Bioimaging and the future of whole-organismal developmental physiology

Comp Biochem Physiol A Mol Integr Physiol. 2025 Feb:300:111783. doi: 10.1016/j.cbpa.2024.111783. Epub 2024 Nov 23.

Abstract

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.

Keywords: Comparative developmental physiology; Computer vision; Deep learning; Developmental physiology; Embryonic development; Imaging; Phenomics.

Publication types

  • Review

MeSH terms

  • Animals
  • Deep Learning
  • Developmental Biology* / methods
  • Humans
  • Image Processing, Computer-Assisted / methods