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.

Human immune cell subsets develop in immunodeficient mice following reconstitution with human CD34+ hematopoietic stem cells. These “humanized” mice are useful models to study human immunology and human-tropic infections, autoimmunity, and cancer. However, some human immune cell subsets are unable to fully develop or acquire full functional capacity due to a lack of cross-reactivity of many growth factors and cytokines between species. Conventional dendritic cells (cDCs) in mice are categorized into cDC1, which mediate T helper (Th)1 and CD8+ T cell responses, and cDC2, which mediate Th2 and Th17 responses. The likely human equivalents are CD141+ DC and CD1c+ DC subsets for mouse cDC1 and cDC2, respectively, but the extent of any interspecies differences is poorly characterized. Here, we exploit the fact that human CD141+ DC and CD1c+ DC develop in humanized mice, to further explore their equivalency in vivo. Global transcriptome analysis of CD141+ DC and CD1c+ DC isolated from humanized mice demonstrated that they closely resemble those in human blood. Activation of DC subsets in vivo, with the TLR3 ligand poly I:C, and the TLR7/8 ligand R848 revealed that a core panel of genes consistent with DC maturation status were upregulated by both subsets. R848 specifically upregulated genes associated with Th17 responses by CD1c+ DC, while poly I:C upregulated IFN-λ genes specifically by CD141+ DC. MYCL expression, known to be essential for CD8+ T cell priming by mouse DC, was specifically induced in CD141+ DC after activation. Concomitantly, CD141+ DC were superior to CD1c+ DC in their ability to prime naïve antigen-specific CD8+ T cells. Thus, CD141+ DC and CD1c+ DC share a similar activation profiles in vivo but also have induce unique signatures that support specialized roles in CD8+ T cell priming and Th17 responses, respectively. In combination, these data demonstrate that humanized mice provide an attractive and tractable model to study human DC in vitro and in vivo.

Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. 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 both infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data.

SummaryEarly-onset Alzheimer disease (AD)-like pathology in Down syndrome is commonly attributed to an increased dosage of the amyloid precursor protein (APP) gene. To test this in an isogenic human model, we deleted the supernumerary copy of the APP gene in trisomic Down syndrome induced pluripotent stem cells or upregulated APP expression in euploid human pluripotent stem cells using CRISPRa. Cortical neuronal differentiation shows that an increased APP gene dosage is responsible for increased β-amyloid production, altered Aβ42/40 ratio, and deposition of the pyroglutamate (E3)-containing amyloid aggregates, but not for several tau-related AD phenotypes or increased apoptosis. Transcriptome comparisons demonstrate that APP has a widespread and temporally modulated impact on neuronal gene expression. Collectively, these data reveal an important role for APP in the amyloidogenic aspects of AD but challenge the idea that increased APP levels are solely responsible for increasing specific phosphorylated forms of tau or enhanced neuronal cell death in Down syndrome-associated AD pathogenesis.