Classification of cancer cell lines using matrix-assisted laser desorption/ionization time‑of‑flight mass spectrometry and statistical analysis

Serafim,Vlad; Shah,Ajit; Puiu,Maria; Andreescu,Nicoleta; Coricovac,Dorina; Nosyrev,Alexander; Spandidos,Demetrios   A.; Tsatsakis,Aristides   M.; Dehelean,Cristina; Pinzaru,Iulia

doi:10.3892/ijmm.2017.3083

Classification of cancer cell lines using matrix-assisted laser desorption/ionization time‑of‑flight mass spectrometry and statistical analysis

Authors:
- Vlad Serafim
- Ajit Shah
- Maria Puiu
- Nicoleta Andreescu
- Dorina Coricovac
- Alexander Nosyrev
- Demetrios A. Spandidos
- Aristides M. Tsatsakis
- Cristina Dehelean
- Iulia Pinzaru
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Affiliations: Center of Genomic Medicine, ‘Victor Babes’ University of Medicine and Pharmacy, Timisoara 300041, Romania, Department of Natural Sciences, Middlesex University, London NW4 4BT, UK, Department of Toxicology, Faculty of Pharmacy, ‘Victor Babes’ University of Medicine and Pharmacy, 300041 Timisoara, Romania, Central Chemical Laboratory of Toxicology, I.M. Sechenov First Moscow State Medical University, 119991 Moscow, Russia, Laboratory of Clinical Virology, Medical School, University of Crete, 71003 Heraklion, Greece, Department of Forensic Sciences and Toxicology, Faculty of Medicine, University of Crete, 71003 Heraklion, Greece
Published online on: July 27, 2017 https://doi.org/10.3892/ijmm.2017.3083
Pages: 1096-1104
Copyright: © Serafim et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Over the past decade, matrix-assisted laser desorption/ionization time‑of‑flight mass spectrometry (MALDI‑TOF MS) has been established as a valuable platform for microbial identification, and it is also frequently applied in biology and clinical studies to identify new markers expressed in pathological conditions. The aim of the present study was to assess the potential of using this approach for the classification of cancer cell lines as a quantifiable method for the proteomic profiling of cellular organelles. Intact protein extracts isolated from different tumor cell lines (human and murine) were analyzed using MALDI‑TOF MS and the obtained mass lists were processed using principle component analysis (PCA) within Bruker Biotyper® software. Furthermore, reference spectra were created for each cell line and were used for classification. Based on the intact protein profiles, we were able to differentiate and classify six cancer cell lines: two murine melanoma (B16‑F0 and B164A5), one human melanoma (A375), two human breast carcinoma (MCF7 and MDA‑MB‑231) and one human liver carcinoma (HepG2). The cell lines were classified according to cancer type and the species they originated from, as well as by their metastatic potential, offering the possibility to differentiate non‑invasive from invasive cells. The obtained results pave the way for developing a broad‑based strategy for the identification and classification of cancer cells.

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1	Kallow W, Erhard M, Shah HN, Raptakis E and Welker M: MALDI-TOF MS for Microbial Identification: Years of Experimental Development to an Established Protocol. Mass Spectrometry for Microbial Proteomics. Shah HN and Gharbia SE: John Wiley & Sons, Ltd; Chichester: pp. 255–276. 2010, View Article : Google Scholar
2	Shah HN and Gharbia SE: Mass Spectrometry for Microbial Proteomics. John Wiley and Sons Ltd; Chichester, UK: 2010, View Article : Google Scholar
3	Yang C, He Z and Yu W: Comparison of public peak detection algorithms for MALDI mass spectrometry data analysis. BMC Bioinformatics. 10:42009. View Article : Google Scholar : PubMed/NCBI
4	Jarman KH, Daly DS, Anderson KK and Wahl KL: A new approach to automated peak detection. Chemom Intell Lab Syst. 69:61–76. 2003. View Article : Google Scholar
5	Singhal N, Kumar M, Kanaujia PK and Virdi JS: MALDI-TOF mass spectrometry: An emerging technology for microbial identification and diagnosis. Front Microbiol. 6:7912015. View Article : Google Scholar : PubMed/NCBI
6	Karger A, Bettin B, Lenk M and Mettenleiter TC: Rapid characterisation of cell cultures by matrix-assisted laser desorption/ionisation mass spectrometric typing. J Virol Methods. 164:116–121. 2010. View Article : Google Scholar
7	Zhang X, Scalf M, Berggren TW, Westphall MS and Smith LM: Identification of mammalian cell lines using MALDI-TOF and LC-ESI-MS/MS mass spectrometry. J Am Soc Mass Spectrom. 17:490–499. 2006. View Article : Google Scholar : PubMed/NCBI
8	Povey JF, O'Malley CJ, Root T, Martin EB, Montague GA, Feary M, Trim C, Lang DA, Alldread R, Racher AJ, et al: Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling. J Biotechnol. 184:84–93. 2014. View Article : Google Scholar : PubMed/NCBI
9	Wang P, Gao Q, Suo Z, Munthe E, Solberg S, Ma L, Wang M, Westerdaal NA, Kvalheim G and Gaudernack G: Identification and characterization of cells with cancer stem cell properties in human primary lung cancer cell lines. PLoS One. 8:e570202013. View Article : Google Scholar : PubMed/NCBI
10	Dirks WG and Drexler HG: Authentication of Cancer Cell Lines by DNA Fingerprinting. Cancer Cell Culture: methods and Protocols. 1st edition. Langdon SP: Humana Press; Totowa, NJ: pp. 33–42. 2004
11	Greve B, Kelsch R, Spaniol K, Eich HT and Götte M: Flow cytometry in cancer stem cell analysis and separation. Cytometry A. 81:284–293. 2012. View Article : Google Scholar : PubMed/NCBI
12	Duraiyan J, Govindarajan R, Kaliyappan K and Palanisamy M: Applications of immunohistochemistry. J Pharm Bioallied Sci. 4(Suppl 2): S307–S309. 2012. View Article : Google Scholar : PubMed/NCBI
13	Coricovac DE, Moacă EA, Pinzaru I, Cîtu C, Soica C, Mihali CV, Păcurariu C, Tutelyan VA, Tsatsakis A and Dehelean CA: Biocompatible Colloidal Suspensions Based on Magnetic Iron Oxide Nanoparticles: Synthesis, Characterization and Toxicological Profile. Front Pharmacol. 8:1542017. View Article : Google Scholar : PubMed/NCBI
14	Hillenkamp F and Karas M: Mass spectrometry of peptides and proteins by matrix-assisted ultraviolet laser desorption/ionization. Methods Enzymol. 193:280–295. 1990. View Article : Google Scholar : PubMed/NCBI
15	Flatley B, Malone P and Cramer R: MALDI mass spectrometry in prostate cancer biomarker discovery. Biochim Biophys Acta. 1844:940–949. 2014. View Article : Google Scholar
16	Cho YT, Chiang YY, Shiea J and Hou MF: Combining MALDI-TOF and molecular imaging with principal component analysis for biomarker discovery and clinical diagnosis of cancer. Genomic Medicine, Biomarkers and Health Sciences. 4:3–6. 2012. View Article : Google Scholar
17	Rodrigo MA, Zitka O, Krizkova S, Moulick A, Adam V and Kizek R: MALDI-TOF MS as evolving cancer diagnostic tool: A review. J Pharm Biomed Anal. 95:245–255. 2014. View Article : Google Scholar : PubMed/NCBI
18	Nawarak J, Huang-Liu R, Kao SH, Liao HH, Sinchaikul S, Chen ST and Cheng SL: Proteomics analysis of A375 human malignant melanoma cells in response to arbutin treatment. Biochim Biophys Acta. 1794:159–167. 2009. View Article : Google Scholar
19	Heger Z, Rodrigo MA, Krizkova S, Zitka O, Beklova M, Kizek R and Adam V: Identification of estrogen receptor proteins in breast cancer cells using matrix-assisted laser desorption/ionization time of flight mass spectrometry (Review). Oncol Lett. 7:1341–1344. 2014.PubMed/NCBI
20	Corona G, De Lorenzo E, Elia C, Simula MP, Avellini C, Baccarani U, Lupo F, Tiribelli C, Colombatti A and Toffoli G: Differential proteomic analysis of hepatocellular carcinoma. Int J Oncol. 36:93–99. 2010.
21	Kriegsmann J, Kriegsmann M and Casadonte R: MALDI TOF imaging mass spectrometry in clinical pathology: A valuable tool for cancer diagnostics (Review). Int J Oncol. 46:893–906. 2015. View Article : Google Scholar
22	Delorme A, Sejnowski T and Makeig S: Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimage. 34:1443–1449. 2007. View Article : Google Scholar
23	Bodanese B, Silveira FL, Zângaro RA, Pacheco MTT, Pasqualucci CA and Silveira L Jr: Discrimination of basal cell carcinoma and melanoma from normal skin biopsies in vitro through Raman spectroscopy and principal component analysis. Photomed Laser Surg. 30:381–387. 2012. View Article : Google Scholar : PubMed/NCBI
24	Alexandrov T: MALDI imaging mass spectrometry: Statistical data analysis and current computational challenges. BMC Bioinformatics. 13(Suppl 16): S112012.PubMed/NCBI
25	Norris JL and Caprioli RM: Imaging mass spectrometry: A new tool for pathology in a molecular age. Proteomics Clin Appl. 7:733–738. 2013. View Article : Google Scholar : PubMed/NCBI
26	Bumbrah GS and Sharma RM: Raman spectroscopy - Basic principle, instrumentation and selected applications for the characterization of drugs of abuse. Egypt J Forensic Sci. 6:209–215. 2016. View Article : Google Scholar
27	Ghebremedhin M, Heitkamp R, Yesupriya S, Clay B and Crane NJ: Accurate and rapid differentiation of Acinetobacter baumannii strains by Raman spectroscopy: a comparative study. J Clin Microbiol. 55:2480–2490. 2017. View Article : Google Scholar : PubMed/NCBI
28	Geiger T, Wehner A, Schaab C, Cox J and Mann M: Comparative proteomic analysis of eleven common cell lines reveals ubiquitous but varying expression of most proteins. Mol Cell Proteomics. 11:M111.0140502012. View Article : Google Scholar : PubMed/NCBI
29	Pan C, Kumar C, Bohl S, Klingmueller U and Mann M: Comparative proteomic phenotyping of cell lines and primary cells to assess preservation of cell type-specific functions. Mol Cell Proteomics. 8:443–450. 2009. View Article : Google Scholar :
30	Mladkova J, Sanda M, Matouskova E and Selicharova I: Phenotyping breast cancer cell lines EM-G3, HCC1937, MCF7 and MDA-MB-231 using 2-D electrophoresis and affinity chromatography for glutathione-binding proteins. BMC Cancer. 10:4492010. View Article : Google Scholar : PubMed/NCBI
31	Nagaraja GM, Othman M, Fox BP, Alsaber R, Pellegrino CM, Zeng Y, Khanna R, Tamburini P, Swaroop A and Kandpal RP: Gene expression signatures and biomarkers of noninvasive and invasive breast cancer cells: Comprehensive profiles by representational difference analysis, microarrays and proteomics. Oncogene. 25:2328–2338. 2006. View Article : Google Scholar
32	Danciu C, Falamas A, Dehelean C, Soica C, Radeke H, Barbu-Tudoran L, Bojin F, Pînzaru SC and Munteanu MF: A characterization of four B16 murine melanoma cell sublines molecular fingerprint and proliferation behavior. Cancer Cell Int. 13:752013. View Article : Google Scholar : PubMed/NCBI
33	Avram S, Coricovac DE, Pavel IZ, Pinzaru I, Ghiulai R, Baderca F, Soica C, Muntean D, Branisteanu DE, Spandidos DA, et al: Standardization of A375 human melanoma models on chicken embryo chorioallantoic membrane and Balb/c nude mice. Oncol Rep. 38:89–99. 2017. View Article : Google Scholar : PubMed/NCBI