High-throughput bioprinting of the nasal epithelium using patient-derived nasal epithelial cells

Abstract

Human nasal epithelial cells (hNECs) are an essential cell source for the reconstruction of the respiratory pseudostratified columnar epithelium composed of multiple cell types in the context of infection studies and disease modeling. Hitherto, manual seeding has been the dominant method for creating nasal epithelial tissue models. However, the manual approach is slow, low-throughput and has limitations in terms of achieving the intricate 3D structure of the natural nasal epithelium in a uniform manner. 3D Bioprinting has been utilized to reconstruct various epithelial tissue models, such as cutaneous, intestinal, alveolar, and bronchial epithelium, but there has been no attempt to use of 3D bioprinting technologies for reconstruction of the nasal epithelium. In this study, for the first time, we demonstrate the reconstruction of the nasal epithelium with the use of primary hNECs deposited on Transwell inserts via droplet-based bioprinting (DBB), which enabled high-throughput fabrication of the nasal epithelium in Transwell inserts of 24-well plates. DBB of nasal progenitor cells ranging from one-tenth to one-half of the cell seeding density employed during the conventional cell seeding approach enabled a high degree of differentiation with the presence of cilia and tight-junctions over a 4-week air-liquid interface culture. Single cell RNA sequencing of these cultures identified five major epithelial cells populations, including basal, suprabasal, goblet, club, and ciliated cells. These cultures recapitulated the pseudostratified columnar epithelial architecture present in the native nasal epithelium and were permissive to respiratory virus infection. These results denote the potential of 3D bioprinting for high-throughput fabrication of nasal epithelial tissue models not only for infection studies but also for other purposes such as disease modeling, immunological studies, and drug screening.

BioMapAI: Artificial Intelligence Multi-Omics Modeling of Myalgic Encephalomyelitis / Chronic Fatigue Syndrome

“…However, the crux for long-term postviral infection syndromes like ME/CFS is not necessarily pinpointing an exact diagnosis or tracing disease origins 76 , 14 (typically infections 77 ), but rather addressing the chronic, multifaceted symptoms that significantly impacts patients' quality of life 78 , 79 . Our study introduces a highly nuanced approach to link physiological changes in gut microbiome, plasma metabolome, and immune status, with host symptoms, moving beyond the initial causes of the disease 80,81 . Importantly, we validated key biomarkers in external cohorts 28 , 29 , 30 , 31 , despite significant demographic and methodological differences between the studies.…”