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 sin...

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. cerevis...

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 ...

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Chemistry

16.0K 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.

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

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 ...

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 ...

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 ...

Large-scale Top-down Proteomics Using Capillary Zone Electrophoresis Tandem Mass Spectrometry

Capillary zone electrophoresis-electrospray ionization-tandem mass spectrometry (CZE-ESI-MS/MS) has been recognized as a useful tool for top-down proteomics that aims to characterize proteoforms in complex proteomes. However, the application of CZE-MS/MS for large-scale top-down proteomics has been impeded by the low sample-loading capacity and narrow separation window of CZE. Here, a protocol is described using CZE-MS/MS with a microliter-scale sample-loading volume and a 90-min separation window for large-scale top-down proteomics. The CZE-MS/MS platform is based on a linear polyacrylamide (LPA)-coated separation capillary with extremely low electroosmotic flow, a dynamic pH-junction-based online sample concentration method with a high efficiency for protein stacking, an electro-kinetically pumped sheath flow CE-MS interface with extremely high sensitivity, and an ion trap mass spectrometer with high mass resolution and scan speed. The platform can be used for the high-resolution characterization of simple intact protein samples and the large-scale characterization of proteoforms in various complex proteomes. As an example, a highly efficient separation of a standard protein mixture and a highly sensitive detection of many impurities using the platform is demonstrated. As another example, this platform can produce over 500 proteoform and 190 protein identifications from an Escherichia coli proteome in a single CZE-MS/MS run.

A detailed protocol is described for the separation, identification, and characterization of proteoforms in protein samples using capillary zone electrophoresis-electrospray ionization-tandem mass spectrometry (CZE-ESI-MS/MS). The protocol can be used for the high-resolution characterization of proteoforms in simple protein samples and the large-scale identification of proteoforms in complex proteome samples.

Capillary zone electrophoresis-electrospray ionization-tandem mass spectrometry (CZE-ESI-MS/MS) has been recognized as a useful tool for top-down ...

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 ...

Stable Aqueous Suspensions of Manganese Ferrite Clusters with Tunable Nanoscale Dimension and Composition

Manganese ferrite clusters (MFCs) are spherical assemblies of tens to hundreds of primary nanocrystals whose magnetic properties are valuable in diverse applications. Here we describe how to form these materials in a hydrothermal process that permits the independent control of product cluster size (from 30 to 120 nm) and manganese content of the resulting material. Parameters such as the total amount of water added to the alcoholic reaction media and the ratio of manganese to iron precursor are important factors in achieving multiple types of MFC nanoscale products. A fast purification method uses magnetic separation to recover the materials making production of grams of magnetic nanomaterials quite efficient. We overcome the challenge of magnetic nanomaterial aggregation by applying highly charged sulfonate polymers to the surface of these nanomaterials yielding colloidally stable MFCs that remain non-aggregating even in highly saline environments. These non-aggregating, uniform, and tunable materials are excellent prospective materials for biomedical and environmental applications.

We report a one-pot hydrothermal synthesis of manganese ferrite clusters (MFCs) that offers independent control over material dimension and composition. Magnetic separation allows rapid purification while surface functionalization using sulfonated polymers ensures the materials are non-aggregating in biologically relevant medium. The resulting products are well positioned for biomedical applications.

Manganese ferrite clusters (MFCs) are spherical assemblies of tens to hundreds of primary nanocrystals whose magnetic properties are valuable in diverse ...

Primary Organ Identification in Medicinal Plants Using  &lt;sup&gt;13&lt;/sup&gt;C&lt;sub&gt;6&lt;/sub&gt;&amp;#45;Glucose Labeling 

13C6&#45;Glucose Labeling Associated with LC&#45;MS&#58; Identification of Plant Primary Organs in Secondary Metabolite Synthesis

This paper presents a novel and efficient method for certifying primary organs involved in secondary metabolite synthesis. As the most important secondary metabolite in Parispolyphylla var. yunnanensis (Franch.) Hand. -Mzt. (PPY), Paris saponin (PS) has a variety of pharmacological activities and PPY is in increasing demand. This study established leaf, rhizome, and stem-vascular-bundle 13C6-Glucose feeding and non-feeding four treatments to precisely certify the primary organs involved in Paris saponins VII (PS VII) synthesis. By combining liquid chromatography-mass spectrometry (LC-MS), the 13C/12C ratios of leaf, rhizome, stem, and root in different treatments were quickly and accurately calculated, and four types of PS isotopic ion peak(M&#8722;) ratios were found: (M+1) &#8722;/M&#8722;, (M+2) &#8722;/M&#8722;, (M+3) &#8722;/M&#8722; and (M+4) &#8722;/M&#8722;. The results showed that the ratio of 13C/12C in the rhizomes of the stem-vascular-bundle and rhizome feeding treatments was significantly higher than that in the non-feeding treatment. Compared to the non-feeding treatment, the ratio of PS VII molecules (M+2) &#8722;/M&#8722; in the leaves increased significantly under leaf and stem-vascular-bundle feeding treatments. Simultaneously, compared to the non-feeding treatment, the ratio of PS VII molecules (M+2) &#8722;/M&#8722; in the leaves under rhizome treatment showed no significant difference. Furthermore, the ratio of PS VII molecules (M+2) &#8722;/M&#8722; in the stem, root, and rhizome showed no differences among the four treatments. Compared to the non-feeding treatment, the ratio of the Paris saponin II (PS II) molecule (M+2) &#8722;/M&#8722; in leaves under leaf feeding treatment showed no significant difference, and the (M+3) &#8722;/M&#8722; ratio of PS II molecules in leaves under leaf feeding treatment were lower. The data confirmed that the primary organ for the synthesizing of PS VII is the leaves. It lays the foundation for future identification of the primary organs and pathways involved in the synthesis of secondary metabolites in medicinal plants.

Author Spotlight: Plant Primary Organs Profiling Using &lt;sup&gt;13&lt;/sup&gt;C&lt;sub&gt;6&lt;/sub&gt;&amp;#45;Glucose Labeling and LC&amp;#45;MS

The developed method of 13C6-Glucose labeling combined with liquid chromatography high-resolution mass spectrometry is versatile and lays the foundation for future studies on the primary organs and pathways involved in the synthesis of secondary metabolites in medicinal plants, as well as the comprehensive utilization of these secondary metabolites.

This paper presents a novel and efficient method for certifying primary organs involved in secondary metabolite synthesis. As the most important secondary ...