Identification of the signalling cascades and molecular markers is critical to the improvement of early detection, monitoring, and treatment of chronic cardiovascular disease. Study of the molecular basis in clinical samples is challenged by the limited access to human tissues from patients and confounding variables associated with sample accessibility, collection, processing and storage, driving the need to develop new approaches. "Organ-on-a-chip" (OOC) is an emerging technology constituting microengineered biomimetic systems with possible applications ranging from disease modelling, drug screening, to organ regeneration. Heart-on-a-chip devices incorporating human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes can be designed to be more similar to native human heart and amenable to study at the protein level compared with commonly available rodent models, facilitating facile and correct screening of potential small-molecule therapeutics for heart disease.
In collaborative effort between Emili, Gramolini and Radisic labs, we developed an integrative strategy for comparative quantitative phosphoproteomic profiling on three sets of samples, including surgical explants from patients with hypertrophic cardiomyopathy, a transaortic-constriction mouse model, and Biowire II, a heart-on-a-chip model of cardiac fibrosis. We reported the development of an unbiased quantitative mass spectrometry-based workflow, which is standardized from sample preparation to data analysis, and designed for parallel monitoring of signalling pathways in quantity-limited specimens. Using deep global phosphoproteomic profiling as a mechanistically informative framework, we characterized the signalling perturbations in fibrotic Biowire tissues compared with patients and animal samples. All disease-affected sample types were compared with matched non-pathological controls.
This systematic analysis resulted in the identification and relative quantification of thousands of proteins, phosphoproteins and phosphorylation sites across the complete sample cohort. The generation of deep quantitative data enabled pathway-enrichment analysis, revealing selective disruptions in dozens of specific biological processes and signalling pathways. In addition to enabling precise molecular analysis, the combined analytical framework provides a unified high-content approach for identifying functionally relevant clinical targets and screening potential therapeutics relying on simple-to-implement functional readouts. Proof of principle drug testing was performed for selection of anti-fibrotic compounds targeting one of the identified kinases, glycogen synthase kinase-3 (GSK3), consistent in all three models as a key mediator of fibrosis.
In its present form, this data set can serve as a useful resource for dissecting myocardial fibrosis pathways. With appropriate modifications, this platform is not limited to the study of fibrosis alone but can be applied to investigate a wide range of cardiac disorders. The greatest potential lies in the future applications of this methodology to profiling larger patient sample cohorts with the purpose of expanding our knowledge of cardiac pathology at the pathway level, and its use as a functional output in identifying potential therapeutics when combined with high-content compound screening methods.