Quantitative multivariate analysis of dynamic multicellular morphogenic trajectories

• Type of Material: Article
• Authors: Douglas E. White, Georgia Institute of Technology; Jonathan B. Sylvester, Georgia Institute of Technology; Thomas J. Levario, Georgia Institute of Technology; Hang Lu, Georgia Institute of Technology; Jeffrey T. Streelman, Georgia Institute of Technology; Todd McDevitt, Emory University; Melissa Kemp, Emory University
• Language: English
• Date: 2015-01-01
• Publisher: Royal Society of Chemistry
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © The Royal Society of Chemistry 2015.
• Final Published Version (URL): http://dx.doi.org/10.1039/c5ib00072f
• Title of Journal or Parent Work: Integrative Biology
• ISSN: 1757-9694
• Volume: 7
• Issue: 7
• Start Page: 825
• End Page: 833
• Grant/Funding Information: Trainee support was provided in part by the NSF Stem Cell Biomanufacturing IGERT (DGE 0965945, for DEW).; Funding for this work was provided by the NSF Emergent Behaviors of Integrated Cellular Systems Science and Technology Center (CBET 9039511), NIH R01 EB010061 (TCM), and the National Science Foundation NSF IOS 1146275 (JTS).
• Keywords: GENE; Life Sciences & Biomedicine; Science & Technology; FATE; Cell Biology; EMBRYONIC STEM-CELLS; MODEL; PLURIPOTENCY; MEDIATED CONTROL; NETWORKS; EXPRESSION
• Research Categories: Engineering, Biomedical; Biology, Cell

Abstract

Interrogating fundamental cell biology principles that govern tissue morphogenesis is critical to better understanding of developmental biology and engineering novel multicellular systems. Recently, functional micro-tissues derived from pluripotent embryonic stem cell (ESC) aggregates have provided novel platforms for experimental investigation; however elucidating the factors directing emergent spatial phenotypic patterns remains a significant challenge. Computational modelling techniques offer a unique complementary approach to probe mechanisms regulating morphogenic processes and provide a wealth of spatio-temporal data, but quantitative analysis of simulations and comparison to experimental data is extremely difficult. Quantitative descriptions of spatial phenomena across multiple systems and scales would enable unprecedented comparisons of computational simulations with experimental systems, thereby leveraging the inherent power of computational methods to interrogate the mechanisms governing emergent properties of multicellular biology. To address these challenges, we developed a portable pattern recognition pipeline consisting of: the conversion of cellular images into networks, extraction of novel features via network analysis, and generation of morphogenic trajectories. This novel methodology enabled the quantitative description of morphogenic pattern trajectories that could be compared across diverse systems: computational modelling of multicellular structures, differentiation of stem cell aggregates, and gastrulation of cichlid fish. Moreover, this method identified novel spatio-temporal features associated with different stages of embryo gastrulation, and elucidated a complex paracrine mechanism capable of explaining spatiotemporal pattern kinetic differences in ESC aggregates of different sizes.