We thank SP Gygi and GM Church for help and guidance. This work was supported by a CNRS chaire d'excellence to ACT.

NOTE: This method was previously described12.
1. Protein Isolation
Prepare 1 mg of cellular protein by lysing cells, preferably by physical methods such as French press, bead beating, or sonication. Avoid lysozyme-mediated cell lysis because the enzyme will confound mass spectrometry measurements.
2. TCA Precipitation of Proteins
Add 1 volume trichloroacetic acid (TCA) to 4 volumes protein and chill on ice for 10 min&#160;to precipitate proteins. Centrifuge at 12,000 x g for 5 min at 4 &#176;C and remove the supernatant. Resuspend the pellet in 1 ml of ice-cold acetone and centrifuge at 12,000 x g for 5 min at 4 &#176;C. Remove the supernatant and invert the tube on the bench to dry the pellet for 15 min. Store protein pellets at -80 &#176;C.
3. Denature Proteins and Reduce Disulfide Bonds
Resuspend proteins to ~2 mg/ml in 500 &#956;l denaturation and reduction buffer (either 4 M urea or 3% SDS in 50 mM HEPES pH 8.5, 5 mM DTT). Optionally, include a protease inhibitor in the buffer. Incubate proteins for 30 min at 56 &#176;C, followed by 10 min at room temperature.
4. Aklylate Free Sulfhydryl Groups to Irreversibly Disrupt Disulfide Bond Formation
Prepare fresh 0.3 M iodoacetamide in water. CAUTION! Iodoacetamide is highly toxic. Add 25 &#956;l 0.3 M iodoacetamide (15 mM final concentration) to 500 &#956;l protein and incubate for 20 min in the dark at room temperature. Quench iodoacetamide by adding 10 &#956;l of 300 mM DTT (5 mM final DTT concentration). Store alkylated proteins at -80 &#176;C.
5. Protein Digestion
TCA precipitate proteins (as described in step 2) and resuspend in 1 ml of 50 mM HEPES (pH 8.2), 1 M urea. Prepare a stock solution of Lysyl endoproteinase (Lys-C) in water at a concentration of 2 &#181;g/&#181;l and add 5 &#181;l to the protein solution. Incubate the mixture for 16 hr at room temperature. Ensure the final Lys-C concentration is 10 ng/&#181;l and the protein-to-LysC ratio (w/w) is 1/50 to 1/200. Resuspend 20 &#956;g sequencing grade trypsin in 40 &#956;l of 50 mM acetic acid, add 5 &#181;l (10 &#181;g trypsin) to the Lys-C digest, and incubate for 6 hr at 37 &#176;C. Use the same protease concentration and protease-to-protein ratios for Lys-C as used for trypsin.
6. Reversed-phase Peptide Extraction
<ol>
	<li>Acidify peptides by adding trifluoroacetic acid (TFA) to a final concentration of 0.5% (pH&#8776;2). Attach a C18 column to an extraction manifold. Use the highest possible flow rate for all steps except loading and elution of the peptides.</li>
	<li>Wet column with 6 ml acetonitrile (ACN). Wash column with 6 ml 80% ACN, 0.1% TFA, then equilibrate with 6 ml 0.1% TFA. Do not allow the column to run dry between steps.</li>
	<li>Stop vacuum pressure and load 500 &#956;g of peptides onto the column at a flow rate of approximately 1 ml/min. Once peptides have bound to the column, restart vacuum and wash with 6 ml 0.1% TFA, then with 3 ml of citric acid buffer (0.09 M citric acid, 0.23 M Na2HPO4, pH 5.5). 
	Note: Higher peptide amounts can be labeled but the C18 column binding capacity should be at least two-fold higher than the peptide quantity to avoid sample loss.</li>
</ol>
7. On-column Peptide Labeling by Reductive Dimethylation (ReDi Labeling)
Perform this step under a chemical hood as hydrogen cyanide is released in low concentration during the labeling process.
<ol>
	<li>Prepare 12 ml of &#8220;light&#8221; and &#8220;heavy&#8221; ReDi buffers to methylate peptide free amines. Light ReDi buffer consists of 0.8% formaldehyde and 0.12 M sodium cyanoborohydride carrying hydrogens in their natural isotopic distributions in citric acid buffer. Heavy ReDi buffer consists of 0.8% deuterated formaldehyde and 0.12 M deuterated sodium cyanoborohydride in citric acid buffer.</li>
	<li>Incubate column containing peptides by adding 10 ml either light or heavy ReDi buffer to the peptide-containing columns at flow rate of 1 ml/min and repeat to ensure complete labeling. Wash column with 6 ml 0.1% TFA and then with 1 ml 0.5%&#160;acetic acid.</li>
	<li>Stop vacuum and elute labeled peptides first with 1 ml 40% ACN, 0.5% acetic acid, then with 1 ml 80% ACN, 0.5% acetic acid using a flow rate of approximately 0.5 ml/min. If desired, measure the labeling efficiency of individual samples by mass spectrometry before mixing heavy and light samples (see &#8220;Representative Results&#8221;). Mix 1:1 heavy and light-labeled peptide samples to be quantified by mass spectrometry.</li>
</ol>
8. Separate Peptide Mixture by Basic pH Reversed Phase (BPRP) Chromatography
Basic pH reversed phase (BPRP) chromatography to separate the peptide mixture into multiple fractions, which are independently analyzed by LC-MS/MS to increase proteome coverage.
<ol>
	<li>Fractionate the peptide mixture on a C18-HPLC column by applying a gradient of increasing ACN concentration in 10 mM ammonium bicarbonate (pH 8). Start with 5% (v/v) ACN for 5 min, increase to 35% ACN in 60 min, and then to 90% ACN in 1 min. Retain the 90% ACN for 4 min before reducing the ACN to 5% to re-equilibrate the column for 9 min. Collect 96 fractions of equal volume in a 96-well plate (A1 to H12). Monitor the fractionation using a UV detector at 220 nm while peptides are eluting off the column (10-70 min for the conditions described here).</li>
	<li>Combine fractions from wells A1, C1, E1, and G1 (fraction A1), from wells B1, D1, F1, and H1 (fraction B1), from wells A2, C2, E2, and G2 (fraction A2) and accordingly for the remaining fractions. Remove the solvent using a vacuum centrifuge. Resuspend peptides from fractions A1, B2, A3, B4, A5, B6, A7, B8, A9, B10, A11, and B12 in 130 &#181;l of 1 M urea/0.5% TFA and purify using StageTips as described in step 9. Store fractions B1, A2, B3, A4, B5, A6, B7, A8, B9, A10, B11, and A12 at -20 &#186;C.</li>
</ol>
9. Purify Peptides by STop and Go Extraction (StageTips)
Prepare C18-StageTip7 microcolumns by packing 200 &#956;l pipette tips with two C18 disks with an internal diameter (ID) of 1.07 mm. Put Stage Tips into Eppendorf tubes. Use a microcentrifuge to wash tips with 130 &#956;l of methanol, then 130 ul 80% ACN, 0.5% acetic acid. Equilibrate StageTips with 130 &#956;l 0.1% TFA. Transfer peptide mixture to StageTips and wash with 130 &#956;l 0.1% TFA, then 40 &#956;l 0.1% TFA, then 40 &#956;l 0.5% acetic acid. Elute peptides first with 20 &#956;l 40% ACN, 0.5% acetic acid, then 20 &#956;l 80% ACN, 0.5% acetic acid. Combine eluates and dry by vacuum filtration.
10. Microcapillary LC-MS/MS
<ol>
	<li>Dissolve peptides in 1-5 &#956;l 5% formic acid, 5% ACN to a concentration of approximately 1 &#956;g/&#956;l. Resolve ~1 &#956;g peptides on a 100 &#956;m &#215; 20 cm C18-reversed phase HPLC column with a gradient of 6-22% ACN in 0.125% formic acid applied over 75 or 100 min at a flow rate of ~300 nl/min.</li>
	<li>Identify peptides by using an LTQ Orbitrap Velos12 or similar liquid chromatography-mass spectrometry platform with a mass spectrometer providing high-resolution and high mass accuracy. Operate the mass spectrometer in data-dependent mode with a full MS scan (resolution of 60,000) acquired in the Orbitrap analyzer. Generate linear ion trap MS/MS spectra for the 20 most abundant ions detected in the full MS spectrum. Set automatic gain control (AGC) targets to 1 x&#160;106 for the full MS and 2,000 for MS/MS. Set maximum ion accumulation times to 1,000 msec for MS and 150 msec for MS/MS. Exclude fragmented peptide precursor ions from further selection from MS/MS for 20&#8211;60 sec.</li>
</ol>
11. MS/MS Data Acquisition
Identify peptides by comparing MS/MS spectra RAW files to a theoretical database with an algorithm such as SEQUEST8 using these parameters (Table 1).
Table 1. Peptide Database Search Parameters.
<table border="0" cellpadding="0" cellspacing="0" width="600">
	<tbody>
		<tr height="19">
			<td height="19" rowspan="3" style="height:19px;" width="180">General Parameters</td>
			<td>fully tryptic digestion with up to 2 missed cleavages</td>
		</tr>
		<tr height="19">
			<td>25 ppm precursor ion tolerance</td>
		</tr>
		<tr height="19">
			<td>1.0 Da fragment ion tolerance</td>
		</tr>
		<tr height="35">
			<td height="35" rowspan="2" style="height:36px;">Static Modifications</td>
			<td>+57.02146 Da on cysteine, carboxyamidomethylation</td>
		</tr>
		<tr height="19">
			<td>+28.03130 Da on lysine and the peptide N-terminus, light dimethylation label</td>
		</tr>
		<tr height="19">
			<td height="19" rowspan="2" style="height:19px;">Dynamic Modifications</td>
			<td>+15.99491 Da on methionine, oxidation</td>
		</tr>
		<tr height="19">
			<td>+6.03766 Da on lysine and the peptide N-terminus, heavy dimethylation label</td>
		</tr>
	</tbody>
</table>
<ol>
	<li>Filter peptides to a 1% false discovery rate with a method such as the target-decoy9 strategy using a database of open reading frames in the actual and reversed orientations.</li>
</ol>
12. Peptide Quantification
Calculate the areas of heavy and light pairs of MS1 extracted ion chromatograms (MS1 peak areas) and peptide signal-to-noise (S/N) ratios10. Include peptide pairs only when their average signal-to-noise ratio is above five. Quantify relative abundance of a peptide in the two samples as the ratio of MS1 peak areas of heavy and light versions of the same peptide (MS1 peak area ratio). Calculate relative protein abundances as the median MS1 peak area ratio for all peptides in the protein.

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The authors declare no competing financial interests or other conflicts of interest.

Several points make stable isotope labeling of peptides using reductive dimethylation (ReDi labeling) an attractive method for quantitative proteomics: inexpensive labeling reagents (reagents cost less than $1 per sample), fast reaction rate (~10 min), absence of side products, high reproducibility (Figures&#160;3, 4), stable reaction products, ability to use any protease, and high ionization efficiency of labeled peptides. Chemical labeling by ReDi is also advantageous relative to metabolic labeling since it does not require strains or cell lines with specific amino acid auxotrophies or growth on a synthetic medium. As such, ReDi can be applied to nearly any type of protein sample including novel microbes12 for which few mutant strains are available13 and human stem cells14, among others15.
A limitation of ReDi labeling is a lower ability to multiplex samples relative to other methods such as isobaric labeling (e.g., iTRAQ or TMT), for which currently up to 8 samples can be simultaneously quantified16. The method we describe here allows the quantitative comparison of two differentially labeled samples. Additional isotopic combinations of formaldehyde and cyanoborohydride can be used to produce up to 3 labels that differ by at least 4 Da17 and ReDi labeling has recently been extended to 5 multiplexed samples18. Another challenge of ReDi labeling is that deuterated peptides elute slightly before light ones when using reversed-phase chromatography. To control for this &#34;deuterium effect&#34;, peptide quantification should always be based on the entire extracted ion chromatogram (MS1 peak area) instead of intensities from one scan.
The ReDi proteomics protocol described here includes numerous improvements11,15,17 made since the first description of reduction dimethyl labeling of peptides for mass spectrometry5. We described an &#34;on-column&#34; labeling method to allow higher peptide amounts and the labeling efficiency can be verified before sample mixing, but &#34;in solution&#34; and &#34;online&#34; labeling methods exist as well19. In addition to proteome quantification, ReDi labeling is being applied for isoform determination20 and can be combined with other methods to analyze post-translational modifications such as phosphorylation11,18 acetylation21, and glycosylation22. Because of its versatility and inexpensive, quantitative chemistry, ReDi labeling will be increasingly applied to quantitative proteomics in new and exciting ways.

Measuring concentration differences of many proteins between complex samples is a central challenge in proteomics. Increasingly, this is being done by labeling proteins in each sample with different isotopic tags, combining the samples, and using mass spectrometry to quantify concentration differences. Several methods exist for stable isotopic labeling of proteins and peptides. 15N labeling1 and SILAC2 introduce isotopic labels metabolically in vivo, whereas iCAT3, iTRAQ4, and reduction dimethylation5 add stable isotope tags after protein extraction and digestion. Among these methods, reductive dimethylation (ReDi labeling) is gaining popularity as an inexpensive, reproducible method to quantify protein concentration differences in nearly any type of sample.
ReDi labeling involves reacting peptides with formaldehyde to form a Schiff base, which is then reduced by cyanoborohydride. This reaction dimethylates free amino groups on N-termini and lysine side chains and monomethylates N-terminal prolines. The protocol described here methylates peptides in sample 1 with a &#8220;light&#8221; label using reagents with hydrogen atoms in their natural isotopic distribution and sample 2 with a &#8220;heavy&#8221; label using deuterated formaldehyde and cyanoborohydride (Figure&#160;1). Each dimethylated amino group on a peptide results in a mass difference of 6.0377 Da between light and heavy forms, which is employed to distinguish between the two forms using a mass spectrometer. Specifically, relative peptide abundances are quantified as the ratio of MS1 extracted ion chromatogram areas (MS1 peak area ratio) of light and heavy version for each peptide ion pair. The relative abundance of a protein is calculated as the median MS1 peak area ratio among all peptides in the protein. In this report, we describe a robust protocol for conducting ReDi labeling experiments by LC-MS/MS that includes reversed-phase peptide solid-phase extraction, on-column ReDi labeling, peptide fractionation by basic pH reversed phase (BPRP) chromatography, and purification of peptide mixtures using StageTips (Figure&#160;2). We discuss advantages and limitations of using ReDi labeling for quantitative proteomics.

<table border="0" cellpadding="0" cellspacing="0" width="973">
	<tbody>
		<tr height="19">
			<td height="19" style="height:19px;width:377px;">trichloroacetic acid</td>
			<td style="width:141px;">Sigma-Aldrich</td>
			<td style="width:136px;">T9159</td>
			<td style="width:319px;">protein precipitation</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">acetone</td>
			<td>Sigma-Aldrich</td>
			<td style="width:136px;">650501</td>
			<td>protein precipitation</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">Sodium dodecyl sulfate</td>
			<td>Sigma-Aldrich</td>
			<td style="width:136px;">71736</td>
			<td>denature, reduce protein</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">sodium hydroxide</td>
			<td>Sigma-Aldrich</td>
			<td style="width:136px;">S8045</td>
			<td>denature, reduce protein</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">DL-Dithiothreitol</td>
			<td>Sigma-Aldrich</td>
			<td style="width:136px;">43816</td>
			<td>denature, reduce, alkylate protein</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">protease Inhibitor Complete Mini Cocktail</td>
			<td>Roche</td>
			<td>4693124001</td>
			<td>denature, reduce protein</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">iodoacetamide</td>
			<td>Sigma-Aldrich</td>
			<td>I6125</td>
			<td>alkylate protein</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">HEPES</td>
			<td>Sigma-Aldrich</td>
			<td style="width:136px;">H7523</td>
			<td>resuspend, extract, label protein</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">calcium chloride</td>
			<td>Sigma-Aldrich</td>
			<td style="width:136px;">C5670</td>
			<td>resuspend protein</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;width:377px;">Lysyl Endoprotease</td>
			<td>Wako Chemicals</td>
			<td style="width:136px;">129-02541</td>
			<td>protein digestion</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">sequencing grade trypsin</td>
			<td>Promega</td>
			<td style="width:136px;">V5111</td>
			<td>protein digestion</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">acetic acid</td>
			<td>Sigma-Aldrich</td>
			<td style="width:136px;">320099</td>
			<td>protein digestion</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">trifluoroacetic acid</td>
			<td>Sigma-Aldrich</td>
			<td>299537</td>
			<td>Reversed-phase peptide extraction</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">tC18 Sep-Pak C18 cartridges</td>
			<td>Waters</td>
			<td>WAT054960</td>
			<td>Reversed-phase peptide extraction</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">extraction manifold</td>
			<td>Waters</td>
			<td>WAT200609</td>
			<td>Reversed-phase peptide extraction</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">acetonitrile</td>
			<td>Sigma-Aldrich</td>
			<td style="width:136px;">14261</td>
			<td>various</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">formaldehyde</td>
			<td>Sigma-Aldrich</td>
			<td>252549</td>
			<td>&#8220;light&#8221; peptide labeling</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">cyanoborohydride</td>
			<td>Sigma-Aldrich</td>
			<td style="width:136px;">71435</td>
			<td>&#8220;light&#8221; peptide labeling</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">deuterated formaldehyde</td>
			<td>Sigma-Aldrich</td>
			<td>492620</td>
			<td>&#8220;heavy&#8221; peptide labeling</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">sodium cyanoborodeuteride</td>
			<td>CDN isotopes</td>
			<td style="width:136px;">D-1797</td>
			<td>&#8220;heavy&#8221; peptide labeling</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">MES</td>
			<td>Sigma-Aldrich</td>
			<td>M3671</td>
			<td>peptide labeling</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">C18-HPLC column (4.6 x 250 mm, 5 &#181;m particle size)</td>
			<td>Agilent</td>
			<td style="width:136px;">770450-902</td>
			<td>basic pH reversed-phase chromatography</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">formic acid</td>
			<td>Sigma-Aldrich</td>
			<td style="width:136px;">399388</td>
			<td>various</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">C18 Empore Disks</td>
			<td>3M</td>
			<td style="width:136px;">14-386-3&#160;</td>
			<td>STAGE tips</td>
		</tr>
		<tr height="19">
			<td height="19" style="height:19px;">methanol</td>
			<td>Sigma-Aldrich</td>
			<td style="width:136px;">494437</td>
			<td>STAGE tips</td>
		</tr>
	</tbody>
</table>

quantitative proteomics using reductive dimethylation for stable isotope labeling

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

We evaluated the accuracy, precision, and reproducibility of ReDi labeling using Saccharomyces cerevisiae and Clostridium phytofermentans whole cell lysates. We first quantified the ReDi labeling efficiency of a mix of C. phytofermentans protein lysates from cellulose (heavy labeled, H) and glucose (light label, L) cultures. When filtered to a 1% peptide false discovery rate, this sample contained 11,194 unique peptide sequences with a 98% ReDi labeling efficiency. Unfractionated S. cerevisiae protein lysate was similarly labeled with H or L reagents, mixed at various ratios, and analyzed. Protein expression differences (log2 (median MS1 peak areas)) reproducibly reflect the ratios at which the H and L samples were mixed across a wide range of mixing ratios (Figure&#160;3). Specifically, the fold-change of 99% of the proteins were measured as being smaller than 1.6 fold for the 1:1 mixed samples. In the 1:10 and 10:1 samples, 99% of proteins were within 3.8 fold of the expected ratio, showing an increase in the standard deviation at greater distance from a 1:1 mixture. When ReDi labeling was applied to the Clostridium phytofermentans proteome, we quantified more than 2,000 proteins with 94% proteins measured within 2-fold levels for replicate cultures growing on glucose (Figure&#160;4A). Protein fold changes for duplicate pairs of cultures (glucose versus cellulose) were also highly correlated (r2=0.82), (Figure&#160;4B). S. cerevisiae comparisons (Figure&#160;3) are from a single culture and thus show technical reproducibility of ReDi based quantitative proteomics. C. phytofermentans measurements (Figures&#160;4A, B) compare replicate cultures so expression differences represent both measurement error and biological variation between cultures. Together, these experiments support that ReDi proteomics is an accurate and reproducible method to quantify protein expression differences between complex samples.
<img alt="Figure 1" fo:content-width="5in" src="/files/ftp_upload/51416/51416fig1highres.jpg" width="500" /> 
Figure 1. Reductive dimethylation of peptides using heavy and light reagents to dimethylate free amines. The same peptide labeled with heavy versus light reagents has a +6.0377 Da mass shift per free amine. The peptide shown here is labeled both at the N-terminus and on a lysine side chain. Image adapted from Reference 11. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51416/51416fig1highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" fo:content-width="5in" src="/files/ftp_upload/51416/51416fig2highres.jpg" width="500" /> 
Figure 2. Overview of protocol for quantitative proteomics by reductive dimethylation. Red numbers above arrows correspond to steps described in the protocol. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51416/51416fig2highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" fo:content-width="5in" src="/files/ftp_upload/51416/51416fig3highres.jpg" width="500" /> 
Figure 3. Evaluation of ReDi labeling using Saccharomyces cerevisiae whole cell lysates. Samples from a single culture were labeled with either heavy (H) and light (L) reagents, mixed at various ratios (1H:10L, 1H:5L, 1H:2L, 1H:1L, 2H:1L, 5H:1L, and 10H:1L), and protein differences between H and L samples were quantified as log2 median MS1 peak area ratios (log2 H/L ratio). The R2 value of a linear trend line through all data points was 0.96 (black line); the Pearson correlation was 0.98. Confidence boundaries (red lines) show the absolute perpendicular distance from the trend line within which 95% of the data points were found for each sample. Image adapted from Reference 12. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51416/51416fig3highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" fo:content-width="5in" src="/files/ftp_upload/51416/51416fig4highres.jpg" width="500" /> 
Figure 4. Quantification of ReDi-labeled Clostridium phytofermentans proteins from cultures growing of different carbon sources. A) Protein expression in a glucose culture relative to a replicate glucose culture, a hemicellulose culture, and a cellulose culture. The fraction of proteins expressed within twofold levels: glucose-glucose (94%), glucose-hemicellulose (80%), and glucose-cellulose (49%). B)&#160;Fold changes in protein expression for glucose versus cellulose cultures are highly correlated (r2=0.82) for duplicate pairs of cultures. Images adapted from Reference 12. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/51416/51416fig4highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Quantitative Proteomics Using Reductive Dimethylation for Stable Isotope Labeling at JoVE.com

Quantitative Proteomics Using Reductive Dimethylation for Stable Isotope Labeling

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

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

quantitative-proteomics-using-reductive-dimethylation-for-stable

Nanyang Technological University

Research

JoVE Journal

Chemistry

16.1K Views.  Genoscope. Stable isotope labeling of peptides by reductive dimethylation (ReDi labeling) is a rapid, inexpensive strategy for accurate mass spectrometry-based quantitative proteomics. Here we demonstrate a robust method for preparation and analysis of protein mixtures using the ReDi approach that can be applied to nearly any sample type. 

Video: Quantitative Proteomics Using Reductive Dimethylation for Stable Isotope Labeling

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS)

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. The approach offers an unbiased and in-depth analysis that can enable the development of diagnostic tests, novel therapies, and further our understanding of disease processes. The inherent chemical diversity of the metabolome creates significant analytical challenges and there is no single experimental approach that can detect all metabolites. Additionally, the biological variation in individual metabolism and the dependence of metabolism on environmental factors necessitates large sample numbers to achieve the appropriate statistical power required for meaningful biological interpretation. To address these challenges, this tutorial outlines an analytical workflow for large scale non-targeted metabolite profiling of serum by UPLC-MS. The procedure includes guidelines for sample organization and preparation, data acquisition, quality control, and metabolite identification and will enable reliable acquisition of data for large experiments and provide a starting point for laboratories new to non-targeted metabolite profiling by UPLC-MS.

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to investigate metabolism. This article outlines a typical workflow utilized for non-targeted metabolite profiling of serum including sample organization and preparation, data acquisition, data analysis, quality control, and metabolite identification.

Non-targeted metabolite profiling by ultra performance liquid chromatography coupled with mass spectrometry (UPLC-MS) is a powerful technique to ...

Improved In-gel Reductive &#946;-Elimination for Comprehensive O-linked and Sulfo-glycomics by Mass Spectrometry

Separation of proteins by SDS-PAGE followed by in-gel proteolytic digestion of resolved protein bands has produced high-resolution proteomic analysis of biological samples. Similar approaches, that would allow in-depth analysis of the glycans carried by glycoproteins resolved by SDS-PAGE, require special considerations in order to maximize recovery and sensitivity when using mass spectrometry (MS) as the detection method. A major hurdle to be overcome in achieving high-quality data is the removal of gel-derived contaminants that interfere with MS analysis. The sample workflow presented here is robust, efficient, and eliminates the need for in-line HPLC clean-up prior to MS. Gel pieces containing target proteins are washed in acetonitrile, water, and ethyl acetate to remove contaminants, including polymeric acrylamide fragments. O-linked glycans are released from target proteins by in-gel reductive &#946;-elimination and recovered through robust, simple clean-up procedures. An advantage of this workflow is that it improves sensitivity for detecting and characterizing sulfated glycans. These procedures produce an efficient separation of sulfated permethylated glycans from non-sulfated (sialylated and neutral) permethylated glycans by a rapid phase-partition prior to MS analysis, and thereby enhance glycomic and sulfoglycomic analyses of glycoproteins resolved by SDS-PAGE.

In order to comprehensively explore the diversity of O-linked glycans, a new procedure for in-gel reductive &#946;-elimination, combined with permethylation and a rapid phase-partition method, is applied to the analysis of O-linked glycans directly released from glycoproteins resolved by SDS-PAGE and amenable to subsequent glycomic analysis by mass spectrometry.

Separation of proteins by SDS-PAGE followed by in-gel proteolytic digestion of resolved protein bands has produced high-resolution proteomic analysis of ...

Quantitative and Qualitative Examination of Particle-particle Interactions Using Colloidal Probe Nanoscopy

Colloidal Probe Nanoscopy (CPN), the study of the nano-scale interactive forces between a specifically prepared colloidal probe and any chosen substrate using the Atomic Force Microscope (AFM), can provide key insights into physical interactions present within colloidal systems. Colloidal systems are widely existent in several applications including, pharmaceuticals, foods, paints, paper, soil and minerals, detergents, printing and much more.1-3 Furthermore, colloids can exist in many states such as emulsions, foams and suspensions. Using colloidal probe nanoscopy one can obtain key information on the adhesive properties, binding energies and even gain insight into the physical stability and coagulation kinetics of the colloids present within. Additionally, colloidal probe nanoscopy can be used with biological cells to aid in drug discovery and formulation development. In this paper we describe a method for conducting colloidal probe nanoscopy, discuss key factors that are important to consider during the measurement, and show that both quantitative and qualitative data that can be obtained from such measurements.

Colloidal probe nanoscopy can be used within a variety of fields to gain insight into the physical stability and coagulation kinetics of colloidal systems and aid in drug discovery and formulation sciences using biological systems. The method described within provides a quantitative and qualitative means to study such systems.

Colloidal Probe Nanoscopy (CPN), the study of the nano-scale interactive forces between a specifically prepared colloidal probe and any chosen substrate ...

CN-GELFrEE - Clear Native Gel-eluted Liquid Fraction Entrapment Electrophoresis

Protein complexes perform an array of crucial cellular functions. Elucidating their non-covalent interactions and dynamics is paramount for understanding the role of complexes in biological systems. While the direct characterization of biomolecular assemblies has become increasingly important in recent years, native fractionation techniques that are compatible with downstream analysis techniques, including mass spectrometry, are necessary to further expand these studies. Nevertheless, the field lacks a high-throughput, wide-range, high-recovery separation method for native protein assemblies. Here, we present clear native gel-eluted liquid fraction entrapment electrophoresis (CN-GELFrEE), which is a novel separation modality for non-covalent protein assemblies. CN-GELFrEE separation performance was demonstrated by fractionating complexes extracted from mouse heart. Fractions were collected over 2 hr and displayed discrete bands ranging from ~30 to 500 kDa. A consistent pattern of increasing molecular weight bandwidths was observed, each ranging ~100 kDa. Further, subsequent reanalysis of native fractions via SDS-PAGE showed molecular-weight shifts consistent with the denaturation of protein complexes. Therefore, CN-GELFrEE was proved to offer the ability to perform high-resolution and high-recovery native separations on protein complexes from a large molecular weight range, providing fractions that are compatible with downstream protein analyses.

This protocol describes how to prepare and perform clear native gel-eluted liquid fraction entrapment electrophoresis (CN-GELFrEE), a native separation technique for non-covalent biomolecular assemblies and proteins from heterogeneous samples that is compatible with various downstream protein analysis techniques.

Protein complexes perform an array of crucial cellular functions. Elucidating their non-covalent interactions and dynamics is paramount for understanding ...

A Quantitative Glycomics and Proteomics Combined Purification Strategy

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

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

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

Standards for Quantitative Metalloproteomic Analysis Using Size Exclusion ICP-MS

Metals are essential for protein function as cofactors to catalyze chemical reactions. Disruption of metal homeostasis is implicated in a number of diseases including Alzheimer's and Parkinson's disease, but the exact role these metals play is yet to be fully elucidated. Identification of metalloproteins encounters many challenges and difficulties. Here we report an approach that allows metalloproteins in complex samples to be quantified. This is achieved using size exclusion chromatography coupled with inductively coupled plasma - mass spectrometry (SEC-ICP-MS). Using six known metalloproteins, the size exclusion column can be calibrated and the respective trace elements (iron, copper, zinc, cobalt, iodine) can be used for quantification. SEC-ICP-MS traces of human brain and plasma are presented. The use of these metalloprotein standards provides the means to quantitatively compare metalloprotein abundances between biological samples. This technique is poised to help shed light on the role of metalloproteins in neurodegenerative disease as well as other diseases where imbalances in trace elements are implicated.

Size exclusion chromatography hyphenated with inductively coupled plasma - mass spectrometry (ICP-MS) is a powerful tool to measure changes in the abundance of metalloproteins directly from biological samples. Here we describe a set of metalloprotein standards used to estimate molecular mass and the amount of metal associated with unknown proteins.

Metals are essential for protein function as cofactors to catalyze chemical reactions. Disruption of metal homeostasis is implicated in a number of ...

Highly Stable, Functional Hairy Nanoparticles and Biopolymers from Wood Fibers: Towards Sustainable Nanotechnology

Nanoparticles, as one of the key materials in nanotechnology and nanomedicine, have gained significant importance during the past decade. While metal-based nanoparticles are associated with synthetic and environmental hassles, cellulose introduces a green, sustainable alternative for nanoparticle synthesis. Here, we present the chemical synthesis and separation procedures to produce new classes of hairy nanoparticles (bearing both amorphous and crystalline regions) and biopolymers based on wood fibers. Through periodate oxidation of soft wood pulp, the glucose ring of cellulose is opened at the C2-C3 bond to form 2,3-dialdehyde groups. Further heating of the partially oxidized fibers (e.g., T = 80 &#176;C) results in three products, namely fibrous oxidized cellulose, sterically stabilized nanocrystalline cellulose (SNCC), and dissolved dialdehyde modified cellulose (DAMC), which are well separated by intermittent centrifugation and co-solvent addition. The partially oxidized fibers (without heating) were used as a highly reactive intermediate to react with chlorite for converting almost all aldehyde to carboxyl groups. Co-solvent precipitation and centrifugation resulted in electrosterically stabilized nanocrystalline cellulose (ENCC) and dicarboxylated cellulose (DCC). The aldehyde content of SNCC and consequently surface charge of ENCC (carboxyl content) were precisely controlled by controlling the periodate oxidation reaction time, resulting in highly stable nanoparticles bearing more than 7 mmol functional groups per gram of nanoparticles (e.g., as compared to conventional NCC bearing &#60;&#60; 1 mmol functional group/g). Atomic force microscopy (AFM), transmission electron microscopy (TEM), and scanning electron microscopy (SEM) attested to the rod-like morphology. Conductometric titration, Fourier transform infrared spectroscopy (FTIR), nuclear magnetic resonance (NMR), dynamic light scattering (DLS), electrokinetic-sonic-amplitude (ESA) and acoustic attenuation spectroscopy shed light on the superior properties of these nanomaterials.

Synthesis schemes to prepare highly stable wood fiber-based hairy nanoparticles and functional cellulose-based biopolymers have been detailed.

Nanoparticles, as one of the key materials in nanotechnology and nanomedicine, have gained significant importance during the past decade. While ...

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

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

Peptide and Protein Quantification Using Automated Immuno-MALDI iMALDI

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

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

Rapid Nanoprobe Signal Enhancement by In Situ Gold Nanoparticle Synthesis

The use of nanoprobes such as gold, silver, silica or iron-oxide nanoparticles as detection reagents in bioanalytical assays can enable high sensitivity and convenient colorimetric readout. However, high densities of nanoparticles are typically needed for detection. The available synthesis-based enhancement protocols are either limited to gold and silver nanoparticles or rely on precise enzymatic control and optimization. Here, we present a protocol to enhance the colorimetric readout of gold, silver, silica, and iron oxide nanoprobes. It was observed that the colorimetric signal can be improved by up to a 10000-fold factor. The basis for such signal enhancement strategies is the chemical reduction of Au3+ to Au0. There are several chemical reactions that enable the reduction of Au3+ to Au0. In the protocol, Good&#39;s buffers and H2O2 are used and it is possible to favor the deposition of Au0 onto the surface of existing nanoprobes, in detriment of the formation of new gold nanoparticles. The protocol consists of the incubation of the microarray with a solution consisting of chloroauric acid and H2O2 in 2-(N-morpholino)ethanesulfonic acid pH 6 buffer following the nanoprobe-based detection assay. The enhancement solution can be applied to paper and glass-based sensors. Moreover, it can be used in commercially available immunoassays as demonstrated by the application of the method to a commercial allergen microarray. The signal development requires less than 5 min of incubation with the enhancement solution and the readout can be assessed by naked eye or low-end image acquisition devices such as a table-top scanner or a digital camera.

Rapid Nanoprobe Signal Enhancement by In Situ Gold Nanoparticle Synthesis

In this work, a protocol for signal enhancement of nanoprobe-based biosensing is presented. The protocol is based on the reduction of chloroauric acid onto the surface of existing nanoprobes that consist of gold, silver, silica or iron-oxide nanoparticles.

The use of nanoprobes such as gold, silver, silica or iron-oxide nanoparticles as detection reagents in bioanalytical assays can enable high sensitivity ...

Preparation of Stable Bicyclic Aziridinium Ions and Their Ring-Opening for the Synthesis of Azaheterocycles

Bicyclic aziridinium ions were generated by the removal of an appropriate leaving group through internal nucleophilic attack by nitrogen atom in the aziridine ring. The utility of bicyclic aziridinium ions, specifically 1-azoniabicyclo[3.1.0]hexane and 1-azoniabicyclo[4.1.0]heptane tosylate highlighted in the aziridine ring openings by the nucleophile with the release of the ring strain to yield the corresponding ring-expanded azaheterocycles such as pyrrolidine, piperidine and azepane with diverse substituents on the ring in regio- and stereospecific manner. Herein, we report a simple and convenient method for the preparation of the stable 1-azabicyclo[4.1.0]heptane tosylate followed by selective ring opening via a nucleophilic attack either at the bridge or at the bridgehead carbon to yield piperidine and azepane rings, respectively. This synthetic strategy allowed us to prepare biologically active natural products containing piperidine and azepane motif including sedamine, allosedamine, fagomine and balanol in highly efficient manner.

Bicyclic aziridinium ions such as 1-azoniabicyclo[4.1.0]heptane tosylate were generated from 2-[4-tolenesulfonyloxybutyl]aziridine, which was utilized for the preparation of substituted piperidines and azepanes via regio- and stereospecific ring-expansion with various nucleophiles. This highly efficient protocol allowed us to prepare diverse azaheterocycles including natural products such as fagomine, febrifugine analogue and balanol.

Bicyclic aziridinium ions were generated by the removal of an appropriate leaving group through internal nucleophilic attack by nitrogen atom in the ...