LinRace: cell division history reconstruction of single cells using paired lineage barcode and gene expression data

Xinhai Pan, Hechen Li, Pranav Putta and Xiuwei Zhang ()
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Xinhai Pan: Georgia Institute of Technology
Hechen Li: Georgia Institute of Technology
Pranav Putta: Georgia Institute of Technology
Xiuwei Zhang: Georgia Institute of Technology

Nature Communications, 2023, vol. 14, issue 1, 1-15

Abstract: Abstract Lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes in single cells, which allows for inference of cell lineage and cell types at the whole organism level. While most state-of-the-art methods for lineage reconstruction utilize only the lineage barcode data, methods that incorporate gene expressions are emerging. Effectively incorporating the gene expression data requires a reasonable model of how gene expression data changes along generations of divisions. Here, we present LinRace (Lineage Reconstruction with asymmetric cell division model), which integrates lineage barcode and gene expression data using asymmetric cell division model and infers cell lineages and ancestral cell states using Neighbor-Joining and maximum-likelihood heuristics. On both simulated and real data, LinRace outputs more accurate cell division trees than existing methods. With inferred ancestral states, LinRace can also show how a progenitor cell generates a large population of cells with various functionalities.

Date: 2023
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DOI: 10.1038/s41467-023-44173-3

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