notes for: materials and methods · materials (polymers, membranes, connectors etc.) for chip...

Supporting Information:

Notes for: Materials and methods

Microfluidic chips, fabrication and pre-treatment BBB Chip: Molds for microfluidic channels with a width, height and length of 1 mm, 0.2, lower channel 1

mm upper channel and 20 mm, respectively, were designed with SolidWorks® software (Dassault Systèmes

SolidWorks Corp. Waltham, MA, USA) and produced by Fineline stereolithography (Proto Labs, Inc.

Maple Plain, MN, USA). Microfluidic devices were subsequently produced by soft lithography. Briefly, a

degassed 10:1 base:crosslinking mix of Sylgard 184 polydimethylsiloxane (PDMS, Dow Corning, Inc.

Auburn MI, USA) was poured onto the mold and allowed to crosslink at 80 °C for 18 hrs. Inlets and outlets

of 1.5 mm diameter were punched in the molded PDMS and the device was bonded to a 100 µm layer of

spincoated PDMS by pre-treating with oxygen plasma at 50 W for 20 seconds in a PFE-100 (Plasma Etch,

Inc. Carson City, NV, USA) and then pressing the surfaces together. A porous polyester membrane (IP4IT,

Louvain-la-Neuve, Belgium, polyethylene terephthalate, 0.4 µm pores, density 4 x106/cm2) was

sandwiched between the two microfluidic channels during bonding.

Brain Chip: The Brain Chip is composed of two parts of polycarbonate (PC) forming the channels and a

porous membrane ((IP4IT, Louvain-la-Neuve, Belgium), polycarbonate 5 µm diameter pores; density 4

x106/cm2) separating the channels (Fig. 1d and Supplementary Fig. 1b). The two PC parts were designed

with SolidWorks software (Dassault Systèmes, SolidWorks Corp. Waltham, MA, USA) and produced by

micromachining. The PC parts were cleaned then sonicated 2x15 min in mild detergent solution and 15 min

in water. Thereafter, the PC parts were dried with compressed air and incubated overnight at 65°C for

complete drying. In order to polish the surfaces, the PC parts were briefly exposed to dichloromethane

(DCM) (Sigma-Aldrich, St Louis, MO, USA) vapor and dried in a dust free environment at room

temperature for 24 hrs. The porous PC membrane was cut with a UV laser (Protolaser U3, LPFK Laser and

Nature Biotechnology: doi:10.1038/nbt.4226

Electronics, Garbsen, Germany). The PC porous membrane was placed between the two PC parts, manually

aligned and glued with Kapton tape ® (E.I du Pont de Nemours and Company. Wilmington, DE, USA).

Once the chips parts were aligned and glued, the parts were bonded by vapor bonding: The chip was placed

in a vacuum chamber containing 4 ml of DCM for 30 minutes to allow bonding of the parts. After the

bonding treatment, the Kapton tape ® was removed and the chips were ready to use.

A chip base and manifolds were designed with SolidWorks (Dassault Systèmes, SolidWorks Corp.

Waltham, MA, USA) and fabricated in PC by micromachining. After 2 weeks of neural cell culture (see

below) the TOPASÒ (PolyLinks, Asheville, NC, USA) substrates were assembled into the chip. The

TOPASÒ was first deposited in the bottom of the base. Then, a PDMS gasket (molded with an aperture

corresponding to the neuronal growth area) was placed on top of the TOPASÒ substrate. The PC chip was

finally placed on top of the gasket and tight seal was formed by screwing the manifolds to the base. Media

reservoirs were fixed on one manifold using Luer-lock connectors. Reservoirs consisted of 5 ml syringes

from which the top was cut. The plungers were cut and a biopsy punch used to create a minimal opening to

the atmosphere. Connectors were fixed to the other manifold. The connector linked to the bottom channel

(neuronal channel) was blocked from flow and the connector linked to the upper channel was connected to

a peristaltic pump (IPC series 16 channels, Ismatec, Germany). This configuration prevented all shear stress

on the neuronal constructs while enabling diffusion through the PC porous membrane.

Notes for Cell culture

TranswellÔ cell culture: 24-well Transwell inserts (Corning, Tewksbury, MA, USA), 0.4 µm, polyethylene

terephthalate membrane, were coated with fibronectin (Sigma-Aldrich, St Louis, MO, USA) and collagen

IV (Sigma-Aldrich, St Louis, MO, USA) at 200 µg/ml in cell culture grade water (ultra-pure H2O) for 2

hrs. The inserts were inverted and pericytes or astrocytes were seeded at 6.25×103 cells per insert. After 2

hrs of incubation, the inserts were placed in 24-well plates and seeded with hBMVEC (CellSystems,

Kirkland, WA, USA) at 2×104 cells per insert. Media was exchanged to BBB media (see below) with 250


µM cAMP (Abcam, Cambridge, MA, USA) and 17.4 µM RO20-1724 (St Cruz Biotech, St Cruz, CA,

USA). Transendothelial electrical resistance (TEER) values were measured after 120 hrs of culture using

an EndOhm (World Precision Instruments, Sarasota, FL, USA. Paracellular diffusion was assayed by

applying 0.1 mg/ml cascade blue hydrazine sodium salt (530Da, Thermo Fisher Scientific, Waltham, MA,

USA) and 0.1 mg/ml BSA-555 (66 kDa, Thermo Fisher Scientific, Waltham, MA, USA) apically and allow

to permeate into the basal chamber for 45 min.

BBB and Brain Chip media: All media components were from Life Technologies (Life Technologies,

Carlsbad, CA, USA) unless otherwise stated. Endothelial media was based on M199 supplemented with

N2 (1:100), endothelial cell growth supplement (ECGS) (15 µg/ml Sigma-Aldrich, St Louis, MO, USA),

PenStrep (100 U/mL), FBS (2.5% v/v), heparin (5 µg/ml, Sigma-Aldrich, St Louis, MO, USA),

hydrocortisone (0.5 µg/ml, Sigma-Aldrich, St Louis, MO, USA), EGF (10 ng/ml) , FGF (10 ng/ml) , cAMP

(250 µM, Abcam, Cambridge, MA, USA) and RO20-1724 (17.4 µM St Cruz Biotech, Santa Cruz, CA,

USA). The Brain Chip media, also used for the perivascular compartment of the BBB Chip was based on

Neurobasal supplemented with B27 (1:50), N2 (1:100), laminin (1 µg/ml, Sigma-Aldrich, St Louis, MO,

USA), BDNF (20 ng/ml Peprotech, Rocky Hill, NJ, USA), GDNF (20 ng/ml Peprotech, Rocky Hill, NJ,

USA), Penstrep (100 U/mL). C13 labelled glucose (Cambridge Isotope Laboratory, Tewksbury, MA)

experiments was carried out in normal glucose free media, alternatively with C13 labeled lactate or pyruvate

(both from (Cambridge Isotope Laboratory, Tewksbury, MA).

Practical guidance in setting up the BBB-Brain-BBB Chip system: Microfluidic devices are available from

several commercial vendors and the specific designs used in this system can be fabricated at non-profit

foundries with standard techniques. Care should be taken to use medical grade or cell culture quality


materials (polymers, membranes, connectors etc.) for chip components and tubing when possible.

Moreover, it is essential to find good methods of sterilization, preferably autoclaving or plasma treatments,

that are compatible with the materials of all components and to practice high-level tissue culture techniques,

since the many parts of the system makes it susceptible to infections. Minimize the dead volume of the

system by the design of reservoir volumes, tubing length and connection design and account for dead

volumes in the specific assays. In this study, we used commercially available primary cells to allow for

direct availability to other labs. All cells should be validated (i.e. protein expression of relevant markers,

barrier function, etc.) in both regular well-plates and un-coupled chips before application in coupled chip

set-ups. Likewise, alternative common media of the directly connected compartments, the Brain Chip media

in our system, should be validated in regular well-plates and un-coupled chips before usage in coupled

system. Experimental planning is required to allow for differences in cell maturation time, in our hands, the

Brain Chip cells mature for 3-4 weeks, whereas the BBB Chip cells require 3-4 days.

Notes for Analytical methods aGluR2 ELISA: White 96-well Maxisorb plates (NUNC, Roskilde, Denmark) were coated with goat anti-

mouse-IgG, Fcγ-specific (Jackson Labs, Bar Harbor, ME, USA) in sodium carbonate buffer (pH 9.4) for 2

hrs at room temperature or overnight at 4°C. After three washes in PBS, plates were blocked in 1% Biotin-

free BSA (Sigma-Aldrich, St Louis, MO, USA) in PBS for 1 hr at room temperature, followed by three

additional washes. Samples and standards were added for 2 hrs at room temperature, followed by three

washes. Biotin_SP (long spacer) AffiniPure F(ab')2 Fragment Donkey Anti-Mouse I IgG (Jackson Labs,

Bar Harbor, ME, USA) was added in blocking buffer for 2 hrs at room temperature. Following three washes,

Streptavidin poly-HRP40 (FitzGerald Industries, Acton, MA, USA) was added for 30 min. BM

Chemiluminescence substrate ELISA Substrate (Sigma-Aldrich, St Louis, MO, USA) was added to the

wells after washing and the signal was read in a BioTek Neo (BioTek Instruments, Inc. Winooski, VT,

USA) after a few minutes. Standard curves were fitted to a nonlinear regression, four-parameter logistic,

4PL plot in Prism (Graphpad, La Jolla, CA, USA).


Live dead assay: A working solution of ethidium homodimer 8 µM was prepared by dissolving ethidium

homodimer 2 mM (ThermoFischer Scientific, Waltham, MA, USA) in culture medium. The chips were

incubated in the ethidium homodimer working solution for 20 min at 37°C. The chips were then fixed in

4% paraformaldehyde (Sigma-Aldrich, St Louis, MO, USA) for 10 min at room temperature. The cells

were then rinsed 3 times with phosphate buffer. 4’,6-Diamidino-2-Phenylindole, dihydrochloride (DAPI, 5

mg/ml, ThermoFischer Scientific, Waltham, MA, USA) was incubated at a dilution 1:200 for 45 min at

37°C with the cells to stain the nuclei. ProLong Gold Antifade reagent (Molecular Probes Life

Technologies, Grand Island, NY, USA) was added to preserve the samples and glass coverslips are affixed

using transparent nail polish. Samples were imaged using an Olympus confocal microscope (Olympus,

Center Valley, PA, USA) with appropriate lasers. A custom-made MATLAB® (The MathWorks, Inc.,

Natick, MA, USA) code (SI Script 5 below) was used to measure the total number of cells (nuclei stained

with DAPI) and the number of dead cells (nuclei stained with ethidium homodimer). The number of live

cells was derived by subtracting the number of dead cells to the total number of cells.

Fixation, staining and imaging: Chips were rinsed in pre-warmed phosphate-buffered saline and fixed in

4% paraformaldehyde (Sigma-Aldrich, St Louis, MO, USA) for 10 minutes (Brain Chip) to 20 minutes

(BBB Chip) at room temperature. Immunocytochemistry was carried out after permeabilization in

phosphate-buffered saline with 0.05-0.1% Triton X-100 (Sigma-Aldrich, St Louis, MO, USA) and blocking

for 30 minutes in 3-5% Bovine Serum Albumin (Jackson ImmunoResearch, West Grove, PA, USA) or 10%

goat serum in phosphate-buffered saline with 0.05-0.1% Triton X-100. Primary antibodies were applied in

2% goat serum or 0.5% BSA over-night at 4°C or at room temperature for 1.5 hrs. The following primary

antibodies were used for immunocytochemistry experiments: rabbit anti-glial fibrillary acidic protein

(GFAP) (DAKO, Agilent, St Clara, CA, USA, 1:100), mouse anti-vascular endothelial (VE)-cadherin

(Abcam, Cambridge, MA, USA 1:100), mouse anti-zona occludens-1 (ZO-1) (Invitrogen 1:100), anti-β-

III-tubulin (Sigma, St. Louis, MO, USA 1:200), anti-neurofilament (Abcam, Cambridge, MA, USA 1:100),

anti-glial fibrillary acidic protein (GFAP, Abcam, Cambridge, MA, USA, 1:200), anti-occludin (Invitrogen


1:100), anti-claudin5 (Invitrogen 1:100) anti-tyrosine hydroxylase, anti-GAD1/GAD67, and anti-VGLUT

1 (all Abcam, Cambridge, MA, USA 1:100). Cells were washed three times in phosphate-buffered saline

with 0.05-0.1 % BSA, followed by staining with secondary antibody for 30-60 min at room temperature.

The secondary antibodies were anti-rabbit, anti-goat or anti-mouse IgG conjugated with Alexa Fluor-488,

Alexa Fluor-555, or Alexa Fluor-647 (Invitrogen, Carlsbad, CA, USA). Hoechst (10 mg/ml, Invitrogen,

Carlsbad, CA, USA) was used at a dilution of 1:5000 for nuclei staining. For staining of F-actin, Alexa

Fluor-488-phalloidin or Alexa Fluor-647-phalloidin (Invitrogen, Carlsbad, CA, USA) were used at dilution

of 1:30. CellMaskTM (Invitrogen, Carlsbad, CA, USA) was used at 1:500 x dilutions in PBS for 30 min,

followed by rinsing in PBS. ProLong Gold Antifade reagent (Molecular Probes Life Technologies, Grand

Island, NY, USA) was added to preserve the samples and glass coverslips are affixed using transparent nail

polish. Prepared slides were either imaged immediately or stored at 4oC. Imaging was carried out on a Zeiss

710 LSM (Zeiss, Oberkochen, Germany) or Olympus confocal microscope or Olympus VS120 Slide

Scanner (Olympus, Center Valley, PA, USA) with appropriate filter cubes. Image processing was done in

FIJI1 or Imaris (Bitplane, Zürich, Switzerland).

Mass spectrometry – metabolomics: All chemicals were from Sigma, St. Louis, MO, USA unless otherwise

noted. To prepare media samples and calibration samples for liquid chromatograph mass spectrometry (LC-

MS) analysis, 20 µL of sample was mixed with 30 µL of an internal standards solution (10 µM D4-

Succinate, D11-Methamphetamine, D11-Amphetamine, in Acetonitrile). After centrifugation at 18000 g

for 10 minutes, 40 µL of supernatant was transferred to glass micro inserts. All samples were kept at -80°C

until analysis.

LC–MS analyses were modified from Maddocks et al.2 and were performed on an Orbitrap Q-

Exactive (Thermo Scientific, Waltham, MA, USA) in line with an Ultimate 3000 LC (Thermo Scientific,

Waltham, MA, USA), Thermo Xcalibur 3.0.63 (Thermo Scientific Inc, Waltham, MA, USA). The Exactive

operated in the polarity-switching mode with positive voltage 3.0 kV and negative voltage 4.0 kV. Column

hardware consisted of a Sequant ZIC-pHILIC column (2.1 × 150 mm, 5 µm, Millipore, Billerica, MA,


USA). Flow rate was 200 µl min−1, buffers consisted of acetonitrile 97% in water for B, and 20 mM

ammonium carbonate, 0.1% ammonium hydroxide in water for A. Gradient ran from 100% to 40% B in

20 min, then to 0% B in 10 min. After maintaining B at 0% for 5 min, it was ramped to 100% over 5 min

and kept at 100% for 10 min. Metabolites were identified and quantified using Trace Finder, and Compound

Discoverer 2.0 software (Thermo Scientific, Waltham, MA, USA). Pyruvate, lactate, leucine, glucose,

glutamine, glutamate, cystein, a-ketoglutarate, N-AcetylAspartate, oxaloacetate and GABA standard

curves were produced for quantifying those metabolites. For the metabolomics analysis, all other

metabolites were identified using Compound Discoverer, mainly on the basis of exact mass within 5 p.p.m.,

and by comparison with the MZCloud MSMS library when possible. For the 13C incorporation

experiments, area for the m/z corresponding to each isotope of the metabolites were plotted and integrated.

Errors in percentage of isotopes were estimated using the standard curve samples. For each intensity of

each isotope in the standard curve, the error was calculated by comparing with the theoretical values. In the

samples, each isotope of the metabolites of interest was assigned an error based on 1.5x the error calculated

for the standard with intensity just below it. The errors were assigned based on the isotope ranks (i.e. the

isotope with the highest intensity in the sample was assigned the corresponding error calculated for the 0C13

isotope in the standard, the second most intense isotope in the sample the error calculated for the 1C13

isotope in the standard, etc.). All concentrations were calculated from the standard curves based on the areas

of the 0C13 isotope divided by the corresponding internal standard (D11-methamphetamine for

methamphetamine and p-hydroxymethamphetamine, D11-amphetamine for amphetamine, and D4-

succinate for all other quantified metabolites). Concentrations for the other isotopes were estimated from

the concentration calculated for 0C13 isotopes and the isotopic ratios. The GABA concentration in the

secretome (the portion escaping the synaptic cleft) of a healthy neuronal culture is low. GABA was mainly

detected in its labeled (C13) form, given the higher sensitivity of C13 detection and the quantification was

done accordingly.

Metabolic pathways were identified by using Mummichog 1.0.33,4, a Python® based software

which predict network activity and significantly affected pathways from high throughput metabolomics.


Mass spectrometry analysis- Label-free Proteomics

Each sample was submitted for single LC-MS/MS experiment that was performed on a LTQ Orbitrap Elite

(Thermo Scientific, Waltham, MA, USA), XCalibur 3.0 (Thermo Scientific Inc, Waltham, MA, USA),

equipped with Waters (Milford, MA, USA) NanoAcquity HPLC pump Peptides were separated onto a 100

µm inner diameter microcapillary trapping column packed first with approximately 5 cm of C18 Reprosil

resin (5 µm, 100 Å, Dr. Maisch GmbH, Germany) followed by analytical column ~20 cm of Reprosil resin

(1.8 µm, 200 Å, Dr. Maisch, GmbH, Germany). Separation was achieved through applying a gradient from

5–27% ACN in 0.1% formic acid over 90 min at 200 nl min−1. Electrospray ionization was enabled through

applying a voltage of 1.8 kV using a home-made electrode junction at the end of the microcapillary column

and sprayed from fused silica pico tips (New Objective, Woburn, MA, USA). The LTQ Orbitrap Elite was

operated in the data-dependent mode for the mass spectrometry methods. The mass spectrometry survey

scan was performed in the Orbitrap in the range of 395 –1,800 m/z at a resolution of 6 × 104, followed by

the selection of the twenty most intense ions (TOP20) for CID-MS2 fragmentation in the Ion trap using a

precursor isolation width window of 2 m/z, automatic gain control (AGC) setting of 10,000, and a maximum

ion accumulation of 200 ms. Singly charged ion species were not subjected to CID fragmentation.

Normalized collision energy was set to 35 V and an activation time of 10 ms, AGC was set to 50,000, the

maximum ion time was 200 ms. Ions in a 10 ppm m/z window around ions selected for MS2 were excluded

from further selection for fragmentation for 60 s.

The proteomic datasets of each of the BBB-Brain-BBB Chip compartments in coupled and uncoupled

configuration were added to the center of computational mass spectrometry, and can be found in the

following data base:

http://massive.ucsd.edu/ProteoSAFe/status.jsp?task=6370ee74ab3e45f897396a8a5cac3412


Notes for Modelling methods COMSOL Simulation Computational Model

Fluid Dynamics and Chemical Species Transport and Reaction Kinetics were model by using COMSOL

(COMSOL, Multiphysics® 5.0, Stockholm, Sweden).

Fluid Dynamics: To estimate the fluid dynamics through the microfluidic devices, a simplified version of

the incompressible Navier-Stokes equation was used:

(1)

(2)

where, ρ is fluid density [kg/m3], u is flow rate [m/s], P is pressure [Pa], and µ is dynamic viscosity [Pa·s].

This modified version of the equation can be used because the Reynold’s number is low enough in the

device that convective terms become negligible5. In this model, the properties of water (at 37°C) were used

to estimate those of the media in the experimental setup with ρ = 9933 kg/m3 [6] and µ = 6.9 x 10-4 Pa·s7.

The model also utilizes the Brinkman equation, another modified form of the Navier-Stokes

equation:

(3)

where K is convective permeability [m2] and ε is porosity. This equation models the flow through porous

media present in the devices, namely polycarbonate membranes. The membranes were estimated to have a

convective permeability of 3.7 x 10-15 [8] and a porosity of 0.3 [by manufacturer - IP4IT, Louvain-la-Neuve,

Belgium].

Chemical Species Transport and Reaction Kinetics:

Transport of the chemical species, in this model, was assumed to follow the generic convection-diffusion

equation:

(4)

𝜌𝑑𝑢𝑑𝑡

= −𝛻𝑃 + µ𝛻+𝑢

∇ · 𝑢 = 0

𝜌𝑑𝑢𝑑𝑡

= −∇𝑃 + µ∇+𝑢 −µ𝛫𝜀𝑢

2324+ 𝛻(−𝐷𝛻𝑐) = −𝑢𝛻𝑐 + 𝑅


where c is species concentration, D is diffusivity, u is the velocity field (solution to the fluid dynamics

equations), and R is the reaction rate described by either consumption or production of the species from

cells in the device5. Here, we describe the transport of three different chemical species: cascade blue, bovine

serum albumin, and oxygen. The diffusion coefficients for each chemical species in an aqueous solution,

through the polycarbonate membrane, and through the cellular tissue are presented in Supplementary

Table 9, along with the initial concentrations of each species, as were measured and calculated.

The reaction rates, R, presented here were assumed to follow Hill-type functions, and more

specifically, Michaelis-Menten kinetics9–11:

(5)

where Rmax is the maximum reaction rate of the process (Rmax < 0 for consumption reactions and Rmax > 0

for production reactions), c is the concentration of the chemical species (dependent variable), Km is the

concentration of the chemical species where the reaction rate is half of its maximum, and n is the Hill

constant (n = 1 for Michaelis-Menten kinetics). The current model presents reactions of oxygen

consumption by all cell types. The specific parameters used for each of these reactions are described in

Supplementary Table 10.

𝑅 = :𝑅;<=𝑐>

𝑐> + 𝐾;>

0 In the presence of cells


SI Tables:

Supplementary Information Table 1a. Detailed information for p values in the manuscript, unpaired refers to t-tests

Figure Comparison

Test Significance P value Parameters Parameters

1.g ANOVA Bonferroni's multiple

comparisons

**** <0.0001 F (3, 11) = 4119

1.h ANOVA Bonferroni's multiple

comparisons

**** <0.0001 F (3, 10) = 1008313

3.m Unpaired Two tailed ** 0.0092 t:4.718 df:4 3.n Unpaired Two tailed * 0.0497

t:2.782 df:4

3.o Unpaired Two tailed * 0.0114 t:4.437 df:4 5.c:

Pyruvate (Vascular)

Unpaired Two tailed *** 0.0008 t:5.272 df:8

Lactate (Vascular)

Unpaired Two tailed ** 0.0056 t:3.750 df:8

Glutamine (Vascular)

Unpaired Two tailed ns 0.6178 t:0.5191 df:8

Glutamate (Vascular)

Unpaired Two tailed **** <0.0001 t:10.85 df:8

Pyruvate (Perivasc)


Lactate (Perivasc)


Glutamine (Perivasc)


Glutamate (Perivasc)


Lactate (Vascular vs

Perivasc)

Unpaired Two tailed ** 0.0074

t:3.065

df:16

6.b: Coupled vs.

C13 Glc ANOVA Bonferroni's

multiple comparison

s

**** <0.0001 F (3, 10) = 55,55

Coupled vs. C13Lac

ANOVA Bonferroni's multiple

comparisons

**** <0.0001 F (3, 10) = 55,55

Coupled vs. C13Pyr


comparisons

**** <0.0001 F (3, 10) = 55,55

6.c: Coupled vs.

C13 Glc ANOVA Bonferroni's

multiple comparison

s

* 0.0124 F (3, 10) = 11,36

Coupled vs. C13Lac


comparisons

** 0.0073 F (3, 10) = 11,36

Coupled vs. C13Pyr


comparisons

** 0.0048 F (3, 10) = 11,36


Supplementary Information Table 1b. Detailed information for p values for SI

SI Figure Comparison Test Significance P value Parameters 4.a.

Vessel1 vs.

Perivasc

ANOVA Bonferroni's multiple comparisons

**** <0.0001 F (3, 9) = 219,2

Vessel1 vs.

Brain


**** <0.0001 F (3, 9) = 219,2

Vessel1 vs.

Vessel2 ANOVA Bonferroni's multiple

comparisons **** <0.0001 F (3, 9) = 219,2

Perivasc

vs. Brain


ns 0.6420

F (3, 9) = 219,2

Brain vs.

Vessel2 ANOVA Bonferroni's multiple

comparisons * 0.0025 F (3, 9) = 219,2

4.b.

Vessel1 vs. Perivasc


**** <0.0001 F (3, 9) = 2041

Vessel1 vs.

Brain ANOVA Bonferroni's multiple

comparisons **** <0.0001 F (3, 9) = 2041

Vessel1

vs. Vessel2


**** <0.0001 F (3, 9) = 2041

Perivasc vs.

Brain ANOVA Bonferroni's multiple

comparisons n.s >0,9999

F (3, 9) = 2041

Brain

vs. Vessel2


n.s 0,3166

F (3, 9) = 2041

7.c Cntrl

Live-dead ANOVA Bonferroni's multiple

comparisons **** <0.0001 F (3, 63) =

3740 Meth

Live-dead ANOVA Bonferroni's multiple

comparisons **** <0.0001 F (3, 63) =

3740 Cntrl-Meth

Live ANOVA Bonferroni's multiple

comparisons n.s. >0.9999

F (3, 63) =

3740 Cntrl-Meth

Dead ANOVA Bonferroni's multiple

comparisons n.s. >0.9999

F (3, 63) =

3740 8.a

Vessel 1 vs. Vessel

1 Out


**** <0.0001 F (5, 12) = 155,1

Vessel 1

vs. Perivasc. 1


**** <0.0001 F (5, 12) = 155,1

Perivasc. 1 vs. Brain


ns 0.1182 F (5, 12) = 155,1

Brain vs. Perivasc. 2


ns >0.9999 F (5, 12) = 155,1

Brain vs. Vessel 2

out


ns >0.9999 F (5, 12) = 155,1

Supplementary table 1. The statistical tests, p values, t, F, dF and significance for figures in the main body

(Supplementary table 1 a) and for the SI Figs (Table b). Unpaired refers to t-tests.


Supplementary Information Table 2 Metabolites with more than 2-fold change as response to Meth

Outlet of BBBinflux

Endo

Outlet of BBBinflux Peri/Astro

Outlet of Brain Chip

Outlet of BBBefflux Peri/Astro

Outlet of BBBefflux

Endo Adenine Adenine Adenine Adenine Adenine

D-(-)-Fructose12 D-(-)-Fructose12

D-(-)-Fructose12 L-(+)-Lactic acid12 L-(+)-Lactic acid12 L-(+)-Lactic acid12

L-Aspartic acid L-Aspartic acid L-Aspartic acid L-Aspartic acid

L-Threonic acid13 L-Threonic acid13 L-Threonic acid13 L-Threonic acid13 Indole-3-acetic acid13 Indole-3-acetic acid13

4-Toluic acid 4-Toluic acid Hippuric acid Hippuric acid

Pyruvic acid13 Pyruvic acid13

Uric acid14 Uric acid14 N-

Acetylneuraminic acid 4-Oxoproline Methionine13 Glycine13

Linoleic acid Citric acid13 Maltol N-

Acetylaspartic acid12,

15,16

4-Acetamidobutanoi

c acid Vanillin13 Hypoxanthine

Supplementary table 2. Metabolites were identified with more than 2-fold change (Blue-increased, Red-

decreased) as a results of Meth addition. Compound Discoverer 2.0 was used to analyze the untargeted

spectrum in order to identify the significant changes in the mass spectrometric profiles before and after the

addition of Meth. Each column exhibits the major potential metabolites, which change more than 2-fold as

a response to Meth (N=3). Each metabolite was identified by KEGG to the associated pathway. The same

metabolites identified in more than one compartment are marked with gray background. References show

literature where the same metabolites where identified after Meth exposure. Note that Hippuric acid is a

likely metabolite from Meth.


Supplementary Information Table 3 Significant metabolic pathways that are expressed in the BBBinflux Endo (Vessel 1)

Before treatment with Meth

During treatment with Meth

Leukotriene metabolism Leukotriene metabolism Glycerophospholipid

metabolism Glycerophospholipid

metabolism Xenobiotics metabolism Xenobiotics metabolism

Pyruvate Metabolism b-Alanine metabolism

Lysine metabolism Prostaglandin formation from arachidonate

Valine, leucine and isoleucine degradation

Arginine and Proline Metabolism

Glycine, serine, alanine and threonine metabolism

Vitamin H (biotin) metabolism

Purine metabolism Drug metabolism other

enzymes Aspartate and asparagine

metabolism Ascorbate (Vitamin C) and

Aldarate Metabolism Putative anti-inflammatory metabolites formation from

EPA Urea cycle/amino group

metabolism D4&E4neuroprostanes

formation Supplementary table 3. Significant metabolic pathways which change in the BBBinflux Endo (Vessel 1)

compartment, were identified by using Mummichog3,4. The pathways were identified by assigning each

metabolite identified by KEGG (potential identification based on accurate mass only) to the associated

pathway. While some pathways are still significantly expressed after Meth addition (gray background),

there are still pathways that are significantly different after the Meth addition.


Supplementary Information Table 4

Significant metabolic pathways that are expressed in the BBBinflux Peri/Astro (Perivasc 1)

Supplementary table 4. Significant metabolic pathways, which change in the BBBinflux Peri/Astro

(Perivasc 1) compartment, were identified by using Mummichog3,4. The pathways were identified by

assigning each metabolite, identified by KEGG (potential identification based on accurate mass only) to

the associated pathway. While some pathways remain significantly expressed after Meth addition (gray

background), there are pathways that are significantly different after the Meth addition.





Urea cycle/amino group metabolism


Lysine metabolism Lysine metabolism Glycerophospholipid


metabolism Vitamin B3 (nicotinate and nicotinamide) metabolism

Vitamin B3 (nicotinate and nicotinamide) metabolism

Drug metabolism other enzymes


Aspartate and asparagine metabolism

Aspartate and asparagine metabolism



Alanine and Aspartate Metabolism

Alanine and Aspartate Metabolism

Nitrogen metabolism Nitrogen metabolism Fructose and mannose

metabolism Fructose and mannose

metabolism b-Alanine metabolism b-Alanine metabolism C21steroid hormone

biosynthesis and metabolism

C21steroid hormone biosynthesis and

metabolism Glycosphingolipid

metabolism Glycosphingolipid

metabolism Vitamin B9 (folate)

metabolism Glutamate metabolism

Sialic acid metabolism Hexose phosphorylation

TCA cycle Putative anti-inflammatory metabolites formation from

EPA Glycolysis and

Gluconeogenesis Glutathione Metabolism

Histidine metabolism


Supplementary Information Table 5 Significant metabolic pathways that are expressed in the Brain Chip



Lysine metabolism Lysine metabolism Glycerophospholipid

metabolism Bile acid biosynthesis

Pyruvate Metabolism Squalene and cholesterol biosynthesis

Xenobiotics metabolism Arginine and Proline Metabolism

Leukotriene metabolism Drug metabolism cytochrome P450

Valine, leucine and isoleucine degradation



Supplementary table 5. Significant metabolic pathways, which change in the Brain Chip compartment,

were identified by using Mummichog3,4. The pathways were identified by assigning each metabolite,

identified by KEGG (potential identification based on accurate mass only) to the associated pathway. While

some pathways remain significantly expressed after Meth addition (gray background), there are pathways

that are significantly different after the Meth addition.


Supplementary Information Table 6 Significant metabolic pathways that are expressed in the BBBefflux Peri/Astro (Perivasc 2)



Histidine metabolism Histidine metabolism Methionine and cysteine

metabolism Methionine and cysteine

metabolism Glycine, serine, alanine

and threonine metabolism Glycine, serine, alanine

and threonine metabolism Purine metabolism Purine metabolism

Pyruvate Metabolism Pyruvate Metabolism NGlycan biosynthesis NGlycan biosynthesis



Lysine metabolism Lysine metabolism Caffeine metabolism Caffeine metabolism Phosphatidylinositol

phosphate metabolism Phosphatidylinositol

phosphate metabolism Glycosphingolipid

biosynthesis globoseries Glycosphingolipid

biosynthesis globoseries Pyrimidine metabolism Pyrimidine metabolism Glycerophospholipid


metabolism

Propanoate metabolism Arginine and Proline Metabolism

Selenoamino acid metabolism

Glycolysis and Gluconeogenesis

Fructose and mannose metabolism

Hexose phosphorylation Glutathione Metabolism

Carbon fixation Alanine and Aspartate

Metabolism Glutamate metabolism Sialic acid metabolism Butanoate metabolism b-Alanine metabolism Starch and Sucrose

Metabolism Tryptophan metabolism

Pentose phosphate pathway

Aminosugars metabolism Supplementary table 6. Significant metabolic pathways, which change in the BBBefflux Peri/Astro

(Perivasc 2) compartment, were identified by using Mummichog3,4. The pathways were identified by

assigning each metabolite, identified by KEGG (potential identification based on accurate mass only), to

the associated pathway. While some pathways remain significantly expressed after Meth addition (gray

background), there are pathways that are significantly different after the Meth addition.


Supplementary Information Table 7 Significant metabolic pathways that are expressed in the BBBefflux Endo (Vessel 2)





D4&E4neuroprostanes formation

D4&E4neuroprostanes formation

Butanoate metabolism Butanoate metabolism Propanoate metabolism Propanoate metabolism

Vitamin B5 CoA biosynthesis from

pantothenate

Vitamin B5 CoA biosynthesis from

pantothenate CoA Catabolism CoA Catabolism Vitamin H (biotin)

metabolism Vitamin H (biotin)

metabolism Leukotriene metabolism Leukotriene metabolism

Pyruvate Metabolism Pyruvate Metabolism Vitamin B2 (riboflavin)

metabolism Vitamin B2 (riboflavin)

metabolism Bile acid biosynthesis Ubiquinone Biosynthesis

Lysine metabolism Drug metabolism cytochrome P450

Alkaloid biosynthesis II Urea cycle/amino group metabolism

Supplementary table 7. Significant metabolic pathways, which change in the BBBefflux Endo (Vessel 2)

compartment, were identified by using Mummichog3,4. The pathways were identified by assigning each

metabolite, identified by KEGG (potential identification based on accurate mass only), to the associated

pathway. While some pathways remain significantly expressed after Meth addition (gray background),

there are pathways that are significantly different after the Meth addition.


Supplementary table 8 Correlated Metabolic and Proteomics pathways

Vessel Perivasc Brain

tRNA Charging tRNA Charging tRNA Charging

L-carnitine Biosynthesis L-carnitine Biosynthesis L-carnitine Biosynthesis Glutathione Biosynthesis

Glutathione Biosynthesis

Glutathione Biosynthesis

γ-glutamyl Cycle γ-glutamyl Cycle γ-glutamyl Cycle Purine Ribonucleosides Degradation to Ribose-

1-phosphate

Purine Ribonucleosides Degradation to Ribose-

1-phosphate

Purine Ribonucleosides Degradation to Ribose-

1-phosphate Cysteine Biosynthesis

III (mammalia) Cysteine Biosynthesis

III (mammalia) Cysteine Biosynthesis

III (mammalia)

Histamine Degradation Histamine Degradation Histamine Degradation

Dopamine Receptor Signaling



L-cysteine Degradation I

Glycine Degradation (Creatine Biosynthesis)

Glycine Degradation (Creatine Biosynthesis)

Adenosine Nucleotides Degradation II

Tyrosine Biosynthesis IV

Supplementary table 8 The table shows canonical pathways which show significant change in both the

secretion of the associated metabolites and proteomic expression due to fluidic coupling of the BBB-Brain-

BBB Chips.


Supplementary table 9

Diffusion coefficients of the different chemical species used in our model

Chemical Species

Diffusivity in

Aqueous Medium

[m2/s]

Diffusivity

through PC

Membrane [m2/s]

Diffusivity through Cell

Monolayer [m2/s]

Initial

Concentration

[mol/m3]

Cascade Blue 6e-10 [17] 6e-12

(empirical data)

11e-12

(empirical data)

0.084

(experimental

parameter)

Bovine Serum

Albumin 5.9e-11 [18]

6e-12

(empirical data)

1e-16

(empirical data)

0.0015

(experimental

parameter

Oxygen 3e-9 [9] 2.9e-12 [19] 6e-10 [20] 0.21 [21]

Supplementary table 9. Diffusion coefficients of the different chemical species represented in the model

and the initial concentrations of each species being perfused into the device.

Supplementary table 10

Kinetic Parameters

Cell Type Reaction Km [µM] Rmax [mol/m3/s]

Endothelial Oxygen

Consumption 1[22] -0.034[11]

Astrocyte/

Pericyte

Oxygen

Consumption 1[22] Assume same as neurons

Neuron/

Astrocyte

Oxygen

Consumption 1[22] -0.077 (Seahorse Data)

Supplementary table 10: Kinetic Parameters for the consumption of oxygen by all cell types.


Supplementary Information Table 11 Simulated medium conditions with varying carbon source concentrations

Medium condition 1 2 3 4 Carbon sources, vmax

held constant L-lactate + pyruvate - Glucose -

Carbon sources, vmax varied Glucose Glucose L-lactate +

pyruvate L-lactate + pyruvate

Supplementary table 11. GABA secretion was maximized in each condition to observe the impact

of external metabolites on GABA exchange. The glucose uptake vmax was varied in the presence of either

constant L-lactate and pyruvate uptake (cond. 1, defined by Lewis et al.23 as 0.0058 µmol/gWB/min), or no

L-lactate and pyruvate uptake (cond. 2). Similarly, L-lactate and pyruvate uptake vmax was varied in the

presence of either constant glucose uptake (cond. 3 defined by Lewis et al. as 0.2900 µmol/gWB/min), or

no glucose uptake (cond. 4).


SI Scripts

SI Script 1: Proteomics_Bioprocess_

#------------------------------------------------------------------------------- # Name: Proteomics_Bioprocess_BarGraph # Purpose: Generate bar graphs illustrating break down of bioprocess # composition of mass spec protein expression data # Author: spsheehy # # Created: 08/03/2017 # Copyright: (c) spsheehy 2017 #------------------------------------------------------------------------------- import pandas as pd import numpy as np import matplotlib.pyplot as plt if __name__ == '__main__': # List of input file names filenames = [ 'BOC_Linked_Proteomap_Input.csv','BOC_Solo_Proteomap_Input.csv', 'AP_Out_Proteomap_Input.csv', 'AP_In_Proteomap_Input.csv', 'Endo_Out_Proteomap_Input.csv','Endo_In_Proteomap_Input.csv', ] conditions = ['Neuronal C. Linked', 'Neuronal C. Unlinked', 'Peri/Astro Linked', 'Peri/Astro Unlinked', 'Endo Linked', 'Endo Unlinked'] sample_info = zip(filenames,conditions) processed_samples = pd.DataFrame() for filename, condition in sample_info: data = pd.DataFrame.from_csv(filename) total_abundance = data['Abundance'].sum() bioprocess = pd.Series(data['Level 2'].values.ravel()).unique() columns = ['Process Abundance','Percent of Dataset'] processed_data = pd.DataFrame(index=bioprocess, columns=columns) for process in bioprocess: process_abundance = data.loc[data['Level 2'] == process]['Abundance'].sum()


processed_data.set_value(process,columns[0],process_abundance) percent_abundance = (process_abundance/total_abundance)*100 processed_data.set_value(process,columns[1],percent_abundance) sample_data = pd.Series(data = processed_data['Percent of Dataset'], index = processed_data.index) processed_samples = processed_samples.append(sample_data, ignore_index=True) #output_file = "%s_%s.csv" % (condition, 'Output') #processed_data.to_csv(output_file) processed_samples = processed_samples.transpose() processed_samples.columns = conditions processed_samples.to_csv('Processed_Samples.csv') colors = {'Biosynthesis':'sienna', 'Cell Growth and Death':'green', 'Cellular Community':'gold', 'Central Carbon Metabolism':'sandybrown', 'Cytoskeleton':'crimson', 'DNA Maintenance':'lightskyblue', 'Development':'palevioletred', 'Energy Metabolism':'saddlebrown', 'Folding, Sorting, and Degradation':'midnightblue', 'Immune System':'plum', 'Membrane Transport':'turquoise', 'Metabolism':'darkorange', 'Neurodegenerative Diseases':'black', 'Signal Transduction':'teal', 'Signaling Molecules and Interaction':'lightseagreen', 'Transcription':'navy', 'Translation':'indigo', 'Vesicular transport':'maroon', 'Nervous System':'grey', 'Immune Diseases':'silver' } processed_samples = processed_samples.transpose() processed_samples.plot(kind='barh', color=map(colors.get,processed_samples.columns), stacked=True) plt.xticks(fontsize=20) plt.yticks(fontsize=20) plt.xlabel('Percent of Dataset (%)', fontsize=20, weight='bold') #plt.ylim(0,3) plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize=14) plt.show()


SI Script 2: Proteomics_PCA_Scatterplots #------------------------------------------------------------------------------- # Name: Proteomics_PCA_Scatterplots # Purpose: Generate scatterplots of mass spec PCA # # Author: spsheehy # # Created: 5/17/2017 # Copyright: (c) spsheehy 2017 #------------------------------------------------------------------------------- import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.decomposition import PCA def MergeDataFrames(DF1, DF2, join_type='outer'): Combined = pd.concat([DF1,DF2], join=join_type) Combined = Combined.groupby(Combined.index).first().dropna() return Combined def PCA_Graph(csv_input_filename, fig_output_filename): # Read in mass spec data from samples of interest input_data = pd.read_csv(csv_input_filename) # Get the numerical values to perform PCA on num_data = np.array(input_data._get_numeric_data()) # Create a list of the unique bioprocess IDs present in the data set input_bioprocess = pd.unique(input_data.Bioprocess.ravel()) # Perform PCA on the numerical data data_pca = PCA(n_components=2) pca_results = data_pca.fit(num_data).transform(num_data) # Add the eigenvalues from the PCA to the input data set and output to a # new csv file input_data['PCA 1st PC'] = pca_results[:,0] input_data['PCA 2nd PC'] = pca_results[:,1] # Graph the results of the PCA on a scatter plot, labeling each point # according to its corresponding bioprocess ID # Color marker lables for the scatterplot (one for each unique bioprocess ID)


color_markers = ['gv', 'bd', 'm^', 'ks', 'y<', 'rh', 'c>', 'gp', 'bs', 'rd', 'k^', 'yo', 'gh','bv', 'rv', 'gd', 'y^', 'cs', 'go', 'b^', 'mo', 'ko', 'yv', 'ro', 'cv'] color_marker_dict = dict(zip(input_bioprocess, color_markers)) # To keep the legend legible, only add bioprocess ID label the first time # it is encountered first_time = dict(zip(input_bioprocess, np.repeat([True], len(input_bioprocess)))) # Create a font object to control the font parameters for the plot axis labels font = {'family' : 'arial', 'weight' : 'bold', 'size' : 28} plt.rc('font', **font) fig = plt.figure() ax = fig.add_subplot(1,1,1) #sub = fig.add_subplot(1,1,1) for i in range(len(pca_results)): if(first_time[input_data['Bioprocess'][i]]): ax.scatter(pca_results[i, 0], pca_results[i, 1], s=70, c=color_marker_dict[input_data['Bioprocess'][i]][0], marker=color_marker_dict[input_data['Bioprocess'][i]][1],label=input_data['Bioprocess'][i]) first_time[input_data['Bioprocess'][i]] = False else: ax.scatter(pca_results[i, 0], pca_results[i, 1], s=70, c=color_marker_dict[input_data['Bioprocess'][i]][0], marker=color_marker_dict[input_data['Bioprocess'][i]][1]) # Label scatterplot axes and add bioprocess ID legend to figure ax.set_xlabel('First Principle Component', fontsize=18, weight='bold') ax.set_xlim([-2,8]) ax.set_ylabel('Second Principle Component', fontsize=18, weight='bold') ax.set_ylim([-2,4]) ax.tick_params(axis='both', which='major', labelsize=18) lgd = ax.legend(bbox_to_anchor=(1.05, 1), loc=2, ncol=1, borderaxespad=0.) fig.savefig(fig_output_filename, bbox_extra_artists=(lgd,), bbox_inches='tight') plt.show() return input_data def main(): pass if __name__ == '__main__':


main() # List of mass spec data input file names, and PCA graph output file names # for each data set to be analyzed filenames = [['PCA_Input_Data_1.csv','PCA_Scatterplot_Image_1.png','PCA_Results_Output_File_1.csv'], ['PCA_Input_Data_2.csv','PCA_Scatterplot_Image_2.png','PCA_Results_Output_File_2.csv']] # Run PCA on each input file in the filenames list and save the output # PCA graph to an image file for in_file, out_file, pca_out_file in filenames: analysis_results = PCA_Graph(in_file, out_file) analysis_results.to_csv(pca_out_file)


SI Script 3: Neurons Astrocytes FBA Exogenous

%% Neuron-Astrocyte Flux Balance Analysis % Performs flux balance analysis on the GABA-ergic model 'iNL403_GABA.mat'. % Modifies neuron-astrocyte genome-scale metabolic model to reflect % experimental conditions in Brain-on-a-Chip device, and tests dependence % of neuronal GABA production on lactate and pyruvate from the environment, % and on glucose from the environment. Metabolic model sourced from: Lewis % N.E. et al., Nat. Biotech. 2010. FBA optimization performed using COBRA % Toolbox v2.0, Schellenberger et al. Nat. Protocols, 2011. Solver: Gurobi % 6. % % Alan R. Pacheco, Boston University Graduate Program in Bioinformatics % 17-Jul 2016 %% Initialize the COBRA Toolbox % initCobraToolbox %% Load model filename = 'iNL403_GABA.mat'; S = load(filename,'-mat'); Name = whos('-file',filename); model = Name.name; model = S.(model); %% Run FBA to get ATP demand fluxes FBAsoln = optimizeCbModel(model,'max',0); ATP_As_min = FBAsoln.x(10); % astrocyte ATP_Ne_min = FBAsoln.x(603); % neuron %% Correct gln-glu-gaba exchange directionalities % correct directionality of gaba exchange model.S(find(model.S(:,668)),668) = -model.S(find(model.S(:,668)),668); model.lb(668)=0; model.ub(668)=1000; model.lb(536)=0; %% Close interstitial lac and pyruvate transport and make lac and pyr come from extracellular to neuron % constrain lac and pyr to neuron from interstitial model.lb([880 983])=0;


model.ub([880 983])=0; % constrain lac and pyr from astrocyte to interstitial model.lb([567 580])=0; model.ub([567 580])=0; % Create new extracellular -> neuron lactate reaction model.S(872,1068) = 1; % lac-L [cN] model.S(561,1068) = 1; % h[cN] model.S(168,1068) = -1; % lac-L[e] model.S(161,1068) = -1; % h[e] model.rxns{1068} = 'L-LACt2r_neu'; model.rxnNames{1068} = 'L-lactate reversible transport via proton symport Neuron'; model.c(1068) = 0; model.rev(1068) = 1; model.lb(1068) = -1000; model.ub(1068) = 1000; % Create new extracellular -> neuron pyruvate reaction model.S(708,1069) = 1; % pyr [cN] model.S(561,1069) = 1; % h[cN] model.S(181,1069) = -1; % pyr[e] model.S(161,1069) = -1; % h[e] model.rxns{1069} = 'PYRt2r_neu'; model.rxnNames{1069} = 'pyruvate reversible transport via proton symport Neuron'; model.c(1069) = 0; model.rev(1069) = 1; model.lb(1069) = -1000; model.ub(1069) = 1000; %% Change objective to be GABA output by neuron but still keep ATP demand minimum model.c(:) = 0; obj = [823]; model.c(obj)=1; model.lb(10)=ATP_As_min; % Astrocyte ATP demand model.lb(603)=ATP_Ne_min; % Neuron ATP demand %% Vary global intake of glc, lac, pyr, and record effect on GABA production lac_lb_orig = model.lb(45); pyr_lb_orig = model.lb(58); glc_lb_orig = model.lb(33); met_up_range = [-0.6:0.001:0]'; model.ub(45)=0; model.ub(58)=0; model.ub(33)=0;


% vary glucose with constant lactate and pyruvate for i = 1:length(met_up_range) model.lb(33) = met_up_range(i); FBAsoln = optimizeCbModel(model,'max',0); if numel(FBAsoln.x)==0 gaba_exc_glc_1(i) = 0; else gaba_exc_glc_1(i) = FBAsoln.x(823); end end model.lb(33) = glc_lb_orig; % vary glucose without lactate or pyruvate model.lb(45)=0; model.lb(58)=0; for i = 1:length(met_up_range) model.lb(33) = met_up_range(i); FBAsoln = optimizeCbModel(model,'max',0); if numel(FBAsoln.x)==0 gaba_exc_glc_2(i) = 0; else gaba_exc_glc_2(i) = FBAsoln.x(823); end end model.lb(33) = glc_lb_orig; % vary lactate and pyruvate together with constant glucose for i = 1:length(met_up_range) model.lb(45) = met_up_range(i); model.lb(58) = met_up_range(i); FBAsoln = optimizeCbModel(model,'max',0); if numel(FBAsoln.x)==0 gaba_exc_lacpyr_1(i) = 0; else gaba_exc_lacpyr_1(i) = FBAsoln.x(823); end end model.lb(45) = lac_lb_orig; model.lb(58) = pyr_lb_orig; % vary lactate and pyruvate together without glucose model.lb(33) = 0; for i = 1:length(met_up_range) model.lb(45) = met_up_range(i); model.lb(58) = met_up_range(i); FBAsoln = optimizeCbModel(model,'max',0); if numel(FBAsoln.x)==0 gaba_exc_lacpyr_2(i) = 0; else


gaba_exc_lacpyr_2(i) = FBAsoln.x(823); end end dataMat = [gaba_exc_glc_1',gaba_exc_glc_2',gaba_exc_lacpyr_1',gaba_exc_lacpyr_2']; %% Plotting close all % Plot GABA as a function of overall glc,lac,pyr intake figure plot(-met_up_range,gaba_exc_glc_1,'Linewidth',4) hold on plot(-met_up_range,gaba_exc_glc_2,'Linewidth',2) hold on plot(-met_up_range,gaba_exc_lacpyr_1,'Linewidth',2) hold on plot(-met_up_range,gaba_exc_lacpyr_2,'Linewidth',2) title('Production of GABA') legend('Varied glucose uptake (with constant lactate and pyruvate)','Varied glucose uptake (no lactate or pyruvate)','Varied lactate and pyruvate uptake (with constant glucose)','Varied lactate and pyruvate uptake (no glucose)','Location','southeast') xlabel('Metabolite uptake flux (umol/gWB/min)') ylabel('Synaptic cleft GABA flux (umol/gWB/min)')


SI Script 4: Neurons Astrocytes FBA

%% Neuron-Astrocyte Flux Balance Analysis % Performs flux balance analysis on the GABA-ergic model 'iNL403_GABA.mat'. % Modifies neuron-astrocyte genome-scale metabolic model to reflect % experimental conditions in Brain-on-a-Chip device, and tests dependence % of neuronal GABA production on lactate and pyruvate from the astrocyte, % and on glucose from the environment. Metabolic model sourced from: Lewis % N.E. et al., Nat. Biotech. 2010. FBA optimization performed using COBRA % Toolbox v2.0, Schellenberger et al. Nat. Protocols, 2011. Solver: Gurobi % 6. % % Alan R. Pacheco, Boston University Graduate Program in Bioinformatics % 17-Jul 2016 %% Initialize the COBRA Toolbox % initCobraToolbox %% Load model filename = 'iNL403_GABA.mat'; S = load(filename,'-mat'); Name = whos('-file',filename); model = Name.name; model = S.(model); %% Run FBA to get ATP demand fluxes FBAsoln = optimizeCbModel(model,'max',0); ATP_As_min = FBAsoln.x(10); % astrocyte ATP_Ne_min = FBAsoln.x(603); % neuron %% Correct gln-glu-gaba exchange directionalities % correct directionality of gaba exchange model.S(find(model.S(:,668)),668) = -model.S(find(model.S(:,668)),668); model.lb(668)=0; model.ub(668)=1000; model.lb(536)=0; %% Make lactate and pyruvate interstitial fluxes unidirectional (astrocyte -> neuron) model.lb([880 983])=0; model.ub([567 580])=0;


model.ub(580)=-0.4660; model.lb(983)=0.4660; %% Change objective to be GABA output by neuron but still keep ATP demand minimum model.c(:) = 0; obj = [823]; model.c(obj)=1; model.lb(10)=ATP_As_min; % Astrocyte ATP demand model.lb(603)=ATP_Ne_min; % Neuron ATP demand %% Vary global intake of glc, lac, pyr, and record effect on GABA production lac_lb_orig = model.lb(45); pyr_lb_orig = model.lb(58); glc_lb_orig = model.lb(33); met_up_range = [-0.6:0.001:0]'; model.ub(45)=0; model.ub(58)=0; model.ub(33)=0; % vary glucose with constant lactate and pyruvate for i = 1:length(met_up_range) model.lb(33) = met_up_range(i); FBAsoln = optimizeCbModel(model,'max',0); if numel(FBAsoln.x)==0 gaba_exc_glc_1(i) = 0; else gaba_exc_glc_1(i) = FBAsoln.x(823); end end model.lb(33) = glc_lb_orig; % vary glucose without lactate or pyruvate model.lb(45)=0; model.lb(58)=0; for i = 1:length(met_up_range) model.lb(33) = met_up_range(i); FBAsoln = optimizeCbModel(model,'max',0); if numel(FBAsoln.x)==0 gaba_exc_glc_2(i) = 0; else gaba_exc_glc_2(i) = FBAsoln.x(823); end end model.lb(33) = glc_lb_orig; % vary lactate and pyruvate together with constant glucose for i = 1:length(met_up_range)


model.lb(45) = met_up_range(i); model.lb(58) = met_up_range(i); FBAsoln = optimizeCbModel(model,'max',0); if numel(FBAsoln.x)==0 gaba_exc_lacpyr_1(i) = 0; else gaba_exc_lacpyr_1(i) = FBAsoln.x(823); end end model.lb(45) = lac_lb_orig; model.lb(58) = pyr_lb_orig; % vary lactate and pyruvate together without glucose model.lb(33) = 0; for i = 1:length(met_up_range) model.lb(45) = met_up_range(i); model.lb(58) = met_up_range(i); FBAsoln = optimizeCbModel(model,'max',0); if numel(FBAsoln.x)==0 gaba_exc_lacpyr_2(i) = 0; else gaba_exc_lacpyr_2(i) = FBAsoln.x(823); end end dataMat = [gaba_exc_glc_1',gaba_exc_glc_2',gaba_exc_lacpyr_1',gaba_exc_lacpyr_2']; %% Plotting close all % Plot GABA as a function of overall glc,lac,pyr intake figure plot(-met_up_range,gaba_exc_glc_1,'Linewidth',4) hold on plot(-met_up_range,gaba_exc_glc_2,'Linewidth',2) hold on plot(-met_up_range,gaba_exc_lacpyr_1,'Linewidth',2) hold on plot(-met_up_range,gaba_exc_lacpyr_2,'Linewidth',2) title('Production of GABA') legend('Varied glucose uptake (with constant lactate and pyruvate)','Varied glucose uptake (no lactate or pyruvate)','Varied lactate and pyruvate uptake (with constant glucose)','Varied lactate and pyruvate uptake (no glucose)','Location','southeast') xlabel('Metabolite uptake flux (umol/gWB/min)') ylabel('Synaptic cleft GABA flux (umol/gWB/min)')


SI Script 5: Live Dead Cell Counting

function CellLiveDeath

%%%Syntax%%%

% matrices begin with a capital letter

% single number variables begin with a lower case

savedirectory=uigetdir('E:\Resultats_PostDoc_DBG\Louise\SMC-

Uterus_Ki67','Select a folder in which saving the results');

[imgfilenuc,pathnamenuc] = uigetfile('*.tif','Select a tif image of

neurons stained with DAPI','Multiselect','on');

[imgfile,pathname] = uigetfile('*.tif','Select a tif image of neurons

stained with Ethidium','Multiselect','on'); % open a dialog box

displaying "Select a tif image of neurons treated with betaIII tubulin"

to choose the stacks to open. Returns a string array with the names and

paths of the image

l=length(imgfile);

%%%%%%% Prompt for nuclei images ("nuc" added to the end of each

variable)

prompt_nuc={'enter the threshold value(0<T<1):','Change contrast (1 if

yes, 0 if no)','Enter the maximum nuclei area (in pi)','Enter the minimum

nuclei area (in pi)'};% creates a prompt in which user enter the desired

threshold

dlg_title_nuc='Parameters for nuclei stacks';

num_lines_nuc=1; % 3 lines that defines the values for inputdlg function


def_nuc={'0.20','1','10000','150'}; %give default values for the prompt,

user will change these values

Answer_nuc=inputdlg(prompt_nuc,dlg_title_nuc,num_lines_nuc,def_nuc);

tnuc=str2num(Answer_nuc{1});

contrast_nuc=str2num(Answer_nuc{2});

maxarean=str2num(Answer_nuc{3});

minarean=str2num(Answer_nuc{4});

%%%%%%%

%%%%%%% Prompt for Ethidium images (same name of variable as nuclei

minus the nuc added to the end of each variable)

prompt={'enter the threshold value(0<T<1):','Change contrast (1 if yes,

0 if no)','Enter the maximum nuclei area (in pi)','Enter the minimum

nuclei area (in pi)'};% creates a prompt in which user enter the desired

threshold

dlg_title='Parameters for nuclei stacks';

num_lines=1; % 3 lines that defines the values for inputdlg function

def={'0.10','1','3000','100'}; %give default values for the prompt, user

will change these values

Answer_et=inputdlg(prompt,dlg_title,num_lines,def);

t=str2num(Answer_et{1});

contrast=str2num(Answer_et{2});

maxarea=str2num(Answer_et{3});

minarea=str2num(Answer_et{4});


%%%%%%%

% initializes a matrix l lines x 4 columns that will display the results

% and be saved later

Results=cell(l,4);

for q=1:l

Results(q,1)=imgfilenuc(q);

%%%Nuclei-DAPI%%%

img_nuc=strcat(pathnamenuc,imgfilenuc{q});

Temp_nuc = imread(img_nuc);

Temp_nuc8bit=imadjust(Temp_nuc,stretchlim(Temp_nuc,0.00125));

%Temp_nuc8bit=adapthisteq(Temp_nuc,'NumTiles', [100 100],

'ClipLimit', 0.1); %enhance contrast zone by zone (here image divided

in 10 x 10 zones)

%imtool(Gn)

Gn=uint8(Temp_nuc8bit/256);

%Nuc(:,:,m) = Gn;


Kn=medfilt2(Gn,[2 2]);% filtre qui remplace le pixel par la mÈdiane

des pixels de la zone dÈfinie par le masque (taille du masque spÈcifiÈe

entre crochets)

%imtool(Kn)

Hn=mat2gray(Kn); %transform an image with 0 to 255 (integers) to an

image between 0 and 1 (class:double)

%imtool(Hn)

Bin_nuc=im2bw(Hn,tnuc);

%imtool(Bin_nuc)

Clean1_nuc=bwmorph(Bin_nuc,'clean');

%imtool(Clean1_nuc)

In=bwmorph(Clean1_nuc,'fill');

nuc=strcat(savedirectory,'\',imgfilenuc{q},'nuclei_DAPI','.jpg');

% 2 lines to save an image of the DAPI staining as control

imwrite(Gn,nuc);

[Ln,k]=bwlabel(In);% pour ÈnumÈrer les objets (n=nbre d'objets) de

la figure I. l est la nouvelle figure labelisÈe

%imtool(L)


Cmap=colormap(jet(k)); %defines the colormap that is going to be

used to label the nuclei

NucRGB=label2rgb(Ln,Cmap,'k'); % creates a matrix in which the

object detected in bwlabel will be automatically colored

nucrgb=strcat(savedirectory,'\',imgfilenuc{q},'nuclei_detected','.jpg'

);

imwrite(NucRGB,nucrgb);

Dn=regionprops(Ln,'area','perimeter');% on crÈe un 'structure

array' qui contient les valeurs de area et perimeter de tous les objets

Arn=[Dn.Area];

Pern=[Dn.Perimeter];

%on crÈe 2 nouveaux arrays qui contiennent les surfaces et pÈrimËtres

de tous les objets

% boucle pour choisir la surface des objets ‡ sÈlectionner, entre

Amin et Amax

Narean=size(Arn); Npern=size(Pern);x=1;i=1;Numbn=[];

while i<=k-1;

i=i+1;

if (Arn(i)>minarean)&&(Arn(i)<maxarean);

Arbn(x)=Arn(i);

Perbn(x)=Pern(i);

Numbn(x)=1;

x=x+1;


end

end

number_nuclei=sum(Numbn);

Results{q,2}=number_nuclei;

%%%%%%%%%%Nuclei-Ethidium%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

img=strcat(pathname,imgfile{q});

Temp= imread(img);

Temp_8bit=imadjust(Temp,stretchlim(Temp,0.00125));

%gn=adapthisteq(maxImage_nuc,'NumTiles', [10 10], 'ClipLimit',

0.1); %enhance contrast zone by zone (here image divided in 10 x 10

zones)

%imtool(Gn)

G=uint8(Temp_8bit/256);

%Nuc(:,:,m) = Gn;

K=medfilt2(G,[2 2]);% filtre qui remplace le pixel par la mÈdiane

des pixels de la zone dÈfinie par le masque (taille du masque spÈcifiÈe

entre crochets)

%imtool(Kn)


H=mat2gray(K); %transform an image with 0 to 255 (integers) to an

image between 0 and 1 (class:double)

%imtool(Hn)

Bin=im2bw(H,t);

%imtool(Bin_nuc)

Clean1=bwmorph(Bin,'clean');

%imtool(Clean1_nuc)

I=bwmorph(Clean1,'fill');

ethidium=strcat(savedirectory,'\',imgfile{q},'nuclei_ethidium','.jpg')

; % 2 lines to save an image of the DAPI staining as control

imwrite(G,ethidium);

[L,e]=bwlabel(I);% pour ÈnumÈrer les objets (n=nbre d'objets) de la

figure I. l est la nouvelle figure labelisÈe

%imtool(L)

Cmap_et=colormap(jet(e)); %defines the colormap that is going to be

used to label the nuclei

EtRGB=label2rgb(L,Cmap_et,'k'); % creates a matrix in which the

object detected in bwlabel will be automatically colored


etrgb=strcat(savedirectory,'\',imgfile{q},'ethidium_detected','.jpg');

imwrite(EtRGB,etrgb);

D=regionprops(L,'area','perimeter');% on crÈe un 'structure array'

qui contient les valeurs de area et perimeter de tous les objets

Ar=[D.Area];

Per=[D.Perimeter];

%on crÈe 2 nouveaux arrays qui contiennent les surfaces et pÈrimËtres

de tous les objets

% boucle pour choisir la surface des objets ‡ sÈlectionner, entre

Amin et Amax

Narea=size(Ar); Nper=size(Per);s=1;v=1;Numb=[];

while v<=e-1;

v=v+1;

if (Ar(v)>minarea)&&(Ar(v)<maxarea);

Ar(s)=Ar(v);

Per(s)=Per(v);

Numb(s)=1;

s=s+1;

end

end

number_ethidium=sum(Numb);

Results{q,3}=number_ethidium;


livecells=number_nuclei-number_ethidium;

Results{q,4}=livecells;

end

name=strcat(savedirectory,'\',imgfile{q},'Results','.mat');

save(name,'Results');

end


SI References

1. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat

Methods 9, 676–682 (2012).

2. Maddocks, O. D. et al. Serine starvation induces stress and p53-dependent metabolic

remodelling in cancer cells. Nature 493, 542–546 (2013).

3. Li, S. et al. Predicting network activity from high throughput metabolomics. PLoS

Comput Biol 9, e1003123 (2013).

4. Li, S. et al. Constructing a fish metabolic network model. Genome Biol 11, R115 (2010).

5. Stone, H. A., Stroock, A. D. & Ajdari, A. Engineering flows in small devices:

microfluidics toward a lab-on-a-chip. Annu. Rev. Fluid Mech. 36, 381–411 (2004).

6. Kell, G. S. Density, thermal expansivity, and compressibility of liquid water from 0. deg.

to 150. deg.. Correlations and tables for atmospheric pressure and saturation reviewed and

expressed on 1968 temperature scale. J. Chem. Eng. Data 20, 97–105 (1975).

7. Kestin, J., Sokolov, M. & Wakeham, W. A. Viscosity of liquid water in the range −8 °C to

150 °C. J. Phys. Chem. Ref. Data 7, 941–948 (1978).

8. Neeves, K. B. & Diamond, S. L. A membrane-based microfluidic device for controlling

the flux of platelet agonists into flowing blood. Lab Chip 8, 701–709 (2008).

9. Avgoustiniatos, E. S. & Colton, C. K. Effect of external oxygen mass transfer resistances

on viability of immunoisolated tissue. Ann N Y Acad Sci 831, 145–167 (1997).

10. de la Torre, R., Yubero-Lahoz, S., Pardo-Lozano, R. & Farre, M. MDMA,

methamphetamine, and CYP2D6 pharmacogenetics: what is clinically relevant? Front

Genet 3, 235 (2012).

11. Jones 3rd, C. I. et al. Endothelial cell respiration is affected by the oxygen tension during


shear exposure: role of mitochondrial peroxynitrite. Am J Physiol Cell Physiol 295, C180-

91 (2008).

12. McClay, J. L. et al. Large-scale neurochemical metabolomics analysis identifies multiple

compounds associated with methamphetamine exposure. Metabolomics 9, 392–402

(2013).

13. 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).

14. Shima, N. et al. Influences of methamphetamine-induced acute intoxication on urinary

and plasma metabolic profiles in the rat. Toxicology 287, 29–37 (2011).

15. Kim, J. E. et al. Metabolic alterations in the anterior cingulate cortex and related cognitive

deficits in late adolescent methamphetamine users. Addict Biol (2016).

doi:10.1111/adb.12473

16. Sailasuta, N., Abulseoud, O., Hernandez, M., Haghani, P. & Ross, B. D. Metabolic

Abnormalities in Abstinent Methamphetamine Dependent Subjects. Subst Abus. 2010, 9–

20 (2010).

17. Turker, N. & Erdogdu, F. Effects of pH and temperature of extraction medium on

effective diffusion coefficient of anthocynanin pigments of black carrot (Daucus carota

var. L.). J. Food Eng. 76, 579–583 (2006).

18. Phillies, G. D. Diffusion of bovine serum albumin in a neutral polymer solution.

Biopolymers 24, 379–386 (1985).

19. Haraya, K. & Hwang, S.-T. Permeation of oxygen, argon and nitrogen through polymer

membranes. J. Memb. Sci. 71, 13–27 (1992).

20. Androjna, C., Gatica, J. E., Belovich, J. M. & Derwin, K. A. Oxygen diffusion through


natural extracellular matrices: implications for estimating ‘critical thickness’ values in

tendon tissue engineering. Tissue Eng Part A 14, 559–569 (2008).

21. Kutty, M. N. Site Selection For Aquaculture: Chemical Features of Water. in United

Nations Development Programme (Food and Agricultural Organization of the United

Nations, 1987).

22. Wagner, B. A., Venkataraman, S. & Buettner, G. R. The Rate of Oxygen Utilization by

Cells. Free Radic Biol Med 51, 700–712 (2011).

23. 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).


notes for: materials and methods · materials (polymers, membranes, connectors etc.) for chip...

Documents