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NeuroElectro.org A window to the world’s neurophysiology data Shreejoy Tripathy University of British Columbia, Canada Twitter:

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Presentation on theme: "NeuroElectro.org A window to the world’s neurophysiology data Shreejoy Tripathy University of British Columbia, Canada Twitter:"— Presentation transcript:

1 NeuroElectro.org A window to the world’s neurophysiology data Shreejoy Tripathy University of British Columbia, Canada

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 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 6 Tripathy et al, 2014 “Experiments were conducted in acutely prepared brain slices of 24- to 28-day-old (65– 120 g) male Wistar rats.”

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

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 10 Electrode type Tripathy et al, in revision

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

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

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 Pavlidis UBC Urban CMU Gerkin ASU 15 Shreejoy Tripathy URL: neuroelectro.org Code: github.com/neuroelectro

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

17 Finding genes most correlated with electrophysiological diversity

18 Assessing predictive power between gene expression and electrophysiology


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