Abstract
Understanding the spatio-temporal processes of tissue growth, including how new cell types emerge and how cells form the tissue architecture, is a fundamental problem in biology. The emerging spatially resolved lineage tracing data, where three modalities, lineage barcodes, gene expression profiles, and spatial locations, are measured for each single cell, provides an unprecedented opportunity to understand these processes. Computational methods that take advantage of all three modalities to reconstruct cell lineage tree and ancestral cell states and locations are needed. We introduce LineageMap, a hybrid lineage inference algorithm that integrates the scalability of distance-based tree reconstruction methods with the flexibility of likelihood-based methods under a unified probabilistic framework. The input to LineageMap is spatially resolved lineage tracing data, where for each single cell, the gene expression, lineage barcode and spatial locations are available. LineageMap enables accurate, interpretable, and scalable inference of high-resolution lineage trees as well as locations of ancestral cells from the tri-modality single-cell data. Across simulated and experimental datasets, LineageMap consistently outperforms existing methods in the accuracy of reconstructed cell lineage trees, while revealing biologically coherent spatiotemporal trajectories. Our framework bridges molecular lineage tracing with spatial and transcriptomic information, advancing computational reconstruction of dynamic cellular ancestries in both time and space. LineageMap is available at: https://github.com/ZhangLabGT/LineageMap.