Project description:Plastic pollution poses a universal yet understudied environmental risk to the immune system. Once ingested, nano- and microplastic particles (MNPs) can translocate from the gut into internal organs, likely via circulation. In humans, MNPs have been detected in macrophages within carotid artery plaques, suggesting that these highly phagocytic cells, may also serve as key targets for MNPs under homeostatic conditions. Kupffer cells (KCs), the liver-resident macrophages, play a crucial role in liver homeostasis by regulating metabolism, clearing opsonized target cells, and serving as the first line of defence against bacteria. Residing within the liver sinusoids, they continuously monitor the bloodstream, efficiently capturing and eliminating pathogens and circulating particles to maintain immune and metabolic balance5. It remains unknown whether KCs efficiently capture and store MNPs and how this might affect liver function. Here, we utilize a mouse model of chronic plastic exposure to assess how ingested MNP influence KC core functions, and thereby also liver function. We show that KCs are the primary hepatic target of MNPs and that 12 weeks of exposure alters their transcriptome and impairs phagocytic capacity, leading to dysregulated liver metabolism. Microplastics, but not nanoplastics, exposure reduces KC-mediated clearance of circulating cells and bacteria and exacerbates diet-induced obesity. These findings suggest that chronic MNP exposure disrupts tissue-specific macrophage functions in a size-dependent manner, with distinct long-term consequences for liver function and overall health.
Project description:To investigate the effecs of commensal papillomavirus immunity on the homeostasis of highly mutated normal skin, spatial transcriptomics (Xenium, 10x Genomics, Pleasanton, CA) was performed on SKH-1 mouse back skin. The mice were treated with mouse papillomavirus (MmuPV1) or virus-like particles (VLP), followed by UV exposure for 25 weeks.
Project description:Cellular plasticity is a hallmark of rare Claudin-low (CL) and metaplastic (MBC) breast cancer subtypes, with a documented overlap whose exact extent is yet unknown, and which are associated to resistance and poor survival. We used spatial transcriptomics to further characterise these plastic subtypes, respectively defined molecularly and histopathologically. We identified 3 putative CL tumours (CL-like) and 4 genomically unstable TNBC samples via molecular analyses, combined with 4 MBCs identified by a breast pathologist.
Project description:Xenium platform was used for the spatial transcriptomic analysis of human DRG neurons, 100 marker genes were selected as the customized probe panel and hybridized to fresh frozen hDRG sections. Manual segmentation of each neuron soma was performed, based on expressions of pan-neuronal marker gene PGP9.5, satellite glia cell marker FAB7B, and the corresponding H.E. staining. In total, 1340 neurons were identified (excluding 75 region-of-interest with poor or unclear neuronal soma morphology in H & E staining) and clustered into 16 groups. The 16 clusters were assigned as different cell types based on marker genes expression.
Project description:10X Genomics Xenium data from human Medulloblastoma samples. The data was acuired in the course of a study performing a comparison of four imaging-based ST methods – RNAscope HiPlex, Molecular Cartography, MERFISH/Merscope, and Xenium – together with sequencing-based ST (Visium). These technologies were used to study cryosections of medulloblastoma with extensive nodularity (MBEN), a tumor chosen for its distinct microanatomical features. Our analysis reveals that automated imaging-based ST methods are well suited to delineating the intricate MBEN microanatomy, capturing cell-type-specific transcriptome profiles. We devise approaches to compare the sensitivity and specificity of the different methods together with their unique attributes to guide method selection based on the research aim. Furthermore, we demonstrate how reimaging of slides after the ST analysis can markedly improve cell segmentation accuracy and integrate additional transcript and protein readouts to expand the analytical possibilities and depth of insights. This study highlights key distinctions between various ST technologies and provides a set of parameters for evaluating their performance. Our findings aid in the informed choice of ST methods and delineate approaches for enhancing the resolution and breadth of spatial transcriptomic analyses, thereby contributing to advancing ST applications in solid tumor research.