Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data.

Enhanced sensitivity to Wnts is an emerging hallmark of a subset of cancers, defined in part by mutations regulating the abundance of their receptors. Whether these mutations identify a clinical opportunity is an important question. Inhibition of Wnt secretion by blocking an essential post-translational modification, palmitoleation, provides a useful therapeutic intervention. We developed a novel potent, orally available PORCN inhibitor, ETC-1922159 (henceforth called ETC-159) that blocks the secretion and activity of all Wnts. ETC-159 is remarkably effective in treating RSPO-translocation bearing colorectal cancer (CRC) patient derived xenografts. This is the first example of effective targeted therapy for this subset of CRC. Consistent with a central role of Wnt signaling in regulation of gene expression, inhibition of PORCN in RSPO3-translocated cancers causes a marked remodeling of the transcriptome, with loss of cell cycle, stem cell, and proliferation genes and an increase in differentiation markers. Inhibition of Wnt signaling by PORCN inhibition holds promise as differentiation therapy in genetically defined human cancers.

Mural cells of the vertebrate brain maintain vascular integrity and function, play roles in stroke and are involved in maintenance of neural stem cells. However, the origins, diversity and roles of mural cells remain to be fully understood. Using transgenic zebrafish, we identified a population of isolated mural lymphatic endothelial cells surrounding meningeal blood vessels. These meningeal mural lymphatic endothelial cells (muLECs) express lymphatic endothelial cell markers and form by sprouting from blood vessels. In larvae, muLECs develop from a lymphatic endothelial loop in the midbrain into a dispersed, nonlumenized mural lineage. muLEC development requires normal signaling through the Vegfc-Vegfd-Ccbe1-Vegfr3 pathway. Mature muLECs produce vascular growth factors and accumulate low-density lipoproteins from the bloodstream. We find that muLECs are essential for normal meningeal vascularization. Together, these data identify an unexpected lymphatic lineage and developmental mechanism necessary for establishing normal meningeal blood vasculature.

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

anndata is a Python package for handling annotated data matrices in memory and on disk, positioned between pandas and xarray. anndata offers a broad range of computationally efficient features including, among others, sparse data support, lazy operations, and a PyTorch interface.