PO.TB04.07 · 肿瘤生物学

2D和3D细胞培养方法对髓母细胞瘤DNA甲基化模式影响的比较分析

Comparative analysis of 2D and 3D cell culture methods on DNA methylation patterns in medulloblastoma

海报缩略图:2D和3D细胞培养方法对髓母细胞瘤DNA甲基化模式影响的比较分析
编号 3424 展板 29 时间 4/20 02:00–05:00 区域 Section 28 主讲 Sigrid Langhans
分会场 In Vitro Models 1: 2D and 3D
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作者与单位 Authors & Affiliations

Karen Sperle1, Laurel Stell2, Micheal Avoseh3, Aderonke Ajongbolo1, Haozhe Zheng4, Darrin Pochan4, Sigrid Langhans1

1Neurology, Nemours Children's Hospital, Wilmington, DE,2Stanford University, Palo Alto, CA,3Nemours Children's Hospital, Wilmington, DE,4Materials Science and Engineering, University of Delaware, Newark, DE

摘要 Abstract

中文摘要
表观遗传过程,包括组蛋白修饰、DNA甲基化、染色质重塑和调控性非编码RNA(如microRNA),在不直接改变基因组的情况下调控基因表达。其中,DNA甲基化涉及在胞嘧啶(C)的第五个碳原子上添加甲基基团,形成5-甲基胞嘧啶(5mC),最常发生在CpG二核苷酸(CpG)处,是研究最多的表观遗传修饰之一。DNA甲基化已被证明影响多种癌症的肿瘤发生、肿瘤进展和治疗反应,包括髓母细胞瘤——儿童中最常见的恶性脑肿瘤。由于多个国际联盟和独立团体进行了广泛的全基因组分析,包括DNA甲基化谱在内的大量基因组数据库已公开可用于髓母细胞瘤,使其适合作为研究不同细胞培养技术是否影响DNA甲基化模式的模型系统。在此,我们试图研究3D细胞培养系统(通常被认为更接近再现体内肿瘤微环境)是否能促进肿瘤细胞系中更接近患者肿瘤的DNA甲基化模式。将最常用的人髓母细胞瘤细胞系(传统上以2D单层培养)进行锚定非依赖性(球体)和基于支架(胶原蛋白、Matrigel、合成肽水凝胶)的3D细胞培养方法。使用Infinium MethylationEPIC BeadChip芯片生成全基因组甲基化谱,并与分子神经病理学组/德国癌症研究中心(DKFZ)脑分类器数据库进行比较。令人惊讶的是,没有一个人髓母细胞瘤细胞系被认为匹配(评分等于或高于0.9),最初测试的五个细胞系中有三个的值小于0.3,不符合研究标准。此外,即使应用不同的标准化方法以减少非生物学伪影,对于给定细胞系,各细胞系之间的评分存在相当大的差异,但培养条件(2D与3D)之间无差异。因此,基于球体或基于支架的3D细胞培养方法并未转化为与患者来源甲基化谱更好的一致性,表明这些模型复制患者肿瘤谱的潜力有限。此外,我们的数据揭示,即使结合先进的细胞培养技术,生物学模型的选择也是构建准确疾病模型的关键组成部分。
查看英文原文 English abstract
Epigenetic processes including histone modifications, DNA methylation, chromatin remodeling, and regulatory non-coding RNAs such as microRNAs regulate gene expression without direct alterations to the genome. Among these, DNA methylation which involves the addition of a methyl group to the fifth carbon of cytosine (C), forming 5-methylcytosine (5mC) most frequently in CpG dinucleotides (CpGs), is one of the most studied epigenetic modifications. DNA methylation has been shown to influence tumor development, tumor progression and therapy response in various cancers, including medulloblastoma, the most common malignant brain tumor in children. Due to the extensive genome-wide profiling done by several international consortia and independent groups, extensive genomic databases, including DNA methylation profiles, are publicly available for medulloblastoma making it suitable as a model system to study whether different cell culture techniques influence DNA methylation patterns. Here we sought to investigate whether 3D cell culture systems, commonly referred to as more closely recapitulating in vivo tumor microenvironments, promote DNA methylation patterns in tumor cell lines that are more closely aligned with those of patient tumors. Most commonly used human medulloblastoma cell lines, traditionally cultured as 2D monolayers, were subjected to both anchorage-independent (spheroids) and scaffold-based (collagen, Matrigel, synthetic peptide hydrogel) 3D cell culture methods. Genome-wide methylation profiles were generated using the Infinium MethylationEPIC BeadChip Arrays and compared to the Molecular Neuropathology group/Deutsches Krebsforschungszentrum (DKFZ) brain classifier database. Surprisingly, none of the human medulloblastoma cell lines was considered a match (score equal to or higher than 0.9) and three out of the five cell lines initially tested had values of less than 0.3 and did not meet the study criterion. Moreover, there was considerable differences in scores between cell lines but not between culture condition (2D versus 3D) for a given cell line even when applying different normalization methods to reduce non-biological artifacts. Thus, spheroid- or scaffold-based 3D cell culture methods did not translate into better alignment with patient-derived methylation profiles and suggests limited potential for these models to replicate patient tumor profiles. Furthermore, our data reveal that even in conjunction with advanced cell culture technology, the choice of biological model is a crucial component in engineering accurate disease models.
利益披露 Disclosure
K. Sperle, None.. L. Stell, None.. M. Avoseh, None.. A. Ajongbolo, None.. H. Zheng, None.. D. Pochan, None.. S. Langhans, None.

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