The pancreas is a large glandular organ with mixed exocrine and endocrine functions, located in the abdominal cavity behind the stomach. The endocrine portion, 1-2 % of its total volume, is represented by islets of Langerhans and plays a significant role in the pathophysiology of diabetes. The islets mainly contain β cells, which produce and release the hormonal protein insulin into the bloodstream in order to reduce glucose concentrations in the blood [1]. Dysfunction of this regulatory mechanism can lead to the development of type 2 diabetes mellitus (T2DM), a chronic metabolic disorder characterized by increased glucose levels in the blood and caused by either the resistance to insulin or the inability of β cells to produce (enough) insulin, or a combination of both [2,3]. The number of people affected by T2DM is growing world-wide, driven by the spread of obesity. The absence of characteristic symptoms complicates early diagnosis of the disease and can lead to premature death if left untreated [4]. For all these reasons, in order to get a better understanding of the complex pathophysiology leading to the onset of T2DM and its progression, elucidation of the molecular mechanisms underlying the disorder is paramount. This PhD thesis aims to characterize, at the molecular level, the pancreas, focusing particularly on the islet of Langerhans and on their involvement in type two diabetes. cells failure, in type two diabetes, is caused by several factors: environmental factors, such as high-fat diets and sedentary lifestyle, and genetic predisposition [5]. Individuals with high fasting levels of plasma free fatty acids (FFAs) have an elevated risk of developing T2DM [6]. In fact, prolonged exposure to FFAs impairs insulin secretion in vivo and in vitro [7,8] inducing β cells death [9]. Palmitate is the most common saturated FFA in human plasma and it has been used in vitro studies on isolated islets or β cells lines to investigate the mechanisms of lipotoxicity. Prolonged exposure to palmitate may promote the inhibition of insulin transcription [10], the induction of ER stress in β cells [11,12], the production of reactive oxygen species (ROS) [13], and ceramides [14] and finally to cells death. Some evidence suggest that palmitate could induce these effects through defects in mitochondrial function [13,15]. Nowadays, the relationship of lipotoxicity mechanisms to mitochondrial function is not well understood and remain under investigation. As far as mitochondria concerns, they play a central role in coupling glucose metabolism to insulin secretion. Mitochondrial dysfunction impairs glucose stimulated insulin secretion and may promote β cells death. Moreover, mitochondria are the major source of ROS but also the target of their damaging effects. An overproduction of free radicals in β cells by the mitochondrial respiratory chain produces peroxidation of mitochondrial membrane [16], impairment of ATP production [16] and damage of mitochondrial DNA [17] which regulates oxidative phosphorylation process involved in the insulin secretion from pancreatic β cells. The molecular mechanisms by which palmitate affects β cells function and survival, have been studied using different approaches such as RNA-based studies [18] and proteomic analysis [19]. Very recently, Cnop et al. [18], mapped the transcriptome of human islets of Langherans, by using RNA-sequencinq (RNA-seq), following a 48h exposure to the saturated FFA palmitate and suggesting novel mechanisms of palmitate-induced β cells dysfunction and death. Little is known about mitochondrial responses to induced-palmitate stress and about the mechanisms through which glucagon-like peptide-1 (GLP-1) exerts its potential protective effect in β cells mitochondrial dysfunction. Brun et al. [20], using pharmacological and siRNA approaches, investigated the mitochondrial responses in isolated INS-1E cells mitochondria preparations exposed to different stressors: glucose, fatty acids and oxidative stress. They suggested a selective modification in expression levels of energy sensors and mitochondrial carriers after these different stress conditions. As far as the proteomic approach concerns, only one paper showed the changes of INS-1E mitochondrial proteome after stress induced by high glucose exposure [21]. The purpose of the first part of this thesis was to investigate, for the first time, the lipotoxic effect of palmitate on mitochondria from rat INS-1E cells in the presence and in the absence of GLP-1 by using proteomics and metabolomics approaches. A different expression of mitochondrial proteins was evaluated by using two-dimensional electrophoresis (2-DE) coupled to tandem mass spectrometry (MS/MS) and quantitative shotgun analysis. The use of 2-DE allowed to validate shot-gun results and to overcome the limit of this technique by evaluating potential transformations which could occur in mitochondrial proteins such as post-translational modifications and protein degradation. Moreover, the metabolomic differences targeting aminoacids and carnitines, since they are related to the mitochondrial metabolism and activity, were measured. The study of mitochondrial alteration in rat INS-1E cells after treatment with palmitate and/or GLP- constitutes an important starting point before moving to the study of human cells and towards a better understanding of mitochondrial dysfunction in the context of type two diabetes. The second part of this thesis focused on the development of ultra-high resolution mass spectrometry imaging methods for the analysis of proteins in mouse and human pancreas tissues. The ability of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to simultaneously record the distributions of hundreds of molecules in tissue makes it a powerful discovery method for molecular pathology. MALDI-MSI combines the chemical specificity of modern mass spectrometry with the imaging capabilities of microscopy; it allows a highly multiplexed and untargeted analysis of biomolecular ions and enables their localization within the tissue section [22]. In clinical applications and diagnostics MALDI-MSI has been used to analyse a large variety of analyte classes, such as xenobiotics [23], metabolites [24], lipids [25,26], N-linked glycans [27,28], peptides [29], and proteins [30,31]. Sample preparation is critical to the success of a MALDI-MSI experiment, and must be optimized prior to any clinical investigation. Several reports on method development for protein analysis from different tissues have been published, and indicate that the optimum sample preparation method may be tissue type and application specific [32–34]. To date only a few studies have been published for MALDI-MSI of pancreas tissues. The ability of the MALDI-MSI to measure the peptide hormones located in the endocrine and exocrine pancreas was shown [35–37]. Minerva et al. [38,39] reported two different methods for the analysis of endogenous peptides from the pancreases of obese and wild type mice. Another three studies focused on the analysis of proteolytic peptides from pancreas [40–42], one of which compared healthy and type 1 diabetes [42]. Four studies focused on the analysis of intact proteins from pancreas: a 3D MALDI-MSI datasets from mouse pancreas in the mass m/z range 1600-15000 had been registered [43], and three focused on biomarker discovery on pancreas cancer tissue (ductal cancer, pre-neoplastic pancreatic lesions, pancreatic adenocarcinoma and insulinoma) [44–46]. When analysing intact proteins in clinical tissue samples the possibility of post-translational modifications (PTMs) and proteolytic processing must be considered, especially for pancreas tissue which is characterized by rapid post mortem degradation [47]. The analysis of intact proteins allows the identification of any proteoforms by retaining any PTMs or proteolytic processing, and which can be clinically very relevant. Poté et al. [48] have demonstrated that a specific protein acetylation was indicative of microvascular invasion in hepatocellular carcinoma, and a specific truncation of thymosin beta 4 has been found to be associated with stromal activation in breast cancer and patient survival in malignant melanoma [49]. MALDI-MSI of intact proteins has been performed predominantly using time-of-flight (TOF) based mass spectrometers, operated in linear mode [50,51]. Linear MALDI-TOF systems provide limited resolving power and mass accuracy (50-200 ppm) [52], which complicates protein identity assignments by mass matching MSI datasets with liquid chromatography (LC) MS/MS-based protein identifications. Recently Fourier transform ion cyclotron resonance (FTICR) mass spectrometry has been adapted for MALDI profiling [53–55] and MALDI-MSI [56]. MALDI-FTICR-MSI provides the high mass accuracy and high resolving power required to analyse intact proteins (≤ 17.000 m/z) with isotopic resolution, and to assign protein identities with additional confidence [56]. In the current work the workflows for the MSI analysis of intact proteins directly from pancreas tissue by MALDI-TOF-MS and MALDI-FTICR-MS had been developed. Method development, with special emphasis on sample preparation (e.g., tissue washing, matrix choice, MALDI-matrix deposition) was performed on mouse pancreas tissues. Afterward, the method optimization was extended to the analysis of endogenous peptides. The embedding of the tissue in a supporting material allows easy handling and precise microtoming of sections. In clinical laboratories, for histological applications, tissues cut on cryostat microtomes are usually embedded in the optimal cutting temperature (OCT) polymer. However, care should be taken to avoid contamination of the tissue sections with OCT, because its components can lead to ion suppression during mass spectrometry analysis by MALDI-TOF-MS. Recently, there is evidence [57] that it is feasible to analyse lipids from tissues embedded in OCT compound by MALDI-MSI after extensive tissue washing using water-based solutions. Also Green-Mitchell et al. [42] in the study on on-tissue reduction of insulin, used OCT embedded pancreas tissues. Seeley et al. [58] in a review of 2008 also reported that, after washing steps to remove OCT, “[…] spectra obtained from OCT-embedded samples are virtually identical to those obtained from fresh frozen tissue”. However, most of the studies principally showed how the PEG contamination in the spectra is reduced after removing OCT compound with suitable washing steps [34,59], but any of them showed the comparison between data from OCT-embedded and non-embedded tissues after the application of the same sample preparation procedure. On this basis, here an in-depth comparison between mass spectrometry imaging data obtained from OCT-embedded and non-embedded tissues was performed. The optimized methods were applied to a small set of human pancreas samples (3x T2DM and 3x control), so that small endocrine compartments (islets of Langerhans) may be analysed in control and pathological tissues. In particular, human pancreas samples were collected from the same individual both OCT-embedded and non-embedded. References [1] Gittes, G., Developmental biology of the pancreas: a comprehensive review. Developmental biology 2009, 326, 4–35. [2] Halban, P., Polonsky, K., Bowden, D., Hawkins, M., et al., β-cell failure in type 2 diabetes: postulated mechanisms and prospects for prevention and treatment. Diabetes care 2014, 37, 1751–8. [3] Kahn, B., Flier, J., Obesity and insulin resistance. Journal of Clinical Investigation 2000, 106, 473–481. [4] Olokoba, A., Obateru, O., Olokoba, L., Type 2 diabetes mellitus: a review of current trends. Oman medical journal 2012, 27, 269–73. [5] Kahn, S., Hull, R., Utzschneider, K., Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 2006, 444, 840–846. [6] Wang, L., Folsom, A.R., Zheng, Z.-J., Pankow, J.S., Eckfeldt, J.H., Plasma fatty acid composition and incidence of diabetes in middle-aged adults: the Atherosclerosis Risk in Communities (ARIC) Study. The American Journal of Clinical Nutrition 2003, 78, 91–98. [7] Kashyap, Belfort, Gastaldelli, Pratipanawatr, et al., A Sustained Increase in Plasma Free Fatty Acids Impairs Insulin Secretion in Nondiabetic Subjects Genetically Predisposed to Develop Type 2 Diabetes. Diabetes 2003, 52, 2461–2474. [8] SAKO, GRILL, A 48-hour Lipid Infusion in the Rat Time-Dependently Inhibits Glucose-Induced Insulin Secretion and B Cell Oxidation Through a Process Likely Coupled to Fatty Acid Oxidation. Endocrinology 1990, 127, 1580–1589. [9] Cnop, Hannaert, Hoorens, Eizirik, Pipeleers, Inverse Relationship Between Cytotoxicity of Free Fatty Acids in Pancreatic Islet Cells and Cellular Triglyceride Accumulation. Diabetes 2001, 50, 1771–1777. [10] Kelpe, C., Moore, P., Parazzoli, S., Wicksteed, B., et al., Palmitate Inhibition of Insulin Gene Expression Is Mediated at the Transcriptional Level via Ceramide Synthesis. Journal of Biological Chemistry 2003, 278, 30015–30021. [11] Laybutt, Preston, Åkerfeldt, Kench, et al., Endoplasmic reticulum stress contributes to beta cell apoptosis in type 2 diabetes. Diabetologia 2007, 50, 752–763. [12] Cunha, D., Hekerman, P., Ladrière, L., Bazarra-Castro, A., et al., Initiation and execution of lipotoxic ER stress in pancreatic β-cells. Journal of Cell Science 2008, 121, 2308–2318. [13] Carlsson, Borg, H., Welsh, Sodium Palmitate Induces Partial Mitochondrial Uncoupling and Reactive Oxygen Species in Rat Pancreatic Islets in Vitro 1 1999. [14] Shimabukuro, M., Higa, M., Zhou, Y.-T., Wang, M.-Y., et al., Lipoapoptosis in Beta-cells of Obese Prediabeticfa/fa Rats ROLE OF SERINE PALMITOYLTRANSFERASE OVEREXPRESSION. Journal of Biological Chemistry 1998, 273, 32487–32490. [15] Lowell, B., Shulman, G., Mitochondrial Dysfunction and Type 2 Diabetes. Science 2005, 307, 384–387. [16] Li, N., Frigerio, F., Maechler, P., The sensitivity of pancreatic β-cells to mitochondrial injuries triggered by lipotoxicity and oxidative stress. Biochemical Society Transactions 2008, 36, 930–934. [17] Chen, X., Wang, X., Kaufman, B.A., Butow, R.A., Aconitase Couples Metabolic Regulation to Mitochondrial DNA Maintenance. Science 2005, 307, 714–717. [18] Cnop, M., Abdulkarim, B., Bottu, G., Cunha, D., et al., RNA Sequencing Identifies Dysregulation of the Human Pancreatic Islet Transcriptome by the Saturated Fatty Acid Palmitate. Nestle Nutr Works Se 2014, 63, 1978–1993. [19] Maris, M., Robert, S., Waelkens, E., Derua, R., et al., Role of the saturated nonesterified fatty acid palmitate in beta cell dysfunction. Journal of proteome research 2012, 12, 347–62. [20] Brun, He, K., Lupi, Boehm, The diabetes-linked transcription factor Pax4 is expressed in human pancreatic islets and is activated by mitogens and GLP-1 2008. [21] Ahmed, M., Muhammed, S., Kessler, B., Salehi, A., Mitochondrial proteome analysis reveals altered expression of voltage dependent anion channels in pancreatic β-cells exposed to high glucose. Islets 2010, 2, 283–292. [22] McDonnell, L., Heeren, R., Imaging mass spectrometry. Mass Spectrometry Reviews 2007, 26, 606–643. [23] Trim, P., Henson, C., Avery, J., McEwen, A., et al., Matrix-assisted laser desorption/ionization-ion mobility separation-mass spectrometry imaging of vinblastine in whole body tissue sections. Analytical chemistry 2008, 80, 8628–34. [24] Esteve, C., Tolner, E., Shyti, R., van den Maagdenberg, A., McDonnell, L., Mass spectrometry imaging of amino neurotransmitters: a comparison of derivatization methods and application in mouse brain tissue. Metabolomics : Official journal of the Metabolomic Society 2016, 12, 30. [25] Ly, A., Schöne, C., Becker, M., Rattke, J., et al., High-resolution MALDI mass spectrometric imaging of lipids in the mammalian retina. Histochemistry and Cell Biology 2014, 143, 453–62. [26] Sjövall, P., Lausmaa, J., Johansson, B., Mass spectrometric imaging of lipids in brain tissue. Analytical Chemistry 2004, 76, 4271–4278. [27] Holst, S., Heijs, B., de Haan, N., van Zeijl, R., et al., Linkage-Specific in Situ Sialic Acid Derivatization for N-Glycan Mass Spectrometry Imaging of Formalin-Fixed Paraffin-Embedded Tissues. Analytical chemistry 2016, 88, 5904–13. [28] Powers, T., Neely, B., Shao, Y., Tang, H., et al., MALDI imaging mass spectrometry profiling of N-glycans in formalin-fixed paraffin embedded clinical tissue blocks and tissue microarrays. PloS one 2014, 9, e106255. [29] Heijs, B., Carreira, R., Tolner, E., de Ru, A., et al., Comprehensive Analysis of the Mouse Brain Proteome Sampled in Mass Spectrometry Imaging. Analytical Chemistry 2015, 87. [30] Chaurand, P., Latham, J., Lane, K., Mobley, J., et al., Imaging mass spectrometry of intact proteins from alcohol-preserved tissue specimens: bypassing formalin fixation. Journal of proteome research 2008, 7, 3543–55. [31] Yang, J., Caprioli, R., Matrix Precoated Targets for Direct Lipid Analysis and Imaging of Tissue. Anal Chem 2013, 85, 2907–2912. [32] Seeley, E., Oppenheimer, S., Mi, D., Chaurand, P., Caprioli, R., Enhancement of protein sensitivity for MALDI imaging mass spectrometry after chemical treatment of tissue sections. J Am Soc Mass Spectr 2008, 19, 1069–1077. [33] Martin-Lorenzo, M., Balluff, B., Sanz-Maroto, A., Zeijl, R.J.M., et al., 30μm spatial resolution protein MALDI MSI: In-depth comparison of five sample preparation protocols applied to human healthy and atherosclerotic arteries. Journal of Proteomics 2014, 108, 465–468. [34] Enthaler, B., Bussmann, T., Pruns, J., Rapp, C., et al., Influence of various on‐tissue washing procedures on the entire protein quantity and the quality of matrix‐assisted laser desorption/ionization spectra. Rapid Communications in Mass Spectrometry 2013, 27, 878–884. [35] Ergin, B., Meding, S., Langer, R., Kap, M., et al., Proteomic Analysis of PAXgene-Fixed Tissues. Journal of Proteome Research 2010, 9, 5188–5196. [36] Deininger, S.-O., Cornett, D., Paape, R., Becker, M., et al., Normalization in MALDI-TOF imaging datasets of proteins: practical considerations. Analytical and Bioanalytical Chemistry 2011, 401, 167–181. [37] McDonnell, L., van Remoortere, A., de Velde, N., van Zeijl, R., Deelder, A., Imaging mass spectrometry data reduction: automated feature identification and extraction. Journal of the American Society for Mass Spectrometry 2010, 21, 1969–78. [38] Minerva, Boonen, Menschaert, Landuyt, et al., Linking mass spectrometric imaging and traditional peptidomics: a validation in the obese mouse model. Analytical chemistry 2011, 83, 7682–91. [39] Minerva, L., Clerens, S., Baggerman, G., Arckens, L., Direct profiling and identification of peptide expression differences in the pancreas of control and ob/ob mice by imaging mass spectrometry. Proteomics 2008, 8, 3763–74. [40] Djidja, M.-C., Claude, E., Snel, M., Scriven, P., et al., MALDI-Ion Mobility Separation-Mass Spectrometry Imaging of Glucose-Regulated Protein 78 kDa (Grp78) in Human Formalin-Fixed, Paraffin-Embedded Pancreatic Adenocarcinoma Tissue Sections. Journal of Proteome Research 2009, 8, 4876–4884. [41] Casadonte, R., Kriegsmann, M., Zweynert, F., Friedrich, K., et al., Imaging mass spectrometry to discriminate breast from pancreatic cancer metastasis in formalin‐fixed paraffin‐embedded tissues. PROTEOMICS 2014, 14, 956–964. [42] Green-Mitchell, S., Cazares, L., Semmes, Nadler, J., Nyalwidhe, J., On-tissue identification of insulin: in situ reduction coupled with mass spectrometry imaging. Proteomics. Clinical applications 2011, 5, 448–53. [43] Oetjen, J., Veselkov, K., Watrous, J., McKenzie, J., et al., Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry. GigaScience 2015, 4, 20. [44] Walch, A., Rauser, S., Deininger, S.-O., Höfler, H., MALDI imaging mass spectrometry for direct tissue analysis: a new frontier for molecular histology. Histochemistry and cell biology 2008, 130, 421–434. [45] Grüner, B., Hahne, H., Mazur, P., Trajkovic-Arsic, M., et al., MALDI imaging mass spectrometry for in situ proteomic analysis of preneoplastic lesions in pancreatic cancer. PloS one 2012, 7, e39424. [46] McDonnell, L., Corthals, G., Willems, S., van Remoortere, A., et al., Peptide and protein imaging mass spectrometry in cancer research. J Proteomics 2010, 73, 1921–1944. [47] Scholz, B., Sköld, K., Kultima, K., Fernandez, C., et al., Impact of temperature dependent sampling procedures in proteomics and peptidomics--a characterization of the liver and pancreas post mortem degradome. Mol. Cell Proteomics 2011, 10, M900229MCP200. [48] Poté, N., Alexandrov, T., Le Faouder, J., Laouirem, S., et al., Imaging mass spectrometry reveals modified forms of histone H4 as new biomarkers of microvascular invasion in hepatocellular carcinomas. Hepatology 2013, 58, 983–94. [49] Hardesty, W.M., Kelley, M.C., Mi, D., Low, R.L., Caprioli, R.M., Protein signatures for survival and recurrence in metastatic melanoma. J Proteomics 2011, 74, 1002–14. [50] Signor, L., Boeri Erba, E., Matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometric analysis of intact proteins larger than 100 kDa. Journal of visualized experiments : JoVE 2013, e50635. [51] Riaz, S., Alam, S., Akhtar, Proteomic identification of human serum biomarkers in diabetes mellitus type 2. Journal of pharmaceutical and biomedical analysis 2010, 51, 1103–7. [52] Lewis, K., Wei, J., Siuzdak, G., Encyclopedia of Analytical Chemistry, 2006. [53] Nicolardi, S., Bogdanov, B., Deelder, A., Palmblad, M., van der Burgt, Y., Developments in FTICR-MS and Its Potential for Body Fluid Signatures. International Journal of Molecular Sciences 2015, 16, 27133–27144. [54] Fleurbaaij, F., Kraakman, M., Claas, E., Knetsch, C., et al., TypingPseudomonas aeruginosaIsolates with Ultrahigh Resolution MALDI-FTICR Mass Spectrometry. Analytical Chemistry 2016, 88, 5996–6003. [55] Nicolardi, S., Switzar, L., Deelder, A., Palmblad, M., van der Burgt, Y., Top-Down MALDI-In-Source Decay-FTICR Mass Spectrometry of Isotopically Resolved Proteins. Analytical Chemistry 2015, 87, 3429–3437. [56] Spraggins, J., Rizzo, D., Moore, J., Rose, K., et al., MALDI FTICR IMS of Intact Proteins: Using Mass Accuracy to Link Protein Images with Proteomics Data. Journal of The American Society for Mass Spectrometry 2015, 26, 974–985. [57] Angel, P., Spraggins, J., Baldwin, Caprioli, R., Enhanced sensitivity for high spatial resolution lipid analysis by negative ion mode matrix assisted laser desorption ionization imaging mass spectrometry. Analytical chemistry 2012, 84, 1557–64. [58] Seeley, E., Caprioli, R., Molecular imaging of proteins in tissues by mass spectrometry. Proceedings of the National Academy of Sciences 2008, 105, 18126–18131. [59] Zhang, W., Sakashita, S., Taylor, P., Tsao, M.S., Moran, M.F., Comprehensive proteome analysis of fresh frozen and optimal cutting temperature (OCT) embedded primary non-small cell lung carcinoma by LC-MS/MS. Methods 2015, 81, 50–5.
Piga, I. (2016). Proteomics tools and mass spectrometry imaging techniques for the molecular characterization of pancreas.
Proteomics tools and mass spectrometry imaging techniques for the molecular characterization of pancreas
PIGA, ISABELLA
2016-01-01
Abstract
The pancreas is a large glandular organ with mixed exocrine and endocrine functions, located in the abdominal cavity behind the stomach. The endocrine portion, 1-2 % of its total volume, is represented by islets of Langerhans and plays a significant role in the pathophysiology of diabetes. The islets mainly contain β cells, which produce and release the hormonal protein insulin into the bloodstream in order to reduce glucose concentrations in the blood [1]. Dysfunction of this regulatory mechanism can lead to the development of type 2 diabetes mellitus (T2DM), a chronic metabolic disorder characterized by increased glucose levels in the blood and caused by either the resistance to insulin or the inability of β cells to produce (enough) insulin, or a combination of both [2,3]. The number of people affected by T2DM is growing world-wide, driven by the spread of obesity. The absence of characteristic symptoms complicates early diagnosis of the disease and can lead to premature death if left untreated [4]. For all these reasons, in order to get a better understanding of the complex pathophysiology leading to the onset of T2DM and its progression, elucidation of the molecular mechanisms underlying the disorder is paramount. This PhD thesis aims to characterize, at the molecular level, the pancreas, focusing particularly on the islet of Langerhans and on their involvement in type two diabetes. cells failure, in type two diabetes, is caused by several factors: environmental factors, such as high-fat diets and sedentary lifestyle, and genetic predisposition [5]. Individuals with high fasting levels of plasma free fatty acids (FFAs) have an elevated risk of developing T2DM [6]. In fact, prolonged exposure to FFAs impairs insulin secretion in vivo and in vitro [7,8] inducing β cells death [9]. Palmitate is the most common saturated FFA in human plasma and it has been used in vitro studies on isolated islets or β cells lines to investigate the mechanisms of lipotoxicity. Prolonged exposure to palmitate may promote the inhibition of insulin transcription [10], the induction of ER stress in β cells [11,12], the production of reactive oxygen species (ROS) [13], and ceramides [14] and finally to cells death. Some evidence suggest that palmitate could induce these effects through defects in mitochondrial function [13,15]. Nowadays, the relationship of lipotoxicity mechanisms to mitochondrial function is not well understood and remain under investigation. As far as mitochondria concerns, they play a central role in coupling glucose metabolism to insulin secretion. Mitochondrial dysfunction impairs glucose stimulated insulin secretion and may promote β cells death. Moreover, mitochondria are the major source of ROS but also the target of their damaging effects. An overproduction of free radicals in β cells by the mitochondrial respiratory chain produces peroxidation of mitochondrial membrane [16], impairment of ATP production [16] and damage of mitochondrial DNA [17] which regulates oxidative phosphorylation process involved in the insulin secretion from pancreatic β cells. The molecular mechanisms by which palmitate affects β cells function and survival, have been studied using different approaches such as RNA-based studies [18] and proteomic analysis [19]. Very recently, Cnop et al. [18], mapped the transcriptome of human islets of Langherans, by using RNA-sequencinq (RNA-seq), following a 48h exposure to the saturated FFA palmitate and suggesting novel mechanisms of palmitate-induced β cells dysfunction and death. Little is known about mitochondrial responses to induced-palmitate stress and about the mechanisms through which glucagon-like peptide-1 (GLP-1) exerts its potential protective effect in β cells mitochondrial dysfunction. Brun et al. [20], using pharmacological and siRNA approaches, investigated the mitochondrial responses in isolated INS-1E cells mitochondria preparations exposed to different stressors: glucose, fatty acids and oxidative stress. They suggested a selective modification in expression levels of energy sensors and mitochondrial carriers after these different stress conditions. As far as the proteomic approach concerns, only one paper showed the changes of INS-1E mitochondrial proteome after stress induced by high glucose exposure [21]. The purpose of the first part of this thesis was to investigate, for the first time, the lipotoxic effect of palmitate on mitochondria from rat INS-1E cells in the presence and in the absence of GLP-1 by using proteomics and metabolomics approaches. A different expression of mitochondrial proteins was evaluated by using two-dimensional electrophoresis (2-DE) coupled to tandem mass spectrometry (MS/MS) and quantitative shotgun analysis. The use of 2-DE allowed to validate shot-gun results and to overcome the limit of this technique by evaluating potential transformations which could occur in mitochondrial proteins such as post-translational modifications and protein degradation. Moreover, the metabolomic differences targeting aminoacids and carnitines, since they are related to the mitochondrial metabolism and activity, were measured. The study of mitochondrial alteration in rat INS-1E cells after treatment with palmitate and/or GLP- constitutes an important starting point before moving to the study of human cells and towards a better understanding of mitochondrial dysfunction in the context of type two diabetes. The second part of this thesis focused on the development of ultra-high resolution mass spectrometry imaging methods for the analysis of proteins in mouse and human pancreas tissues. The ability of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to simultaneously record the distributions of hundreds of molecules in tissue makes it a powerful discovery method for molecular pathology. MALDI-MSI combines the chemical specificity of modern mass spectrometry with the imaging capabilities of microscopy; it allows a highly multiplexed and untargeted analysis of biomolecular ions and enables their localization within the tissue section [22]. In clinical applications and diagnostics MALDI-MSI has been used to analyse a large variety of analyte classes, such as xenobiotics [23], metabolites [24], lipids [25,26], N-linked glycans [27,28], peptides [29], and proteins [30,31]. Sample preparation is critical to the success of a MALDI-MSI experiment, and must be optimized prior to any clinical investigation. Several reports on method development for protein analysis from different tissues have been published, and indicate that the optimum sample preparation method may be tissue type and application specific [32–34]. To date only a few studies have been published for MALDI-MSI of pancreas tissues. The ability of the MALDI-MSI to measure the peptide hormones located in the endocrine and exocrine pancreas was shown [35–37]. Minerva et al. [38,39] reported two different methods for the analysis of endogenous peptides from the pancreases of obese and wild type mice. Another three studies focused on the analysis of proteolytic peptides from pancreas [40–42], one of which compared healthy and type 1 diabetes [42]. Four studies focused on the analysis of intact proteins from pancreas: a 3D MALDI-MSI datasets from mouse pancreas in the mass m/z range 1600-15000 had been registered [43], and three focused on biomarker discovery on pancreas cancer tissue (ductal cancer, pre-neoplastic pancreatic lesions, pancreatic adenocarcinoma and insulinoma) [44–46]. When analysing intact proteins in clinical tissue samples the possibility of post-translational modifications (PTMs) and proteolytic processing must be considered, especially for pancreas tissue which is characterized by rapid post mortem degradation [47]. The analysis of intact proteins allows the identification of any proteoforms by retaining any PTMs or proteolytic processing, and which can be clinically very relevant. Poté et al. [48] have demonstrated that a specific protein acetylation was indicative of microvascular invasion in hepatocellular carcinoma, and a specific truncation of thymosin beta 4 has been found to be associated with stromal activation in breast cancer and patient survival in malignant melanoma [49]. MALDI-MSI of intact proteins has been performed predominantly using time-of-flight (TOF) based mass spectrometers, operated in linear mode [50,51]. Linear MALDI-TOF systems provide limited resolving power and mass accuracy (50-200 ppm) [52], which complicates protein identity assignments by mass matching MSI datasets with liquid chromatography (LC) MS/MS-based protein identifications. Recently Fourier transform ion cyclotron resonance (FTICR) mass spectrometry has been adapted for MALDI profiling [53–55] and MALDI-MSI [56]. MALDI-FTICR-MSI provides the high mass accuracy and high resolving power required to analyse intact proteins (≤ 17.000 m/z) with isotopic resolution, and to assign protein identities with additional confidence [56]. In the current work the workflows for the MSI analysis of intact proteins directly from pancreas tissue by MALDI-TOF-MS and MALDI-FTICR-MS had been developed. Method development, with special emphasis on sample preparation (e.g., tissue washing, matrix choice, MALDI-matrix deposition) was performed on mouse pancreas tissues. Afterward, the method optimization was extended to the analysis of endogenous peptides. The embedding of the tissue in a supporting material allows easy handling and precise microtoming of sections. In clinical laboratories, for histological applications, tissues cut on cryostat microtomes are usually embedded in the optimal cutting temperature (OCT) polymer. However, care should be taken to avoid contamination of the tissue sections with OCT, because its components can lead to ion suppression during mass spectrometry analysis by MALDI-TOF-MS. Recently, there is evidence [57] that it is feasible to analyse lipids from tissues embedded in OCT compound by MALDI-MSI after extensive tissue washing using water-based solutions. Also Green-Mitchell et al. [42] in the study on on-tissue reduction of insulin, used OCT embedded pancreas tissues. Seeley et al. [58] in a review of 2008 also reported that, after washing steps to remove OCT, “[…] spectra obtained from OCT-embedded samples are virtually identical to those obtained from fresh frozen tissue”. However, most of the studies principally showed how the PEG contamination in the spectra is reduced after removing OCT compound with suitable washing steps [34,59], but any of them showed the comparison between data from OCT-embedded and non-embedded tissues after the application of the same sample preparation procedure. On this basis, here an in-depth comparison between mass spectrometry imaging data obtained from OCT-embedded and non-embedded tissues was performed. The optimized methods were applied to a small set of human pancreas samples (3x T2DM and 3x control), so that small endocrine compartments (islets of Langerhans) may be analysed in control and pathological tissues. In particular, human pancreas samples were collected from the same individual both OCT-embedded and non-embedded. References [1] Gittes, G., Developmental biology of the pancreas: a comprehensive review. Developmental biology 2009, 326, 4–35. [2] Halban, P., Polonsky, K., Bowden, D., Hawkins, M., et al., β-cell failure in type 2 diabetes: postulated mechanisms and prospects for prevention and treatment. Diabetes care 2014, 37, 1751–8. [3] Kahn, B., Flier, J., Obesity and insulin resistance. Journal of Clinical Investigation 2000, 106, 473–481. [4] Olokoba, A., Obateru, O., Olokoba, L., Type 2 diabetes mellitus: a review of current trends. Oman medical journal 2012, 27, 269–73. [5] Kahn, S., Hull, R., Utzschneider, K., Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 2006, 444, 840–846. [6] Wang, L., Folsom, A.R., Zheng, Z.-J., Pankow, J.S., Eckfeldt, J.H., Plasma fatty acid composition and incidence of diabetes in middle-aged adults: the Atherosclerosis Risk in Communities (ARIC) Study. The American Journal of Clinical Nutrition 2003, 78, 91–98. [7] Kashyap, Belfort, Gastaldelli, Pratipanawatr, et al., A Sustained Increase in Plasma Free Fatty Acids Impairs Insulin Secretion in Nondiabetic Subjects Genetically Predisposed to Develop Type 2 Diabetes. Diabetes 2003, 52, 2461–2474. [8] SAKO, GRILL, A 48-hour Lipid Infusion in the Rat Time-Dependently Inhibits Glucose-Induced Insulin Secretion and B Cell Oxidation Through a Process Likely Coupled to Fatty Acid Oxidation. Endocrinology 1990, 127, 1580–1589. [9] Cnop, Hannaert, Hoorens, Eizirik, Pipeleers, Inverse Relationship Between Cytotoxicity of Free Fatty Acids in Pancreatic Islet Cells and Cellular Triglyceride Accumulation. Diabetes 2001, 50, 1771–1777. [10] Kelpe, C., Moore, P., Parazzoli, S., Wicksteed, B., et al., Palmitate Inhibition of Insulin Gene Expression Is Mediated at the Transcriptional Level via Ceramide Synthesis. Journal of Biological Chemistry 2003, 278, 30015–30021. [11] Laybutt, Preston, Åkerfeldt, Kench, et al., Endoplasmic reticulum stress contributes to beta cell apoptosis in type 2 diabetes. Diabetologia 2007, 50, 752–763. [12] Cunha, D., Hekerman, P., Ladrière, L., Bazarra-Castro, A., et al., Initiation and execution of lipotoxic ER stress in pancreatic β-cells. Journal of Cell Science 2008, 121, 2308–2318. [13] Carlsson, Borg, H., Welsh, Sodium Palmitate Induces Partial Mitochondrial Uncoupling and Reactive Oxygen Species in Rat Pancreatic Islets in Vitro 1 1999. [14] Shimabukuro, M., Higa, M., Zhou, Y.-T., Wang, M.-Y., et al., Lipoapoptosis in Beta-cells of Obese Prediabeticfa/fa Rats ROLE OF SERINE PALMITOYLTRANSFERASE OVEREXPRESSION. Journal of Biological Chemistry 1998, 273, 32487–32490. [15] Lowell, B., Shulman, G., Mitochondrial Dysfunction and Type 2 Diabetes. Science 2005, 307, 384–387. [16] Li, N., Frigerio, F., Maechler, P., The sensitivity of pancreatic β-cells to mitochondrial injuries triggered by lipotoxicity and oxidative stress. Biochemical Society Transactions 2008, 36, 930–934. [17] Chen, X., Wang, X., Kaufman, B.A., Butow, R.A., Aconitase Couples Metabolic Regulation to Mitochondrial DNA Maintenance. Science 2005, 307, 714–717. [18] Cnop, M., Abdulkarim, B., Bottu, G., Cunha, D., et al., RNA Sequencing Identifies Dysregulation of the Human Pancreatic Islet Transcriptome by the Saturated Fatty Acid Palmitate. Nestle Nutr Works Se 2014, 63, 1978–1993. [19] Maris, M., Robert, S., Waelkens, E., Derua, R., et al., Role of the saturated nonesterified fatty acid palmitate in beta cell dysfunction. Journal of proteome research 2012, 12, 347–62. [20] Brun, He, K., Lupi, Boehm, The diabetes-linked transcription factor Pax4 is expressed in human pancreatic islets and is activated by mitogens and GLP-1 2008. [21] Ahmed, M., Muhammed, S., Kessler, B., Salehi, A., Mitochondrial proteome analysis reveals altered expression of voltage dependent anion channels in pancreatic β-cells exposed to high glucose. Islets 2010, 2, 283–292. [22] McDonnell, L., Heeren, R., Imaging mass spectrometry. Mass Spectrometry Reviews 2007, 26, 606–643. [23] Trim, P., Henson, C., Avery, J., McEwen, A., et al., Matrix-assisted laser desorption/ionization-ion mobility separation-mass spectrometry imaging of vinblastine in whole body tissue sections. Analytical chemistry 2008, 80, 8628–34. [24] Esteve, C., Tolner, E., Shyti, R., van den Maagdenberg, A., McDonnell, L., Mass spectrometry imaging of amino neurotransmitters: a comparison of derivatization methods and application in mouse brain tissue. Metabolomics : Official journal of the Metabolomic Society 2016, 12, 30. [25] Ly, A., Schöne, C., Becker, M., Rattke, J., et al., High-resolution MALDI mass spectrometric imaging of lipids in the mammalian retina. Histochemistry and Cell Biology 2014, 143, 453–62. [26] Sjövall, P., Lausmaa, J., Johansson, B., Mass spectrometric imaging of lipids in brain tissue. Analytical Chemistry 2004, 76, 4271–4278. [27] Holst, S., Heijs, B., de Haan, N., van Zeijl, R., et al., Linkage-Specific in Situ Sialic Acid Derivatization for N-Glycan Mass Spectrometry Imaging of Formalin-Fixed Paraffin-Embedded Tissues. Analytical chemistry 2016, 88, 5904–13. [28] Powers, T., Neely, B., Shao, Y., Tang, H., et al., MALDI imaging mass spectrometry profiling of N-glycans in formalin-fixed paraffin embedded clinical tissue blocks and tissue microarrays. PloS one 2014, 9, e106255. [29] Heijs, B., Carreira, R., Tolner, E., de Ru, A., et al., Comprehensive Analysis of the Mouse Brain Proteome Sampled in Mass Spectrometry Imaging. Analytical Chemistry 2015, 87. [30] Chaurand, P., Latham, J., Lane, K., Mobley, J., et al., Imaging mass spectrometry of intact proteins from alcohol-preserved tissue specimens: bypassing formalin fixation. Journal of proteome research 2008, 7, 3543–55. [31] Yang, J., Caprioli, R., Matrix Precoated Targets for Direct Lipid Analysis and Imaging of Tissue. Anal Chem 2013, 85, 2907–2912. [32] Seeley, E., Oppenheimer, S., Mi, D., Chaurand, P., Caprioli, R., Enhancement of protein sensitivity for MALDI imaging mass spectrometry after chemical treatment of tissue sections. J Am Soc Mass Spectr 2008, 19, 1069–1077. [33] Martin-Lorenzo, M., Balluff, B., Sanz-Maroto, A., Zeijl, R.J.M., et al., 30μm spatial resolution protein MALDI MSI: In-depth comparison of five sample preparation protocols applied to human healthy and atherosclerotic arteries. Journal of Proteomics 2014, 108, 465–468. [34] Enthaler, B., Bussmann, T., Pruns, J., Rapp, C., et al., Influence of various on‐tissue washing procedures on the entire protein quantity and the quality of matrix‐assisted laser desorption/ionization spectra. Rapid Communications in Mass Spectrometry 2013, 27, 878–884. [35] Ergin, B., Meding, S., Langer, R., Kap, M., et al., Proteomic Analysis of PAXgene-Fixed Tissues. Journal of Proteome Research 2010, 9, 5188–5196. [36] Deininger, S.-O., Cornett, D., Paape, R., Becker, M., et al., Normalization in MALDI-TOF imaging datasets of proteins: practical considerations. Analytical and Bioanalytical Chemistry 2011, 401, 167–181. [37] McDonnell, L., van Remoortere, A., de Velde, N., van Zeijl, R., Deelder, A., Imaging mass spectrometry data reduction: automated feature identification and extraction. Journal of the American Society for Mass Spectrometry 2010, 21, 1969–78. [38] Minerva, Boonen, Menschaert, Landuyt, et al., Linking mass spectrometric imaging and traditional peptidomics: a validation in the obese mouse model. Analytical chemistry 2011, 83, 7682–91. [39] Minerva, L., Clerens, S., Baggerman, G., Arckens, L., Direct profiling and identification of peptide expression differences in the pancreas of control and ob/ob mice by imaging mass spectrometry. Proteomics 2008, 8, 3763–74. [40] Djidja, M.-C., Claude, E., Snel, M., Scriven, P., et al., MALDI-Ion Mobility Separation-Mass Spectrometry Imaging of Glucose-Regulated Protein 78 kDa (Grp78) in Human Formalin-Fixed, Paraffin-Embedded Pancreatic Adenocarcinoma Tissue Sections. Journal of Proteome Research 2009, 8, 4876–4884. [41] Casadonte, R., Kriegsmann, M., Zweynert, F., Friedrich, K., et al., Imaging mass spectrometry to discriminate breast from pancreatic cancer metastasis in formalin‐fixed paraffin‐embedded tissues. PROTEOMICS 2014, 14, 956–964. [42] Green-Mitchell, S., Cazares, L., Semmes, Nadler, J., Nyalwidhe, J., On-tissue identification of insulin: in situ reduction coupled with mass spectrometry imaging. Proteomics. Clinical applications 2011, 5, 448–53. [43] Oetjen, J., Veselkov, K., Watrous, J., McKenzie, J., et al., Benchmark datasets for 3D MALDI- and DESI-imaging mass spectrometry. GigaScience 2015, 4, 20. [44] Walch, A., Rauser, S., Deininger, S.-O., Höfler, H., MALDI imaging mass spectrometry for direct tissue analysis: a new frontier for molecular histology. Histochemistry and cell biology 2008, 130, 421–434. [45] Grüner, B., Hahne, H., Mazur, P., Trajkovic-Arsic, M., et al., MALDI imaging mass spectrometry for in situ proteomic analysis of preneoplastic lesions in pancreatic cancer. PloS one 2012, 7, e39424. [46] McDonnell, L., Corthals, G., Willems, S., van Remoortere, A., et al., Peptide and protein imaging mass spectrometry in cancer research. J Proteomics 2010, 73, 1921–1944. [47] Scholz, B., Sköld, K., Kultima, K., Fernandez, C., et al., Impact of temperature dependent sampling procedures in proteomics and peptidomics--a characterization of the liver and pancreas post mortem degradome. Mol. Cell Proteomics 2011, 10, M900229MCP200. [48] Poté, N., Alexandrov, T., Le Faouder, J., Laouirem, S., et al., Imaging mass spectrometry reveals modified forms of histone H4 as new biomarkers of microvascular invasion in hepatocellular carcinomas. Hepatology 2013, 58, 983–94. [49] Hardesty, W.M., Kelley, M.C., Mi, D., Low, R.L., Caprioli, R.M., Protein signatures for survival and recurrence in metastatic melanoma. J Proteomics 2011, 74, 1002–14. [50] Signor, L., Boeri Erba, E., Matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometric analysis of intact proteins larger than 100 kDa. Journal of visualized experiments : JoVE 2013, e50635. [51] Riaz, S., Alam, S., Akhtar, Proteomic identification of human serum biomarkers in diabetes mellitus type 2. Journal of pharmaceutical and biomedical analysis 2010, 51, 1103–7. [52] Lewis, K., Wei, J., Siuzdak, G., Encyclopedia of Analytical Chemistry, 2006. [53] Nicolardi, S., Bogdanov, B., Deelder, A., Palmblad, M., van der Burgt, Y., Developments in FTICR-MS and Its Potential for Body Fluid Signatures. International Journal of Molecular Sciences 2015, 16, 27133–27144. [54] Fleurbaaij, F., Kraakman, M., Claas, E., Knetsch, C., et al., TypingPseudomonas aeruginosaIsolates with Ultrahigh Resolution MALDI-FTICR Mass Spectrometry. Analytical Chemistry 2016, 88, 5996–6003. [55] Nicolardi, S., Switzar, L., Deelder, A., Palmblad, M., van der Burgt, Y., Top-Down MALDI-In-Source Decay-FTICR Mass Spectrometry of Isotopically Resolved Proteins. Analytical Chemistry 2015, 87, 3429–3437. [56] Spraggins, J., Rizzo, D., Moore, J., Rose, K., et al., MALDI FTICR IMS of Intact Proteins: Using Mass Accuracy to Link Protein Images with Proteomics Data. Journal of The American Society for Mass Spectrometry 2015, 26, 974–985. [57] Angel, P., Spraggins, J., Baldwin, Caprioli, R., Enhanced sensitivity for high spatial resolution lipid analysis by negative ion mode matrix assisted laser desorption ionization imaging mass spectrometry. Analytical chemistry 2012, 84, 1557–64. [58] Seeley, E., Caprioli, R., Molecular imaging of proteins in tissues by mass spectrometry. Proceedings of the National Academy of Sciences 2008, 105, 18126–18131. [59] Zhang, W., Sakashita, S., Taylor, P., Tsao, M.S., Moran, M.F., Comprehensive proteome analysis of fresh frozen and optimal cutting temperature (OCT) embedded primary non-small cell lung carcinoma by LC-MS/MS. Methods 2015, 81, 50–5.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
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