Methods for proteome analysis of obesity (Adipose tissue)

Transcription

1 Methods for proteome analysis of obesity (Adipose tissue) I. Sample preparation and liquid chromatography-tandem mass spectrometric analysis Instruments, softwares, and materials AB SCIEX Triple TOF 5600 System (AB SCIEX, Massachusetts, USA) Nano-Frontier nlc (Hitachi High Technologies, Tokyo, Japan) Monolith Trap column C (Hitachi High Technologies, Tokyo, Japan) MonoCap for Fast-flow analytical column ( , GL Sciences, Tokyo, Japan) Shake Master Neo Ver.1.0 (BMS-M10N21, BMS, Tokyo, Japan) Savant SpeedVac (SPD111V, Thermo Scientific, Yokohama, Japan) Himac Compact Centrifuge (CF16RXII, Hitachi, Tokyo, Japan) Ultimate Nano LC (Thermo Scientific, Yokohama, Japan) MonoSpin SCX ( , GL Science, Tokyo Japan) MonoTip C18 ( , GL Science, Tokyo Japan) Lysyl endopeptidase ( , Wako Pure Industries, Osaka, Japan) Trypsin (V5111, Promega, Wisconsin, USA) Fluoraldehyde o-phthalaldehyde (OPA) Reagent Solution (#26025, Thermo Scientific, Yokohama, Japan) Mascot Ver. 2.4 (Matrix Science, London, UK) ( 2DICAL2 Ver (Mitsui Knowledge Industry, Tokyo, Japan) Procedures 1. Delipidation and Trypsin Digestion 1.1. Fat tissue (~10 mg) was pulverized by Shake Master Neo, washed twice with methanol, and then dried in a SpeedVac concentrator The water suspension (400 μl) was heated at 95 C for 10 min, extracted twice with diethyl ether, and the water layer was dried in a concentrator To the sample, sodium deoxycholate solution (SDC, 2%, 50 μl), urea (5 M, 20 μl), ammonium hydrogen carbonate (NH4HCO3, 1M, 5 μl), and water (7.8 μl) were added and vortexed well The suspension was digested with lysyl endopeptidase (2 μg) at 37 C for 3 h, and then with trypsin (6.6 μg) at 37 C for h Formic acid solution (5%, 20 μl) was added to precipitate the SDC After centrifugation, the supernatant was extracted twice with ethyl acetate, and then twice with an organic solvent mixture (butanol/diisopropyl ether) The water layer was dried and warmed in a concentrator to remove NH4HCO3. 2. Tryptic peptide purification 2.1. MonoSpin SCX columns were washed with 100 μl of methanol, 100 μl of 0.5 M HCl, and 200 μl of loading buffer A by passing them by centrifugation The tryptic digests dissolved in 100 μl of loading buffer A (2% CH3CN, 5 mm NaCl, and 0.1% trifluoroacetic acid (TFA)) were loaded onto the MonoSpin SCX column by centrifugation, and the flow through fraction was loaded again onto the same column The column was washed twice with 200 μl of wash buffer A (50% CH3CN, 5 mm NaCl, and 0.1% TFA), and then with 200 μl of loading buffer A Adsorbed peptides were eluted twice with 100 μl of elution buffer A (40% CH3CN, 1

2 500mM NaCl, and 0.1% TFA) by centrifugation The eluted peptides were dried in a concentrator. 3. Peptide enrichment and desalting 3.1. MonoTip C18 cartridges were washed with 100 μl each of methanol, washing buffer B (80% CH3CN and 0.1% TFA), and loading buffer B by centrifugation Half of the peptide solution dissolved in 100 μl of loading buffer B (2% CH3CN and 0.1% TFA) was separately taken up to the 2 cartridges, and the solution was passed through the cartridge at least 10 times The cartridges were washed twice with 100 μl each of loading buffer B Adsorbed peptides were eluted with elution buffer B (50 μl, 40% CH3CN and 0.1%TFA) by repeatedly passing the elution buffer more than 10 times. The eluates from the 2 cartridges were combined and dried in a concentrator The purified peptides were dissolved in 40 μl of 0.1% formic acid, and the peptide concentration was determined by the OPA method. 4. LC-MS/MS analysis and peptide identification 4.1. Peptide identification was performed with the liquid chromatography coupled with tandem mass spectrometric analysis (LC-MS/MS) by the aid of Mascot software. Quantitative estimation of peptides was carried out using 2DICAL2 software The peptide solution (~300 ng of peptides), prepared by Procedure 3, were separated by LC using a linear gradient elution of 2% to 11.6% of CH3CN for 5 min, then 11.6% to 45.2% of CH3CN for 60 min. Ionized peptides and its fragment ions generated by the collision-induced dissociation were analyzed by a Triple-TOF 5600 mass spectrometer Peptides were identified by MS/MS spectra using the Mascot software, and their quantitative analysis was carried out using the 2DICAL2 software[1]. Data quality control and functional assessment of the liquid chromatography and mass spectrometer 1. LC-MS/MS analysis was performed by the Trap-and-Elute method. Calibration of a Triple-TOF 5600 mass spectrometer was performed every 10 samples using trypsin-digested bovine serum albumin MS standard (TD-BSA-S). To confirm the retention times and peak intensities, selected TD-BSA-S peptides were monitored. Peak intensity of each peptide was determined as an average of duplicate measurements. 2. In the case of fat tissues, contaminants were occasionally included in the finally purified fraction. To confirm the purity, a portion of the solution was subjected to nano LC and its elution profile of absorbance 210 nm was monitored. The samples showing deviated profiles were not subjected to the LC-MS/MS analysis. II. Protein quantitation in the proteome analysis and its quality control 1. Criteria for identification of trypsin-digested peptides 1.1. The parameter sets for MS/MS ion search were shown below; Database: SwissProt_2014.fasta 2

3 Enzyme: Trypsin Miss cleavages allowed: Up to one Variable modification: Methionine oxidation Peptide tolerance: 20 ppm MS/MS tolerance: 0.05 Da 1.2. The peptides identified with an expectation value p<0.05 were considered as reliable If multiple peptides with high sequence similarity were selected for the same peptide peak, the peptide sequence is not determined unambiguously and is excluded from the identified peptide list PeptideProphet analysis[2] was also performed and the results were used to select peptide peaks for the protein quantification, as described in Quantitation of tryptic peptides by the non-labeling method 2.1. MS peak intensities did not always correlate with absolute amounts of peptides. However, if samples of the same tissue were processed by the fixed method, the peptide peak intensity ratio of the same peptide is expected to correlate with the relative abundance of the peptides. This is the principle of the non-labeling quantitative proteomic analysis method In the 2DICAL software, the MS peaks of the same peptides were adjusted by the specific program and superimposed, and the average peak intensity was calculated and compared between the sample groups If the same peptide was identified as various charged forms (2+, 3+, 4+), the highest peak is selected for the quantitative analysis. 3. Selection method of tryptic peptide peaks for protein quantification 3.1. The protein levels in the target tissue were evaluated by using multiple peptide peak data from each protein The peptide peak data are filtered by the following rule; a. If the weight value in PeptideProphet analysis was less than 0.5, the peptide is excluded. b. If multiple peptides showed nearly equal values in both m/z and retention time (within 0.05Da and 0.6 min each), these peptides are excluded except the value of number of sibling peptides (NSP)-adjusted probability in the PeptideProphet analysis was more than 0.8. c. The peptides including methionine are excluded because methionine residue is easily oxidized during the sample preparation. d. Peptides generated by miss cleavages of trypsin/lysyl endopeptidase are excluded. e. Peptides with the low peak intensity (mean intensity of the group is less than 500) are excluded. f. Peptides of which peaks are detected in less than 90 % of samples are excluded. 4. Correction of filtered peptides data 4.1. Fat tissues in the obesity study were divided into three groups; D: Visceral fat of obesity patients N: Subcutaneous fat of obesity patients 3

4 C: Perirenal fat of non-obesity patients 4.2. The mean ratio of intensity of each tryptic peptide between Groups D and N was calculated excluding outliers exceeding 2 standard deviations from the averages. Then, the intensity of data set of Group N was corrected by the above mean ratio In the same way, Groups D and C was compared, and the intensity of data set of Group C was corrected. 5. Protein quantification and statistical analysis A. Comparison between two groups 5.1. To detect statistically significant changes of protein levels, unpaired t-test analysis between case and control groups was firstly performed on the data of tryptic peptide levels The changes of protein levels were evaluated by averaging levels of multiple tryptic peptides from the same protein using their p values as criteria Proteins were classified as shown below; Among the tryptic peptides from one protein, A: Two or more peptides showed p < 0.05 B1: One peptide showed p <0.05 and another showed p<0.2 B2: One peptide showed p <0.05 and its intensity was higher than 2,000 B3: The product of p1 and p2 for two tryptic peptides was lower than 0.01, where p1 and p2 were the two smallest p values for tryptic peptides. B4: three or more tryptic peptides showed p <0.2 C: Others * In some cases, both increased and decreased tryptic peptides from the same protein are observed. When the number of increased peptides is more than twice that of decreased peptides, the protein level is judged as increasing and vice versa. In the other cases not included in criteria A to B4 (Class C), the protein level is judged as not significantly changed Protein level in each class is calculated by the following methods; Class A, B3, B4: Mean of two tryptic peptides is adopted as the protein level Class B1, B2: Peak intensity of the tryptic peptide that showed p < 0.05 is adopted Class C, Mean of peak intensities of two highest tryptic peptides is adopted. If only one tryptic peptide was identified, its peak intensity is adopted Statistical analysis between two groups was performed using unpaired t-test on protein levels calculated as described above Proteins of Class A are judged to show significant changes between the groups and marked as 1 in significance column. B. Comparison among three groups 5.7. Protein quantification is performed by the Top3 method (established by Drs. Ono and Yamada of National Cancer Center Research Institute), and unpaired t-test is used for detecting statistical significance between groups. 4

5 References: [1] Ono, M. et al. Label-free quantitative proteomics using large peptide data sets generated by nanoflow liquid chromatography and mass spectrometry. Mol. Cell. Proteomics. 5, (2006) [2] Keller, A. et al. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search. Anal. Chem. 74, (2002) 5