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Biology subjects

McDole, K.

Publications and source records attributed to McDole, K..

2 recordsLinked to original sources

Hingepoint emergence in mammalian spinal neurulation

Neurulation is the process in early vertebrate embryonic development during which the neural plate folds to form the neural tube. Spinal neural tube folding in the posterior neuropore changes over time, first showing a median hingepoint, then both the median hingepoint and dorsolateral hingepoints, followed by dorsolateral hingepoints only. The biomechanical mechanism of hingepoint formation in the mammalian neural tube is poorly understood. Here, we employ a mechanical finite element model to study neural tube formation. The computational model mimics the mammalian neural tube using microscopy data from mouse and human embryos. While intrinsic curvature at the neural plate midline has been hypothesized to drive neural tube folding, intrinsic curvature was not sufficient for tube closure in our simulations. We achieved neural tube closure with an alternative model combining mesoderm expansion, non-neural ectoderm expansion and neural plate adhesion to the notochord. Dorsolateral hingepoints emerged in simulations with low mesoderm expansion and zippering. We propose that zippering provides the biomechanical force for dorsolateral hingepoint formation in settings where the neural plate lateral sides extend above the mesoderm. Together, these results provide a new perspective on the biomechanical and molecular mechanism of mammalian spinal neurulation.

developmental biology↗

Automated Reconstruction of Whole-Embryo Cell Lineages by Learning from Sparse Annotations

We present a method for automated nucleus identification and tracking in time-lapse microscopy recordings of entire developing embryos. Our method combines deep learning and global optimization to enable complete lineage reconstruction from sparse point annotations, and uses parallelization to process multi-terabyte light-sheet recordings, which we demonstrate on three common model organisms: mouse, zebrafish, Drosophila. On the most difficult dataset (mouse), our method correctly reconstructs 75.8% of cell lineages spanning 1 hour, compared to 31.8% for the previous state of the art, thus enabling biologists to determine where and when cell fate decisions are made in developing embryos, tissues, and organs.

bioinformatics↗