Project description:The post-natal development and maturation of the mammalian heart involve highly intricate processes that remain incompletely understood, particularly concerning the molecular signature and roles of the diverse cell types involved. In this study, we present a comprehensive dataset generated from murine hearts at three key post-natal developmental stages using Spatio-temporal Enhanced Resolution Omics-Sequencing (Stereo-seq), an advanced spatially resolved transcriptomic technology. This dataset encompasses spatial transcriptomes of approximately 0.186 million individual cells within intact sections of murine hearts at post-natal developmental stages. Our dataset serves as a valuable resource for investigating the mechanisms underlying mammalian heart development and maturation. Through initial analyses, we identified distinct cell types and their spatial distributions, including 93,826 cardiomyocytes within a single heart section. This extensive dataset provides researchers with opportunities for data mining and facilitates diverse analyses, including studies on transcriptional regulation, cell-to-cell communication, and the functional activities of genes and signalling molecules during critical phases of heart development.
Project description:The recent development of spatial omics enables single-cell profiling of the transcriptome and the 3D organization of the genome in a spatially resolved manner. A spatial epigenomics method would expand the repertoire of spatial omics tools and accelerate our understanding of the spatial regulation of cellular processes and tissue functions. Here, we developed an imaging approach for spatially resolved profiling of epigenetic modifications in single cells
Project description:Understanding the spatial distribution of T cells is pivotal to decrypting immune dysfunction in cancer. Current spatially resolved transcriptomics fall short in directly annotating T cell receptors (TCRs), limiting the comprehension of anti-cancer immunity. We introduce a novel technology, Spatially Resolved T Cell Receptor Sequencing (SPTCR-seq), integrating target enrichment and long-read sequencing for highly sensitive TCR sequencing. This approach yields an on-target rate of ~85%, and via a bespoke computational pipeline, it provides meticulous spatial mapping, error correction, and UMI refinement. SPTCR-seq outperforms PCR-based methods, offering superior reconstruction of the complete TCR architecture, inclusive of V, D, J regions and the vital complementarity-determining region 3 (CDR3). Applying SPTCR-seq, we reveal local T cell diversity, clonal expansion, and transcriptional evolution across spatially distinct niches in glioblastoma, identifying critical involvement of NK and B cells in spatial T cell adaptation. SPTCR-seq, by bridging spatially resolved omics and TCR sequencing, stands as a robust tool for exploring T cell dysfunction in cancers and beyond.
Project description:To evaluate the potential utility of the CBTi-seq for spatially resolved transcriptomics tissue analysis, we performed a proof-of-concept experiment by microneedle sampling and analyzing three typical mouse brain regions (cerebral cortex (Ccx), corpus callosum (Cc), and hippocampus (Hi)) under the specific zone. CBTi-seq enables spatially resolved transcriptomics analysis in the tissue microenvironment, and holds great potential for revealing the spatial and temporal characteristics and functional heterogeneity of cells.
Project description:Pancreatic ductal adenocarcinoma (PDAC) exhibits profound molecular heterogeneity and poor prognosis, necessitating novel tailored therapies. The basal and classical subtypes - driven by glycolysis versus lipid metabolism - have distinct prognostic implications. We mapped PDAC molecular subtype heterogeneity, capturing spatially-resolved gene expression signatures and generating a comprehensive high-resolution dataset of 42,035 spatial spots. Subtype assignments were validated via multiplex immunofluorescence and quantitative analyses in patient-derived organoids. Our analysis resolved cancer cell signatures, deconvoluted intra-tumoral heterogeneity, and delineated a classical-to-basal trajectory. We identified metabolically ‘hot’, high-grade tumor niches characterized by concurrent enrichment of glycolysis and lipogenesis across both subtypes, nominating them as subtype-agnostic therapeutic targets. Preclinical models demonstrated that despite the basal subtype’s glycolysis dependence, both classical and basal tumors are susceptible to glycolysis inhibition. This work challenges the dogma of subtype-specific therapeutic silos and demonstrates highly adaptable energetic niches as reservoirs to drive tumor progression.