We have only begun to scratch the surface in understanding mammalian development. An overwhelming caveat is the lack of topographical transcriptomic information to correlate signaling cues and cell-cell interactions within the hierarchy of cell fate decisions. Spatially resolved transcriptomic technologies are promising tools to fill this gap. However, the small field of view and imbalance between resolution and transcript capture of current methodologies precludes their systematic application to study relatively large and multilayered embryos. Here, we have combined DNA nanoball (DNB) patterned arrays and in situ RNA capture to create SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq). This approach allows transcriptomic profiling of large histological sections with high resolution, sensitivity, and reproducibility. We have applied Stereo-seq to study the kinetics of transcriptional variation, the networks of transcription factor binding events and their relationship with morphogens across spatial domains of gene expression in a time course of mouse organogenesis. We have used this information to detect the emergence of tissue-specific cell identities such as early neuroblast populations in the late neural tube stage or the gradients of neuronal specification in the neocortex. Furthermore, we have mapped the expression of a panel of developmental disease-related loci to define the spatiotemporal windows of tissue vulnerability. Our panoramic atlas constitutes a essential resource to investigate longstanding questions concerning the molecular basis of normal and abnormal mammalian development.

Here we apply Stereo-seq to generate a spatially-resolved transcriptomic description of the postnatal day 7 (P7) murine whole brain sagittal section near the middle line. Our study comprehensively dissected the anatomical regions, gene expression and gene regulatory network patterns and cell type localization at whole brain scale.

Despite the liver is one of the largest and most important organs in mammals, hepatic cell responses in homeostasis and perturbation are yet poorly understood. This is due to the difficulty of systematically studying multiple cell types in different cell states and locations, many of which are transient. Here, we used Stereo-seq (Spatio-Temporal Enhanced REsolution Omics-sequencing) combined with high-throughput single-cell transcriptomic analysis to profile murine liver homeostasis and regeneration after partial resection. Our integrative analysis dissects with unprecedented resolution the transcriptomic gradients controlling liver cell function at whole lobe scale, carefully defining how genes and gene regulatory networks are modulated through intercellular communication. Among other important regulators, we identified the transcriptional cofactor TBL1XR1 as an inflammation-induced master switch derepressing genes necessary for hepatocyte proliferation including metabolic genes and Wnt/β-catenin signaling pathway components. Our works lays the foundation for future high-definition spatiotemporal studies of liver malfunction.

Cholestatic injuries, characterized by regional damage around the periportal region, lack curative therapies and cause considerable mortality. In this study, we generated a high-definition spatiotemporal atlas during cholestatic injury and repair by Stereo-seq and single-cell transcriptomics. We uncovered that cholangiocytes function as a periportal hub (cholangio-hub) by integrating multiple signals with neighboring cells. Feedback between cholangiocytes and lipid-associated macrophages (LAM) was detected in the cholangio-hub, which is related to the differentiation of LAM, a recently identified subpopulation of macrophages crucial in tissue injury. Moreover, the cholangio-hub highly expressed TGFβ, which is associated with cholangiocyte conversion of liver progenitor-like cells during injury and dampened proliferation of periportal hepatocytes during recovery. Importantly, spatiotemporal analysis revealed a key inhibitory rheostat for hepatocyte proliferation. Our data provide a comprehensive resource for demarcating regional cholestatic injuries.

The developing brain has a complex and well-organized anatomical structure comprising different types of neural and non-neural cells. Stem cells, progenitors, and newborn neurons tightly interact with their neighbouring cells and tissue microenvironment, and this intricate interplay ultimately shapes the output of neurogenesis. Given the relevance of spatial cues during brain development, we acknowledge the necessity for a transcriptomics atlas within the tissue context accessible to the neurodevelopmental community. To fulfil this need, we offer an open-access spatial gene expression browser of the embryonic mouse brain at the peak of neurogenesis. Using 10x Visium technology, we generated spatially-resolved RNAseq data from E13.5 embryonic brain sections. Unsupervised clustering reliably defined specific cell type populations of diverse lineages and maturational states. Differential expression analysis revealed unique transcriptional signatures across specific embryonic brain areas, uncovering novel features inherent to particular anatomical domains. Furthermore, we integrated single-cell RNAseq data from E13.5 mouse brains into our Spatial Transcriptomics data, adding tissue context to single-cell resolution. In summary, we provide a valuable tool that enables the exploration and discovery of unforeseen molecular players involved in neurogenesis, particularly in the crosstalk between different cell types.

Revealing the relationships between the tissue structures and transcriptomic information is significant for understanding the mechanisms and principles underpinning the biological processes in lung development and diseases. The widely transcriptomic technics, including bulk and single-cell RNA sequencing, cannot accurately restore the tissue location of the gene expression. The advanced spatial transcriptomic tools can fill this gap properly with in-situ RNA capturing and spatial barcode labeling and translating. However, the published spatial transcriptome data of lung is rare or low-quality. Here we harnessed the large-view and high-resolution capabilities of Stereo-seq, one of the most advanced spatial transcriptome tools, in a 5-week-old female mouse to uncover the lung functions with their tissue location resolved. We also found a gradient expression of a bunch of genes related to cell proliferation along the axis of the proximal-to-distal trachea. All the data provided by the study pave the way for further studies in lung development and disease in the future.

Transcriptional heterogeneity among malignant cells of a tumor has been studied in individual cancer types and shown to be organized into cancer cell states; however, it remains unclear to what extent these states span tumor types, constituting general features of cancer. Here, we perform a pan-cancer single-cell RNA-Seq analysis across 15 cancer types and identify a catalog of gene modules whose expression defines recurrent cancer cell states including ‘stress’, ‘interferon response’, ‘epithelial-mesenchymal transition’, ‘metal response’, ‘basal’ and ‘ciliated’. Spatial transcriptomic analysis linked the interferon response in cancer cells to T cells and macrophages in the tumor microenvironment. Using mouse models, we further found that induction of the interferon response module varies by tumor location and is diminished upon elimination of lymphocytes. Our work provides a framework for studying how cancer cell states interact with the tumor microenvironment to form organized systems capable of immune evasion, drug resistance, and metastasis.

Although melanoma is notorious for its high degree of heterogeneity and plasticity, the origin and magnitude of cell state diversity remains poorly understood. Equally, it is not known whether melanoma growth and metastatic dissemination are supported by overlapping or distinct melanoma subpopulations. By combining mouse genetics, unbiased lineage tracing and quantitative modelling, single-cell and spatial transcriptomics, we provide evidence of a hierarchical model of tumour growth that mirrors the cellular and molecular logic underlying embryonic neural crest cell fate specification and differentiation. Our findings indicate that tumorigenic competence is associated with a spatially localized perivascular niche environment, a phenotype acquired through a NOTCH3-dependent intercellular communication pathway established by endothelial cells. Consistent with a model in which only a fraction of melanoma cells is fated to fuel growth, temporal single-cell tracing of a population of melanoma cells harbouring a mesenchymal-like state revealed that these cells do not contribute to primary tumour growth but, instead, constitutes a pool of metastatic-initiating cells that can switch cell identity while disseminating to secondary organs. Our data provide a spatially and temporally resolved map of the diversity and trajectories of cancer cell states within the evolving melanoma ecosystem and suggest that the ability to support growth and metastasis are limited to distinct pools of melanoma cells. The observation that these phenotypic competencies can be dynamically acquired upon exposure to specific niche signals warrant the development of therapeutic strategies that interfere with the cancer cell reprogramming activity of such microenvironmental cues.

Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for Oncology Discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis that provide a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.

The basic analysis steps of spatial transcriptomics involves obtaining gene expression information from both space and cells. This process requires a set of tools to be completed, and existing tools face performance issues when dealing with large data sets. These issues include computationally intensive spatial localization, RNA genome alignment, and excessive memory usage in large chip scenarios. These problems affect the applicability and efficiency of the process. To address these issues, a high-performance and accurate spatial transcriptomics data analysis workflow called Stereo-Seq Analysis Workflow (SAW) has been developed for the Stereo-Seq technology developed by BGI. This workflow includes mRNA spatial position reconstruction, genome alignment, gene expression matrix generation and clustering, and generate results files in a universal format for subsequent personalized analysis. The excutation time for the entire analysis process is ~148 minutes on 1G reads 1*1 cm chip test data, 1.8 times faster than unoptimized workflow.