Many metazoan organs are comprised of branching trees of epithelial tubes; how patterning occurs in these trees is a fundamental problem of development. Commonly, branch tips fill the volume of the organ approximately uniformly, e.g., in mammalian lung, airway branch tips are dispersed roughly uniformly throughout the volume of the lung. In contrast, in the developing metanephric kidney, the tips of the ureteric bud tree are located close to the outer surface of the kidney rather than filling the kidney. Here, we describe a simple alteration in the branching rules that accounts for the difference between the kidney pattern that leads to tips near the organ surface versus previously known patterns that lead to the branch tips being dispersed throughout the organ. We further use a simple toy model to deduce from first principles how this rule change accounts for the differences in the two types of trees.

cAMP-dependent protein kinase A (PKA) is a ubiquitously expressed serine/threonine kinase that regulates a variety of cellular functions. Here, we demonstrate that endothelial PKA activity is essential for vascular development, specifically regulating the transition from sprouting to stabilization of nascent vessels. Inhibition of endothelial PKA by endothelial cell-specific expression of dominant-negative PKA in mice led to perturbed vascular development, hemorrhage and embryonic lethality at mid-gestation. During perinatal retinal angiogenesis, inhibition of PKA resulted in hypersprouting as a result of increased numbers of tip cells. In zebrafish, cell autonomous PKA inhibition also increased and sustained endothelial cell motility, driving cells to become tip cells. Although these effects of PKA inhibition were highly reminiscent of Notch inhibition effects, our data demonstrate that PKA and Notch independently regulate tip and stalk cell formation and behavior.

The mucosal epithelium consists of polarized cells with distinct apical and basolateral membranes that serve as functional and physical barriers to external pathogens. The apical surface of the epithelium constitutes the first point of contact between mucosal pathogens, such as Pseudomonas aeruginosa, and their host. We observed that binding of P. aeruginosa aggregates to the apical surface of polarized cells led to the striking formation of an actin-rich membrane protrusion with inverted polarity, containing basolateral lipids and membrane components. Such protrusions were associated with a spatially localized host immune response to P. aeruginosa aggregates that required bacterial flagella and a type III secretion system apparatus. Host protrusions formed de novo underneath bacterial aggregates and involved the apical recruitment of a Par3/Par6α/aPKC/Rac1 signaling module for a robust, spatially localized host NF-κB response. Our data reveal a role for spatiotemporal epithelial polarity changes in the activation of innate immune responses.

Ageing is characterized by a progressive loss of physiological integrity, leading to impaired function and increased vulnerability to death¹. Despite rapid advances over recent years, many of the molecular and cellular processes that underlie the progressive loss of healthy physiology are poorly understood². To gain a better insight into these processes, here we generate a single-cell transcriptomic atlas across the lifespan of Mus musculus that includes data from 23 tissues and organs. We found cell-specific changes occurring across multiple cell types and organs, as well as age-related changes in the cellular composition of different organs. Using single-cell transcriptomic data, we assessed cell-type-specific manifestations of different hallmarks of ageing-such as senescence³, genomic instability⁴ and changes in the immune system². This transcriptomic atlas-which we denote Tabula Muris Senis, or 'Mouse Ageing Cell Atlas'-provides molecular information about how the most important hallmarks of ageing are reflected in a broad range of tissues and cell types.

Here we present a compendium of single-cell transcriptomic data from the model organism Mus musculus that comprises more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations and enable the direct and controlled comparison of gene expression in cell types that are shared between tissues, such as T lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3'-end counting, enabled the survey of thousands of cells at relatively low coverage, whereas the other, full-length transcript analysis based on fluorescence-activated cell sorting, enabled the characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology.

Molecular characterization of cell types using single-cell transcriptome sequencing is revolutionizing cell biology and enabling new insights into the physiology of human organs. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. Using multiple tissues from a single donor enabled identification of the clonal distribution of T cells between tissues, identification of the tissue-specific mutation rate in B cells, and analysis of the cell cycle state and proliferative potential of shared cell types across tissues. Cell type-specific RNA splicing was discovered and analyzed across tissues within an individual.

Cell-free RNA from liquid biopsies can be analyzed to determine disease tissue of origin. We extend this concept to identify cell types of origin using the Tabula Sapiens transcriptomic cell atlas as well as individual tissue transcriptomic cell atlases in combination with the Human Protein Atlas RNA consensus dataset. We define cell type signature scores, which allow the inference of cell types that contribute to cell-free RNA for a variety of diseases.