Various acute and chronic inflammatory stimuli increase the number and activity of pulmonary mucus-producing goblet cells, and goblet cell hyperplasia and excess mucus production are central to the pathogenesis of chronic pulmonary diseases. However, little is known about the transcriptional programs that regulate goblet cell differentiation. Here, we show that SAM-pointed domain-containing Ets-like factor (SPDEF) controls a transcriptional program critical for pulmonary goblet cell differentiation in mice. Initial cell-lineage-tracing analysis identified nonciliated secretory epithelial cells, known as Clara cells, as the progenitors of goblet cells induced by pulmonary allergen exposure in vivo. Furthermore, in vivo expression of SPDEF in Clara cells caused rapid and reversible goblet cell differentiation in the absence of cell proliferation. This was associated with enhanced expression of genes regulating goblet cell differentiation and protein glycosylation, including forkhead box A3 (Foxa3), anterior gradient 2 (Agr2), and glucosaminyl (N-acetyl) transferase 3, mucin type (Gcnt3). Consistent with these findings, levels of SPDEF and FOXA3 were increased in mouse goblet cells after sensitization with pulmonary allergen, and the proteins were colocalized in goblet cells lining the airways of patients with chronic lung diseases. Deletion of the mouse Spdef gene resulted in the absence of goblet cells in tracheal/laryngeal submucosal glands and in the conducting airway epithelium after pulmonary allergen exposure in vivo. These data show that SPDEF plays a critical role in regulating a transcriptional network mediating the goblet cell differentiation and mucus hyperproduction associated with chronic pulmonary disorders.

Goblet cell hyperplasia and mucous hypersecretion contribute to the pathogenesis of chronic pulmonary diseases including cystic fibrosis, asthma, and chronic obstructive pulmonary disease. In the present work, mouse SAM pointed domain-containing ETS transcription factor (SPDEF) mRNA and protein were detected in subsets of epithelial cells lining the trachea, bronchi, and tracheal glands. SPDEF interacted with the C-terminal domain of thyroid transcription factor 1, activating transcription of genes expressed selectively in airway epithelial cells, including Sftpa, Scgb1a1, Foxj1, and Sox17. Expression of Spdef in the respiratory epithelium of adult transgenic mice caused goblet cell hyperplasia, inducing both acidic and neutral mucins in vivo, and stainined for both acidic and neutral mucins in vivo. SPDEF expression was increased at sites of goblet cell hyperplasia caused by IL-13 and dust mite allergen in a process that was dependent upon STAT-6. SPDEF was induced following intratracheal allergen exposure and after Th2 cytokine stimulation and was sufficient to cause goblet cell differentiation of Clara cells in vivo.

‘LungGENS’, our previously developed web tool for mapping single-cell gene expression in the developing lung, has been well received by the pulmonary research community. With continued support from the ‘LungMAP’ consortium, we extended the scope of the LungGENS database to accommodate transcriptomics data from pulmonary tissues and cells from human and mouse at different stages of lung development. Lung Gene Expression Analysis (LGEA) web portal is an extended version of LungGENS useful for the analysis, display and interpretation of gene expression patterns obtained from single cells, sorted cell populations and whole lung tissues. The LGEA web portal is freely available at http://research.cchmc.org/pbge/lunggens/mainportal.html.

Rationale: Goblet cell metaplasia accompanies common pulmonary disorders that are prone to recurrent viral infections. Mechanisms regulating both goblet cell metaplasia and susceptibility to viral infection associated with chronic lung diseases are incompletely understood.Objectives: We sought to identify the role of the transcription factor FOXA3 in regulation of goblet cell metaplasia and pulmonary innate immunity.Methods: FOXA3 was identified in airways from patients with asthma and chronic obstructive pulmonary disease. We produced transgenic mice conditionally expressing Foxa3 in airway epithelial cells and developed human bronchial epithelial cells expressing Foxa3. Foxa3-regulated genes were identified by immunostaining, Western blotting, and RNA analysis. Direct binding of FOXA3 to target genes was identified by chromatin immunoprecipitation sequencing correlated with RNA sequencing.Measurements and Main Results: FOXA3 was highly expressed in airway goblet cells from patients with asthma and chronic obstructive pulmonary disease. FOXA3 was induced by either IL-13 or rhinovirus. Foxa3 induced goblet cell metaplasia and enhanced expression of a network of genes mediating mucus production. Paradoxically, FOXA3 inhibited rhinovirus-induced IFN production, IRF-3 phosphorylation, and IKKe expression and inhibited viral clearance and expression of genes required for antiviral defenses, including MDA5, RIG-I, TLR3, IRF7/9, and nuclear factor-kB.Conclusions: FOXA3 induces goblet cell metaplasia in response to infection or Th2 stimulation. Suppression of IFN signaling by FOXA3 provides a plausible mechanism that may serve to limit ongoing Th1 inflammation during the resolution of acute viral infection; however, inhibition of innate immunity by FOXA3 may contribute to susceptibility to viral infections associated with chronic lung disorders accompanied by chronic goblet cell metaplasia.

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Laser capture microdissection (LCM)-enabled region-specific tissue analyses are critical to better understand complex multicellular processes. However, current proteomics workflows entail several manual sample preparation steps and are challenged by the microscopic mass-limited samples generated by LCM, impacting measurement robustness, quantification and throughput. Here, we coupled LCM with a proteomics workflow that provides fully automated analysis of proteomes from microdissected tissues. Benchmarking against the current state-of-the-art in ultrasensitive global proteomics (FASP workflow), our approach demonstrated significant improvements in quantification (~2-fold lower variance) and throughput (>5 times faster). Using our approach we for the first time characterized, to a depth of >3,400 proteins, the ontogeny of protein changes during normal lung development in microdissected alveolar tissue containing only 4,000 cells. Our analysis revealed seven defined modules of coordinated transcription factor-signaling molecule expression patterns, suggesting a complex network of temporal regulatory control directs normal lung development with epigenetic regulation fine-tuning pre-natal developmental processes.

Generating detailed and accurate organogenesis models using single-cell RNA-seq data remains a major challenge. Current methods have relied primarily on the assumption that descendant cells are similar to their parents in terms of gene expression levels. These assumptions do not always hold for in vivo studies, which often include infrequently sampled, unsynchronized, and diverse cell populations. Thus, additional information may be needed to determine the correct ordering and branching of progenitor cells and the set of transcription factors (TFs) that are active during advancing stages of organogenesis. To enable such modeling, we have developed a method that learns a probabilistic model that integrates expression similarity with regulatory information to reconstruct the dynamic developmental cell trajectories. When applied to mouse lung developmental data, the method accurately distinguished different cell types and lineages. Existing and new experimental data validated the ability of the method to identify key regulators of cell fate.

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