1. CARMEN for the storage and analysis of rich datasets obtained from 60 and 4,096 channels MEA recordings of retinal activity Evelyne Sernagor

2. Spontaneous activity in the immature retina Present during a short developmental window Consists of recurring bursts in RGCs, correlated between neighbouring cells, resulting in propagating waves Episodes occur every few minutes Patterns change with development Important for wiring the visual system

3. Retinal waves are believed to drive the wiring of retinal projections Important wave features Asynchrony between both eyes Wave spatial extent Synchronisation between neighbouring ganglion cells

4. Visualising and characterising retinal waves necessitates techniques to record neural activity from large cell assemblies in the RGC layer Multielectrode arrays (MEAs)

5. . Sekirnjak C et al. J Neurophysiol 2006;95:3311-3327 ©2006 by American Physiological Society

6. We have made extensive use of 60 channels MEAs (Multichannel Systems) Electrode diameter 30  m Electrode centre-to-centre separation 100-200  m

8. Data processing from MCS MEA recordings 1. Spike threshold detection on all electrodes 2. Ascii files of spike time stamps 3. Burst and wave detection algorithms developed in R by Stephen Eglen and Jennifer Simonotto Services on the CARMEN portal Fourplot Service (Stephen Eglen) Reads data in from all MEA retinal data formats currently on CARMEN MEA Movie Calculator Service Creates an animated.gif file showing bursting over time on a MEA grid. Burst Analysis Service (Jennifer Simonotto) Takes text file spike times from multi-electrode array data, and computes network and burst characteristics, outputting a pdf with figures showing burst and network MEA Burst and IBI Cumulative Distribution Calculator Service This service calculates the cumulative distribution functions for burst lengths and inter-burst intervals of spike time series, returning pdfs of the burst length distribution versus burst length, the inter-burst interval distribution versus the interval length, and also returns comma separated variable files for x-y coordinates of the above plots.

9. Newcastle Cambridge U. Washington Seattle UC Berkeley UC Santa Cruz UC Davis Exchange data and share analytical codes Perform cross-labs analysis of retinal waves data using CARMEN analytical tools We have established an international network involving labs investigating retinal waves using MEAs Edinburgh

10. Burst, Network and Propagation Analysis of Retinal Waves - Cross lab comparison Jennifer Simonotto Propagation analysis MEA Spike time data Network analysis Burst analysis

11. Mammalian retinal waves: 3 distinct developmental stages Stage I - Before synapse formation (Gap junctions, adenosine) Bipolar cells glu GABA gly ACh Retinal Ganglion Cell Stage III (P9-P15) Glutamatergic waves Inhibitory amacrine cells Starburst Amacrine cells X Cholinergic Starburst Amacrine cells ACh Retinal Ganglion Cell Stage II (late gestation to P9) Cholinergic waves ACh Retinal Ganglion Cell GABA gly Inhibitory amacrine cells Starburst Amacrine cells Stage II + GABA (P4-P9)

12. Despite fundamental developmental changes in network organization, no consistent changes in wave dynamics have been reported Possible reasons: Retinal area viewed is too small Spatio-temporal resolution of the recordings is not high enough

13. The Active Pixel Sensor (APS) MEA (collaboration with Luca Berdondini, Alessandro Maccione and Mauro Gandolfo, IIT) Camera chip Pixels are metallic electrodes instead of light sensors 4,096 electrodes (64x64 array) Spatial resolution of 21  m (el. diameter, 42  m centre-to-centre) - spatial resolution comparable to neuronal somata in intact networks Can acquire at full frame rate of 7.8kHz L. Berdondini, et al., Lab On Chip, 2009. K. Imfeld, et al., IEEE Transactions on Biomedical Engineering, Vol. 55, Issue 8, 2008. L. Berdondini, et al., IEEE-ICECS, 2001

14. Data acquisition and processing similar to light imager Each metallic electrode represents one pixel Activity acquired with a frame grabber Fast signal acquisition performed as a sequence of frames by encoding extracellular voltage signals as pixels data. Single microelectrode raw data is reconstructed by combining single pixel data from sequential frames. Activity movies “functional electrophysiological imaging”

15. www.3brain.com

16. Newcastle Edinburgh Cambridge IIT Genova U. Washington Seattle UC Berkeley UC Santa Cruz UC Davis

17. Activity movies Raw signals 2.67 mm 1.6 mV P5 retina Visualization based either on signal variance (e.g. within 5ms window) or on electrical potential

18. Screenshot from BrainWave

19. Spike detection

20. The BrainWave developers (Mauro Gandolfo and Alessandro Maccione) are soon going to add a data export tool directly to CARMEN Spike time stamps files exported to Matlab

21. Spikes extraction and visualization of spike trains P10 retina Movie of firing rates R (Stephen Eglen) Movie of detected waves Based on burst analysis Matlab (Matthias Hennig) Raster plot

22. Spatiotemporal resolution is important!!!!! Down-sampling to 8x8 electrodes 42  m diameter (2x2 APS channels) 240  m pitch (6 APS channels) APS 64x64 electrodes 21  m diameter 40  m pitch P3 retina

23. Data processing from APS MEA recordings 1. Spike threshold detection on all electrodes 2. Export to MATLAB files of spike time stamps (also possible to export.MAT files of raw data) 3. Burst and wave detection algorithms developed in MATLAB by Matthias Hennig 4. Additional algorithms to compute wave trajectories and cluster analysis (Mauro Gandolfo, Matlab) and wave spatial extent (Stephen Eglen, R). Services on the CARMEN portal (Matt Down) Bursts Detection Finds bursts of activity from spike times, for each channel in an MEA. Analyse Waves APS2 This service takes the output from burst detection 2 and classifies the bursts into waves

24. Developmental changes in wave spatiotemporal patterns Stage II Slow Random initiation points Random patterns More widespread Stage III Faster More spatially restricted Repetitive patterns

25. Developmental changes in wave spatiotemporal patterns

26. Thanks to… Newcastle Matt Down James van Coppenhagen Jennifer Simonotto Rolando Berlinguer-Palmini Patrick Degenaar Christopher Adams Cambridge Stephen Eglen Edinburgh Matthias Hennig Funders BBSRC, EPSRC, IIT CARMEN Colin Ingram Tom Jackson Mike Weeks Mark Jessop Genova Luca Berdondini (IIT) Alessandro Maccione (IIT) Mauro Gandolfo (Univ. of Genova) Kilian Imfeld (3Brain, Switzerland) USA collaborators who made data available Rachel Wong Marla Feller Leo Chalupa David Feldheim and Alan Litke