Comparative proteome analysis across non-small cell lung cancer cell lines
Non-small cell lung cancer (NSCLC) cell lines are widely used model systems to study molecular aspects of lung cancer. Comparative and in-depth proteome expression data across many NSCLC cell lines has not been generated yet, but would be of utility for the investigation of candidate targets and markers in oncogenesis. We employed a SILAC reference approach to perform replicate proteome quantifications across 23 distinct NSCLC cell lines. On average, close to 4000 distinct proteins were identified and quantified per cell line. These included many known targets and diagnostic markers, indicating that our proteome expression data represents a useful resource for NSCLC pre-clinical research. To assess proteome diversity within the NSCLC cell line panel, we performed hierarchical clustering and principal component analysis of proteome expression data. Our results indicate that general proteome diversity among NSCLC cell lines supersedes potential effects common to K-Ras or epidermal growth factor receptor (EGFR) oncoprotein expression. However, we observed partial segregation of EGFR or KRAS mutant cell lines for certain principal components, which reflected biological differences according to gene ontology enrichment analyses. Moreover, statistical analysis revealed several proteins that were significantly overexpressed in KRAS or EGFR mutant cell lines. Biological significance Despite enormous progress in molecular characterization and targeted therapy NSCLC represents a major cause for cancer-related deaths. While pre-clinical models such as NSCLC cell lines have been studied on the genomic and transcriptional level, proteome composition is poorly characterized. We conducted quantitative profiling across 23 NSCLC cell lines and studied global proteome diversity in relation to the presence of oncogenic KRAS or EGFR mutations. Notably, in-depth bioinformatics analysis pointed to prominent biological processes as well as up-regulated proteins in KRAS and EGFR mutant cells, highlighting the utility of cancer cell proteomics to identify target or biomarker candidates in the context of specific oncogenic mechanisms.
Sample Processing Protocol
23 NSCLC cell lines were grown in intermediate isotope labeled L-D4 14N2-lysine (Lys4) and L-13C614N4-arginine (Arg6) and heavy isotope-labeled L-13C615N2-lysine (Lys8) and L-13C615N4-arginine (Arg10) SILAC medium. All cells were cultivated in SILAC medium for a minimum of six doubling times to obtain an incorporation efficiency for the labeled amino acids of at least 95%. For harvest, cells were washed twice with ice-cold PBS and lysed directly on the plates by the addition of ice-cold lysis buffer (8 M urea, 50 mM Tris, pH 8.2, 75 mM NaCl, 1 mM EDTA, 1 mM EGTA, 1 mM PMSF, 10 mM sodium fluoride, 10 mM β-glycerophosphat, 2.5 mM sodium orthovanadate, 50 ng/ml calyculin A, 10 µg/ml aprotinin, 10 µg/ml leupeptin and 1:100 (v/v) phosphatase inhibitor cocktails 1 and 3 (Sigma)). After sonication, cell debris was sedimented by centrifugation and the protein concentration was determined by a Bradford assay. Equal protein amounts of a super SILAC reference sample (composed of 13 cell lines) and lysates from intermediate and heavy SILAC labeled cell lines (50 µg protein each) were mixed and subsequently subjected to reduction (10 mM DTT, 30 min at room temperature) and alkylation (55 mM iodoacetoamide for 30 min at room temperature). Proteins were separated by gel electrophoresis with ready-made 10% NuPAGE® Bis-Tris gels (Invitrogen) according to the manufacturer's instructions. Resolved proteins were stained using the Colloidal Blue staining kit (Invitrogen). In all SILAC experiments, gels were cut into ten slices followed by in-gel digestion with trypsin (Promega, 1:100 (w/w), 37°C, overnight). Subsequently, peptides were extracted, purified with C18-StageTips and subjected to MS analysis as described (Rappsilber et al., 2007). All LC-MS/MS analyses were performed on an LTQ-Orbitrap (Orbitrap Discovery, Thermo Fisher Scientific). Peptide samples were loaded in solvent A (0.5% acetic acid) by an Agilent 1200 nanoflow system (Agilent Technologies) on a 15 cm fused silica emitter (New Objective) packed in-house with reversed phase material (Reprosil-Pur C18-AQ, 3 µm, Dr. Maisch GmbH; Germany) at a flow of 500 nl/min. The bound peptides were eluted by a gradient from 10% to 60% of solvent B (80% acetonitrile, 0.5% acetic acid) at a flow rate of 200 nl/min and sprayed directly into the mass spectrometer by applying a spray voltage of 2.2 kV using a nanoelectrospray ion source (Proxeon Biosystems). The mass spectrometer was operated in the positive ion mode and with a data-dependent switch between MS and MS/MS acquisition. To improve mass accuracy in the MS mode, the lock-mass option was enabled as described (Olsen et al., 2005). Full scans were acquired in the orbitrap at a resolution r = 30,000 and a target value of 1,000,000 ions. The five most intense ions detected in the MS scan were selected for collision induced dissociation in the LTQ at a target value of 5000 ion counts. The resulting fragmentation spectra were also recorded in the linear ion trap. Ions that were once selected for data dependent acquisition were dynamically excluded for 90 sec from further fragmentation. Mass spectrometric settings were: no sheath and auxiliary gas flow; heated capillary temperature, 220°C; normalized collision energy, 35% and an activation q = 0.25.
Data Processing Protocol
All MS raw files were collectively processed with MaxQuant (version 184.108.40.206) (Cox et al., 2008) applying the Andromeda search engine (Cox et al., 2011). Data were searched against a concatenated forward and reversed version of the human Swiss-Prot database (version: 02.2012) comprising 25,874 database entries and 175 frequently detected contaminants (such as porcine trypsin, human keratins and Lys-C). The minimal peptide length was set to 6 amino acids, trypsin was selected as proteolytic enzyme and maximally 2 missed cleavage sites were allowed. Carbamidomethylation of cysteine residues was set as fixed modification while oxidation of methionine, and protein N-acetylation were allowed as variable modifications. As MaxQuant automatically extracts isotopic SILAC peptide triplets, the corresponding isotopic forms of lysine and arginine were automatically selected. The maximal mass deviation of precursor and fragment masses was set to 20 ppm and 0.5 Da before internal mass recalibration by MaxQuant. For protein quantification only proteins with at least two quantified razor and/or unique peptides were considered. The match between runs function was enabled with a time window of 2 min. A false discovery rate (FDR) of 0.01 was applied to both protein and peptide identification. The MaxQuant results were uploaded to the MaxQB database (version 2.5) for further analysis (Schaab et al., 2012). The MaxQuant proteinGroups table was used for proteome data analysis. The initial measure for relative protein abundance is provided as the normalized ratio between heavy or intermediate SILAC labeled cell line samples and the light labeled SILAC reference material. For further analysis, MaxQuant protein ratios were log2-transformed and identifications flagged as reverse or contaminant hits were excluded.
Kathrin Grundner-Culemann, Evotec (München) GmbH
Henrik Daub, 1: Evotec (München) GmbH, Am Klopferspitz 19a, 82151 Martinsried, Germany 2: Cell Signaling Group, Department of Molecular Biology, Max Planck Institute of Biochemistry, Am Klopferspitz 18, 82152 Martinsried, Germany ( lab head )
Grundner-Culemann K, Nikolaj Dybowski J, Klammer M, Tebbe A, Schaab C, Daub H. Comparative proteome analysis across non-small cell lung cancer cell lines. J Proteomics. 2015 Sep 8. pii: S1874-3919(15)30119-6 PubMed: 26361996