Bayesian inference of lineage trees by joint analysis of single-cell multimodal lineage-tracing data with BiLinT.

BiLinT accurately reconstructs cell lineage trees by jointly analyzing gene expression and lineage barcodes, achieving high resolution in single-cell data.

• Why it matters: Understanding cell lineage relationships is crucial for studying development and disease, but current methods often analyze data modalities separately, limiting accuracy.
• What they did: The approach employs a Bayesian framework that combines barcode evolution modeled as a continuous-time Markov chain with gene expression dynamics via an Ornstein-Uhlenbeck process, applied to multiple datasets.
• The result: BiLinT delivers precise lineage reconstructions, uncovers clonal structures linked to differentiation, and reveals developmental fate biases, advancing insights into cellular development.