Identification and characterization of multiple Paneth cell types in the mouse small intestine

The small intestinal crypts harbor secretory Paneth cells (PCs) which express bactericidal peptides that are crucial for maintaining intestinal homeostasis. Considering the diverse environmental conditions throughout the course of the small intestine, multiple subtypes of PCs are expected to exist. We applied single-cell RNA-sequencing of PCs combined with deep bulk RNA-sequencing on PC populations of different small intestinal locations and discovered several expression-based PC clusters. Some of these are discrete and resemble tuft cell-like PCs, goblet cell (GC)-like PCs, PCs expressing stem cell markers, and atypical PCs. Other clusters are less discrete but appear to be derived from different locations along the intestinal tract and have environment-dictated functions such as food digestion and antimicrobial peptide production. A comprehensive spatial analysis using Resolve Bioscience was conducted, leading to the identification of different PC's transcriptomic identities along the different compartments of the intestine, but not between PCs in the crypts themselves

SpatialData : an open and universal data framework for spatial omics

Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study. SpatialData is a user-friendly computational framework for exploring, analyzing, annotating, aligning and storing spatial omics data that can seamlessly handle large multimodal datasets

SpatialData: an open and universal data framework for spatial omics

Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study.ISSN:1548-7105ISSN:1548-709

A TCF4-dependent gene regulatory network confers resistance to immunotherapy in melanoma

To better understand intrinsic resistance to immune checkpoint blockade (ICB), we established a comprehensive view of the cellular architecture of the treatment -naive melanoma ecosystem and studied its evolution under ICB. Using single -cell, spatial multi-omics, we showed that the tumor microenvironment promotes the emergence of a complex melanoma transcriptomic landscape. Melanoma cells harboring a mesenchymal-like (MES) state, a population known to confer resistance to targeted therapy, were significantly enriched in early on -treatment biopsies from non -responders to ICB. TCF4 serves as the hub of this landscape by being a master regulator of the MES signature and a suppressor of the melanocytic and antigen presentation transcriptional programs. Targeting TCF4 genetically or pharmacologically, using a bromodomain inhibitor, increased immunogenicity and sensitivity of MES cells to ICB and targeted therapy. We thereby uncovered a TCF4-dependent regulatory network that orchestrates multiple transcriptional programs and contributes to resistance to both targeted therapy and ICB in melanoma