We would like to acknowledge the UF-ICBR proteomics facility for their processing of our samples. This work was supported in part by the National Institutes of Health R01 HL136759-01A1 (CAP).

The Institutional Animal Care and Use Committee from University of Florida approved all animal studies.
1. Preparation of reagents
<ol>
	<li>Prepare CHAPS Lysis Buffer (150 mM KCl, 50 mM HEPES pH = 7.4, 0.1% CHAPS, and 1 protease inhibitor cocktail tablet per 50 mL of buffer). Buffer without protease inhibitors can be stored at 4 &#176;C for up to 6 months or buffer with protease inhibitor stored at -20 &#176;C up to 1 year.</li>
	<li>Prepare 100 mM triethylammonium bicarbonate (TEAB): Add 500 &#181;L of 1 M TEAB to 4.5 mL of ultrapure water.</li>
	<li>Prepare 200 mM tris (2-carboxyethyl) phosphine hydrochloride (TCEP): Add 70 &#181;L of 0.5 M TCEP, a denaturing reagent, to 70 &#181;L of ultrapure water. Then add 35 &#181;L of the 1 M TEAB.</li>
	<li>Prepare 5% hydroxylamine: Add 50 &#181;L of 50% hydroxylamine to 450 &#181;L of 100 mM TEAB.</li>
</ol>
2. Protein extraction
<ol>
	<li>Isolate quadriceps muscle from a euthanized mouse according to an IACUC approved protocol. Freeze and maintain at -80 &#176;C or continue with the protocol for immediate use.</li>
	<li>Cut to isolate approximately 10 mg of fresh or frozen quadriceps mouse tissue. Separate fibers using tweezers when working with skeletal muscle. Alternatively, if working with cell cultures, resuspend ~3 x 106 cells in 300 &#181;L of CHAPS lysis buffer and skip to step 2.4.</li>
	<li>Homogenize tissue using a bead disrupter using 2 mL tubes filled with approximately 200 &#181;L of 1 mm zirconia/silica beads and 500 &#181;L of CHAPS lysis buffer. Scale up or down as appropriate (e.g., 5 mg of tissue in 250 &#181;L of CHAPS lysis buffer).</li>
	<li>Perform sonication (10x for 10 s each with 50% amplitude and 30 s intervals on ice) to release protein bound to DNA. The same DNA degradation results can be achieved either with syringe lysis by passing the lysate 10x through a 21 G needle attached to a 1 mL syringe, or by benzonase incubation (44 U/mL) at 37 &#176;C for 30 min.</li>
	<li>Centrifuge the lysate at 16,000 x g for 10 min at 4 &#176;C and transfer the supernatant to a new centrifuge tube.</li>
</ol>
3. Protein measurement
<ol>
	<li>Determine the protein concentration of the supernatant using established protocols (see Table of Materials). 
	NOTE: It is best to use samples at &#8805;2 &#181;g/&#181;L, but less concentrated samples may also be used. If a less concentrated sample is used it will be necessary to appropriately adjust the volumes of the reducing/alkylating reagents in step 5.1.</li>
	<li>Prepare a BSA standard curve dilution using CHAPS lysis buffer.</li>
	<li>Follow the manufacturer&#39;s instructions, and after 15 min, read the absorbance at 750 nm.</li>
</ol>
4. Reducing/alkylating reagent treatment
<ol>
	<li>Transfer 200 &#181;g of protein per condition into a new centrifuge tube and adjust to a final volume of 100 &#181;L using CHAPS lysis buffer. It is possible to scale up to 200 &#181;L when the protein concentration is too low, but do not forget to appropriately adjust the volume of reducing/alkylating reagent.</li>
	<li>Add 5 &#181;L of the 200 mM TCEP and incubate samples at 55 &#176;C for 1 h.</li>
	<li>Immediately prior to use, prepare 375 mM iodoacetamide by dissolving one tube of iodoacetamide (i.e., 9 mg) into 132 &#181;L of 100 mM TEAB. Protect this solution from light.</li>
	<li>Add 5 &#181;L of 375 mM iodoacetamide to the sample and incubate for 30 min at room temperature (RT) protected from light.</li>
</ol>
5. Methanol/chloroform precipitation20
<ol>
	<li>Add 400 &#181;L of methanol to each 100 &#181;L of protein and briefly vortex samples.</li>
	<li>Centrifuge at 9,000 x g for 10 s at RT. This is to incorporate liquids deposited on the sides of the sample tube.</li>
	<li>Add 100 &#181;L of chloroform to the mixture and briefly vortex. Use 200 &#181;L of chloroform if the sample has a high concentration of phospholipids.</li>
	<li>Centrifuge at 9,000 x g for 10 s at RT. This is to incorporate liquids deposited on the sides of the sample tube.</li>
	<li>Add 300 &#181;L of water and vortex vigorously. It is important to obtain a homogenous solution.</li>
	<li>Centrifuge at 9,000 x g for 1 min at RT. Be extremely careful to avoid disturbing the layers when transferring the tube to a rack. 
	NOTE: The tube should now contain three phases: 1) Top layer (i.e., supernatant), a mixture of water and methanol; 2) Middle layer (i.e., interphase), white precipitated protein; and 3) Bottom layer (i.e., bottom phase), chloroform.</li>
	<li>Carefully remove the supernatant.</li>
	<li>Add 300 &#181;L of methanol to the remaining interphase and bottom phase. Vortex vigorously.</li>
	<li>Centrifuge at 9,000 x g for 2 min at RT. Be extremely careful to avoid disturbing the layers when transferring the tube to a rack.</li>
	<li>Carefully remove the supernatant.</li>
	<li>Gently aspirate as much liquid as possible under a stream of air (e.g., using a vacuum concentrator) at RT until the pellet is just a bit moist (~10 min). As the necessary time may be different for each sample, check every 2 min to assess. Store the pellet at -80 &#176;C until further processing.</li>
</ol>
6. Protein digestion
<ol>
	<li>Resuspend the precipitated protein pellet in 100 &#181;L of TEAB lysis buffer. 
	NOTE: It is optional to measure the protein concentration at this step.</li>
	<li>Immediately prior to use, prepare 1 &#181;g/&#181;L trypsin by adding 100 &#181;L of the trypsin storage solution (50 mM acetic acid) to the bottom of the 100 &#181;g trypsin glass vial and incubate for 5 min at RT. Store remaining reagent in single-use doses at -80 &#176;C.</li>
	<li>Add 2.5 &#181;L of trypsin per 100 &#181;g of protein. Digest the sample overnight at 37 &#176;C. This step is crucial for complete solubilization of the protein; do not modify these conditions. Following the digestion, it is optional to measure the protein concentration using standard protein assays.</li>
</ol>
7. Peptide labeling
<ol>
	<li>Immediately prior to use, equilibrate the TMT label kit reagents to RT.</li>
	<li>Dissolve each of the 0.8 mg TMT tag vials through the addition of 41 &#181;L of anhydrous acetonitrile to each tube. Incubate the reagent for 5 min at RT with occasional vortexing. Briefly centrifuge the tubes. 
	NOTE: A concentration of 0.8 mg of TMT tag is usually enough to label two sets. Other investigators, however, have demonstrated that this concentration can be reduced further and still yield reliable data15.</li>
	<li>Carefully add 41 &#181;L of the TMT label reagent to each 100 &#181;L sample.</li>
	<li>Incubate the reaction for 1 h at RT.</li>
	<li>Add 8 &#181;L of 5% hydroxylamine to the sample and incubate for 15 min to quench the reaction.</li>
	<li>Split the samples into equal amounts in a new centrifuge tube and store at -80 &#176;C. 
	NOTE: In this step the samples are stable and can be submitted for mass spectroscopy. It is optional to measure concentration at this point using standard protein assays.</li>
</ol>
8. Mass spectroscopy
<ol>
	<li>Submit the samples to a proteomics facility (this study used the UF ICBR Proteomics Core facility), where all samples are combined and purified using C18 spin columns. 
	NOTE: Discuss with the core facility how to submit the samples prior to preparing them to confirm the exact steps they prefer for submissions.</li>
	<li>Request the following procedures per combined multiplex sample: solid phase extraction, HPLC (SCX, SE), zip tip, and LC-MS/MS (2 h gradient for protein ID, if &#62;10QE Plus).</li>
	<li>Once the data are collected, the core facility will process RAW files using vendor-supplied software for protein identification.</li>
</ol>
9. Data analysis
<ol>
	<li>Data are typically delivered from the core back to the user in the 7z format, which can require approximately 16 GB of disk space per each dataset (in this case 11 samples). For data processing, make sure a computer is available that is at least 3.4 GHz.</li>
	<li>Extract files using 7-Zip File Manager. These extracted files contain RAW data, pdStudy format file, and pdResultView format file. Save all files for further analyses.</li>
	<li>Open file using Proteome Discovery 2.2 Software. 
	NOTE: The file format is &#34;File name.pdStudy&#34;. If the &#34;File name.pdResultView&#34; is opened it is not possible to select the control sample.</li>
	<li>Select control samples on &#34;Samples&#34; panel.</li>
	<li>Open Result by selecting ID on &#34;Analysis Results&#34; panel.</li>
	<li>Export to spreadsheet software.</li>
	<li>Save raw data (all proteins identified).</li>
	<li>Open the spreadsheet software file. This will contain all proteins that have been identified.</li>
	<li>In the spreadsheet software file use the &#34;Filter&#34; function to screen &#34;Protein FDR Confidence: Combined&#34; in high (column B), &#34;#Unique Peptides&#34; higher than 2 (column K), and either one of &#34;Abundance Ratio&#34; blank exclusively (column S up to W).</li>
	<li>Insert a column for &#34;p-value&#34; calculation with the function 
	=TTEST(control group,experimental group, tails, type)</li>
	<li>Insert a column for &#34;Statistical Significance&#34; with the function 
	=IF(p-value&#60;0.05, &#34;Significance&#34;,&#34;NS&#34;)</li>
	<li>Use the &#34;Filter&#34; function to screen &#34;Statistical Significance&#34; showing &#34;Significance&#34;. The result shows the analyzed proteins with the statistical significance in the control group and experimental group.</li>
	<li>Determine significantly higher or lower protein expression abundance in the experimental group compared to the control group, insert a column for &#34;Regulation&#34; with the function 
	=IF(AVERAGE(controlgroup)&#62;AVERAGE(experimentalgroup),&#34;Upregulated&#34;,&#34;Downregulated&#34;)</li>
</ol>
10. Methods to evaluate significant hits
<ol>
	<li>To identify protein-protein interactions between the significant hits identified in the TMT studies, use Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) version 11.021: <a href="https://string-db.org/" target="_blank">https://string-db.org/</a></li>
	<li>To classify by groups (i.e., molecular function, biological processes, and protein classes) use Protein Analysis through Evolutionary Relationships (PANTHER) ontology classification software22: <a href="http://www.pantherdb.org/" target="_blank">http://www.pantherdb.org/</a></li>
	<li>To identify protein interactions in a variety of pathways, use pathway analysis software23.</li>
</ol>
11. Proteomic data upload to a repository bank
<ol>
	<li>To submit proteomic data to the Proteomics IDEntificantions Database (PRIDE) or Mass Spectrometry Interactive Virtual Environment (MassIVE) include the following information: the peak list files (processed mass spectrum files in a standard format such as mzXML, mzML, or MGF), result files (spectrum identifications in a standard format such as mzIdentML or mzTab), and raw spectrum files (raw mass spectrum files in a nonstandard or instrument-specific format such as .RAW files or .WIFF files).</li>
	<li>To submit, create an account and include information such as affiliation and project details. Then, select the files listed in step 11.1 and upload them.</li>
	<li>To create an official dataset, run a submission workflow on these uploaded files. 
	NOTE: Following submission, the dataset will be private in the repository bank. With the private option, the data are only available to authorized users. There are two additional options: 1) Shared dataset, which gives access to journal reviewers and collaborators; or 2) Public dataset, which will show up in public dataset searches. Another important feature of these repositories is the ability to update the uploaded data and associate subsequent publications with the existing dataset.</li>
</ol>

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The authors have nothing to disclose.

To successfully prepare samples for proteomic analysis using TMT-based isobaric stable isotope labeling methodologies, it is crucial to perform protein extractions very carefully at 4 &#176;C and to use a lysis buffer that contains a protease inhibitor cocktail24,25. The protease inhibitor cocktail is a crucial reagent to avoid unexpected protein degradation during protein digestion. One key difference between our protocol and the current one provided by the vendor is that we strongly recommend the use of CHAPS lysis buffer based upon our experience with mammalian cells and tissues. We also suggest using a methanol/chloroform protein precipitation approach for both cell pellets and tissues.
Ideally, protein extraction, measurement, reducing/alkylating reagent treatments, and methanol/chloroform precipitations are all performed on the same day. Following this recommendation will result in more accurate protein concentrations for subsequent labeling. The protein precipitation step is important for the removal of reagents that will interfere with tandem mass spectrometry. Including the precipitation step significantly enhances the resolution of TMT26. In sum, the major advantages of our TMT protocol are the high labeling efficiencies for different types of samples, its reproducibility, and the reliable data acquired.
As the multiplex nature of this TMT untargeted proteomics strategy continues to expand, it will progressively enhance the ability of researchers across a wide variety of fields to make novel discoveries. Specifically in the biomedical field, we and others have found this technology increasingly informative in studies exploring novel mechanisms of action in disease and relative impacts of various therapeutics. For all of these reasons, this powerful technology complements the repertoire of other OMICS approaches used in modern research studies and provides key information that can guide further therapeutic development.

The term &#34;proteomics&#34; was first defined as the large-scale characterization of the entire protein complement of a cell, tissue, or organism1. Proteomic analyses enable the investigation of mechanisms and cellular processes involved in disease development, therapeutic pathways, and healthy systems using techniques to perform relative quantitation of protein expression levels2. The initial descriptions of such studies were published in 1975 and demonstrated the use of two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) for this purpose1,3. The 2D method separates proteins based on charge (isoelectric focusing, IEF) and molecular mass (sodium dodecyl sulfate polyacrylamide gel electrophoresis, or SDS-PAGE)4. For years, the combination of 2D-PAGE and subsequent tandem mass spectrometry performed on each gel component was the most common untargeted protein expression analysis technique performed and identified numerous previously unknown protein expression profiles5,6. General disadvantages to the 2D-PAGE approach are that it is time-consuming, does not work well for hydrophobic proteins, and there are limitations in the total number of proteins assessed due to low sensitivity7,8.
The stable isotope labeling by amino acids in cell culture (SILAC) method became the next popular approach to identify and quantify protein abundance in samples9. It consists of metabolic labeling of cells that are incubated in medium lacking a standard essential amino acid and supplemented with an isotope-labeled version of that specific amino acid10. The advantage of this technique is its efficiency and precise labeling9. The main limitation to the SILAC approach is primarily the reduced cell growth rate caused by isotope label incorporation, which can be particularly challenging in relatively sensitive cell lines modeling human diseases11.
In 2003, a novel and robust proteomics technique involving tandem mass tag (TMT) isobaric labels was introduced to the field12. TMT labeling is a powerful method due to its increased sensitivity to detect relative protein expression levels and posttranslational modifications13. As of this publication date, TMT kits have been developed that can simultaneously label 6, 10, 11, or 16 samples. As a result, it is possible to measure peptide abundance in multiple conditions with biological replicates at the same time14,15,16. We recently used TMT to characterize the cardiac proteomic profile of a mouse model of Barth Syndrome (BTHS)17. In so doing, we were able to demonstrate widespread improvement in the cardiac profiles of BTHS mice treated with gene therapy and identify novel proteins impacted by BTHS that revealed novel therapeutic pathways involved in cardiomyopathies.
Here, we describe a detailed method to perform multiplex TMT quantitative proteomics analyses using tissue samples or cell pellets. It can be beneficial to perform the sample preparation and labeling prior to submission to a core because the labeled tryptic peptides are more stable than raw frozen samples, not all cores have experience handling all sample types, and preparing samples in a laboratory can save time for cores, which often have long backlogs. For detailed descriptions of the mass spectroscopy portion of this process please see Kirshenbaum et al. and Perumal et al.18,19.
The sample preparation protocol consists of the following major steps: extraction, quantification, precipitation, digestion, and labeling. The major benefits of this optimized protocol are that it reduces the costs of labeling, improves protein extraction, and consistently generates high-quality data. In addition, we describe how to analyze TMT data to screen thousands of proteins in a short amount of time. We hope that this protocol encourages other research groups to consider incorporating this powerful methodology into their studies.

<table><tbody><tr><td>1 M Triethylammonium bicarbonate (TEAB), 50 mL</td><td>Thermo Fisher</td><td>90114</td><td>Reagent for protein labeling</td></tr><tr><td>50% Hydroxylamine, 5 mL</td><td>Thermo Fisher</td><td>90115</td><td>Reagent for protein labeling</td></tr><tr><td>Acetic acid</td><td>Sigma</td><td>A6283</td><td>Reagent for protein digestion</td></tr><tr><td>Anhydrous acetonitrile, LC-MS Grade</td><td>Thermo Fisher</td><td>51101</td><td>Reagent for protein labeling</td></tr><tr><td>Benzonaze nuclease</td><td>Sigma-Aldrich</td><td>E1014</td><td>DNA shearing</td></tr><tr><td>Bond-Breaker TCEP solution, 5 mL</td><td>Thermo Fisher</td><td>77720</td><td>Reagent for protein labeling</td></tr><tr><td>BSA standard</td><td>Thermo</td><td>23209</td><td>Reagent for protein measurement</td></tr><tr><td>CHAPS</td><td>Thermo Fisher</td><td>28300</td><td>Reagent for protein extraction</td></tr><tr><td>Chloroform</td><td>Fisher</td><td>BP1145-1</td><td>Reagent for protein precipitation</td></tr><tr><td>cOmplete, EDTA-free Protease Inhibitor Cocktail Tablet</td><td>Roche</td><td>4693132001</td><td>Reagent for protein extraction</td></tr><tr><td>DC Protein Assay</td><td>BioRad</td><td>500-0116</td><td>Reagent for protein measurement</td></tr><tr><td>Excel</td><td>Microsoft Office</td><td></td><td>Software for data analyses</td></tr><tr><td>Heat block</td><td>VWR analog</td><td>12621-104</td><td>Equipment for protein digestion incubation</td></tr><tr><td>HEPES</td><td>Sigma</td><td>RDD002</td><td>Reagent for protein extraction</td></tr><tr><td>Methanol</td><td>Fisher</td><td>A452-4</td><td>Reagent for protein precipitation</td></tr><tr><td>Pierce Trypsin Protease, MS Grade</td><td>Thermo Fisher</td><td>90058</td><td>Reagent for protein digestion</td></tr><tr><td>Potassium chloride</td><td>Sigma</td><td>46436</td><td>Reagent for protein extraction</td></tr><tr><td>Sigma Plot 14.0</td><td>Sigma Plot 14.0</td><td></td><td>Software for data analyses</td></tr><tr><td>Sonicator</td><td>Fisher Scientific</td><td>FB120</td><td>DNA shearing</td></tr><tr><td>Spectra Max i3x Multi-Mode Detection Platform</td><td>Molecular Devices</td><td></td><td>Plate reader for protein measurement</td></tr><tr><td>Thermo Scientific Pierce Quantitative Colorimetric Peptide Assay</td><td>Thermo Fisher</td><td>23275</td><td>Reagent for protein measurement</td></tr><tr><td>Thermo Scientific Pierce Quantitative Fluorescent Peptide Assay</td><td>Thermo Fisher</td><td>23290</td><td>Reagent for protein measurement</td></tr><tr><td>Thermo Scientific Proteome Discoverer Software</td><td>Thermo Fisher</td><td>OPTON-30945</td><td>Software for data analyses</td></tr><tr><td>TMT 10plex Isobaric Label Reagent Set 0.8 mg, sufficient reagents for one 10plex isobaric experiment</td><td>Thermo Fisher</td><td>90110</td><td>Reagent for protein labeling</td></tr><tr><td>TMT11-131C Label Reagent 5 mg</td><td>Thermo Fisher</td><td>A34807</td><td>Reagent for protein labeling</td></tr><tr><td>Water, LC-MS Grade</td><td>Thermo Fisher</td><td>51140</td><td>Reagent for protein extraction</td></tr></tbody></table>

tmt sample preparation for proteomics facility submission and subsequent data analysis

Proteomic technologies are powerful methodologies that can aid our understanding of mechanisms of action in biological systems by providing a global view of the impact of a disease, treatment, or other condition on the proteome as a whole. This report provides a detailed protocol for the extraction, quantification, precipitation, digestion, labeling, and subsequent data analysis of protein samples. Our optimized TMT labeling protocol requires a lower tag-label concentration and achieves consistently reliable data. We have used this protocol to evaluate protein expression profiles in a variety of mouse tissues (i.e., heart, skeletal muscle, and brain) as well as cells cultured in vitro. In addition, we demonstrate how to evaluate thousands of proteins from the resulting dataset.

Healthy and diseased cells were lysed in CHAPS buffer, prepared as detailed in our TMT labeling method, and submitted to the University of Florida Interdisciplinary Center for Biotechnology Research (UF-ICBR) Proteomics Core for Liquid Chromatography with tandem mass spectrometry. Following data acquisition and delivery from the core, the dataset was opened in vendor-supplied software and the following cutoff filters were applied: &#8805;2 unique peptides, reporter ions for each protein sample present in all channels, and include only significantly altered proteins (p &#8804; 0.05). Table 1 summarizes the data: 39,653 total peptides, of which 7,211 have equal or greater than two unique peptides, and 3,829 include reporter ions for all channels. The p values for these 3,829 peptides were calculated by Student&#39;s t test and p &#8804; 0.05 was considered significant. In addition, a fold-change cutoff was used to determine the relative distribution of proteins from diseased compared to healthy cells: downregulated (blue) or upregulated (red) (Figure 1).
The list of significantly dysregulated protein expression was assessed using the PANTHER ontology classification system and STRING analyses. Panther analyses showed a categorized list of proteins based upon significantly lower (Figure 2A) or higher abundance in diseased cells based upon molecular function (Figure 2B). String analyses of proteins of significantly lower (Figure 2C) and higher (Figure 2D) abundance identified multiple interactions and strong associations between proteins.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60970/60970fig1.jpg" /> 
Figure 1: Volcano plot displaying proteins whose abundance was not significantly altered (black), significantly lowered (blue), or significantly increased (red) in diseased vs. healthy control cells. <a href="https://www.jove.com/files/ftp_upload/60970/60970fig1large.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/60970/60970fig2.jpg" /> 
Figure 2: Representative evaluations of significantly dysregulated hits identified by PANTHER (A, B) and String (C, D) of significantly lower or higher abundance proteins. <a href="https://www.jove.com/files/ftp_upload/60970/60970fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Total peptides</td>
			<td>Total identified</td>
			<td>&#8805; 2 unique peptides</td>
			<td>Quantified proteins</td>
			<td colspan="2">Significantly altered proteins</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td></td>
			<td></td>
			<td>Low</td>
			<td>High</td>
		</tr>
		<tr>
			<td>39653</td>
			<td>7211</td>
			<td>4457</td>
			<td>3829</td>
			<td>296</td>
			<td>108</td>
		</tr>
	</tbody>
</table>
Table 1: Representative table of quantified proteins per dataset analysis.

Watch this Scientific Journal Video about TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis at JoVE.com

TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

We present an optimized tandem mass tag (TMT) labeling protocol that includes detailed information for each of the following steps: protein extraction, quantification, precipitation, digestion, labeling, submission to a proteomics facility, and data analyses.

Proteomic technologies are powerful methodologies that can aid our understanding of mechanisms of action in biological systems by providing a global view ...

tmt-sample-preparation-for-proteomics-facility-submission-subsequent

Research

JoVE Journal

Biochemistry

12.5K Views.  University of Florida. We present an optimized tandem mass tag (TMT) labeling protocol that includes detailed information for each of the following steps: protein extraction, quantification, precipitation, digestion, labeling, submission to a proteomics facility, and data analyses.

Video: TMT Sample Preparation for Proteomics Facility Submission and Subsequent Data Analysis

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

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

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

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

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

Environment

Industrialized, Artificial Intelligence-guided Laser Microdissection for Microscaled Proteomic Analysis of the Tumor Microenvironment

The tumor microenvironment (TME) represents a complex ecosystem comprised of dozens of distinct cell types, including tumor, stroma, and immune cell populations. To characterize proteome-level variation and tumor heterogeneity at scale, high-throughput methods are needed to selectively isolate discrete cellular populations in solid tumor malignancies. This protocol describes a high-throughput workflow, enabled by artificial intelligence (AI), that segments images of hematoxylin and eosin (H&#38;E)-stained, thin tissue sections into pathology-confirmed regions of interest for selective harvest of histology-resolved cell populations using laser microdissection (LMD). This strategy includes a novel algorithm enabling the transfer of regions denoting cell populations of interest, annotated using digital image software, directly to laser microscopes, thus enabling more facile collections. Successful implementation of this workflow was performed, demonstrating the utility of this harmonized method to selectively harvest tumor cell populations from the TME for quantitative, multiplexed proteomic analysis by high-resolution mass spectrometry. This strategy fully integrates with routine histopathology review, leveraging digital image analysis to support enrichment of cellular populations of interest and is fully generalizable, enabling harmonized harvests of cell populations from the TME for multiomic analyses.

This protocol describes a high-throughput workflow for artificial intelligence-driven segmentation of pathology-confirmed regions of interest from stained, thin tissue section images for enrichment of histology-resolved cell populations using laser microdissection. This strategy includes a novel algorithm enabling the transfer of demarcations denoting cell populations of interest directly to laser microscopes.

The tumor microenvironment (TME) represents a complex ecosystem comprised of dozens of distinct cell types, including tumor, stroma, and immune cell ...

Cancer Research

Resin-Assisted Capture Coupled with Isobaric Tandem Mass Tag Labeling for Multiplexed Quantification of Protein Thiol Oxidation

Reversible oxidative modifications on protein thiols have recently emerged as important mediators of cellular function. Herein we describe the detailed procedure of a quantitative redox proteomics method that utilizes resin-assisted capture (RAC) in combination with tandem mass tag (TMT) isobaric labeling and liquid chromatography-tandem mass spectrometry (LC-MS/MS) to allow multiplexed stochiometric quantification of oxidized protein thiols at the proteome level. The site-specific quantitative information on oxidized cysteine residues provides additional insight into the functional impacts of such modifications. 
The workflow is adaptable across many sample types, including cultured cells (e.g., mammalian, prokaryotic) and whole tissues (e.g., heart, lung, muscle), which are initially lysed/homogenized and with free thiols being alkylated to prevent artificial oxidation. The oxidized protein thiols are then reduced and captured by a thiol-affinity resin, which streamlines and simplifies the workflow steps by allowing the proceeding digestion, labeling, and washing procedures to be performed without additional transfer of proteins/peptides. Finally, the labeled peptides are eluted and analyzed by LC-MS/MS to reveal comprehensive stoichiometric changes related to thiol oxidation across the entire proteome. This method greatly improves the understanding of the role of redox-dependent regulation under physiological and pathophysiological states related to protein thiol oxidation.

Protein thiol oxidation has significant implications under normal physiological and pathophysiological conditions. We describe the details of a quantitative redox proteomics method, which utilizes resin-assisted capture, isobaric labeling, and mass spectrometry, enabling site-specific identification and quantification of reversibly oxidized cysteine residues of proteins.

Reversible oxidative modifications on protein thiols have recently emerged as important mediators of cellular function. Herein we describe the detailed ...

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.

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.

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

Chemistry

A Mass Spectrometry-Based Approach to Identify Phosphoprotein Phosphatases and their Interactors

Most cellular processes are regulated by dynamic protein phosphorylation. More than three-quarters of proteins are phosphorylated, and phosphoprotein phosphatases (PPPs) coordinate over 90% of all cellular serine/threonine dephosphorylation. Deregulation of protein phosphorylation has been implicated in the pathophysiology of various diseases, including cancer and neurodegeneration. Despite their widespread activity, the molecular mechanisms controlling PPPs and those controlled by PPPs are poorly characterized. Here, a proteomic approach termed phosphatase inhibitor beads and mass spectrometry (PIB-MS) is described to identify and quantify PPPs, their posttranslational modifications, and their interactors in as little as 12 h using any cell line or tissue. PIB-MS utilizes a non-selective PPP inhibitor, microcystin-LR (MCLR), immobilized on sepharose beads to capture and enrich endogenous PPPs and their associated proteins (termed the PPPome). This method does not require the exogenous expression of tagged versions of PPPs or the use of specific antibodies. PIB-MS offers an innovative way to study the evolutionarily conserved PPPs and expand our current understanding of dephosphorylation signaling.

Here, we present a protocol for the enrichment of endogenous phosphoprotein phosphatases and their interacting proteins from cells and tissues and their identification and quantification by mass spectrometry-based proteomics.

Most cellular processes are regulated by dynamic protein phosphorylation. More than three-quarters of proteins are phosphorylated, and phosphoprotein ...

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Mass spectrometry (MS)-based proteomics enables comprehensive proteome analysis across a wide range of biological samples, including cells, tissues, and body fluids. Formalin-fixed, paraffin-embedded (FFPE) tissue sections, commonly used for long-term archiving, have emerged as valuable resources for proteomic studies. Beyond their storage benefits, researchers can isolate regions of interest (ROIs) from normal tissue regions through collaborative efforts with pathologists. Despite this potential, a streamlined approach for proteomic experiments encompassing ROI isolation, proteomic sample preparation, and MS analysis remains lacking. In this protocol, an integrated workflow that combines macrodissection of ROIs, suspension trapping-based sample preparation, and high-throughput MS analysis is presented. Through this approach, the ROIs of patients' FFPE tissues, consisting of benign serous cystic neoplasms (SCN) and precancerous intraductal papillary mucinous neoplasms (IPMN) diagnosed by pathologists, were macrodissected, collected, and analyzed, resulting in high proteome coverage. Furthermore, molecular differences between the two distinct pancreatic cystic neoplasms were successfully identified, thus demonstrating the applicability of this approach for advancing proteomic research with FFPE tissues.

Here, we establish a mass spectrometry-based proteomic method using isolated regions of interest in formalin-fixed, paraffin-embedded tissue sections. This protocol is used to analyze proteome from specific tissue areas in archived formalin-fixed, paraffin-embedded tissue sections.

Mass spectrometry (MS)-based proteomics enables comprehensive proteome analysis across a wide range of biological samples, including cells, tissues, and ...

Profiling Thiol Redox Proteome Using Isotope Tagging Mass Spectrometry

Pseudomonas syringae pv. tomato strain DC3000 not only causes bacterial speck disease in Solanum lycopersicum but also on Brassica species, as well as on Arabidopsis thaliana, a genetically tractable host plant1,2. The accumulation of reactive oxygen species (ROS) in cotyledons inoculated with DC3000 indicates a role of ROS in modulating necrotic cell death during bacterial speck disease of tomato3. Hydrogen peroxide, a component of ROS, is produced after inoculation of tomato plants with Pseudomonas3. Hydrogen peroxide can be detected using a histochemical stain 3'-3' diaminobenzidine (DAB)4. DAB staining reacts with hydrogen peroxide to produce a brown stain on the leaf tissue4. ROS has a regulatory role of the cellular redox environment, which can change the redox status of certain proteins5. Cysteine is an important amino acid sensitive to redox changes. Under mild oxidation, reversible oxidation of cysteine sulfhydryl groups serves as redox sensors and signal transducers that regulate a variety of physiological processes6,7. Tandem mass tag (TMT) reagents enable concurrent identification and multiplexed quantitation of proteins in different samples using tandem mass spectrometry8,9. The cysteine-reactive TMT (cysTMT) reagents enable selective labeling and relative quantitation of cysteine-containing peptides from up to six biological samples. Each isobaric cysTMT tag has the same nominal parent mass and is composed of a sulfhydryl-reactive group, a MS-neutral spacer arm and an MS/MS reporter10. After labeling, the samples were subject to protease digestion. The cysteine-labeled peptides were enriched using a resin containing anti-TMT antibody. During MS/MS analysis, a series of reporter ions (i.e., 126-131 Da) emerge in the low mass region, providing information on relative quantitation. The workflow is effective for reducing sample complexity, improving dynamic range and studying cysteine modifications. Here we present redox proteomic analysis of the Pst DC3000 treated tomato (Rio Grande) leaves using cysTMT technology. This high-throughput method has the potential to be applied to studying other redox-regulated physiological processes.

Reactive oxygen species level is elevated when cells encounter stress conditions. Here we show the example of 3'-3' diaminobenzidine staining as well as cysTMT labeling and mass spectrometry to profile the redox proteome in Pseudomonas syringae treated tomato leaves.

Pseudomonas syringae pv. tomato strain DC3000 not only causes bacterial speck disease in Solanum lycopersicum but also on Brassica species, as well as on ...

Biology

High-throughput and Deep-proteome Profiling by 16-plex Tandem Mass Tag Labeling Coupled with Two-dimensional Chromatography and Mass Spectrometry

Isobaric tandem mass tag (TMT) labeling is widely used in proteomics because of its high multiplexing capacity and deep proteome coverage. Recently, an expanded 16-plex TMT method has been introduced, which further increases the throughput of proteomic studies. In this manuscript, we present an optimized protocol for 16-plex TMT-based deep-proteome profiling, including protein sample preparation, enzymatic digestion, TMT labeling reaction, two-dimensional reverse-phase liquid chromatography (LC/LC) fractionation, tandem mass spectrometry (MS/MS), and computational data processing. The crucial quality control steps and improvements in the process specific for the 16-plex TMT analysis are highlighted. This multiplexed process offers a powerful tool for profiling a variety of complex samples such as cells, tissues, and clinical specimens. More than 10,000 proteins and posttranslational modifications such as phosphorylation, methylation, acetylation, and ubiquitination in highly complex biological samples from up to 16 different samples can be quantified in a single experiment, providing a potent tool for basic and clinical research.

Presented here is an optimized high-throughput protocol developed with 16-plex tandem mass tag reagents, enabling quantitative proteome profiling of biological samples. Extensive basic pH fractionation and high-resolution LC-MS/MS mitigate ratio compression and provide deep proteome coverage.

Isobaric tandem mass tag (TMT) labeling is widely used in proteomics because of its high multiplexing capacity and deep proteome coverage. Recently, an ...

Comprehensive Workflow of Mass Spectrometry-based Shotgun Proteomics of Tissue Samples

Recent advances in mass spectrometry have resulted in deep proteomic&#160;analysis along with the generation of&#160;robust and&#160;reproducible datasets. However, despite the considerable technical advancements, sample preparation from biospecimens such as patient blood, CSF, and tissue still poses considerable challenges. For identifying biomarkers, tissue proteomics often provides an attractive sample source to translate the research findings from the bench to the clinic. It can reveal potential candidate biomarkers for early diagnosis of cancer and neurodegenerative diseases such as Alzheimer&#39;s disease, Parkinson&#39;s disease, etc. Tissue proteomics also yields a wealth of systemic information based on the abundance of proteins and helps to address interesting biological questions.
Quantitative proteomics analysis can be grouped into two broad categories: a label-based and a label-free approach. In the label-based approach, proteins or peptides are labeled using stable isotopes such as SILAC (stable isotope labeling with amino acids in cell culture) or by chemical tags such as ICAT (isotope-coded affinity tags), TMT (tandem mass tag) or iTRAQ (isobaric tag for relative and absolute quantitation). Label-based approaches have the advantage of more accurate quantitation of proteins and using isobaric labels, multiple samples can be analyzed in a single experiment. The label-free approach provides a cost-effective alternative to label-based approaches. Hundreds of patient samples belonging to a particular cohort can be analyzed and compared with other cohorts based on clinical features. Here, we have described an optimized quantitative proteomics workflow for tissue samples using label-free and label-based proteome profiling methods, which is crucial for applications in life sciences, especially biomarker discovery-based projects.

The described protocol provides an optimized quantitative proteomics analysis of tissue samples using two approaches: label-based and label free quantitation. Label-based approaches have the advantage of more accurate quantitation of proteins, while a label-free approach is more cost-effective and used to analyze hundreds of samples of a cohort.