Template-based mapping of dynamic motifs in tissue morphogenesis

Tissue morphogenesis relies on repeated use of dynamic behaviors at the levels of intracellular structures, individual cells, and cell groups. Rapidly accumulating live imaging datasets make it increasingly important to formalize and automate the task of mapping recurrent dynamic behaviors (motifs), as it is done in speech recognition and other data mining applications. Here, we present a “template-based search” approach for accurate mapping of sub- to multi-cellular morphogenetic motifs using a time series data mining framework. We formulated the task of motif mapping as a subsequence matching problem and solved it using dynamic time warping, while relying on high throughput graph-theoretic algorithms for efficient exploration of the search space. This formulation allows our algorithm to accurately identify the complete duration of each instance and automatically label different stages throughout its progress, such as cell cycle phases during cell division. To illustrate our approach, we mapped cell intercalations during germband extension in the early Drosophila embryo. Our framework enabled statistical analysis of intercalary cell behaviors in wild-type and mutant embryos, comparison of temporal dynamics in contracting and growing junctions in different genotypes, and the identification of a novel mode of iterative cell intercalation. Our formulation of tissue morphogenesis using time series opens new avenues for systematic decomposition of tissue morphogenesis.Author summary: Much like many ballet dances that are choreographed by combining the same movements in different ways, tissue morphogenesis relies on combinations of highly conserved sub- to multi-cellular behaviors, or motifs, such as cell division, death and intercalation. Algorithms for systematic mapping of morphogenetic motifs are essential for making the most of rapidly accumulating live imaging datasets. Here, we present a template-based strategy for motif mapping using an integrated time series data mining and graph theoretic approach. Rather than relying on a set of manually-crafted criteria, our algorithm identifies instances of the motif based on similarity to a user-provided example or template. This strategy allows our algorithm to accurately identify the complete duration of each instance and automatically label different stages throughout its progress, such as cell cycle phases during cell division. As an illustration, we mapped intercalary cell behaviors during convergence-extension movements in wild-type and mutant Drosophila embryos. Our results revealed statistical properties in the dynamics of T1-cell rearrangements and multicellular rosettes and identified a new mode of iterative cell intercalation. Our time series approach opens new avenues for systematic decomposition and analysis of tissue morphogenesis.