DNA methylome-based validation of induced sputum as an effective protocol to study lung immunity: construction of a classifier of pulmonary cell types.
Jyotirmoy DasNina IdhLiv Ingunn Bjoner SikkelandJakob PauesMaria LermPublished in: Epigenetics (2021)
Flow cytometry is a classical approach used to define cell types in peripheral blood. While DNA methylation signatures have been extensively employed in recent years as an alternative to flow cytometry to define cell populations in peripheral blood, this approach has not been tested in lung-derived samples. Here, we compared bronchoalveolar lavage with a more cost-effective and less invasive technique based on sputum induction and developed a DNA methylome-based algorithm that can be used to deconvolute the cell types in such samples. We analysed the DNA methylome profiles of alveolar macrophages and lymphocytes cells isolated from the pulmonary compartment. The cells were isolated using two different methods, sputum induction and bronchoalveolar lavage. A strong positive correlation between the DNA methylome profiles of cells obtained with the two isolation methods was found. We observed the best correlation of the DNA methylomes when both isolation methods captured cells from the lower parts of the lungs. We also identified unique patterns of CpG methylation in DNA obtained from the two cell populations, which can be used as a signature to discriminate between the alveolar macrophages and lymphocytes by means of open-source algorithms. We validated our findings with external data and obtained results consistent with the previous findings. Our analysis opens up a new possibility to identify different cell populations from lung samples and promotes sputum induction as a tool to study immune cell populations from the lung.
Keyphrases
- peripheral blood
- dna methylation
- single cell
- flow cytometry
- cell therapy
- induced apoptosis
- circulating tumor
- mycobacterium tuberculosis
- cystic fibrosis
- single molecule
- machine learning
- genome wide
- cell free
- cell cycle arrest
- pulmonary tuberculosis
- randomized controlled trial
- stem cells
- deep learning
- artificial intelligence
- mesenchymal stem cells
- cell death
- genetic diversity
- pi k akt
- high glucose