Electrophysiology is a branch of physiology that pertains to the electrical activity of cells and tissues, and the electrical recording techniques that enable measurement of this activity. Electrophysiological data are typically collected using single-electrodes, electrode arrays, and fluorescent imaging systems. They are the most common type of data collected in basic studies of cardiac cells and tissues, as well as many other types of excitable cells. The reason for this is that these data can be used to relate molecular function to the electrical activity of cardiac muscle cells in health and disease. Establishing this relationship is a critically important task, since sudden cardiac death (SCD) from electrical arrhythmia is now the leading cause of mortality in the Western world, exceeding cancer in deaths per year. The number of cardiac electrophysiological (CEP) studies supported by the NHLBI, and the quantity and variety of data collected in these studies of heart function, vastly exceeds that for genetic, transcriptional or proteomic studies. While there are resources (e.g., dbGaP, dbSNP, GEO, ArrayExpress, World-2DPAGE Repository, etc) for the documentation and dissemination of these latter data types, no infrastructure exists for managing and sharing celular, tissue, and whole-heart electrophysiological data. The lack of this infrastructure means that these data are being """"""""lost"""""""" in the sense that they exist only in the labs of those who collect it. They are seldom, if ever, disseminated by any means other than publication in peer-reviewed journals. Published data sets are limited to a few ideal examples presented in a way (images) that does not support re-use and further quantitative analysis. To address this problem, our goal is to create software tools for annotating, storing, sharing, and querying CEP data, including both primary data from measurement instrumentation and metadata regarding experimental protocols and results of data analyses. The Cardiovascular Research Grid Project will host a national repository for these experimental data and metadata. The creation of this national repository will, for the first time, make it possible for researchers to organize, archive, and search their own data and share it with others. It will enable re-use of the data so that other groups can mine the data for new results, use the data to design new experiments, and to formulate computational models of cell, tissue and heart function in health and disease. The ability to annotate data according to species, disease, and animal models of disease (including de-identified data from human tissue), as well as other ways, will support deep analysis of the electrophysiological basis of heart disease. Each of these capabilities will help provide fundamentally important insights into the mechanisms of cardiac arrhythmias, and possible therapeutic approaches that can help reduce risk of SCD.

Public Health Relevance

The work in this proposal will create a publicly available infrastructure for managing cardiac electrophysiological data. These data provide insights into the fundamental cause of heart disease. Their availability will enhance our understanding, and will contribute to the treatment of heart disease, the number one cause of mortality in the Western world.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
5R01HL103727-02
Application #
8312531
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Larkin, Jennie E
Project Start
2011-08-05
Project End
2015-05-31
Budget Start
2012-06-01
Budget End
2013-05-31
Support Year
2
Fiscal Year
2012
Total Cost
$455,180
Indirect Cost
$177,631
Name
Johns Hopkins University
Department
Biostatistics & Other Math Sci
Type
Schools of Engineering
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Winslow, Raimond L; Granite, Stephen; Jurado, Christian (2016) WaveformECG: A Platform for Visualizing, Annotating, and Analyzing ECG Data. Comput Sci Eng 18:36-46
Quinn, T A; Granite, S; Allessie, M A et al. (2011) Minimum Information about a Cardiac Electrophysiology Experiment (MICEE): standardised reporting for model reproducibility, interoperability, and data sharing. Prog Biophys Mol Biol 107:4-10