Inflammation

Signaling protein linked to cell aging in scarred human lungs

Analyses including 1,004 human lung samples linked SLIT2, a signaling protein, to aging-associated changes in connective-tissue cells in a lung-scarring disease.

Paper by Liudan Gu, Ziyi Zuo, Jian Liu, +4 more authorsLiangxing Wang · First Affiliated Hospital of Wenzhou Medical University
Figure 1. Transcriptomic deconvolution reveals fibroblast enrichment across IPF and aging.
Open the figure at full size
Figure 1. Transcriptomic deconvolution reveals fibroblast enrichment across IPF and aging.Transcriptomic deconvolution reveals fibroblast enrichment across IPF and aging.Gu et al.

Our read

Mechanisms
Studied:
Study in people

Editor of record: Boris Djordjevic · AgingResearch.News

Follow @agingbrief

Human lung samples from people with idiopathic pulmonary fibrosis, a progressive lung-scarring disease, and controls underwent integrated analyses of gene activity and DNA chemical tags. The work included 1,004 samples and six single-cell datasets.

The analysis identified five types of fibroblasts, cells that make connective tissue. It inferred a possible shift from a type common in normal lungs toward one enriched in diseased lungs. Cells showing senescence, a state in which cells stop dividing, appeared biased toward that shift. The SLIT2 gene was preferentially active in the normal-lung type. In experiments with MRC-5 human fetal lung fibroblasts grown in the lab, adding SLIT2 protein reduced senescence induced by the chemical bleomycin and scarring-promoting signals to other fibroblasts. Reducing SLIT2 worsened both. Separately, chemical tags near SLIT2 correlated with lower gene activity and co-occurred with DNA-based signs of accelerated aging.

Why it matters

How aging contributes to lung scarring remains poorly understood. The authors identify SLIT2 as a potential therapeutic target in aging-associated pulmonary fibrosis.

Caveats

The authors note that the proposed cell-state shift came from data collected at one point in time, not from tracking cells as they changed.

From the paper

Limitation

First, trajectory inference is based on cross-sectional single-cell data and reflects computationally inferred state relationships rather than experimentally validated lineage conversion.

Discussion

Quoted word for word from the full text on link.springer.com.

Comments

Be the first to comment.