title: Automated image analysis identifies signaling pathways regulating distinct signatures of cardiac myocyte hypertrophy.
abstract: Cardiac hypertrophy is controlled by a complex signal transduction and gene regulatory network, containing multiple layers of crosstalk and feedback. While numerous individual components of this network have been identified, understanding how these elements are coordinated to regulate heart growth remains a challenge. Past approaches to measure cardiac myocyte hypertrophy have been manual and often qualitative, hindering the ability to systematically characterize the network's higher-order control structure and identify therapeutic targets. Here, we develop and validate an automated image analysis approach for objectively quantifying multiple hypertrophic phenotypes from immunofluorescence images. This approach incorporates cardiac myocyte-specific optimizations and provides quantitative measures of myocyte size, elongation, circularity, sarcomeric organization, and cell-cell contact. As a proof-of-concept, we examined the hypertrophic response to α-adrenergic, β-adrenergic, tumor necrosis factor (TNFα), insulin-like growth factor-1 (IGF-1), and fetal bovine serum pathways. While all five hypertrophic pathways increased myocyte size, other hypertrophic metrics were differentially regulated, forming a distinct phenotype signature for each pathway. Sarcomeric organization was uniquely enhanced by α-adrenergic signaling. TNFα and α-adrenergic pathways markedly decreased cell circularity due to increased myocyte protrusion. Surprisingly, adrenergic and IGF-1 pathways differentially regulated myocyte-myocyte contact, potentially forming a feed-forward loop that regulates hypertrophy. Automated image analysis unlocks a range of new quantitative phenotypic data, aiding dissection of the complex hypertrophic signaling network and enabling myocyte-based high-content drug screening.
authors.affiliation: Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908-0759, USA.
authors.forename: Gregory T
authors.initials: GT
authors.lastname: Bass
authors.forename: Karen A
authors.initials: KA
authors.lastname: Ryall
authors.forename: Ashwin
authors.initials: A
authors.lastname: Katikapalli
authors.forename: Brooks E
authors.initials: BE
authors.lastname: Taylor
authors.forename: Stephen T
authors.initials: ST
authors.lastname: Dang
authors.forename: Scott T
authors.initials: ST
authors.lastname: Acton
authors.forename: Jeffrey J
authors.initials: JJ
authors.lastname: Saucerman
mesh_terms: D000316:Adrenergic alpha-Agonists / Q000494:pharmacology; D000318:Adrenergic beta-Agonists / Q000494:pharmacology; D000818:Animals; D006332:Cardiomegaly / Q000473:pathology; D002448:Cell Adhesion; D049108:Cell Enlargement / Q000187:drug effects*; D048430:Cell Shape; D048429:Cell Size / Q000187:drug effects; D002478:Cells, Cultured; D007091:Image Processing, Computer-Assisted; D007334:Insulin-Like Growth Factor I / Q000494:pharmacology; D007545:Isoproterenol / Q000494:pharmacology; D032383:Myocytes, Cardiac / Q000187:drug effects / Q000378:metabolism / Q000502:physiology*; D010641:Phenotype; D010656:Phenylephrine / Q000494:pharmacology; D051381:Rats; D017207:Rats, Sprague-Dawley; D012518:Sarcomeres / Q000378:metabolism; D015398:Signal Transduction; D059010:Single-Cell Analysis / Q000379:methods; D014409:Tumor Necrosis Factor-alpha / Q000494:pharmacology
chemical_list: D000316:Adrenergic alpha-Agonists; D000318:Adrenergic beta-Agonists; D014409:Tumor Necrosis Factor-alpha; D010656:Phenylephrine; D007334:Insulin-Like Growth Factor I; D007545:Isoproterenol