AI Article Synopsis
- A new computational method called SpaTrio was developed to integrate single-cell multi-omics and spatial transcriptomics data for a better understanding of tissue information at a spatial scale.
- SpaTrio uses probabilistic alignment, demonstrating strong accuracy and reliability through simulations and biological dataset evaluations.
- The method helps uncover complex spatial relationships and gene expression regulations in cells, revealing valuable insights into the multimodal biology of tissues.
Article Abstract
Capturing and depicting the multimodal tissue information of tissues at the spatial scale remains a significant challenge owing to technical limitations in single-cell multi-omics and spatial transcriptomics sequencing. Here, we developed a computational method called SpaTrio that can build spatial multi-omics data by integrating these two datasets through probabilistic alignment and enabling further analysis of gene regulation and cellular interactions. We benchmarked SpaTrio using simulation datasets and demonstrated its accuracy and robustness. Next, we evaluated SpaTrio on biological datasets and showed that it could detect topological patterns of cells and modalities. SpaTrio has also been applied to multiple sets of actual data to uncover spatially multimodal heterogeneity, understand the spatiotemporal regulation of gene expression, and resolve multimodal communication among cells. Our data demonstrated that SpaTrio could accurately map single cells and reconstruct the spatial distribution of various biomolecules, providing valuable multimodal insights into spatial biology.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10726534 | PMC |
| http://dx.doi.org/10.1016/j.xgen.2023.100446 | DOI Listing |
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