Human airway submucosal gland organoids to study respiratory inflammation and infection [bulk RNA-seq]
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1130065 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| SRR29657309_1.fastq.gz | Fastqsanger.gz | |||
| SRR29657309_2.fastq.gz | Fastqsanger.gz | |||
| SRR29657310_1.fastq.gz | Fastqsanger.gz | |||
| SRR29657310_2.fastq.gz | Fastqsanger.gz |
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Project description:Human airway submucosal gland organoids to study respiratory inflammation and infection
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Project description:The human airway lining consists of two physiologically distinct compartments: the surface airway epithelium (SAE) and the submucosal glands (SMGs). Despite their critical role, SMGs have remained largely overlooked in airway in vitro modeling of respiratory inflammation and infection. In this study, we leverage long-term cultured organoids derived separately from SAE and SMG to investigate their unique physiological characteristics. Single-cell RNA sequencing (scRNA-seq) analysis confirms that these organoid models accurately replicate the cellular heterogeneity inherent to each tissue type. Specifically, SMG organoids are enriched in MUC5B-producing mucous cells and generate alpha-Smooth Muscle Actin (αSMA)-expressing myoepithelial cells. We identify ANPEP/CD13 as a specific cell surface marker for SMG secretory cells. Exposure to cytokines elicits distinct transcriptomic responses in SMG secretory cells, providing insights into the cellular mechanisms underpinning inflammatory pathogenesis. Infection assays with human alpha-coronavirus 229E (HCoV-229E) reveal a selective vulnerability of CD13-positive secretory cells within SMG organoids, triggering a specific unfolded protein response associated with endoplasmic reticulum (ER) stress. These findings broaden the utility of airway organoids for precision modeling of respiratory (patho-)physiology
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Project description:The human airway lining consists of two physiologically distinct compartments: the surface airway epithelium (SAE) and the submucosal glands (SMGs). Despite their critical role, SMGs have remained largely overlooked in airway in vitro modeling of respiratory inflammation and infection. In this study, we leverage long-term cultured organoids derived separately from SAE and SMG to investigate their unique physiological characteristics. Single-cell RNA sequencing (scRNA-seq) analysis confirms that these organoid models accurately replicate the cellular heterogeneity inherent to each tissue type. Specifically, SMG organoids are enriched in MUC5B-producing mucous cells and generate alpha-Smooth Muscle Actin (αSMA)-expressing myoepithelial cells. We identify ANPEP/CD13 as a specific cell surface marker for SMG secretory cells. Exposure to cytokines elicits distinct transcriptomic responses in SMG secretory cells, providing insights into the cellular mechanisms underpinning inflammatory pathogenesis. Infection assays with human alpha-coronavirus 229E (HCoV-229E) reveal a selective vulnerability of CD13-positive secretory cells within SMG organoids, triggering a specific unfolded protein response associated with endoplasmic reticulum (ER) stress. These findings broaden the utility of airway organoids for precision modeling of respiratory (patho-)physiology
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Project description:The human airway lining consists of two physiologically distinct compartments: the surface airway epithelium (SAE) and the submucosal glands (SMGs). Despite their critical role, SMGs have remained largely overlooked in airway in vitro modeling of respiratory inflammation and infection. In this study, we leverage long-term cultured organoids derived separately from SAE and SMG to investigate their unique physiological characteristics. Single-cell RNA sequencing (scRNA-seq) analysis confirms that these organoid models accurately replicate the cellular heterogeneity inherent to each tissue type. Specifically, SMG organoids are enriched in MUC5B-producing mucous cells and generate alpha-Smooth Muscle Actin (αSMA)-expressing myoepithelial cells. We identify ANPEP/CD13 as a specific cell surface marker for SMG secretory cells. Exposure to cytokines elicits distinct transcriptomic responses in SMG secretory cells, providing insights into the cellular mechanisms underpinning inflammatory pathogenesis. Infection assays with human alpha-coronavirus 229E (HCoV-229E) reveal a selective vulnerability of CD13-positive secretory cells within SMG organoids, triggering a specific unfolded protein response associated with endoplasmic reticulum (ER) stress. These findings broaden the utility of airway organoids for precision modeling of respiratory (patho-)physiology
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Project description:Human airway submucosal gland organoids to study respiratory inflammation and infection [RNA-seq]
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Project description:The airways and the alveoli of the human respiratory tract are lined by two distinct types of epithelium. We previously established long-term expanding human lung epithelial organoids from lung tissues and developed a ‘proximal’ differentiation protocol to generate mucociliary airway organoids, yet the derivation of alveolar organoids from adult lung has remained a challenge. Here we defined a ‘distal’ differentiation approach to generate alveolar organoids from the same source that allows the establishment of airway organoids. Alveolar organoids are enriched for AT1 and AT2 cells and functionally simulate the alveolar epithelium. AT2 cells in lung organoids act as the progenitor cells from which alveolar organoids emerge. Moreover, we demonstrate productive SARS-CoV-2 infection of alveolar organoids. We further optimize 2-dimensional (2D) airway organoids. When differentiated under a slightly acidic pH, these 2D airway organoids sustain enhanced viral replication and better recapitulate the high infectivity of SARS-CoV-2. Moreover, the optimized 2D airway organoids can model IgG transcytosis across the airway epithelium. Collectively, we establish a bipotential organoid culture system that can reproducibly expand the entire human respiratory epithelium in vitro for modeling respiratory diseases, including COVID-19.
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