請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91132完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 曾宇鳳 | zh_TW |
| dc.contributor.advisor | Yufeng Jane Tseng | en |
| dc.contributor.author | 姚柏仰 | zh_TW |
| dc.contributor.author | Po-Yang Yao | en |
| dc.date.accessioned | 2023-11-13T16:09:37Z | - |
| dc.date.available | 2023-11-14 | - |
| dc.date.copyright | 2023-11-13 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-10-04 | - |
| dc.identifier.citation | 1. Norris, J.L. and R.M. Caprioli, Analysis of tissue specimens by matrix-assisted laser desorption/ionization imaging mass spectrometry in biological and clinical research. Chemical reviews, 2013. 113(4): p. 2309-2342.
2. Laskin, J. and I. Lanekoff, Ambient mass spectrometry imaging using direct liquid extraction techniques. Analytical chemistry, 2016. 88(1): p. 52-73. 3. Tamura, K., et al., Discovery of lipid biomarkers correlated with disease progression in clear cell renal cell carcinoma using desorption electrospray ionization imaging mass spectrometry. Oncotarget, 2019. 10(18): p. 1688. 4. Neumann, E.K., et al., Spatial metabolomics of the human kidney using MALDI trapped ion mobility imaging mass spectrometry. Analytical Chemistry, 2020. 92(19): p. 13084-13091. 5. Andersson, M., et al., Imaging mass spectrometry of proteins and peptides: 3D volume reconstruction. Nature methods, 2008. 5(1): p. 101-108. 6. Pareek, V., et al., Metabolomics and mass spectrometry imaging reveal channeled de novo purine synthesis in cells. Science, 2020. 368(6488): p. 283-290. 7. Taylor, M.J., J.K. Lukowski, and C.R. Anderton, Spatially resolved mass spectrometry at the single cell: recent innovations in proteomics and metabolomics. Journal of the American Society for Mass Spectrometry, 2021. 32(4): p. 872-894. 8. Drake, R.R., et al., Defining the human kidney N‐glycome in normal and cancer tissues using MALDI imaging mass spectrometry. Journal of mass spectrometry, 2020. 55(4): p. e4490. 9. DeLaney, K., A. Phetsanthad, and L. Li, Advances in high‐resolution maldi mass spectrometry for neurobiology. Mass spectrometry reviews, 2022. 41(2): p. 194-214. 10. Zhang, J., et al., Evaluation of the tumor-targeting efficiency and intratumor heterogeneity of anticancer drugs using quantitative mass spectrometry imaging. Theranostics, 2020. 10(6): p. 2621. 11. Hinners, P., M. Thomas, and Y.J. Lee, Determining fingerprint age with mass spectrometry imaging via ozonolysis of triacylglycerols. Analytical chemistry, 2020. 92(4): p. 3125-3132. 12. Buchberger, A.R., et al., Mass spectrometry imaging: a review of emerging advancements and future insights. Analytical chemistry, 2018. 90(1): p. 240. 13. Ifa, D.R. and L.S. Eberlin, Ambient ionization mass spectrometry for cancer diagnosis and surgical margin evaluation. Clinical chemistry, 2016. 62(1): p. 111-123. 14. Taylor, A.J., A. Dexter, and J. Bunch, Exploring ion suppression in mass spectrometry imaging of a heterogeneous tissue. Analytical chemistry, 2018. 90(9): p. 5637-5645. 15. Sun, Y., et al., Spatially Resolved Metabolomics Method for Mapping the Global Molecular Landscape of Whole-Body Zebrafish (Danio rerio) Using Ambient Mass Spectrometry Imaging. Analytical Chemistry, 2023. 16. Neumann, E.K., et al., Multimodal imaging mass spectrometry: next generation molecular mapping in biology and medicine. Journal of the American Society for Mass Spectrometry, 2020. 31(12): p. 2401-2415. 17. Porta Siegel, T., et al., Mass spectrometry imaging and integration with other imaging modalities for greater molecular understanding of biological tissues. Molecular Imaging and Biology, 2018. 20(6): p. 888-901. 18. Tian, X., et al., Multimodal imaging of amyloid plaques: fusion of the single-probe mass spectrometry image and fluorescence microscopy image. Analytical chemistry, 2019. 91(20): p. 12882-12889. 19. Ryan, D.J., et al., MicroLESA: integrating autofluorescence microscopy, in situ micro-digestions, and liquid extraction surface analysis for high spatial resolution targeted proteomic studies. Analytical chemistry, 2019. 91(12): p. 7578-7585. 20. Perry, W.J., et al., Visualizing Staphylococcus aureus pathogenic membrane modification within the host infection environment by multimodal imaging mass spectrometry. Cell Chemical Biology, 2022. 29(7): p. 1209-1217. e4. 21. Van de Plas, R., et al., Image fusion of mass spectrometry and microscopy: a multimodality paradigm for molecular tissue mapping. Nature methods, 2015. 12(4): p. 366-372. 22. Abdelmoula, W.M., et al., Automatic 3D nonlinear registration of mass spectrometry imaging and magnetic resonance imaging data. Analytical chemistry, 2019. 91(9): p. 6206-6216. 23. Tata, A., et al., Contrast agent mass spectrometry imaging reveals tumor heterogeneity. Analytical chemistry, 2015. 87(15): p. 7683-7689. 24. Verbeeck, N., et al., Connecting imaging mass spectrometry and magnetic resonance imaging-based anatomical atlases for automated anatomical interpretation and differential analysis. Biochimica et Biophysica Acta (BBA)-Proteins and Proteomics, 2017. 1865(7): p. 967-977. 25. Flint, L.E., et al., Characterization of an aggregated three-dimensional cell culture model by multimodal mass spectrometry imaging. Analytical Chemistry, 2020. 92(18): p. 12538-12547. 26. Vermeulen, I., et al., Multimodal molecular imaging in drug discovery and development. Drug Discovery Today, 2022. 27. Vos, D.N., et al., Experimental and data analysis considerations for three-dimensional mass spectrometry imaging in biomedical research. Molecular imaging and biology, 2021. 23: p. 149-159. 28. Li, X., et al., Nanoscale three-dimensional imaging of drug distributions in single cells via laser desorption post-ionization mass spectrometry. Journal of the American Chemical Society, 2021. 143(51): p. 21648-21656. 29. Paine, M.R., et al., Three-dimensional mass spectrometry imaging identifies lipid markers of medulloblastoma metastasis. Scientific reports, 2019. 9(1): p. 2205. 30. Balluff, B., R.M. Heeren, and A.M. Race, An overview of image registration for aligning mass spectrometry imaging with clinically relevant imaging modalities. Journal of Mass Spectrometry and Advances in the Clinical lab, 2022. 23: p. 26-38. 31. Andreasen, N.C. and S. Olsen, Negative v positive schizophrenia: Definition and validation. Archives of general psychiatry, 1982. 39(7): p. 789-794. 32. McCutcheon, R.A., J.H. Krystal, and O.D. Howes, Dopamine and glutamate in schizophrenia: biology, symptoms and treatment. World Psychiatry, 2020. 19(1): p. 15-33. 33. Uno, Y. and J.T. Coyle, Glutamate hypothesis in schizophrenia. Psychiatry and clinical neurosciences, 2019. 73(5): p. 204-215. 34. Chen, B.-S. and K.W. Roche, Regulation of NMDA receptors by phosphorylation. Neuropharmacology, 2007. 53(3): p. 362-368. 35. Finke, C., et al., Cognitive deficits following anti-NMDA receptor encephalitis. Journal of Neurology, Neurosurgery & Psychiatry, 2012. 83(2): p. 195-198. 36. Jones, C.A., D. Watson, and K. Fone, Animal models of schizophrenia. British journal of pharmacology, 2011. 164(4): p. 1162-1194. 37. Jaaro-Peled, H., Gene models of schizophrenia: DISC1 mouse models. Progress in brain research, 2009. 179: p. 75-86. 38. Neill, J.C., et al., Animal models of cognitive dysfunction and negative symptoms of schizophrenia: focus on NMDA receptor antagonism. Pharmacology & therapeutics, 2010. 128(3): p. 419-432. 39. Lodge, D.J. and A.A. Grace, Hippocampal dysregulation of dopamine system function and the pathophysiology of schizophrenia. Trends in pharmacological sciences, 2011. 32(9): p. 507-513. 40. Kristiansen, L.V., et al., NMDA receptors and schizophrenia. Current opinion in pharmacology, 2007. 7(1): p. 48-55. 41. Labrie, V., A.H. Wong, and J.C. Roder, Contributions of the D-serine pathway to schizophrenia. Neuropharmacology, 2012. 62(3): p. 1484-1503. 42. Balu, D.T., et al., Multiple risk pathways for schizophrenia converge in serine racemase knockout mice, a mouse model of NMDA receptor hypofunction. Proceedings of the National Academy of Sciences, 2013. 110(26): p. E2400-E2409. 43. Basu, A.C., et al., Targeted disruption of serine racemase affects glutamatergic neurotransmission and behavior. Molecular psychiatry, 2009. 14(7): p. 719-727. 44. Matveeva, T.M., et al., Sociality deficits in serine racemase knockout mice. Brain and Behavior, 2019. 9(10): p. e01383. 45. Yang, D., et al., Lipid metabolism and storage in neuroglia: role in brain development and neurodegenerative diseases. Cell & Bioscience, 2022. 12(1): p. 1-16. 46. Aureli, M., et al., Lipid membrane domains in the brain. Biochimica et Biophysica Acta (BBA)-Molecular and Cell Biology of Lipids, 2015. 1851(8): p. 1006-1016. 47. Migrenne, S., et al., Brain lipid sensing and nervous control of energy balance. Diabetes & metabolism, 2011. 37(2): p. 83-88. 48. Couttas, T.A., et al., Current State of Fluid Lipid Biomarkers for Personalized Diagnostics and Therapeutics in Schizophrenia Spectrum Disorders and Related Psychoses: A Narrative Review. Frontiers in Psychiatry, 2022. 13. 49. Berger, G.E., et al., Implications of lipid biology for the pathogenesis of schizophrenia. Australian & New Zealand journal of psychiatry, 2002. 36(3): p. 355-366. 50. Yao, J.K., D.P. van Kammen, and J.A. Gurklis, Abnormal incorporation of arachidonic acid into platelets of drug-free patients with schizophrenia. Psychiatry research, 1996. 60(1): p. 11-21. 51. Ross, B.M., et al., Differential alteration of phospholipase A2 activities in brain of patients with schizophrenia. Brain research, 1999. 821(2): p. 407-413. 52. Zhuo, C., et al., Lipidomics of the brain, retina, and biofluids: from the biological landscape to potential clinical application in schizophrenia. Translational Psychiatry, 2020. 10(1): p. 391. 53. Wood, P.L., Targeted lipidomics and metabolomics evaluations of cortical neuronal stress in schizophrenia. Schizophrenia Research, 2019. 212: p. 107-112. 54. Taha, A.Y., et al., Altered fatty acid concentrations in prefrontal cortex of schizophrenic patients. Journal of psychiatric research, 2013. 47(5): p. 636-643. 55. Schmitt, A., et al., Altered thalamic membrane phospholipids in schizophrenia: a postmortem study. Biological psychiatry, 2004. 56(1): p. 41-45. 56. McNamara, R.K., et al., Abnormalities in the fatty acid composition of the postmortem orbitofrontal cortex of schizophrenic patients: gender differences and partial normalization with antipsychotic medications. Schizophrenia research, 2007. 91(1-3): p. 37-50. 57. Hamazaki, K., K.H. Choi, and H.-Y. Kim, Phospholipid profile in the postmortem hippocampus of patients with schizophrenia and bipolar disorder: no changes in docosahexaenoic acid species. Journal of psychiatric research, 2010. 44(11): p. 688-693. 58. Wood, P.L., et al., Lipidomics reveals dysfunctional glycosynapses in schizophrenia and the G72/G30 transgenic mouse. Schizophrenia research, 2014. 159(2-3): p. 365-369. 59. Ghosh, S., R.A. Dyer, and C.L. Beasley, Evidence for altered cell membrane lipid composition in postmortem prefrontal white matter in bipolar disorder and schizophrenia. Journal of Psychiatric Research, 2017. 95: p. 135-142. 60. Matsumoto, J., et al., Decreased 16: 0/20: 4-phosphatidylinositol level in the post-mortem prefrontal cortex of elderly patients with schizophrenia. Scientific reports, 2017. 7(1): p. 45050. 61. Esaki, K., et al., Evidence for altered metabolism of sphingosine-1-phosphate in the corpus callosum of patients with schizophrenia. Schizophrenia bulletin, 2020. 46(5): p. 1172-1181. 62. Shimamoto-Mitsuyama, C., et al., Lipid pathology of the corpus callosum in schizophrenia and the potential role of abnormal gene regulatory networks with reduced microglial marker expression. Cerebral Cortex, 2021. 31(1): p. 448-462. 63. Sano, F., et al., Associations between prefrontal PI (16: 0/20: 4) lipid, TNC mRNA, and APOA1 protein in schizophrenia: A trans-omics analysis in post-mortem brain. Frontiers in Psychiatry, 2023. 14: p. 1145437. 64. Pei, J.-C., et al., Therapeutic potential and underlying mechanism of sarcosine (N-methylglycine) in N-methyl-D-aspartate (NMDA) receptor hypofunction models of schizophrenia. Journal of Psychopharmacology, 2019. 33(10): p. 1288-1302. 65. Deininger, S.-O., et al., Normalization in MALDI-TOF imaging datasets of proteins: practical considerations. Analytical and bioanalytical chemistry, 2011. 401: p. 167-181. 66. Alexandrov, T., et al., METASPACE: A community-populated knowledge base of spatial metabolomes in health and disease. BioRxiv, 2019: p. 539478. 67. Palmer, A., et al., FDR-controlled metabolite annotation for high-resolution imaging mass spectrometry. Nature methods, 2017. 14(1): p. 57-60. 68. Wishart, D.S., et al., HMDB 4.0: the human metabolome database for 2018. Nucleic acids research, 2018. 46(D1): p. D608-D617. 69. Wadie, B., et al., METASPACE-ML: Metabolite annotation for imaging mass spectrometry using machine learning. bioRxiv, 2023: p. 2023.05. 29.542736. 70. Ovchinnikova, K., et al., OffsampleAI: artificial intelligence approach to recognize off-sample mass spectrometry images. BMC bioinformatics, 2020. 21(1): p. 1-11. 71. Smets, T., et al., Evaluation of distance metrics and spatial autocorrelation in uniform manifold approximation and projection applied to mass spectrometry imaging data. Analytical chemistry, 2019. 91(9): p. 5706-5714. 72. Fonville, J.M., et al., Hyperspectral visualization of mass spectrometry imaging data. Analytical chemistry, 2013. 85(3): p. 1415-1423. 73. Aramaki, S., et al., Lipidomics-based tissue heterogeneity in specimens of luminal breast cancer revealed by clustering analysis of mass spectrometry imaging: A preliminary study. Plos one, 2023. 18(5): p. e0283155. 74. Kümmel, T., et al., Rapid brain structure and tumour margin detection on whole frozen tissue sections by fast multiphotometric mid-infrared scanning. Scientific reports, 2021. 11(1): p. 11307. 75. Smets, T., E. Waelkens, and B. De Moor, Prioritization of m/z-values in mass spectrometry imaging profiles obtained using uniform manifold approximation and projection for dimensionality reduction. Analytical chemistry, 2020. 92(7): p. 5240-5248. 76. Hartigan, J.A. and M.A. Wong, Algorithm AS 136: A k-means clustering algorithm. Journal of the royal statistical society. series c (applied statistics), 1979. 28(1): p. 100-108. 77. Aranganayagi, S. and K. Thangavel. Clustering categorical data using silhouette coefficient as a relocating measure. in International conference on computational intelligence and multimedia applications (ICCIMA 2007). 2007. IEEE. 78. Pang, Z., et al., MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights. Nucleic acids research, 2021. 49(W1): p. W388-W396. 79. Kirkpatrick, B. and B.J. Miller, Inflammation and schizophrenia. Schizophrenia bulletin, 2013. 39(6): p. 1174-1179. 80. Müller, N., et al., The role of inflammation in schizophrenia. Frontiers in neuroscience, 2015. 9: p. 372. 81. Zheng, W., et al., The possible role of the Akt signaling pathway in schizophrenia. Brain research, 2012. 1470: p. 145-158. 82. Osaki, M., M.a. Oshimura, and H. Ito, PI3K-Akt pathway: its functions and alterations in human cancer. Apoptosis, 2004. 9: p. 667-676. 83. Acosta-Martinez, M. and M.Z. Cabail, The PI3K/Akt pathway in meta-inflammation. International journal of molecular sciences, 2022. 23(23): p. 15330. 84. Foster, D.A., Phosphatidic acid signaling to mTOR: signals for the survival of human cancer cells. Biochimica et Biophysica Acta (BBA)-Molecular and Cell Biology of Lipids, 2009. 1791(9): p. 949-955. 85. Lutkewitte, A.J. and B.N. Finck, Regulation of signaling and metabolism by lipin-mediated phosphatidic acid phosphohydrolase activity. Biomolecules, 2020. 10(10): p. 1386. 86. Yao, J., P. Yasaei, and D. Van Kammen, Increased turnover of platelet phosphatidylinositol in schizophrenia. Prostaglandins, leukotrienes and essential fatty acids, 1992. 46(1): p. 39-46. 87. Roychaudhuri, R., H. Atashi, and S.H. Snyder, Serine Racemase mediates subventricular zone neurogenesis via fatty acid metabolism. Stem Cell Reports, 2023. 88. Maugard, M., et al., l-Serine links metabolism with neurotransmission. Progress in Neurobiology, 2021. 197: p. 101896. 89. Riecan, M., et al., Branched and linear fatty acid esters of hydroxy fatty acids (FAHFA) relevant to human health. Pharmacology & Therapeutics, 2022. 231: p. 107972. 90. Wood, P.L., Fatty acyl esters of hydroxy fatty acid (FAHFA) lipid families. Metabolites, 2020. 10(12): p. 512. 91. Yore, M.M., et al., Discovery of a class of endogenous mammalian lipids with anti-diabetic and anti-inflammatory effects. Cell, 2014. 159(2): p. 318-332. 92. Thomas, S.A., et al. Dimensionality reduction of mass spectrometry imaging data using autoencoders. in 2016 IEEE symposium series on computational intelligence (SSCI). 2016. IEEE. 93. Abdelmoula, W.M., et al., Peak learning of mass spectrometry imaging data using artificial neural networks. Nature communications, 2021. 12(1): p. 5544. 94. Gardner, W., et al., Two-Dimensional and Three-Dimensional Time-of-Flight Secondary Ion Mass Spectrometry Image Feature Extraction Using a Spatially Aware Convolutional Autoencoder. Analytical Chemistry, 2022. 94(22): p. 7804-7813. 95. Yamada, H., et al., Changes of Mass Spectra Patterns on a Brain Tissue Section Revealed by Deep Learning with Imaging Mass Spectrometry Data. Journal of the American Society for Mass Spectrometry, 2022. 33(9): p. 1607-1614. 96. Hu, H. and J. Laskin, Emerging Computational Methods in Mass Spectrometry Imaging. Advanced Science, 2022: p. 2203339. 97. Hu, H., J.P. Bindu, and J. Laskin, Self-supervised clustering of mass spectrometry imaging data using contrastive learning. Chemical Science, 2022. 13(1): p. 90-98. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91132 | - |
| dc.description.abstract | 質譜成像是一種具有空間解析能力的分析技術，能提供不同分子的空間分佈情況，以便識別出空間模式、梯度或局部分子變化。質譜影像可以與其他的成像模式結合，如顯微鏡、螢光成像或磁共振成像，以獲取互補的信息，使研究人員能夠對樣本有更全面、完整的觀察。然而，傳統的二維質譜成像無法提供樣本在z軸上的詳細訊息，這使得分析和解釋複雜的三維結構變得具有挑戰性。三維質譜成像具有提升深度剖析能力並實現對給定樣本特定分子體積的定量分析潛力。它能夠評估組織內代謝物濃度的空間變化。三維質譜影像的研究持續增加，表明對探索質譜影像三維特性的興趣正在上升。
思覺失調症是一種影響人們思維、情感和行為的精神疾病。其中一個思覺失調症的假說是NMDA（N-甲基-D-天冬氨酸）受體的功能不足，這導致思覺失調症的臨床症狀。越來越多的新證據表明脂質代謝與思覺失調症之間可能存在著關聯。我們提供一個以思覺失調症鼠腦為例的三維質譜成像重建流程。透過該流程，可以將實驗所得不同深度的二維質譜影像重組成三維質譜影像，從而了解分子在三維空間中的分佈。透過比較思覺失調和正常小鼠海馬迴、紋狀體和內側前額葉皮層中的脂質強度，我們發現這三個腦區中的特定脂質在兩組樣本間存在顯著差異。 | zh_TW |
| dc.description.abstract | Mass spectrometry imaging (MSI) is a spatially resolved analytical technique that provides spatial distributions of different molecules, allowing for the identification of spatial patterns, gradients, or localized molecular changes. MSI can be combined with various imaging modalities, including microscopy, fluorescence imaging, and magnetic resonance imaging (MRI), to obtain complementary information, which enables researchers to have a more comprehensive and intact view of the sample. However, traditional 2D MSI does not provide detailed information along the z-axis of the sample. This makes it challenging to analyze and interpret complex 3D structures. Three-dimensional (3D) MSI has the potential to enhance depth profiling capabilities and allow for quantitative analysis of the volume. It enables the assessment of spatial variations in metabolite concentrations throughout a tissue volume. Research on 3D MSI is increasing, indicating a rising interest in exploring the 3D aspects of MSI.
Schizophrenia is a psychiatric disorder characterized by cognitive, emotional, and behavioral disturbances. One of the hypotheses of schizophrenia is the hypofunction of NMDA (N-methyl-D-aspartate) receptors, which leads to the clinical symptoms of schizophrenia. An increasing body of emerging evidence indicates a potential association between lipid metabolism and schizophrenia. We provided a three-dimensional mass spectrometry imaging reconstruction protocol using schizophrenic mouse brains as an example. With this protocol, the 2D MSI data acquired at different depths can be reconstructed into a 3D MSI, allowing us to understand the spatial distribution of molecules in a 3D space. By comparing the lipid intensities in the hippocampus, striatum, and medial prefrontal cortex (mPFC) between schizophrenia and wild-type mice, we identified specific lipids in these three brain regions that showed significant differences between the two groups. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-11-13T16:09:37Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-11-13T16:09:37Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌謝 i
中文摘要 ii ABSTRACT iii CONTENTS v LIST OF FIGURES viii LIST OF TABLES x GLOSSARY xi Chapter 1 Introduction 1 1.1 Mass Spectrometry Imaging 1 1.1.1 The Basics of MSI 1 1.1.2 Multimodal Imaging with MSI 2 1.2 Three-dimensional (3D) mass spectrometry imaging 3 1.2.1 The Symptoms of Schizophrenia 6 1.2.2 The Hypotheses of Schizophrenia 6 1.2.3 Mouse models of schizophrenia 7 1.2.4 Serine racemase-deficient (SR-/-) mice 8 1.2.5 Lipid Disturbances in Schizophrenia 9 Chapter 2 Materials and Methods 12 2.1 Experimental Procedures 12 2.1.1 Sample Preparation 12 2.1.2 DESI MSI experiment 13 2.2 MSI data Processing 13 2.2.1 On-tissue Spectra Extraction and Peak Picking 13 2.2.2 Normalization 14 2.2.3 Metabolite Annotation 14 2.3 Data Compression 15 2.3.1 Dimensionality Reduction 16 2.3.2 Hyperspectral Visualization 17 2.3.3 Image Segmentation 18 2.4 3D MSI Reconstruction and Analysis 19 2.4.1 Optical Image Processing 20 2.4.2 Consecutive Histological Images and Multimodal Image Registration 20 2.4.3 Region of interest (ROI) selection 21 2.4.4 Three-dimensional Reconstruction 22 2.4.5 Statistical Analysis 22 Chapter 3 Results and Discussion 23 3.1 Molecular Annotation of MSI Data 23 3.2 Hyperspectral visualization provides histoanatomical structure information 24 3.3 The clustering results of the four samples have similar spatial distributions 31 3.4 The results of statistical analysis indicate the significant differences in certain lipids between the SR-/- samples and WT samples 36 3.4.1 Phosphatidylinositols (PI) have significantly higher levels in the hippocampus of SR-/- mice 37 3.4.2 Phosphatidic acids (PAs) have relatively higher levels in the striatum of SR-/- mice 40 3.4.3 Multiple phosphatidylserine (PS) species show relatively higher levels in the mPFC of SR-/- mice 42 3.5 Limitations and Future Perspectives 45 3.5.1 The Limitation 45 3.5.2 The Future Perspective 45 Chapter 4 Conclusion 47 References 48 | - |
| dc.language.iso | en | - |
| dc.subject | 質譜影像 | zh_TW |
| dc.subject | 脂質 | zh_TW |
| dc.subject | 小鼠模型 | zh_TW |
| dc.subject | 失覺失調症 | zh_TW |
| dc.subject | 三維質譜影像 | zh_TW |
| dc.subject | 質譜影像 | zh_TW |
| dc.subject | 脂質 | zh_TW |
| dc.subject | 小鼠模型 | zh_TW |
| dc.subject | 失覺失調症 | zh_TW |
| dc.subject | 三維質譜影像 | zh_TW |
| dc.subject | Mass spectrometry imaging | en |
| dc.subject | lipid | en |
| dc.subject | Mass spectrometry imaging | en |
| dc.subject | three-dimensional mass spectrometry imaging | en |
| dc.subject | schizophrenia | en |
| dc.subject | mouse model | en |
| dc.subject | lipid | en |
| dc.subject | mouse model | en |
| dc.subject | schizophrenia | en |
| dc.subject | three-dimensional mass spectrometry imaging | en |
| dc.title | 用於增強空間分析和分子定位的三維質譜影像重建流程：以思覺失調小鼠大腦為例 | zh_TW |
| dc.title | 3D Mass Spectrometry Imaging Reconstruction Protocol for Enhanced Spatial Profiling and Molecular Mapping with Example Application of Schizophrenia Mouse Brain | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 蕭明熙;賴文崧;張瑞峰 | zh_TW |
| dc.contributor.oralexamcommittee | Ming-Shi Shiao;Wen-Sung Lai;Ruey-Feng Chang | en |
| dc.subject.keyword | 質譜影像,三維質譜影像,失覺失調症,小鼠模型,脂質, | zh_TW |
| dc.subject.keyword | Mass spectrometry imaging,three-dimensional mass spectrometry imaging,schizophrenia,mouse model,lipid, | en |
| dc.relation.page | 53 | - |
| dc.identifier.doi | 10.6342/NTU202304283 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2023-10-05 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | - |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-1.pdf 未授權公開取用 | 3.03 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。