The authors acknowledge Vanderbilt University Start-up Funds and NIH award (R01GM117191) to RASR.

<ol>
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B., Robinson, R. A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sample+Multiplexing+Strategies+in+Quantitative+Proteomics.">Sample Multiplexing Strategies in Quantitative Proteomics.</a> Analytical Chemistry. 91 (1), 178-189 (2019).</li><li>Everley, R. A., Kunz, R. C., McAllister, F. E., Gygi, S. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Increasing+Throughput+in+Targeted+Proteomics+Assays:+54-Plex+Quantitation+in+a+Single+Mass+Spectrometry+Run.">Increasing Throughput in Targeted Proteomics Assays: 54-Plex Quantitation in a Single Mass Spectrometry Run.</a> Analytical Chemistry. 85 (11), 5340-5346 (2013).</li><li>Shiio, Y., Aebersold, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+proteome+analysis+using+isotope-coded+affinity+tags+and+mass+spectrometry.">Quantitative proteome analysis using isotope-coded affinity tags and mass spectrometry.</a> Nature Protocol. 1 (1), 139-145 (2006).</li><li>Gygi, S. P., 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=Quantitative+analysis+of+complex+protein+mixtures+using+isotope-coded+affinity+tags.">Quantitative analysis of complex protein mixtures using isotope-coded affinity tags.</a> Nature Biotechnology. 17 (10), 994-999 (1999).</li><li>Thompson, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=TMTpro:+Design,+Synthesis,+and+Initial+Evaluation+of+a+Proline-Based+Isobaric+16-Plex+Tandem+Mass+Tag+Reagent+Set.">TMTpro: Design, Synthesis, and Initial Evaluation of a Proline-Based Isobaric 16-Plex Tandem Mass Tag Reagent Set.</a> Analytical Chemistry. 91 (24), 15941-15950 (2019).</li><li>Frost, D. C., Feng, Y., Li, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=21-plex+DiLue+Isobaric+Tags+for+High-Throughput+Quantitative+Proteomics.">21-plex DiLue Isobaric Tags for High-Throughput Quantitative Proteomics.</a> Anal. Chem. 92 (12), 8228-8234 (2020).</li><li>Evans, A. R., Robinson, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+combined+precursor+isotopic+labeling+and+isobaric+tagging+(cPILOT)+approach+with+selective+MS(3)+acquisition.">Global combined precursor isotopic labeling and isobaric tagging (cPILOT) approach with selective MS(3) acquisition.</a> Proteomics. 13 (22), 3267-3272 (2013).</li><li>Robinson, R. A., Evans, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enhanced+sample+multiplexing+for+nitrotyrosine-modified+proteins+using+combined+precursor+isotopic+labeling+and+isobaric+tagging.">Enhanced sample multiplexing for nitrotyrosine-modified proteins using combined precursor isotopic labeling and isobaric tagging.</a> Analytical Chemistry. 84 (11), 4677-4686 (2012).</li><li>King, C. D., Dudenhoeffer, J. D., Gu, L., Evans, A. R., Robinson, R. A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enhanced+Sample+Multiplexing+of+Tissues+Using+Combined+Precursor+Isotopic+Labeling+and+Isobaric+Tagging+(cPILOT).">Enhanced Sample Multiplexing of Tissues Using Combined Precursor Isotopic Labeling and Isobaric Tagging (cPILOT).</a> Journal of Visual Experiments. (123), e55406 (2017).</li><li>King, C. D., Robinson, R. A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluating+Combined+Precursor+Isotopic+Labeling+and+Isobaric+Tagging+Performance+on+Orbitraps+To+Study+the+Peripheral+Proteome+of+Alzheimer's+Disease.">Evaluating Combined Precursor Isotopic Labeling and Isobaric Tagging Performance on Orbitraps To Study the Peripheral Proteome of Alzheimer's Disease.</a> Analytical Chemistry. , (2020).</li><li>Evans, A. R., Gu, L., Guerrero, R., Robinson, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Global+cPILOT+analysis+of+the+APP/PS-1+mouse+liver+proteome.">Global cPILOT analysis of the APP/PS-1 mouse liver proteome.</a> Proteomics &amp; Clinical Applications. 9 (9-10), 872-884 (2015).</li><li>Gu, L., Evans, A. R., Robinson, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sample+multiplexing+with+cysteine-selective+approaches:+cysDML+and+cPILOT.">Sample multiplexing with cysteine-selective approaches: cysDML and cPILOT.</a> Journal of American Society of Mass Spectrometer. 26 (4), 615-630 (2015).</li><li>Ting, L., Rad, R., Gygi, S. P., Haas, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MS3+eliminates+ratio+distortion+in+isobaric+multiplexed+quantitative+proteomics.">MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics.</a> Nature Methods. 8 (11), 937-940 (2011).</li><li>Frost, D. C., Rust, C. J., Robinson, R. A. S., Li, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Increased+N,N-Dimethyl+Leucine+Isobaric+Tag+Multiplexing+by+a+Combined+Precursor+Isotopic+Labeling+and+Isobaric+Tagging+Approach.">Increased N,N-Dimethyl Leucine Isobaric Tag Multiplexing by a Combined Precursor Isotopic Labeling and Isobaric Tagging Approach.</a> Analytical Chemistry. 90 (18), 10664-10669 (2018).</li><li>Gu, L., Robinson, R. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+endogenous+measurement+of+S-nitrosylation+in+Alzheimer's+disease+using+oxidized+cysteine-selective+cPILOT.">High-throughput endogenous measurement of S-nitrosylation in Alzheimer's disease using oxidized cysteine-selective cPILOT.</a> Analyst. 141 (12), 3904-3915 (2016).</li><li>Amin, B., Ford, K. I., Robinson, R. A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+proteomics+to+study+aging+in+rabbit+liver.">Quantitative proteomics to study aging in rabbit liver.</a> Mechanisms of Ageing and Development. 187, 111227 (2020).</li></ol>

The authors have nothing to disclose.

cPILOT is an enhanced multiplexing strategy that can analyze up to 24 samples in a single experiment. The multiplexing capacity depends on the number of available isotopic and isobaric tagging combinations. Introduction of the TMTpro7, which is capable of tagging 16 samples in single experiment, can push the limits of cPILOT to 32-plex. cPILOT consists of multiple pipetting steps and requires extensive care and user skills to perform sample preparation. Even with an expert user, manual errors are inevitable, which invites the use of robotic platforms to process samples in the cPILOT strategy. Since cPILOT utilizes pH dependent tagging of the peptides, the pH needs to be maintained for the light and the heavy dimethylated set of samples. Mildly acidic-basic pH can result in dimethylation of both N-termini and lysine residues. An advantage of cPILOT is that it requires only half of the isobaric tags since peptide N-termini are occupied with the dimethyl groups. This affords a greater number of samples to be labelled at half the cost. Handling larger sample numbers requires that reagent exposure times are similar for the first and the last sample in a batch. A pipette dispenser that can accommodate up to 32 samples in parallel can best be achieved with the use of robotic liquid handling devices.
In order to process multiple samples by cPILOT, the manual workflow was amended to incorporate automation. The robotic liquid handler used in this study has two pods with 96-channel and 8-channel pipetting abilities, with a gripper to place the plates in the available 28 deck locations. The liquid handler is integrated with a positive pressure apparatus, orbital shaker, and a device to heat/cool samples in the 96 well plate. The positive pressure apparatus assists in performing buffer exchanges in the SPE plates during clean-up, while the orbital shaker helps to vortex/mix the samples. The robotic platform was programmed to aspirate and dispense buffers and samples to 96-well plates, incubate, vortex samples, and transfer plates. Liquids with different viscosities, such as acetonitrile and water, require specific pipetting considerations that can also be programmed into the method.
The cPILOT workflow, starting from protein quantification by BCA to labeling the peptides with isobaric tags (i.e., TMT), was performed on the liquid handler system. The complete protocol was scaled to use 96 deep well plates that can hold 2 mL per well. The buffers were prepared prior to the start of the experiment and added to the 96 well plate so as to allow parallel sample processing. In the present study, 22 workflow replicates of mouse liver homogenate were added to the deep well plates and taken through the cPILOT protocol. Finally, a single sample consisting of the 22-plex equimolar mouse liver tagged peptides was injected to the mass spectrometer. Reporter ion intensities corresponding to peptide abundances in the samples demonstrated that samples processed with the liquid handler have lower CVs than the manual protocol (data not shown). The robotic platform also greatly improved the reproducibility of sample processing. Reproducibility and robustness are very important factors while processing large numbers of samples. Pipetting errors can lead to complete mis-interpretation of the data and here the robotic platform provided low inter-sample variation. Also using the robotic platform for cPILOT reduced the time required to prepare samples. For example, after developing the automated method, it required 2.5 h to process 22 samples in comparison to 7.5 h for manual cPILOT. Experiments are on-going in our laboratory to further evaluate comparisons of the manual and automated cPILOT workflows. Based on previous reports from our laboratory, the CV%&#8217;s of protein reporter ion intensities in the manual cPILOT were&#160;on average 20% with some outliers exceeding this value12.
cPILOT is a chemical derivatization strategy at the peptide level, that can be used for any sample type such as cells, tissues, and body fluids. cPILOT offers enhanced sample multiplexing and with the incorporation of automation can facilitate high-throughput sample multiplexing in proteomics. This throughput is necessary to further advance disease and biological understanding and biomarker discovery.

Mass spectrometry (MS)-based proteomics is an indispensable research tool in identifying disease specific biomarkers, understanding disease progression, and creating leads for therapeutic development. This can be achieved from a range of disease-related clinical samples such as blood serum/plasma, proximal fluids, and tissues1,2. Proteomics biomarker discovery and validation have recently gained significant consideration due to the power of sample multiplexing strategies3,4. Sample multiplexing is a technique that enables simultaneous comparison and quantification of two or more sample conditions within a single MS injection5,6. Sample multiplexing is achieved by barcoding peptides or proteins from multiple samples with chemical, enzymatic, or metabolic tags and obtaining MS information from all samples in a single MS or MS/MS experiment. Among the available isobaric tags are isobaric tagging reagents (iTRAQ), commercial tandem mass tags (TMT), and in house synthesized isobaric N,N-dimethyl leucine (DiLeu) reagents with capabilities up to 16-plex7 and&#160;21-plex8, respectively.
Combined precursor isotopic labeling and isobaric tagging (cPILOT) is an enhanced sample multiplexing technology. cPILOT combines isotopic labeling of peptide N-termini with light [&#8722;(CH3)2] and heavy [&#8722;(13C2H3)2] isotopes at low pH (&#8764;2.5), which keeps the lysine residue available for subsequent high pH (8.5) isobaric labeling using TMT, DiLeu, or iTRAQ tagging3,9,10,11,12,13,14. The dual labeling scheme of the cPILOT strategy is depicted in&#160;Supplemental Figure 1 with two samples using an example peptide. The accuracy and precision of the TMT based quantification at the MS2 level can be compromised&#160;due to the presence of contaminating co-isolated and co-fragmented ions termed as the interference effect15. This limitation in inaccurate reporter ion ratios can be overcome with the help of tribrid Orbitrap mass spectrometers. For example, the interference effect can be overcome by isolating a peak in a dimethylated pair at the MS1 level in the mass spectrometer, subjecting the light or heavy peak to MS2 fragmentation in the linear ion trap and then subjecting the most intense MS2 fragment for HCD-MS3 to obtain quantitative information. In order to increase the chances of selecting the peptides without lysine amines available for generating reporter ions, a selective MS3 acquisition based on the y-1 fragment also can be used and is an approach which can result in a higher percentage of peptides quantifiable with cPILOT9. The combination of light and heavy labeling increases sample multiplexing capabilities by a factor of 2x to that achieved with individual isobaric tags. We have recently used cPILOT to combine up to 24 samples in a single experiment with DiLeu reagents16. Additionally cPILOT has been used to study oxidative post-translational modifications14 including protein nitration17, other global proteomes9, and has demonstrated applications across multiple tissue samples in an Alzheimer&#8217;s disease mouse model11.
Robust sample preparation is a critical step in a cPILOT experiment and can be time-consuming, laborious, and extensive. Enhanced sample multiplexing requires extensive pipetting and highly skilled laboratory personnel, and there are several factors that can heavily influence the reproducibility of the experiment. For example, careful handling of samples is necessary to ensure similar reaction times for all samples and to maintain appropriate buffer pH for light and heavy dimethylated samples. Furthermore, manual preparation of tens to hundreds of samples can introduce high experimental error. Therefore, to reduce sample preparation variability, improve quantitative accuracy, and increase experimental throughput, we developed an automated cPILOT workflow. Automation is achieved using a robotic liquid handling device that can complete many aspects of the workflow (Figure 1). Sample preparation from protein quantification to peptide labeling was performed on an automated liquid handler. The automated liquid handler is integrated with a positive pressure apparatus (PPA) for buffer exchanges between the solid-phase extraction (SPE) plates, orbital shaker, and a heating/cooling device. The robotic platform contains 28 deck locations to accommodate plates and buffers. There are two pods with a gripper to transfer the plates within the deck locations: a 96-channel fixed volume pipetting head (5-1100 &#181;L) and 8 channel variable volume probes (1-1000 &#181;L). The robotic platform is controlled using a software. The user needs to be professionally trained prior to using the robotic liquid handler. The present study focuses on automating the manual cPILOT workflow, which can be labor intensive for processing more than 12 samples in a single batch. In order to increase the throughput of the cPILOT approach11, we transferred the cPILOT protocol to a robotic liquid handler to process more than 10 samples in parallel. The automation also allows similar reactions for each sample in parallel during various steps of the sample preparation process, which required highly trained users to achieve during manual cPILOT. This protocol focuses on the implementation of the automated liquid handling device to carry out cPILOT. The present study describes the protocol for using this automated system and demonstrates its performance using a 22-plex &#8220;proof-of-concept&#8221; analysis of mouse liver homogenates.

<table border="0" cellpadding="0" cellspacing="0" style="width:1651px;" width="1651">
	<colgroup>
		<col />
		<col />
		<col />
		<col />
	</colgroup>
	<tbody>
		<tr height="20">
			<td height="20" style="height:20px;width:367px;">0.6 mL eppendorf tubes, 500 pk</td>
			<td style="width:203px;">Fisher Scientific</td>
			<td style="width:159px;">04-408-120</td>
			<td style="width:923px;">Any brand of 0.6 mL eppendorf tubes are sufficient</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">0.65 &#181;m Ultrafree MC DV centrifugal filter units</td>
			<td>EMD Millipore</td>
			<td>UFC30DV00</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">1.5 mL eppendorf tubes, 500 pk</td>
			<td>Fisher Scientific</td>
			<td>05-408-129</td>
			<td>Any brand of 1.5 mL eppendorf tubes are sufficient</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">2 ml black deep well plate</td>
			<td>Analytical Sales and Services, Inc.</td>
			<td>59623-23BKGC</td>
			<td>Any brand of black 96-well plate is sufficient</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">2 ml clear deep well plate</td>
			<td>VWR</td>
			<td>75870-796</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Acetic Acid</td>
			<td>J.T. Baker</td>
			<td>9508-01</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Acetonitrile - MS Grade</td>
			<td>Fisher Scientific</td>
			<td>A955-4</td>
			<td>4 L quantity is not necessary</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Agilent 500&#181;L plate</td>
			<td>Agilent</td>
			<td>203942-100</td>
			<td>Reagent plate for adding buffers</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Ammonium formate</td>
			<td>Acros Organics</td>
			<td>208-753-9</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Ammonium hydroxide solution (28 - 30%)</td>
			<td>Sigma Aldrich</td>
			<td>320145-500ML</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Analytical balance</td>
			<td>Mettler Toledo</td>
			<td>AL54</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">BCA protein assay kit</td>
			<td>Pierce Thermo Fisher Scientific</td>
			<td>23227</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Biomek i7 hybrid</td>
			<td>Beckmann</td>
			<td></td>
			<td>Any liquid handling device with ability to use positive pressure, heating/cooling and Vortex the samples.</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">C18 packing material (2.5 &#181;m, 100 &#197;)</td>
			<td>Bruker</td>
			<td></td>
			<td>This item is no longer available from Bruker. Alternative packing material with listed specifications will be sufficient</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Centrifuge with plate rotor</td>
			<td>Thermo Scientific</td>
			<td>69720</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Micro 21R Centrifuge</td>
			<td>Sorval</td>
			<td>5437</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Dionex 3000 UHPLC</td>
			<td>Thermo Scientific</td>
			<td></td>
			<td>This model is no longer available. Any nano LC with an autosampler is sufficient.</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Dithiothreiotol (DTT)</td>
			<td>Fisher Scientific</td>
			<td>BP172-5</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Formaldehyde (13CD2O) solution; 20 wt % in D2O, 98 atom % D, 99 atom % 13C</td>
			<td>Sigma Aldrich, Chemistry</td>
			<td>596388-1G</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Formaldehyde (CH2O) solution; 36.5 - 38% in H2O</td>
			<td>Sigma Aldrich, Life Science</td>
			<td>F8775-25ML</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Formic Acid</td>
			<td>Fluka Analytical</td>
			<td>94318-250ML-F</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Fusion Lumos Mass Spectrometer</td>
			<td>Thermo Scientific</td>
			<td></td>
			<td>This model is no longer available. Other high resolution instruments (e.g. Orbitrap Elite, Orbitrap Fusion, or Orbitrap Fusion Lumos) can be used.</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Hydroxylamine hydrochloride</td>
			<td>Sigma Aldrich, Chemistry</td>
			<td>255580-100G</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Iodoacetamide (IAM)</td>
			<td>Acros Organics</td>
			<td>144-48-9</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Isobaric Tagging Kit (TMT 11-plex)</td>
			<td>Thermo Fisher Scientific</td>
			<td>90061</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">L-1-tosylamido-2 phenylethyl cholormethyl ketone (TPCK)-treated Trypsin from bovine pancreas</td>
			<td>Sigma Aldrich, Life Science</td>
			<td>T1426-100MG</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">L-Cysteine</td>
			<td>Sigma Aldrich, Chemistry</td>
			<td>168149-25G</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Mechanical Homogenizer (i.e. FastPrep-24 5G)</td>
			<td>MP Biomedicals</td>
			<td>116005500</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">pH 10 buffer</td>
			<td>Fisher Scientific</td>
			<td>06-664-261</td>
			<td>Any brand of pH buffer 10 is sufficient</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">pH 7 buffer</td>
			<td>Fisher Scientific</td>
			<td>06-664-260</td>
			<td>Any brand pH buffer 7 is sufficient</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">pH meter (Tris compatiable)</td>
			<td>Fisher Scientific (Accumet)</td>
			<td>13-620-183</td>
			<td>Any brand of a pH meter is sufficient</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Protein software (e.g. Proteome Discoverer)</td>
			<td>Thermo Scientific</td>
			<td></td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Reservior plate 200ml</td>
			<td>Agilent</td>
			<td>204017-100</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Sodium Cyanoborodeuteride; 96 atom % D, 98% CP</td>
			<td>Sigma Aldrich, Chemistry</td>
			<td>190020-1G</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Sodium Cyanoborohydride; reagent grade, 95%</td>
			<td>Sigma Aldrich</td>
			<td>156159-10G</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Speed-vac</td>
			<td>Thermo Scientific</td>
			<td>SPD1010</td>
			<td>any brand of speed vac that can accommodate a deep well plate is sufficient</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Stir plate</td>
			<td>VWR</td>
			<td>12365-382</td>
			<td>Any brand of stir plates are sufficient</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Targa 20 mg SPE plates</td>
			<td>Nest Group, Inc.</td>
			<td>HNS S18V</td>
			<td>These are C18 cartridges</td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Triethyl ammonium bicarbonate (TEAB) buffer</td>
			<td>Sigma Aldrich, Life Science</td>
			<td>T7408-100ML</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Tris</td>
			<td>Biorad</td>
			<td>161-0716</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Biomek 24-Place Tube Rack Holder</td>
			<td>Beckmann</td>
			<td>373661</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Urea</td>
			<td>Biorad</td>
			<td>161-0731</td>
			<td></td>
		</tr>
		<tr height="20">
			<td height="20" style="height:20px;">Water - MS Grade</td>
			<td>Fisher Scientific</td>
			<td>W6-4</td>
			<td>4 L quantity is not necessary</td>
		</tr>
	</tbody>
</table>

automated sample multiplexing by using combined precursor isotopic labeling and isobaric tagging (cpilot)

We have introduced a high throughput quantitative proteomics workflow, combined precursor isotopic labeling and isobaric tagging (cPILOT) capable of multiplexing up to 22 or 24 samples with tandem mass tags or isobaric N,N-dimethyl leucine isobaric tags, respectively, in a single experiment. This enhanced sample multiplexing considerably reduces mass spectrometry acquisition times and increases the utility of the expensive commercial isobaric reagents. However, the manual process of sample handling and pipetting steps in the strategy can be labor intensive, time consuming, and introduce sample loss and quantitative error. These limitations can be overcome through the incorporation of automation. Here we transferred the manual cPILOT protocol to an automated liquid handling device that can prepare large sample numbers (i.e., 96 samples) in parallel. Overall, automation increases feasibility and reproducibility of cPILOT and allows for broad usage by other researchers with comparable automation devices.

Figure 2 shows representative MS data of a peptide identified in all 22 reporter ion channels from a 22-plex cPILOT experiment, including workflow replicates. Figure 2 (top) depicts a doubly charged peak pair separated by 4 Da m/z spacing indicating a single dimethyl group incorporated into the peptide. The light and heavy dimethylated peak pairs were isolated and fragmented independently to yield the sequence of the peptide. The sequence of the peptide is G(dimethyl)AAELMQQK(TMT-11plex) and corresponds to the protein Betaine-homocysteine S-methyl transferase. The most intense fragment ions for both the light and heavy dimethylated peaks (not shown) were further isolated for MS3 fragmentation and the reporter ions (m/z 126-131) are shown in Figure 2 (bottom). The reporter ion intensities are directly proportionate to the peptide abundance in the sample. The peptide abundance of the samples implies the pipetting ability of the robotic platform is fairly uniform across the 22 samples. Overall, this 22-plex cPILOT experiment resulted in 1326 (1209-light/1181-heavy) proteins identifications resulting from 3098 (6137-light/5872-heavy) peptides (Table 4). Figure 3 shows the box plot of log10 abundance versus total reporter ion intensities across all the 22 channels showing lesser inter-well/inter-sample variability. Evaluation of the total automation was done by examining the error in reporter ion abundance across each protein in the 22 samples. Figure 4 shows that sample processing with the robotic platform resulted in very low CV values. Specifically, across the 3098 peptides identified the average CV in reporter ion abundance was 12.36 % and 15.03 % for light and heavy dimethylated peptides, respectively. Among these peptides 2032 of these peptides had reporter ion signal above the minimum threshold and were deemed quantifiable.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/61342/61342fig1.jpg" /> 
Figure 1. Experimental workflow to process 22 samples in parallel with an automated cPILOT protocol. <a href="https://www.jove.com/files/ftp_upload/61342/61342fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/61342/61342fig2.jpg" /> 
Figure 2. Quantification of peptides across 22 samples. Example MS (top) and MS3 (bottom) spectra of the peptide G(dimethyl)AAELMQQK(TMT-11plex) quantified in 22-plex automated cPILOT experiment for light dimethylated (bottom left) and heavy dimethylated (bottom right) peaks. <a href="https://www.jove.com/files/ftp_upload/61342/61342fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/61342/61342fig3.jpg" /> 
Figure 3. Box plot of total reporter ion intensities versus log10 abundance of 22 samples using proteome discoverer 2.3. The RAW file was searched twice for light and heavy peptides, proteins IDs separately with TMT as dynamic modification, light (+28.031 Da) and heavy (+36.076 &#38; +35.069 Da) dimethylation at peptide N-termini as static modification. A combined search with all the above modifications was run using Proteome Discover 2.3 to obtain the Log 10 Abundance of peptide intensities across all the channels. <a href="https://www.jove.com/files/ftp_upload/61342/61342fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/61342/61342fig4.jpg" /> 
Figure 4. Violin plots of co-efficient of variation of peptide abundance from summed reporter ion intensities across channels 126-131 m/z. The peptide was quantified with an average CV value of 12.36 and 15.03 for light (2373) and heavy (2533) quantifiable peptides. <a href="https://www.jove.com/files/ftp_upload/61342/61342fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Figure 1. Illustration of the cPILOT with a single peptide. Showing the isotopic labeling of two different samples and isobaric tagging with TMT126, the resulting mixture was injected to MS for LC-MS3. <a href="https://www.jove.com/files/ftp_upload/61342/61342supfig1large.jpg" target="_blank">Please click here to download this file.</a>
<table border="1" fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td>Variable Name</td>
			<td>Value</td>
			<td>Description</td>
		</tr>
		<tr>
			<td>DesaltSamp1</td>
			<td>1065</td>
			<td>Volume to be used for desalt step 1</td>
		</tr>
		<tr>
			<td>DesaltSamp2</td>
			<td>392</td>
			<td>Volume to be used for desalt step 2</td>
		</tr>
		<tr>
			<td>DesaltSamp3</td>
			<td>100</td>
			<td>Volume to be used for desalt step 3</td>
		</tr>
		<tr>
			<td>DevMode</td>
			<td>FALSE</td>
			<td>False will cut-down the incubation times to 30sec- True will follow the incubation timing in the protocol.</td>
		</tr>
		<tr>
			<td>DTTVol</td>
			<td>3</td>
			<td>Volume of DTT</td>
		</tr>
		<tr>
			<td>FilterPlate</td>
			<td>Targa</td>
			<td>Plate used for desalting</td>
		</tr>
		<tr>
			<td>FilterPlateVol</td>
			<td>600</td>
			<td>Volume for desalting</td>
		</tr>
		<tr>
			<td>HAWaterWashes</td>
			<td>FALSE</td>
			<td>Number of water washes on the SPE plate</td>
		</tr>
		<tr>
			<td>IAMVol</td>
			<td>2</td>
			<td>Volume of iodoacetamide</td>
		</tr>
		<tr>
			<td>PeptideTMTVol</td>
			<td>12.5</td>
			<td>Volume of peptide for TMT labelling</td>
		</tr>
		<tr>
			<td>pressure</td>
			<td>100</td>
			<td>mbar pressure at PPA</td>
		</tr>
		<tr>
			<td>TempOffSet</td>
			<td>1</td>
			<td>Change in temperature</td>
		</tr>
		<tr>
			<td>TMTVol</td>
			<td>10</td>
			<td>Isobaric tag volume to be added</td>
		</tr>
		<tr>
			<td>TrisVol</td>
			<td>800</td>
			<td>Volume to dilute sample prior to digestion</td>
		</tr>
		<tr>
			<td>TrypsinVol</td>
			<td>2</td>
			<td>Volume of trypsin</td>
		</tr>
		<tr>
			<td>UsePopTimer</td>
			<td>TRUE</td>
			<td>True displays the options to apply pressure on plate if required</td>
		</tr>
	</tbody>
</table>
Table 1. List of variables used in automated cPILOT protocol.
<table border="1" fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td>DilSource</td>
			<td>DilWell</td>
			<td>Dest</td>
			<td>DestWell</td>
			<td>DilVolume</td>
			<td>StockSource</td>
			<td>StockWell</td>
			<td>SampleVol</td>
			<td>SampleID</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>A1</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>1</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>A2</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>2</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>A3</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>3</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>A4</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>4</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>A5</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>5</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>A6</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>6</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>A7</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>7</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>A8</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>8</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>A9</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>9</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>A10</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>10</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>A11</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>11</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>A12</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>12</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>B1</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>13</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>B2</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>14</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>B3</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>15</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>B4</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>16</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>B5</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>17</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>B6</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>18</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>B7</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>19</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>B8</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>20</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>B9</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>21</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>B10</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>22</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>B11</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>23</td>
		</tr>
		<tr>
			<td>8M_Urea</td>
			<td>1</td>
			<td>Samples</td>
			<td>B12</td>
			<td>90</td>
			<td>Stock_Samples</td>
			<td>A1</td>
			<td>10</td>
			<td>24</td>
		</tr>
	</tbody>
</table>
Table 2. Volume of mouse liver homogenate and 8 M urea.
<table border="1" fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td>SourceWell</td>
			<td>SourceWell2</td>
			<td>Reporter Ion</td>
			<td>DestWell1</td>
			<td>DestWell2</td>
			<td>Volume</td>
			<td>SampleID</td>
		</tr>
		<tr>
			<td>A1</td>
			<td>C1</td>
			<td>126</td>
			<td>A1</td>
			<td>E1</td>
			<td>10</td>
			<td>1</td>
		</tr>
		<tr>
			<td>A3</td>
			<td>C3</td>
			<td>127N</td>
			<td>A2</td>
			<td>E2</td>
			<td>10</td>
			<td>2</td>
		</tr>
		<tr>
			<td>A5</td>
			<td>C5</td>
			<td>127C</td>
			<td>A3</td>
			<td>E3</td>
			<td>10</td>
			<td>3</td>
		</tr>
		<tr>
			<td>A7</td>
			<td>C7</td>
			<td>128N</td>
			<td>A4</td>
			<td>E4</td>
			<td>10</td>
			<td>4</td>
		</tr>
		<tr>
			<td>A9</td>
			<td>C9</td>
			<td>128C</td>
			<td>A5</td>
			<td>E5</td>
			<td>10</td>
			<td>5</td>
		</tr>
		<tr>
			<td>A11</td>
			<td>C11</td>
			<td>129N</td>
			<td>A6</td>
			<td>E6</td>
			<td>10</td>
			<td>6</td>
		</tr>
		<tr>
			<td>B2</td>
			<td>D2</td>
			<td>129C</td>
			<td>A7</td>
			<td>E7</td>
			<td>10</td>
			<td>7</td>
		</tr>
		<tr>
			<td>B4</td>
			<td>D4</td>
			<td>130N</td>
			<td>A8</td>
			<td>E8</td>
			<td>10</td>
			<td>8</td>
		</tr>
		<tr>
			<td>B6</td>
			<td>D6</td>
			<td>130C</td>
			<td>A9</td>
			<td>E9</td>
			<td>10</td>
			<td>9</td>
		</tr>
		<tr>
			<td>B8</td>
			<td>D8</td>
			<td>131N</td>
			<td>A10</td>
			<td>E10</td>
			<td>10</td>
			<td>10</td>
		</tr>
		<tr>
			<td>B10</td>
			<td>D10</td>
			<td>131C</td>
			<td>A11</td>
			<td>E11</td>
			<td>10</td>
			<td>11</td>
		</tr>
	</tbody>
</table>
Table 3. Total number of peptides, proteins and peptide spectral matches (PSMs).
<table border="1" fo:keep-together.within-page="1">
	<tbody>
		<tr>
			<td></td>
			<td colspan="2">Automated cPILOT</td>
		</tr>
		<tr>
			<td></td>
			<td>Light</td>
			<td>Heavy</td>
		</tr>
		<tr>
			<td>Proteins</td>
			<td>1209</td>
			<td>1181</td>
		</tr>
		<tr>
			<td>Peptides</td>
			<td>6137</td>
			<td>5872</td>
		</tr>
		<tr>
			<td>PSMs</td>
			<td>14948</td>
			<td>16762</td>
		</tr>
	</tbody>
</table>
Table 4. Barcoding the isobaric tags with the light and heavy labelled samples.

Watch this Scientific Journal Video about Automated Sample Multiplexing by using Combined Precursor Isotopic Labeling and Isobaric Tagging cPILOT at JoVE.com

Automated Sample Multiplexing by using Combined Precursor Isotopic Labeling and Isobaric Tagging cPILOT

Combined precursor isotopic labeling and isobaric tagging (cPILOT) is an enhanced sample multiplexing strategy that is capable of increasing the number of samples that can be analyzed simultaneously with available isobaric tags. Incorporation of a robotic platform has greatly increased experimental throughput, reproducibility, and quantitative accuracy.

Automated Sample Multiplexing by using Combined Precursor Isotopic Labeling and Isobaric Tagging (cPILOT)

We have introduced a high throughput quantitative proteomics workflow, combined precursor isotopic labeling and isobaric tagging (cPILOT) capable of ...

automated-sample-multiplexing-using-combined-precursor-isotopic

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JoVE Journal

Chemistry

5.5K Views.  Vanderbilt University. Combined precursor isotopic labeling and isobaric tagging (cPILOT) is an enhanced sample multiplexing strategy that is capable of increasing the number of samples that can be analyzed simultaneously with available isobaric tags. Incorporation of a robotic platform has greatly increased experimental throughput, reproducibility, and quantitative accuracy.

Video: Automated Sample Multiplexing by using Combined Precursor Isotopic Labeling and Isobaric Tagging cPILOT

Quantitative Proteomics Using Reductive Dimethylation for Stable Isotope Labeling

Stable isotope labeling of peptides by reductive dimethylation (ReDi labeling) is a method to accurately quantify protein expression differences between samples using mass spectrometry. ReDi labeling is performed using either regular (light) or deuterated (heavy) forms of formaldehyde and sodium cyanoborohydride to add two methyl groups to each free amine. Here we demonstrate a robust protocol for ReDi labeling and quantitative comparison of complex protein mixtures. Protein samples for comparison are digested into peptides, labeled to carry either light or heavy methyl tags, mixed, and co-analyzed by LC-MS/MS. Relative protein abundances are quantified by comparing the ion chromatogram peak areas of heavy and light labeled versions of the constituent peptide extracted from the full MS spectra. The method described here includes sample preparation by reversed-phase solid phase extraction, on-column ReDi labeling of peptides, peptide fractionation by basic pH reversed-phase (BPRP) chromatography, and StageTip peptide purification. We discuss advantages and limitations of ReDi labeling with respect to other methods for stable isotope incorporation. We highlight novel applications using ReDi labeling as a fast, inexpensive, and accurate method to compare protein abundances in nearly any type of sample.

Stable isotope labeling of peptides by reductive dimethylation (ReDi labeling) is a rapid, inexpensive strategy for accurate mass spectrometry-based quantitative proteomics. Here we demonstrate a robust method for preparation and analysis of protein mixtures using the ReDi approach that can be applied to nearly any sample type.

Stable isotope labeling of peptides by reductive dimethylation (ReDi labeling) is a method to accurately quantify protein expression differences between ...

High Precision Zinc Isotopic Measurements Applied to Mouse Organs

We present a procedure to measure with high precision zinc isotope ratios in mouse organs. Zinc is composed of 5 stable isotopes (64Zn, 66Zn, 67Zn, 68Zn and 70Zn) which are naturally fractionated between mouse organs. We first show how to dissolve the different organs in order to free the Zn atoms; this step is realized by a mixture of HNO3 and H2O2. We then purify the zinc atoms from all the other elements, in particular from isobaric interferences (e.g., Ni), by anion-exchange chromatography in a dilute HBr/HNO3 medium. These first two steps are performed in a clean laboratory using high purity chemicals. Finally, the isotope ratios are measured by using a multi-collector inductively-coupled-plasma mass-spectrometer, in low resolution. The samples are injected using a spray chamber and the isotopic fractionation induced by the mass-spectrometer is corrected by comparing the ratio of the samples to the ratio of a standard (standard bracketing technique).&#160;This full typical&#160;procedure produces an isotope ratio with a 50 ppm (2 s.d.) reproducibility.

We present the technique to measure with high precision zinc isotope ratios in mouse organs.

We present a procedure to measure with high precision zinc isotope ratios in mouse organs. Zinc is composed of 5 stable isotopes (64Zn, 66Zn, 67Zn, 68Zn ...

A Quantitative Glycomics and Proteomics Combined Purification Strategy

There is a growing desire in the biological and clinical sciences to integrate and correlate multiple classes of biomolecules to unravel biology, define pathways, improve treatment, understand disease, and aid biomarker discovery. N-linked glycosylation is one of the most important and robust post-translational modifications on proteins and regulates critical cell functions such as signaling, adhesion, and enzymatic function. Analytical techniques to purify and analyze N-glycans have remained relatively static over the last decade. While accurate and effective, they commonly require significant expertise and resources. Though some high-throughput purification schemes have been developed, they have yet to find widespread adoption and often rely on the enrichment of glycopeptides. One promising method, developed by Thomas-Oates et al., filter aided N-glycan separation (FANGS), was qualitatively demonstrated on tissues. Herein, we adapted FANGS to plasma and coupled it to the individuality normalization when labeling with glycan hydrazide tags strategy in order to achieve accurate relative quantification by liquid chromatography mass spectrometry and enhanced electrospray ionization. Furthermore, we designed new functionality to the protocol by achieving tandem, shotgun proteomics and glycosylation site analysis on hen plasma. We showed that N-glycans purified on filter and derivatized by hydrophobic hydrazide tags were comparable in terms of abundance and class to those by solid phase extraction (SPE); the latter is considered a gold standard in the field. Importantly, the variability in the two protocols was not statistically different. Proteomic data that was collected in-line with glycomic data had the same depth compared to a standard trypsin digest. Peptide deamidation is minimized in the protocol, limiting non-specific deamidation detected at glycosylation motifs. This allowed for direct glycosylation site analysis, though the protocol can accommodate 18O site labeling as well. Overall, we demonstrated a new in-line high-throughput, unbiased, filter based protocol for quantitative glycomics and proteomics analysis.

A high-throughput protocol was developed for combined proteomics and glycomics purification and LC-MS/MS quantification in plasma. Deamidation analysis of N-linked glycosylation motifs was specific to deglycosylated sites. Accurate quantitation of N-glycans was achieved by coupling filter aided N-glycan separation to the individuality normalization when labeling with glycan hydrazide tags strategy.

There is a growing desire in the biological and clinical sciences to integrate and correlate multiple classes of biomolecules to unravel biology, define ...

Characterization, Quantification and Compound-specific Isotopic Analysis of Pyrogenic Carbon Using Benzene Polycarboxylic Acids (BPCA)

Fire-derived, pyrogenic carbon (PyC), sometimes called black carbon (BC), is the carbonaceous solid residue of biomass and fossil fuel combustion, such as char and soot. PyC is ubiquitous in the environment due to its long persistence, and its abundance might even increase with the projected increase in global wildfire activity and the continued burning of fossil fuel. PyC is also increasingly produced from the industrial pyrolysis of organic wastes, which yields charred soil amendments (biochar). Moreover, the emergence of nanotechnology may also result in the release of PyC-like compounds to the environment. It is thus a high priority to reliably detect, characterize and quantify these charred materials in order to investigate their environmental properties and to understand their role in the carbon cycle.
Here, we present the benzene polycarboxylic acid (BPCA) method, which allows the simultaneous assessment of PyC&#39;s characteristics, quantity and isotopic composition (13C and 14C) on a molecular level. The method is applicable to a very wide range of environmental sample materials and detects PyC over a broad range of the combustion continuum, i.e., it is sensitive to slightly charred biomass as well as high temperature chars and soot. The BPCA protocol presented here is simple to employ, highly reproducible, as well as easily extendable and modifiable to specific requirements. It thus provides a versatile tool for the investigation of PyC in various disciplines, ranging from archeology and environmental forensics to biochar and carbon cycling research.

Characterization, Quantification and Compound-specific Isotopic Analysis of Pyrogenic Carbon Using Benzene Polycarboxylic Acids BPCA

We present the benzene polycarboxylic acid (BPCA) method for assessing pyrogenic carbon (PyC) in the environment. The compound-specific approach uniquely provides simultaneous information about the characteristics, quantity and isotopic composition (13C and 14C) of PyC.

Fire-derived, pyrogenic carbon (PyC), sometimes called black carbon (BC), is the carbonaceous solid residue of biomass and fossil fuel combustion, such as ...

Site Directed Spin Labeling and EPR Spectroscopic Studies of Pentameric Ligand-Gated Ion Channels

Ion channel gating is a stimulus-driven orchestration of protein motions that leads to transitions between closed, open, and desensitized states. Fundamental to these transitions is the intrinsic flexibility of the protein, which is critically modulated by membrane lipid-composition. To better understand the structural basis of channel function, it is necessary to study protein dynamics in a physiological membrane environment. Electron Paramagnetic Resonance (EPR) spectroscopy is an important tool to characterize conformational transitions between functional states. In comparison to NMR and X-ray crystallography, the information obtained from EPR is intrinsically of lower resolution. However, unlike in other techniques, in EPR there is no upper-limit to the molecular weight of the protein, the sample requirements are significantly lower, and more importantly the protein is not constrained by the crystal lattice forces. Therefore, EPR is uniquely suited for studying large protein complexes and proteins in reconstituted systems. In this article, we will discuss general protocols for site-directed spin labeling and membrane reconstitution using a prokaryotic proton-gated pentameric Ligand-Gated Ion Channel (pLGIC) from Gloeobacter violaceus (GLIC) as an example. A combination of steady-state Continuous Wave (CW) and Pulsed (Double Electron Electron Resonance-DEER) EPR approaches will be described that will enable a complete quantitative characterization of channel dynamics.

This article describes methods for site-directed spin labeling and reconstitution of pentameric ligand-gated channels for Electron Paramagnetic Resonance studies. This protocol can be adapted for any membrane protein. The reconstitution method described here can also be used for patch-clamp measurements of macroscopic and single-channel currents in a defined lipid system.

Ion channel gating is a stimulus-driven orchestration of protein motions that leads to transitions between closed, open, and desensitized states. ...

Peptide and Protein Quantification Using Automated Immuno-MALDI (iMALDI)

Mass spectrometry (MS) is one of the most commonly used technologies for quantifying proteins in complex samples, with excellent assay specificity as a result of the direct detection of the mass-to-charge ratio of each target molecule. However, MS-based proteomics, like most other analytical techniques, has a bias towards measuring high-abundance analytes, so it is challenging to achieve detection limits of low ng/mL or pg/mL in complex samples, and this is the concentration range for many disease-relevant proteins in biofluids such as human plasma. To assist in the detection of low-abundance analytes, immuno-enrichment has been integrated into the assay to concentrate and purify the analyte before MS measurement, significantly improving assay sensitivity. In this work, the immuno- Matrix-Assisted Laser Desorption/Ionization (iMALDI) technology is presented for the quantification of proteins and peptides in biofluids, based on immuno-enrichment on beads, followed by MALDI-MS measurement without prior elution. The anti-peptide antibodies are functionalized on magnetic beads, and incubated with samples. After washing, the beads are directly transferred onto a MALDI target plate, and the signals are measured by a MALDI-Time of Flight (MALDI-TOF) instrument after the matrix solution has been applied to the beads. The sample preparation procedure is simplified compared to other immuno-MS assays, and the MALDI measurement is fast. The whole sample preparation is automated with a liquid handling system, with improved assay reproducibility and higher throughput. In this article, the iMALDI assay is used for determining the peptide angiotensin I (Ang I) concentration in plasma, which is used clinically as readout of plasma renin activity for the screening of primary aldosteronism (PA).

Peptide and Protein Quantification Using Automated Immuno-MALDI iMALDI

A protocol for the protein quantification in complex biological fluids using automated immuno-MALDI (iMALDI) technology is presented.

Mass spectrometry (MS) is one of the most commonly used technologies for quantifying proteins in complex samples, with excellent assay specificity as a ...

Automated 90Sr Separation and Preconcentration in a Lab-on-Valve System at Ppq Level

A quick, automated and portable system for the separation and determination of radiostrontium in aqueous samples, using Sr-resin and multi sequential flow injection analysis, has been developed. The concentrations of radioactive strontium were determined by flow scintillation counting, allowing for on-line and also on-site determination. The proposed system can determine radioactive strontium at industrial relevant levels without further modification using overall analysis time of less than 10 min per aqueous sample. The limit of the detection is 320 fg&#183;g-1 (1.7 Bq/g).

Automated 90Sr Separation and Preconcentration in a Lab-on-Valve System at Ppq Level

Here, we present the portable autoRAD platform for quick radiometric separation and determination of 90Sr, an important fission product highly pertinent to nuclear waste.

A quick, automated and portable system for the separation and determination of radiostrontium in aqueous samples, using Sr-resin and multi sequential flow ...

Imine Metathesis by Silica-Supported Catalysts Using the Methodology of Surface Organometallic Chemistry

With this protocol, a well-defined singlesite silica-supported heterogeneous catalyst [(&#8801;Si-O-)Hf(=NMe)(&#951;1-NMe2)] is designed and prepared according to the methodology developed by surface organometallic chemistry (SOMC). In this framework, catalytic cycles can be determined by isolating crucial intermediates. All air-sensitive materials are handled under inert atmosphere (using gloveboxes or a Schlenk line) or high vacuum lines (HVLs, &#60;10-5 mbar). The preparation of SiO2-700 (silica dehydroxylated at 700 &#176;C) and subsequent applications (the grafting of complexes and catalytic runs) requires the use of HVLs and double-Schlenk techniques. Several well-known characterization methods are used, such as Fourier-transform infrared spectroscopy (FTIR), elemental microanalysis, solid-state nuclear magnetic resonance spectroscopy (SSNMR), and state-of-the-art dynamic nuclear polarization surface enhanced NMR spectroscopy (DNP-SENS). FTIR and elemental microanalysis permit scientists to establish the grafting and its stoichiometry. 1H and 13C SSNMR allows the structural determination of the hydrocarbon ligands coordination sphere. DNP SENS is an emerging powerful technique in solid characterization for the detection of poorly sensitive nuclei (15N, in our case). SiO2-700 is treated with about one equivalent of the metal precursor compared to the amount of surface silanol (0.30 mmol&#183;g-1) in pentane at room temperature. Then, volatiles are removed, and the powder samples are dried under dynamic high vacuum to afford the desired materials [(&#8801;Si-O-)Hf(&#951;2&#960;-MeNCH2)(&#951;1-NMe2)(&#951;1-HNMe2)]. After a thermal treatment under high vacuum, the grafted complex is converted into metal imido silica complex [(&#8801;Si-O-)Hf(=NMe)(&#951;1-NMe2)]. [(&#8801;Si-O-)Hf(=NMe)(&#951;1-NMe2)] effectively promotes the metathesis of imines, using the combination of two imine substrates, N-(4-phenylbenzylidene)benzylamine, or N-(4-fluorobenzylidene)-4-fluoroaniline, with N-benzylidenetert-butylamine as substrates. A significantly lower conversion is observed with the blank runs; thus, the presence of the imido group in [(&#8801;Si-O-)Hf(=NMe)(&#951;1-NMe2)] is correlated to the catalytic performance.

A new group IV metal catalyst for imine metathesis is prepared by grafting amine metal complex onto dehydroxylated silica. Surface metal fragments are characterized using FT-IR, elemental microanalysis, and solid-state NMR spectroscopy. Further dynamic nuclear polarization surface enhanced NMR spectroscopy experiments complement the determination of the coordination sphere.

With this protocol, a well-defined singlesite silica-supported heterogeneous catalyst [(&#8801;Si-O-)Hf(=NMe)(&#951;1-NMe2)] is designed and prepared ...

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

The single deuterium substitution in porphycene leads to an asymmetric molecular geometry, which may affect the double proton transfer process in the porphycene molecule. In this study, we applied an enhanced QM/MM method called SITS-QM/MM to investigate hydrogen/deuterium (H/D) isotope effects on the double proton transfer in porphycene. Distance changes in SITS-QM/MM molecular dynamics simulations suggested that the deuterium substituted porphycene adopted the stepwise double proton transfer mechanism. The structural analysis and the free energy shifts of double proton transfer process indicated that the asymmetric isotopic substitution subtly compressed the covalent hydrogen bonds and may alter the original transition state location.

A protocol that uses enhanced QM/MM method to investigate the isotopic effect on the double proton transfer process in porphycene is presented here.

The single deuterium substitution in porphycene leads to an asymmetric molecular geometry, which may affect the double proton transfer process in the ...

Sample Preparation in Quartz Crystal Microbalance Measurements of Protein Adsorption and Polymer Mechanics

In this study, we present various examples of how thin film preparation for quartz crystal microbalance experiments informs the appropriate modeling of the data and determines which properties of the film can be quantified. The quartz crystal microbalance offers a uniquely sensitive platform for measuring fine changes in mass and/or mechanical properties of an applied film by observing the changes in mechanical resonance of a quartz crystal oscillating at high frequency. The advantages of this approach include its experimental versatility, ability to study changes in properties over a wide range of experimental time lengths, and the use of small sample sizes. We demonstrate that, based on the thickness and shear modulus of the layer deposited on the sensor, we can acquire different information from the material. Here, this concept is specifically exploited to display experimental parameters resulting in mass and viscoelastic calculations of adsorbed collagen on gold and polyelectrolyte complexes during swelling as a function of salt concentration.

The quartz crystal microbalance can provide accurate mass and viscoelastic properties for films in the micron or submicron range, which is relevant for investigations in biomedical and environmental sensing, coatings, and polymer science. The sample thickness influences which information can be obtained from the material in contact with the sensor.

In this study, we present various examples of how thin film preparation for quartz crystal microbalance experiments informs the appropriate modeling of ...