bioRxivCell BiologyPreprint
A Multimodal Spatial and Epigenomic Atlas of Human Adult Lung Topography
Thu Elizabeth DuongiD, Dinh H. DiepiD, Kimberly Y. ConkliniD, Indy Bui, Jeffrey M. PurkersoniD, Eric BooneiD, Jacqueline OlnessiD, Sahil PateliD, Beverly F PengiD, Colin KerniD, Zoey ZhaoiD, Ravi S. MisraiD, Heidie L. HuyckiD, Jamie M. VerheydeniD, Zea BorokiD, Yun ZhangiD, Richard H. ScheuermanniD, Quan ZhuiD, Gail H. DeutschiD, James S. HagoodiD, Xin SuniD, Kun ZhangiD, Gloria PryhuberiD
Abstract
Developing high-resolution reference maps of disease-susceptible spatial niches is a critical step to mitigating the profound effects of lung disease. Here, we present an integrated multimodal single-nucleus human lung atlas (snHLA) profiling 746,047 nuclei from 49 mapped lung blocks spanning clinically relevant distal airways, alveoli, and interstitium across 11 healthy adults. Integrating snRNA-seq and SNARE-seq2, which co-assays chromatin accessibility and gene expression from the same nucleus, we resolved 70 molecularly distinct populations and captured 332,846 accessible chromatin regions, nominating new transcriptional regulators of human lung cell diversity. Spatial transcriptomics using MERFISH mapped 25 cell populations across 7 structural neighborhoods and multiplexed immunofluorescence localized cell subtypes and distal airway-defining protein markers, expanding and validating distinct lung structure-specific cell populations. This open access snHLA and companion Cell Type and Marker Gene Dictionary with anatomically aligned nomenclature delivers a foundational resource at an unprecedented resolution to interrogate the origins of lung pathophysiology.
- organism: human
- assay: scRNA-seq
- assay: spatial transcriptomics
- assay: ATAC-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: spatial domain detection
- approach: deep learning
- approach: statistical model
- Citations
- 3
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