Slabaugh, G. | bioRxiv Science

bioRxiv2023

Deep learning models to map osteocyte networks can successfully distinguish between young and aged bone

Osteocytes, the most abundant and mechanosensitive cells in bone tissue, play a pivotal role in bone homeostasis and mechano-responsiveness, orchestrating the intricate balance between bone formation and resorption under daily activity. Studying osteocyte connectivity and understanding their intricate arrangement within the lacunar canalicular network (LCN) is essential for unraveling bone physiology. This is particularly true as our bones age, which is associated with decreased integrity of the osteocyte network, disrupted mass transport, and lower sensitivity to the mechanical stimuli that allow the skeleton to adapt to changing demands. Much work has been carried out to investigate this relationship, often involving high resolution microscopy of discrete fragments of this network, alongside advanced computational modelling of individual cells. However, traditional methods of segmenting and measuring osteocyte connectomics are time-consuming and labour-intensive, often hindered by human subjectivity and limited throughput. In this study, we explore the application of deep learning and computer vision techniques to automate the segmentation and measurement of osteocyte connectomics, enabling more efficient and accurate analysis. We compare several state-of-the-art computer vision models (U-Nets and Vision Transformers) to successfully segment the LCN, finding that an Attention U-Net model can accurately segment and measure 81.8% of osteocytes and 42.1% of dendritic processes, when compared to manual labelling. While further development is required, we demonstrate that this degree of accuracy is already sufficient to distinguish between bones of young (2 month old) and aged (36 month old) mice, as well as capturing the degeneration induced by genetic modification of osteocytes. By harnessing the power of these advanced technologies, further developments can unravel the complexities of osteocyte networks in unprecedented detail, revolutionising our understanding of bone health and disease.

bioengineering↗

bioRxiv2023

Cell-Vision Fusion: A Swin Transformer-based Approach to Predicting Kinase Inhibitor Mechanism of Action from Cell Painting Data

Image-based profiling of the cellular response to drug compounds has proven to be an effective method to characterize the morphological changes resulting from chemical perturbation experiments. This approach has been useful in the field of drug discovery, ranging from phenotype-based screening to identifying a compounds mechanism of action or toxicity. As a greater amount of data becomes available however, there are growing demands for deep learning methods to be applied to perturbation data. In this paper we applied the transformer-based SwinV2 computer vision architecture to predict the mechanism of action of 10 kinase inhibitor compounds directly from raw images of the cellular response. This method outperforms the standard approach of using image-based profiles, multidimensional feature set representations generated by bioimaging software. Furthermore, we combined the best performing models for three different data modalities, raw images, image-based profiles and compound chemical structures, to form a fusion model, Cell-Vision Fusion (CVF). This approach classified the kinase inhibitors with 69.79% accuracy and 70.56% F1 score, 4.20% and 5.49% greater, respectively, than the best performing image-based profile method. Our work provides three techniques, specific to Cell Painting images, which enable the SwinV2 architecture to train effectively, and explores approaches to combat the significant batch effects present in large Cell Painting perturbation datasets.

cell biology↗