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.
Across diverse microbiotas, species abundances vary in time with distinctive statistical behaviors that appear to generalize across hosts, but the origins and implications of these patterns remain unclear. Here, we show that many of these macroecological patterns can be quantitatively recapitulated by a simple class of consumer-resource models, in which the metabolic capabilities of different species are randomly drawn from a common statistical distribution. Our model parametrizes the consumer-resource properties of a community using only a small number of global parameters, including the total number of resources, typical resource fluctuations over time, and the average overlap in resource-consumption profiles across species. We show that variation in these macroscopic parameters strongly affects the time series statistics generated by the model, and we identify specific sets of global parameters that can recapitulate macroecological patterns across wide-ranging microbiotas, including the human gut, saliva, and vagina, as well as mouse gut and rice, without needing to specify microscopic details of resource consumption. These findings suggest that resource competition may be a dominant driver of community dynamics. Our work unifies numerous time series patterns under a simple model, and provides an accessible framework to infer macroscopic parameters of effective resource competition from longitudinal studies of microbial communities.
Members of microbial communities interact via a plethora of mechanisms, including resource competition, cross-feeding, and pH modulation. However, the relative contributions of these mechanisms to community dynamics remain uncharacterized. Here, we develop a framework to distinguish the effects of resource competition from other interaction mechanisms by integrating data from growth measurements in spent media, synthetic community assembly, and metabolomics with consumer resource models. When applied to human gut commensals, our framework revealed that resource competition alone could explain most pairwise interactions. The resource-competition landscape inferred from metabolomic profiles of individual species predicted assembly compositions, demonstrating that resource competition is a dominant driver of in vitro community assembly. Moreover, the identification and incorporation of interactions other than resource competition, including pH-mediated effects and cross-feeding, improved model predictions. Our work provides an experimental and modeling framework to characterize and quantify interspecies interactions in vitro that should advance mechanistically principled engineering of microbial communities.
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