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In the human lung, terminal bronchioles (TBs), the most distal conducting airways, open to respiratory bronchioles (RBs) that lead to alveolar region where gas exchange takes place. This transition occurs in pulmonary lobules, lung tissue units supplied by pre-TBs, which inside the lobules give rise to TBs. Accumulating evidence suggests that structural remodeling and loss of pre-TBs and TBs underlies progressive irreversible airflow limitation in chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide. Understanding the nature of these changes at single-cell level has so far been limited by poor accessibility of pre-TBs and TBs. Here, we introduce a novel method of region-precise airway dissection, which enable capture of the entire anatomical continuum of peripheral airways, from pre-TBs to RBs, and associated alveolar region within the lobule. Such approach allowed us to identify terminal airway-enriched secretory cells (TASCs), a unique epithelial cell population of distal airways expressing secretoglobin 3A2 (SCGB3A2) and/or surfactant protein B (SFTPB). TASCs were enriched in TBs and, particularly, areas of TB-RB transition and exhibited an intermediate, broncho-alveolar molecular pattern. TASC frequency was markedly decreased in pre-TBs and TBs of COPD patients compared to those in non-diseased lungs, paralleled by changes in cellular composition of region-specific vascular and immune microenvironments. In vitro regeneration assays identified basal cells (BCs) of pre-TBs and TBs as the cellular origin of TASCs in the human lung. Generation of TASCs by these region-specific progenitors was suppressed by IFN-γ signaling that was augmented in distal airways of COPD patients. Thus, altered maintenance of region-specific cellular organization of pre-TBs and TBs represents the biological basis of distal airway pathology in COPD.

Multiplexed imaging and spatial transcriptomics enable highly resolved spatial characterization of cellular phenotypes, but still largely depend on laborious manual annotation to understand higher-order patterns of tissue organization. As a result, higher-order patterns of tissue organization are poorly understood and not systematically connected to disease pathology or clinical outcomes. To address this gap, we developed UTAG, a novel method to identify and quantify microanatomical tissue structures in multiplexed images without human intervention. Our method combines information on cellular phenotypes with the physical proximity of cells to accurately identify organ-specific microanatomical domains in healthy and diseased tissue. We apply our method to various types of images across physiological and disease states to show that it can consistently detect higher level architectures in human organs, quantify structural differences between healthy and diseased tissue, and reveal tissue organization patterns with relevance to clinical outcomes in cancer patients.

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