Single-cell and spatial transcriptomics enables probabilistic inference of cell type topography
By Alma Andersson, J. Bergenstråhle, Michaela Asp, L. Bergenstråhle, A. Jurek, J. Fernández Navarro, J. Lundeberg. Communications Biology, 2020
396 citations recorded by the source corpus.
The field of spatial transcriptomics is rapidly expanding, and with it the repertoire of available technologies. However, several of the transcriptome-wide spatial assays do not operate on a single cell level, but rather produce data comprised of contributions from a – potentially heterogeneous – mixture of cells. Still, these techniques are attractive to use when examining complex tissue specimens with diverse cell populations, where complete expression profiles are required to properly capture their richness. Motivated by an interest to put gene expression into context and delineate the spatial arrangement of cell types within a tissue, we here present a model-based probabilistic method that uses single cell data to deconvolve the cell mixtures in spatial data. To illustrate the capacity of our method, we use data from different experimental platforms and spatially map cell types from the mouse brain and developmental heart, which arrange as expected. Alma Andersson et al. present a probabilistic framework that integrates single-cell and bulk spatial transcriptomics in order to spatially map cell types onto their respective tissues. They apply their method to the developing human heart and mouse brain to demonstrate the power of the technique.
Subfields: Immunology, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/aaca267515ad4de8ee7ecb872b13f57c0002beee
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Citations in the corpus
- Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics (2022, neutral): “28 and 29).”
- Spatial components of molecular tissue biology (2022, neutral): “3b and Table 1) 130–139 .”
- Cell2location maps fine-grained cell types in spatial transcriptomics (2022, background): “Although cell2location is related to existing transcriptomic decomposition methods1,3,5,6,18, it offers features to (1) borrow statistical strength across locations with similar cell composition, (2) account for batch variation across slides as well as variation in mRNA detection sensitivity and (3)”
- Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution (2022, methodology): “RCTD had the highest AS score (0.94), followed by Stereoscope (0.92).”