Efforts to decipher chronic lung disease and to reconstitute functional lung tissue through regenerative medicine have been hampered by an incomplete understanding of cell-cell interactions governing tissue homeostasis. Because the structure of mammalian lungs is highly conserved at the histologic level, we hypothesized that there are evolutionarily conserved homeostatic mechanisms that keep the fine architecture of the lung in balance. We have leveraged single-cell RNA sequencing techniques to identify conserved patterns of cell-cell cross-talk in adult mammalian lungs, analyzing mouse, rat, pig, and human pulmonary tissues. Specific stereotyped functional roles for each cell type in the distal lung are observed, with alveolar type I cells having a major role in the regulation of tissue homeostasis. This paper provides a systems-level portrait of signaling between alveolar cell populations. These methods may be applicable to other organs, providing a roadmap for identifying key pathways governing pathophysiology and informing regenerative efforts.

Extracellular matrix is a key component of many products in regenerative medicine. Multiple regenerative medicine products currently in the clinic are comprised of human or xenogeneic extracellular matrix. In addition, whole-organ regeneration exploits decellularized native organs as scaffolds for organotypic cell culture. However, precise understanding of the constituents of such extracellular matrix-based implants and scaffolds has sorely lagged behind their use. We present here an advanced protein extraction method using known quantities of proteotypic 13C-labeled peptides to quantify matrix proteins in native and decellularized lung tissues. Using quantitative proteomics that produce picomole-level measurements of a large number of matrix proteins, we show that a mild decellularization technique (“Triton/SDC”) results in near-native retention of laminins, proteoglycans, and other basement membrane and ECM-associated proteins. Retention of these biologically important glycoproteins and proteoglycans is quantified to be up to 27-fold higher in gently-decellularized lung scaffolds compared to scaffolds generated using a previously published decellularization regimen. Cells seeded onto this new decellularized matrix also proliferate robustly, showing positive staining for proliferating cell nuclear antigen (PCNA). The high fidelity of the gently decellularized scaffold as compared to the original lung extracellular matrix represents an important step forward in the ultimate recapitulation of whole organs using tissue-engineering techniques. This method of ECM and scaffold protein analysis allows for better understanding, and ultimately quality control, of matrices that are used for tissue engineering and human implantation. These results should advance regenerative medicine in general, and whole organ regeneration in particular.

There is a growing body of work dedicated to producing acellular lung scaffolds for use in regenerative medicine by decellularizing donor lungs of various species. These scaffolds typically undergo substantial matrix damage due to the harsh conditions required to remove cellular material (e.g., high pH, strong detergents), lengthy processing times, or pre-existing tissue contamination from microbial colonization. In this work, a new decellularization technique is described that maintains the global tissue architecture, key matrix components, mechanical composition and cell-seeding potential of lung tissue while effectively removing resident cellular material. Acellular lung scaffolds were produced from native porcine lungs using a combination of Triton X-100 and sodium deoxycholate (SDC) at low concentrations in 24 hours. We assessed the effect of matrix decellularization by measuring residual

Single-cell RNA-sequencing data has revolutionized our ability to understand 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 in a way that informs tissue biology 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 signaling networks are compared between tissue systems. Connectome focuses on computational and graphical tools designed to analyze and explore cell–cell connectivity patterns across disparate single-cell datasets and reveal biologic insight. We present approaches to quantify focused network topologies and discuss some of the biologic theory leading to their design.