Inference of animal life history strategies from organismal-scale single-cell transcriptomics
Identifying cell types driving trade-offs between key biological tasks
The overall goal of this project is to explore the possibility of inferring life history traits, their trade-offs, and potential regulatory mechanisms from high-dimensional, organism-scale, single cell transcriptomics of planarian flatworms using the framework of Pareto optimality theory. With funding from the Explore!Tech grant as well as our in-kind contribution, we have collected three key datasets needed for the project, comprising ~3 million cells from ~450 individual planarians with body sizes ranging from ~2-20 mm, for two reproductively distinct strains (sexual and asexual), and under different starvation conditions. From these data, we mapped the changes in cell type proportions with size. As a proof of principle, we fit the relative cell type abundances from our asexual worm dataset to the Pareto Task Inference framework. We found that the animals occupy a triangular cell composition space, reflecting trade-offs between three key biological tasks. We also identified the specific cell types that drive these trade-offs and are currently experimentally manipulating their proportions to test causal relationships between cell composition, biological tasks, and life history trade-offs. We hope that this work will provide a new framework for studying the mechanistic basis of life history evolution in animals.
An individually-resolved single cell atlas of asexual Schmidtea mediterranea of
different sizes. Courtesy of Amrutha Palavalli.