Synthetic Physiology: Manipulating and Measuring Biological Pattern Formation With Light

Abstract

Multicellular tissues and organisms execute complex physiological functions through precise spatiotemporal coordination of their constituent cells. Cells achieve this coordination through long-range intercellular communication pathways – for example, electrical signaling synchronizes the beating heart, and biochemical morphogen signals organize the developing embryo. It is an outstanding challenge in quantitative biology to develop mathematical models (borrowing from e.g. control theory or statistical physics) that explain how robust pattern formation emerges from these long-range interactions. It is generally challenging to connect theoretical models to experimental demonstrations due to the difficulty of manipulating and measuring physiological dynamics in complex tissues. The emergence of optogenetic tools has opened new opportunities in experimental biology. By placing signaling pathways under the control of light, one can leverage methods from optical physics in order to pattern biological signals with precise agility in space and time. By combining these tools with complementary optical methods for mapping resultant patterns with high spatial resolution, one can create high-dimensional biological interfaces in order to study pattern formation. Optogenetic tools have achieved widespread adoption in the study of the brain, but they have only begun to be extended to study biological pattern formation in other contexts like the developing embryo. The first portion of this thesis presents ‘synthetic electrophysiology’, a new framework for studying bioelectrical pattern formation. By deploying all-optical electrophysiology in engineered tissues with designed electrical components, we study electrical tissues as dynamical systems. In tissues comprised of synthetic excitable cells, we show that geometry is a fundamental determinant of stability and chaos in biological pacemakers, and we show that excitable cells can be composed into bioelectric circuits capable of primitive information processing and memory. We also show that electrically bistable tissues can form reaction-diffusion patterns of electrical domains which undergo phase transitions via spontaneous symmetry breaking. These previously unobserved classes of stable electrical patterns suggest a potential role for electrophysiology during embryonic development. This strategy of ‘bioelectrical engineering’ can articulate subtle aspects of electrophysiological patterning that may be obscured by the complexity of the embryo \textit{in vivo}. The latter portion of this thesis describes progress towards developing tools for optical manipulation and measurement of spatial patterns of gene expression. The ability to spatially pattern morphogen signals in the embryo, and to map the complete transcriptional response of a system to a given signal, could enable the pressure-testing of mutually consistent morphogen models which classical methods cannot disambiguate. First, we describe a new optogenetic platform for patterning the Nodal morphogen signal in the zebrafish embryo. By fusing the Nodal receptors acvr1b/acvr2b to the photoassociating domain pair CRY2/CIB1, we convert blue photons into Nodal morphogens. Deploying these reagents in a platform for patterning illumination in dozens of embryos in parallel enables us to systematically study the spatiotemporal requirements of Nodal signaling. We next discuss a new platform for using photochemical patterning of DNA barcodes to layer spatial information onto droplet microfluidics-based single cell RNA sequencing (scRNAseq) methods. By attaching these DNA ‘zipcodes’ as barcoded features on the cell surface, we can introduce spatial information onto scRNAseq while retaining the single-cell discretization and full-transcriptome depth of droplet methods. Combining optogenetic morphogen patterning with spatially resolved transcriptomics may enable more powerful studies of how the embryo processes morphogen patterns as dynamical signals. We conclude by discussing future prospects for this strategy of ‘synthetic physiology’ to unveil quantitative physical principles which underlie the robustness and reliability of biological patterning.