Biology subjects

Raredon, M. S. B.

Publications and source records attributed to Raredon, M. S. B..

4 recordsLinked to original sources

bioRxiv2022

In vitro engineering of the lung alveolus

Therapeutic lung regeneration is predicated upon successful reconstitution of lung alveoli, the functional units of gas exchange. Here, we identify requisite multimodal cues that are critical to reconstructing the alveolar epithelium and alveoli in lung scaffolds. Alveolar reconstruction in vitro is divided into several distinct phases. In the first phase, endothelial cells coordinate with fibroblasts and select exogenous factors to promote alveolar scaffold population with surfactant-secreting alveolar epithelial type 2 cells (AEC2s). After formation of organized epithelial alveolar-like structures, subsequent withdrawal of Wnt and FGF agonism synergizes with tidal-level mechanical strain to induce differentiation of AEC2s to squamous type 1 AECs (AEC1s) in cultured alveoli, in situ. These results outline a rational strategy to engineer an alveolus of AEC2s and AEC1s contained within epithelial-mesenchymal-endothelial units, and reveal the critical interplay amongst biochemical, cellular, and mechanical niche cues within the reconstituting alveolus.

bioengineering↗

bioRxiv2022

SARS-CoV-2 leverages airway epithelial protective mechanism for viral infection

Despite much concerted effort to better understand SARS-CoV-2 viral infection, relatively little is known about the dynamics of early viral entry and infection in the airway. Here we analyzed a single-cell RNA sequencing dataset of early SARS-CoV-2 infection in a humanized in vitro model, to elucidate key mechanisms by which the virus triggers a cell-systems-level response in the bronchial epithelium. We find that SARS-CoV-2 virus preferentially enters the tissue via ciliated cell precursors, giving rise to a population of infected mature ciliated cells, which signal to basal cells, inducing further rapid differentiation. This feed-forward loop of infection is mitigated by further cell-cell communication, before interferon signaling begins at three days post-infection. These findings suggest hijacking by the virus of potentially beneficial tissue repair mechanisms, possibly exacerbating the outcome. This work both elucidates the interplay between barrier tissues and viral infections, and may suggest alternative therapeutic approaches targeting non-immune response mechanisms.

systems biology↗

bioRxiv2022

Comprehensive visualization of cell-cell interactions in single-cell and spatial transcriptomics with NICHES

SummaryRecent years have seen the release of several toolsets that reveal cell-cell interactions from single-cell data. However, all existing approaches leverage mean celltype gene expression values, and do not preserve the single-cell fidelity of the original data. Here, we present NICHES (Niche Interactions and Communication Heterogeneity in Extracellular Signaling), a tool to explore extracellular signaling at the truly single-cell level. NICHES allows embedding of ligand-receptor signal proxies to visualize heterogeneous signaling archetypes within cell clusters, between cell clusters, and across experimental conditions. When applied to spatial transcriptomic data, NICHES can be used to reflect local cellular microenvironment. NICHES can operate with any list of ligand-receptor signaling mechanisms and is compatible with existing single-cell packages and pseudotime techniques. NICHES is also a user friendly and extensible program, allowing rapid analysis of cell-cell signaling at single-cell resolution. Availability and implementationNICHES is an open-source software implemented in R under academic free license v3.0 and it is available at github.com/msraredon/NICHES. Use-case vignettes are available at https://msraredon.github.io/NICHES/. Contactmichasam.raredon@yale.edu; yuval.kluger@yale.edu

bioinformatics↗

bioRxiv2021

Connectome: computation and visualization of cell-cell signaling topologies in single-cell systems data

Single-cell RNA-sequencing data can revolutionize our understanding of the patterns of cell-cell and ligand-receptor connectivity that influence the function of tissues and organs. However, the quantification and visualization of these patterns are major computational and epistemological challenges. Here, we present Connectome, a software package for R which facilitates rapid calculation, and interactive exploration, of cell-cell signaling network topologies contained in single-cell RNA-sequencing data. Connectome can be used with any reference set of known ligand-receptor mechanisms. It has built-in functionality to facilitate differential and comparative connectomics, in which complete mechanistic networks are quantitatively compared between systems. Connectome includes computational and graphical tools designed to analyze and explore cell-cell connectivity patterns across disparate single-cell datasets. We present approaches to quantify these topologies and discuss some of the biologic theory leading to their design.

systems biology↗