Original articleAutomated image analysis identifies signaling pathways regulating distinct signatures of cardiac myocyte hypertrophy
Highlights
► Automated image analysis method for cardiac myocyte hypertrophy. ► New quantitative measure of sarcomeric organization. ► TNFα and α-adrenergic signaling have opposite effects on myocyte elongation. ► Sarcomeric organization is uniquely enhanced by α-adrenergic signaling. ► α- and β-adrenergic pathways enhance myocyte–myocyte contact.
Introduction
Cardiac hypertrophy is the growth of individual cardiac myocytes in response to stress. Physiologic stresses like exercise and pregnancy cause an adaptive, reversible hypertrophy while pathologic stresses such as myocardial infarction lead to maladaptive hypertrophy and heart failure [1]. But major challenges remain in identifying, understanding, and ultimately controlling the molecular circuits that regulate heart growth [2]. Rather than acting through a common mechanism, these pathways form a dense web of interactions that has eluded therapeutic approaches to date [3]. The complexity of cardiac signaling networks indicates that integrative systems approaches will be critical for understanding and treating heart failure [4], [5].
Cultured neonatal rat ventricular myocytes have been widely used to study the signaling pathways that initiate cardiac hypertrophy [6]. While neonatal myocytes cannot replicate the native 3D environment and later stages of heart failure, most hypertrophic pathways and genes studied in vivo were first implicated in cultured cells [7]. Previous approaches to imaging hypertrophy in cultured myocytes have been low-throughput and subjective, which has generally limited individual studies to a single node or pathway. Systems-wide analysis of hypertrophy networks will require new quantitative, scalable phenotypic screening approaches [2]. One such approach is high-content cell imaging [8], which has emerged in the pharmaceutical industry for improved target validation compared with traditional biochemical screens [9]. Similarly, systems analyses of the myocyte hypertrophy network may benefit from automated imaging approaches that are amenable to testing larger numbers of pharmacologic or genetic perturbations.
Here, we develop a method for automated image analysis that enables high-content imaging of cardiac myocyte hypertrophy. In addition to myocyte size, several additional hypertrophic phenotypes are quantified including myocyte elongation, sarcomeric organization, and cell–cell contact. As a proof of principle, we measured phenotypic responses to five main classes of hypertrophic pathways: α-adrenergic, β-adrenergic, cytokine (tumor necrosis factor, TNFα), growth factor (insulin-like growth factor-1, IGF-1), and serum (fetal bovine serum, FBS) targeted pathways. Our image analysis approach reveals that while these pathways share a common response in myocyte size, their regulation of other hypertrophic metrics is remarkably distinct.
Section snippets
Cardiac myocyte hypertrophy experiments
Neonatal ventricular myocytes were isolated from 1 to 2 day old Sprague–Dawley rats using the Neomyts isolation kit (Cellutron, Baltimore MD). All procedures were performed in accordance with the Guide for the Care and Use of Laboratory Animals published by the US National Institutes of Health and approved by the University of Virginia Institutional Animal Care and Use Committee. Myocytes were initially cultured in plating media (Dulbecco Modified Eagle Media, 17% M199, 10% horse serum, 5% fetal
Automated cell segmentation accurately identifies myocyte morphology
Several features of the automated cell segmentation algorithm (described above in Methods) were particularly important for accurate identification of cardiac myocytes. As shown in Fig. 2A, thresholding the α-actinin channel before cell segmentation enabled more precise identification of cell-background borders. This is a challenging issue in α-actinin-labeled myocytes due to the complex periodic structure of the myofilaments. We evaluated several algorithms for automated segmentation of myocyte
Discussion
New unbiased phenotypic screening approaches are needed to more systematically characterize the hypertrophic signaling network and identify novel therapeutic targets [2]. Previous work has demonstrated high-content imaging of cancer cell lines and other cell types [8], [12], which enables systematic perturbation studies such as genome-wide RNA interference [19], [20]. Here, we developed an automated image analysis approach optimized for high-content imaging of cardiac myocyte hypertrophy. A
Disclosures
The authors declared none.
Acknowledgements
The authors thank Matthew Kraeutler, Renata Polanowska-Grabow, and Jason Yang for technical assistance and discussion. This work was supported by National Institutes of Health (grant HL094476), the American Heart Association (grant 0830470N), and the University of Virginia FEST program.
References (32)
- et al.
Are transgenic mice the ‘alkahest’ to understanding myocardial hypertrophy and failure?
J Mol Cell Cardiol
(2009) - et al.
With great power comes great responsibility: using mouse genetics to study cardiac hypertrophy and failure
J Mol Cell Cardiol
(2009) - et al.
A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen
Cell
(2006) - et al.
Substrate stiffness affects the functional maturation of neonatal rat ventricular myocytes
Biophys J
(2008) - et al.
Relation between myocyte disarray and outcome in hypertrophic cardiomyopathy
Am J Cardiol
(2001) - et al.
Cardiac plasticity
N Engl J Med
(2008) - et al.
Regulation of cardiac hypertrophy by intracellular signalling pathways
Nat Rev Mol Cell Biol
(2006) - et al.
Tackling heart failure in the twenty-first century
Nature
(2008) - et al.
Cardiovascular networks: systems-based approaches to cardiovascular disease
Circulation
(2010) - et al.
Computational models reduce complexity and accelerate insight into cardiac signaling networks
Circ Res
(2011)
Linking microscopy and high content screening in large-scale biomedical research
Methods Mol Biol
The beautiful cell: high-content screening in drug discovery
Anal Bioanal Chem
Threshold selection method from gray-level histograms
IEEE Trans Syst Man Cybern
Voronoi-based segmentation of cells on image manifolds
Lect Notes Comput Sci
Cell Profiler: image analysis software for identifying and quantifying cell phenotypes
Genome Biol
Segmentation of fluorescence microscopy cell images using unsupervised mining
Open Med Inform J
Cited by (29)
CmyoSize: An ImageJ macro for automated analysis of cardiomyocyte size in images of routine histology staining
2022, Annals of AnatomyCitation Excerpt :Moreover, while JavaCyte analysis do not involve cardiomyocyte nuclei detection or shape criteria for accurate cell selection, it express cell size only by its minimum Feret diameter, which was found to underestimate the actual cardiomyocyte size (Winters et al., 2020). Another automated pipeline, by Bass et al. (2012), implements morphological standardization (namely, nuclei detection and exclusion of non-myocyte cells) for selecting cardiomyocytes, and calculates the cell area, perimeter, elongation, circularity, sarcomeric organization, and cell-to-cell contact. However, it also requires immunofluorescence images and overlooks that size and shape of the myocytes vary depending on their orientation (Varshney et al., 2021; Xu et al., 2016).
A high-throughput ratiometric method for imaging hypertrophic growth in cultured primary cardiac myocytes
2019, Journal of Molecular and Cellular CardiologyHigh-content phenotypic assay for proliferation of human iPSC-derived cardiomyocytes identifies L-type calcium channels as targets
2019, Journal of Molecular and Cellular CardiologyCitation Excerpt :Image analysis scripts were developed in MATLAB to automatically segment and classify the cells. Nuclear segmentation methods adapted from Bass et al. were applied to images of nuclei stained with Hoechst 33342 or DAPI [38]. Briefly, a median filter with a window size of three pixels was applied to smooth the images, and the blurred nuclei were segmented using an Otsu threshold.
Disrupting the key circadian regulator CLOCK leads to age-dependent cardiovascular disease
2017, Journal of Molecular and Cellular CardiologyPhenotypic screen quantifying differential regulation of cardiac myocyte hypertrophy identifies CITED4 regulation of myocyte elongation
2014, Journal of Molecular and Cellular CardiologyCitation Excerpt :Previous work has established a role for these 15 agonists in hypertrophy [14,16], but the contributions of these pathways to distinct hypertrophic features and relative dominance among the agonists to specific phenotypic outputs were less characterized. We previously compared changes in shape and sarcomere organization between four of the hypertrophic agonists, PE, Iso, IGF1, and TNFα [11]. We extended this work to measure transcript abundance in addition to shape with a more diverse panel of agonists.
Hypertrophic reprogramming of the left ventricle: Translation to the ECG
2012, Journal of ElectrocardiologyCitation Excerpt :As part of this, signaling and transcriptional programs emerge wherein a wide range of genes are reactivated. Some evidence suggests that cell size is regulated by shared signaling pathways, but cell shape and sarcomeric organization are regulated by distinct pathways.10 Current understanding does not allow us to parse the effects of all those genes and pathways yet, but it is thought that some confer benefit whereas others are maladaptive.