药学学报

Endothelial cell	Co-culture cell	ECM	Permeability measurement/10^-6 cm²·s^-1	TEER/Ω·cm²	Shear stress/10^-3 Pa	Protein expression	Ref.
HUVEC hCMEC/D3	Primary brain Astrocytes Rats cortical neurons	Collagen I	FD^a10 kDa FD 70 kDa	3.3-65.8	N.A.	N.A.	ZO-1 VE-cadherin	Adriani^[35]
hBMVEC	Human brain pericytes Human brain astrocytes	Collagen I	FD 3 kDa	2-5	N.A.	100	ZO-1 VE-cadherin F-actin	Herland^[48]
hiPS-derived BMEC	Primary rat astrocytes	Collagen I Fibronectin	FD 4 kDa FD 20 kDa FD 70 kDa	0.01-0.1	2000-4000	2-3	ZO-1 F-actin Claudin-5	Wang^[61]
RBE4	Human neutrophils	Collagen I	FD 40 kDa	N.A.	N.A.	N.A.	ZO-1	Cho^[31]
RBE4	Neonatal rat astrocytes	N.A.	TRD^b 40 kDa	1.1-41	N.A.	0.4-7.6	ZO-1	Deosarkar^[47]
bEnd.3	Mouse astrocyte (C8D1A) Immortalized mouse pericytes	Fibronectin	Urea Mannitol Dexamethasone	1.1 0.3-0.6 2.9	320	160	N.A.	Wang^[62]
hBMEC	Primary human brain pericytes Primary astrocytes Human cortical glutamatergic neurons from hiPSCs	Laminin	FD 10 kDa FD 70 kDa	N.A.	5000-30000	2	ZO-1	Brown^[37]
bEnd.3	N.A.	Collagen I Fibronectin	FD 40 kDa Mannitol	2.27×10^-5-6.5×10^-4 3.9	N.A.	N.A.	ZO-1	Kim^[29]
bEnd.3	Astrocytes(C8D1A) Rat astrocytes (C6)	Fibronectin Collagen IV Fibroectin	FD 4 kDa FD 20 kDa FD 70 kDa Propidium iodide	N.A.	50-280	0.08 1500	ZO-1 ZO-1	Booth^[19] Booth^[63]
hCMEC/D3	N.A.	Collagen I	N.A.	N.A.	Static 40 Flow 120 TNF-α12	580	ZO-1	Griep^[64]
hCMEC/D3	Human astrocytes	Collagen I Matrigel	FD 4 kDa -TNF-α -Flow	4.0-9.0 0.6-1.2	Static 180-220 Flow 220-1000	50	ZO-1	Partyka^[46]
RBE4	N.A. (ACM)	Fibronectin	FD 3-5 kDa	N.A.	N.A.	3	ZO-1 Claudin	Prabhakarpandian^[34]
RBE4	E18 neural cells Astrocytes Microglia	Fibronectin	A488 dextran 3 kDa	N.A.	N.A.	N.A.	ZO-1	Achyuta^[53]
HUVEC	N.A. (ACM)	N.A.	FD 4 kDa FD 40 kDa FD 70 kDa	N.A.	N.A.	N.A.	ZO-1	Yeon^[65]
hCMEC/D3 Primary rat brain endothelial cells	Primary brain pericytes Primary brain astrocytes	Collagen I Collagen IV	NaFL^c376 Da FD 4.4 kDa EBA^d67 kDa	1.57-1.61 1.32-1.55 0.15-0.51	10-30	150	ZO-1	Walter^[38]
bEnd.3	Astrocytes(C8D1A)	Collagen I Collagen IV Fibronectin	FD 70 kDa	0.6	N.A.	500	Claudin-5	Sellgren^[41]
hBMVEC	Human primary astrocytes	Collagen I	TMR dextran^e3 kDa TMR dextran 40 kDa	N.A.	1000-1240	N.A.	N.A.	Merkel^[45]
bEnd.3	N.A.	N.A.	N.A.	N.A.	1000-1150	15	Claudin 5	Falanga^[33]
hBMVECs	Human astrocytes Human pericytes Human hippocampal neural stem cells	Fibronectin Collagen IV Laminin	CB dextran^f530 Da BSA-555 67 kDa	11.2 ± 0.8 0.27 ± 0.02	N.A.	2	VE-Cadherin F-actin	Maoz^[40]
iPSC-ECs	Human brain pericytes Human brain astrocytes	Fibronectin	FD 10 kDa FD 40 kDa	0.22-1.2 0.089-0.66	N.A.	N.A.	ZO-1 Claudin-5 Occluding	Campisi^[39]
hBMVEC	Human astrocytes Human pericytes	Laminin	FD 10 kDa	N.A.	100	N.A.	ZO-1 Claudin-5	Brown^[36]
fBMVECs aBMVECs	Astrocyte Pericyte	Collagen I	NaFL 376 Da CB dextran 3 kDa TMR dextran 40 kDa	N.A.	N.A.	1000	ZO-1 Claudin-5 Occludin Tricellulin	Andrews^[66]
hCMEC/D3	U251	Matrigel	NaFL 376 Da FD 70 kDa	7.16 ± 0.81 1.41 ± 0.15	N.A.	110	VE-Cadherin F-actin	Shao^[67]
HUVEC	Normal human lung fibroblast Cortex neural cell	N.A.	FD 20 kDa FD 70 kDa	0.45 ± 0.11 0.36 ± 0.05	N.A.	N.A.	ZO-1	Bang^[68]

Endothelial cell	Co-culture cell	ECM	Permeability measurement/10^-6 cm²·s^-1	TEER/Ω·cm²	Shear stress/10^-3 Pa	Protein expression	Ref.
HUVEC hCMEC/D3	Primary brain Astrocytes Rats cortical neurons	Collagen I	FD^a10 kDa FD 70 kDa	3.3-65.8	N.A.	N.A.	ZO-1 VE-cadherin	Adriani^[35]
hBMVEC	Human brain pericytes Human brain astrocytes	Collagen I	FD 3 kDa	2-5	N.A.	100	ZO-1 VE-cadherin F-actin	Herland^[48]
hiPS-derived BMEC	Primary rat astrocytes	Collagen I Fibronectin	FD 4 kDa FD 20 kDa FD 70 kDa	0.01-0.1	2000-4000	2-3	ZO-1 F-actin Claudin-5	Wang^[61]
RBE4	Human neutrophils	Collagen I	FD 40 kDa	N.A.	N.A.	N.A.	ZO-1	Cho^[31]
RBE4	Neonatal rat astrocytes	N.A.	TRD^b 40 kDa	1.1-41	N.A.	0.4-7.6	ZO-1	Deosarkar^[47]
bEnd.3	Mouse astrocyte (C8D1A) Immortalized mouse pericytes	Fibronectin	Urea Mannitol Dexamethasone	1.1 0.3-0.6 2.9	320	160	N.A.	Wang^[62]
hBMEC	Primary human brain pericytes Primary astrocytes Human cortical glutamatergic neurons from hiPSCs	Laminin	FD 10 kDa FD 70 kDa	N.A.	5000-30000	2	ZO-1	Brown^[37]
bEnd.3	N.A.	Collagen I Fibronectin	FD 40 kDa Mannitol	2.27×10^-5-6.5×10^-4 3.9	N.A.	N.A.	ZO-1	Kim^[29]
bEnd.3	Astrocytes(C8D1A) Rat astrocytes (C6)	Fibronectin Collagen IV Fibroectin	FD 4 kDa FD 20 kDa FD 70 kDa Propidium iodide	N.A.	50-280	0.08 1500	ZO-1 ZO-1	Booth^[19] Booth^[63]
hCMEC/D3	N.A.	Collagen I	N.A.	N.A.	Static 40 Flow 120 TNF-α12	580	ZO-1	Griep^[64]
hCMEC/D3	Human astrocytes	Collagen I Matrigel	FD 4 kDa -TNF-α -Flow	4.0-9.0 0.6-1.2	Static 180-220 Flow 220-1000	50	ZO-1	Partyka^[46]
RBE4	N.A. (ACM)	Fibronectin	FD 3-5 kDa	N.A.	N.A.	3	ZO-1 Claudin	Prabhakarpandian^[34]
RBE4	E18 neural cells Astrocytes Microglia	Fibronectin	A488 dextran 3 kDa	N.A.	N.A.	N.A.	ZO-1	Achyuta^[53]
HUVEC	N.A. (ACM)	N.A.	FD 4 kDa FD 40 kDa FD 70 kDa	N.A.	N.A.	N.A.	ZO-1	Yeon^[65]
hCMEC/D3 Primary rat brain endothelial cells	Primary brain pericytes Primary brain astrocytes	Collagen I Collagen IV	NaFL^c376 Da FD 4.4 kDa EBA^d67 kDa	1.57-1.61 1.32-1.55 0.15-0.51	10-30	150	ZO-1	Walter^[38]
bEnd.3	Astrocytes(C8D1A)	Collagen I Collagen IV Fibronectin	FD 70 kDa	0.6	N.A.	500	Claudin-5	Sellgren^[41]
hBMVEC	Human primary astrocytes	Collagen I	TMR dextran^e3 kDa TMR dextran 40 kDa	N.A.	1000-1240	N.A.	N.A.	Merkel^[45]
bEnd.3	N.A.	N.A.	N.A.	N.A.	1000-1150	15	Claudin 5	Falanga^[33]
hBMVECs	Human astrocytes Human pericytes Human hippocampal neural stem cells	Fibronectin Collagen IV Laminin	CB dextran^f530 Da BSA-555 67 kDa	11.2 ± 0.8 0.27 ± 0.02	N.A.	2	VE-Cadherin F-actin	Maoz^[40]
iPSC-ECs	Human brain pericytes Human brain astrocytes	Fibronectin	FD 10 kDa FD 40 kDa	0.22-1.2 0.089-0.66	N.A.	N.A.	ZO-1 Claudin-5 Occluding	Campisi^[39]
hBMVEC	Human astrocytes Human pericytes	Laminin	FD 10 kDa	N.A.	100	N.A.	ZO-1 Claudin-5	Brown^[36]
fBMVECs aBMVECs	Astrocyte Pericyte	Collagen I	NaFL 376 Da CB dextran 3 kDa TMR dextran 40 kDa	N.A.	N.A.	1000	ZO-1 Claudin-5 Occludin Tricellulin	Andrews^[66]
hCMEC/D3	U251	Matrigel	NaFL 376 Da FD 70 kDa	7.16 ± 0.81 1.41 ± 0.15	N.A.	110	VE-Cadherin F-actin	Shao^[67]
HUVEC	Normal human lung fibroblast Cortex neural cell	N.A.	FD 20 kDa FD 70 kDa	0.45 ± 0.11 0.36 ± 0.05	N.A.	N.A.	ZO-1	Bang^[68]

基于微流控芯片的体外血脑屏障模型构建与应用研究进展

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蔡颖 ¹^,² , 陈阳 ¹ , 周雯婷 ¹ , 廖跃华 ³ , 洪战英 ¹^,^* , 柴逸峰 ¹

药学学报 | 综述 2019,54(2): 269-280

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药学学报 | 综述 2019, 54(2): 269-280

基于微流控芯片的体外血脑屏障模型构建与应用研究进展

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蔡颖¹^,², 陈阳¹, 周雯婷¹, 廖跃华³, 洪战英¹^,^*, 柴逸峰¹

作者信息

1.第二军医大学药学院, 上海 200433

2.福建中医药大学药学院, 福建福州 350122

3.上海健康医学院, 上海 201318

通讯作者:

*洪战英, Tel:86-21-81871261-85, E-mail:hongzhy001@163.com

Research advancement in the construction and applications of microfluidic devices for in vitro blood-brain barrier research

Ying CAI¹^,², Yang CHEN¹, Wen-ting ZHOU¹, Yue-hua LIAO³, Zhan-ying HONG¹^,^*, Yi-feng CHAI¹

Affiliations

1. School of Pharmacy, Second Military Medical University, Shanghai 200433, China

2. School of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China

3. Shanghai University of Medicine & Health Sciences, Shanghai 201318, China

出版时间: 2019-02-12 doi: 10.16438/j.0513-4870.2018-0773

文章导航

摘要

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血脑屏障（blood-brain barrier，BBB）能控制血脑两侧的物质转运，保证中枢神经组织内环境的稳定，对神经系统疾病药物的研发有着重要的影响。建立体外高保真BBB模型对BBB功能进行研究，对药物、毒素等的屏障渗透性的评估等具有重要意义。然而，BBB结构的复杂性导致其难以在体外较好的复制，BBB芯片可以使系统微型化、减少细胞和培养基用量，同时可以诱导剪切力产生，与传统体外BBB模型相比具有一定优势。本文对BBB芯片模型的建立，模型的表征方法及其在神经炎症、脑肿瘤研究和药物评价方面的应用进行综述，为建立更可靠的体外BBB模型提供参考。

关键词

血脑屏障 / 微流控芯片 / 模型建立 / 表征方法 / 应用

Abstract

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The blood-brain barrier (BBB) not only maintains the stability of the environment within the central nervous system by controlling the transport of substances on both sides of the blood and brain, but also plays an important role in the R&D of new drugs for neurological disorders. The establishment of an in vitro high-fidelity model to study BBB function is imperative for assessing barrier permeability of drugs and xenobiotics. However, the complexity of the BBB structure makes it difficult to replicate with an in vitro model. Compared to the traditional in vitro BBB model, the BBB-on-chip provides certain advantages in miniaturizing the system, reducing the amount of cells and medium required, and allowing simultaneously induction of shear stress. We review here the BBB-on-chip models from their establishment and characterization to applications in research of neuroinflammation, brain tumor and drug evaluation.

Key words

blood-brain barrier / microfluidic chip / model establishment / characterization / application

引用本文

蔡颖, 陈阳, 周雯婷, 廖跃华, 洪战英, 柴逸峰. 基于微流控芯片的体外血脑屏障模型构建与应用研究进展. 药学学报, 2019 , 54 (2) : 269 -280 . DOI: 10.16438/j.0513-4870.2018-0773

Ying CAI, Yang CHEN, Wen-ting ZHOU, Yue-hua LIAO, Zhan-ying HONG, Yi-feng CHAI. Research advancement in the construction and applications of microfluidic devices for in vitro blood-brain barrier research[J]. Acta Pharmaceutica Sinica, 2019 , 54 (2) : 269 -280 . DOI: 10.16438/j.0513-4870.2018-0773

正文

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血脑屏障(blood-brain barrier, BBB)是血液循环和神经组织之间一种特殊生物界面, 主要由无窗孔脑微血管内皮细胞(brain microvessel endothelia cell, BMEC)及紧密连接(tight junctions, TJs)、毛细血管基底膜及周细胞、星形胶质细胞足突共同组成^{[1, 2]}。它可以控制中枢神经系统(central nervous system, CNS)与血液、脑脊液、脑间质液之间的物质交换, 阻挡病原生物和其他大分子物质由血液循环进入脑室和脑组织, 并将脑内代谢产物排出, 在维持脑内环境稳态和药物向CNS的运输中起着至关重要的作用^{[3, 4]}。

神经系统疾病和脑部肿瘤的治疗需要透过BBB从而有效地将药物输送到CNS^[5], BBB对CNS候选药物的渗透性将直接影响药效。目前, 一些脂溶性小分子已用于治疗癫痫、精神分裂症^[6], 而98%以上的小分子化合物和几乎全部的大分子神经药物无法透过BBB产生药效, 因此, CNS新药研发的失败率远高于其他药物, 开发能透过BBB并对CNS疾病有疗效的药物仍然是一个艰巨的挑战^{[7, 8]}。

微流控芯片技术将微反应腔、微通道和其他功能单元集成到一块几平方厘米(甚至更小)的芯片上, 通过连接这些功能单元和通道网络来操纵微流体从而实现在芯片上进行样品制备、反应、分离、检测等操作^[9]。微流控芯片因其微纳尺度与细胞大小匹配、密闭培养方式接近生理环境、微流体网络灵活可控、可高通量设计应用等特点, 具备模拟可控生物化学和物理因素的优势, 已逐渐成为体外细胞培养的理想技术平台^{[10, 11]}, 对于建立BBB体外高保真模型具有一定优势。本文主要综述了近年来微流控芯片在建立BBB模型方面的研究进展。

1 血脑屏障

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1.1 血脑屏障结构与功能

BBB中, BMEC之间重叠覆盖, 跨膜蛋白、胞质附着蛋白(zonula occludens protein, ZO)及细胞骨架蛋白共同组成了TJs, 使相邻的内皮细胞更加紧密地连接在一起, 形成结构致密的屏障^[12-14]。其中, 跨膜蛋白包括咬合蛋白(occludin)、闭合蛋白(claudin)和连接黏附分子3种完整的膜蛋白; 胞质附着蛋白有3种, 即闭合小环蛋白ZO-1、ZO-2和ZO-3;而纤维状肌动蛋白(F-actin)是一种重要的细胞骨架蛋白^{[13, 14]}。约85%脑毛细血管表面被基底膜外大量的星形胶质细胞终足包被。镶嵌在内皮基膜上的周细胞与内皮细胞紧密连接, 夹在脑微血管内皮细胞和星形胶质细胞终足间, 形成三明治结构^[15] (图 1)^[4]。

BBB既可以用作调节被动和主动转运的功能性物理屏障, 也可以用作代谢和免疫屏障^[16]。TJs将相邻内皮细胞质膜紧密连在一起形成了物理屏障, 限制了离子和亲水小分子物质通过旁细胞途径渗透。主动转运屏障通过特定膜转运蛋白、囊泡机制和多药耐药转运蛋白调节潜在有害物质外排。代谢屏障则由胞内外代谢有毒化合物的酶构成, 它们可以将底物转化为低渗、低毒的化合物。物理和代谢屏障使98%的小分子和几乎全部大分子物质不能通过BBB, 免疫屏障可以阻止细菌和病毒进入大脑。

1.2 BBB转运机制与体内外BBB模型

亲脂性小分子化合物可通过被动扩散透过BBB, 亲水性化合物则被TJs限制通过旁细胞扩散透过BBB。大脑所必需的极性分子可通过各种转运蛋白转运, 分子量较大的肽和蛋白质主要通过与血-脑膜表面的受体结合促进内吞作用进行转运, 而外排转运系统可主动将毒性代谢物和异源性物质外排, 以维持大脑正常的生理功能^{[17, 18]}。

目前, BBB研究采用体内和体外模型。体内模型直接利用整个活生物体, 而体外模型则利用细胞构建人工环境来模拟体内BBB结构和环境^[19]。体内模型的优势在于它们可以反映BBB环境的复杂性, 提供更接近人类表型的环境^[20]。但是, 体内模型成本高、费时费力且具有伦理争议^[21]。体外模型则具有成本低、简单易控、模型相对稳定、适合高通量筛选的特点^[22]。体外BBB模型多建立在静态Transwell培养系统中, 但该模型无法复制BBB的关键特征, 如血流、剪切力等。近年来, 体外动态BBB模型(DIV-BBB)被开发出来, 它们利用中空纤维模拟BBB结构, 通过变速泵将培养液抽入到装置中产生剪切力从而模拟体内环境^[23-25]。然而, DIV-BBB壁厚(150 μm)显著高于Transwell膜厚度(10 μm), 不利于细胞间相互作用, 且模型达到稳态需要更长的时间^[26]。

2 BBB芯片模型的构建

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BBB芯片是指利用微流控芯片技术构建易调控、接近在体微环境的体外BBB。与现有模型相比, BBB芯片模型具有许多优点: ①功能体积小、节约材料、成本低^[27]; ②介质交换快、能快速达到稳态; ③可提供平行、可控的动态微环境; ④培养膜更薄可以减少共培养细胞间距; ⑤高通量; ⑥易于对被测物进行分析和受控递送; ⑦能够整合到生物传感器中以长期监测BBB功能^[28]。但是目前BBB芯片仍无法完全反映体内复杂的微环境, 无法替代体内模型。

2.1 BBB芯片设计

2.1.1 BBB芯片结构

BBB芯片结构主要分为两类, 一类是血管结构(图 2)^[29-31], 另一类是神经血管单元(neurovascular unit, NVU)的生理结构(图 3)^[32-35]。

血管结构芯片复制脑微毛细血管的3D管状结构和脑微血管的微观尺寸并模拟其功能。Marino等^[30]采用双光子光刻技术制成平均直径为10 μm的多孔血管状结构。Kim等^[29]用微针和3D打印框架制造了一系列由Ⅰ型胶原组成的微通道, 通过在圆柱形胶原微通道的腔表面培养脑内皮细胞, 在体外重建了脑微血管。

NVU的体内生理结构可分为两种:垂直双培养腔结构^[36-38]和平面并列多通道结构^[39], 它们均有血管室和脑室, 通过向血管室连续注入培养液, 可模拟血管内流体状态, 更接近人体真实内环境。其中垂直双培养腔结构中间由多孔薄膜分割且上、下两层细胞培养腔均与微流体通道和芯片微电极相连^[32], 通过芯片微电极对电阻抗信号进行采集, 实时监测芯片内细胞层的生理活性情况。垂直双培养腔结构细胞共培养有效面积更大, 适合进行药物筛选但无法实现接触式共培养。平面并列多通道结构可通过通道间多孔栅栏结构和柱状结构^[35]实现细胞的接触培养且适合显微观察与操作, 适用于分析BBB结构中细胞间相互影响, 但难以集成电极对细胞层进行实时监测。

2.1.2 BBB芯片膜材料

垂直双培养腔BBB芯片中使用的膜材料主要为聚碳酸脂(polycarbonate, PC)膜, 聚对苯二甲酸(polyethylene terephthalate, PET)膜^{[38, 40]}、聚乙烯(polyethylene, PE)膜^[33]使用较少。PC薄膜耐冲击性好, 可支持剪切力下细胞的黏附。但PC薄膜是光学半透明的, 无法通过相差显微镜观察细胞, 需要对细胞进行荧光染色且不能随时观察。PE膜透明度更高, 但比较柔软, 对细胞的黏附支撑不如PC膜。Sellgren等^[41]分别采用聚四氟乙烯(poly tetra fluoroethylene, PTFE)膜与PE膜, 将小鼠星形胶质细胞与bEnd.3细胞进行共培养, 用相差显微镜观察发现, 在生理剪切力作用下, PTFE膜能支持细胞单层存活, 而PE膜上细胞脱落。免疫荧光以及渗透性测定结果均表明PTFE膜比PE膜更适合在剪切力下支持细胞附着, 成功利用亲水化PTFE纳米多孔膜建立了BBB芯片模型。

2.2 芯片上细胞培养

2.2.1 培养模式

BBB芯片模型和其他体外BBB模型大多使用动物来源的细胞。一般认为牛、猪等大型动物细胞更适合建立体外BBB模型, 但是由于取材难度较大^[42-44], 所以常采用大鼠细胞作为实验材料。

根据BBB芯片模型所包含细胞种类数不同, 可以分为单培养模型和共培养模型。单培养模型的优点是简单, 缺点是只培养BMEC一种细胞, 缺乏不同类型细胞间的交流。共培养模型除了内皮细胞外, 还包含了BBB中的其他细胞, 因此能更好地模拟其体内组织结构, 形成更紧密的屏障。现阶段有以下几种共培养模式:内皮细胞-星形胶质细胞二元共培养模型^[45-47]以及内皮细胞-星形胶质细胞-周细胞^{[48, 49]}、内皮细胞-星形胶质细胞-神经元^{[35, 37]}、内皮细胞-周细胞-神经元三元共培养模型^[37]。

脑微血管内皮细胞-星形胶质细胞共培养模型中, 两种细胞的相互作用能增强内皮细胞间的TJs, 促进细胞表现型更接近于体内状态, 增强其屏障性。Jeong等^[32]用组胺对内皮细胞单培养、内皮细胞与星形胶质细胞共培养BBB芯片的屏障功能进行了测试和比较, 组胺可破坏紧密连接屏障增加内皮细胞通透性。结果发现:组胺处理前, 共培养芯片跨膜电阻(trans-endothelial electrical resistant, TEER)值显著高于单培养芯片, 组胺处理后, 共培养芯片TEER值无明显改变而单培养芯片TEER值迅速下降; 单培养模型小分子葡聚糖的渗透系数是共培养模型的2.48倍。结果表明星形胶质细胞与内皮细胞相互作用形成的屏障更紧密且具有更高的弹性。Walter等^[38]在芯片上建立了两种不同的BBB模型: hCMEC/D3单培养模型和脑血管内皮细胞-神经胶质细胞-周细胞三元共培养模型, 发现三元共培养BBB模型显示出更高的TEER值、更高的TJ蛋白表达和更低的渗透率, 说明周细胞和星形胶质细胞对BBB的完整性有协同促进作用。

2.2.2 培养环境

BBB芯片上细胞培养环境包括生物和物理的微环境, 其中生物培养环境主要由一些细胞外基质(extracellular matrix, ECM)形成。脑毛细血管基底膜在细胞相互作用和细胞行为调控中起着关键作用, 它由一层连接性细胞外基质蛋白(约50 nm)组成, 如Ⅳ型胶原蛋白、层粘连蛋白、硫酸乙酰肝素蛋白多糖和纤连蛋白^[50]。ECM可以形成间质基底膜, 提供细胞附着的框架, 因此在体外BBB模型中需要可靠的ECM^[51]。目前, BBB芯片模型通常通过在微通道上涂覆纤连蛋白、胶原蛋白或层粘连蛋白来模拟3D体内微环境。除此之外, 也会使用多聚阳离子化合物多聚赖氨酸(poly-L-lysine, PLL)对微通道表面进行修饰, 促进细胞的贴壁和生长^[52]。Achyuta等^[53]采用模块化方法建立了由神经元单元、血管单元组成的NVU芯片系统。神经元单元将大鼠皮质细胞在涂有PLL的孔中培养10天, 血管单元通道用纤连蛋白包被后, 静态培养大鼠脑内皮细胞2天。通过活/死染色、免疫荧光、渗透性实验对BBB芯片模型进行表征, 结果表明NVU芯片建立的BBB功能良好, 可为药物筛选、神经系统基础研究等提供一个新的平台。近年来, 利用凝胶材料实现细胞3D培养成为体外模拟ECM环境的重要手段。凝胶材料大多具有良好的生物相容性, 尤其是从动物组织内提取的基质胶, 不仅含有转化生长因子、上表皮生长因子等多种细胞生长因子, 而且含有胶原蛋白、层粘连蛋白、纤连蛋白等蛋白质骨架, 能够提供细胞黏附的特异位点, 较好地模拟了细胞在体内存活的微环境^{[32, 54]}。

流体剪切力是细胞力学微环境中一种重要的机械力, 是细胞培养的物理环境构成因素, 它通常由血液或组织间液流动产生, 并普遍存在于生命体内^[55]。病理性剪切力会导致内皮功能障碍, 并扰乱促动脉粥样硬化、抗动脉粥样硬化, 血栓形成和炎症状态之间的微妙平衡, 导致各种血管病变, 包括缺血性卒中^{[56, 57]}。正常生理水平剪切力对内皮细胞分化和细胞功能具有积极作用^{[58, 59]}。脑微毛细血管(直径10 μm)中, 血流流速通常为6~12 nL·min^-1, 相应的剪切力为0.3~2 Pa。细胞接种到芯片上后, 可以通过注射泵、重力等方式引入流动流体向细胞施加一定剪切力。在BBB芯片模型上已经证实了剪切力可以使BBB更加紧密, 但剪切力的应用尚未标准化。Cucullo等^[60]在流体中培养hCMEC/D3, 与静态培养相比TEER值增加了近20倍, 表明了流体流动和剪切力对BBB形成的重要性。Jeong等^[32]通过调节施加到BBB芯片上的培养基的流速而获得不同的剪切力, 检测了8种不同剪切力条件下培养的内皮细胞的ZO-1表达程度。当BBB芯片上的剪切力增加时, ZO-1表达程度显著的增加, 当剪切力过高时, ZO-1表达水平降低。

2.3 BBB芯片模型表征与评价

测量跨膜电阻值(TEER)、分析渗透性和对已知标记物进行免疫荧光染色是评价BBB功能的3种基本方法, 可以提供足够的信息来评价BBB模型屏障融合的均匀性和紧密性(表 1)^{[19, 29, 31, 33-41, 45-48, 53, 61-68]}。

2.3.1 跨膜电阻值的测定

TEER是评价生物屏障严密性的一种常用方法^[69-72], 常将其与体内值(通常为1 800~2 000 Ω·cm²)进行比较, 用于确定体外BBB的完整性和紧密性。

通过在细胞层上施加一定的电位差, 可以确定测量区域的跨细胞层的离子流电阻。TEER主要代表细胞旁转运的电阻:细胞层越紧密, 离子和其他带电物质可通过的细胞屏障间隙越少, 电阻越高^[71]。

目前, BBB芯片模型大多依赖于集成电极和交流电流测量, 以分别减少离子路径和电极表面结垢。电阻与通道的横截面积成反比, 因此, 微流体通道通常具有高电阻, 集成电极时需注意将电极固定在适当位置, 以防止电极位置的差异导致测量误差。使用适当频率的交流电流可防止电极和浓度极化以及其他直流电对细胞产生的影响, 优于用直流电测量TEER。除此之外, 温度和培养基的离子组成等因素会影响TEER值测量, 应尽量保持不变。Brown等^[37]使用四点阻抗传感方法测量其设计的NVU芯片的TEER, 电流源探针连接在“H”层的入口和血管腔出口之间, 确保激发电流流过脑室并通过内皮层, 感应电压探针连接在血管腔室入口和“H”层出口之间, 通过监测阻抗与探头频率变化, 可以确定监测屏障TEER值具有最高灵敏度的频率(图 4)^[37]。结果表明在15 kHz时阻抗的变化灵敏度最高且内皮细胞在培养12天期间TEER显著增加。将NVU芯片在33 ℃冷刺激12 h, 细胞活性不变, TJs受损, TEER显著降低。

与渗透率测定相比, TEER测量是一种快速、非侵入性的评估屏障紧密度的方法, 将电极材料集成到BBB芯片上可以进行实时测量, 跟踪评价特定模型中细胞层的完整性, 并记录实验诱导的屏障特性变化, 具有更大的优势。此外, 在BBB芯片上应用组胺可以观察到TEER值的瞬时下降和恢复, 可以验证模型用于长期测试的稳定性。

2.3.2 渗透性实验

除了测量TEER外, 渗透性测定同样是评价BBB完整性的重要方法。通过使用荧光标记的分子示踪剂, 可以量化透过BBB的分子, 量化屏障的渗透性, 将BBB芯片模型与其他体内外模型进行比较^[73]。渗透性测定中通常使用的分子有小分子蔗糖、荧光素钠或大分子多糖如异硫氰酸荧光素(fluorescein isothiocyanate isomer, FITC)葡聚糖。通过使用分子量不同的荧光示踪剂, 可以评估TJs的质量。例如大分子葡聚糖在不成熟的内皮层中也可能具有低渗性, 而小分子蔗糖仅能被高度分化的内皮层有效阻断。体外实验中, 蔗糖的渗透系数为1×10^-6~30×10^-6 cm·s^-1, 4 kDa葡聚糖为5×10^-6~10×10^-6 cm·s^-1。

通过将示踪剂加入BBB芯片模型的血管室, 然后收集脑室中的流出物, 可以测定其渗透性。如果这种渗透性与生理水平一致, 则表明建立的BBB芯片可用于测试候选药物^[20]。与Transwell系统相比, BBB芯片中的分析物可以以恒定的流速从顶端通道透过细胞层和微孔膜到达下层通道并被收集, 因此, 在整个测定过程中分析物跨膜浓度差可以保持恒定, 而在静态Transwell系统中, 浓度差随时间推移而降低。

评价BBB芯片功能的另一个重要方法是通过已知底物研究多药耐药泵的外排。其中一种常用方法是用P-糖蛋白(P-glycoprotein, P-gp)的荧光底物(如罗丹明123)注入血管腔中, 随着时间的推移追踪细胞的荧光强度。此外, 也可以进行药物渗透性测定, 测量值与已知的体外/体内渗透性数据相关, 也可以表征BBB芯片功能的完整性。Wang等^[61]用大分子荧光示踪剂FITC-葡聚糖(4、20和70 kDa)和模型药(咖啡因、西咪替丁和多柔比星)全面评估建立的BBB芯片的渗透性, 通过微量培养板分光荧光计测定腔内外FITC-葡聚糖或多柔比星的样品浓度, LC-MS/MS测定咖啡因和西咪替丁的浓度。FITC-葡聚糖的低渗性、渗透的尺寸依赖性以及与体内数据相当的模型药物渗透系数证实了构建的BBB的完整性, 表明该模型能够模拟生理BBB功能, 可成为筛选候选药物的工具。

2.3.3 显微观察

显微镜分析广泛用于研究BBB的形态和结构。采用免疫化学方法对细胞进行染色, 并通过荧光显微镜检测BBB相关蛋白表达, 如黏附和紧密连接蛋白(ZO-1, occludin, claudin-5), 外排转运蛋白和NVU的不同细胞类型(如内皮细胞的血管性血友病因子、星形胶质细胞的胶质纤维酸性蛋白和周细胞的α-平滑肌肌动蛋白), 是表征BBB芯片细胞屏障最直观的方法。

Adriani等^[35]采用对芯片上构建BBB模型的不同细胞以及TJ蛋白进行染色, 研究三维微流控装置中的多种细胞类型。双皮质素(doublecortin, DCX)是特异于未成熟神经元的微管相关蛋白, 抗DCX抗体可以清晰地显示细胞体和神经突; 用抗胶质纤维酸性蛋白的抗体可以鉴定星形胶质细胞; 细胞间连接蛋白VE-cadherin和ZO-1、F-actin可以表征内皮细胞及其TJs; 即刻早期基因c-Fos作为神经元激活的标志。结果显示:神经元呈现突起生长, 星形胶质细胞呈典型的星型形态, 内皮细胞在通道中形成紧密单层并表达ZO-1 (图 5)^[35]。

3 BBB芯片模型的应用

收起

目前, BBB芯片模型已逐渐应用于CNS疾病的病理生理研究、药物BBB渗透性的筛选以及治疗药物的研发等领域。

3.1 神经炎症模型

在神经退行性疾病中, 通过星形胶质细胞和小胶质细胞分泌致炎细胞因子诱导神经元损伤是神经炎症产生的主要机制之一^[74]。在此病理生理过程中, 血管内皮细胞和单核细胞等直接或间接参与细胞间的相互作用, 是产生炎症反应所必需的。因此, 建立BBB芯片模型来模拟神经炎症和神经退行性疾病并对其进行研究是必要的。

Cho等^[31]将hBMEC接种在包被有PLL和Ⅰ型胶原的微通道中, 应用肿瘤坏死因子-α (tumor necrosis factor-α, TNF-α)诱导神经炎症状态并通过注入中性粒细胞建立氧糖剥夺缺血模型。结果表明, 该模型暴露于TNF-α以及处于缺血条件下, BBB被破坏。但该模型有一定局限: ①未使用星形胶质细胞或其他神经元细胞; ② TNF-α是一种细胞分泌蛋白, 而不是有毒物质或神经炎症的诱导物质。为了更好地理解神经炎症间的复杂关系及其对BBB功能的影响, Brown等^[36]建立了双培养腔NVU芯片, 应用脂多糖以及白细胞介素-1β、TNF-α和单核细胞趋化蛋白的细胞因子混合物, 通过细胞因子检测和质谱分析以研究BBB对两种不同神经炎症驱动因子的响应。研究表明, 该系统能够模拟神经炎症和促炎细胞因子激活对BBB损伤的初始影响。相反, 当BBB暴露于细胞因子时, 代谢途径的改变可以诱导BBB的恢复。

目前, 利用微流控技术模拟神经炎症的研究很多, 但大多研究集中在内皮和血管的相互作用, 而不是免疫细胞的免疫反应引起的神经元损伤和功能丧失。

3.2 脑肿瘤研究

脑部的肿瘤细胞转移涉及通过BBB上的生物化学和物理复杂微环境的细胞迁移。BBB芯片模型可以应用于对血脑屏障功能的影响、肿瘤细胞的迁移、外渗等行为的研究。

Shumakovich等^[75]利用不同通道宽度(50、10和3 μm)的BBB芯片来研究乳腺癌细胞在星形胶质细胞条件培养基(astrocyte conditioned medium, ACM)中的迁移能力。结果表明, 10 μm通道中肿瘤细胞的侧向诱导和核畸形达到最佳平衡, 细胞迁移速度最快。该模型可用于研究特定环境中肿瘤细胞的迁移能力。Tourovskaia等^[76]利用微流控技术模拟了血管新生, 建立了BBB和癌细胞外渗模型, 研究了前列腺癌细胞(PC3, 高转移性)或乳腺癌细胞(BT-474, 低转移性)的迁移情况。

Xu等^[77]设计了一种含16个功能单元的BBB芯片, 每个功能单元具有4个BBB区域, 每个区域由血管室和脑室组成, 用于研究不同肿瘤细胞的迁移。结果发现, 肺腺癌细胞(A549)和乳腺癌细胞(MDA-MB-231)显示出比黑素瘤细胞(M624)更大的迁移, 均对BBB有一定损伤, 而肝癌细胞(BEL-7402)在72 h内未透过BBB。通过进一步研究肺腺癌细胞(A549)在TNF-α诱导的炎性微血管系统中的黏附/外渗, 发现A549细胞增加了对炎症性内皮的黏附^[78]。

3.3 药物评价

由于体外研究缺乏合适的生理性BBB模型, 严重阻碍了抗脑肿瘤药物及其他神经系统疾病治疗药物的研发和筛选进程, BBB芯片模型的发展有望为体外CNS药物的筛选提供快速高效的平台。

Booth课题组^[63]在μBBB芯片上进行胶质瘤细胞和bEnd.3细胞的共培养, 在具有功能性屏障的μBBB上测量7种脑靶向药物(乙琥胺、加巴喷丁、舍曲林、舒尼替尼、曲索罗地、伐尼克兰、PF-3084014)亚毒性水平的渗透系数。结果表明, 7种药物浓度低于10 μmol·L^-1时, 对动态BBB模型中的脑内皮细胞没有毒性作用。当体内脑/血浆比率(B/P)从0.42增加到26.8时, 每种药物的相应渗透系数平均对数值从−4.06增加到−3.63。在该动态模型中测得的渗透系数与体内B/P有很好的相关性(R² > 0.85), 证明该模型具备预测BBB药物清除率的潜力。

Shao等^[67]在多孔膜上对HCMEC/D3进行动态培养并模拟药物入脑, 以诱导靶细胞产生细胞毒性。模型药物舒尼替尼输送到微通道中被动透过BBB, 经固相萃取预处理后, 用电喷雾四极杆飞行时间质谱仪直接对其渗透量进行定量分析。此外, 将渗透药物与下游的胶质瘤细胞U251 (培养在琼脂糖凝胶内)共同孵育, 以研究药物诱导的细胞毒性。舒尼替尼的渗透性与文献报道高度相关, 在48 h的治疗后, 在三维支架中培养的U251细胞的存活率比二维高出近6%, 与现有报道结果一致。这些结果表明, 该平台为药物渗透性和细胞毒性检测提供了有效的工具, 对CNS药物的研究和开发具有重要价值(图 6)^[67]。

Terrell-Hall等^[79]在芯片的外室和中央室中分别接种内皮细胞、星形胶质细胞或乳腺癌细胞并在外室施加流体以模拟生理流动条件, 建立BBB和血液肿瘤屏障(blood tumor barrier, BTB)模型, 用于评估两种系统对潜在抗癌药物的渗透性。结果表明, BTB的屏障虽然遭到一定程度破坏, 但仍然保留了对药物的选择性渗透以及P-gp的有效外排, 与体内尽管屏障在许多病变中遭到破坏, P-gp功能仍能保留的报道一致; 曲妥珠单抗抗体可透过BBB和BTB模型, 但是数量很少, 低于有效水平^[80]。

4 小结与展望

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BBB芯片技术为仿生人BBB模型的发展提供了新的机遇。BBB芯片可以与其他仿生器官连接, 以研究大脑与其他组织之间的信息传递。BBB芯片主要有血管、NVU两种结构类型, 通过在芯片上进行细胞单培养以及二元、三元共培养来构建体外BBB模型, 并通过测定TEER值、渗透率以及显微观察来评价芯片模型。目前, BBB芯片主要应用于建立神经炎症模型、模拟脑肿瘤的转移以及对药物的药效和毒性评价。随着微流控技术的不断发展, BBB芯片将可用于发展BBB的疾病模型; 预测健康和疾病状态下药物透过人体BBB的渗透性; 评估药物透过BBB进入脑组织的疗效; 并确定BBB在与仿生脑组织整合后对神经退行性疾病或脑肿瘤进展的贡献。

当前, BBB芯片开发领域还面临许多问题: ① BBB芯片构建生物环境技术尚未成熟, 缺乏规范的量化参数及评价标准; ②目前用于建模的细胞多是大鼠脑内皮细胞, 但不可避免的物种差异阻碍了它们对临床药物开发的可译性。原代hBMEC表型漂移和高成本阻碍了其广泛使用, 而永生化hBMEC表现出较差的单层完整性和TJ蛋白的低表达。迄今为止开发的各种基于干细胞的BBB模型报告的TEER和渗透率存在较大差异。③ BBB芯片多数还停留在建立表征阶段, 实际应用还比较少。未来BBB芯片发展方向应着重于将评估芯片BBB模型的可测量参数标准化, 形成规范化技术体系, 为其实际应用奠定坚实的基础。相信随着微流控芯片技术的发展, BBB芯片模型将在不久的将来对生物医学科学和药物开发产生深远的影响。

基金

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国家自然科学基金资助项目(81373376)
国家自然科学基金资助项目(81872829)

参考文献

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文献

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文章信息

doi: 10.16438/j.0513-4870.2018-0773

接收时间：2018-08-23
首发时间：2026-01-26
出版时间：2019-02-12

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作者

出版历史

收稿日期：2018-08-23
修回日期：2018-11-15

基金

国家自然科学基金资助项目(81373376)

国家自然科学基金资助项目(81872829)

作者信息

1.第二军医大学药学院, 上海 200433

2.福建中医药大学药学院, 福建福州 350122

3.上海健康医学院, 上海 201318

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*洪战英, Tel:86-21-81871261-85, E-mail:hongzhy001@163.com

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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RefWorks
TxT

Endothelial cell	Co-culture cell	ECM	Permeability measurement/10^-6 cm²·s^-1	TEER/Ω·cm²	Shear stress/10^-3 Pa	Protein expression	Ref.
HUVEC hCMEC/D3	Primary brain Astrocytes Rats cortical neurons	Collagen I	FD^a10 kDa FD 70 kDa	3.3-65.8	N.A.	N.A.	ZO-1 VE-cadherin	Adriani^[35]
hBMVEC	Human brain pericytes Human brain astrocytes	Collagen I	FD 3 kDa	2-5	N.A.	100	ZO-1 VE-cadherin F-actin	Herland^[48]
hiPS-derived BMEC	Primary rat astrocytes	Collagen I Fibronectin	FD 4 kDa FD 20 kDa FD 70 kDa	0.01-0.1	2000-4000	2-3	ZO-1 F-actin Claudin-5	Wang^[61]
RBE4	Human neutrophils	Collagen I	FD 40 kDa	N.A.	N.A.	N.A.	ZO-1	Cho^[31]
RBE4	Neonatal rat astrocytes	N.A.	TRD^b 40 kDa	1.1-41	N.A.	0.4-7.6	ZO-1	Deosarkar^[47]
bEnd.3	Mouse astrocyte (C8D1A) Immortalized mouse pericytes	Fibronectin	Urea Mannitol Dexamethasone	1.1 0.3-0.6 2.9	320	160	N.A.	Wang^[62]
hBMEC	Primary human brain pericytes Primary astrocytes Human cortical glutamatergic neurons from hiPSCs	Laminin	FD 10 kDa FD 70 kDa	N.A.	5000-30000	2	ZO-1	Brown^[37]
bEnd.3	N.A.	Collagen I Fibronectin	FD 40 kDa Mannitol	2.27×10^-5-6.5×10^-4 3.9	N.A.	N.A.	ZO-1	Kim^[29]
bEnd.3	Astrocytes(C8D1A) Rat astrocytes (C6)	Fibronectin Collagen IV Fibroectin	FD 4 kDa FD 20 kDa FD 70 kDa Propidium iodide	N.A.	50-280	0.08 1500	ZO-1 ZO-1	Booth^[19] Booth^[63]
hCMEC/D3	N.A.	Collagen I	N.A.	N.A.	Static 40 Flow 120 TNF-α12	580	ZO-1	Griep^[64]
hCMEC/D3	Human astrocytes	Collagen I Matrigel	FD 4 kDa -TNF-α -Flow	4.0-9.0 0.6-1.2	Static 180-220 Flow 220-1000	50	ZO-1	Partyka^[46]
RBE4	N.A. (ACM)	Fibronectin	FD 3-5 kDa	N.A.	N.A.	3	ZO-1 Claudin	Prabhakarpandian^[34]
RBE4	E18 neural cells Astrocytes Microglia	Fibronectin	A488 dextran 3 kDa	N.A.	N.A.	N.A.	ZO-1	Achyuta^[53]
HUVEC	N.A. (ACM)	N.A.	FD 4 kDa FD 40 kDa FD 70 kDa	N.A.	N.A.	N.A.	ZO-1	Yeon^[65]
hCMEC/D3 Primary rat brain endothelial cells	Primary brain pericytes Primary brain astrocytes	Collagen I Collagen IV	NaFL^c376 Da FD 4.4 kDa EBA^d67 kDa	1.57-1.61 1.32-1.55 0.15-0.51	10-30	150	ZO-1	Walter^[38]
bEnd.3	Astrocytes(C8D1A)	Collagen I Collagen IV Fibronectin	FD 70 kDa	0.6	N.A.	500	Claudin-5	Sellgren^[41]
hBMVEC	Human primary astrocytes	Collagen I	TMR dextran^e3 kDa TMR dextran 40 kDa	N.A.	1000-1240	N.A.	N.A.	Merkel^[45]
bEnd.3	N.A.	N.A.	N.A.	N.A.	1000-1150	15	Claudin 5	Falanga^[33]
hBMVECs	Human astrocytes Human pericytes Human hippocampal neural stem cells	Fibronectin Collagen IV Laminin	CB dextran^f530 Da BSA-555 67 kDa	11.2 ± 0.8 0.27 ± 0.02	N.A.	2	VE-Cadherin F-actin	Maoz^[40]
iPSC-ECs	Human brain pericytes Human brain astrocytes	Fibronectin	FD 10 kDa FD 40 kDa	0.22-1.2 0.089-0.66	N.A.	N.A.	ZO-1 Claudin-5 Occluding	Campisi^[39]
hBMVEC	Human astrocytes Human pericytes	Laminin	FD 10 kDa	N.A.	100	N.A.	ZO-1 Claudin-5	Brown^[36]
fBMVECs aBMVECs	Astrocyte Pericyte	Collagen I	NaFL 376 Da CB dextran 3 kDa TMR dextran 40 kDa	N.A.	N.A.	1000	ZO-1 Claudin-5 Occludin Tricellulin	Andrews^[66]
hCMEC/D3	U251	Matrigel	NaFL 376 Da FD 70 kDa	7.16 ± 0.81 1.41 ± 0.15	N.A.	110	VE-Cadherin F-actin	Shao^[67]
HUVEC	Normal human lung fibroblast Cortex neural cell	N.A.	FD 20 kDa FD 70 kDa	0.45 ± 0.11 0.36 ± 0.05	N.A.	N.A.	ZO-1	Bang^[68]

Endothelial cell

Co-culture cell

ECM

Permeability measurement/10^-6 cm²·s^-1

TEER/Ω·cm²

Shear stress/10^-3 Pa

Protein expression

Ref.

HUVEC
hCMEC/D3

Primary brain Astrocytes
Rats cortical neurons

Collagen I

FD^a10 kDa
FD 70 kDa

3.3-65.8

N.A.

ZO-1
VE-cadherin

Adriani^[35]

hBMVEC

Human brain pericytes
Human brain astrocytes

Collagen I

FD 3 kDa

2-5

N.A.

100

ZO-1
VE-cadherin
F-actin

Herland^[48]

hiPS-derived BMEC

Primary rat astrocytes

Collagen I
Fibronectin

FD 4 kDa
FD 20 kDa
FD 70 kDa

0.01-0.1

2000-4000

2-3

ZO-1
F-actin
Claudin-5

Wang^[61]

RBE4

Human neutrophils

Collagen I

FD 40 kDa

N.A.

ZO-1

Cho^[31]

RBE4

Neonatal rat astrocytes

N.A.

TRD^b 40 kDa

1.1-41

N.A.

0.4-7.6

ZO-1

Deosarkar^[47]

bEnd.3

Mouse astrocyte (C8D1A)
Immortalized mouse pericytes

Fibronectin

Urea
Mannitol
Dexamethasone

1.1
0.3-0.6
2.9

320

160

N.A.

Wang^[62]

hBMEC

Primary human brain pericytes
Primary astrocytes
Human cortical glutamatergic neurons from hiPSCs

Laminin

FD 10 kDa
FD 70 kDa

N.A.

5000-30000

ZO-1

Brown^[37]

bEnd.3

N.A.

Collagen I
Fibronectin

FD 40 kDa
Mannitol

2.27×10^-5-6.5×10^-4
3.9

N.A.

ZO-1

Kim^[29]

bEnd.3

Astrocytes(C8D1A)
Rat astrocytes (C6)

Fibronectin
Collagen IV
Fibroectin

FD 4 kDa
FD 20 kDa
FD 70 kDa
Propidium iodide

N.A.

50-280

0.08

1500

ZO-1

ZO-1

Booth^[19]

Booth^[63]

hCMEC/D3

N.A.

Collagen I

N.A.

Static 40
Flow 120
TNF-α12

580

ZO-1

Griep^[64]

hCMEC/D3

Human astrocytes

Collagen I
Matrigel

FD 4 kDa -TNF-α
-Flow

4.0-9.0
0.6-1.2

Static 180-220
Flow 220-1000

ZO-1

Partyka^[46]

RBE4

N.A. (ACM)

Fibronectin

FD 3-5 kDa

N.A.

ZO-1
Claudin

Prabhakarpandian^[34]

RBE4

E18 neural cells
Astrocytes
Microglia

Fibronectin

A488 dextran 3 kDa

N.A.

ZO-1

Achyuta^[53]

HUVEC

N.A. (ACM)

N.A.

FD 4 kDa
FD 40 kDa
FD 70 kDa

N.A.

ZO-1

Yeon^[65]

hCMEC/D3 Primary rat brain endothelial cells

Primary brain pericytes
Primary brain astrocytes

Collagen I
Collagen IV

NaFL^c376 Da
FD 4.4 kDa
EBA^d67 kDa

1.57-1.61
1.32-1.55
0.15-0.51

10-30

150

ZO-1

Walter^[38]

bEnd.3

Astrocytes(C8D1A)

Collagen I
Collagen IV
Fibronectin

FD 70 kDa

0.6

N.A.

500

Claudin-5

Sellgren^[41]

hBMVEC

Human primary astrocytes

Collagen I

TMR dextran^e3 kDa
TMR dextran 40 kDa

N.A.

1000-1240

N.A.

Merkel^[45]

bEnd.3

N.A.

1000-1150

Claudin 5

Falanga^[33]

hBMVECs

Human astrocytes Human pericytes
Human hippocampal neural stem cells

Fibronectin
Collagen IV
Laminin

CB dextran^f530 Da
BSA-555 67 kDa

11.2 ± 0.8
0.27 ± 0.02

N.A.

VE-Cadherin
F-actin

Maoz^[40]

iPSC-ECs

Human brain pericytes
Human brain astrocytes

Fibronectin

FD 10 kDa
FD 40 kDa

0.22-1.2
0.089-0.66

N.A.

ZO-1
Claudin-5
Occluding

Campisi^[39]

hBMVEC

Human astrocytes
Human pericytes

Laminin

FD 10 kDa

N.A.

100

N.A.

ZO-1
Claudin-5

Brown^[36]

fBMVECs
aBMVECs

Astrocyte
Pericyte

Collagen I

NaFL 376 Da
CB dextran 3 kDa
TMR dextran 40 kDa

N.A.

1000

ZO-1
Claudin-5
Occludin
Tricellulin

Andrews^[66]

hCMEC/D3

U251

Matrigel

NaFL 376 Da
FD 70 kDa

7.16 ± 0.81
1.41 ± 0.15

N.A.

110

VE-Cadherin
F-actin

Shao^[67]

HUVEC

Normal human lung fibroblast
Cortex neural cell

N.A.

FD 20 kDa
FD 70 kDa

0.45 ± 0.11
0.36 ± 0.05

N.A.

ZO-1

Bang^[68]