Benchmarking mapping algorithms for cell-type annotating in mouse brain by integrating single-nucleus RNA-seq and Stereo-seq data.

Limited gene capture efficiency and spot size of spatial transcriptome (ST) data pose significant challenges in cell-type characterization. The heterogeneity and complexity of cell composition in the mammalian brain make it more challenging to accurately annotate ST data from brain. Many algorithms attempt to characterize subtypes of neuron by integrating ST data with single-nucleus RNA sequencing (snRNA-seq) or single-cell RNA sequencing. However, assessing the accuracy of these algorithms on Stereo-seq ST data remains unresolved. Here, we benchmarked 9 mapping algorithms using 10 ST datasets from four mouse brain regions in two different resolutions and 24 pseudo-ST datasets from snRNA-seq. Both actual ST data and pseudo-ST data were mapped using snRNA-seq datasets from the corresponding brain regions as reference data. After comparing the performance across different areas and resolutions of the mouse brain, we have reached the conclusion that both robust cell-type decomposition and SpatialDWLS demonstrated superior robustness and accuracy in cell-type annotation. Testing with publicly available snRNA-seq data from another sequencing platform in the cortex region further validated our conclusions. Altogether, we developed a workflow for assessing suitability of mapping algorithm that fits for ST datasets, which can improve the efficiency and accuracy of spatial data annotation.

Molecular, Metabolic, and Subcellular Mapping of the Tumor Immune Microenvironment via 3D Targeted and Non-Targeted Multiplex Multi-Omics Analyses.

Most platforms used for the molecular reconstruction of the tumor-immune microenvironment (TIME) of a solid tumor fail to explore the spatial context of the three-dimensional (3D) space of the tumor at a single-cell resolution, and thus lack information about cell-cell or cell-extracellular matrix (ECM) interactions. To address this issue, a pipeline which integrated multiplex spatially resolved multi-omics platforms was developed to identify crosstalk signaling networks among various cell types and the ECM in the 3D TIME of two FFPE (formalin-fixed paraffin embedded) gynecologic tumor samples. These platforms include non-targeted mass spectrometry imaging (glycans, metabolites, and peptides) and Stereo-seq (spatial transcriptomics) and targeted seqIF (IHC proteomics). The spatially resolved imaging data in a two- and three-dimensional space demonstrated various cellular neighborhoods in both samples. The collection of spatially resolved analytes in a voxel (3D pixel) across serial sections of the tissue was also demonstrated. Data collected from this analytical pipeline were used to construct spatial 3D maps with single-cell resolution, which revealed cell identity, activation, and energized status. These maps will provide not only insights into the molecular basis of spatial cell heterogeneity in the TIME, but also novel predictive biomarkers and therapeutic targets, which can improve patient survival rates.

Region-specific transcriptomic responses to obesity and diabetes in macaque hypothalamus.

The hypothalamus plays a crucial role in the progression of obesity and diabetes; however, its structural complexity and cellular heterogeneity impede targeted treatments. Here, we profiled the single-cell and spatial transcriptome of the hypothalamus in obese and sporadic type 2 diabetic macaques, revealing primate-specific distributions of clusters and genes as well as spatial region, cell-type-, and gene-feature-specific changes. The infundibular (INF) and paraventricular nuclei (PVN) are most susceptible to metabolic disruption, with the PVN being more sensitive to diabetes. In the INF, obesity results in reduced synaptic plasticity and energy sensing capability, whereas diabetes involves molecular reprogramming associated with impaired tanycytic barriers, activated microglia, and neuronal inflammatory response. In the PVN, cellular metabolism and neural activity are suppressed in diabetic macaques. Spatial transcriptomic data reveal microglia's preference for the parenchyma over the third ventricle in diabetes. Our findings provide a comprehensive view of molecular changes associated with obesity and diabetes.

Cellular atlases of ovarian microenvironment alterations by diet and genetically-induced obesity.

Obesity, which can arise from genetic or environmental factors, has been shown to cause serious damages to the reproductive system. The ovary, as one of the primary regulators of female fertility, is a complex organ comprised of heterogeneous cell types that work together to maintain a normal ovarian microenvironment (OME). Despite its importance, the effect of obesity on the entire ovary remains poorly documented. In this study, we performed ovary single-cell and nanoscale spatial RNA sequencing to investigate how the OME changed under different kinds of obesity, including high-fat diet (HFD) induced obesity and Leptin ablation induced obesity (OB). Our results demonstrate that OB, but not HFD, dramatically altered the proportion of ovarian granulosa cells, theca-interstitial cells, luteal cells, and endothelial cells. Furthermore, based on the spatial dynamics of follicular development, we defined four subpopulations of granulosa cell and found that obesity drastically disrupted the differentiation of mural granulosa cells from small to large antral follicles. Functionally, HFD enhanced follicle-stimulating hormone (FSH) sensitivity and hormone conversion, while OB caused decreased sensitivity, inadequate steroid hormone conversion, and impaired follicular development. These differences can be explained by the differential expression pattern of the transcription factor Foxo1. Overall, our study provides a powerful and high-resolution resource for profiling obesity-induced OME and offers insights into the diverse effects of obesity on female reproductive disorders.

Single-cell and spatiotemporal transcriptomic analyses reveal the effects of microorganisms on immunity and metabolism in the mouse liver.

The gut-liver axis is a complex bidirectional communication pathway between the intestine and the liver in which microorganisms and their metabolites flow from the intestine through the portal vein to the liver and influence liver function. In a sterile environment, the phenotype or function of the liver is altered, but few studies have investigated the specific cellular and molecular effects of microorganisms on the liver. To this end, we constructed single-cell and spatial transcriptomic (ST) profiles of germ-free (GF) and specific-pathogen-free (SPF) mouse livers. Single-cell RNA sequencing (scRNA-seq) and single-nucleus RNA sequencing (snRNA-seq) revealed that the ratio of most immune cells was altered in the liver of GF mice; in particular, natural killer T (NKT) cells, IgA plasma cells (IgAs) and Kupffer cells (KCs) were significantly reduced in GF mice. Spatial enhanced resolution omics sequencing (Stereo-seq) confirmed that microorganisms mediated the accumulation of Kupffer cells in the periportal zone. Unexpectedly, IgA plasma cells were more numerous and concentrated in the periportal vein in liver sections from SPF mice but less numerous and scattered in GF mice. ST technology also enables the precise zonation of liver lobules into eight layers and three patterns based on the gene expression level in each layer, allowing us to further investigate the effects of microbes on gene zonation patterns and functions. Furthermore, untargeted metabolism experiments of the liver revealed that the propionic acid levels were significantly lower in GF mice, and this reduction may be related to the control of genes involved in bile acid and fatty acid metabolism. In conclusion, the combination of sc/snRNA-seq, Stereo-seq, and untargeted metabolomics revealed immune system defects as well as altered bile acid and lipid metabolic processes at the single-cell and spatial levels in the livers of GF mice. This study will be of great value for understanding host-microbiota interactions.

Spatial transcriptomics: Technologies, applications and experimental considerations.

The diverse cell types of an organ have a highly structured organization to enable their efficient and correct function. To fully appreciate gene functions in a given cell type, one needs to understand how much, when and where the gene is expressed. Classic bulk RNA sequencing and popular single cell sequencing destroy cell structural organization and fail to provide spatial information. However, the spatial location of gene expression or of the cell in a complex tissue provides key clues to comprehend how the neighboring genes or cells cross talk, transduce signals and work together as a team to complete the job. The functional requirement for the spatial content has been a driving force for rapid development of the spatial transcriptomics technologies in the past few years. Here, we present an overview of current spatial technologies with a special focus on the commercially available or currently being commercialized technologies, highlight their applications by category and discuss experimental considerations for a first spatial experiment.

VT3D: a visualization toolbox for 3D transcriptomic data.

Data visualization empowers researchers to communicate their results that support scientific reasoning in an intuitive way. Three-dimension (3D) spatially resolved transcriptomic atlases constructed from multi-view and high-dimensional data have rapidly emerged as a powerful tool to unravel spatial gene expression patterns and cell type distribution in biological samples, revolutionizing the understanding of gene regulatory interactions and cell niches. However, limited accessible tools for data visualization impede the potential impact and application of this technology. Here we introduce VT3D, a visualization toolbox that allows users to explore 3D transcriptomic data, enabling gene expression projection to any 2D plane of interest, 2D virtual slice creation and visualization, and interactive 3D data browsing with surface model plots. In addition, it can either work on personal devices in standalone mode or be hosted as a web-based server. We apply VT3D to multiple datasets produced by the most popular techniques, including both sequencing-based approaches (Stereo-seq, spatial transcriptomics, and Slide-seq) and imaging-based approaches (MERFISH and STARMap), and successfully build a 3D atlas database that allows interactive data browsing. We demonstrate that VT3D bridges the gap between researchers and spatially resolved transcriptomics, thus accelerating related studies such as embryogenesis and organogenesis processes. The source code of VT3D is available at https://github.com/BGI-Qingdao/VT3D, and the modeled atlas database is available at http://www.bgiocean.com/vt3d_example.

Resolving the gene expression maps of human first-trimester chorionic villi with spatial transcriptome.

The placenta is important for fetal development in mammals, and spatial transcriptomic profiling of placenta helps to resolve its structure and function. In this study, we described the landscape of spatial transcriptome of human placental villi obtained from two pregnant women at the first trimester using the modified Stereo-seq method applied for paraformaldehyde (PFA) fixation samples. The PFA fixation of human placenta villi was better than fresh villi embedded in optimum cutting temperature (OCT) compound, since it greatly improved tissue morphology and the specificity of RNA signals. The main cell types in chorionic villi such as syncytiotrophoblasts (SCT), villous cytotrophoblasts (VCT), fibroblasts (FB), and extravillous trophoblasts (EVT) were identified with the spatial transcriptome data, whereas the minor cell types of Hofbauer cells (HB) and endothelial cells (Endo) were spatially located by deconvolution of scRNA-seq data. We demonstrated that the Stereo-seq data of human villi could be used for sophisticated analyses such as spatial cell-communication and regulatory activity. We found that the SCT and VCT exhibited the most ligand-receptor pairs that could increase differentiation of the SCT, and that the spatial localization of specific regulons in different cell types was associated with the pathways related to hormones transport and secretion, regulation of mitotic cell cycle, and nutrient transport pathway in SCT. In EVT, regulatory pathways such as the epithelial to mesenchyme transition, epithelial development and differentiation, and extracellular matrix organization were identified. Finally, viral receptors and drug transporters were identified in villi according to the pathway analysis, which could help to explain the vertical transmission of several infectious diseases and drug metabolism efficacy. Our study provides a valuable resource for further investigation of the placenta development, physiology and pathology in a spatial context.

High-resolution 3D spatiotemporal transcriptomic maps of developing Drosophila embryos and larvae.

Drosophila has long been a successful model organism in multiple biomedical fields. Spatial gene expression patterns are critical for the understanding of complex pathways and interactions, whereas temporal gene expression changes are vital for studying highly dynamic physiological activities. Systematic studies in Drosophila are still impeded by the lack of spatiotemporal transcriptomic information. Here, utilizing spatial enhanced resolution omics-sequencing (Stereo-seq), we dissected the spatiotemporal transcriptomic changes of developing Drosophila with high resolution and sensitivity. We demonstrated that Stereo-seq data can be used for the 3D reconstruction of the spatial transcriptomes of Drosophila embryos and larvae. With these 3D models, we identified functional subregions in embryonic and larval midguts, uncovered spatial cell state dynamics of larval testis, and revealed known and potential regulons of transcription factors within their topographic background. Our data provide the Drosophila research community with useful resources of organism-wide spatiotemporally resolved transcriptomic information across developmental stages.

Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis.

A major challenge in understanding vertebrate embryogenesis is the lack of topographical transcriptomic information that can help correlate microenvironmental cues within the hierarchy of cell-fate decisions. Here, we employed Stereo-seq to profile 91 zebrafish embryo sections covering six critical time points during the first 24 h of development, obtaining a total of 152,977 spots at a resolution of 10 × 10 × 15 µm3 (close to cellular size) with spatial coordinates. Meanwhile, we identified spatial modules and co-varying genes for specific tissue organizations. By performing the integrated analysis of the Stereo-seq and scRNA-seq data from each time point, we reconstructed the spatially resolved developmental trajectories of cell-fate transitions and molecular changes during zebrafish embryogenesis. We further investigated the spatial distribution of ligand-receptor pairs and identified potentially important interactions during zebrafish embryo development. Our study constitutes a fundamental reference for further studies aiming to understand vertebrate development.

The single-cell stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leaves.

Understanding the complex functions of plant leaves requires a thorough characterization of discrete cell features. Although single-cell gene expression profiling technologies have been developed, their application in characterizing cell subtypes has not been achieved yet. Here, we present scStereo-seq (single-cell spatial enhanced resolution omics sequencing) that enabled us to show the bona fide single-cell spatial transcriptome profiles of Arabidopsis leaves. Subtle but significant transcriptomic differences between upper and lower epidermal cells have been successfully distinguished. Furthermore, we discovered cell-type-specific gene expression gradients from the main vein to the leaf edge, which led to the finding of distinct spatial developmental trajectories of vascular cells and guard cells. Our study showcases the importance of physical locations of individual cells for exerting complex biological functions in plants and demonstrates that scStereo-seq is a powerful tool to integrate single-cell location and transcriptome information for plant biology study.