A Digital Framework to Build, Visualize and Analyze a Gene Expression Atlas with Cellular Resolution in Zebrafish Early Embryogenesis

Castro-González, Carlos; Luengo-Oroz, Miguel; Duloquin, Louise; Savy, Thierry; Rizzi, B; Desnoulez, Sophie; Doursat, René; Kergosien, Yannick; Ledesma-Carbayo, María J.; Bourgine, Paul; Peyriéras, Nadine; Santos, A.

doi:10.1371/journal.pcbi.1003670

Cited by 26 publications

(32 citation statements)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Registering images that correspond to different populations is useful to quantify the spatial proximity of neurons and thus helps estimate the putative connectivity of neurons. Similarly interesting results for the zebrafish (Danio rerio) were also reported recently [3], [4], [12]. Sophisticated volumetric image registration methods have been developed in the biomedical imaging field.…”

supporting

confidence: 68%

3-D Registration of Biological Images and Models: Registration of microscopic images and its uses in segmentation and annotation

Long²,

Peng³

2015

IEEE Signal Process. Mag.

View full text Add to dashboard Cite

Registration of microscopic images and its uses in segmentation and annotation ] 3-D Registration of Biological Images and Models © istock photo.com/beano5 IEEE SIGNAL PROCESSING MAGAZINE [71] JANuARy 2015semantic properties such as the identities or categories to objects or patterns. Segmentation and annotation are critical to address important biological questions (e.g., quantification of gene expression patterns, generation of the ontology databases, and digital atlases of model animals).From Image-To-Image regIsTraTIon To model-To-Image regIsTraTIon Registration is often needed to compare, fuse, or quantify objects or patterns in images. In many cases, registration is also required to map images to models and vice versa. In these latter situations, a model often consists of geometric shape description of the anatomy or spatial layout of biological objects in the respective images. Image-to-Image RegIstRatIonMany system biology studies rely on aligning images of gene expressions in different cell populations [6]- [8] or specimens that correspond to different developmental times [9]. In several recent brain mapping projects of the Drosophila (fruit fly), it became critical to align a number of 3-D confocal images of the insect's brains. Each fly had been genetically engineered to express fluorescent proteins in a specific population of neurons, which were aligned to a standard space so that they could be compared with each other [ Figure 1(a)]. The FlyCircuit project in Taiwan [10] and the FlyLight project at the Janelia Research Campus of the Howard Hughes Medical Institute [11] each generated tens of thousands of 3-D fruit fly brain image stacks represented some of the biggest neuroscience efforts to date to understand the brain's structure. In each of these brains, some neuron populations are labeled using genetic methods. In both projects, registration of brain images is crucial. Registering images that correspond to the same population is useful to quantify the intrapopulation variability of neurons, which can further help define the meaningful neuron types. Registering images that correspond to different populations is useful to quantify the spatial proximity of neurons and thus helps estimate the putative connectivity of neurons. Similarly interesting results for the zebrafish (Danio rerio) were also reported recently [3], [4], [12]. Sophisticated volumetric image registration methods have been developed in the biomedical imaging field. Many methods, such as mutual information registration [13], spline-based elastic registration [14], invariant moment feature-based registration [15], and congealing registration [16], [17], have been widely used and extended to align molecular and cellular images. However, since many of them were originally designed for magnetic resonance imaging and computer tomography data, in many cases it remains challenging to use them easily and effectively in aligning the microscopy images that have larger-scale and fuzzier contents.Two major challenges in biological image registra...

show abstract

supporting

confidence: 68%

3-D Registration of Biological Images and Models: Registration of microscopic images and its uses in segmentation and annotation

Long²,

Peng³

2015

IEEE Signal Process. Mag.

View full text Add to dashboard Cite

show abstract

“…19 Specifically, elastic registration techniques 20 have been of interest for merging gene expression datasets acquired from different samples to generate virtual models and atlases [21][22][23][24][25][26][27][28][29][30][31][32][33] and will be useful in our present case to merge datasets obtained from the same sample (lineage map and gene expression domain) under different conditions (live and postfixation). Much work has been done to develop registration techniques for various medical imaging modalities 10-13 that have been extended to microscopy images 14-18 including 3-D images of model organism embryos.…”

Section: Introductionmentioning

confidence: 99%

Combined lineage mapping and gene expression profiling of embryonic brain patterning using ultrashort pulse microscopy and image registration

et al. 2014

View full text Add to dashboard Cite

During embryogenesis, presumptive brain compartments are patterned by dynamic networks of gene expression. The spatiotemporal dynamics of these networks, however, have not been characterized with sufficient resolution for us to understand the regulatory logic resulting in morphogenetic cellular behaviors that give the brain its shape. We have developed a new, integrated approach using ultrashort pulse microscopy [a high-resolution, two-photon fluorescence (2PF)-optical coherence microscopy (OCM) platform using 10-fs pulses] and image registration to study brain patterning and morphogenesis in zebrafish embryos. As a demonstration, we used time-lapse 2PF to capture midbrain-hindbrain boundary morphogenesis and a wnt1 lineage map from embryos during brain segmentation. We then performed in situ hybridization to deposit NBT/BCIP, where wnt1 remained actively expressed, and reimaged the embryos with combined 2PF-OCM. When we merged these datasets using morphological landmark registration, we found that the mechanism of boundary formation differs along the dorsoventral axis. Dorsally, boundary sharpening is dominated by changes in gene expression, while ventrally, sharpening may be accomplished by lineage sorting. We conclude that the integrated visualization of lineage reporter and gene expression domains simultaneously with brain morphology will be useful for understanding how changes in gene expression give rise to proper brain compartmentalization and structure.

show abstract

“…In that sense, and like the other frameworks mentioned above, our elementary rules are not a lot more sophisticated than the classical cellular automata (Turing patterns)27 and generative grammars (L-systems)28 of pattern formation (stripes, spots, branches) in animals and plants—yet at the same time they are capable of exhibiting complex developmental structures that none of these models can. The schematic representations of biological objects in MecaGen are also designed to allow comparisons between the simulated specimen and a ‘reconstructed specimen' obtained by algorithmic processing of 3D+time microscopy imaging29303132. In sum, this framework should be a valuable tool for developmental biologists to create a model of the spatiotemporal transformations of embryonic tissues and calculate their quantitative difference with biological data.…”

mentioning

confidence: 99%

A cell-based computational model of early embryogenesis coupling mechanical behaviour and gene regulation

et al. 2017

Self Cite

View full text Add to dashboard Cite

The study of multicellular development is grounded in two complementary domains: cell biomechanics, which examines how physical forces shape the embryo, and genetic regulation and molecular signalling, which concern how cells determine their states and behaviours. Integrating both sides into a unified framework is crucial to fully understand the self-organized dynamics of morphogenesis. Here we introduce MecaGen, an integrative modelling platform enabling the hypothesis-driven simulation of these dual processes via the coupling between mechanical and chemical variables. Our approach relies upon a minimal ‘cell behaviour ontology' comprising mesenchymal and epithelial cells and their associated behaviours. MecaGen enables the specification and control of complex collective movements in 3D space through a biologically relevant gene regulatory network and parameter space exploration. Three case studies investigating pattern formation, epithelial differentiation and tissue tectonics in zebrafish early embryogenesis, the latter with quantitative comparison to live imaging data, demonstrate the validity and usefulness of our framework.

show abstract

A Digital Framework to Build, Visualize and Analyze a Gene Expression Atlas with Cellular Resolution in Zebrafish Early Embryogenesis

Cited by 26 publications

References 44 publications

3-D Registration of Biological Images and Models: Registration of microscopic images and its uses in segmentation and annotation

3-D Registration of Biological Images and Models: Registration of microscopic images and its uses in segmentation and annotation

Combined lineage mapping and gene expression profiling of embryonic brain patterning using ultrashort pulse microscopy and image registration

A cell-based computational model of early embryogenesis coupling mechanical behaviour and gene regulation

Contact Info

Product

Resources

About