Matthew J. Szucs, Marc H. Wadsworth, Alexandra Provost Braun, Rushdy Ahmad, Seth Rakoff-Nahoum, Travis K. Hughes, Alex K. Shalek, Robert Langer, Jeffrey M. Karp, Steven A. Carr, Xiaolei Yin, Lauren Levy, Jose Ordovas-Montanes, Melanie A. MacMullan, Benjamin E. Mead, Dustin A. Ammendolia, James J. Collins, Prerna Bhargava, Institute for Medical Engineering and Science, Massachusetts Institute of Technology. Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology. Department of Biological Engineering, Massachusetts Institute of Technology. Department of Chemical Engineering, Massachusetts Institute of Technology. Department of Chemistry, Massachusetts Institute of Technology. Synthetic Biology Center, Koch Institute for Integrative Cancer Research at MIT, Mead, Benjamin Elliott, Ordovas-Montanes, Jose Manuel, Braun, Alexandra Provost, Levy, Lauren, Saluja, Prerna Bhargava, Yin, Xiaolei, Hughes, Travis K., Wadsworth, Marc Havens, Ahmad, Rushdy, Carr, Steven A, Langer, Robert S, Collins, James J., Shalek, Alexander K, and Karp, Jeffrey
Background Single-cell genomic methods now provide unprecedented resolution for characterizing the component cell types and states of tissues such as the epithelial subsets of the gastrointestinal tract. Nevertheless, functional studies of these subsets at scale require faithful in vitro models of identified in vivo biology. While intestinal organoids have been invaluable in providing mechanistic insights in vitro, the extent to which organoid-derived cell types recapitulate their in vivo counterparts remains formally untested, with no systematic approach for improving model fidelity. Results Here, we present a generally applicable framework that utilizes massively parallel single-cell RNA-seq to compare cell types and states found in vivo to those of in vitro models such as organoids. Furthermore, we leverage identified discrepancies to improve model fidelity. Using the Paneth cell (PC), which supports the stem cell niche and produces the largest diversity of antimicrobials in the small intestine, as an exemplar, we uncover fundamental gene expression differences in lineage-defining genes between in vivo PCs and those of the current in vitro organoid model. With this information, we nominate a molecular intervention to rationally improve the physiological fidelity of our in vitro PCs. We then perform transcriptomic, cytometric, morphologic and proteomic characterization, and demonstrate functional (antimicrobial activity, niche support) improvements in PC physiology. Conclusions Our systematic approach provides a simple workflow for identifying the limitations of in vitro models and enhancing their physiological fidelity. Using adult stem cell-derived PCs within intestinal organoids as a model system, we successfully benchmark organoid representation, relative to that in vivo, of a specialized cell type and use this comparison to generate a functionally improved in vitro PC population. We predict that the generation of rationally improved cellular models will facilitate mechanistic exploration of specific disease-associated genes in their respective cell types. Keywords: Single-cell RNA-seq; Chemical biology; Stem cell-derived models; Paneth cell; Intestinal organoid; Intestinal stem cell; Differentiation; Systems biology, National Cancer Institute (U.S.) (Grant P30-CA14051), National Institutes of Health (U.S.) (Grant DE013023), National Institutes of Health (U.S.) (Grant HL095722), National Institutes of Health (U.S.) (Grant 1DP2OD020839), National Institutes of Health (U.S.) (Grant 2U19AI089992), National Institutes of Health (U.S.) (Grant 2R01HL095791), National Institutes of Health (U.S.) (Grant 1U54CA217377), National Institutes of Health (U.S.) (Grant 2P01AI039671), National Institutes of Health (U.S.) (Grant 5U24AI118672), National Institutes of Health (U.S.) (Grant 2RM1HG006193), National Institutes of Health (U.S.) (Grant 1R33CA202820), National Institutes of Health (U.S.) (Grant 1R01HL126554), National Institutes of Health (U.S.) (Grant 1R01DA046277), National Institutes of Health (U.S.) (Grant 1R01AI138546), Bill & Melinda Gates Foundation (Grant OPP1139972), Bill & Melinda Gates Foundation (Grant OPP1137006), Bill & Melinda Gates Foundation (Grant OPP1116944)