Power-laws in collagen-fiber density across tissue types fits viscoelasticity above gelation and identifies a mechano-regulome

Brain is soft, muscle is stiffer, and cartilage and bone are relatively rigid. While water is the most abundant compound in animals and collagen is the most abundant protein, it is also known that collagen-fiber networks differ in density among adult tissues and are absent in early embryos. Here, we refined Second Harmonic Generation (SHG) imaging to pair with viscoelasticity measurements of live adult and embryonic tissues and with tissue collagenase treatments to progressively degrade collagen. The combined approaches have allowed us to show that pan-tissue mechanics scale above gelation with collagen-fiber densities (z≈1.4 power-law). Tissue water content and cell densities confirm the trends. For cell-rich soft tissues, scaling aligns best with in-vitro results for cellularized collagen-I gels, whereas stronger scaling for collagen-dense tissues such as tendon aligns best with acellular gels. A mechanism of strain-stabilized fibers can explain mechano-regulation and soft tissue scaling for healthy and developing tissues as well as high collagen-I in human liver cancer. Mining public expression profiles across adult human tissues also identifies a collagen-centered supercluster of ~1000 genes including mechanosensitive nuclear Lamin-A, Yap1, Vinculin, and Piezo-1, and many exhibit power-law scaling versus collagen-I. Lamin-A is one of the most mutated human genes, leading to heart and muscle diseases, lipodystrophy, and accelerated againg, and our experiments show the expected scaling of Lamin-A with collagen-I. The latter conforms to tissue-nucleus compliance-matching and illustrates how viscoelasticity of tissue-spanning collagen-fiber networks tunes a cell's mechano-regulome. Lastly, understanding the basis for the physical properties of real tissues is fundamental to biology but can also guide applications that include the explainability of tissue predictions from big biodata via 'artificial intelligence' and more realistic designs for organoids.