Dissecting the cellular and spatial tissue clock of the human lung and peripheral blood

Icahn School Of Medicine At Mount Sinai, New York NY

Investigators

Abstract

PROJECT SUMMARY With the unprecedented increase in age of the global population, there is a critical need for improving our understanding of the cellular, molecular, and spatial tissue underpinnings of aging biology. New insight into the age-related mechanisms and pathways in the human lung have the potential to uncover new therapeutic opportunities to modulate the cellular microenvironment, decelerate the lung aging process, and decrease the susceptibility of the elderly to numerous respiratory diseases. We propose to combine highly-parallelized, single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomics profiling with advanced computational analyses to characterize the effect of age on the human lung and peripheral blood cellular microenvironment. As part of the Human Lung Cell Atlas network, we have helped build an integrated scRNA-seq census of hundreds of healthy individuals. The integrated scRNA-seq data set will provide us with the necessary statistical power to decipher the cell composition, cell type expression, and regulatory differences between different age groups of healthy lung and blood donors. We will also use machine learning to model the effect of cell type specific immunomodulators/pathways on lung cell compostion and to identify novel therapeutic targets in order to better understand the underlying aging mechanisms and how they associate with age. Finally, we will validate our computational modeling by measuring the spatial cellular organization of normal lung tissue samples spanning different age groups as well as testing the anti-aging effect of the most promising therapeutic targets on lung function and cellular communication. Our study promises to improve our understanding of the effect of age on cell-cell interactions, gene expression programs, and multicellular communities in the human lung and peripheral blood, identify/validate new treatment strategies, and uncover new biomarkers that can improve the quality of life for elderly individuals. Finally, our data-driven, computational approach can be extended to study the relationship between age and the cellular microenvironment in other tissues or organs and help combat the effect of aging throughtout the human body.