Integrating -Omic Technologies across Modality, Space, and Time to Decipher Remodeling in Cardiac Disease

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  Fig. 1. A. Research scientists commonly aim to explain the molecular mechanisms underpinning phenotypes. The “central dogma” of biology explains how information encoded in the genome is first transcribed and then translated into proteins that ultimately determine phenotypes. However, at each stage additional information is incorporated (with some exemplar additional mechanisms shown) that means measurements correspond more directly to phenotypic outcomes. B. The correlation between transcript and protein abundance measured in the heart tissue of a single donor by Wang et al. [13], where each datapoint corresponds to a single gene. For some genes, such as PKP2 in the upper inset, transcript levels strongly correlate with protein abundance (R2 = 0.78), indicating that most regulation occurs at the transcriptional level. For others, such as PRDX1 in the lower inset, transcript levels show little correlation with protein abundance (R2 = 0.04), suggesting that post-transcriptional regulation plays a larger role. C. From samples of cardiac tissue, -omics methods can be applied to bulk samples, or variants now allow measurements to be resolved by cell or within space