Salisbury, T. | Search bioRxiv

bioRxiv2024

Differential proliferation regulates multi-tissue morphogenesis during embryonic axial extension: Integrating viscous modeling and experimental approaches.

The study of how mechanical interactions and different cellular behaviors affect tissues and embryo shaping has been and remains an important challenge in biology. Axial extension is a morphogenetic process that results in the acquisition of the elongated shape of the vertebrate embryonic body. Several adjacent tissues are involved in the process, including the tissues that form the spinal cord and musculoskeletal system: the neural tube and the paraxial mesoderm, respectively. Although we have a growing understanding of how each of these tissues elongates, we still need to fully understand the morphogenetic consequences of their growth and mechanical interactions. In this study, we develop a 2D multi-tissue continuum-based mathematical model to simulate and study how differential growth, tissue biophysical properties, and mechanical interactions affect the morphogenesis of the embryonic body during axial extension. Our model captures the long-term dynamics of embryonic posterior tissues previously observed in vivo by time-lapse imaging of bird embryos. It reveals the underestimated influence of differential tissue proliferation rates in inter-tissue interaction and shaping by capturing the relative impact of this process on tissue dynamics. We verified the predictions of our model in quail embryos by showing that decreasing the rate of cell proliferation in the paraxial mesoderm affects long-term tissue dynamics and shaping of both the paraxial mesoderm and the neighboring neural tube. Overall, our work provides a new theoretical platform to consider the long-term consequences of tissue differential growth and mechanical interactions on morphogenesis.

developmental biology↗

bioRxiv2020

Synthesis of diagnostic quality cancer pathology images

Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to synthesize medical images. In this study, we evaluated the efficacy of generative adversarial networks (GANs) to synthesize high resolution pathology images of ten histological types of cancer, including five cancer types from The Cancer Genome Atlas (TCGA) and the five major histological subtypes of ovarian carcinoma. The quality of these images was assessed using a comprehensive survey of board-certified pathologists (n = 9) and pathology trainees (n = 6). Our results show that the real and synthetic images are classified by histotype with comparable accuracies, and the synthetic images are visually indistinguishable from real images. Furthermore, we trained deep convolutional neural networks (CNNs) to diagnose the different cancer types and determined that the synthetic images perform as well as additional real images when used to supplement a small training set. These findings have important applications in proficiency testing of medical practitioners and quality assurance in clinical laboratories. Furthermore, training of computer-aided diagnostic systems can benefit from synthetic images where labeled datasets are limited (e.g., rare cancers). We have created a publicly available website where clinicians and researchers can attempt questions from the image survey at http://gan.aimlab.ca/.

bioinformatics↗