354 Chapter 13 Additionally, the present focus on applying second- and third-generation sequencing technologies may lead to optimization in the full analysis of the underlying epigenetic and genetic regulation in each cardiomyocyte. For example, second-generation sequencing by Split-Pool Ligation-based Transcriptome sequencing (SPLIT-seq) has been used to define the in vivo cell composition from 27 healthy donors and 18 dilated cardiomyopathy patient hearts.29 Notably, cardiomyocytes displayed the common disease-associated cell states, whereas fibroblasts and myeloid cells undergo dramatic diversification. Fibroblasts in the DCM hearts displayed a robust activation signature that included αSMA and COL1A1 expression. Interestingly, we observed a similar upregulation of the fibroblast activation signature (COL1A1, αSMA, and FN1) in DCM patient-derived spheroids harboring the PLN-R14del mutation (Chapter 12). Together, this study provided a comprehensive analysis of the cellular and transcriptomic landscape of DCM hearts and uncovered cell type-specific transcriptional programs and disease-associated cell states, which is a valuable resource for the investigation of human heart failure.29 However, future unbiased and spatiotemporal RNA sequencing and proteomics attempts could identify key regulators of the pathological features of the R14del cardiomyopathy.30 Importantly, the identification of clinically relevant pathological features may inform us of disease pathology and hint at other potential therapeutic opportunities. Finally, knowing when and why these pathophysiological traits derail would be the key to unravelling the optimal time window to apply the most suitable therapeutic intervention. Another option is third-generation sequencing (long-range sequencing), by direct RNA sequencing by Oxford Nanopore Technologies, which makes the direct sequencing of full-length RNA transcripts and post-transcriptional RNA modifications analysis possible. However, both SPLIT-seq and Nanopore sequencing suffer from high costs, and limited sequencing depth, something the single-cell community has not had huge success with, including ourselves. However, with the optimization of large hiPSC-CM batches, and improved knowledge of the fixation and library preparations, it would be a matter of time before these second- and third-generation techniques can be applied to study 3D in vitro hiPSC-CM models for the initial complete disease genome evaluation. Although transcriptomics is currently the most readily-used single-cell-omics level analysis, post-translational protein modifications can affect protein interactions and also play a major role in disease pathogenesis. Quantitative proteomic analysis of hiPSC-CM reveals lineage-specific protein profiles in hiPSC-derived Marfan syndrome smooth muscle cells31 and titin-mutated hiPSC-CMs32. Still, combined analysis of modified proteomics (for example phosphorylated proteomics) with proteomics is rare. Here, the development of micro- and modified-proteomics will allow wide protein dynamic ranges and high levels of identifications in low numbers of cells.33 The advancement of RNA-seq and proteome technology opens a new chapter in the study of the pathological mechanisms, as displayed recently in hiPSC-CMs from hypoplastic left heart disease patients.34 At this point, we can only reinforce the importance of multi-omics analysis of hiPSC-based 3D structures for the initial in vitro screening of pathological mechanisms. Clearly, by integrating
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