Stathis Megas, Anna Wilbrey-Clark, Aidan Maartens, Sarah A Teichmann, Kerstin B Meyer
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引用次数: 0
Abstract
Over the last decade, single-cell genomics has revealed remarkable heterogeneity and plasticity of cell types in the lungs and airways. The challenge now is to understand how these cell types interact in three-dimensional space to perform lung functions, facilitating airflow and gas exchange while simultaneously providing barrier function to avoid infection. An explosion in novel spatially resolved gene expression technologies, coupled with computational tools that harness machine learning and deep learning, now promise to address this challenge. Here, we review the most commonly used spatial analysis workflows, highlighting their advantages and limitations, and outline recent developments in machine learning and artificial intelligence that will augment how we interpret spatial data. Together these technologies have the potential to transform our understanding of the respiratory system in health and disease, and we showcase studies in lung development, COVID-19, lung cancer, and fibrosis where spatially resolved transcriptomics is already providing novel insights.
期刊介绍:
Since 1939, the Annual Review of Physiology has been highlighting significant developments in animal physiology. The journal covers diverse areas, including cardiovascular physiology, cell physiology, ecological, evolutionary, and comparative physiology, endocrinology, gastrointestinal physiology, neurophysiology, renal and electrolyte physiology, respiratory physiology, and special topics.