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Engineered human blood–brain barrier microfluidic model for vascular permeability analyses

Nature Protocols volume 17, pages 95–128 (2022)Cite this article

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Abstract

The blood–brain barrier (BBB) greatly restricts the entry of biological and engineered therapeutic molecules into the brain. Due to challenges in translating results from animal models to the clinic, relevant in vitro human BBB models are needed to assess pathophysiological molecular transport mechanisms and enable the design of targeted therapies for neurological disorders. This protocol describes an in vitro model of the human BBB self-assembled within microfluidic devices from stem-cell-derived or primary brain endothelial cells, and primary brain pericytes and astrocytes. This protocol requires 1.5 d for device fabrication, 7 d for device culture and up to 5 d for downstream imaging, protein and gene expression analyses. Methodologies to measure the permeability of any molecule in the BBB model, which take 30 min per device, are also included. Compared with standard 2D assays, the BBB model features relevant cellular organization and morphological characteristics, as well as values of molecular permeability within the range expected in vivo. These properties, coupled with a functional brain endothelial expression profile and the capability to easily test several repeats with low reagent consumption, make this BBB model highly suitable for widespread use in academic and industrial laboratories.

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Fig. 1: Summary of protocol steps for the formation of BBB MVNs in the micro- and macrodevices, along with downstream applications following 7 d of culture in the devices.
Fig. 2: Summary of protocol steps for the measurement of permeability in the BBB MVNs by confocal microscopy of fluorophore-labeled molecules.
Fig. 3: Summary of protocol steps for the measurement of permeability in the BBB MVNs by collection and analysis of interstitial fluid after intravascular pressurization.
Fig. 4: Summary of protocol steps for the fabrication of the microdevice with an SU-8 wafer and the macrodevice with a 3D printed mold (Steps 1–2 in the protocol).
Fig. 5: Summary of protocol steps for the addition of EC monolayers in the side medium reservoirs on day 4 of culture (Steps 15–19 in the protocol).
Fig. 6: Schematic visualization of molecular perfusion through the BBB MVNs (Steps 21–22 in the protocol).
Fig. 7: Fabrication of flow stoppers and pressure line to prepare the BBB MVN device for pressurization (Steps 38–43 in the protocol).
Fig. 8: Access and collection of the BBB MVN gel within PDMS microfluidic devices (Steps 62–64 in the protocol).
Fig. 9: Immunofluorescence staining for various proteins of interest in the BBB MVNs with iPS-ECs (Steps 52–59 in the protocol).
Fig. 10: Immunofluorescence staining for various proteins of interest in the BBB MVNs with iPS-ECs (Steps 52–59 in the protocol).
Fig. 11: Scanning electron microscopy images of histological sections collected from BBB MVNs fixed in 2% PFA, 2% glutaraldehyde in 0.15 M cacodylate buffer and sectioned using the methodology described in Fang et al.70.
Fig. 12: EC culture conditions and isolation for gene analysis.
Fig. 13: Assessing transport across the BBB MVNs.
Fig. 14: Functional permeability assay.
Fig. 15: Evaluating secreted cytokines in the BBB MVNs.

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Data availability

Source data are provided with this paper. All raw data needed to generate the figures presented in this work are available in the source data files. Raw image files are available from the corresponding author upon request.

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Acknowledgements

The authors thank L. Possenti for help with writing the ImageJ Macro, J. Whisler and K. Haase for initial design of the macrodevice, and L. Vega for help with the 3D printing mold for the macrodevice. C.H. is supported by the Ludwig Center for Molecular Oncology Graduate Fellowship and by the National Cancer Institute (U01 CA202177). G.S.O. is supported by Amgen Inc. Y.J. and R.K. acknowledge support from the Cure Alzheimer’s Fund and the National Institute of Neurological Disorders and Stroke (R21NS105027).

Author information

Author notes

  1. These authors contributed equally: Cynthia Hajal, Giovanni S. Offeddu, Yoojin Shin.

Authors and Affiliations

  1. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Cynthia Hajal & Roger D. Kamm

  2. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Giovanni S. Offeddu, Yoojin Shin, Shun Zhang & Roger D. Kamm

  3. Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA

    Olga Morozova

  4. Amgen Research, Amgen Inc., Cambridge, MA, USA

    Olga Morozova, Dean Hickman & Charles G. Knutson

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Contributions

Y.S. and C.H. developed and optimized the BBB MVN protocol; G.S.O. developed the transport measurement protocols; C.H., G.S.O., Y.S., D.H., C.G.K. and R.D.K. designed the experiments; C.H., G.S.O., Y.S., S.Z. and O.M. performed the experiments; C.H. and G.S.O. analyzed the data; C.H., G.S.O. and Y.S. designed the figure schematics; C.H. and G.S.O. wrote the first draft of the manuscript, and all authors contributed to its final form.

Corresponding author

Correspondence to Roger D. Kamm.

Ethics declarations

Competing interests

R.D.K. is a cofounder of AIM Biotech, which markets microfluidic systems for 3D culture.

Additional information

Peer review information Nature Protocols thanks Luca Cucullo and Loes Segerink for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol:

Campisi, M. et al. Biomaterials 180, 117–129 (2018): https://doi.org/10.1016/j.biomaterials.2018.07.014

Offeddu, G. S. et al. Biomaterials 212, 115–125 (2019): https://doi.org/10.1016/j.biomaterials.2019.05.022

Offeddu, G. S. et al. Small 15, 1902393 (2019): https://doi.org/10.1002/smll.201902393

Chen, M. B. et al. Nat. Protoc. 12, 865–880 (2017): https://doi.org/10.1038/nprot.2017.018

Shin, Y. et al. Nat. Protoc. 7, 1247–59 (2012): https://doi.org/10.1038/nprot.2012.051

Extended data

Extended Data Fig. 1 Morphological properties of the BBB MVNs over time starting at day 2 of culture after device seeding.

Parameters considered are (a) average vessel branch length (µm), (b) average vessel diameter (µm), and (c) number of branches per ROI12,69,73.

Source data

Extended Data Fig. 2 Steps for vascular permeability measurements via confocal microscopy.

(a) Representative images of perfused plasma IgG (green) at times 0 and 12 min, and increasing matrix intensity over time; the single data points represent the same single pixels in the matrix imaged over time. Scale bar, 100 μm. (b) Permeability of plasma IgG in the BBB MVNs over time. At short times (<12 min), variability in the measurement derives from low signal-to-noise ratio. At long times (>20 min), higher permeability values derive from progressive perfusate diffusion from the side channels. (c) Comparison in 40 kDa dextran permeability measured in the micro and macro devices; n = 3 device repeats, each the average of 3 regions of interest (ROIs). The higher permeability in the micro device derives from increased perfusate diffusion from the side channels over the same time.

Source data

Extended Data Fig. 3 Steps for vascular permeability measurements via fluid sampling.

(a) Example vascular and matrix intensities measured for IgG as a function of depth imaged within the device. Both intensities are normalized to the vascular intensity near the bottom glass. They decrease with depth as a result of light scattering, falling within the background intensity range (shaded area) in the case of the matrix intensity. (b) Example microscope z-drift measured during a typical experiment. (c) Permeability of plasma IgG as a function of imaged region of interest (ROI) size. Average and standard deviation between 3 devices are reported. For ROIs smaller than 600 µm by side, the varying permeability values are an artifact due to incomplete capture of the BBB MVN average morphology.

Source data

Extended Data Fig. 4 Effective permeability of FITC (assumed σ = 0) across the BBB MVNs as a function of intravascular pressure applied.

The slope of the linear fit represents the hydraulic conductivity Lp14.

Source data

Extended Data Fig. 5 Immunofluorescence staining for various proteins of interest in the BBB MVNs with iPS-ECs (steps 52-59 in the protocol).

(a) Staining of water channel protein (aquaporin 4) in ACs in the BBB MVNs. (b) Staining of glycocalyx protein (hyaluronic acid) in the BBB MVNs. (c) Staining of tight junction protein (claudin-5) in the BBB MVNs. Scale bars are 100 µm for (a-b) and 50 µm for (c).

Extended Data Fig. 6 Immunofluorescence staining for various proteins of interest in the BBB MVNs with HBMECs (steps 52-59 in the protocol).

(a) Staining of PC marker (PDGFR-β) in the MVNs, along with F-actin to visualize interactions between HBMECs and PCs. (b) Staining of AC marker (GFAP) in the MVNs, along with F-actin to visualize interactions between HBMECs and ACs. (c) Staining of tight junction protein (ZO-1) in the BBB MVNs with HBMECs. (d) Staining of basement membrane proteins (collagen IV and laminin) in the BBB MVNs with HBMECs. Scale bars are 100 µm for (a, d) and 50 µm for (b-c).

Extended Data Fig. 7 Gene levels measured via qRT-PCR in the different conditions described in Fig. 12a for tight junctions.

(a) Claudin 1, (b) claudin 3, (c) claudin 5, (d) occludin, and (e) ZO-1.

Extended Data Fig. 8 Gene levels measured via qRT-PCR in the different conditions described in Fig. 12a for adherens junctions.

(a) VE-cadherin, (b) JAM-A, and EC adhesion markers (c) PDGF-B and (d) VCAM1.

Extended Data Fig. 9 Gene levels measured via qRT-PCR in the different conditions described in Fig. 12a for transporter receptors.

(a) LRP1, (b) LAT1, (c) CAT1, (d) GLUT1, (e) TfR, (f) BCRP, (g) MOT1, (h) CERP, (i) MRP1, (j) MRP2, (k) RAGE, (l) MFSD2A, and (m) P-GP.

Supplementary information

Supplementary Information

Supplementary Figs. 1 and 2, Supplementary Table 1 and Supplementary Method.

Reporting Summary

Supplementary Data 1

CAD file 1 for macro device

Supplementary Data 2

CAD file 2 for micro device

Supplementary Data 3

Raw data to graph and obtain statistical measures for Fig. 12 and Extended Data Figs. 7–9.

Supplementary Software 1

Macro code for permeability measurements using ImageJ

Supplementary Software 2

Classifier model for permeability measurements using ImageJ

Supplementary Table 2

Spreadsheet with template for permeability measurements using ImageJ

Source data

Source Data Fig. 11

Raw data to graph and obtain statistical measures.

Source Data Fig. 13

Raw data to graph and obtain statistical measures.

Source Data Fig. 14

Raw data to graph and obtain statistical measures.

Source Data Fig. 15

Raw data to graph and obtain statistical measures.

Source Data Extended Data Fig. 1

Raw data to graph and obtain statistical measures.

Source Data Extended Data Fig. 2

Raw data to graph and obtain statistical measures.

Source Data Extended Data Fig. 3

Raw data to graph and obtain statistical measures.

Source Data Extended Data Fig. 4

Raw data to graph and obtain statistical measures.

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Hajal, C., Offeddu, G.S., Shin, Y. et al. Engineered human blood–brain barrier microfluidic model for vascular permeability analyses. Nat Protoc 17, 95–128 (2022). https://doi.org/10.1038/s41596-021-00635-w

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