A new tractable method for generating Human Alveolar Macrophage Like cells in vitro to study lung inflammatory processes and diseases

Alveolar macrophages (AM) are unique lung resident myeloid cells and often the first cell type to contact airborne pathogens. The contribution of human AMs (HAM) to pulmonary diseases remains poorly understood due to the difficulty in accessing them from human donors and their rapid phenotypic change during in vitro culture. There remains an unmet need for cost effective methods for generating human cells with a HAM phenotype, particularly important for translational studies and clinical benefits to humans, including analyses of host cell responses to vaccine candidates. We developed cell culture conditions that mimic the lung alveolar environment in humans using lung lipids, that is, Infasurf (calfactant, natural bovine surfactant) and lung-associated cytokines (granulocyte macrophage colony–stimulating factor, transforming growth factor-ß, and interleukin 10) that facilitate the conversion of blood-obtained monocytes to an AM-like (AML) phenotype and function in tissue culture. Similar to HAM, AML cells are particularly susceptible to both Mycobacterium tuberculosis and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. This study reveals the importance of alveolar space components in the development and maintenance of HAM phenotype and function and provides a readily accessible model to study HAM in infectious and inflammatory disease processes, as well as therapies and vaccines. Overall design: Freshy isolated human alveolar macrophages (HAM, n=2) and 2h adherent monocyte derived macrophages (MDM, n=3) and alveolar macrophage-like (AML, n=3) cells were washed once with 1% Dulbecco's phosphate-buffered saline (DPBS) (Gibco), lysed in TRIzol (Invitrogen) and RNA was isolated using Direct-zol RNA Miniprep kit, R2052 (Zymo Research) as per the manufacturer's instructions. Isolated RNA was quantified using the Qubit 4 Fluorimeter (Invitrogen). RNA quality was assessed with the 4200 TapeStation System (Agilent). Samples with an RNA integrity number (RIN) higher than 7 were used for RNA-seq. RNA-seq libraries were prepared from 300ng of total RNA using the mRNA-seq library preparation and 50bp single read sequencing with approximately 25-30M reads per sample in the NEB Next RNA Ultra Kit (Qiagen) with poly (A) enrichment. RNA sequencing was carried out using the HiSeq 3000 platform (Illumina). Raw sequencing reads were preprocessed using Trim Galore! to trim the adapters and low-quality sequences. Trimmed reads were mapped to the human hg38 reference genome using HISAT2. Read counts for each sample were obtained using feature Counts. Differential gene expression analysis was carried out using DESeq2. The Benjamini-Hochberg procedure was used to control the false discovery rate (FDR), and the shrunken log2 fold changes (LFCs) were calculated using the adaptive shrinkage (ash) estimator with an Empirical Bayes approach. Genes with FDR-adjusted p-value <0.05 and LFC of more than 1 or less than -1 were considered to be differentially expressed. Gene Set Enrichment Analysis (GSEA) was carried out to identify over-represented gene ontology (GO) terms and biological pathways. Pathway and network analyses were performed using Ingenuity Pathway Analysis (IPA) (Qiagen). Functional protein association networks were analyzed using StringApp and network visualization was generated using Cytoscape version 3.8.2. Heatmaps of specific genes were generated using the pheatmap package in R.