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目录 contents

    摘要

    脑毛细血管上的特殊结构单元为大脑提供氧气和养分,与此同时形成一种限制性屏障,称为血脑屏障(BBB),该结构单元由单层脑微血管内皮细胞构成,内皮细胞外侧的周细胞、基膜以及星形胶质细胞的足突也参与了血脑屏障的形成. 血脑屏障是一种选择性渗透屏障,大多数中枢神经系统候选药物在血脑屏障中的渗透性差,用实验动物进行药物筛选具有成本高、周期长、成功率低等缺点. 此外,直接在人体中试验有违道德伦理,但建立可靠的体外血脑屏障模型可以简化实验过程、缩短试验周期、实验结果更易测定,因此建立体外BBB模型可以极大地加快中枢神经系统药物的研发. 目前已研究的模型主要可以分为3类:单培养、共培养、三培养,这些模型由简单到复杂,与体内血脑屏障的相似性也越来越高. 本文就目前现有的血脑屏障模型进行综述,以期未来在体外BBB模型设计中有新的思路.

    Abstract

    The special structural unit on the brain's capillaries supplies oxygen and nutrients to the brain, while at the same time forming a restrictive barrier called blood-brain barrier (BBB). The structural unit is mainly composed of a single layer of brain microvascular endothelial cells, surrounding by endothelial cells, as well as the neurons, microglia, and astrocytes, are involved in the formation of the blood-brain barrier. The blood-brain barrier is a selective permeation barrier,most of the central nervous system drug candidates have poor permeability in the blood-brain barrier. The disadvantages of drug screening experiments in laboratory animals are high cost, long cycle, and low accuracy. Besides, directly test in the human body is unethical. But establishing a reliable model of extracorporeal blood-brain barrier can simplify the experimental process, shorten the test period, and make the experimental results easier to determine. Therefore, the establishment of an in vitro BBB model can greatly accelerate the development of central nervous system drugs. The models that have been studied can be divided into three categories: single culture, co-culture, and triple culture. These models range from simple to complex, and more similar to blood-brain barrier in vivo. This article reviews the current blood-brain barrier model, and we expect it can provide new ideas for the design of in vitro BBB models in the future.

    人中枢神经系统的一大典型特征就是血脑屏[1](blood-brain barrier,BBB)(图1),它由单层的脑微血管内皮细胞(brain microvessel endothelial cells,BMECs)和包裹着BMECs的周细胞(brain microvessel pericytes,BMPC)、星型胶质细胞(astrocytes,AS)、神经元(neurons)、小胶质细胞(microglia)组[2]. 其中血管内皮细胞间存在紧密连[3],并且被一层连续不断的基膜包裹着. 除此之外,脑毛细血管表面还缺少外周毛细血管所具有的小孔,使得外源性的物质进出脑组织更加困[2,4,5]. 血脑屏障是一种选择性渗透屏[6],其严格控制物质进出大脑,保护脑组织不受有害物质攻击,维持脑组织正常生理环境和稳态.

    血脑屏障是一种选择性渗透屏障,大多数中枢神经系统(central nervous system,CNS)候选药物在血脑屏障中的渗透性差,进行CNS药物筛选最为准确的方法是在人脑中进行实验,其可以真实地反映药物分子的透过情况,但有违道德伦理. 此外利用实验动物进行药物筛选成本高、周期长、成功率低,得到的结果大约有50%不能转化为准确的人体反应结[2]. 为了克服以上两种方式的缺陷,基于细胞培养的体外BBB模型应运而生,建立可靠的体外血脑屏障模型可以简化实验过程、缩短试验周期、使实验现象更加可视化、并且实现高通量筛选,这些优势可以极大地加快中枢神经系统药物的研发. 测量跨内皮细胞间电阻(transendothelial electrical resistance,TEER)、分析渗透性和对已知标记进行免疫染色的3种方法可以为BBB模型提供适当信息,反映模型屏障的完整性和紧密度. TEER是一种广泛使用的参数,用于表征和评估内皮细胞和上皮细胞单层屏障紧密连接的完整性. 跨屏障测量TEER可以提供有关屏障性能的实时信[7]. 目前,已构建出使用猪、鼠或人细胞进行单培养、共培养和三培养的模[6]. 在几种体外BBB模型研究中显示,BMECs与AS共培养可诱导BMECs形成体内血脑屏障的特性,例如形成紧密连[8,9,10].

    微流体系统正在成为细胞培养和系统分析的新自动化技术平[2],微流控芯片细胞实验室可以看做是把细胞操作及控制等部件集成或基本集成到一块几平方厘米的芯片上, 由微通道形成网络, 以可控流体贯穿整个系统,整个体系灵活可[11]. 我们将这些系统的性能与传统的Transwell 小室模型进行了比较. 本文旨在介绍和评价BBB的各种不同模型.

  • 1 单培养的BBB模型

  • 1.1 单细胞培养Transwell模型

    Shi[12]为了验证5种新型磁流体在血脑屏障的渗透性,依据Bennett [13]文献中记载的方法制作了一种Transwell血脑屏障模型. 他们将胶原包被好的0.4 μm四氟乙烯膜铺在6.5 mm Transwell-COL上,并将整个装置插入到24孔板中,将小鼠脑微血管内皮细胞(b.End3)按照一定的密度接种于膜上,用DMEM完全培养基培养至汇合,之后替换为无血清DMEM / Ham's F12培养基培养96 h. 用异硫氰酸荧光素标记的葡聚糖验证其模型的渗透性. 随着无血清培养基培养时间的增加,渗透性的下降显示血脑屏障模型的成功收[14],这与Bennett[13]结果相似. Su[15]为验证一种新型纳米复合材料G23-alg-Fe3O4 NPs能否通过BBB,也将b.End3铺在Transwell膜上,进行单培养以构建体外BBB模型(图2a),但均未验证内皮细胞之间形成屏障的完整性,以及是否形成了BBB特有的膜蛋白及紧密连接,也未用阴性和阳性药物作为对照证实该模型的可靠性,所以该模型的可靠性及与体内BBB的相似程度,有待深究.

    图1
                            血脑屏障结构单元

    图1 血脑屏障结构单元

    Fig. 1 The blood-brain barrier structural unit

    注:BBB靠近血液层为脑微血管内皮细胞,周围环绕着周细胞、神经元、小胶质细胞和星形细胞末端足底.

    图2
                            单培养BBB模型

    图2 单培养BBB模型

    Fig. 2 Single cell culture BBB mode

    注:(a)脑微血管内皮细胞单培养Transwell模型;(b)微流体微血管血脑屏障模型;(c)质谱联用微流体平台.

  • 1.2 微流控芯片BBB

    Prabhakarpandian[16]利用深反应离子刻蚀技术加工硅基底片,研制了微流体的血脑屏障微血管模型(synthetic microvasculature model of the blood-brain barrier,SyM-BBB). SyM-BBB平台(图2b)由塑料和光学透明的微流体芯片组成,包含顶侧腔室和基底侧隔室两个室,两个室之前由微材料间隔并留有3 μm的孔间隙,由分别独立的微脉管道在顶层腔室注入和排出永生化大鼠脑内皮细胞系(RBE4),基底侧注入星形胶质细胞条件培养基一起培养,该设计模拟了人体微脉管系统的发散和会聚分叉特性. 生化分析显示紧密连接分子表达的上调,渗透性研究显示完整的BBB. 最后,在SyM-BBB中成功进行转运蛋白测定,验证了该模型的功能,目前尚不清楚阻隔层在这项研究中的有效性. 由于缺乏星形胶质细胞的诱导,单细胞培养的结果通常应该谨慎对待. Yang[17]的研究结果表明,星形胶质细胞对诱导和维持血脑屏障的限制作用是至关重要的. 但与传统的Transwell模型相比,SyM-BBB是有所进步的. 该模型模拟了体内微脉管系统的分叉和会聚的特点,这一点的设计更加符合人体的生理特征,并且微流体系也提供了一定的剪切应力.

    Shao[18]为了测定CNS药物的渗透性和细胞毒性,进行CNS药物筛选,基于膜的微芯片技术构建了一种血脑屏障模型(图2c). 其将多孔聚碳酸酯(PC)膜组装在两个刻蚀好微流通道的聚二甲基硅氧烷(PDMS)片中间,形成第一微芯片. 该微芯片上部为μBBB单元,底部为3D细胞室,以分别重建体内BBB结构和3D脑微环境,细胞培养室之间由通道连接(图2c). 与Prabhakarpandian[16]不同的是,Shao[18]采用的是人脑微血管内皮细胞(hCMEC / D3)接种在μBBB室膜的上侧,排除了种间差[19]. 3D细胞腔室注射包封于琼脂糖溶液中的U251(人胶质瘤细胞)细胞,用于3D细胞培养. Summerfield[20]的文献介绍了用负载脑细胞的琼脂糖凝胶来重建大脑微环境,可以更好地模拟体内药物的扩散. 为了验证其药物筛选的可行性,将舒尼替尼作为阳性药物,以确定整个BBB的渗透性和对脑瘤细胞的细胞毒性. 对于渗透性测定,在用μSPE(微固相萃取)柱消除离子源基质[21],连接到质谱仪(ESI-Q-TOF MS),直接测定渗透的舒尼替尼含量,所得渗透性数据和细胞毒性数据与先前报道的结果一致. 这些结果表明,该模型为药物渗透性和细胞毒性研究提供了有效的工具,对CNS药物的研发具有重要价值. 在以单培养为基础的BBB体外模型中,此模型是新颖且较为先进的,在应用和渗透性测定方法上都远远优于Transwell模型,也为今后血脑屏障体外模型研究提供了一定思路,值得在此基础上向共培养体系发展.

  • 2 共培养的BBB模型

  • 2.1 共培养 Transwell 模型

    张水华[22]利用Transwell 小室将BMECs和AS共培养制作BBB模型(图3a). 其首先将大鼠脑微血管内皮细胞接种于鼠尾胶原包被好的Transwell上室聚碳酸酯膜上,培养3 d后,去除嘌呤霉素,将细胞培养池置于底部含有AS的12 孔板内,7 d后聚碳酸酯膜上形成致密的单层内皮细胞. 虽然Transwell 模型在设计上相对简单,成本也较低,但其一大缺点在于忽略了腔内血细胞与血液的流动,缺乏剪切应力,以及无法很好地模拟体内BBB生理结构特征. 已经证明剪切应力可以促进内皮细胞的进一步分化,并在促进BBB表型分化和维持脑血管系统中发挥关键作[9,23].

    图3
                            共培养BBB模型

    图3 共培养BBB模型

    Fig. 3 Co-culture BBB mode

    注:(a)脑微血管内皮细胞与星型胶质细胞共培养Transwell模型;(b)3D流动培养体系的3D流动室和膜;(c)流动的中空纤维模型.

    Varga [24]按照Wilhelm[25]的方法分离原代大鼠脑内皮细胞(RBEC)、周细胞和神经胶质细胞,构建与图3a相似的模型,通过将周细胞铺板到12孔Transwell过滤器(孔径0.4 μm)的背面,并将RBEC铺在上侧来构建体外BBB模型. 并将此模型置于含有神经胶质的Cell Zscope仪器中,测量TEER及核-壳纳米粒子靶向介导的犬尿酸通过血脑屏障的渗透[24]. 培养至一定密度后,向上室内皮细胞层提供550 nmol/L氢化可的松. 相关研究已经证实,向培养基中补加氢化可的松可增加单层细胞之间的紧密[26,27],调控紧密连接蛋白的表[28,29].

    尽管使用Transwell模型有很多的缺点,但共培养Transwell体系优于单培养体系,形成的屏障作用也更加紧密. Paradis[30]利用类似Transwell模型模拟二维的血脑屏障,通过测定模型的TEER和单核细胞迁移率也证明了这一点,BMECs单培养5 d的TEER为(98±10)Ω·cm2 ,单核细胞迁移率为2.01%,AS单培养的TEER为(47±6)Ω·cm2,单核细胞迁移率为1.14%,BMECs与AS共培养的TEER为(180±19)Ω·cm2,单核细胞迁移率为0.89%. 由测试数据分析可知,共培养的TEER显著提升,但与人体实际数据有一定差距,据报道人体跨BBB电阻值在(1 500~8 000)Ω·cm2[31,32],(150~200)Ω·cm2被认为是体外BBB具有限制性作用的最低限[33]. 而用PGE2(前列腺素E2)处理的BMECs与AS共培养组,趋化因子CCR7表达上调,单核细胞的迁移率显著增加,TEER无明显下降. 这再次说明了与星形胶质细胞共培养可以明显提高BBB屏障的紧密性和完整性,同时证明PGE2处理可以提高BBB的渗透性. 因此,在今后的BBB模型构建时,可以考虑用PGE2处理细胞体系.

  • 2.2 3D流动系统共培养

    在BBB模型构建中,原代人脑内皮细胞的可用性是有限[34],因此Takeshita[35]用温敏猿猴病毒-40大T抗原(ts-SV40-LT)转染BMECs,得到了永生化的人脑微血管内皮细胞(hBMVEC);用ts-SV40-LT转染AS,得到了永生化的人星形胶质细胞(hAST[36] . 为了实现在流动下测定白细胞迁移率,其利用hBMVEC和hAST构建了体外BBB模型,该模型由3D流量泵、3D流动室、3D流动膜3部分组成. 3D流动室有3个独立的储存器,可与3D流动膜组装在一起(图3b). 3D流动膜由轨道蚀刻的聚碳酸盐制成,具有3 μm的孔径. 将hAST接种于鼠尾胶原包被的膜近腔背面,33℃孵育1 h后,用镊子轻轻翻转膜; 将hBMVEC接种于腔侧,33℃星形胶质细胞条件培养基(ACM)中共培养2 d;然后置于37℃培养,24 h后在培养基中添加一定的TNF-α和IFN-γ(37℃活化细胞 24 h),将膜小心转入3D流动室中;通过蠕动泵灌注PBMC(外周血单核细胞),血细胞计数器计数PBMC迁移量. 使用该模型,作者证明了hAST可以促进白细胞迁移并增加hBMVEC的BBB功能. 但该模型形成BBB屏障的完整性以及和体内BBB的相似性仍未知. 此外这种将脑内皮细胞与星形胶质细胞在多孔膜的相对侧培养的方式,与脑毛细血管的解剖组织结构不匹[5],在脑组织中内皮细胞实际上是由周细胞(平滑肌细胞)包裹,在维持毛细血管结构的完整性和增强内皮细胞的屏障特性方面起关键作[37,38]. 但与共培养Transwell模型相比,该模型是有所进步的,弥补了Transwell模型无法提供模仿血流的剪切应力的缺点.

  • 2.3 流动的中空纤维模型

    Cucullo[39]的研究证明,与Transwell模型相比,使用中空纤维流动系统可以显著增加TEER值,该模型降低了亲水性分子蔗糖和菊粉的渗透性,并且有着更长的使用寿命. 为了验证药物分子在血脑屏障的渗透性,从而辅助药物优化设计, Neuhaus[40]构建了一种基于流动的中空纤维来培养细胞系的BBB体外模型(图3c). 此模型由中空纤维筒、中间源(储存瓶)和泵系统组成,中空纤维固定在透明的聚碳酸酯室中,并通过管道连接到中型储液瓶,介质(星形胶质细胞条件培养基)通过泵系统循环,推动内腔入口注射器,并拉动内腔出口注射器使永生化的猪脑微血管内皮细胞系(PBMEC / C1-2)接种在内腔中空纤维的内表面上,所使用的PBMEC / C1细胞系在体外BBB模型研究中被证明是有价值[41]. 而大鼠神经胶质瘤细胞(C1)则接种在毛细管外空间(ECS),在ACM中进行共培养. 其渗透性研究选择用中枢神经系统药物苯并二氮卓类进行测试. 结果表明,单层PBMEC / C1-2的紧密度显著增强. 此模型虽然可以重复利用,但是笨重的设计忽略了系统的可伸缩性,并且无法直接观察到脑微血管内皮细胞的形态变化,可视性差,具有一定的局限性.

  • 2.4 3D微流动态装置模拟

    Xu[42]利用空气泵和聚二甲基硅氧烷(PDMS)制作了一种3D微流体装置. 该微流体装置利用PDMS制作了16个独立功能单元,每个功能单元之间由微通道相互连接,并且每个功能单元由4个均一的血脑屏障模型组成. 每个血脑屏障单元有两根管,一根位于模拟的脑区可输注天然细胞外基质(ECM)、胶原,另一根位于模拟的血管区用于引入流体流动. 其BBB构建时,先将胶原明胶和ECM输入,之后将星形胶质细胞悬浮于血管区通道中,并附着于凝胶化ECM的内侧表面. 星形胶质细胞贴壁之后,将BMECs悬浮于血管区通道中,并附着于星形胶质细胞,最后引入流体. 在连续流动下,血管通道中将BMECs和星形胶质细胞共培养(图4). 作者通过检测BMECs的紧密连接蛋白(ZO-1,Claudin-5)和黏附蛋白(VE-Cadherin)的表达情况、TEER、不同癌细胞透过BBB向脑区的迁移能力、亲水小分子荧光素钠的透过率、亲脂性分子替莫唑胺(TMZ)和亲水性分子CBP、DDP、5-Fu的透过率,并且测定了替莫唑胺对不同肿瘤细胞的IC50. 实验结果表明,TMZ可以顺利透过血脑屏障界面抑制肿瘤细胞生长,而亲水性的药物分子无法通过BBB. 证实了该模型的有效性,也突显了其可作为抗脑瘤药物筛选模型的应用价值.

    图4
                            3D微流动态BBB模型结构单元

    图4 3D微流动态BBB模型结构单元

    Fig. 4 3D microfluidic dynamic BBB model building block

    注:附着在凝胶化天然细胞外基质上的为星形胶质细胞,脑微血管内皮细胞与星形胶质细胞之间无聚碳酸酯膜,整个屏障区域在连续流动的流体中共培养.

    与传统Transwell模型相比,微装置的分隔通道配置可以操控血管流动并独立地将细胞和营养物输送到血管区或脑区. Partyka[43]的研究结果表明,血流所产生的机械应力是物质穿过脑微血管壁或沿着脑微血管壁传输的重要调节因子,而这种微流动态模型恰恰模拟了体内的血流情况. 功能单元的平行设计有助于以高通量方式进行药物渗透性测定. 血管内皮细胞和星型胶质细胞相互作用并紧密融合在一起,其TEER值最高可达(1 298± 86)Ω·cm2,与体内电阻值基本相符,并且两种细胞之前没有Transwell小室中的聚碳酸酯膜载[44],这与传统Transwell试验常见的间接相互作用明显不同. 与中空纤维模型相比,该模型也弥补了其在伸缩性和可视性上的缺点. 作者在ECM上创建了内皮-星形胶质细胞层共培养的、生理学相关的BBB模型,具有模拟血管流动和平行进行多个实验的能力,并且该模型测试了药物透过BBB对多种脑瘤细胞的抑制活性,这为在体外进行抗脑瘤药物筛选提供可能,可以说其在体外血脑屏障的研究上是有突破性进展的,为体外BBB研究提供了新的方法和思路.

  • 3 三重共培养体系

  • 3.1 三培养的Transwell模型

    随着对BBB体外模型的深入研究,科研人员认识到单培养脑微血管内皮细胞容易使体外BBB失去其在体内的特定特征,神经血管单元的细胞在诱导形成BBB体内特征中起重要作[45],从体内脑部微血管解剖组织结构考虑,三培养的体系更符合体内组织结[5]. 因此,脑内皮细胞的单培养物逐渐被共培养系统取代. 周细胞是形态上最接近脑内皮细胞并且共享共同基底膜的细[46]. Hayashi[47]发现,共培养周细胞(BMPC)能够收紧脑内皮细胞中的细胞旁屏障,类似于星形胶质细胞. 利用这一特点,Nakagawa[7]设计了一种大鼠脑微血管内皮细胞与周细胞、星形胶质细胞三培养的BBB模型(图5a). 将AS接种在12孔板的底部,而在共培养开始的前一天将BMPC铺在倒置的薄膜上,BMPC贴壁后将RBEC铺在腔室内,并放置在含有星形胶质细胞的12孔板中. 结果发现,在周细胞和星形胶质细胞存在的情况下,大鼠脑内皮细胞的紧密连接蛋白occludin、claudin-5和ZO-1表达增强,并在细胞边界处有典型的定位. 测得其TEER为400 Ω·cm2,荧光素钠的内皮渗透性系数(Pe)为3×10 -6 cm/s. 为了进一步表征该模型,作者使用19种已知体内BBB渗透性参数的化合物,进行药物渗透性分析. 结果显示,体外与体内BBB渗透性数据具有良好的相关性(R2=0.89),说明一定程度上三培养体系的结构与体内环境相似. Transwell虽然设计制备难度不大,但是其无法模拟体内的微环境,比如体内血液的流动等. 三培养体系与单培养的Transwell模型相比,仍是具有优势的,例如模型的跨内皮细胞电阻明显提高,紧密连接蛋白表达增强,这也预示着今后的BBB模型设计应该向三培养体系甚至多培养体系靠近.

    图5
                            三培养的BBB模型

    图5 三培养的BBB模型

    Fig. 5 Triple culture BBB model

    注:(a)脑微血管内皮细胞与星型胶质细胞、周细胞共培养Transwell模型;(b)三培养3D微流体BBB模型;(c)3D神经血管微流体模型.

  • 3.2 三培养3D微流体模型

    Wang[48]利用软光刻SU-8硅晶片和PDMS制成的单层装置,将0.4 μm的聚碳酸酯膜夹在单层装置之间,上下层都有独立的微流通道,胶合上下两层模型后得到外部模型. 前期处理用鼠尾胶原包被后灭菌,在下层通道中注入鼠的周细胞后将装置倒置,使周细胞贴在聚碳酸酯膜的背侧稳定后,将鼠的星型胶质细胞注入下层通道,最后在上层通道中注入鼠的脑微血管内皮细胞,形成一个三培养体系的BBB体外模型(图5b). 活/死测定表明所有培养细胞的存活率高达21 d. 跨内皮电阻(TEER)值证实培养3 d后完整单层细胞的形成,并且与单一和共培养模型相比,显示出更好的统计学数据. 测定水溶性药物[14 C]-甘露醇和[14 C] -脲、脂溶性药物地塞米松的渗透性,其结果发现三培养模型具有更好的选择渗透性. 此外,培养 18 d后[14 C] -甘露醇在整个培养模型中的渗透性参数(0.3×10 -6 cm/s)与报告的体内BBB参数(0.2×10 -6 ~ 0.6×10 -6 cm/s)相符. 数理统计显示,三培养模型中紧密连接孔半径(r = 1.2 nm)与报道的体内BBB半径(r = 0.8 nm)近似. 最后,模型显示出P-糖蛋白外排泵有功能性表达,并随着培养天数的增加而增强. 这些结果共同表明,三培养模型是BBB的稳健体外模型,与体内反应结果也更为相似.

  • 3.3 3D神经血管微流体模型

    神经血管单元是一个复杂的,相互依赖的系统,由神经元和神经支持细胞,如星形胶质细胞,以及血管系统的细胞如内皮细胞、周细胞和平滑肌细胞组成. 神经血管单元中的每种细胞在传递和处理神经信号或维持神经正常功能中起着重要作[49]. 虽然BBB体外模型研究已经取得了很大进展,但是目前仍没有体外模型能够在筛选中枢神经系统药物的同时,定量评估它们对神经细胞生长和功能的影响. Adriani[50]构建了一种3D神经血管微流体模型(图5c),在一些2D微流体平台中已经研究了类似的培养条件,总结了部分的神经血管功能,但它们未能解决对体内许多细胞过程至关重要的三维(3D)细胞组[51,52,53]. Adriani[50]构建的微流体装置是由PDMS制成的单层装置,然后通过软光刻SU-8硅晶片得到外部模型. 其中两个中央3D水凝胶通道用于培养神经组织的神经元和星形胶质细胞,两侧有两个流体介质通道,其中一个用于培养人脑微血管内皮细胞. 每个水凝胶通道设置了9个梯形结构,使得每个通道之间可以相互连通. 使用10 ku和70 ku的葡聚糖作为荧光追踪剂来评估内皮屏障的渗透性. 神经元的形态学和功能分析能够定量评估神经元反应,抗DCX抗体可以清晰显示细胞体和神经突,抗胶质纤维酸性蛋白(GFAP)的抗体鉴定星形胶质细胞,免疫细胞化学和共聚焦成像结果显示共培养系统中的每种细胞类型都表现出细胞特异性形态并表达特征性细胞标记物,如脑内皮细胞的紧密连接蛋白(ZO-1)及F-肌动蛋白(F-actin)和VE-钙黏蛋白(VE-cadherin).其BBB屏障功能的完整性评估未用测量TEER来评估,而是用分子荧光葡聚糖在不同培养条件下的渗透系数与结合蛋白质的免疫荧光标记相结合来表示屏障的完整性. 作者新颖地采用神经递质谷氨酸能否通过BBB屏障、激活神经元引起钙浓度的变化来验证单层内皮细胞的选择性. 钙成像技术的应用为体外BBB研究提供了新的测试理念与方法,但无法测得TEER,仅通过渗透性测验和紧密连接蛋白荧光标记,可能在BBB屏障完整性上存在质疑. 与Transwell模型相比,3D微流体平台具有许多优势,例如减少样品体积、降低成本、精确控制介质流量和试剂输[53,54]. 除此之外该平台还支持制备更复杂3D体外神经血管模型,包括添加存在于神经血管系统中的其他细胞类型,例如周细胞和小胶质细胞. 因此,随着3D神经血管微流体模型研究的深入,定会建立出更加完善、更加符合体内BBB生理特征的模型.

  • 4 结语

    随着对体内血脑屏障生理组成结构研究的更为深入,体外模型的模拟程度越来越相似,从BEMCs单培养模型,到与AS共培养,再到与AS、BMPC三培养模型,同时也运用了很多新的材料来构建外部模型,赋予了模型构建的可变性与可塑性,而不是纯粹、简单的Transwell小室模型. 目前使用较多的制作模型材料是PDMS和SU-8硅晶片,通过软光刻得到各类的外部模型. 但有些模型设计忽视了提供模仿血液流动的剪切应力,还有些模型忽视了与周细胞共培养的作用,相关文献记载了剪切应[43]与周细胞对建立体外BBB的作[47].

    目前我们认为构建体外BBB模型应有以下几个要点:a. 三培养或多培养体系;b. 有循环流动的模仿血流剪切应力的微流通道;c. 可以进行抗脑瘤细胞活性筛选测试;d. 模型可检验细胞形态及屏障完整性(TEER)、紧密连接蛋白、渗透性,还需要设计阳性与阴性药物进行对比验证,以此来证明体外模型的可信度及与体内的相似程度. 相信随着BBB模型的深入研究,其模型设计会越来越合理,并且会有更加先进的与细胞培养更加贴合的分子材料应用于BBB模型制作,在模型可信度、相似性验证方面会有更多、更加权威的方法,在应用方面可以进行高通量中枢神经系统药物筛选. 目前很少有将体外BBB模型用于筛选抗脑瘤化合物的实例,也缺乏模型能够进行高通量筛选能通过BBB的小分子,在这些方面的研究可能成为下一步BBB模型设计的创新点. 本文通过分析和评价各种不同的BBB模型,提出了构建BBB体外模型的要点,以期为BBB模型研究提供新的思路.

    Tel:15182434757, E-mail: dqsun@swust.edu.cn

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    • 3

      Sarkadi B, Homolya L, Szakács G, et al. Human multidrug resistance ABCB and ABCG transporters: participation in a chemoimmunity defense system. Physiological Reviews,2006, 86(4): 1179-1236

    • 4

      Panula P, Joo F, Rechardt L. Evidence for the presence of viable endothelial cells in cultures derived from dissociated rat brain. Experientia, 1978, 34(1): 95-97

    • 5

      Abbott N J, Rönnbäck L, Hansson E. Astrocyte-endothelial interactions at the blood–brain barrier. Nature Reviews Neuroscience, 2006, 7(1): 41-53

    • 6

      Paradis A, Leblanc D, Dumais N. Optimization of an in vitro human blood-brain barrier model: application to blood monocyte transmigration assays. MethodsX, 2016, 3:25-34

    • 7

      Nakagawa S, Deli M A, Kawaguchi H, et al. A new blood–brain barrier model using primary rat brain endothelial cells, pericytes and astrocytes. Neurochemistry International, 2009, 54(3-4): 253-263

    • 8

      Raub T J, Kuentzel S L, Sawada G A. Permeability of bovine brain microvessel endothelial cells in vitro: barrier tightening by a factor released from astroglioma cells. Experimental Cell Research, 1992, 199(2): 330-340

    • 9

      Rubin L, Hall D, Porter S, et al. A cell culture model of the blood-brain barrier. The Journal of Cell Biology,1991, 115(6): 1725-1735

    • 10

      Chesne C, Dehouck M, Jolliet-Riant P, et al. Drug transfer across the blood-brain barrier: comparison of in vitro and in vivo models [M]. Frontiers in Cerebral Vascular Biology. Springer. 1993: 113-115

    • 11

      秦建华, 刘婷姣, 林炳承. 微流控芯片细胞实验室. 色谱, 2009, 27(5): 655-661

      Qing J H, Liu T J, Lin B C. Chinese Journal of Chromntography, 2009, 27(5): 655-661

    • 12

      Shi D, Sun L, Mi G, et al. Controlling ferrofluid permeability across the blood-brain barrier model. Nanotechnology, 2014, 25(7): 075101

    • 13

      Bennett J, Basivireddy J, Kollar A, et al. Blood-brain barrier disruption and enhanced vascular permeability in the multiple sclerosis model EAE. Journal of Neuroimmunology, 2010, 229(1-2): 180-191

    • 14

      Canfield S G, Stebbins M J, Morales B S, et al. An isogenic blood-brain barrier model comprising brain endothelial cells, astrocytes, and neurons derived from human induced pluripotent stem cells. Journal of Neurochemistry, 2017, 140(6): 874-888

    • 15

      Su C-H, Tsai C-Y, Tomanek B, et al. Evaluation of blood-brain barrier-stealth nanocomposites for in situ glioblastoma theranostics applications. Nanoscale,2016, 8(15): 7866-7870

    • 16

      Prabhakarpandian B, Shen M-C, Nichols J B, et al. SyM-BBB: a microfluidic blood brain barrier model. Lab on a Chip, 2013, 13(6): 1093-1101

    • 17

      Yang J, Aschner M. Developmental aspects of blood-brain barrier (BBB) and rat brain endothelial (RBE4) cells as in vitro model for studies on chlorpyrifos transport. Neurotoxicology, 2003, 24(4-5): 741-745

    • 18

      Shao X, Gao D, Chen Y, et al. Development of a blood-brain barrier model in a membrane-based microchip for characterization of drug permeability and cytotoxicity for drug screening. Analytica Chimica Acta, 2016, 934:186-193

    • 19

      Deo A K, Theil F-P, Nicolas J-M. Confounding parameters in preclinical assessment of blood-brain barrier permeation: an overview with emphasis on species differences and effect of disease states. Molecular Pharmaceutics, 2013, 10(5): 1581-1595

    • 20

      Summerfield S G, Stevens A J, Cutler L, et al. Improving the in vitro prediction of in vivo central nervous system penetration: integrating permeability, P-glycoprotein efflux, and free fractions in blood and brain. Journal of Pharmacology and Experimental Therapeutics, 2006, 316(3): 1282-1290

    • 21

      Giordano B C, Burgi D S, Hart S J, et al. On-line sample pre-concentration in microfluidic devices: a review. Analytica Chimica Acta, 2012, 718(5):11-24

    • 22

      张水华,季龙凤,马璟. 非接触式共培养体外血脑屏障模型的跨膜电阻及通透性. 中国药理学与毒理学杂志, 2012, 26(6): 882-887

      Zhang S H, Ji L F, Ma J. Chinese Journal of Pharmacology and Toxicology, 2012, 26(6): 882-887

    • 23

      Cucullo L, Mcallister M S, Kight K, et al. A new dynamic in vitro model for the multidimensional study of astrocyte-endothelial cell interactions at the blood-brain barrier. Brain Research, 2002, 951(2): 243-254

    • 24

      Varga N, Csapó E, Majláth Z, et al. Targeting of the kynurenic acid across the blood-brain barrier by core-shell nanoparticles. European Journal of Pharmaceutical Sciences, 2016, 86: 67-74

    • 25

      Wilhelm I, Fazakas C, Krizbai I A. In vitro models of the blood-brain barrier. Acta Neurobiol Exp (Wars), 2011, 71(1): 113-128

    • 26

      Weidenfeller C, Schrot S, Zozulya A, et al. Murine brain capillary endothelial cells exhibit improved barrier properties under the influence of hydrocortisone. Brain Research, 2005, 1053(1-2): 162-174

    • 27

      Schrot S, Weidenfeller C, Schäffer T E, et al. Influence of hydrocortisone on the mechanical properties of the cerebral endothelium in vitro. Biophysical Journal, 2005, 89(6): 3904-3910

    • 28

      Förster C, Silwedel C, Golenhofen N, et al. Occludin as direct target for glucocorticoid-induced improvement of blood-brain barrier properties in a murine in vitro system. The Journal of Physiology, 2005, 565(2): 475-486

    • 29

      Förster C, Burek M, Romero I A, et al. Differential effects of hydrocortisone and TNFα on tight junction proteins in an in vitro model of the human blood-brain barrier. The Journal of Physiology, 2008, 586(7): 1937-1949

    • 30

      Paradis A, Leblanc D, Dumais N. Optimization of an in vitro human blood-brain barrier model: application to blood monocyte transmigration assays. MethodsX, 2016, 3: 25-34

    • 31

      Crone C, Olesen S. Electrical resistance of brain microvascular endothelium. Brain Research, 1982, 241(1): 49-55

    • 32

      Butt A M, Jones H C. Effect of histamine and antagonists on electrical resistance across the blood-brain barrier in rat brain-surface microvessels. Brain Research, 1992, 569(1): 100-105

    • 33

      Reichel A, Begley D J, Abbott N J. An overview of in vitro techniques for blood-brain barrier studies [M]. The Blood-Brain Barrier. Springer. 2003: 307-324

    • 34

      Lippmann E S, Azarin S M, Kay J E, et al. Derivation of blood-brain barrier endothelial cells from human pluripotent stem cells. Nature Biotechnology, 2012, 30(8): 783-791

    • 35

      Takeshita Y, Obermeier B, Cotleur A, et al. An in vitro blood-brain barrier model combining shear stress and endothelial cell/astrocyte co-culture. Journal of Neuroscience Methods, 2014, 232:165-172

    • 36

      Shimizu F, Sano Y, Tominaga O, et al. Advanced glycation end-products disrupt the blood-brain barrier by stimulating the release of transforming growth factor-β by pericytes and vascular endothelial growth factor and matrix metalloproteinase-2 by endothelial cells in vitro. Neurobiology of Aging, 2013, 34(7): 1902-1912

    • 37

      Armulik A, Genové G, Betsholtz C. Pericytes: developmental, physiological, and pathological perspectives, problems, and promises. Developmental Cell, 2011, 21(2): 193-215

    • 38

      Betsholtz C, Lindblom P, Gerhardt H. Role of pericytes in vascular morphogenesis [M]. Mechanisms of Angiogenesis. Springer. 2005: 115-125

    • 39

      Cucullo L, Aumayr B, Rapp E, et al. Drug delivery and in vitro models of the blood-brain barrier. Curr Opin Drug Discov Devel, 2005, 8(1): 89-99

    • 40

      Neuhaus W, Trzeciak J, Lauer R, et al. APTS-labeled dextran ladder: a novel tool to characterize cell layer tightness. Journal of Pharmaceutical and Biomedical Analysis, 2006, 40(4): 1035-1039

    • 41

      Vanier G, Szczotka A, Friedl P, et al. Haemophilus parasuis invades porcine brain microvascular endothelial cells. Microbiology, 2006, 152(1): 135-142

    • 42

      Xu H, Li Z, Yu Y, et al. A dynamic in vivo-like organotypic blood-brain barrier model to probe metastatic brain tumors. Scientific Reports, 2016, 6: 36670

    • 43

      Partyka P P, Godsey G A, Galie J R, et al. Mechanical stress regulates transport in a compliant 3D model of the blood-brain barrier. Biomaterials, 2017, 115: 30-39

    • 44

      Xue Q, Liu Y, Qi H, et al. A novel brain neurovascular unit model with neurons, astrocytes and microvascular endothelial cells of rat. International Journal of Biological Sciences, 2013, 9(2): 174-189

    • 45

      Cecchelli R, Berezowski V, Lundquist S, et al. Modelling of the blood-brain barrier in drug discovery and development. Nature Reviews Drug Discovery, 2007, 6(8): 650-661

    • 46

      Lai C-H, Kuo K-H. The critical component to establish in vitro BBB model: pericyte. Brain Research Reviews, 2005, 50(2): 258-265

    • 47

      Hayashi K, Nakao S, Nakaoke R, et al. Effects of hypoxia on endothelial/pericytic co-culture model of the blood-brain barrier. Regulatory Peptides, 2004, 123(1-3): 77-83

    • 48

      Wang J D, Khafagy E-S, Khanafer K, et al. Organization of endothelial cells, pericytes, and astrocytes into a 3D microfluidic in vitro model of the blood-brain Barrier. Molecular Pharmaceutics, 2016, 13(3): 895-906

    • 49

      Neuwelt E A, Bauer B, Fahlke C, et al. Engaging neuroscience to advance translational research in brain barrier biology. Nature Reviews Neuroscience, 2011, 12(3): 169-182

    • 50

      Adriani G, Ma D, Pavesi A, et al. A 3D neurovascular microfluidic model consisting of neurons, astrocytes and cerebral endothelial cells as a blood-brain barrier. Lab on a Chip, 2017, 17(3): 448-459

    • 51

      Griep L, Wolbers F, De Wagenaar B, et al. BBB on chip: microfluidic platform to mechanically and biochemically modulate blood-brain barrier function. Biomedical Microdevices, 2013, 15(1): 145-150

    • 52

      Yeon J H, Na D, Choi K, et al. Reliable permeability assay system in a microfluidic device mimicking cerebral vasculatures. Biomedical Microdevices, 2012, 14(6): 1141-1148

    • 53

      East E, Golding J P, Phillips J B. Engineering an integrated cellular interface in three-dimensional hydrogel cultures permits monitoring of reciprocal astrocyte and neuronal responses. Tissue Engineering Part C: Methods, 2012, 18(7): 526-536

    • 54

      Pavesi A, Adriani G, Tay A, et al. Engineering a 3D microfluidic culture platform for tumor-treating field application. Scientific Reports, 2016, 6(1): 26584

林兰

机 构:西南科技大学生命科学与工程学院,绵阳 621000

Affiliation:College of Life Science and Engineering, Southwest University of Science and Technology, Mianyang 621000, China

孙德群

机 构:西南科技大学生命科学与工程学院,绵阳 621000

Affiliation:College of Life Science and Engineering, Southwest University of Science and Technology, Mianyang 621000, China

角 色:通讯作者

Role:Corresponding author

html/pibbcn/20190123/alternativeImage/cf490c55-37be-4afe-b12c-2196c4bad15e-F001.png
html/pibbcn/20190123/alternativeImage/cf490c55-37be-4afe-b12c-2196c4bad15e-F002.png
html/pibbcn/20190123/alternativeImage/cf490c55-37be-4afe-b12c-2196c4bad15e-F003.png
html/pibbcn/20190123/alternativeImage/cf490c55-37be-4afe-b12c-2196c4bad15e-F004.png
html/pibbcn/20190123/alternativeImage/cf490c55-37be-4afe-b12c-2196c4bad15e-F005.png

图1 血脑屏障结构单元

Fig. 1 The blood-brain barrier structural unit

图2 单培养BBB模型

Fig. 2 Single cell culture BBB mode

图3 共培养BBB模型

Fig. 3 Co-culture BBB mode

图4 3D微流动态BBB模型结构单元

Fig. 4 3D microfluidic dynamic BBB model building block

图5 三培养的BBB模型

Fig. 5 Triple culture BBB model

image /

BBB靠近血液层为脑微血管内皮细胞,周围环绕着周细胞、神经元、小胶质细胞和星形细胞末端足底.

(a)脑微血管内皮细胞单培养Transwell模型;(b)微流体微血管血脑屏障模型;(c)质谱联用微流体平台.

(a)脑微血管内皮细胞与星型胶质细胞共培养Transwell模型;(b)3D流动培养体系的3D流动室和膜;(c)流动的中空纤维模型.

附着在凝胶化天然细胞外基质上的为星形胶质细胞,脑微血管内皮细胞与星形胶质细胞之间无聚碳酸酯膜,整个屏障区域在连续流动的流体中共培养.

(a)脑微血管内皮细胞与星型胶质细胞、周细胞共培养Transwell模型;(b)三培养3D微流体BBB模型;(c)3D神经血管微流体模型.

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      Paradis A, Leblanc D, Dumais N. Optimization of an in vitro human blood-brain barrier model: application to blood monocyte transmigration assays. MethodsX, 2016, 3:25-34

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      Nakagawa S, Deli M A, Kawaguchi H, et al. A new blood–brain barrier model using primary rat brain endothelial cells, pericytes and astrocytes. Neurochemistry International, 2009, 54(3-4): 253-263

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      Raub T J, Kuentzel S L, Sawada G A. Permeability of bovine brain microvessel endothelial cells in vitro: barrier tightening by a factor released from astroglioma cells. Experimental Cell Research, 1992, 199(2): 330-340

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      Su C-H, Tsai C-Y, Tomanek B, et al. Evaluation of blood-brain barrier-stealth nanocomposites for in situ glioblastoma theranostics applications. Nanoscale,2016, 8(15): 7866-7870

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      Prabhakarpandian B, Shen M-C, Nichols J B, et al. SyM-BBB: a microfluidic blood brain barrier model. Lab on a Chip, 2013, 13(6): 1093-1101

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      Zhang S H, Ji L F, Ma J. Chinese Journal of Pharmacology and Toxicology, 2012, 26(6): 882-887

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      Cucullo L, Mcallister M S, Kight K, et al. A new dynamic in vitro model for the multidimensional study of astrocyte-endothelial cell interactions at the blood-brain barrier. Brain Research, 2002, 951(2): 243-254

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      Varga N, Csapó E, Majláth Z, et al. Targeting of the kynurenic acid across the blood-brain barrier by core-shell nanoparticles. European Journal of Pharmaceutical Sciences, 2016, 86: 67-74

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      Wilhelm I, Fazakas C, Krizbai I A. In vitro models of the blood-brain barrier. Acta Neurobiol Exp (Wars), 2011, 71(1): 113-128

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      Weidenfeller C, Schrot S, Zozulya A, et al. Murine brain capillary endothelial cells exhibit improved barrier properties under the influence of hydrocortisone. Brain Research, 2005, 1053(1-2): 162-174

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      Schrot S, Weidenfeller C, Schäffer T E, et al. Influence of hydrocortisone on the mechanical properties of the cerebral endothelium in vitro. Biophysical Journal, 2005, 89(6): 3904-3910

    • 28

      Förster C, Silwedel C, Golenhofen N, et al. Occludin as direct target for glucocorticoid-induced improvement of blood-brain barrier properties in a murine in vitro system. The Journal of Physiology, 2005, 565(2): 475-486

    • 29

      Förster C, Burek M, Romero I A, et al. Differential effects of hydrocortisone and TNFα on tight junction proteins in an in vitro model of the human blood-brain barrier. The Journal of Physiology, 2008, 586(7): 1937-1949

    • 30

      Paradis A, Leblanc D, Dumais N. Optimization of an in vitro human blood-brain barrier model: application to blood monocyte transmigration assays. MethodsX, 2016, 3: 25-34

    • 31

      Crone C, Olesen S. Electrical resistance of brain microvascular endothelium. Brain Research, 1982, 241(1): 49-55

    • 32

      Butt A M, Jones H C. Effect of histamine and antagonists on electrical resistance across the blood-brain barrier in rat brain-surface microvessels. Brain Research, 1992, 569(1): 100-105

    • 33

      Reichel A, Begley D J, Abbott N J. An overview of in vitro techniques for blood-brain barrier studies [M]. The Blood-Brain Barrier. Springer. 2003: 307-324

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      Lippmann E S, Azarin S M, Kay J E, et al. Derivation of blood-brain barrier endothelial cells from human pluripotent stem cells. Nature Biotechnology, 2012, 30(8): 783-791

    • 35

      Takeshita Y, Obermeier B, Cotleur A, et al. An in vitro blood-brain barrier model combining shear stress and endothelial cell/astrocyte co-culture. Journal of Neuroscience Methods, 2014, 232:165-172

    • 36

      Shimizu F, Sano Y, Tominaga O, et al. Advanced glycation end-products disrupt the blood-brain barrier by stimulating the release of transforming growth factor-β by pericytes and vascular endothelial growth factor and matrix metalloproteinase-2 by endothelial cells in vitro. Neurobiology of Aging, 2013, 34(7): 1902-1912

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      Betsholtz C, Lindblom P, Gerhardt H. Role of pericytes in vascular morphogenesis [M]. Mechanisms of Angiogenesis. Springer. 2005: 115-125

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      Vanier G, Szczotka A, Friedl P, et al. Haemophilus parasuis invades porcine brain microvascular endothelial cells. Microbiology, 2006, 152(1): 135-142

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      Xu H, Li Z, Yu Y, et al. A dynamic in vivo-like organotypic blood-brain barrier model to probe metastatic brain tumors. Scientific Reports, 2016, 6: 36670

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      Partyka P P, Godsey G A, Galie J R, et al. Mechanical stress regulates transport in a compliant 3D model of the blood-brain barrier. Biomaterials, 2017, 115: 30-39

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      Xue Q, Liu Y, Qi H, et al. A novel brain neurovascular unit model with neurons, astrocytes and microvascular endothelial cells of rat. International Journal of Biological Sciences, 2013, 9(2): 174-189

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      Cecchelli R, Berezowski V, Lundquist S, et al. Modelling of the blood-brain barrier in drug discovery and development. Nature Reviews Drug Discovery, 2007, 6(8): 650-661

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      Lai C-H, Kuo K-H. The critical component to establish in vitro BBB model: pericyte. Brain Research Reviews, 2005, 50(2): 258-265

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      Hayashi K, Nakao S, Nakaoke R, et al. Effects of hypoxia on endothelial/pericytic co-culture model of the blood-brain barrier. Regulatory Peptides, 2004, 123(1-3): 77-83

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      Wang J D, Khafagy E-S, Khanafer K, et al. Organization of endothelial cells, pericytes, and astrocytes into a 3D microfluidic in vitro model of the blood-brain Barrier. Molecular Pharmaceutics, 2016, 13(3): 895-906

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      Neuwelt E A, Bauer B, Fahlke C, et al. Engaging neuroscience to advance translational research in brain barrier biology. Nature Reviews Neuroscience, 2011, 12(3): 169-182

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      Adriani G, Ma D, Pavesi A, et al. A 3D neurovascular microfluidic model consisting of neurons, astrocytes and cerebral endothelial cells as a blood-brain barrier. Lab on a Chip, 2017, 17(3): 448-459

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      Griep L, Wolbers F, De Wagenaar B, et al. BBB on chip: microfluidic platform to mechanically and biochemically modulate blood-brain barrier function. Biomedical Microdevices, 2013, 15(1): 145-150

    • 52

      Yeon J H, Na D, Choi K, et al. Reliable permeability assay system in a microfluidic device mimicking cerebral vasculatures. Biomedical Microdevices, 2012, 14(6): 1141-1148

    • 53

      East E, Golding J P, Phillips J B. Engineering an integrated cellular interface in three-dimensional hydrogel cultures permits monitoring of reciprocal astrocyte and neuronal responses. Tissue Engineering Part C: Methods, 2012, 18(7): 526-536

    • 54

      Pavesi A, Adriani G, Tay A, et al. Engineering a 3D microfluidic culture platform for tumor-treating field application. Scientific Reports, 2016, 6(1): 26584