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bioRxiv · 10.1101/2020.02.10.941690

An Image-Based Data-Driven Analysis of Cellular Architecture in a Developing Tissue

Hartmann, J.·Wong, M.·Gallo, E.·Gilmour, D.
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Abstract

Quantitative microscopy is becoming increasingly crucial in efforts to disentangle the complexity of organogenesis, yet adoption of the potent and ever-growing toolbox provided by modern data science has been slow, primarily because data-driven tools focus on "big data" whereas microscopy produces "rich data". We tackle this issue using a newly developed algorithm for point cloud-based morphometry to unpack the rich information encoded in high-resolution 3D image data into a straightforward numerical representation. This allowed us to employ machine learning for multi-modal data integration of cell morphology, intracellular organization, gene expression and annotated contextual knowledge that facilitates data exploration. We apply these techniques to construct and explore a quantitative atlas of cellular architecture for the zebrafish posterior lateral line primordium, an experimentally tractable model of complex self-organized organogenesis. In doing so, we are able to retrieve both previously established and novel biologically relevant patterns, demonstrating the potential of our data-driven approach.

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Hartmann, J., Wong, M., Gallo, E., Gilmour, D.. 2020-02-10. An Image-Based Data-Driven Analysis of Cellular Architecture in a Developing Tissue. https://doi.org/10.1101/2020.02.10.941690

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