site consists of amino acids, and the spacer consists of polyethylene glycol. The cleaved substrate will generate a unique molecular weight using a cleaved amino acid. One of the merits of this method is that it may be carried out in one pot using crude samples, and it is also suitable for assessing multiple samples. In this article, we describe a simple experimental method optimized with samples extracted from mouse lung tissue, including tissue extraction, placement of digestive substrates into samples, purification of digestive substrates under different pH conditions, and measurement of the substrates&#39; molecular weight using MALDI-TOF mass spectrometry. In summary, this technique allows for the identification of protease specificity in crude samples derived from tissue extracts using MALDI-TOF mass spectrometry, which may easily be scaled up for multiple sample processing.

Here, we present a protocol to determine protease specificity in crude mouse tissue extracts using MALDI-TOF spectrometry.

Proteases have several biological functions, including protein activation/inactivation and food digestion. Identifying protease specificity is important ...

Proofreading and DNA Repair Assay Using Single Nucleotide Extension and MALDI-TOF Mass Spectrometry Analysis

The maintenance of the genome and its faithful replication is paramount for conserving genetic information. To assess high fidelity replication, we have developed a simple non-labeled and non-radio-isotopic method using a matrix-assisted laser desorption ionization with time-of-flight (MALDI-TOF) mass spectrometry (MS) analysis for a proofreading study. Here, a DNA polymerase [e.g., the Klenow fragment (KF) of Escherichia coli DNA polymerase I (pol I) in this study] in the presence of all four dideoxyribonucleotide triphosphates is used to process a mismatched primer-template duplex. The mismatched primer is then proofread/extended and subjected to MALDI-TOF MS. The products are distinguished by the mass change of the primer down to single nucleotide variations. Importantly, a proofreading can also be determined for internal single mismatches, albeit at different efficiencies. Mismatches located at 2-4-nucleotides (nt) from the 3' end were efficiently proofread by pol I, and a mismatch at 5 nt from the primer terminus showed only a partial correction. No proofreading occurred for internal mismatches located at 6 - 9 nt from the primer 3' end. This method can also be applied to DNA repair assays (e.g., assessing a base-lesion repair of substrates for the endo V repair pathway). Primers containing 3' penultimate deoxyinosine (dI) lesions could be corrected by pol I. Indeed, penultimate T-I, G-I, and A-I substrates had their last 2 dI-containing nucleotides excised by pol I before adding a correct ddN 5'-monophosphate (ddNMP) while penultimate C-I mismatches were tolerated by pol I, allowing the primer to be extended without repair, demonstrating the sensitivity and resolution of the MS assay to measure DNA repair.

A non-labeled, non-radio-isotopic method for DNA polymerase proofreading and a DNA repair assay was developed by using high-resolution MALDI-TOF mass spectrometry and a single nucleotide extension strategy. The assay proved to be very specific, simple, rapid, and easy to perform for proofreading and repair patches shorter than 9-nucleotides.

The maintenance of the genome and its faithful replication is paramount for conserving genetic information. To assess high fidelity replication, we have ...

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 ...

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 ...

Rapid Determination of Antibody-Antigen Affinity by Mass Photometry

Measurements of the specificity and affinity of antigen-antibody interactions are critically important for medical and research applications. In this protocol, we describe the implementation of a new single-molecule technique, mass photometry (MP), for this purpose. MP is a label- and immobilization-free technique that detects and quantifies molecular masses and populations of antibodies and antigen-antibody complexes on a single-molecule level. MP analyzes the antigen-antibody sample within minutes, allowing for the precise determination of the binding affinity and simultaneously providing information on the stoichiometry and the oligomeric state of the proteins. This is a simple and straightforward technique that requires only picomole quantities of protein and no expensive consumables. The same procedure can be used to study protein-protein binding for proteins with a molecular mass larger than 50 kDa. For multivalent protein interactions, the affinities of multiple binding sites can be obtained in a single measurement. However, the single-molecule mode of measurement and the lack of labeling imposes some experimental limitations. This method gives the best results when applied to measurements of sub-micromolar interaction affinities, antigens with a molecular mass of 20 kDa or larger, and relatively pure protein samples. We also describe the procedure for performing the required fitting and calculation steps using basic data analysis software.

We describe a single-molecule approach to antigen-antibody affinity measurements using mass photometry (MP). The MP-based protocol is fast, accurate, uses a very small amount of material, and does not require protein modification.

Measurements of the specificity and affinity of antigen-antibody interactions are critically important for medical and research applications. In this ...