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In-Depth Profiling of the Peripheral Blood Mononuclear Cells Proteome for Clinical Blood Proteomics

DOI: 10.1155/2014/129259

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Abstract:

Peripheral blood mononuclear cells (PBMCs) are an easy accessible cellular part of the blood organ and, along with platelets, represent the only site of active gene expression in blood. These cells undergo immunophenotypic changes in various diseases and represent a peripheral source of monitoring gene expression and posttranslational modifications relevant to many diseases. Little is known about the source of many blood proteins and we hypothesise that release from PBMCs through active and passive mechanisms may account for a substantial part of the plasma proteome. The use of state-of-the-art proteomic profiling methods in PBMCs will enable minimally invasive monitoring of disease progression or response to treatment and discovery of biomarkers. To achieve this goal, detailed mapping of the PBMC proteome using a sensitive, robust, and quantitative methodological setup is required. We have applied an indepth gel-free proteomics approach using tandem mass tags (TMT), unfractionated and SCX fractionated PBMC samples, and LC-MS/MS with various modulations. This study represents a benchmark in deciphering the PBMC proteome as we provide a deep insight by identifying 4129 proteins and 25503 peptides. The identified proteome defines the scope that enables PBMCs to be characterised as cellular major biomarker pool within the blood organ. 1. Introduction Peripheral blood mononuclear cells (PBMCs) constitute the cellular part of the blood organ containing all blood cells with a round nucleus. PBMCs are mainly comprised of monocytes, T cells, B cells, natural killer (NK) cells, and dendritic cells. Thus, the PBMCs contain different cell types that play important roles in the immune system monitoring immune-relevant events and respond in an inflammatory manner [1]. In recent years PBMCs have received growing attention as surrogate markers of several diseases. For example, in vitro data describe the response in PBMCs upon contact with diseased cells [2]. PBMCs can be obtained relatively easy from routinely collected blood samples, and therefore they provide direct access to physiologically relevant (immune) proteins without the well-known analytical difficulties of native human plasma originating from the presence of highly abundant proteins [3]. So far, most Omics studies utilising PBMCs were transcriptional profiling experiments in the context of inflammatory (e.g., preeclampsia, rheumatoid arthritis, and chronic pancreatitis) and malignant (e.g., chronic lymphocytic leukaemia and renal cell carcinoma) diseases [4–8]. Although these studies revealed a number of

References

[1]  V. J. Haudek-Prinz, P. Klepeisz, A. Slany et al., “Proteome signatures of inflammatory activated primary human peripheral blood mononuclear cells,” Journal of Proteomics, vol. 76, pp. 150–162, 2012.
[2]  M. Nowak, M. Klink, E. Glowacka et al., “Production of cytokines during interaction of peripheral blood mononuclear cells with autologous ovarian cancer cells or benign ovarian tumour cells,” Scandinavian Journal of Immunology, vol. 71, no. 2, pp. 91–98, 2010.
[3]  N. L. Anderson and N. G. Anderson, “The human plasma proteome: history, character, and diagnostic prospects,” Molecular & Cellular Proteomics, vol. 1, no. 11, pp. 845–867, 2002.
[4]  C.-J. Sun, L. Zhang, and W.-Y. Zhang, “Gene expression profiling of maternal blood in early onset severe preeclampsia: identification of novel biomarkers,” Journal of Perinatal Medicine, vol. 37, no. 6, pp. 609–616, 2009.
[5]  C. J. Edwards, J. L. Feldman, J. Beech et al., “Molecular profile of peripheral blood mononuclear cells from patients with rheumatoid arthritis,” Molecular Medicine, vol. 13, no. 1-2, pp. 40–58, 2007.
[6]  M. Bluth, Y.-Y. Lin, H. Zhang, D. Viterbo, and M. Zenilman, “Use of gene expression profiles in cells of peripheral blood to identify new molecular markers of acute pancreatitis,” Archives of Surgery, vol. 143, no. 3, pp. 227–233, 2008.
[7]  A. R. Whitney, M. Diehn, S. J. Popper et al., “Individuality and variation in gene expression patterns in human blood,” Proceedings of the National Academy of Sciences of the United States of America, vol. 100, no. 4, pp. 1896–1901, 2003.
[8]  N. C. Twine, J. A. Stover, B. Marshall et al., “Disease-associated expression profiles in peripheral blood mononuclear cells from patients with advanced renal cell carcinoma,” Cancer Research, vol. 63, no. 18, pp. 6069–6075, 2003.
[9]  A. Belle, A. Tanay, L. Bitincka, R. Shamir, and E. K. O'Shea, “Quantification of protein half-lives in the budding yeast proteome,” Proceedings of the National Academy of Sciences of the United States of America, vol. 103, no. 35, pp. 13004–13009, 2006.
[10]  D. Vergara, F. Chiriacò, R. Acierno, and M. Maffia, “Proteomic map of peripheral blood mononuclear cells,” Proteomics, vol. 8, no. 10, pp. 2045–2051, 2008.
[11]  V. J. Haudek, A. Slany, N. C. Gundacker, H. Wimmer, J. Drach, and C. Gerner, “Proteome maps of the main human peripheral blood constituents,” Journal of Proteome Research, vol. 8, no. 8, pp. 3834–3843, 2009.
[12]  L. Wang, Y. Dai, S. Qi et al., “Comparative proteome analysis of peripheral blood mononuclear cells in systemic lupus erythematosus with iTRAQ quantitative proteomics,” Rheumatology International, vol. 32, no. 3, pp. 585–593, 2012.
[13]  G. Maccarrone, C. Rewerts, M. Lebar, C. W. Turck, and D. M. de Souza, “Proteome profiling of peripheral mononuclear cells from human blood,” Proteomics, vol. 13, no. 5, pp. 893–897, 2013.
[14]  L. Dayon, A. Hainard, V. Licker et al., “Relative quantification of proteins in human cerebrospinal fluids by MS/MS using 6-plex isobaric tags,” Analytical Chemistry, vol. 80, no. 8, pp. 2921–2931, 2008.
[15]  T. Farrah, E. W. Deutsch, G. S. Omenn et al., “A high-confidence human plasma proteome reference set with estimated concentrations in Peptideatlas,” Molecular & Cellular Proteomics, vol. 10, no. 9, 2011.
[16]  K. Kuhn, C. Baumann, J. Tommassen, and T. Prinz, “TMT labelling for the quantitative analysis of adaptive responses in the meningococcal proteome,” Methods in Molecular Biology, vol. 799, pp. 127–141, 2012.
[17]  D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists,” Nucleic Acids Research, vol. 37, no. 1, pp. 1–13, 2009.
[18]  D. W. Huang, B. T. Sherman, and R. A. Lempicki, “Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources,” Nature Protocols, vol. 4, no. 1, pp. 44–57, 2009.
[19]  V. Rachakonda, T. H. Pan, and W. D. Le, “Biomarkers of neurodegenerative disorders: how good are they?” Cell Research, vol. 14, no. 5, pp. 349–358, 2004.
[20]  L. Dayon, B. Sonderegger, and M. Kussmann, “Combination of gas-phase fractionation and ms3 acquisition modes for relative protein quantification with isobaric tagging,” Journal of Proteome Research, vol. 11, no. 10, pp. 5081–5089, 2012.
[21]  J. Muntel, M. Hecker, and D. Becher, “An exclusion list based label-free proteome quantification approach using an LTQ Orbitrap,” Rapid Communications in Mass Spectrometry, vol. 26, no. 6, pp. 701–709, 2012.
[22]  M. Rucevic, D. Hixson, and D. Josic, “Mammalian plasma membrane proteins as potential biomarkers and drug targets,” Electrophoresis, vol. 32, no. 13, pp. 1549–1564, 2011.
[23]  M. Pellicanò, M. Bulati, S. Buffa et al., “Systemic immune responses in Alzheimer's disease: in vitro mononuclear cell activation and cytokine production,” Journal of Alzheimer's Disease, vol. 21, no. 1, pp. 181–192, 2010.
[24]  O. C. Maes, H. M. Schipper, H. M. Chertkow, and E. Wang, “Methodology for discovery of Alzheimer's disease blood-based biomarkers,” Journals of Gerontology A, vol. 64, no. 6, pp. 636–645, 2009.
[25]  C. Onore, A. Enstrom, P. Krakowiak et al., “Decreased cellular IL-23 but not IL-17 production in children with autism spectrum disorders,” Journal of Neuroimmunology, vol. 216, no. 1-2, pp. 126–129, 2009.
[26]  G. Nardo, S. Pozzi, M. Pignataro et al., “Amyotrophic lateral sclerosis multiprotein biomarkers in peripheral blood mononuclear cells,” PLoS ONE, vol. 6, no. 10, Article ID e25545, 2011.
[27]  M. Shipkova and E. Wieland, “Peripheral biomarkers as a complementary tool to TDM for individualizing immunosuppression in transplantation medicine. 3.5. Surface markers of lymphocyte activation and markers of cell proliferation,” Clinica Chimica Acta, vol. 413, no. 17-18, pp. 1338–1349, 2012.
[28]  H. Zola, B. Swart, A. Banham et al., “CD molecules 2006—human cell differentiation molecules,” Journal of Immunological Methods, vol. 319, no. 1-2, pp. 1–5, 2007.
[29]  A. Murashima, Y. Takasaki, M. Ohgaki, H. Hashimoto, T. Shirai, and S. Hirose, “Activated peripheral blood mononuclear cells detected by murine monoclonal antibodies to proliferating cell nuclear antigen in active lupus patients,” Journal of Clinical Immunology, vol. 10, no. 1, pp. 28–37, 1990.
[30]  G. C. McAlister, E. L. Huttlin, W. Haas et al., “Increasing the multiplexing capacity of tmts using reporter ion isotopologues with isobaric masses,” Analytical Chemistry, vol. 84, no. 17, pp. 7469–7478, 2012.
[31]  H. L. Byers, J. Campbell, P. van Ulsen et al., “Candidate verification of iron-regulated Neisseria meningitidis proteins using isotopic versions of tandem mass tags (TMT) and single reaction monitoring,” Journal of Proteomics, vol. 73, no. 2, pp. 231–239, 2009.

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