Project description:Using Multiome and previously published sc/snRNA-seq data, we studied eight anatomical regions of the human heart including left and right ventricular free walls (LV and RV), left and right atria (LA and RA), left ventricular apex (AX), interventricular septum (SP), sino-atrial node (SAN) and atrioventricular node (AVN). For the first time, we profile the cells of the human cardiac conduction system, revealing their distinctive repertoire of ion channels, G-protein coupled receptors and cell-cell interactions. We map the identified cells to spatial transcriptomic data to discover cellular niches within the eight regions of the heart.
Project description:Using Multiome and previously published sc/snRNA-seq data, we studied eight anatomical regions of the human heart including left and right ventricular free walls (LV and RV), left and right atria (LA and RA), left ventricular apex (AX), interventricular septum (SP), sino-atrial node (SAN) and atrioventricular node (AVN). For the first time, we profile the cells of the human cardiac conduction system, revealing their distinctive repertoire of ion channels, G-protein coupled receptors and cell-cell interactions. We map the identified cells to spatial transcriptomic data to discover cellular niches within the eight regions of the heart.
Project description:Using Multiome and previously published sc/snRNA-seq data, we studied eight anatomical regions of the human heart including left and right ventricular free walls (LV and RV), left and right atria (LA and RA), left ventricular apex (AX), interventricular septum (SP), sino-atrial node (SAN) and atrioventricular node (AVN). For the first time, we profile the cells of the human cardiac conduction system, revealing their distinctive repertoire of ion channels, G-protein coupled receptors and cell-cell interactions. We map the identified cells to spatial transcriptomic data to discover cellular niches within the eight regions of the heart.
Project description:These samples are part of a study to provide a spatially resolved single-cell multiomics map of human trophoblast differentiation in early pregnancy. Here we profiled human implantation sites, decidual and placental samples from 6-9 PCW by 10x multiome snRNA-seq/snATAC-seq.
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: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.
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.