InsideOutside: an accessible algorithm for classifying interior and exterior points, with applications in embryology.

ABSTRACT: A crucial aspect of embryology is relating the position of individual cells to the broader geometry of the embryo. A classic example of this is the first cell-fate decision of the mouse embryo, where interior cells become inner cell mass and exterior cells become trophectoderm. Fluorescent labelling, imaging, and quantification of tissue-specific proteins have advanced our understanding of this dynamic process. However, instances arise where these markers are either not available, or not reliable, and we are left only with the cells' spatial locations. Therefore, a simple, robust method for classifying interior and exterior cells of an embryo using spatial information is required. Here, we describe a simple mathematical framework and an unsupervised machine learning approach, termed inside

SUBMITTER: Strawbridge SE 

PROVIDER: S-EPMC10461464 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature