Presentation Abstract

Program#/Poster#: 648.12/E67
Presentation Title: Predicting the intrinsic properties of diverse neuron types from gene expression
Location: Hall F-J
Presentation time: Tuesday, Oct 16, 2012, 4:00 PM - 5:00 PM
Authors: *S. J. TRIPATHY1, J. SAVITSKAYA2, R. C. GERKIN1,2, N. N. URBAN1,2;
1Ctr. for the Neural Basis of Cognition, 2Biol., Carnegie Mellon Univ., Pittsburgh, PA
Abstract: Brains achieve efficient function through implementing a division of labor, in which different neurons serve distinct computational roles. One striking way in which neuron types differ is in their electrophysiology properties. These properties arise through combinations of ion channels that collectively define the computations that a neuron performs on its inputs. Though the electrophysiology of many neuron types has been previously characterized, these data exist across thousands of journal articles, making cross-study neuron-to-neuron comparisons difficult. Furthermore, the recent collection of datasets describing the differential expression of each gene in the genome throughout the brain raises the exciting possibility of linking neuron genetics with neuron function.
Here, using a combination of manual and automated methods, we describe a methodology to curate neuron electrophysiology information into a centralized database. We then combine this information with datasets on neuron gene expression from the Allen Brain Institute with the goal of predicting differences in neuron physiology from differences in gene expression. Using purely automated approaches, we show that electrophysiology properties can in fact be predicted from gene expression. For example, we show that the uncertainty in a neuron’s resting membrane potential can be lowered from an average of 10.5 mV to 8 mV when incorporating information about neuronal gene expression. These findings suggest that more refined and more accurate data curation approaches can possibly further reduce the uncertainty of electrophysiology parameters. Ultimately, we hope that these methods may allow for aspects of neuron physiology to be determined from existing gene expression datasets alone, in the absence of additional neurophysiology experiments.
Disclosures:  S.J. Tripathy: None. J. Savitskaya: None. R.C. Gerkin: None. N.N. Urban: None.
Keyword(s): GENE EXPRESSION
NEUROINFORMATICS
INTRINSIC PROPERTIES
Support: NSF Graduate Research Fellowship
NIH F32 DC010535
NIH R01 DC0005798
[Authors]. [Abstract Title]. Program No. XXX.XX. 2012 Neuroscience Meeting Planner. New Orleans, LA: Society for Neuroscience, 2012. Online.

2012 Copyright by the Society for Neuroscience all rights reserved. Permission to republish any abstract or part of any abstract in any form must be obtained in writing by SfN office prior to publication.




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