Picturing of the Lung Tumor Cellular Composition by Multispectral Flow Cytometry

The lung tumor microenvironment plays a critical role in the tumorigenesis and metastasis of lung cancer, resulting from the crosstalk between cancer cells and microenvironmental cells. Therefore, comprehensive identification and characterization of cell populations in the complex lung structure is crucial for development of novel targeted anti-cancer therapies. Here, a hierarchical clustering approach with multispectral flow cytometry was established to delineate the cellular landscape of murine lungs under steady-state and cancer conditions. Fluorochromes were used multiple times to be able to measure 24 cell surface markers with only 13 detectors, yielding a broad picture for whole-lung phenotyping. Primary and metastatic murine lung tumor models were included to detect major cell populations in the lung, and to identify alterations to the distribution patterns in these models. In the primary tumor models, major altered populations included CD324(+) epithelial cells, alveolar macrophages, dendritic cells, and blood and lymph endothelial cells. The number of fibroblasts, vascular smooth muscle cells, monocytes (Ly6C(+) and Ly6C(-)) and neutrophils were elevated in metastatic models of lung cancer. Thus, the proposed clustering approach is a promising method to resolve cell populations from complex organs in detail even with basic flow cytometers.

Automated summary

The cellular landscape of murine lungs under steady-state and cancer conditions was elucidated using a hierarchical clustering approach with multispectral flow cytometry. This approach showed promise in resolving cell populations in complex organs, even with basic flow cytometers. The study identified major altered populations in primary and metastatic murine lung tumor models and provided a comprehensive understanding of the lung tumor microenvironment.