Nature communicationsJul 15, 2026

Cellular features and aging signs in the human lung tissue

Longevity & Aging Weekly Brief ↗PubMed ↗DOI ↗OA ↗

Updated Jul 16, 2026

Abstract

Analysis of 184 lung samples reveals age-associated changes in cell types and functions linked to lung aging.

Specific cell types show distinct markers of aging, indicating varying responses to age-related stress.
Alveolar cells demonstrate reduced proliferation and signaling related to cell growth as age increases.
Myeloid cells exhibit a decrease in certain macrophage types along with increased mitochondrial dysfunction and inflammation.
T cells in lung and blood samples expand with age, showing increased activity and signs of exhaustion.
Altered interactions between myeloid and T cells suggest changes in immune responses could occur with aging.
Machine learning models may help predict biological age and identify potential biomarkers related to lung aging.

Simplified

Aging affects lung function, predisposing older adults to respiratory diseases; however, the cellular and molecular mechanisms of lung aging are not fully understood. Leveraging single-cell and spatial transcriptomics data from 184 and 70 lung parenchyma samples, respectively, we present an analytical platform to dissect the cell composition, gene expression modules, and regulatory changes linked to multiple hallmarks of lung aging. Our findings show cell type-specific age-association of senescence markers and a decline in alveolar cell proliferation, autocrine WNT signaling, and stemness indicators with advancing age. Analysis of myeloid cells reveals a global reduction in macrophage subsets and a surge in mitochondrial dysfunction and inflammatory signaling. In contrast, lung parenchyma T cells expand with age and exhibit heightened interferon gamma expression, cytotoxic activity, and exhaustion in older lung and blood samples, indicative of age-related immune dysfunction. Cell interaction and spatial analysis demonstrate aberrant myeloid-T cell cross-talk, leading to an increase in T cell chemotaxis and activation. Lastly, we use machine learning to predict lung biological age and identify putative biomarkers of lung aging and disease risk.