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NeuroElectro A window to the world’s neurophysiology data

NeuroElectro.org A window to the world’s neurophysiology data. Shreejoy Tripathy University of British Columbia, Canada Email: stripathy@chibi.ubc.ca Twitter: @ neuronJoy. Main Idea. Given that there is an extensive neuron electrophysiology literature, what can we learn by compiling it?.

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NeuroElectro A window to the world’s neurophysiology data

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  1. NeuroElectro.orgA window to the world’s neurophysiology data Shreejoy Tripathy University of British Columbia, Canada Email: stripathy@chibi.ubc.ca Twitter: @neuronJoy

  2. Main Idea • Given that there is an extensive neuron electrophysiology literature, what can we learn by compiling it? PubMed search: neuron AND (electrophysiology OR biophysical OR neurophysiology) >45K articles

  3. Electrophysiology literature is notoriously heterogeneous

  4. Electrophysiology literature is notoriously heterogeneous Input resistance measurement differences

  5. NeuroElectro overall methodology

  6. Semi-automated text-mining overview • Identify within data tables: • Neuron types (from NeuroLex.org) • Biophysical properties (in normotypic conditions) • Biophysical data values • Experimental conditions defined within methods sections • Text-mined data is then checked by experts “Experiments were conducted in acutely prepared brain slices of 24- to 28-day-old (65–120 g) male Wistar rats.” Tripathy et al, 2014

  7. NeuroElectro.org web interface Code at github.com/neuroelectro Data at neuroelectro.org/api

  8. Database statistics • Currently 100 neuron types, >300 articles

  9. Extensive variability among NeuroElectro data Resting membrane potential Input resistance Netzebrand et al, 1999 mV MΩ Tripathy et al, in revision

  10. Accounting for differences in experimental conditions • Explain variability in electrophysiological data through influence of experimental conditions: • species/strain • electrode type • animal age, • recording temperature • in vitro/in vivo/cell culture • junction potential Electrode type Tripathy et al, in revision

  11. Neuron clustering on basis of electrophysiology 11 Tripathy et al, in revision

  12. Whole-genome correlation of gene expression and electro-diversity Patterns of gene expression Systematic variation among neuron types Electrophysiological phenotypes 20,000 genes Tripathy et al, in revision/in progress

  13. Making hypotheses on electrophysiology - gene expression relationships • Explaining electrophysiological phenotypes in terms of underlying gene expression (and vice versa)

  14. Future directions • Continuing to expand NeuroElectro • More neuron types • More domains • Synaptic plasticity • Continuing to demonstrate the value of data integration • How can we move to a situation where experimentalists are willingly sharing their data?

  15. Acknowledgements ShreejoyTripathy Email: stripathy@chibi.ubc.ca Twitter: @neuronJoy URL: neuroelectro.org Code: github.com/neuroelectro • Pavlidis Lab @ UBC • Urban Lab @ CMU • Gerkin Lab @ ASU

  16. Mapping neuron electrophysiology to gene expression Neuron type resolution Cell layer resolution Neocortex layer 5/6 20,000 genes Neocortex L5/6 pyramidal cell Neuron type to cell layer mapping is approximate. Will be improved in future iterations with high resolution data. Neocortex Neocortex basket cell

  17. Finding genes most correlated with electrophysiological diversity

  18. Assessing predictive power between gene expression and electrophysiology

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