Nuclear instance segmentation and tracking for preimplantation mouse embryos.

For investigations into fate specification and morphogenesis in time-lapse images of preimplantation embryos, automated 3D instance segmentation and tracking of nuclei are invaluable. Low signal-to-noise ratio, high voxel anisotropy, high nuclear density, and variable nuclear shapes can limit the performance of segmentation methods, while tracking is complicated by cell divisions, low frame rates, and sample movements. Supervised machine learning approaches can radically improve segmentation accuracy and enable easier tracking, but they often require large amounts of annotated 3D data.

Dynamics of an incoherent feedforward loop drive ERK-dependent pattern formation in the early Drosophila embryo.

Positional information in development often manifests as stripes of gene expression, but how stripes form remains incompletely understood. Here, we use optogenetics and live-cell biosensors to investigate the posterior brachyenteron (byn) stripe in early Drosophila embryos. This stripe depends on interpretation of an upstream ERK activity gradient and the expression of two target genes, tailless (tll) and huckebein (hkb), that exert antagonistic control over byn.

A novel ground truth dataset enables robust 3D nuclear instance segmentation in early mouse embryos.

For investigations into fate specification and cell rearrangements in live images of preimplantation embryos, automated and accurate 3D instance segmentation of nuclei is invaluable; however, the performance of segmentation methods is limited by the images' low signal-to-noise ratio and high voxel anisotropy and the nuclei's dense packing and variable shapes. Supervised machine learning approaches have the potential to radically improve segmentation accuracy but are hampered by a lack of fully annotated 3D data. In this work, we first establish a novel mouse line expressing near-infrared nuclear reporter H2B-miRFP720.

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.

Chemical Embryology Redux: Metabolic Control of Development.

New studies of metabolic reactions and networks in embryos are making important additions to regulatory models of development, so far dominated by genes and signals. Metabolic control of development is not a new idea and can be traced back to Joseph Needham's 'Chemical Embryology', published in the 1930s. Even though Needham's ideas fell by the wayside with the advent of genetic studies of embryogenesis, they demonstrated that embryos provide convenient models for addressing fundamental questions in biochemistry and are now experiencing a comeback, enabled by the powerful merger of detailed mechanistic studies and systems-level techniques.