Transcriptomics is a significant advance in combining high-throughput sequencing and bioinformatics to explore biological mechanisms [5]. However, sequencing analysis of bulk tissue obscured individual cell phenotypic and functional differences and could not identify the molecular features of single-cell resol...
A good separation according to age and genotype was also observed (Figure 1D; see principal-component analysis on https://www.alzmap.org/). The spots of wild-type (WT) mice 12 and 18 months of age overlap (purple and red in Figure 1D), whereas the AppNL-G-F transcriptomics profile ...
In this study, ESPL status is analyzed. Immune microenvironment analysis reveals an immune suppressive condition in ESPL stages. Principal component and DEGs analysis indicate that cancer-like changes primarily initiate at the HGIN stage. Certain DEGs demonstrate progressive increase or decrease throughout...
Registration of slices to a common anatomical reference space via the Spatial Transcriptomics Analysis Tool (STAnly) allows the unrestricted analysis of transcriptomic data across entire brain slices Our deconvolution approach (used in Figs. 1–4) subdivides a given brain slice into different larger br...
Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide ...
Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequen
Our research focuses on single-cell biology, in particular applying single-cell expression analysis to discover the cell types and lineages of the mouse nervous system. The long-term goal of our research is to map the stable cellular states (‘cell types’) that mammalian organs are made of,...
Most existing spatial transcriptome analysis methods are limited to single-slice spatial domain analysis, as they are unable to integrate gene expression and spatial information across multiple slices. However, with the advancement of spatial transcriptomics, it has become possible to obtain multiple slice...
In spatial transcriptomics (ST), clustering analysis categorizes similar gene expression profiles spatially. Various AI techniques have been developed to enhance this process, utilizing machine learning (ML) and deep learning (DL) methods. Among these, methods like spatially informed clustering and predi...
Here, we profiled the spatiotemporal gene programs of the human embryonic kidneys at 9 and 18 post-conception weeks (PCW) by integrating the application of microarray-based spatial transcriptomics and single-cell transcriptomics. Results We mapped transcriptomic signatures of scRNA-seq cell types upon...