Assistant Professor Johns Hopkins Schoolof Medicine St. Petersburg, Florida, United States
Introduction/Rationale: Tissue-resident immunity (TRI) in gut and liver is highly individualized, but how donor-specific programs coordinate across organs is unclear. We paired TCR mapping with autologous organ coupling using mcirophysiological systems, to chart TRI circuits and test whether they encode stable, donor-programmed immune set-points.
Methods: TRI cells from colon intraepithelial (IEL) and lamina propria (LP) niches, liver, and matched blood were profiled by joint scRNA-seq/scTCR-seq to define states, clonotypes, and inter-site sharing. Ligand–receptor inference nominated stromal, epithelial, endothelial, and APC cues instructing TRI. Donor-matched colon epithelium with IEL/LP leukocytes and primary hepatocytes with liver TRI were reconstituted and fluidically coupled in a two-compartment microphysiological system. Readouts included secretomics, bulk RNA-seq, and responses to LPS, poly(I:C), and 5-OP-RU.
Results: Clonal maps showed tissue-dominant TRM/MAIT and circulating TCM/Teff in blood. IEL and LP shared few clones, and blood shared with liver, indicating liver-biased exchange of antigen-experienced clonotypes. TRI programs diverged by niche (IEL enriched for IL-27/IFN-γ; LP for IFN/chemokine modules). Predicted instructors included LSECs, epithelium, and myeloid APCs. Reconstruction recapitulated donor baselines: one donor showed Th1/Th17-skewed gut signals and stronger LPS-driven hepatic activation; another showed TGF-β-linked regulation and selective 5-OP-RU responsiveness. Gut–liver coupling imposed a retinoid/bile-acid metabolic tone that dampened basal inflammation across donors, yet microbial/viral challenges unmasked individualized hepatic and mucosal responses.
Conclusion: Integrating clonal tracking with autologous organ coupling yields a donor-resolved blueprint of the gut–liver immune axis. TRI clonotypes adapt to niche instruction and a conserved metabolic set-point coexists with sharply donor-specific challenge responses. This framework supports personalized modeling of mucosal immunity.
