In the ever-evolving field of biomedical research, deciphering the intricate relationships between cells and their spatial environment has remained a key challenge. However, a promising solution has emerged in the form of deep learning models. These innovative models leverage the power of artificial intelligence to interpret complex spatial data, opening the door to new insights and discoveries.
One notable breakthrough is the Spatial Transcriptomics Embedded deep learning Model (STEM). Unlike existing methods, STEM employs a unique deep transfer learning approach to analyze single-cell and spatial transcriptomic data. Notably, this model outperforms others in inferring spatial associations, preserving spatial topologies, and identifying genes that dominate cell distributions.
Validation studies have demonstrated STEM’s robust performance and interpretability. By applying this model to real data, researchers have created detailed and accurate maps of cellular spatial relationships, revolutionizing our understanding of tissue heterogeneity and spatial transcriptomics within organs like the liver.
Another notable application of deep learning is the DeepLiver model. This model has been used to map enhancer gene regulatory networks in the mouse liver. The study has revealed fascinating insights into the impact of zonation on gene expression and chromatin accessibility, shedding light on spatial variations within the liver.
Additionally, the Multi-range cell context Decipherer (MENDER) method has propelled spatial omics data analysis further. This advanced method not only identifies tissue structure but also aligns labels across slices automatically. MENDER’s power has uncovered previously unrecognized spatial domains associated with brain aging and subtype differentiations in breast cancer patients, which were previously hidden by traditional single-cell analysis.
Looking ahead, the future of spatial transcriptomics holds great promise. Deep learning models like STEM, DeepLiver, and MENDER will continue pushing the boundaries of our understanding. These tools will undoubtedly play a crucial role in unraveling the complexities of cellular biology, elucidating disease mechanisms, and identifying new therapeutic opportunities. As technology advances and research progresses, deep learning will remain at the forefront of biomedical breakthroughs, driving innovation and unlocking new realms of knowledge in the world of cellular relationships.
The source of the article is from the blog mgz.com.tw