Abstract
Cardiac fibrosis is a pathological process characterized by excessive tissue deposition, matrix remodeling, and tissue stiffening, which eventually leads to organ failure. On a cellular level, the development of fibrosis is associated with the activation of cardiac fibroblasts into myofibroblasts, a highly contractile and secretory phenotype. Myofibroblasts are commonly identified in vitro by the de novo assembly of alpha-smooth muscle actin stress fibers; however, there are few methods to automate stress fiber identification, which can lead to subjectivity and tedium in the process. To address this limitation, we present a computer vision model to classify and segment cells containing alpha-smooth muscle actin stress fibers into 2 classes (α-SMA SF+ and α-SMA SF-), with a high degree of accuracy (cell accuracy: 77%, F1 score 0.79). The model combines standard image processing methods with deep learning techniques to achieve semantic segmentation of the different cell phenotypes. We apply this model to cardiac fibroblasts cultured on hyaluronic acid-based hydrogels of various moduli to induce alpha-smooth muscle actin stress fiber formation. The model successfully predicts the same trends in stress fiber identification as obtained with a manual analysis. Taken together, this work demonstrates a process to automate stress fiber identification in in vitro fibrotic models, thereby increasing reproducibility in fibroblast phenotypic characterization.
Highlights
Cardiac fibrosis is a pathological process characterized by excessive tissue deposition, matrix remodeling, and tissue stiffening, which eventually leads to organ failure
The degree of α-SMA stress fiber formation exists on a spectrum, it is possible to differentiate between cells that contain no stress fibers (α-SMA S F-) and those that contain at least a single stress fiber (α-SMA S F+)
We have developed a computer vision model that is able to segment cells containing α-SMA stress fibers from a standard, 3 channel fluorescence image
Summary
Cardiac fibrosis is a pathological process characterized by excessive tissue deposition, matrix remodeling, and tissue stiffening, which eventually leads to organ failure. Despite the fibroblast heterogeneity in vivo, numerous studies in vitro and in vivo have shown that increasing matrix stiffness is both a c ause[19] and a r esult[20] of increasing α-SMA expression, creating a positive feedback loop that, when uncontrolled, progresses to pro-fibrotic cellular phenotypes Synergistic processes such as TGF-B s ignaling[21,22] and the innate immune r esponse[23] lead to increased myofibroblast activation as identified by α-SMA stress fibers. The organization of α-SMA into stress fibers is responsible for many of the phenotypic behaviors associated with myofibroblast a ctivation[27] and represents a key aspect of the myofibroblast classification process[28] To this end, an automated method of image-based cell identification that is based on fiber structure would both reduce time spent on this tedious task and increase consistency in the field. We sought to train a model to classify cells based on stress fiber presence
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