EmbryoMiner: A new framework for interactive knowledge discovery in large-scale cell tracking data of developing embryos

Abstract

State-of-the-art light-sheet and confocal microscopes allow recording of entire embryos in 3D and over time (3D+t) for many hours. Fluorescently labeled structures can be segmented and tracked automatically in these terabyte-scale 3D+t images, resulting in thousands of cell migration trajectories that provide detailed insights to large-scale tissue reorganization at the cellular level. Here we present EmbryoMiner, a new interactive open-source framework suitable for in-depth analyses and comparisons of entire embryos, including an extensive set of trajectory features. Starting at the whole-embryo level, the framework can be used to iteratively focus on a region of interest within the embryo, to investigate and test specific trajectory-based hypotheses and to extract quantitative features from the isolated trajectories. Thus, the new framework provides a valuable new way to quantitatively compare corresponding anatomical regions in different embryos that were manually selected based on biological prior knowledge. As a proof of concept, we analyzed 3D+t light-sheet microscopy images of zebrafish embryos, showcasing potential user applications that can be performed using the new framework.

Pax6 organizes the anterior eye segment by guiding two distinct neural crest waves

“…Next, we assessed the relationship between the origin and destination of the two NC cell populations contributing to the eye quantitatively. To this end, we imaged Tg(sox10:h2a-tdEosFP) embryos by DSLM and employed our recently developed interactive image analysis framework [25] to trace back the origin of NC cells that arrived at the proximal and distal sides of the eye (S4 Video). NC cells for each destination were selected at two stages (17 and 24 hpf) for precise group selection based on the final location ( Fig 3A).…”

“…Embryos were mounted in cleared fluorinated ethylene propylene tubes in 0.3% (w/v) low-melting point agarose. The pipeline of algorithms for NC cell tracking has been described earlier [25].…”

How Computation Is Helping Unravel the Dynamics of Morphogenesis

“…Image analysis and computer vision methods can now create digital atlases of developing embryos ( Figures 1A-F). These atlases contain spatio-temporal information about cell and tissue morphology, cell lineages, and functional patterns, such as gene expression or protein activity [36][37][38][39][40][41][42]. Moreover, novel visualization tools allow for systematically browsing these digital embryos (Figure 1), and integrating them into numerical simulations and machine learning algorithms [36,42,43].…”

“…Deep Learning tools, such as Convolutional Neural Networks can help to improve the performance under challenging conditions, such as deep-tissue segmentation provided tagged training data [58,59]. Interactive annotation tools for correction and validation are still a suitable approach to generate reliable expert-driven data [36,42,43,60] and potentially allow crowdsourced results [61]. Beyond image data repositories, sharing detailed experiment metadata through systematic frameworks (e.g., based on ontologies) can provide a "Big Data" substrate for machine learning to optimize pipelines.…”

Rule-governed Dynamic Stochastic Equilibration of Multicellular MotionIn VivoDuring Olfactory Neurogenesis

“…The origins of microvillous sensory neurons, however, remain controversial. Our previous work showed that cranial NCCs migrate into the zebrafish OE to form the vast majority of microvillous sensory neurons (Saxena et al, 2013), while fate-mapping work from other groups across multiple species yielded both supportive (Forni et al, 2011;Katoh et al, 2011;Schott et al, 2018) and contradictory (Aguillon et al, 2018) findings, due, in part, to different assumptions about progenitor cell domains. Additionally, recent work suggests that neural plate border cells have greater than expected plasticity, with no clear demarcation between future placodal or NC cells (Roellig et al, 2017), adding further ambiguity to the debate.…”