Micropatterned Neuronal Networks in Culture: Simplicity or Complexity

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Micropatterned Neuronal Networks in Culture
Simplicity or Complexity

• Bruce C. Wheeler
• Department of Bioengineering
• University of Illinois at Urbana-Champaign

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Recording Multichannel Neuronal Network Activity
Arrays from Multichannel Systems
Localization to Electrodes
Example 92 Neurons are Active
Automated Spike Detection and Sorting (Plexon
Inc.)
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Automated Analysis of Data from Neuronal Networks
Example 92 Neurons are Active
Automated Connectivity Analysis and Diagrams
(under development)
NN Cross-Correlations
Color Coded Activity Maps
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Confluence of Technologies

• Microelectrode Array Technologies
• Thomas (c. 1970), Gross (1977), Pine (1980)
• Used with Cell Culture, Isolated Neural Ganglia
• Novak (c. 1985) Brain Slice
• Perforated, flexible arrays (Boppart 1990)
• Micropatterning Technologies
• Letourneau (1980 -- UV), others -- scratching
  glass etc.
• Kleinfeld 1989 (photolithography)
• Corey 1991 (laser ablation, photolithography),
• Whitesides (microprinting) 1990s Corey/Branch
  (direct printing)
• Patterns and Arrays
• Technology Push
• Some of this technology push has become
  commercialized and productive for biomedical
  research

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Analysis of Array Data -- A Sampling
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Brain Slices Acute and Cultured
Novak, J. L., Wheeler, B. C. (1989).
Two-dimensional current source density analysis
of propagation delays for components of
epileptiform bursts in rat hippocampal slices.
Brain Research, 497, 223-230.
Acute Hippocampal Slice
Cultured Hippocampal Slice
Novak Wheeler
Acute Cerebellar Slice
Ulrich Egert http//www.brainworks.uni-freiburg
.de/ projects/mea/meatools/moviedemo/hcotc
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Tensor Biosciences collaborates with Panasonic, a
division of Matshushita, and the University of
California at Irvine
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Cultured Neurons as Sensors
Evidence of Dose/ Response Relationship (Bursts
per minute)
Guenter Gross U. North Texas http//www.cnns.org/
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Drug Analysis by State Space
Burst Duration vs. its Coefficient of Variation
Gross, Harsch, Rhoades, Goepel Odor, drug toxin
analysis ... Biosens. Bioelect. 12 (5), 373, 1973
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Statistical Properties of Interspike Intervals
Statistical distribution of interspike interval
times (top), interburst interval (left) and power
spectrum (right) for three sizes of networks
Segev, Beneviste, Hulata, Cohen, Palevski, Kapon,
Shapira, Ben-Jacob Long term behavior of
lithographically prepared in vitro neuronal
networks Phys Rev Lett 88(11), 118102, 2002.
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Gravitational Clustering

• Gerstein Aertsen
• Goal Graphical Representation of Functional
  Relationships Among a Population of Neurons
• Each neuron is represented by an atom in N space
• When a neuron fires, its atom gains charge, then
  slowly discharges.
• Like charges attract.
• Positions / trajectories are indicative of cell
  assemblies.

Gerstein, G. L., Aertsen, A. M. H. J. (1985).
Representation of cooperative firing activity
among simultaneously recorded neurons. J.
Neurophysiol., 54, 1513-1528.
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Inferred Connections from Spike Trains
Assumed Simulated Network
Projection into Principal Components Plane from
N-1 Space
Gerstein, G. L., Perkel, D. H., Dayhoff, J. E.
(1985). Cooperative Firing Activity in
Simultaneously Recorded Populations of Neurons
Detection and Measurement. J. Neurosci, 5(4),
881-889.
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Multi Dimensional Scaling to Create Maps
(millimeters on a map)
Adapted from Kruskal, J. B., and Wish, M.,
Multidimensional Scaling, p. 31-32, Sage
University paper series on Quantitative
Applications in the Social Sciences, 07-011.
Beverly Hills and London Sage Publications.
Adapted from Borg, I., and Lingoes, J. (1987)
Multi-dimensional Similarity Structure Analysis,
p. 2-4, Springer Verlag, New York
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Goal Map from Neural Correlation
Cross-Correlograms from Simulation
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ISOMAPbuilds map following geodesic distances
Here four simultaneous networks were simulated.
ISOMAP separated the networks and identified
connections within each correctly.
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Analysis of Bursts Across Electrode Arrays
Grey scale, block clustered depiction of
correlation coefficients between all pairs of
electrodes under low (A), medium (B), high (C)
calcium.
Connectivity Diagram corresponding to A, B. Using
Affinities -- a normalized correlation measure
Segev, Baruchi, Hulata, Ben-Jacob Hidden neuronal
correlations in cultured networks Phys Rev Lett
92(11), 118102, 2004.
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Other Correlation Measures

• max correlation over a window
• correlation coefficients
• synchronization index with a reference signal or
  reference neuron
• information theoretic measures
• The difficult part is finding the right measures

l l l
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Different Learning of Frequent vs. Rare Stimuli
Paradigm Stimulate A often (1/5 Hz), B rarely
(1/50 Hz) Early response (dashed) is different
from later (conditioned) response Rare
response is larger GABA mediated i.e. in the
inhibitory network Single cell responses (left)
show same trend as total population response
(right)
Eytan, Brenner, Marom, Selective Adaptation in
networks of cortical neurons J. Nsci. 23(28)
9349, 2003.
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Learning Reinforcing Long Latency Responses
Paradigm Simulate a pair of electrodes at 1/3
Hz Stop when there is a response in 40-60 msec
window twice in ten trials Repeat after
rest Network learns to respond to that stimulus
Shahaf, Marom Learning in Networks of Cortical
Neurons J. Nsci. 21 (22), 8782. 2001
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Electrical Potentiation of Neurons
Stimulating electrode
potentiated
depressed
Recorded neuron
Jimbo, Tateno, Robinson Simultaneous induction of
pathway-specific potentiation ... Biophys J. 76,
670, 1999.
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Culture / Array as Image Processor / Perceptron
Dense Neuronal Culture on Electrode Array.
Arrows Stimulated electrodes. Right electrical
(action potential) responses
Neurons Learn the Letter L Before Training
little difference After Training recognizes L
as opposed to inverted L
Ruaro, Bonifazi, Torre, Toward the Neurocomputer
... TBME, 52, 3, 371, Mar 2005
Responses to Stimulus Along Top Moving Gaussian
Low Pass Filter
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Animats(Potter et al., GaTech, Caltech, U.
FL.http//www.neuro.gatech.edu/groups/potter
Detect spike rates on electrodes Map rate vector
to display position Map position to stimulus
parameter
Goal Directed Network Control Function
Non-goal directed
Trajectory in Display space
Spike Rate vs. Inter-Stimulus Interval
Not shown Map from clusters to 2-D space,
feedback function
Clusters of the rate vectors (in arbitrary
coordinate system)
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Designing Networks for Geometrical Simplicity and
Control

• E18 Hippocampal Neurons (Sprague-Dawley Rats)
• At 4 days in vitro nearly Glial Free,
• Brain BitsTM
• Serum Free Defined Medium (B27/Neurobasal)
• Low Density (usually 200 cells / mm2)

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Micropatterning Technologies
Printed polylysine
insulator (3um)
metal
neuron
Substrate
Metal electrode
Metals platinum, indium tin oxide, titanium
nitride, gold Insulators silicon nitride,
silicon dioxide, glass, polyimide,
PDMS Permissive polylysine, laminin,
Nonpermissive PEG, chondroitin sulfate,
Microcontact Printing Photoresist
Patterning Microfluidic Deposition Laser
Ablation Microchannels Covalently linked Or
physisorbed
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Cells migrate to the pattern
1 DIV
Number of cells
7 DIV
Days in vitro
14 DIV
Area 0.96 mm2
Arrows indicate cells growing in off-pattern
area
AR0201-G7637
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Electrical Activity Density Dependent, Emerges
with Glia Maturation
Date of 1st Onset of Activity
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Localization enhances recordability
92 discriminable neurons
10 Day Culture, initially localized to 50 um
circles around electrodes
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Recorded Spikes
Occasional high amplitude (100-500 uV) and field
potentials
Typical Mono-, Bi-, and Tri-phasic low amplitude
(20-100 uV)
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1 2 3
4
1
2
3
4
2
1
3
4
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Fraction of Active Sites Suggests 10 to 20 um is
Close Enough -- for a Cell Body
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Patterned Neuronal Networks Show Normal Behavior

• Examples of
• Propagation of signals
• Variety of Input / Output functions
• Rudimentary learning or plasticity
• Engagement of different circuits with increasing
  stimulation

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Basic Geometrical Pattern 40 um wide
lines(Numbers are count of neurons within 30?m
of each electrode)
11 10 5 9 6 5
1 4 10 3 2 8 4 4
1 9 1 9 6 7 2 10
2 10 1 0 3 5 5 5
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Lines Show Independent Network Activity
L I N E 3
Spike Rate (Hz)
L I N E 4
Time (sec)
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Cross Correlograms Suggest Action Potential
Propagation
Reference CH 85, bin 0.1ms, range ?2 ms
600 ?s
300 ?s
Ref.
Reference
?200 ?s
Lag Time (-2 to 2 msec)
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Stimulation of Propagated Activity
4.2 ms
Delays suggest AP propagation
4.4 ms
4.8 ms
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Stimulated Activity Appears to Propagate with Two
Different Conduction Velocities
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14
y 0.0117x 4
Electrically coupled?
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2
R
0.9859
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Velocity 0.09 m/s
Delay (0-16 ms)
8
6
y 0.0016x 3.8579
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Action potential?
2
0.9538
R
2
Velocity 0.63 m/s
0
0
200
400
600
800
1000
1200
Distance (0 - 1200 um)
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Stimulation Evokes a Variety of Responses
Spike probability
Time (0 to 0.5 sec)
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8 Days
Stimulation Shows Incremental Network Involvement
20 Days
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STM 0.6 V
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STM 0.8 V
S
41
STM 1.0 V
S
42
STM 1.2 V
S
43
STM 1.4 V
S
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Input output relation
Fan Out
Fan In
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27 day old culture, stains for neurofilament,
GFAP, nuclei
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Stimulating 41 Recording from 44
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Stimulating 44 Recording from 41
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Stimulating 43 Recording from 41
41
41
43
43
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Stimulating 43 Recording from 44
3 ms
6 ms
43
44
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Spontaneous Activity Cross correlogram (41 44)
Bin 0.5 ms
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Proposed Connectivity
Units milliseconds
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• Biological Complexity Complicates Design and
  Interpretation

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Spontaneous Rates Vary Across Array and Age
X spike rate Y time in days position
corresponds to electrode
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Any Pattern is Possible
But Survival/Activity Appear to Depend On
Connectedness
2-25-05ilines1div21-3MPSPEG
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Glia are Essential to Neural Activity
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Glia Colocalize with Neurons
Versus days in culture Glia within 2 um of a
neuron of Glia on Neurons of Neuron area
covered by Glia

Ratio
26 days 87 of Glia stay within 2um from neurons
13 days 95.6 of Glia with neurons Glia/Neuron
0.2

• Red Glia, Green Neuron,
• Pink Glia Neuron

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Even Simple Networks Have High Synapse Count
22 days
15 days
8 days
Synapse/ Neuron
670
450
300
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Neuron/Glia Locations Vary with Respect to
Substrate/Electrodes
Red, yellow Glial layer Green, blue Neurites
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Apparent Neural Progenitor Cells Propagate from
the Neural Bundle
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Two Populations by Drug Response -- two cell
types, or -- two states of maturation?
Characterize by Spontaneous Activity
Response to Stimulation Depends More on Character
of Cells Stimulated
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Complexity Issues for Cultured Neurons

• How many bbiological features can be controlled?
• How many does one need to control?
• An Array makes 60 simultaneous experiments
  possible
• Combinatoric explosion with pairwise correlations
• Working to automate stimulation / recording
  capabilities
• Need to develop paradigms for looking for network
  connectivity

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Thanks to ...
Greg Brewers Lab (Southern Illinois Univ. School
of Medicine) At the UIUC Yoonkey Nam, John
Chang, David Khatami, Rudi Scharnweber, Betty
Ujhelyi, Kate Musick, Faramarz Edalat, Kathy
Motsegood. Former Students Joe Corey, Darren
Branch NSF EIA 0130828 (BITS Program) NIH R21
NS 38617 R55 RR 13320 Subcontract to R01
EB000786 at Georgia Tech (BRP)
Go Illini!