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研究生： 林可法
Lin, Ke-Fa
論文名稱： 微珠式單細胞基因表達實作於胰臟癌小鼠模型分析
Implementation of droplet-based single cell gene expression in pancreatic cancer mouse model
指導教授： 黃柏憲
Huang, Po-Hsien
學位類別： 碩士
Master
系所名稱： 醫學院 - 生物化學暨分子生物學研究所
Department of Biochemistry and Molecular Biology
論文出版年： 2019
畢業學年度： 108
語文別： 英文
論文頁數： 65
中文關鍵詞： 胰臟癌 、胰臟腺癌 、單細胞及微流道系統
外文關鍵詞： microfluidic system
相關次數： 點閱：67  下載：0 
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    • 時至今日，更全面及詳盡的分析需求增加，大組織定序已無法完整表達胰 臟癌複雜性。胰臟腺癌(PDAC)為一複雜且極具侵略性之惡性腫瘤，伴隨早期局 部組織侵犯和轉移，以及對大部分治療具抗性，以上舉例原因皆被認為其因為 極不良癒後。過去癌症研究藉由基因、信使核糖核酸及訊息傳遞路徑分析發現， 胰臟癌亞型大致可分為四種，且各具不同存活率及治療複雜度。然而，相較於 過去全混合組織定序分析，單細胞信使核糖核酸表達分析或許才能完整概括胰 臟癌擴增腫瘤細胞株之異質性。因此我們假設胰臟癌個體差異核和複雜度可能 源於其細胞組成及其種類，且期望建立單細胞信使核糖核酸分析系統用以探討 在小鼠胰臟癌模型中，細胞組成及信使核糖核酸之特性是否在胰臟癌變惡化過 程中發生改變。我們由胰臟癌小鼠模型中，收集各偽胰臟癌進程階段之單細胞 懸浮液，接著利用已建立之液滴式單細胞信使核糖核酸定序技術，同時特徵化 分析各單細胞產生之族群及信使核糖核酸表達量。總結我們的初步研究顯示， 此技術在觀察小鼠癌細胞增生，以及癌症組織化時期參與之細胞族群、種類和 信使核糖核酸表達量分析具相當發展的潛力。

    Alone with the requirement of comprehensive and exhaustive analysis, whole tissue sequencing is unable to fulfill our research on pancreatic cancer complexities. Pancreatic ductal adenocarcinoma (PDAC) is a complex and aggressive malignancy, presenting with early local invasion and metastasis, and is resistant to most therapies, all of which are believed to contribute to its extremely poor prognosis. Cancer subtypes analysis by genome, mRNA expression and signaling pathways of the tumors revealed four subtypes of pancreatic cancer with distinct patterns of survival rate and treatment complexity. However, it might not fully comprise the heterogeneity of expanding tumoral clones based on whole-tissue bulk sequencing results as compared to single cell mRNA expression. We hypothesize that the complexities of pancreatic cancer can be attributed to the composition of cells and cell types and aim to establish a single-cell mRNA analysis system to survey the characteristics of cell-type composition and the mRNA expression in mouse pancreas tissues from normal to disease stages. We collect single-cell suspension at different times after Kras mutation induction from pancreatic cancer mouse animal model. Next, by using the established droplet-based single-cell mRNA sequencing technology, the system is anticipated to simultaneously characterizing individual single-cell population and the mRNA expression. In summary, our pilot study showed that the instrument has potential to be feasible to survey cancer clones and cells participating in fibrosis by changes in cell population, cell type, and the mRNA 3’ expression of mouse tumors.

    INTRODUCTION.........1 PANCREATIC CANCER.........1 KRAS.........3 P53.........4 SINGLE-CELL SEQUENCING.........5 DROP-SEQ.........7 MICROFLUIDIC DEVICE.........8 RATIONALE/HYPOTHESIS.........9 SPECIFIC AIM.........10 MATERIALS AND METHODS.........11 CELL CULTURE.........11 DOXYCYCLINE PREPARATION.........11 MOUSE STRAIN.........11 GENOTYPING.........12 PANCREATITIS AND PANCREATIC TUMORIGENESIS ANIMAL MODEL.........12 SINGLE-CELL SUSPENSION.........13 BARCODED MICROPARTICLES PREPARATION.........13 MICROFLUIDIC DEVICE DESIGN AND FABRICATION.........14 DROP-SEQ PROCEDURE-STAMPS GENERATION.........14 DROP-SEQ PROCEDURE-STAMPS AMPLIFICATION & LIBRARY PREPARATION.........15 AMPURE BEAD PURIFICATION.........16 THE QUBIT 2.0 FLUOROMETER.........17 AGILENT 2100 BIOANALYZER.........17 DROP-SEQ DATA CLUSTERING ANALYSIS.........18 RESULT.........19 1. KRAS MUTATION WAS INDUCED BY DOXYCYCLINE IN P53 DOUBLE DELETION MOUSE MODEL TO IMITATE PANCREATITIS.........19 2. ESTABLISHMENT OF DROPLET-BASED SCRNA-SEQ SYSTEM.........20 3. CENTRIFUGE CONDITION INFLUENCED THE RECOVERY RATE IN DROPLETS BREAKAGE STEP.........21 4. REMAINING DROP-SEQ BEAD WOULD INTERFERE PURIFICATION STEP AND CONTAMINATE PCR PRODUCT.........21 5. DROP-SEQ REQUIRED A SPECIFIC LIBRARY PREPARATION PARAMETER.........22 6. COMBINATION OF THE QUBIT QUANTIFICATION AND BIOANALYZER FRAGMENTS SIZE TO PREDICT MOLECULAR CONCENTRATION OF THE SEQUENCING LIBRARY.........23 7. DETAILING THE RAW FASTQ DATA MANIPULATING PROCESS AND EXTRACT THE CELL BARCODES ANDUMIS. 24 8. REVERSE SEQUENCE DATA CONTAINED AMBIGUOUS NUCLEOTIDE BASE INSTEAD OF BIOLOGICAL INFORMATION.........25 9. COMPUTATIONAL ANALYSIS OF DROP-SEQ RAW DATA FROM PUBLISHED REFERENCE.........25 DISCUSSION.........26 CONCLUSION.........27 FIGURE.........28 REFERENCE.........50 APPENDIX.........56 List of figures FIG. 1 DOXY-INDUCED PANCREATITIS AND PDAC OF MOUSE MODEL.........30 FIG. 2 ARRANGEMENT OF DROP-SEQ AND SCHEMATIC WORKFLOW OF MICROFLUIDIC DEVICE PRODUCTION.........32 FIG. 3 THE DROPLETS OBSERVED UNDER MICROSCOPE AND THE COMPARATION OF DIFFERENT CENTRIFUGE CONDITIONS IN DROPLET BREAKAGE STEP.........34 FIG. 4 REMAINING DROP-SEQ BEAD INFLUENCED PCR PRODUCT PURIFICATION STEP.........35 FIG. 5 LIBRARY PREPARATION PARAMETER AND EXAMPLE OF BIOANALYZER QUALITY CONTROL.........37 FIG. 6 LIBRARY PREPARATION PARAMETER AND EXAMPLE OF BIOANALYZER QUALITY CONTROL.........39 FIG. 7 MANIPULATING THE RAW DATA BY FOLLOWING THE DROP-SEQ COMPUTATIONAL WORKFLOW WAS PRECEDED BY FASTQC ANALYSIS FOR QUALITY CONTROL.........42 FIG. 8 THE N CONTENT PER BASE OF SEQUENCING DATA WAS ANALYZED BY FASTQC.........44 FIG. 9 DEMONSTRATION OF MAIN CELL TYPES BY T-DISTRIBUTED STOCHASTIC NEIGHBOR EMBEDDING (T-SNE) PLOT.........46 TABLE. 1-1 TABLE. 1-2 TABLE. 2 SINGLE-CELL ANALYSIS IN PANCREATIC TISSUE.........47 SINGLE-CELL ANALYSIS IN PANCREATIC TISSUE.........48 SINGLE-CELL ANALYSIS BASED ON DROP-SEQ SYSTEM.........49

    1. Ansari, D., et al., Pancreatic cancer: yesterday, today and tomorrow. Future Oncol, 2016. 12(16): p. 1929-46.
    2. Da Costa, J.M.J.M., On the morbid
    anatomy
    3. Kleeff, J., et al., Pancreatic cancer. Nat Rev Dis Primers, 2016. 2: p.
    16022.
    4. He, J., et al., 2564 resected periampullary adenocarcinomas at a single
    institution: trends over three decades. HPB (Oxford), 2014. 16(1): p. 83-90.
    5. Rahib, L., et al., Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States.
    Cancer Res, 2014. 74(11): p. 2913-21.
    6. Waddell, N., et al., Whole genomes redefine the mutational landscape of
    pancreatic cancer. Nature, 2015. 518(7540): p. 495-501.
    7. Shen, Y., et al., KRAS and BRAF mutations in prostate carcinomas of
    Chinese patients. Cancer Genet Cytogenet, 2010. 198(1): p. 35-9.
    8. Cho, N.Y., et al., BRAF and KRAS mutations in prostatic adenocarcinoma.
    Int J Cancer, 2006. 119(8): p. 1858-62.
    9. Wang, X.S., et al., Characterization of KRAS rearrangements in metastatic
    prostate cancer. Cancer Discov, 2011. 1(1): p. 35-43.
    10. Sionov, R.V. and Y. Haupt, The cellular response to p53: the decision
    between life and death. Oncogene, 1999. 18(45): p. 6145-57.
    11. Prives, C. and P.A. Hall, The p53 pathway. J Pathol, 1999. 187(1): p. 112-
    26.
    12. Vousden, K.H. and X. Lu, Live or let die: the cell's response to p53. Nat Rev Cancer, 2002. 2(8): p. 594-604.
    13. Lacroix, M., R.A. Toillon, and G. Leclercq, p53 and breast cancer, an update. Endocr Relat Cancer, 2006. 13(2): p. 293-325.
    14. Kubbutat, M.H., S.N. Jones, and K.H. Vousden, Regulation of p53 stability by Mdm2. Nature, 1997. 387(6630): p. 299-303.
    15. Haupt, Y., et al., Mdm2 promotes the rapid degradation of p53. Nature, 1997. 387(6630): p. 296-9.
    16. Honda, R., H. Tanaka, and H. Yasuda, Oncoprotein MDM2 is a ubiquitin ligase E3 for tumor suppressor p53. FEBS Lett, 1997. 420(1): p. 25-7.
    17. Cahilly-Snyder, L., et al., Molecular analysis and chromosomal mapping of amplified genes isolated from a transformed mouse 3T3 cell line. Somat Cell Mol Genet, 1987. 13(3): p. 235-44.
    18. Rodriguez, M.S., et al., Multiple C-terminal lysine residues target p53 for ubiquitin-proteasome-mediated degradation. Mol Cell Biol, 2000. 20(22): p. 8458- 67.
    19. Lai, Z., et al., Human mdm2 mediates multiple mono-ubiquitination of p53 by a mechanism requiring enzyme isomerization. J Biol Chem, 2001. 276(33): p. 31357-67.
    20. Maki, C.G., J.M. Huibregtse, and P.M. Howley, In vivo ubiquitination and proteasome-mediated degradation of p53(1). Cancer Res, 1996. 56(11): p. 2649-54.
    21. Scian, M.J., et al., Tumor-derived p53 mutants induce oncogenesis by transactivating growth-promoting genes. Oncogene, 2004. 23(25): p. 4430-43.
    22. Lu, X., P53: A Target and a Biomarker of Cancer Therapy?, in Recent Advances in Cancer Research and Therapy. 2012, Elsevier Inc. p. 197-213.
    23. Tang, F., et al., mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods, 2009. 6(5): p. 377-82.
    24. Margulies, M., et al., Genome sequencing in microfabricated high-density picolitre reactors. Nature, 2005. 437(7057): p. 376-80.
    25. Kumar, R.M., et al., Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature, 2014. 516(7529): p. 56-61.
    26. Picelli, S., et al., Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat Methods, 2013. 10(11): p. 1096-8.
    27. Shalek, A.K., et al., Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature, 2013. 498(7453): p. 236-40. 28. Huang, H.L., et al., Trypsin-induced proteome alteration during cell
    subculture in mammalian cells. J Biomed Sci, 2010. 17: p. 36.
    29. Kolodziejczyk, A.A., et al., The technology and biology of single-cell RNA
    sequencing. Mol Cell, 2015. 58(4): p. 610-20.
    30. Olsen, T.K. and N. Baryawno, Introduction to Single-Cell RNA
    Sequencing. Curr Protoc Mol Biol, 2018. 122(1): p. e57.
    31. Ramsköld, D., et al., Full-length mRNA-Seq from single-cell levels of RNA
    and individual circulating tumor cells. Nat Biotechnol, 2012. 30(8): p. 777-82. 32. Islam, S., et al., Characterization of the single-cell transcriptional
    landscape by highly multiplex RNA-seq. Genome Res, 2011. 21(7): p. 1160-7.
    33. Islam, S., et al., Quantitative single-cell RNA-seq with unique molecular
    identifiers. Nat Methods, 2014. 11(2): p. 163-6.
    34. Hashimshony, T., et al., CEL-Seq: single-cell RNA-Seq by multiplexed
    linear amplification. Cell Rep, 2012. 2(3): p. 666-73.
    35. Fan, J., et al., Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat Methods, 2016. 13(3): p. 241-4.
    36. Kharchenko, P.V., L. Silberstein, and D.T. Scadden, Bayesian approach to
    single-cell differential expression analysis. Nat Methods, 2014. 11(7): p. 740-2. 37. Macosko, E.Z., et al., Highly Parallel Genome-wide Expression Profiling
    of Individual Cells Using Nanoliter Droplets. Cell, 2015. 161(5): p. 1202-1214. 38. Thorsen, T., et al., Dynamic pattern formation in a vesicle-generating
    microfluidic device. Phys Rev Lett, 2001. 86(18): p. 4163-6.
    39. Umbanhowar, P.B.P., V. and D.A. Weitz, Monodisperse emulsion
    generation via drop break off in a coflowing stream. Langmuir, 2000. 16(2): p. 347- 351.
    40. Hindson, B.J., et al., High-throughput droplet digital PCR system for absolute quantitation of DNA copy number. Anal Chem, 2011. 83(22): p. 8604-10.
    41. Vogelstein, B. and K.W. Kinzler, Digital PCR. Proc Natl Acad Sci U S A, 1999. 96(16): p. 9236-41.
    42. Beer, N.R., et al., On-chip single-copy real-time reverse-transcription PCR in isolated picoliter droplets. Anal Chem, 2008. 80(6): p. 1854-8.
    43. Utada, A.S., et al., Dripping to jetting transitions in coflowing liquid streams. Phys Rev Lett, 2007. 99(9): p. 094502.
    44. Rehm, B., et al., Underbalanced Drilling: Limits and Extremes. 2012: Elsevier Science Ltd. 629.
    45. Chokkalingam, V., et al., Optimized droplet-based microfluidics scheme for sol-gel reactions. Lab Chip, 2010. 10(13): p. 1700-5.
    46. Shestopalov, I., J.D. Tice, and R.F. Ismagilov, Multi-step synthesis of nanoparticles performed on millisecond time scale in a microfluidic droplet-based system. Lab Chip, 2004. 4(4): p. 316-21.
    47. Waters, A.M. and C.J. Der, KRAS: The Critical Driver and Therapeutic Target for Pancreatic Cancer. Cold Spring Harb Perspect Med, 2018. 8(9).
    48. Zheng, S., et al., Molecular transitions in early progenitors during human cord blood hematopoiesis. Mol Syst Biol, 2018. 14(3): p. e8041.
    49. Campbell, J.N., et al., A molecular census of arcuate hypothalamus and median eminence cell types. Nat Neurosci, 2017. 20(3): p. 484-496.
    50. Muraro, M.J., et al., A Single-Cell Transcriptome Atlas of the Human Pancreas. Cell Syst, 2016. 3(4): p. 385-394.e3.
    51. Wollny, D., et al., Single-Cell Analysis Uncovers Clonal Acinar Cell Heterogeneity in the Adult Pancreas. Dev Cell, 2016. 39(3): p. 289-301.
    52. Baron, M., et al., A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. Cell Syst, 2016. 3(4): p. 346-360.e4.
    53. Stanescu, D.E., et al., Single cell transcriptomic profiling of mouse pancreatic progenitors. Physiol Genomics, 2017. 49(2): p. 105-114.
    54. Dorajoo, R., et al., Single-cell transcriptomics of East-Asian pancreatic islets cells. Sci Rep, 2017. 7(1): p. 5024.
    55. Enge, M., et al., Single-Cell Analysis of Human Pancreas Reveals Transcriptional Signatures of Aging and Somatic Mutation Patterns. Cell, 2017. 171(2): p. 321-330.e14.
    56. Krentz, N.A.J., et al., Single-Cell Transcriptome Profiling of Mouse and hESC-Derived Pancreatic Progenitors. Stem Cell Reports, 2018. 11(6): p. 1551- 1564.
    57. Li, L.C., et al., Single-cell transcriptomic analyses reveal distinct dorsal/ventral pancreatic programs. EMBO Rep, 2018. 19(10).
    58. Sznurkowska, M.K., et al., Defining Lineage Potential and Fate Behavior of Precursors during Pancreas Development. Dev Cell, 2018. 46(3): p. 360-375.e5.
    59. Byrnes, L.E., et al., Lineage dynamics of murine pancreatic development at single-cell resolution. Nat Commun, 2018. 9(1): p. 3922.
    60. Zywitza, V., et al., Single-Cell Transcriptomics Characterizes Cell Types in the Subventricular Zone and Uncovers Molecular Defects Impairing Adult Neurogenesis. Cell Rep, 2018. 25(9): p. 2457-2469.e8.
    61. Shin, D., et al., Multiplexed single-cell RNA-seq via transient barcoding for simultaneous expression profiling of various drug perturbations. Sci Adv, 2019. 5(5): p. eaav2249.
    62. Shulse, C.N., et al., High-Throughput Single-Cell Transcriptome Profiling of Plant Cell Types. Cell Rep, 2019. 27(7): p. 2241-2247.e4.

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