Kaminker, J. | Search bioRxiv

bioRxiv2023

Lung-innervating neurons expressing Tmc3 can induce broncho-constriction and dilation with direct consequences for the respiratory cycle

Sensory neurons of the vagal ganglia (VG) innervate lungs and play a critical role in maintaining airway homeostasis. However, the specific VG neurons that innervate lungs, and the mechanisms by which these neurons sense and respond to airway insults, are not well understood. Here, we identify a subpopulation of lung-innervating VG neurons defined by their expression of Tmc3. Single cell transcriptomics illuminated several subpopulations of Tmc3+ sensory neurons, revealing distinct Piezo2- and Trpv1-expressing subclusters. Furthermore, Tmc3 deficiency in VG neurons leads to global and subcluster specific transcriptional changes related to metabolic and ion channel function. Importantly, we show that broncho-constriction and dilation can be modulated through inhibition or activation of Tmc3+ VG neurons resulting in a decrease or increase of end-expiratory lung volume, respectively. Together, our data show that Tmc3 is a marker of lung-innervating neurons and may play a pivotal role in maintaining fundamental inspiratory and expiratory processes. SignificanceHarnessing the neuronal mechanisms that regulate lung function offers potential alternatives to existing corticosteroid treatment regimens for respiratory illness associated with acute bronchoconstriction including asthma, COPD, and emphysema. Our findings define Transmembrane channel-like 3, Tmc3, as a marker of lung-innervating sensory neurons, identify distinct subpopulations of Tmc3+ neurons with unique transcriptional profiles, and show that activation or inhibition of these neurons has a significant impact on airway function. Our work highlights potential avenues of novel targeted intervention in respiratory conditions driven by dysfunctional neuronal reflexes.

neuroscience↗

bioRxiv2023

Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages

Single-cell RNA-seq (scRNA-seq) studies have profiled over 100 million human cells across diseases, developmental stages, and perturbations to date. A singular view of this vast and growing expression landscape could help reveal novel associations between cell states and diseases, discover cell states in unexpected tissue contexts, and relate in vivo cells to in vitro models. However, these require a common, scalable representation of cell profiles from across the body, a general measure of their similarity, and an efficient way to query these data. Here, we present SCimilarity, a metric learning framework to learn and search a unified and interpretable representation that annotates cell types and instantaneously queries for a cell state across tens of millions of profiles. We demonstrate SCimilarity on a 22.7 million cell corpus assembled across 399 published scRNA-seq studies, showing accurate integration, annotation and querying. We experimentally validated SCimilarity by querying across tissues for a macrophage subset originally identified in interstitial lung disease, and showing that cells with similar profiles are found in other fibrotic diseases, tissues, and a 3D hydrogel system, which we then repurposed to yield this cell state in vitro. SCimilarity serves as a foundational model for single cell gene expression data and enables researchers to query for similar cellular states across the entire human body, providing a powerful tool for generating novel biological insights from the growing Human Cell Atlas.

bioinformatics↗