A linked organ-on-chip model of the human neurovascular unit reveals the metabolic coupling of endothelial and neuronal cells (original) (raw)

References

  1. Banks, W.A. From blood-brain barrier to blood-brain interface: new opportunities for CNS drug delivery. Nat. Rev. Drug Discov. 15, 275–292 (2016).
    Google Scholar
  2. Itoh, Y. & Suzuki, N. Control of brain capillary blood flow. J. Cereb. Blood Flow Metab. 32, 1167–1176 (2012).
    Google Scholar
  3. Magistretti, P.J. & Allaman, I. A cellular perspective on brain energy metabolism and functional imaging. Neuron 86, 883–901 (2015).
    Google Scholar
  4. Lecrux, C. & Hamel, E. The neurovascular unit in brain function and disease. Acta Physiol. (Oxf.) 203, 47–59 (2011).
    Google Scholar
  5. Vasilopoulou, C.G., Margarity, M. & Klapa, M.I. Metabolomic analysis in brain research: opportunities and challenges. Front. Physiol. 7, 183 (2016).
    Google Scholar
  6. Warren, M.S. et al. Comparative gene expression profiles of ABC transporters in brain microvessel endothelial cells and brain in five species including human. Pharmacol. Res. 59, 404–413 (2009).
    Google Scholar
  7. Naik, P. & Cucullo, L. In vitro blood-brain barrier models: current and perspective technologies. J. Pharm. Sci. 101, 1337–1354 (2012).
    Google Scholar
  8. Booth, R. & Kim, H. Characterization of a microfluidic in vitro model of the blood-brain barrier (μBBB). Lab Chip 12, 1784–1792 (2012).
    Google Scholar
  9. Prabhakarpandian, B. et al. SyM-BBB: a microfluidic blood brain barrier model. Lab Chip 13, 1093–1101 (2013).
    Google Scholar
  10. Griep, L.M. et al. BBB on chip: microfluidic platform to mechanically and biochemically modulate blood-brain barrier function. Biomed. Microdevices 15, 145–150 (2013).
    Google Scholar
  11. Brown, J.A. et al. Recreating blood-brain barrier physiology and structure on chip: A novel neurovascular microfluidic bioreactor. Biomicrofluidics 9, 054124 (2015).
    Google Scholar
  12. Brown, J.A. et al. Metabolic consequences of inflammatory disruption of the blood-brain barrier in an organ-on-chip model of the human neurovascular unit. J. Neuroinflammation 13, 306 (2016).
    Google Scholar
  13. Ballabh, P., Braun, A. & Nedergaard, M. The blood-brain barrier: an overview: structure, regulation, and clinical implications. Neurobiol. Dis. 16, 1–13 (2004).
    Google Scholar
  14. Banks, W.A. et al. Passage of amyloid beta protein antibody across the blood-brain barrier in a mouse model of Alzheimer's disease. Peptides 23, 2223–2226 (2002).
    Google Scholar
  15. Easton, A.S. & Fraser, P.A. Variable restriction of albumin diffusion across inflamed cerebral microvessels of the anaesthetized rat. J. Physiol. (Lond.) 475, 147–157 (1994).
    Google Scholar
  16. Zhao, Z., Nelson, A.R., Betsholtz, C. & Zlokovic, B.V. Establishment and dysfunction of the blood-brain barrier. Cell 163, 1064–1078 (2015).
    Google Scholar
  17. Uhlén, M. et al. Proteomics. Tissue-based map of the human proteome. Science 347, 1260419 (2015).
    Google Scholar
  18. Jacques, P.F. et al. Relation between folate status, a common mutation in methylenetetrahydrofolate reductase, and plasma homocysteine concentrations. Circulation 93, 7–9 (1996).
    Google Scholar
  19. Ridley, A.J. Rho GTPases and cell migration. J. Cell Sci. 114, 2713–2722 (2001).
    Google Scholar
  20. Bonneh-Barkay, D. & Wiley, C.A. Brain extracellular matrix in neurodegeneration. Brain Pathol. 19, 573–585 (2009).
    Google Scholar
  21. Zamanian, J.L. et al. Genomic analysis of reactive astrogliosis. J. Neurosci. 32, 6391–6410 (2012).
    Google Scholar
  22. Frey, D., Laux, T., Xu, L., Schneider, C. & Caroni, P. Shared and unique roles of CAP23 and GAP43 in actin regulation, neurite outgrowth, and anatomical plasticity. J. Cell Biol. 149, 1443–1454 (2000).
    Google Scholar
  23. Northrop, N.A. & Yamamoto, B.K. Methamphetamine effects on blood-brain barrier structure and function. Front. Neurosci. 9, 69 (2015).
    Google Scholar
  24. Turowski, P. & Kenny, B.A. The blood-brain barrier and methamphetamine: open sesame? Front. Neurosci. 9, 156 (2015).
    Google Scholar
  25. Zhang, X., Banerjee, A., Banks, W.A. & Ercal, N. N-acetylcysteine amide protects against methamphetamine-induced oxidative stress and neurotoxicity in immortalized human brain endothelial cells. Brain Res. 1275, 87–95 (2009).
    Google Scholar
  26. Fujimoto, Y. et al. The pharmacokinetic properties of methamphetamine in rats with previous repeated exposure to methamphetamine: the differences between Long-Evans and Wistar rats. Exp. Anim. 56, 119–129 (2007).
    Google Scholar
  27. Frankel, P.S., Alburges, M.E., Bush, L., Hanson, G.R. & Kish, S.J. Brain levels of neuropeptides in human chronic methamphetamine users. Neuropharmacology 53, 447–454 (2007).
    Google Scholar
  28. Bélanger, M., Allaman, I. & Magistretti, P.J. Brain energy metabolism: focus on astrocyte-neuron metabolic cooperation. Cell Metab. 14, 724–738 (2011).
    Google Scholar
  29. Wishart, D.S. et al. The human cerebrospinal fluid metabolome. J. Chromatogr. B Analyt. Technol. Biomed. Life Sci. 871, 164–173 (2008).
    Google Scholar
  30. Jaeger, C. et al. The mouse brain metabolome: region-specific signatures and response to excitotoxic neuronal injury. Am. J. Pathol. 185, 1699–1712 (2015).
    Google Scholar
  31. Berman, S.M. et al. Changes in cerebral glucose metabolism during early abstinence from chronic methamphetamine abuse. Mol. Psychiatry 13, 897–908 (2008).
    Google Scholar
  32. Zheng, T. et al. The metabolic impact of methamphetamine on the systemic metabolism of rats and potential markers of methamphetamine abuse. Mol. Biosyst. 10, 1968–1977 (2014).
    Google Scholar
  33. Vargas, M.R., Pehar, M., Cassina, P., Beckman, J.S. & Barbeito, L. Increased glutathione biosynthesis by Nrf2 activation in astrocytes prevents p75NTR-dependent motor neuron apoptosis. J. Neurochem. 97, 687–696 (2006).
    Google Scholar
  34. Zanelli, S.A., Solenski, N.J., Rosenthal, R.E. & Fiskum, G. Mechanisms of ischemic neuroprotection by acetyl-L-carnitine. Ann. NY Acad. Sci. 1053, 153–161 (2005).
    Google Scholar
  35. Verleysdonk, S., Martin, H., Willker, W., Leibfritz, D. & Hamprecht, B. Rapid uptake and degradation of glycine by astroglial cells in culture: synthesis and release of serine and lactate. Glia 27, 239–248 (1999).
    Google Scholar
  36. Fredholm, B.B., Chen, J.F., Cunha, R.A., Svenningsson, P. & Vaugeois, J.M. Adenosine and brain function. Int. Rev. Neurobiol. 63, 191–270 (2005).
    Google Scholar
  37. Schousboe, A. & Sonnewald, U. The Glutamate/GABA-Glutamine Cycle (Springer, 2016).
  38. Culic, O., Gruwel, M.L. & Schrader, J. Energy turnover of vascular endothelial cells. Am. J. Physiol. 273, C205–C213 (1997).
    Google Scholar
  39. Turner, D.A. & Adamson, D.C. Neuronal-astrocyte metabolic interactions: understanding the transition into abnormal astrocytoma metabolism. J. Neuropathol. Exp. Neurol. 70, 167–176 (2011).
    Google Scholar
  40. Bröer, S. & Brookes, N. Transfer of glutamine between astrocytes and neurons. J. Neurochem. 77, 705–719 (2001).
    Google Scholar
  41. Wu, G., Haynes, T.E., Li, H. & Meininger, C.J. Glutamine metabolism in endothelial cells: ornithine synthesis from glutamine via pyrroline-5-carboxylate synthase. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 126, 115–123 (2000).
    Google Scholar
  42. Bhatia, S.N. & Ingber, D.E. Microfluidic organs-on-chips. Nat. Biotechnol. 32, 760–772 (2014).
    Google Scholar
  43. Prantil-Baun, R. et al. Physiologically based pharmacokinetic and pharmacodynamic analysis enabled by microfluidically linked organs-on-chips. Annu. Rev. Pharmacol. Toxicol. 58, 37–64 (2018).
    Google Scholar
  44. Uchida, Y. et al. Quantitative targeted absolute proteomics of human blood-brain barrier transporters and receptors. J. Neurochem. 117, 333–345 (2011).
    Google Scholar
  45. Hawrylycz, M.J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).
    Google Scholar
  46. Malik, N. et al. Comparison of the gene expression profiles of human fetal cortical astrocytes with pluripotent stem cell derived neural stem cells identifies human astrocyte markers and signaling pathways and transcription factors active in human astrocytes. PLoS One 9, e96139 (2014).
    Google Scholar
  47. Urich, E., Lazic, S.E., Molnos, J., Wells, I. & Freskgård, P.O. Transcriptional profiling of human brain endothelial cells reveals key properties crucial for predictive in vitro blood-brain barrier models. PLoS One 7, e38149 (2012).
    Google Scholar
  48. Procaccini, C. et al. Role of metabolism in neurodegenerative disorders. Metabolism 65, 1376–1390 (2016).
    Google Scholar
  49. Sims, N.R. & Muyderman, H. Mitochondria, oxidative metabolism and cell death in stroke. Biochim. Biophys. Acta 1802, 80–91 (2010).
    Google Scholar
  50. Giza, C.C. & Hovda, D.A. The neurometabolic cascade of concussion. J. Athl. Train. 36, 228–235 (2001).
    Google Scholar
  51. Li, S. et al. Predicting network activity from high throughput metabolomics. PLOS Comput. Biol. 9, e1003123 (2013).
    Google Scholar
  52. Li, S. et al. Constructing a fish metabolic network model. Genome Biol. 11, R115 (2010).
    Google Scholar
  53. The, M., MacCoss, M.J., Noble, W.S. & Käll, L. Fast and accurate protein false discovery rates on large-scale proteomics data sets with percolator 3.0. J. Am. Soc. Mass Spectrom. 27, 1719–1727 (2016).
    Google Scholar
  54. Eichler, G.S., Huang, S. & Ingber, D.E. Gene Expression Dynamics Inspector (GEDI): for integrative analysis of expression profiles. Bioinformatics 19, 2321–2322 (2003).
    Google Scholar
  55. Liebermeister, W. et al. Visual account of protein investment in cellular functions. Proc. Natl. Acad. Sci. USA 111, 8488–8493 (2014).
    Google Scholar
  56. Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
    Google Scholar
  57. Szklarczyk, D. et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43, D447–D452 (2015).
    Google Scholar
  58. Orth, J.D., Thiele, I. & Palsson, B.O. What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).
    Google Scholar
  59. Varma, A. & Palsson, B.O. Metabolic capabilities of Escherichia coli: I. synthesis of biosynthetic precursors and cofactors. J. Theor. Biol. 165, 477–502 (1993).
    Google Scholar
  60. Lewis, N.E. et al. Large-scale in silico modeling of metabolic interactions between cell types in the human brain. Nat. Biotechnol. 28, 1279–1285 (2010).
    Google Scholar
  61. Duarte, N.C. et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. Proc. Natl. Acad. Sci. USA 104, 1777–1782 (2007).
    Google Scholar
  62. Schellenberger, J. et al. Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0. Nat. Protoc. 6, 1290–1307 (2011).
    Google Scholar
  63. Groves, P.M. & Rebec, G.V. Introduction to Biological Psychology (W.C. Brown, 1988).

Download references

Acknowledgements

This research was supported by the Wyss Institute for Biologically Inspired Engineering at Harvard University, Defense Advanced Research Projects Agency (DARPA) under Cooperative Agreement Number W911NF-12-2-0036 (D.E.I. & K.K.P.), and Sverige-Amerika Stiftelsen, Carl Trygger Stiftelse, Erik och Edith Fernstrom's stiftelse (A.H.). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of DARPA or the US Government. This work also was made possible by access to the microfabrication facilities of the Center for Nanoscale Systems (CNS), a member of the National Nanotechnology Coordinated Infrastructure Network (NNCI), which is supported by the National Science Foundation under NSF award no. 1541959. CNS is part of Harvard University. We also thank T. Ferrante for technical assistance, M. Rosnach and J.P. Ferrier for artwork and technical illustrations, to J.A. Goss for his help with chips design and fabrication, and the Harvard Medical School Neurobiology Imaging Facility (supported in part by NINDS P30 Core Center grant #NS072030) for consultation and instrument availability.

Author information

Author notes

  1. Ben M Maoz, Anna Herland and Edward A FitzGerald: These authors contributed equally to this work.

Authors and Affiliations

  1. Disease Biophysics Group, Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
    Ben M Maoz, Thomas Grevesse, Sean P Sheehy, Stephanie Dauth, Nikita Budnik, Kevin Shores, Alexander Cho, Janna C Nawroth & Kevin Kit Parker
  2. Wyss Institute for Biologically Inspired Engineering at Harvard University, Boston, Massachusetts, USA
    Ben M Maoz, Anna Herland, Edward A FitzGerald, Thomas Grevesse, Alan R Pacheco, Sean P Sheehy, Tae-Eun Park, Stephanie Dauth, Robert Mannix, Kevin Shores, Alexander Cho, Janna C Nawroth, Donald E Ingber & Kevin Kit Parker
  3. Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
    Ben M Maoz
  4. Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
    Ben M Maoz
  5. The Center for Nanoscience and Nanotechnology, Tel Aviv University, Tel Aviv, Israel
    Ben M Maoz
  6. Department of Micro and Nanosystems, KTH Royal Institute of Technology, Stockholm, Sweden
    Anna Herland
  7. Department of Neuroscience, Swedish Medical Nanoscience Center, Karolinska Institute, Stockholm, Sweden
    Anna Herland
  8. Small Molecule Mass Spectrometry Facility, Harvard University, Cambridge, Massachusetts, USA
    Charles Vidoudez
  9. Graduate Program in Bioinformatics and Biological Design Center, Boston University, Boston, Massachusetts, USA
    Alan R Pacheco & Daniel Segrè
  10. Vascular Biology Program and Department of Surgery, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
    Robert Mannix & Donald E Ingber
  11. Department of Biology, Department of Biomedical Engineering, Department of Physics, Boston University, Boston, Massachusetts, USA
    Daniel Segrè
  12. Mass Spectrometry and Proteomics Resource Laboratory, Harvard University, Cambridge, Massachusetts, USA
    Bogdan Budnik
  13. Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts, USA
    Donald E Ingber

Authors

  1. Ben M Maoz
  2. Anna Herland
  3. Edward A FitzGerald
  4. Thomas Grevesse
  5. Charles Vidoudez
  6. Alan R Pacheco
  7. Sean P Sheehy
  8. Tae-Eun Park
  9. Stephanie Dauth
  10. Robert Mannix
  11. Nikita Budnik
  12. Kevin Shores
  13. Alexander Cho
  14. Janna C Nawroth
  15. Daniel Segrè
  16. Bogdan Budnik
  17. Donald E Ingber
  18. Kevin Kit Parker

Contributions

B.M.M., A.H., E.A.F., T.G., D.E.I. and K.K.P. designed the study. C.V. ran and analyzed the MS samples, and A.R.P. and D.S. performed the flux balance analysis modeling. S.P.S. performed bioinformatic analysis for proteomics and MS. A.H., E.A.F. and T.-E.P. conducted the BBB chip culture and B.M.M., T.G. and s.d. performed brain chip culture and imaging. B.M.M., A.H., E.A.F., T.G., S.D. and R.M. performed confocal imaging, N.B. and B.B. conducted proteomic run and analysis. K.S. performed the COMSOL modeling, B.M.M., T.G. and A.C. conducted sample preparation for brain chip. B.M.M., T.G. and J.C.N. contributed to the brain chip design. B.M.M., A.H., E.A.F., T.G., D.E.I. and K.K.P. prepared illustrations and wrote the manuscript.

Corresponding authors

Correspondence toDonald E Ingber or Kevin Kit Parker.

Ethics declarations

Competing interests

D.E.I. holds equity in Emulate, Inc., consults for the company, and chairs its scientific advisory board. K.K.P. is a consultant and a member of the Scientific Advisory Board of Emulate, Inc.

Integrated supplementary information

Supplementary Figure 1 Diagram of BBB and brain chips.

(a) Schematic of the BBB Chip demonstrates the 3 parts of the chip, Top PDMS channel, membrane and Bottom PDMS channel; (b) Image of 2 BBB Chips, the red color represents the bottom (Endo) channel while the blue represents the top (Pericytes/Astrocytes) channel, (c) Schematic of the Brain Chip demonstrates the different parts of the chip, (from top to bottom) the manifold which hold the chip and enables perfusion of the chip, Brain Chip, PDMS gasket, Topas substrate and PC base which hold the chip; (d) Image of Brain Chips, top tube represents the inlet and outlet of the Brain Chips. The orange triangles represent the location of the human neuronal culture. (e) The diagram represents the experimental setup. The red line represents the endothelial media (aBlood) whereas the blue line represents the neuronal media (aCSF).

Supplementary Figure 2 Immunohistochemical characterization of human neural cell cultures in the brain chip and the neurovascular cells in the BBB chip.

(a, b) Confocal images of astrocytes stained for glial fibrillary acidic protein (GFAP, red), neurons stained for β-III-tubulin (βIII, green) and cell nuclei stained with 4′,6-diamidino-2phenylindole (DAPI, blue). (c, d) Confocal images of neurons (βIII, green), synapses (synaptophysin, red) and cell nuclei (DAPI, blue). (e, f) Confocal images of dopaminergic neurons stained for tyrosine hydroxylase (TH, green), astrocytes (GFAP, red) and cell nuclei (DAPI, blue). (g, h) Confocal images of neurons (βIII, green), GABAergic neurons stained for vesicular gamma-aminobutiric acid transporter (VGAT, red) and cell nuclei (DAPI, blue). (i, j) Confocal micrographs of neurons (βIII, green), glutamatergic neurons stained for glutamate decarboxylase (GAD, red) and GABAergic neurons (VGAT, blue). (a, c, e, g, i) scale bars = 3 mm; (b, d, f, h, j) scale bars = 100 μm. (k, l) Demonstration of different cell types that comprise the BBB: (k) Endothelial cells (ZO-1, green, nuclei, blue), (l) Pericytes (Phalloidin, green) and Astrocytes (GFAP, red) and nuclei, blue. Scale bar = 50 μm.(m) Endothelial cells (Occludin, green, nuclei, blue), (n) Endothelial cells (Claudin-5, green, nuclei, blue). More than 10 cultures have been replicated and characterized with immunofluorescence for each condition with similar results.

Supplementary Figure 3 Flow velocity in the model system

(a) Simulation of flow distribution in the coupled chip setup (COMSOL®). Model illustrates precise control of fluidic flow using our microfluidic platform to accurately mimic the differential flow velocities found throughout the BBB-Brain-BBB Chips. Flow distribution confirms our coupled system follows laminar flow with lower velocities applied to the apical channels of the BBB Chip and Brain Chip with a higher velocity found in the basal or vascular component of the BBB Chip.

Supplementary Figure 4 Permeability measurements in the empty BBB-brain-BBB system.

(a) Mean fluorescence intensity measurement of cascade blue-containing medium at different points of the system: vascular component of influx BBB, inlet of Brain Chip, inlet of the brain side of efflux BBB and outlet of the vascular side of efflux BBB. Values are presented as percentage of cascade blue (CB) fluorescence intensity of medium flowing into the system at the inlet vascular part of the influx BBB. N=5 for Vessel 1 and N= 3 for Perivasc.1, Brain and Vessel 2, N representing independent NVU systems, (p values: SI Notes, Supplementary Table 1b.) (b) Mean fluorescence intensity measurement of Alexa-555 labeled BSA (BSA-555) at different points throughout the system. Values are presented as percentage of the BSA-555 intensity values of the medium flowing into the system at the inlet vascular component of the influx BBB microfluidic device (p values: SI Notes, Supplementary Table 1b.). Error bars are SEM, N=5 for Vessel 1 and N= 3 for Perivasc.1, Brain and Vessel 2, significance calculated with one-way ANOVA, Bonferroni post-test.

Supplementary Figure 5 Protein cluster density maps for GEDI Figure 2 and breakdown of the biological processes associated with NVU compartment proteomes.

(a) Mosaics representing the number of proteins that were assigned to each cluster (i.e. tile) in the mosaic after SOM training. These mosaics illustrate the distribution of proteins across the global expression profile GEDI maps shown in Fig. 2. The data are reduced dimensionality by classifying proteins with similar expression profiles into discrete groups that are organized into distinct two-dimensional mosaics. Each tile of these mosaics represents a cluster of proteins and the mapping of proteins to these tiles is conserved across samples to facilitate comparison. Thus, the tiles represent the amount of proteins that have the same expression profile, ranging from low numbers illustrated in blue and high numbers in red. The protein expression for each of the NVU compartments (Endo, Peri/Astro and Neurons) was compared between the uncoupled and coupled conditions. (b). Proteomaps illustrate the KEGG Orthology biological process terms associated with low and high abundance proteins observed in the expression profiles of fluidically uncoupled chips compartments of endothelial cells, pericyte/astrocyte and neuronal/astrocyte cultures contrasted with expression values observed in coupled BBB-Brain-BBB Chips. In these Voronoi diagrams, each individual polygon represents one protein, wherein polygon area is a function of mass abundance. Each polygon is color-coded according to the KEGG Orthology term associated with it, and polygons representing the same KEGG Orthology term are clustered into larger polygons to form the map (Supplementary Video 3, 4, 5, 6, 7, 8). The percentage of the protein expression profiles represented by each KEGG Orthology term shown in the Proteomaps is presented in Fig. 2b. (c) Principle component analysis (PCA) was used to assess the variability in global protein expression observed in the coupled and uncoupled cell populations for each compartment of the BBB and Brain Chips N=3 representing independent NVU systems.

Supplementary Figure 6 GluR2 penetration across the barrier after Meth addition, schematic of experiment shown in Figure 3.

(a) The BBB Chipinflux recapitulates tight barrier preventing penetration of an antibody (anti-GluR2, pink y-shaped objects) from the vascular to the perivascular compartment during control conditions. (b) 24 hrs of Meth addition compromises the barrier, allowing the anti-GluR2 to stain the neurons. Due to the low concentration of Meth reaching the BBB Chipefflux, the barrier stayed intact. (c) 24 hrs after Meth withdrawal resulted in recovery of the Chipinflux while anti-GluR2 remains attached to the neurons.

Supplementary Figure 7 Effect of Meth on the barrier properties of the BBB.

(a-b) Meth dose response of TEER values for a BBB Transwell model. Mean values are represented as percentage of control (untreated well). (a) Meth concentrations were applied 24hrs and (b) recovery for 24 hrs after Meth removal. Error bars are SEM, N=2. (c) Effect of Meth on neuronal viability in the Brain Chip. Cell viability of neurons treated with 1 mM Meth. Dead cells were stained with ethidium homodimer while all cells were stained with DAPI and images were analyzed with a custom MATLAB® script. Results are presented as percentage of live and dead cells, (p values: for each group, the p values for the live vs. dead was <0.0001, SI Notes, Supplementary Table 1b.). Error bars are SEM, N=3 neuronal cultures, were 17–24 field of views were taken for each culture, in order to calculate the mean value for each setup. significance calculated with one-way ANOVA, Bonferroni post-test.

Supplementary Figure 8 Mass balance of Meth in the coupled NVU microfluidic system and Meth uptake by cells in well plates.

(a) Characterization of Meth dilution in BBB-Brain-BBB Chips. Mass spectrometry analysis of the concentration of Meth in different compartments throughout the system: vascular component of the influx BBB (Vessel 1), inlet of the Brain Chip (Perivasc. 1), outlet of the CSF (Brain) and vascular part of the efflux BBB (Vessel 2). Mean values are represented as percentage of the Meth dose flowed into the system (vascular part of the influx BBB), (p values <0.0001 SI Notes, Supplementary Table 1b.). N=3 representing independent NVU systems, significance calculated with one-way ANOVA, Bonferroni post-test (b) Mean values of Meth uptake by the neurovascular cell types in well-plates after 24hrs incubation N=3 representing independent NVU systems.

Supplementary Figure 9 Perivasculature response to Meth.

Immunofluorescence microscopic views of the different cell populations within the influx and efflux BBB Chip in the fluidically coupled NVU system. Perivascular cells stained with astrocyte specific glial fibrillary associated protein (GFAP) and general F-actin staining with Phalloidin demonstrated consistent morphology in the influx and efflux BBB. The cell morphology was compared between steady state (a, b), addition of Meth (c, d), and drug removal (e, f). Administration of Meth for 24 hrs demonstrated only a minor change in perivascular cell morphology of mostly GFAP-negative cells (pericytes and GFAP-negative astrocytes) in the influx BBB (c), whereas efflux BBB maintained the control morphology (d). 24 hrs recovery restored the morphology of the influx BBB Chip (e) which is now identical to the efflux BBB (f). The experiment represented by the immunofluorescence micrographs was repeated 3 times with 2–4 individual NVU systems for each repeat.

Supplementary Figure 10 Untargeted metabolism.

Potential biochemical pathways associated with significant metabolic changes identified by MS analysis (Compound Discoverer) within cells in the vascular compartment of the influx or efflux BBB Chips (Vessel 1 or 2, respectively), perivascular compartments of the influx or efflux BBB Chips (Perivasc 1 or 2, respectively), or the lower compartment of the Brain Chip (Brain) within the coupled NVU system under control conditions (b) or exposed to Meth for 24 hrs (c). Vessel 1 (Blue), Vessel 2 (Magenta), Perivasc 1 (Green), Perivasc 2 (Yellow) and Brain (Black). (c) IPA was used to identify the significant “Disease Pathways” together with “Biofunction” and their regulation which change due to Meth administration, using Z-score, (Z-score scale: red, high Z-score, pathway is upregulated, blue, low Z-score, pathway is downregulated)

Supplementary Figure 11 Metabolic cycle of labelled glucose.

(a) Exploded illustration of the glycolysis and TCA cycles highlighting the modifications to each carbon entity during all steps in the pathway. In our system, we used all carbons C13 labeled glucose (referred to as 6C13; i.e, when a molecule had 2 carbons C13 it was referred as 2C13) to illustrate the glycolysis processes of each carbon. (b) Distribution of the number of C13-labelled carbons in glucose and glutamate in the fluidically coupled NVU system. (c) Distribution of the number of C13-labelled carbons in glucose and glutamate in the uncoupled Brain Chip as it was perfused with C13-labeled glucose, lactate and pyruvate.

Supplementary Figure 12 Theoretical metabolic flux balance analysis.

Theoretical metabolic flux balance analysis of the GABA production in synaptic cleft as the neurons can uptake exogenous pyruvate and lactate without the astrocytes contribution (while Fig. 6d display the exogenous uptake by the astrocytes and directly supplying to neurons). The GABA exchange is shown as a function of the uptake of glucose or equal amounts of lactate and pyruvate in the presence or absence of metabolites in parentheses. All fluxes are reported as μmol•g wet brain−1•min−1.

Supplementary information

Rights and permissions

About this article

Cite this article

Maoz, B., Herland, A., FitzGerald, E. et al. A linked organ-on-chip model of the human neurovascular unit reveals the metabolic coupling of endothelial and neuronal cells.Nat Biotechnol 36, 865–874 (2018). https://doi.org/10.1038/nbt.4226

Download citation