BLADDER CANCER PROTEOME: A MULTIPLEXING APPROACH USING ONLINE 2D RP-RP CHROMATOGRAPHY COUPLED WITH DATA INDEPENDENT ION MOBILITY Lee A Gethings1, Zhuowei Wang2, Bo Wen3, Ju Zhang3, Quanhui Wang3, Liang Lin3, Christopher Hughes1, Johannes PC Vissers1, Richard Lock1, James I Langridge1, Siqi Liu3 1 Waters Corporation, Manchester, United Kingdom, 2Waters Corporation, Beijing, China, 3Beijing Genomics Institute, Beijing, China RESULTS Peptide Comparison A comparative study between the different acquisition strategies (1D MSE, 1D HDMSE and 2D 5 fraction HDMSE) was utilized for the three individual cell lines. All acquisitions were performed in triplicate. Figure 4 are typical chromatograms for individual fractions resulting from a 2D 5 fraction experiment. 70000 25000 20000 50000 # mutant peptides 60000 15000 10000 Mass Spectrometry A Synapt G2-S was used for data acquisition. Data were acquired using data independent analysis (DIA) in conjunction with ion mobility (IM), as shown in Figure 3. Fraction # 1 2 3 4 5 % ACN 10.8 14.0 16.7 20.4 45.0 Fraction 5 # peptides 5000 40000 SV-HUC 5637 0 1D MSE 1D HDMS 2D 5 fraction HDMS T24 30000 20000 SV-HUC 1-1 Fraction 5 11025_005 1: TOF MS ES+ BPI 2.31e5 100 Bioinformatics 10000 0 1D MSE 0 20.00 Fraction 11025_004 SV-HUC 1-1 Fraction 4 40.00 60.00 80.00 2D 5 Fraction HDMS 7000 4 1: TOF MS ES+ BPI 3.33e5 100 0 Fraction 11025_003 SV-HUC 1-1 Fraction 3 40.00 60.00 3 80.00 Time 1: TOF MS ES+ BPI 1.94e5 100 900 800 700 6000 5000 20.00 Figure 7. Normalized protein abundance curve for the SV-HUC cell line, including identifications from 1D MSE (blue), 1D HDMSE (red) and 2D 5 fraction HDMSE (grey) experiments. Coefficient of variance to determine protein quantification reproducibility of all cell lines is shown inset. Time # proteins Single Pump Trapping 1D HDMS Protein Comparison 4000 # mutant proteins The LC-MS peptide data were processed and searched with ProteinLynx GlobalSERVER. A curated human database consisting of protein sequences derived from in-house genomic and transcriptomic studies (BGI) was used for searching. The resulting data was also subjected to pathway analysis using Ingenuity (IPA) software. % Bladder cancer arises from malignancy of cells located within the epithelial lining. It is estimated that there are over 380,000 diagnosed cases worldwide. The presence of blood in the urine and frequent urination are the typical symptoms of the condition. However detailed molecular mechanisms regarding the processes involved for tumor development in cases of bladder cancer are not clearly understood. Therefore using three different human cell lines we aim to use qualitative and quantitative proteomics data to characterize and determine changes in the proteomes related with the type of cancer cell lines, which is useful for discovering potential bladder cancer biomarkers. 2D RP-RP chromatography. Peptides were loaded onto a first dimension column (XBridge BEH 130 C18, 300 µm x 50 mm) using 20 mM ammonium formate (pH 10). A discontinuous step gradient of acetonitrile at a flow rate of 2 µL/min was used to provide a 5 fraction strategy. Second dimension separation was performed as described for 1D chromatography. 500 ng material was loaded on-column (first dimension) for all 2D analyses. % INTRODUCTION 600 500 400 300 200 SV-HUC 100 5637 0 3000 1D MSE 1D HDMS T24 2D 5 Fraction HDMS 2000 % 1000 0 1D MSE 0 20.00 Fraction 11025_002 SV-HUC 1-1 Fraction 2 60.00 2 80.00 Time 1: TOF MS ES+ BPI 1.83e5 100 2D 5 Fraction HDMS Figure 5. Number of identified peptides (top) and proteins (bottom) for the three cell lines over the various acquisition schemes with corresponding mutant information shown inset. Of the mutant proteins identified, approximately 20% show overlap between the three cell lines. Functional analysis using IPA appears to show a strong association of these mutants with cancer derived pathways and networks involving DNA replication, recombination, repair, energy production and nucleic acid metabolism. A total of 465 mutant genes have been identified with 184 selected as potential biomarkers (Figure 9). Figure 9. Functional analysis of the identified mutant proteins from the three cell lines. Of the 796 genes mapped, 465 are positively identified as being associated with bladder cancer. The majority of which originate from the cytoplasm and nucleus. CONCLUSIONS % Figure 1. Transitional cell carcinoma. The most common type of cells responsible for the onset of bladder cancer. 2D RP-RP with dilution 40.00 1D HDMS Label-free LC-IM-DIA-MS quantitation can highlight protein expression fold changes for specific proteins of interest. A example is provided in Figure 8 using hierarchical clustering to group proteins together which share similar expression profiles. METHODS Figure 2. LC configurations: 1D (single pump trapping) and 2D RP-RP with dilution 0 20.00 40.00 60.00 Fraction 1 11025_001 Sample preparation Peptide Identification Intersection low energy % liquid phase separation elevated energy retention time aligned precursor and product ions LC conditions 1D chromatography. 1D LC experiments consisted of a 90 min gradient from 1 to 40% acetonitrile (0.1% formic acid) at 300 nL/min using a nanoACQUITY system, configured in single trapping mode with a HSS T3 1.8 µm C18 reversed phase (75 µm x 15 cm) nanoscale LC column and Symmetry C18 trapping column (180 µm x 20 mm). 100 ng material was loaded on-column for all 1D analyses. Implementing online 2D RP-RP provides an average of 30% more protein identifications compared with 1D LC. Based on 5-fraction 2D RP-RP data, an average of 5800 proteins are identified (over the three cell lines), with 13% being assigned as mutant proteins. Identified mutants can be mapped to 58% of known genes associated with cancer. A large proportion of which are over expressed. A label-free proteomics (IM-DIA-MS) approach has been applied for the analysis of bladder cancer, providing a potential biomarker list of 184 gene candidates. Time 1: TOF MS ES+ BPI 1.53e5 100 Two different bladder cancer cell lines (5637 and T24) were prepared, in addition to the control cell line (SV-HUC-1). In all cases two biological replicates were prepared for each cell line. Cell lines were cultured in media containing fetal bovine serum, which was removed and cell lines rinsed with phosphate buffer solution. Cells were centrifuged, supernatant removed and resuspended with lysis buffer before sonication. Insoluble proteins were separated by centrifugation before gelassisted (trypsin) digestion. 80.00 SV-HUC 1-1 Fraction 1 ion mobility/gas phase separation drift time aligned precursor and product ions Figure 3. Retention and drift time principle ion mobility enabled data-independent analysis (IM-DIA-MS). 0 20.00 40.00 60.00 80.00 Time Figure 4. Representative BPI chromatograms for a 5 fraction experiment of the SV-HUC cell line. Percentages of acetonitrile used for the discontinuous step gradient are shown top left. Peptide and protein identifications following database searching are provided in Figure 5. Comparing 1D MSE with 1D HDMSE, an average increase of 45% in protein identifications is observed. Incorporating the fractionation strategy provides an additional 30%. A proportion of mutant peptides and proteins were also derived from the results (inset of Figure 5). A gauge of reproducibility over technical and biological replicates is provided at the peptide and protein level (Figure 6). Label-free quantitative data over a wide dynamic range is also demonstrated using IM-DIA-MS data (Figure 7). TO DOWNLOAD A COPY OF THIS POSTER, VISIT WWW.WATERS.COM/POSTERS Protein Identification Intersection Figure 6. Identification reproducibility at the peptide and protein level, representing four biological replicates of the T24 cell line. 23788 (39.95%) of the identified peptides replicated in more than two replicates, whilst at the protein level this corresponded to 2091 (42.64%). Figure 8. Subset hierarchical clustering results (log2 converted within sample amounts) for the 5 fraction experiments, showing significantly regulated and unique proteins, contrasting control (SV-HUC) with cancer cell lines (5637 & T24). References 1.An Ion Mobility Assisted Data Independent LC-MS Strategy for the Analysis of Complex Biological Samples. Rodriguez-Suárez E, Hughes C, Gethings L, Giles K, Wildgoose J, Stapels M, Fadgen KE, Geromanos SJ, Vissers JPC, Elortza F, Langridge JI. CAC , 2012(8). 2.Database searching and accounting of multiplexed precursor and product ion spectra from the data independent analysis of simple and complex peptide mixtures. Li GZ, Vissers JP, Silva JC, Golick D, Gorenstein MV, Geromanos SJ. Proteomics. 2009 Mar;9(6):1696-719. ©2013 Waters Corporation
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