Project description:To elucidate the factors that determine nanomedicine tumor uptake, we developed a molecular marker identification platform by integrating microscopic fluorescence images of nanomedicine distribution with spatial transcriptomics (ST) information. When this approach is applied to PEGylated liposomes, molecular markers related to hypoxia, glucose metabolism and apoptosis can be identified as being related to the intratumoral distribution of the nanomedicine. We expect that our method can be applied to explain the distribution of a wide range of nanomedicines, and furthermore, the data obtained from this analysis can be used in precise utilization of the nanomedicine.
Project description:Recently, cell therapy has emerged as a promising treatment option for various disorders. Given the intricate mechanisms of action (MOA) and heterogenous distribution in target tissues inherent to cell therapy, it is necessary to develop more sophisticated, unbiased approaches to evaluate the distribution of administered cells and the molecular changes at a microscopic level. This study introduces a label-free approach for assessing the tissue distribution of administered human mesenchymal stem cells (hMSCs) and their MOA, leveraging spatially resolved transcriptomics (ST) analysis. The hMSCs were introduced into a mouse model with lung fibrosis, followed by the manipulation of ST to visualize the spatial distribution of hMSCs within the tissue. This was achieved by capitalizing on interspecies transcript differences between human and mouse. Furthermore, the method allowed for the examination of molecular changes associated with the spatial distribution of hMSCs. Therefore, our method has the potential to serve as an effective tool for various cell-based therapeutic agents.
Project description:Subplate neurons (SPNs) are among the firstborn neurons in the human fetal cerebral cortex, and play a critical role in establishing intra- and extracortical connections. In this study, we created spatial landscapes and molecular lineages of SPNs by performing spatial transcriptomics, and revealed precise position information of various cortical cell types including subplate neurons and their high diversity in the human fetal cortex. Through the spatial distribution of subplate neurons, we identified the molecular signatures of subplate neurons that are closer to early-born neurons than late-born neurons. We also analyzed human-specific genes and extracellular matrix genes enriched in subplate neurons, highlighting the potential contributions of subplate neurons to cortical neurogenesis and early structural folding.
Project description:We performed spatial transcriptomics to profile compositional and spatial distribution of hetergeneous cell populations in mucinous colorectal adenocarinoma
Project description:These data were used in the spatial transcriptomics analysis of the article titled \\"Single-Cell and Spatial Transcriptomics Analysis of Human Adrenal Aging\\".
Project description:The tumor microenvironment plays a crucial role in soft tissue sarcoma development and response to therapy. We used spatial transcriptomics to analyze the spatial distribution of malignant, immune, and other stromal cells present within soft tissue sarcomas.
Project description:To reveal the spatial distribution and the difference gene expression pattern of cancer cells in colorectal cancer, Visium spatial transcriptomics of four CRC patients was applied
Project description:We developed an analysis pipeline that can extract microbial sequences from Spatial Transcriptomic (ST) data and assign taxonomic labels, generating a spatial microbial abundance matrix in addition to the default host expression matrix, enabling simultaneous analysis of host expression and microbial distribution. We called the pipeline Spatial Meta-transcriptome (SMT) and applied it on both human and murine intestinal sections and validated the spatial microbial abundance information with alternative assays. Biological insights were gained from this novel data that that demonstrated host-microbe interaction at various spatial scales. Finally, we tested experimental modification that can increase microbial capture while preserving host spatial expression quality and, by use of positive controls, quantitatively demonstrated the capture efficiency and recall of our methods. This proof of concept work demonstrates the feasibility of Spatial Meta-transcriptomic analysis, and paves the way for further experimental optimization and application.