Olimpio, E. P. | bioRxiv Science

bioRxiv2018

Long-term expanding human airway organoids for disease modelling.

Organoids are self-organizing 3D structures grown from stem cells that recapitulate essential aspects of organ structure and function. Here we describe a method to establish long-term-expanding human airway organoids from broncho-alveolar biopsies or lavage material. The pseudostratified airway organoid epithelium consists of basal cells, functional multi-ciliated cells, mucus-producing goblet cells, and CC10-secreting club cells. Airway organoids derived from cystic fibrosis (CF) patients allow assessment of CFTR function in an organoid swelling assay. Organoid culture conditions also allow gene editing as well as the derivation of various types of lung cancer organoids. Respiratory syncytial virus (RSV) infection recapitulated central disease features and dramatically increases organoid cell motility, found to be driven by the non-structural viral NS2 protein. We conclude that human airway organoids represent versatile models for the in vitro study of hereditary, malignant, and infectious pulmonary disease.

cell biology↗

bioRxiv2017

Out-of-equilibrium statistical dynamics of spatial pattern generating cellular automata

How living systems generate order from disorder is a fundamental question1-5. Metrics and ideas from physical systems have elucidated order-generating collective dynamics of mechanical, motile, and electrical living systems such as bird flocks and neuronal networks6-8. But suitable metrics and principles remain elusive for many networks of cells such as tissues that collectively generate spatial patterns via chemical signals, genetic circuits, and dynamics representable by cellular automata1,9-11. Here we reveal such principles through a statistical mechanics-type framework for cellular automata dynamics in which cells with ubiquitous genetic circuits generate spatial patterns by switching on and off each others genes with diffusing signalling molecules. Lattices of cells behave as particles stochastically rolling down a pseudo-energy landscape - defined by a spin glass-like Hamiltonian - that is shaped by \"macrostate\" functions and genetic circuits. Decreasing the pseudo-energy increases the spatial patterns orderliness. A new kinetic trapping mechanism - \"pathway trapping\" - yields metastable spatial patterns by preventing minimization of the particles pseudo-energy. Noise in cellular automata reduces the trapping, thus further increases the spatial order. We generalize our framework to lattices with multiple types of cells and signals. Our work shows that establishing statistical mechanics of computational algorithms can reveal collective dynamics of signal-processing in biological and physical networks.

biophysics↗