Figure 1.

Tissue-based hypothesis development and testing in the Big Data Era. (a) Beginning with an idea rooted in biophysics, such as tissue stiffness increasing with tissue levels of the abundant biopolymer collagen 1, one can query publicly available ’omics data sets for three-dimensional (3D) tissue and seek out other factors that correlate with collagen 1. Such data sets are standardized and provide relative concentrations or sequence information, or both. Scaling relations as power laws in log–log plots would be particularly sensible for relationships between polymers, given collagen as an implicit expression of stiffness. The sketched plot illustrates, for example, a gene expression dataset in which two genes increase in relative level when plotted against the relative level of a third gene, whereas one gene remains relatively constant. Self-generated ’omics data or other public data sets, or both, can provide a test of the scaling relationship. (b) Reproducible correlations across ’omics analyses might agree, for example, with an increase in lamin A (LMNA) from soft tissue (brain) to stiff tissue (heart), whereas the B-type lamins (LMNB1 and LMNB2) remain constant, as detected by quantitative mass spectrometry (76). (c) To understand molecular mechanisms for such relationships, reductionist approaches include low dimensionality and sparse cultures on 2D gels of controlled stiffness that are coated equally with collagen 1 for cell adhesion. With such systems, studies of mesenchymal stem cells show that lamin A increases (in relative intensity) from soft gels to stiff gels, with mechanisms involving cytoskeletal stress on the nucleus stabilizing lamin A against phosphorylation and degradation (9).