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Rationale: Remodeling and loss of distal conducting airways, including preterminal and terminal bronchioles (pre-TBs/TBs), underlie progressive airflow limitation in chronic obstructive pulmonary disease (COPD). The cellular basis of these structural changes remains unknown. Objectives: To identify biological changes in pre-TBs/TBs in COPD at single-cell resolution and determine their cellular origin. Methods: We established a novel method of distal airway dissection and performed single-cell transcriptomic profiling of 111,412 cells isolated from different airway regions of 12 healthy lung donors and pre-TBs of 5 patients with COPD. Imaging CyTOF and immunofluorescence analysis of pre-TBs/TBs from 24 healthy lung donors and 11 subjects with COPD were performed to characterize cellular phenotypes at a tissue level. Region-specific differentiation of basal cells isolated from proximal and distal airways was studied using an air-liquid interface model. Measurements and Main Results: The atlas of cellular heterogeneity along the proximal-distal axis of the human lung was assembled and identified region-specific cellular states, including SCGB3A2 + SFTPB + terminal airway-enriched secretory cells (TASCs) unique to distal airways. TASCs were lost in COPD pre-TBs/TBs, paralleled by loss of region-specific endothelial capillary cells, increased frequency of CD8 + T cells normally enriched in proximal airways, and augmented IFN-γ signaling. Basal cells residing in pre-TBs/TBs were identified as a cellular origin of TASCs. Regeneration of TASCs by these progenitors was suppressed by IFN-γ. Conclusions: Altered maintenance of the unique cellular organization of pre-TBs/TBs, including loss of the region-specific epithelial differentiation in these bronchioles, represents the cellular manifestation and likely the cellular basis of distal airway remodeling 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 an approach called UTAG 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 healthy and disease states to show that it can consistently detect higher-level architectures in human tissues, quantify structural differences between healthy and diseased tissue, and reveal tissue organization patterns at the organ scale., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)