Mapping lineage-resolved scRNA-seq data with spatialtranscriptomics using TemSOMap

Spatial transcriptomics (ST) has revolutionized the study of cell spatial organization and cell-cell interactions. However, current ST technologies face limitations such as lower gene coverage and spatial resolution compared to single-cell RNA sequencing (scRNA-seq). Integrating scRNA-seq with ST can address these issues by mapping single cells onto spatial data, thereby inferring their spatial coordinates. During tissue formation, cells derived from the same ancestor often remain spatially proximate, making lineage data valuable for cell location inference. Certain single-cell multi-omics technologies including lineage tracing provide paired gene expression and induced or sometic mutation information in single cells, which can be used to reconstruct the cell lineage tree, representing the clonal relationships of cells. To incorporate this information, we developed TemSOMap (Temporal dynamics guided Spatial Omics Mapping), which infers the spatial coordinates of cells by mapping a paired gene expression and mutation barcode dataset onto a spatial transcriptomics dataset. TemSOMap infers a cell-to-spot mapping matrix by minimizing a loss function incorporating gene expression, cell lineage and cell location information. 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.