To gain a better understanding of the complexity of gene expression in normal and diseased tissues it is important to account for the spatial context and identity of cells in situ...
Read More »Demystifying Single-Cell Spatial Biology with CosMx SMI
CosMx™ Spatial Molecular Imager (SMI) data is flat-out awe-inspiring: even a perfunctory analysis of a single run produces a terabyte of data, gorgeous images (see below), and spatial relationships...
Read More »Celloscope – a probabilistic model for marker-gene-driven cell type deconvolution in spatial transcriptomics data
Spatial transcriptomics maps gene expression across tissues, posing the challenge of determining the spatial arrangement of different cell types. However, spatial transcriptomics spots contain multiple cells...
Read More »spSeudoMap – cell type mapping of spatial transcriptomics using unmatched single-cell RNA-seq data
Since many single-cell RNA-seq (scRNA-seq) data are obtained after cell sorting, such as when investigating immune cells, tracking cellular landscape by integrating single-cell data with spatial...
Read More »SiT – spatial isoform transcriptomics
In situ capturing technologies add tissue context to gene expression data, with the potential of providing a greater understanding of complex biological systems. However, splicing variants and full-length...
Read More »STRIDE – a topic-model-based method to deconvolve spatial transcriptomics using scRNA-seq
The recent advances in spatial transcriptomics have brought unprecedented opportunities to understand the cellular heterogeneity in the spatial context. However, the current limitations of spatial technologies hamper the exploration of cellular localizations...
Read More »PASTE – alignment and integration of spatial transcriptomics data
Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a tissue slice while recording the two-dimensional (2D) coordinates of each spot. Researchers at...
Read More »Computational approach enables spatial mapping of single-cell data within tissues
A new computational approach developed by researchers at The University of Texas MD Anderson Cancer Center successfully combines data from parallel gene-expression profiling methods to create spatial maps of a given tissue at single-cell res...
Read More »STRIDE – accurately decomposing and integrating spatial transcriptomics using single-cell RNA sequencing
The recent advances in spatial transcriptomics have brought unprecedented opportunities to understand the cellular heterogeneity in the spatial context. However, the current limitations of spatial technologies...
Read More »Yale researchers map gene regulation in tissue using novel sequencing technology
Yale researchers are the first to analyze gene regulation patterns in tissue after developing novel spatial epigenetics mapping technology. A team of...
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