An ensemble-averaged, cell density-based digital model of zebrafish embryo development derived from light-sheet microscopy data with single-cell resolution

Abstract

A new era in developmental biology has been ushered in by recent advances in the quantitative imaging of all-cell morphogenesis in living organisms. Here we have developed a light-sheet fluorescence microscopy-based framework with single-cell resolution for identification and characterization of subtle phenotypical changes of millimeter-sized organisms. Such a comparative study requires analyses of entire ensembles to be able to distinguish sample-to-sample variations from definitive phenotypical changes. We present a kinetic digital model of zebrafish embryos up to 16 h of development. The model is based on the precise overlay and averaging of data taken on multiple individuals and describes the cell density and its migration direction at every point in time. Quantitative metrics for multi-sample comparative studies have been introduced to analyze developmental variations within the ensemble. The digital model may serve as a canvas on which the behavior of cellular subpopulations can be studied. As an example, we have investigated cellular rearrangements during germ layer formation at the onset of gastrulation. A comparison of the one-eyed pinhead (oep) mutant with the digital model of the wild-type embryo reveals its abnormal development at the onset of gastrulation, many hours before changes are obvious to the eye.

Fuzzy-based propagation of prior knowledge to improve large-scale image analysis pipelines

“…The immediate availability of a reliable ground truth enables a quantitative validation without the bias observed for manually annotated benchmark data that suffers from intra-and inter-expert variability. As the simulated benchmark is close to the target application of the pipeline, namely quantitatively analyzing terabyte-scale 3D+t fluorescence microscopy images, the developed concepts and algorithms can easily be put into practice, e.g., for false positive reduction of a segmentation algorithm or for segmentationbased multiview fusion [25,43].…”

“…These seeds were then provided to the TWANG algorithm as described in [48] and the segments of different views were combined using a segment-based fusion approach (App. C in Additional file 1 and [25]). Finally, a nearest-neighbor tracking was applied to the detected objects to obtain the movement trajecto- Figure 9: Quantitative performance assessment of a nearest neighbor tracking algorithm (NN) applied on different segmentation results obtained on the SBDE3 data set.…”

The Study of Glioma by Xenotransplantation in Zebrafish Early Life Stages

“…This is due to their small size and optical transparency, making in vivo observations of developmental processes easy to accomplish at single cell resolution (Hendricks and Jesuthasan 2007;Keller et al 2008). Intravital fluorescence microscopy of zebrafish embryos has been further enhanced by the application of light sheet microscopy (Jung et al 2012;Kobitski et al 2015). Although in vivo fluorescence imaging of engrafted tumors has been performed on mammals (Yang et al 2001), they do not offer the high-resolution imaging that can be performed in zebrafish embryos.…”

Generating semi-synthetic validation benchmarks for embryomics

“…For the generation of an exemplary benchmark dataset, we used the spatio-temporal data of an early wild-type zebrafish embryo [14,16]. The displacement vector weights were set to w dir = 1.0, w rep = 1.0 and w nna = 0.1, and K = 10 neighbors were used to estimate the object movements.…”

“…Nevertheless, existing simulated benchmarks are often much simpler than the real application scenarios and mostly focus solely on a single processing step. Challenges such as multiview acquisition and fusion [10][11][12], large file sizes [13,14] and highly dynamic scenes with possibly thousands of objects [15,16], that are frequently observed in state-of-the-art experiments in embryomics using confocal or light-sheet microscopy, are not considered sufficiently yet. To evaluate the performance of an entire image analysis pipeline comprised of seed detection, segmentation, multiview fusion and tracking with a single benchmark, we present a new method that combines simulated fluorescent objects, realistic object movement based on real embryos and the ability to generate challenging large-scale microscopy data in a single framework including various acquisition deficiencies.…”