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Related Experiment Video

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Isolation and Kv Channel Recordings in Murine Atrial and Ventricular Cardiomyocytes
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Transcriptomic analyses of murine ventricular cardiomyocytes.

Morgan Chevalier1, Sarah H Vermij1, Kurt Wyler2

  • 1Ion Channels and Channelopathies Laboratory, Institute for Biochemistry and Molecular Medicine, University of Bern, 3012 Bern, Switzerland.

Scientific Data
|August 22, 2018
PubMed
Summary

This study provides the first transcriptomic data for isolated mouse ventricular cardiomyocytes. These cardiomyocyte-specific gene expression profiles aid research into heart physiology and cardiac arrhythmias.

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Area of Science:

  • Cardiovascular Biology
  • Molecular Biology
  • Genomics

Background:

  • Mice are essential model organisms for cardiac research.
  • Existing transcriptomic data covers whole hearts, but not isolated cardiomyocytes.
  • Understanding cardiomyocyte gene expression is crucial for studying cardiac function and disease.

Purpose of the Study:

  • To generate comprehensive RNA-sequencing data from wildtype and genetically modified murine ventricular cardiomyocytes.
  • To characterize ion channel expression in cardiomyocytes.
  • To provide a valuable resource for researchers studying cardiac physiology and arrhythmias.

Main Methods:

  • RNA-sequencing (RNA-seq) was performed on isolated ventricular cardiomyocytes.
  • Data was collected from wildtype mice and knockout models (CASK, dystrophin, SAP97).
  • Bioinformatic analysis was used to identify expressed genes and ion channels.

Main Results:

  • Novel transcriptomic datasets for isolated murine ventricular cardiomyocytes were generated.
  • Gene expression profiles, including ion channels, were detailed for wildtype and specific knockout models.
  • The data provides insights into the molecular basis of cardiomyocyte function.

Conclusions:

  • This study presents the first cardiomyocyte-specific transcriptomic data resource.
  • The findings facilitate the study of gene function in cardiac physiology and disease.
  • Researchers can utilize this data to investigate gene expression in cardiac arrhythmias.