Lung tissue atlas links cell-type age patterns to a predicted biological age
In single-cell and spatial data from human lung parenchyma samples, the authors report cell-type-specific age associations, including fewer macrophage subsets and more T cells with age, and use machine learning to predict lung biological age. [1]
Automatic summary · not editorially reviewed
The key findings
The analysis used single-cell and spatial transcriptomics data from 184 and 70 human lung parenchyma samples, respectively. [1]
With advancing age, the abstract reports a decline in alveolar cell proliferation, autocrine WNT signaling and stemness indicators. [1]
Machine learning was used to predict lung biological age and identify putative biomarkers of lung aging and disease risk. [1]
The source at a glance
- Study type / format
- Abstract of a transcriptomic analysis using single-cell and spatial data with machine learning
- Population / context
- Human lung parenchyma samples, with older lung and blood samples also mentioned
- Summary based on
- Study abstract
- Original publication
- July 15, 2026
Lung parenchyma samples analysed with single-cell and spatial data
The abstract describes an analytical platform applied to single-cell and spatial transcriptomics data from 184 and 70 human lung parenchyma samples, respectively. The authors state that aging affects lung function and predisposes older adults to respiratory diseases, but that the cellular and molecular mechanisms of lung aging are not fully understood. The platform was used to examine cell composition, gene expression modules and regulatory changes linked to multiple hallmarks of lung aging.
Supporting source [1]
Age-associated shifts in alveolar cells, macrophages and T cells
The findings show cell-type-specific age associations of senescence markers. With advancing age, the abstract reports a decline in alveolar cell proliferation, autocrine WNT signaling and stemness indicators. In myeloid cells, it describes 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 show heightened interferon gamma expression, cytotoxic activity and exhaustion in older lung and blood samples, which the authors call indicative of age-related immune dysfunction.
Supporting source [1]
Myeloid–T cell cross-talk and a machine-learning lung age estimate
Cell interaction and spatial analysis demonstrate aberrant cross-talk between myeloid cells and T cells, leading to increased T cell chemotaxis and activation. Finally, the authors use machine learning to predict lung biological age and to identify putative biomarkers of lung aging and disease risk. The abstract does not report how accurate this prediction was, how the predicted age was validated, or whether the described associations reflect causal mechanisms.
Supporting source [1]
How to interpret this
This is an abstract; the full text was not reviewed. The abstract reports associations from transcriptomic data and does not establish causation, prediction accuracy or clinical utility.
Based on the study abstract; the full paper, tables and supplementary data were not reviewed.
Source & supporting passages
Xu K, Kim GS, Bhagwat A, Meimand SE, Venkat VV, Pham G, Zhang J, Abdul-Ghafar J, Bairakdar MD, Dolasia K, Sahasrabudhe IR, Klausner A, Chen Y, Nguyen TT, Giotti B, Brody R, Kim SJ, Kathiriya JJ, Puleston DJ, Lee PJ, Tsankov AM. · europepmc.org · July 15, 2026