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Meta-analysis of the make-up and properties of in vitro models of the healthy and diseased blood–brain barrier

Nature Biomedical Engineering (2024)Cite this article

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Abstract

In vitro models of the human blood–brain barrier (BBB) are increasingly used to develop therapeutics that can cross the BBB for treating diseases of the central nervous system. Here we report a meta-analysis of the make-up and properties of transwell and microfluidic models of the healthy BBB and of BBBs in glioblastoma, Alzheimer’s disease, Parkinson’s disease and inflammatory diseases. We found that the type of model, the culture method (static or dynamic), the cell types and cell ratios, and the biomaterials employed as extracellular matrix are all crucial to recapitulate the low permeability and high expression of tight-junction proteins of the BBB, and to obtain high trans-endothelial electrical resistance. Specifically, for models of the healthy BBB, the inclusion of endothelial cells and pericytes as well as physiological shear stresses (~10–20 dyne cm–2) are necessary, and when astrocytes are added, astrocytes or pericytes should outnumber endothelial cells. We expect this meta-analysis to facilitate the design of increasingly physiological models of the BBB.

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Fig. 1: Healthy and disease BBB anatomy, cells and features in the CNS.
Fig. 2: Mechanical property and materials of brain tissue including the BBB.
Fig. 3: Cell sources and models for in vitro BBB modelling.
Fig. 4: Model-type-based meta-analyses of BBB models from 1999 to 2024.
Fig. 5: Cell combination, cell ratio and materials-based meta-analyses of BBB models from 1999 to 2024.
Fig. 6: Disease BBB model meta-analyses based on model type and materials.
Fig. 7: BBB model meta-analyses of tight-junction protein expression based on model type, cell combination and materials.
Fig. 8: Proposed engineering design of healthy and disease BBB models based on the meta-analyses.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. All data generated during the study, including source data for the figures, are available from figshare with the identifier https://doi.org/10.6084/m9.figshare.24480850 (ref. 356). Source data are provided with this paper.

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Acknowledgements

This work was partially supported by grants from the University of Maryland Brain and Behavior Institute, the National Cancer Institute (NCI)–University of Maryland Partnership for Integrative Cancer Research Program, and the NCI (R01CA279815). BioRender (Biorender.com) was used to produce part of the illustrations in Figs. 1–3.

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Authors and Affiliations

  1. Fischell Department of Bioengineering, University of Maryland, College Park, MD, USA

    James G. Shamul, Zhiyuan Wang, Hyeyeon Gong, Wenquan Ou, Alisa M. White, Alisa Morss Clyne & Xiaoming He

  2. RNA Mediated Gene Regulation Section, RNA Biology Laboratory, Center for Cancer Research, National Cancer Institute, Frederick, MD, USA

    James G. Shamul & Shuo Gu

  3. Department of Neurosurgery, Mayo Clinic, Jacksonville, FL, USA

    Diogo P. Moniz-Garcia & Alfredo Quiñones-Hinojosa

  4. Robert E. Fischell Institute for Biomedical Devices, University of Maryland, College Park, MD, USA

    Alisa Morss Clyne & Xiaoming He

  5. Brain and Behavior Institute, University of Maryland, College Park, MD, USA

    Alisa Morss Clyne & Xiaoming He

  6. Marlene and Stewart Greenebaum Comprehensive Cancer Center, University of Maryland, Baltimore, MD, USA

    Xiaoming He

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  1. James G. Shamul
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  2. Zhiyuan Wang
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  7. Shuo Gu
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  9. Alfredo Quiñones-Hinojosa
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  10. Xiaoming He
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Contributions

X.H. conceived this project and supervised the data analysis, presentation, interpretation and manuscript writing. J.G.S. gathered meta-data, performed statistical analyses, generated figures, and wrote and revised the manuscript drafts. Z.W., H.G., W.O., A.M.W. and D.P.M.-G. generated/edited figures, tables or video. S.G., A.M.C. and A.Q.-H. edited the manuscript.

Corresponding authors

Correspondence to Alfredo Quiñones-Hinojosa or Xiaoming He.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Transwell insert pore size and material-based analyses.

a, Normalized permeability values of reported co-culture transwell BBB models versus pore size, with respect to (that is, normalized by) the corresponding EC mono-culture models. b, Normalized TEER values of reported co-culture transwell BBB models versus pore size, with respect to (that is, normalized by) the corresponding EC mono-culture models. c, Normalized permeability values of reported co-culture transwell BBB models versus insert materials, with respect to (that is, normalized by) the corresponding EC mono-culture models. d, Normalized TEER values of reported co-culture transwell BBB models versus insert materials, with respect to (that is, normalized by) the corresponding EC mono-culture models. For c-d, statistical analyses were performed using one-way ANOVA with post hoc Tukey’s multiple comparisons tests. There is no statistical significance between any of the groups in c-d. In each panel, every unfilled, hollow circle represents a different, non-outlier data set of the listed model/co-culture conditions. Every filled, solid circle represents an outlier which was not included in any statistical analysis. Error bars represent standard error of the mean. In a-b, a line of best fit is shown together with the line equation and goodness of fit (R2).

Source data

Extended Data Fig. 2 Tracer molecule type and molecular-weight-based analysis.

a, Absolute permeability values of reported EC mono-culture transwell and microfluidic BBB models grouped by tracer type used in the permeability measurement. b, Absolute permeability values of reported EC mono-culture transwell and microfluidic BBB models versus molecular weight of dextran used in the permeability measurement. For a, statistical analyses were performed using one-way ANOVA with post hoc Tukey’s multiple comparisons tests. There is no statistical significance between any of the groups in a. In each panel, every unfilled, hollow circle represents a different, non-outlier data set of the listed model/co-culture conditions. Every filled, solid circle represents an outlier which was not included in any statistical analysis. Error bars represent standard error of the mean. In b, a line of best fit is shown together with the line equation and goodness of fit (R2).

Source data

Extended Data Fig. 3 Cell-ratio-based meta-analyses.

a, Normalized permeability values of bi-culture (pericyte and EC) BBB models versus ratio groups of pericyte (X) to EC (1). Each ratio group represents a range of pericyte (X) to EC ratios (1). b, Normalized permeability values of bi-culture (astrocyte and EC) BBB models versus ratio groups of astrocyte (X) to EC (1). Each ratio group represents a range of astrocyte (X) to EC ratios (1). c, Normalized permeability values of tri-culture (pericyte, astrocyte, and EC) BBB models versus ratio groups of pericyte and astrocyte altogether (X) to EC (1). Each ratio group represents a range of pericyte and astrocyte altogether (X) to EC ratios (1). d, Normalized permeability values of tri-culture (pericyte, astrocyte, and EC) BBB models versus ratio groups of astrocyte (X) to pericyte (1) from these models, with respect to (that is, normalized by) the corresponding EC mono-culture models. For a-c, statistical analyses were performed using one-way ANOVA with post hoc Tukey’s multiple comparisons tests. There is no statistical significance between any of the groups. In b, the dark color-filled, solid circle represents an outlier which was not included in the statistical analysis. In each panel, every unfilled, hollow circle represents a different, non-outlier data set of the listed model/co-culture. For data sets with n < 3, the violin was omitted and only the data points were plotted. For the violin plots in a-c, the coarsely-dashed, thicker line in each violin represents the median (50th percentile) of the data set, and the upper and lower more finely-dashed, thinner lines represent the upper (75th percentile) and lower (25th percentile) quartiles of the data set, respectively. In d, a line of best fit is shown together with the line equation and goodness of fit (R2). NP and NA refer to the number of pericyte (P) and astrocyte (A), respectively. The ranges of NP > NA and NA > NP are shaded in different colors in d.

Source data

Extended Data Fig. 4 A survey of the make-up, permeability value, and engineering tool used for BBB model fabrication.

The make-up includes extracellular matrix (ECM) materials, and cell types. NA represents ‘not applicable’ due to a lack of this data in the specific work.

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Supplementary Video 1

3D reconstruction of 7 Tesla (7 T) MRI illustrating cortical structures.

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Shamul, J.G., Wang, Z., Gong, H. et al. Meta-analysis of the make-up and properties of in vitro models of the healthy and diseased blood–brain barrier. Nat. Biomed. Eng (2024). https://doi.org/10.1038/s41551-024-01250-2

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