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

<ol><li>Diamandis, E. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Mass+spectrometry+as+a+diagnostic+and+a+cancer+biomarker+discovery+tool%3A+opportunities+and+potential+limitations%5BTitle%5D)+AND+%22Molecular+and+Cell+Proteomics%22%5BJournal%5D)">Mass spectrometry as a diagnostic and a cancer biomarker discovery tool: opportunities and potential limitations.</a> Molecular and Cell Proteomics. 3 (4), 367-378 (2004).</li>
<li>Qian, W. -J., Jacobs, J. M., Liu, T., Camp, D. G., Smith, R. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Advances+and+challenges+in+liquid+chromatography-mass+spectrometry-based+proteomics+profiling+for+clinical+applications%5BTitle%5D)+AND+%22Molecular+and+Cellular+Proteomics%22%5BJournal%5D)">Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications.</a> Molecular and Cellular Proteomics. 5 (10), 1727-1744 (2006).</li>
<li>Arul, A. B., Robinson, R. A. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Sample+Multiplexing+Strategies+in+Quantitative+Proteomics%5BTitle%5D)+AND+%22Analytical+Chemistry%22%5BJournal%5D)">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/pubmed?term=((Increasing+Throughput+in+Targeted+Proteomics+Assays%3A+54-Plex+Quantitation+in+a+Single+Mass+Spectrometry+Run%5BTitle%5D)+AND+%22Analytical+Chemistry%22%5BJournal%5D)">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/pubmed?term=((Quantitative+proteome+analysis+using+isotope-coded+affinity+tags+and+mass+spectrometry%5BTitle%5D)+AND+%22Nature+Protocol%22%5BJournal%5D)">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/pubmed?term=((Quantitative+analysis+of+complex+protein+mixtures+using+isotope-coded+affinity+tags%5BTitle%5D)+AND+%22Nature+Biotechnology%22%5BJournal%5D)">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/pubmed?term=((TMTpro%3A+Design%2C+Synthesis%2C+and+Initial+Evaluation+of+a+Proline-Based+Isobaric+16-Plex+Tandem+Mass+Tag+Reagent+Set%5BTitle%5D)+AND+%22Analytical+Chemistry%22%5BJournal%5D)">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/pubmed?term=((21-plex+DiLue+Isobaric+Tags+for+High-Throughput+Quantitative+Proteomics%5BTitle%5D)+AND+%22Anal.+Chem%22%5BJournal%5D)">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/pubmed?term=((Global+combined+precursor+isotopic+labeling+and+isobaric+tagging+%28cPILOT%29+approach+with+selective+MS%283%29+acquisition%5BTitle%5D)+AND+%22Proteomics%22%5BJournal%5D)">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/pubmed?term=((Enhanced+sample+multiplexing+for+nitrotyrosine-modified+proteins+using+combined+precursor+isotopic+labeling+and+isobaric+tagging%5BTitle%5D)+AND+%22Analytical+Chemistry%22%5BJournal%5D)">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/pubmed?term=((Enhanced+Sample+Multiplexing+of+Tissues+Using+Combined+Precursor+Isotopic+Labeling+and+Isobaric+Tagging+%28cPILOT%29%5BTitle%5D)+AND+%22Journal+of+Visual+Experiments%22%5BJournal%5D)">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/pubmed?term=((Evaluating+Combined+Precursor+Isotopic+Labeling+and+Isobaric+Tagging+Performance+on+Orbitraps+To+Study+the+Peripheral+Proteome+of+Alzheimer%27s+Disease%5BTitle%5D)+AND+%22Analytical+Chemistry%22%5BJournal%5D)">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/pubmed?term=((Global+cPILOT+analysis+of+the+APP%2FPS-1+mouse+liver+proteome%5BTitle%5D)+AND+%22Proteomics+%26+Clinical+Applications%22%5BJournal%5D)">Global cPILOT analysis of the APP/PS-1 mouse liver proteome.</a> Proteomics & 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/pubmed?term=((Sample+multiplexing+with+cysteine-selective+approaches%3A+cysDML+and+cPILOT%5BTitle%5D)+AND+%22Journal+of+American+Society+of+Mass+Spectrometer%22%5BJournal%5D)">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/pubmed?term=((MS3+eliminates+ratio+distortion+in+isobaric+multiplexed+quantitative+proteomics%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">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/pubmed?term=((Increased+N%2CN-Dimethyl+Leucine+Isobaric+Tag+Multiplexing+by+a+Combined+Precursor+Isotopic+Labeling+and+Isobaric+Tagging+Approach%5BTitle%5D)+AND+%22Analytical+Chemistry%22%5BJournal%5D)">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/pubmed?term=((High-throughput+endogenous+measurement+of+S-nitrosylation+in+Alzheimer%27s+disease+using+oxidized+cysteine-selective+cPILOT%5BTitle%5D)+AND+%22Analyst%22%5BJournal%5D)">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/pubmed?term=((Quantitative+proteomics+to+study+aging+in+rabbit+liver%5BTitle%5D)+AND+%22Mechanisms+of+Ageing+and+Development%22%5BJournal%5D)">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><tbody><tr><td>0.6 mL eppendorf tubes, 500 pk</td><td>Fisher Scientific</td><td>05-408-120</td><td>Any brand of 0.6 mL eppendorf tubes are sufficient</td></tr><tr><td>0.65 &amp;micro;m Ultrafree MC DV centrifugal filter units</td><td>EMD Millipore</td><td>UFC30DV00</td><td></td></tr><tr><td>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><td>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><td>2 ml clear deep well plate</td><td>VWR</td><td>75870-796</td><td></td></tr><tr><td>Acetic Acid</td><td>J.T. Baker</td><td>9508-01</td><td></td></tr><tr><td>Acetonitrile - MS Grade</td><td>Fisher Scientific</td><td>A955-4</td><td>4 L quantity is not necessary</td></tr><tr><td>Agilent 500&amp;micro;L plate</td><td>Agilent</td><td>203942-100</td><td>Reagent plate for adding buffers</td></tr><tr><td>Ammonium formate</td><td>Acros Organics</td><td>208-753-9</td><td></td></tr><tr><td>Ammonium hydroxide solution (28 - 30%)</td><td>Sigma Aldrich</td><td>320145-500ML</td><td></td></tr><tr><td>Analytical balance</td><td>Mettler Toledo</td><td>AL54</td><td></td></tr><tr><td>BCA protein assay kit</td><td>Pierce Thermo Fisher Scientific</td><td>23227</td><td></td></tr><tr><td>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><td>C18 packing material (2.5 &amp;micro;m, 100 &amp;Aring;)</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><td>Centrifuge with plate rotor</td><td>Thermo Scientific</td><td>69720</td><td></td></tr><tr><td>Micro 21R Centrifuge</td><td>Sorval</td><td>5437</td><td></td></tr><tr><td>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><td>Dithiothreiotol (DTT)</td><td>Fisher Scientific</td><td>BP172-5</td><td></td></tr><tr><td>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><td>Formaldehyde (CH2O) solution; 36.5 - 38% in H2O</td><td>Sigma Aldrich, Life Science</td><td>F8775-25ML</td><td></td></tr><tr><td>Formic Acid</td><td>Fluka Analytical</td><td>94318-250ML-F</td><td></td></tr><tr><td>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><td>Hydroxylamine hydrochloride</td><td>Sigma Aldrich, Chemistry</td><td>255580-100G</td><td></td></tr><tr><td>Iodoacetamide (IAM)</td><td>Acros Organics</td><td>144-48-9</td><td></td></tr><tr><td>Isobaric Tagging Kit (TMT 11-plex)</td><td>Thermo Fisher Scientific</td><td>90061</td><td></td></tr><tr><td>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><td>L-Cysteine</td><td>Sigma Aldrich, Chemistry</td><td>168149-25G</td><td></td></tr><tr><td>Mechanical Homogenizer (i.e. FastPrep-24 5G)</td><td>MP Biomedicals</td><td>116005500</td><td></td></tr><tr><td>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><td>pH 7 buffer</td><td>Fisher Scientific</td><td>06-664-260</td><td>Any brand pH buffer 7 is sufficient</td></tr><tr><td>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><td>Protein software (e.g. Proteome Discoverer)</td><td>Thermo Scientific</td><td></td><td></td></tr><tr><td>Reservior plate 200ml</td><td>Agilent</td><td>204017-100</td><td></td></tr><tr><td>Sodium Cyanoborodeuteride; 96 atom % D, 98% CP</td><td>Sigma Aldrich, Chemistry</td><td>190020-1G</td><td></td></tr><tr><td>Sodium Cyanoborohydride; reagent grade, 95%</td><td>Sigma Aldrich</td><td>156159-10G</td><td></td></tr><tr><td>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><td>Stir plate</td><td>VWR</td><td>12365-382</td><td>Any brand of stir plates are sufficient</td></tr><tr><td>Targa 20 mg SPE plates</td><td>Nest Group, Inc.</td><td>HNS S18V</td><td>These are C18 cartridges</td></tr><tr><td>Triethyl ammonium bicarbonate (TEAB) buffer</td><td>Sigma Aldrich, Life Science</td><td>T7408-100ML</td><td></td></tr><tr><td>Tris</td><td>Biorad</td><td>161-0716</td><td></td></tr><tr><td>Biomek 24-Place Tube Rack Holder</td><td>Beckmann</td><td>373661</td><td></td></tr><tr><td>Urea</td><td>Biorad</td><td>161-0731</td><td></td></tr><tr><td>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.

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JoVE Journal

Chemistry

5.5K Views.  Vanderbilt University. Ons geautomatiseerde cPILOT-protocol maakt het voor onderzoekers gemakkelijker om monsters op een hoge doorvoerwijze te analyseren. Dit is belangrijk omdat het ons in staat stelt om sneller bij biologische informatie over ziekten of verschillende aandoeningen te komen. Het belangrijkste voordeel van deze techniek is dat het helpt om meerdere monsters parallel te verwerken, waardoor bruikbare fouten worden verminderd tijdens het gebruik van hoge doorvoermonsters.Ons laboratorium is geï...

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There is an increasing demand to analyze many biological samples for disease understanding and biomarker discovery. Quantitative proteomics strategies that allow simultaneous measurement of multiple samples have become widespread and greatly reduce experimental costs and times. Our laboratory developed a technique called combined precursor isotopic labeling and isobaric tagging (cPILOT), which enhances sample multiplexing of traditional isotopic labeling or isobaric tagging approaches. Global cPILOT can be applied to samples originating from cells, tissues, bodily fluids, or whole organisms and gives information on relative protein abundances across different sample conditions. cPILOT works by 1) using low pH buffer conditions to selectively dimethylate peptide N-termini and 2) using high pH buffer conditions to label primary amines of lysine residues with commercially-available isobaric reagents (see Table of Materials/Reagents). The degree of sample multiplexing available is dependent on the number of precursor labels used and the isobaric tagging reagent. Here, we present a 12-plex analysis using light and heavy dimethylation combined with six-plex isobaric reagents to analyze 12 samples from mouse tissues in a single analysis. Enhanced multiplexing is helpful for reducing experimental time and cost and more importantly, allowing comparison across many sample conditions (biological replicates, disease stage, drug treatments, genotypes, or longitudinal time-points) with less experimental bias and error. In this work, the global cPILOT approach is used to analyze brain, heart, and liver tissues across biological replicates from an Alzheimer&#39;s disease mouse model and wild-type controls. Global cPILOT can be applied to study other biological processes and adapted to increase sample multiplexing to greater than 20 samples.

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Genetics

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Bacterial vaginosis (BV) is a recurring polymicrobial syndrome that is characterized by a change in the "normal" microbiota from Lactobacillus-dominated to a microbiota dominated by a number of bacterial species, including Gardnerella vaginalis, Atopobium vaginae, and others1-3. This condition is associated with a range of negative health outcomes, including HIV acquisition4, and it can be difficult to manage clinically5. Furthermore, diagnosis of BV has relied on the use of Gram stains of vaginal swab smears that are scored on various numerical criteria6,7. While this diagnostic is simple, inexpensive, and well suited to resource-limited settings, it can suffer from problems related to subjective interpretations and it does not give a detailed profile of the composition of the vaginal microbiota8. Recent deep sequencing efforts have revealed a rich, diverse vaginal microbiota with clear differences between samples taken from individuals that are diagnosed with BV compared to those individuals that are considered normal9,10, which has resulted in the identification of a number of potential targets for molecular diagnosis of BV11,12. These studies have provided a wealth of useful information, but deep sequencing is not yet practical as a diagnostic method in a clinical setting. We have recently described a method for rapidly profiling the vaginal microbiota in a multiplex format using oligonucleotide-coupled fluorescent beads with detection on a Luminex platform13. This method, like current Gram stain-based methods, is rapid and simple but adds the additional advantage of exploiting molecular knowledge arising from sequencing studies in probe design. This method therefore provides a way to profile the major microorganisms that are present in a vaginal swab that can be used to diagnose BV with high specificity and sensitivity compared to Gram stain while providing additional information on species presence and abundance in a semi-quantitative and rapid manner. This multiplex method is expandable well beyond the range of current quantitative PCR assays for particular organisms, which is currently limited to 5 or 6 different assays in a single sample14. Importantly, the method is not limited to the detection of bacteria in vaginal swabs and can be easily adapted to rapidly profile nearly any microbial community of interest. For example, we have recently begun to apply this methodology to the development of diagnostic tools for use in wastewater treatment plants.

We describe a multiplex method for the detection of microorganisms within a sample using oligonucleotide-coupled fluorescent beads. Amplicon from all organisms within a sample is hybridized to a panel of probe-coupled beads. A Luminex or Bio-Plex instrument is used to query each bead for bead type and hybridization signal.

Bacterial vaginosis (BV) is a recurring polymicrobial syndrome that is characterized by a change in the "normal" microbiota from Lactobacillus-dominated ...

Immunology and Infection

Automated Multiplex Immunofluorescence Panel for Immuno-oncology Studies on Formalin-fixed Carcinoma Tissue Specimens

Continued developments in immuno-oncology require an increased understanding of the mechanisms of cancer immunology. The immunoprofiling analysis of tissue samples from formalin-fixed, paraffin-embedded (FFPE) biopsies has become a key tool for understanding the complexity of tumor immunology and discovering novel predictive biomarkers for cancer immunotherapy. Immunoprofiling analysis of tissues requires the evaluation of combined markers, including inflammatory cell subpopulations and immune checkpoints, in the tumor microenvironment. The advent of novel multiplex immunohistochemical methods allows for a more efficient multiparametric analysis of single tissue sections than does standard monoplex immunohistochemistry (IHC). One commercially available multiplex immunofluorescence (IF) method is based on tyramide-signal amplification and, combined with multispectral microscopic analysis, allows for a better signal separation of diverse markers in tissue. This methodology is compatible with the use of unconjugated primary antibodies that have been optimized for standard IHC on FFPE tissue samples. Herein we describe in detail an automated protocol that allows multiplex IF labeling of carcinoma tissue samples with a six-marker multiplex antibody panel comprising PD-L1, PD-1, CD68, CD8, Ki-67, and AE1/AE3 cytokeratins with 4&#8242;,6-diamidino-2-phenylindole as a nuclear cell counterstain. The multiplex panel protocol is optimized in an automated IHC stainer for a staining time that is shorter than that of the manual protocol and can be directly applied and adapted by any laboratory investigator for immuno-oncology studies on human FFPE tissue samples. Also described are several controls and tools, including a drop-control method for fine quality control of a new multiplex IF panel, that are useful for the optimization and validation of the technique.

A detailed protocol for a six-marker multiplex immunofluorescence panel is optimized and performed, using an automated stainer for more consistent results and a shorter procedure time. This approach can be directly adapted by any laboratory for immuno-oncology studies.

Continued developments in immuno-oncology require an increased understanding of the mechanisms of cancer immunology. The immunoprofiling analysis of ...

Cancer Research

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is inherently a single-cell assay, however prior to molecular analysis, the target cells are often prospectively isolated in bulk, thereby losing single-cell resolution. Single-cell gene expression analysis provides a means to understand molecular differences between individual cells in heterogeneous cell populations. In bulk cell analysis an overrepresentation of a distinct cell type results in biases and occlusions of signals from rare cells with biological importance. By utilizing FACS index sorting coupled to single-cell gene expression analysis, populations can be investigated without the loss of single-cell resolution while cells with intermediate cell surface marker expression are also captured, enabling evaluation of the relevance of continuous surface marker expression. Here, we describe an approach that combines single-cell reverse transcription quantitative PCR (RT-qPCR) and FACS index sorting to simultaneously characterize the molecular and immunophenotypic heterogeneity within cell populations.
In contrast to single-cell RNA sequencing methods, the use of qPCR with specific target amplification allows for robust measurements of low-abundance transcripts with fewer dropouts, while it is not confounded by issues related to cell-to-cell variations in read depth. Moreover, by directly index-sorting single-cells into lysis buffer this method, allows for cDNA synthesis and specific target pre-amplification to be performed in one step as well as for correlation of subsequently derived molecular signatures with cell surface marker expression. The described approach has been developed to investigate hematopoietic single-cells, but have also been used successfully on other cell types.
In conclusion, the approach described herein allows for sensitive measurement of mRNA expression for a panel of pre-selected genes with the possibility to develop protocols for subsequent prospective isolation of molecularly distinct subpopulations.

Bulk gene expression measurements cloud individual cell differences in heterogeneous cell populations. Here, we describe a protocol for how single-cell gene expression analysis and index sorting by Florescence Activated Cell Sorting (FACS) can be combined to delineate heterogeneity and immunophenotypically characterize molecularly distinct cell populations.

Immunophenotypic characterization and molecular analysis have long been used to delineate heterogeneity and define distinct cell populations. FACS is ...

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling

Identification and isolation of contagious individuals along with quarantine of close contacts, is critical for slowing the spread of COVID-19. Large-scale testing in a surveillance or screening capacity for asymptomatic carriers of COVID-19 provides both data on viral spread and the follow-up ability to rapidly test individuals during suspected outbreaks. The COVID-19 early detection program at Michigan State University has been utilizing large-scale testing in a surveillance or screening capacity since fall of 2020. The methods adapted here take advantage of the reliability, large sample volume, and self-collection benefits of saliva, paired with a cost-effective, reagent conserving two-dimensional pooling scheme. The process was designed to be adaptable to supply shortages, with many components of the kits and the assay easily substituted. The processes outlined for collecting and processing SARS-CoV-2 samples can be adapted to test for future viral pathogens reliably expressed in saliva. By providing this blueprint for universities or other organizations, preparedness plans for future viral outbreaks can be developed.

The protocol is intended to serve as a blueprint for universities and other organizations considering large-scale testing for SARS-CoV-2 or developing preparedness plans for future viral outbreaks.

Identification and isolation of contagious individuals along with quarantine of close contacts, is critical for slowing the spread of COVID-19. ...

Multiplexed Barcoding Image Analysis for Immunoprofiling and Spatial Mapping Characterization in the Single-Cell Analysis of Paraffin Tissue Samples

Multiplexed imaging technology using antibody barcoding with oligonucleotides, which sequentially detects multiple epitopes in the same tissue section, is an effective methodology for tumor evaluation that improves the understanding of the tumor microenvironment. The visualization of protein expression in formalin-fixed, paraffin-embedded&#160;tissues is achieved when a specific fluorophore is annealed to an antibody-bound barcode via complementary oligonucleotides and then sample imaging is performed; indeed, this method allows for the use of customizable panels of more than 40 antibodies in a single tissue staining reaction. This method is compatible with fresh frozen tissue, formalin-fixed, paraffin-embedded tissue, cultured cells, and peripheral blood mononuclear cells, meaning that researchers can use this technology to view a variety of sample types at single-cell resolution. This method starts with a manual staining and fixing protocol, and all the antibody barcodes are applied using an antibody cocktail. The staining fluidics instrument is fully automated and performs iterative cycles of labeling, imaging, and removing spectrally distinct fluorophores until all the biomarkers have been imaged using a standard fluorescence microscope. The images are then collected and compiled across all the imaging cycles to achieve single-cell resolution for all the markers. The single-step staining and gentle fluorophore removal not only allow for highly multiplexed biomarker analysis but also preserve the sample for additional downstream analysis if desired (e.g., hematoxylin and eosin staining). Furthermore, the image analysis software enables image processing-drift compensation, background subtraction, cell segmentation, and clustering-as well as the visualization and analysis of the images and cell phenotypes for the generation of spatial network maps. In summary, this technology employs a computerized microfluidics system and fluorescence microscope to iteratively hybridize, image, and strip fluorescently labeled DNA probes that are complementary to tissue-bound, oligonucleotide-conjugated antibodies.

Multiplexed barcoding image analysis has recently improved the characterization of the tumor microenvironment, permitting comprehensive studies of cell composition, functional state, and cell-cell interactions. Herein, we describe a staining and imaging protocol using the barcoding of oligonucleotide-conjugated antibodies and cycle imaging, which allows for the use of a high-dimensional image&#160;analysis technique.

Multiplexed imaging technology using antibody barcoding with oligonucleotides, which sequentially detects multiple epitopes in the same tissue section, is ...

Profiling of Pre-micro RNAs and microRNAs using Quantitative Real-time PCR (qPCR) Arrays

Quantitative real-time PCR (QPCR) has emerged as an accurate and valuable tool in profiling gene expression levels.  One of its many advantages is a lower detection limit compared to other methods of gene expression profiling while using smaller amounts of input for each assay.  Automated qPCR setup has improved this field by allowing for greater reproducibility.  Its convenient and rapid setup allows for high-throughput experiments, enabling the profiling of many different genes simultaneously in each experiment.  This method along with internal plate controls also reduces experimental variables common to other techniques.  
We recently developed a qPCR assay for profiling of pre-microRNAs  (pre-miRNAs) using a set of 186 primer pairs.  MicroRNAs have emerged as a novel class of small, non-coding RNAs with the ability to regulate many mRNA targets at the post-transcriptional level.  These small RNAs are first transcribed by RNA polymerase II as a primary miRNA (pri-miRNA) transcript, which is then cleaved into the precursor miRNA (pre-miRNA).  Pre-miRNAs are exported to the cytoplasm where Dicer cleaves the hairpin loop to yield mature miRNAs.  Increases in miRNA levels can be observed at both the precursor and mature miRNA levels and profiling of both of these forms can be useful.  There are several commercially available assays for mature miRNAs; however, their high cost may deter researchers from this profiling technique.  Here, we discuss a cost-effective, reliable, SYBR-based qPCR method of profiling pre-miRNAs.  Changes in pre-miRNA levels often reflect mature miRNA changes and can be a useful indicator of mature miRNA expression.  However, simultaneous profiling of both pre-miRNAs and mature miRNAs may be optimal as they can contribute nonredundant information and provide insight into microRNA processing.  Furthermore, the technique described here can be expanded to encompass the profiling of other library sets for specific pathways or pathogens.

Profiling of Pre-micro RNAs and microRNAs using Quantitative Real-time PCR qPCR Arrays

We will demonstrate the setup and analysis of pre-microRNA 96-well arrays for QPCR using a robot as well as by hand with a Thermo Scientific Matrix multichannel pipette.

Quantitative real-time PCR (QPCR) has emerged as an accurate and valuable tool in profiling gene expression levels.  One of its many advantages is a lower ...

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

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