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.

Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data.

A large number of genomic and imaging datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While much effort has been devoted to capturing information related to biospecimen information and experimental procedures, the metadata standards that describe data matrices and the analysis workflows that produced them are relatively lacking. Detailed metadata schema related to data analysis are needed to facilitate sharing and interoperability across groups and to promote data provenance for reproducibility. To address this need, we developed the Matrix and Analysis Metadata Standards (MAMS) to serve as a resource for data coordinating centers and tool developers. We first curated several simple and complex "use cases" to characterize the types of feature-observation matrices (FOMs), annotations, and analysis metadata produced in different workflows. Based on these use cases, metadata fields were defined to describe the data contained within each matrix including those related to processing, modality, and subsets. Suggested terms were created for the majority of fields to aid in harmonization of metadata terms across groups. Additional provenance metadata fields were also defined to describe the software and workflows that produced each FOM. Finally, we developed a simple list-like schema that can be used to store MAMS information and implemented in multiple formats. Overall, MAMS can be used as a guide to harmonize analysis-related metadata which will ultimately facilitate integration of datasets across tools and consortia. MAMS specifications, use cases, and examples can be found at https://github.com/single-cell-mams/mams/.

Squidpy: a scalable framework for spatial omics analysis.

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.

The Impact of APP on Alzheimer-like Pathogenesis and Gene Expression in Down Syndrome iPSC-Derived Neurons.

Early-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 ß-amyloid production, altered Aß42/40 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.

Human CD141+ Dendritic Cell and CD1c+ Dendritic Cell Undergo Concordant Early Genetic Programming after Activation in Humanized Mice In Vivo.

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.

Mural lymphatic endothelial cells regulate meningeal angiogenesis in the zebrafish.

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.