Tills Oliver, Ibbini Ziad, Spicer John I
Ecophysiology and Development Research Group, School of Biological and Marine Sciences, University of Plymouth, Devon PL4 8AA, UK.
Ecophysiology and Development Research Group, School of Biological and Marine Sciences, University of Plymouth, Devon PL4 8AA, UK.
Comp Biochem Physiol A Mol Integr Physiol. 2025 Feb;300:111783. doi: 10.1016/j.cbpa.2024.111783. Epub 2024 Nov 23.
While omics has transformed the study of biology, concomitant advances made at the level of the whole organism, i.e. the phenome, have arguably not kept pace with lower levels of biological organisation. In this personal commentary we evaluate the importance of imaging as a means of measuring whole organismal developmental physiology. Image acquisition, while an important process itself, has become secondary to image analysis as a bottleneck to the use of imaging in research. Here, we explore the significant potential for increasingly sophisticated approaches to image analysis, including deep learning, to advance our understanding of how developing animals grow and function. Furthermore, unlike many species-specific methodologies, tools and technologies, we explore how computer vision has the potential to be transferable between species, life stages, experiments and even taxa in which embryonic development can be imaged. We identify what we consider are six of the key challenges and opportunities in the application of computer vision to developmental physiology carried out in our lab, and more generally. We reflect on the tangibility of transferrable computer vision models capable of measuring the integrative physiology of a broad range of developing organisms, and thereby driving the adoption of phenomics for developmental physiology. We are at an exciting time of witnessing the move from computer vision as a replacement for manual observation, or manual image analysis, to it enabling a fundamentally more powerful approach to exploring and understanding the complex biology of developing organisms, the quantification of which has long posed a challenge to researchers.
虽然组学改变了生物学研究,但在整个生物体层面(即表型组)取得的相应进展,按理说并未跟上较低生物组织层面的发展步伐。在这篇个人评论中,我们评估了成像作为测量整个生物体发育生理学手段的重要性。图像采集本身虽是一个重要过程,但在成像用于研究的过程中,作为瓶颈,它已变得不如图像分析重要。在此,我们探讨了日益复杂的图像分析方法(包括深度学习)在推进我们对发育中动物生长和功能理解方面的巨大潜力。此外,与许多特定物种的方法、工具和技术不同,我们探讨了计算机视觉如何有可能在不同物种、生命阶段、实验甚至能够对胚胎发育进行成像的分类群之间进行转换。我们确定了我们认为在我们实验室以及更广泛范围内将计算机视觉应用于发育生理学的六个关键挑战和机遇。我们思考了可转移计算机视觉模型的切实可行性,这些模型能够测量广泛发育生物体的综合生理学,从而推动发育生理学中表型组学的应用。我们正处于一个激动人心的时代,见证着计算机视觉从作为手动观察或手动图像分析的替代品,转变为能够实现一种从根本上更强大的方法来探索和理解发育生物体的复杂生物学,而对其进行量化长期以来一直是研究人员面临的挑战。