Peter Molnar Ph.D.

Assistant Professor

NanoScience Technology Center at the University of Central Florida

Hybrid Biological Systems for ‘Functional’ Drug Hybrid Biological Systems for ‘Functional’ Drug Screening, as In Vitro Disease ModelsScreening, as In Vitro Disease Models

or for Lab-On-a-Chip Applicationsor for Lab-On-a-Chip Applications

Peter Molnar, Ph.D.Peter Molnar, Ph.D.NanoScience Technology Center and Burnett NanoScience Technology Center and Burnett

College of Medical SciencesCollege of Medical SciencesUniversity of Central Florida, Orlando, USAUniversity of Central Florida, Orlando, USA

The ‘Long-Term’ Goal:The ‘Long-Term’ Goal:Design and Implementation of Integrated Design and Implementation of Integrated

Functional Biological SystemsFunctional Biological Systems

• Design and Manufacturing of complex biological Design and Manufacturing of complex biological systems based on biological examplessystems based on biological examples

• Integration of single cells and cell assemblies with Integration of single cells and cell assemblies with electronics and controlling systems in closed-loop electronics and controlling systems in closed-loop arrangementarrangement

• Cells as componentsCells as components• Development of functional Development of functional in vitroin vitro test systems for test systems for

basic researchbasic research• Development and Commercialization of novel Development and Commercialization of novel

biosensors for medical or environmental biosensors for medical or environmental monitoring purposesmonitoring purposes

• Development of Commercial functional Development of Commercial functional in vitroin vitro systems for drug development and screening systems for drug development and screening (Disease models)(Disease models)

• Development of novel integrated prosthesisesDevelopment of novel integrated prosthesises• Neuronal or ‘Neuro-mimetic’ information Neuronal or ‘Neuro-mimetic’ information

processing units, controlling systemsprocessing units, controlling systems

Problems:Problems:

Clock wheels and springs clock againChicken cells ???

Novel engineering principles/tools needed – based on self-assembly

Cells have an internal program which is activated by extracellular environmental clues

Problems:

- We do not know the actual state of the cells

- We do not know the necessary signals

- Biological variability

- We do not have the tools / knowledge to present the appropriate clues (spatial and time-dependent chemical signals in closed loop feedback)

Reductionism Reductionism –– alternativesalternatives1. Modeling 1. Modeling 2. Try to rebuild from elements2. Try to rebuild from elements

Cellular engineering = engineering of the extracellular clues which are guiding the internal programs of the cells

Internal Program

Extracellular Signals

Surface

Soluble Factors

Contact Signaling

CELL

ToolsTools

Surface (surface chemistry)

Soluble Factors(serum-free culture)

Systematic modification of:

CELL

Other: Surface topography, 3D environment, concentration gradients, time-dependent processes

Neuronal Engineering – Basic Neuronal Engineering – Basic incompatibilitiesincompatibilities • At the material level biological systems consist of ‘soft At the material level biological systems consist of ‘soft

materials’ (hydrogels) whereas engineered materials materials’ (hydrogels) whereas engineered materials usually have a rigid (solid) structureusually have a rigid (solid) structure

• At the interfacing surfaces level biocompatibility is still a At the interfacing surfaces level biocompatibility is still a critical issue; tissue reactions usually ‘encapsulate’ critical issue; tissue reactions usually ‘encapsulate’ implanted materials thus decreasing the long-term efficacy implanted materials thus decreasing the long-term efficacy of the interfaceof the interface

• At the data representation level biological data are coded in At the data representation level biological data are coded in 4D (XYZ and time) action potential trains, whereas 4D (XYZ and time) action potential trains, whereas computers are using locally stored binary numberscomputers are using locally stored binary numbers

• At computing paradigm level biological systems are At computing paradigm level biological systems are processing information at a highly parallel and structured processing information at a highly parallel and structured (local processing) way whereas silicon-based computers are (local processing) way whereas silicon-based computers are processing information using a serial approachprocessing information using a serial approach

• At the hardware level biological systems are self-At the hardware level biological systems are self-organizing, continuously remodeled. In biological systems organizing, continuously remodeled. In biological systems hardware and software are the same, thus programming hardware and software are the same, thus programming means changing the architecture (synaptic plasticity).means changing the architecture (synaptic plasticity).

Engineered Neuronal Networks for Functional Drug Engineered Neuronal Networks for Functional Drug ScreeningScreening

Idea: Using functionalized self-assembled monolayers combined with advanced surface patterning methods the inherent differentiation and self-organizing program in the neurons can be controlled and guided to form directed networks. Using the appropriate extracellular clues and cell types, different functional pathways of the brain could be recreated in vitro and used for a better understanding of physiology and pathophysiology of the nervous system. Moreover, surface patterns can be registered with surface-embedded extracellular electrodes allowing chronic or high-throughput recordings of synaptic transmission and network dynamics.

Goal: Systematic pharmacological characterization of synaptic transmission in engineered embryonic hippocampal networks with special emphasis on AMPA receptor modulators and metabotropic glutamate receptor agonists and antagonists

Photolithographic patterning of self-assembled monolayers on surfaces for directing cell attachment and axonal growth

Rat embryonic hippocampal cells were plated on the patterns.

Patterned neurons formed functional synapses

Pattern formationPattern formation

Factors Factors affecting affecting pattern pattern formationformation

Feature size, Feature size, line widthline width

??Surface ??Surface chemistry, chemistry, contact contact signaling, signaling, gradients…??gradients…??

ShapeShape

Problems / further studiesProblems / further studies

• Serum-free mediumSerum-free medium

• Cell densityCell density

• Role / introduction of glial cellsRole / introduction of glial cells

• Longevity of the patternsLongevity of the patterns

• What is physiological?What is physiological?

• In vitroIn vitro v.s. v.s. in vivoin vivo

• Single-cell patterns vs. Multiple cell Single-cell patterns vs. Multiple cell patternspatterns

Directed connectivity Directed connectivity in multiple cell patternsin multiple cell patterns

1.7 1.8 1.9 2 2.1 2.2 2.3 2.4 2.5

x 104

0

1

2

3

4

5

6

7

42 52 14 34 54 64 84

-500 -400 -300 -200 -100 0 100 200 300 400 5000

0.005

0.01

0.015

0.02

0.02542 vs 64

Toxin detection with cardiac myocytes

500 1000

-1000

-500

0 sampl e.m cd

ti me 0 1000

500 1000

-1000

-500

0 sampl e.m cd

ti me 0 1000

Time (min)

-40 0 40

% Change

-100

-50

0

50

100

150

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TefluthrinCypermethrin Tetramethrin

Time (mins)-30 -20 -10 0 10 20 30

% Change in Amplitude

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-60

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0

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60

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Time (mins) vs % Change Cypermethrin Time (mins) vs %Change Tefluthrin Time (mins) vs % Change Tetramethrin

Time (min)

-40 0 40

% Change

-100

-50

0

50

100

150

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TefluthrinCypermethrin Tetramethrin

Time (mins)-30 -20 -10 0 10 20 30

% Change in Amplitude

-100

-80

-60

-40

-20

0

20

40

60

80

Time (mins) vs % Change Cypermethrin Time (mins) vs %Change Tefluthrin Time (mins) vs % Change Tetramethrin

Patterning of cardiac Patterning of cardiac cellscells

• New surfaces needed

• Serum required for normal activity

• Applications: excitation reentry, drug screening, toxin detection

Time (s)

0.0 0.5 1.0 1.5 2.0

Membrane Potential (mV)-80

-60

-40

-20

0

20

B

Recreation of the Stretch Reflex Arc Recreation of the Stretch Reflex Arc in Vitroin Vitro

Idea: Random dissociated cell cultures have only a limited use in the study of complex physiological processes or diseases such as Amyotrophic Lateral Sclerosis (ALS) or spinal cord injury. Using surface chemistry and advanced patterning methods a functional model of the spinal stretch reflex arc can be created. This model will be an improvement over current in vitro models that are composed of disorganized culture systems because the interaction between the different cell types will be physiological ensuring a healthy development and in vivo - like functionality. The benefit of this system compared to in vivo models will be the accessibility of each element to experimental manipulations such as selective drug administration or replacement with cells from transgenic animals.

Goal: Develop patterned artificial surfaces integrated with a microfabricated silicon device to create a physiologically realistic in vitro implementation of the stretch reflex arc in order to study normal and pathological behavior of this important functional unit of the spinal cord.

The stretch reflex arcThe stretch reflex arc

Satkunam, L.E. CMAJ. 2003; 169 (11) :1173-9

Photolithographic patterning of Photolithographic patterning of myotubesmyotubes

C2C12 myotubes are forming only on specific surfaces (vitronectin / fibronectin)

Integration of myotubes with AFM Integration of myotubes with AFM cantileverscantilevers

Time (s)

Trigger (V)

PD (V)

A

Trigger (V)

PD (V)

111 121

DCB

108.5Time (s)

Trigger (V)

PD (V)

A

Trigger (V)

PD (V)

111 121

DCB

108.5Time (s)

Trigger (V)

PD (V)

A

Trigger (V)

PD (V)

111 121

DCB

108.5

Determination of transfer characteristics of Determination of transfer characteristics of motoneuronsmotoneurons

Input Output

WN: White noise; CT: EPSCs and IPSCs-Transfer characteristics (Wiener kernel)- Peristimulus time histogram (PSTH)

Motoneuron

h(t)

No Input

WN

CT

No Input

WN

CT

Input Output

WN: White noise; CT: EPSCs and IPSCs-Transfer characteristics (Wiener kernel)- Peristimulus time histogram (PSTH)

Motoneuron

h(t)

No Input

WN

CT

No Input

WN

CT

h(t)

No Input

WN

CT

No Input

WN

CT

Normalized Input (I*RM; mV)

0 20 40 60 80

Output (firing frequency; Hz)0

2

4

6

8

10

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14

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18

Static input/output function

Action potential shape analysis as a high-throughput Toxin Detection tool

Acknowledgement:Acknowledgement:

University of Central FloridaUniversity of Central FloridaHybrid Neuronal Systems LaboratoryHybrid Neuronal Systems LaboratoryJames J. HickmanJames J. HickmanLisa Riedel, ChangJu Chun, Mainak Das, Cassie Gregory, Kerry Lisa Riedel, ChangJu Chun, Mainak Das, Cassie Gregory, Kerry Wilson, Anupama Natarajan, Dinesh MohanWilson, Anupama Natarajan, Dinesh Mohan

Funding:Funding:NIH, DARPA, DOENIH, DARPA, DOE

Summary:Summary:

• We have the basic tools to build functional hybrid We have the basic tools to build functional hybrid biological systemsbiological systems

• We need more experience and knowledge to reliably We need more experience and knowledge to reliably reproduce themreproduce them

• They are promising novel tools for basic research, They are promising novel tools for basic research, environmental monitoring, drug screening, in vitro environmental monitoring, drug screening, in vitro disease models and roboticsdisease models and robotics