Faculty, Staff and Student Publications
Language
English
Publication Date
12-31-2024
Journal
Journal of Bone and Mineral Research
DOI
10.1093/jbmr/zjae151
PMID
39303095
PMCID
PMC11700600
PubMedCentral® Posted Date
9-20-2024
PubMedCentral® Full Text Version
Post-print
Abstract
Recent advancements in deep learning (DL) have revolutionized the capability of artificial intelligence (AI) by enabling the analysis of large-scale, complex datasets that are difficult for humans to interpret. However, large amounts of high-quality data are required to train such generative AI models successfully. With the rapid commercialization of single-cell sequencing and spatial transcriptomics platforms, the field is increasingly producing large-scale datasets such as histological images, single-cell molecular data, and spatial transcriptomic data. These molecular and morphological datasets parallel the multimodal text and image data used to train highly successful generative AI models for natural language processing and computer vision. Thus, these emerging data types offer great potential to train generative AI models that uncover intricate biological processes of bone cells at a cellular level. In this Perspective, we summarize the progress and prospects of generative AI applied to these datasets and their potential applications to bone research. In particular, we highlight three AI applications: predicting cell differentiation dynamics, linking molecular and morphological features, and predicting cellular responses to perturbations. To make generative AI models beneficial for bone research, important issues, such as technical biases in bone single-cell datasets, lack of profiling of important bone cell types, and lack of spatial information, needs to be addressed. Realizing the potential of generative AI for bone biology will also likely require generating large-scale, high-quality cellular-resolution spatial transcriptomics datasets, improving the sensitivity of current spatial transcriptomics datasets, and thorough experimental validation of model predictions.
Keywords
Animals, Humans, Artificial Intelligence, Bone and Bones, Cell Differentiation, Single-Cell Analysis, Transcriptome, artificial intelligence, deep learning, single-cell transcriptomics, spatial transcriptomics, bone cells
Published Open-Access
yes
Recommended Citation
Lu Lu, Noriaki Ono, and Joshua D Welch, "Linking Transcriptome and Morphology in Bone Cells at Cellular Resolution With Generative AI" (2024). Faculty, Staff and Student Publications. 165.
https://digitalcommons.library.tmc.edu/uthdb_docs/165