Spatial transcriptomics
A technique that, building on transcriptome sequencing, retains information about the spatial position of cells. It can reveal the distribution of cells within a tissue and their interactions with neighboring cells, and is a key data source for inferring cell-cell communication networks.
Timeline (last 90 days)
On spatial transcriptomics data, the new method successfully detected tumor boundaries and matched known pathological boundaries.
AI/computational methods can extract the patterns and rules of intercellular communication from single-cell sequencing data, spatial transcriptomics data, and protein-protein interaction networks, treating cell communication as a language system that can be parsed by machine learning.
Intercellular signaling is highly dependent on spatial proximity: a signaling molecule secreted by one cell can only act on target cells near it, so spatial transcriptomic information is crucial for accurately inferring cell communication networks, and the absence of the spatial dimension leads to a large number of false positives.
All Facts (3)
On spatial transcriptomics data, the new method successfully detected tumor boundaries and matched known pathological boundaries.
50%UnverifiedAI/computational methods can extract the patterns and rules of intercellular communication from single-cell sequencing data, spatial transcriptomics data, and protein-protein interaction networks, treating cell communication as a language system that can be parsed by machine learning.
50%UnverifiedIntercellular signaling is highly dependent on spatial proximity: a signaling molecule secreted by one cell can only act on target cells near it, so spatial transcriptomic information is crucial for accurately inferring cell communication networks, and the absence of the spatial dimension leads to a large number of false positives.
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