Epithelial organoid supports resident memory CD8 T cell differentiation.

Resident memory T cells (TRMs) play a vital role in regional immune defense. Although laboratory rodents have been extensively used to study fundamental TRM biology, poor isolation efficiency and low cell survival rates have limited the implementation of TRM-focused high-throughput assays. Here, we engineer a murine vaginal epithelial organoid (VEO)-CD8 T cell co-culture system that supports CD8 TRM differentiation. These in-vitro-generated TRMs are phenotypically and transcriptionally similar to in vivo TRMs. Pharmacological and genetic approaches showed that transforming growth factor ß (TGF-ß) signaling plays a crucial role in their differentiation. The VEOs in our model are susceptible to viral infections and the CD8 T cells are amenable to genetic manipulation, both of which will allow a detailed interrogation of antiviral CD8 T cell biology. Altogether we have established a robust in vitro TRM differentiation system that is scalable and can be subjected to high-throughput assays that will rapidly add to our understanding of TRMs.

DELVE: feature selection for preserving biological trajectories in single-cell data.

Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve .