Imaging, Visualization, and Computation in Developmental Biology

Cutrale, Francesco; Fraser, Scott E.

doi:10.1146/annurev-biodatasci-072018-021305

Cited by 15 publications

(13 citation statements)

References 226 publications

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“…Advances in higher-resolution data collection methods (42), including detailed timecourse tracking of embryonic development by light microscopy, necessitate careful consideration of traditional staging guides. Our studies underscore the importance of finding new ways to finely gauge developmental stages in early brain development: we find that the number of somites is out of sync with prosencephalic development.…”

Section: Discussionmentioning

confidence: 99%

Accurate staging of chick embryonic tissues via deep learning

Groves

Holmshaw

Furley

et al. 2022

Preprint

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Deep learning is a powerful tool for image classification. However, training deep learning models often requires large datasets that are not always available, especially in biological research. Here we leverage a small dataset to train an accurate classifier by employing various data augmentation regimes and Bayesian optimisation. We first generate a fine-grained morphological sub-stage profile of the Hamburger-Hamilton developmental stage 10 chick embryo, finding an asynchrony between somite number and brain morphology. We then leverage our domain knowledge to implement data-driven pre-processing steps, before training a deep convolutional neural network to classify our sub-stages. We find that augmenting our images with a combination of rotation, blur, shear, and cutout transformations is effective in training a high classification accuracy network on a small microscopy dataset. Ensuring reliability of our classifier with saliency analysis, we gain insight into the efficacy of different data augmentation techniques, and identify class-specific features. In summary, we apply domain expertise to design effective data augmentation regimes for training a neural network based image classifier on a small, specialised dataset, classifying the posited developmental sub-stages with up to 90.9% accuracy.

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Section: Discussionmentioning

confidence: 99%

Accurate staging of chick embryonic tissues via deep learning

Groves

Holmshaw

Furley

et al. 2022

Preprint

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“…Long term, high throughput, 3D in vivo imaging with sub cellular and high time-resolution is of great importance in developmental biology (Cutrale et al, 2019). Suitable instruments to perform such measurements are LSFM due to their low phototoxicity and excellent imaging properties.…”

Section: Discussionmentioning

confidence: 99%

Automatic detector synchronization for long‐term imaging using confocal light‐sheet microscopy

Harder

Nagarajan

Odermatt

et al. 2022

Microscopy Res & Technique

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Light sheet fluorescence microscopy (LSFM) is an important tool in developmental biology. In this microscopy technique confocal line detection is often used to improve image contrast. To this end, the image of the illuminating scanned focused laser beam must be mapped onto a line detector. This is not trivial for long‐term observations, since the spatial position of the laser beam and therefore its image on the detector may drift. The problem is aggravated in two‐photon excitation LSFM, since pulsed laser light sources exhibit a lower laser beam pointing stability than continuous wave lasers. Here, we present a procedure for automatic synchronization between the excitation laser and detector, which does not require any additional hardware components and can therefore easily be integrated into existing systems. Since the recorded images are affected by noise, a specific, noise‐tolerant focus metric was developed for calculating the relative displacement, which also allows for autofocusing in the detection direction. Furthermore, we developed an image analysis approach to determine a possible tilt of the excitation laser, which is executed in parallel to the autofocusing and enables the measurement of three solid angles. This allows to automatically correct for the tilting during a measurement. We demonstrated our approach by the observation of the migration of oligodendrocyte precursor cells in two‐day‐old fluorescent Tg(olig2:eGFP) reporter zebrafish larvae over a time span of more than 20 hours.

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“…[ 119,120 ] For example, the 3D convolutional layer network has been used for precise cell tracking and cell division imaging in postimplantation murine embryos. [ 128 ] To conclude, there is an imperative need to develop improvised theoretical models, artificial intelligence algorithms, and quantitative tools to visualize and analyze the resultant multidimensional biological imaging data for both live imaging and fixed specimens.…”

Section: Conclusion and Perspectivementioning

confidence: 99%

Watching the embryo: Evolution of the microscope for the study of embryogenesis

2021

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Embryos and microscopes share a long, remarkable history and biologists have always been intrigued to watch how embryos develop under the microscope. Here we discuss the advances in microscopy which have greatly influenced our current understanding of embryogenesis. We highlight the evolution of microscopes and the optical technologies that have been instrumental in studying various developmental processes. These imaging modalities provide mechanistic insights into the dynamic cellular and molecular events which drive lineage commitment and morphogenetic changes in the developing embryo. We begin the journey with a brief history of microscopy to study embryos.First, we review the principles and optics of light, fluorescence, confocal, and electron microscopy which have been key techniques for imaging cellular and molecular events during embryonic development. Next, we discuss recent key imaging modalities such as light-sheet microscopy, which are suitable for whole embryo imaging. Further, we highlight imaging techniques like multiphoton and super resolution microscopy for beyond light diffraction limit, high resolution imaging. Lastly, we review some of the scattering-based imaging methods and techniques used for imaging human embryos.

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Imaging, Visualization, and Computation in Developmental Biology

Cited by 15 publications

References 226 publications

Accurate staging of chick embryonic tissues via deep learning

Accurate staging of chick embryonic tissues via deep learning

Automatic detector synchronization for long‐term imaging using confocal light‐sheet microscopy

Watching the embryo: Evolution of the microscope for the study of embryogenesis

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