Mapping lineage-resolved scRNA-seq data with spatial transcriptomics using TemSOMap

Spatial transcriptomics (ST) has become a powerful technique that advances the study of cell spatial organization and cell-cell interactions. While ST can preserve location information of cells or spots, limitations of such technologies include lower number of genes, and lower resolution compared to scRNA-seq datasets. These limitations can be alleviated by integrating scRNA-seq data with the ST data. By mapping the single cells onto the spatial data, we can infer the spatial coordinates of the cells from the scRNA-seq dataset. We consider leveraging temporal information in this challenging task of spatial location inference. During tissue formation, cells divided from the same ancestor are likely to be located close to each other in the tissue, thus the cell clonal or lineage information can improve cell location inference. CRISPR/Cas9-based lineage tracing technologies have enabled paired sequencing of cells’ gene expression and lineage barcodes. The lineage barcodes can be used to reconstruct the cell lineage tree, which represents cells’ clonal relationships. In order to incorporate this information, we developed TemSOMap ( Tem poral dynamics guided S patial O mics Map ping), which infers the spatial coordinates of cells by mapping a paired gene expression and lineage barcode dataset onto a spatial transcriptomics dataset. TemSOMap utilizes a machine learning framework to infer a cell-to-spot mapping matrix by minimizing a loss function based on expression and lineage. We show that TemSOMap more accurately infers the spatial location of single cells compared to state-of-the-art baseline methods under various scenarios, using both simulated and real datasets. The resulting lineage-resolved ST data can help us better understand the spatio-temporal dynamics of cells in a tissue. TemSOMap is publicly available at https://github.com/ZhangLabGT/TemSOMap .