Profiling cellular heterogeneity in asthma with single cell multiparameter CyTOF
Autor: | Xiaomei Wang, Geoffrey Chupp, Ruth R. Montgomery, Emma Stewart |
---|---|
Rok vydání: | 2020 |
Předmět: |
Adult
Male 0301 basic medicine Cell type Neutrophils Immunology Inflammation Biology Article 03 medical and health sciences 0302 clinical medicine Immune system Single-cell analysis medicine Humans Immunology and Allergy Mass cytometry Lymphocytes Aged Macrophages Sputum Cell Biology Middle Aged Eosinophil Asthma respiratory tract diseases 030104 developmental biology medicine.anatomical_structure 030220 oncology & carcinogenesis Female Single-Cell Analysis medicine.symptom Cytometry |
Zdroj: | J Leukoc Biol |
ISSN: | 1938-3673 0741-5400 |
Popis: | Asthma is a chronic inflammatory disease of the airways that afflicts over 30 million individuals in the United States and over 300 million individuals worldwide. The inflammatory response in the airways is often characterized by the analysis of sputum, which contains multiple types of cells including neutrophils, macrophages, lymphocytes, and rare bronchial epithelial cells. Subtyping patients using microscopy of the sputum has identified both neutrophilic and eosinophilic infiltrates in airway inflammation. However, with the extensive heterogeneity among these cell types, a higher resolution understanding of the inflammatory cell types present in the sputum is needed to dissect the heterogeneity of disease. Improved recognition of the distinct phenotypes and sources of inflammation in asthmatic granulocytes may identify relevant pathways for clinical management or investigation of novel therapeutic mediators. Here, we employed mass cytometry or cytometry by time-of-flight to quantify frequency and define functional status of sputum derived airway cells in asthmatic patients and healthy controls. This in-depth single cell analysis method identified multiple distinct subtypes of airway immune cells, especially in neutrophils. Significance was discovered by statistical analysis as well as a data-driven unbiased clustering approach. Our multidimensional assessment method identifies differences in cellular function and supports identification of cellular status that may contribute to diverse clinical responses. This technical advance is relevant for studies of pathogenesis and may provide meaningful insights to advance our knowledge of asthmatic inflammation. |
Databáze: | OpenAIRE |
Externí odkaz: |