Reconstructing Cell Lineage Trees from Phenotypic Features with Metric Learning
Da Kuang, Guanwen Qiu, Junhyong Kim
TL;DR
This work presents CellTreeQM, a transformer-based framework that reframes cell lineage reconstruction as a metric-learning problem to recover lineage trees from high-dimensional phenotypic data. By enforcing tree-metric properties via a quartet-based additivity loss, incorporating feature gating, and using a deviation regularizer, the method yields embeddings that enable accurate lineage inference even with limited supervision. The authors establish a Lineage Reconstruction Benchmark and demonstrate superior performance over standard contrastive losses across supervised, weakly supervised, and unsupervised settings on synthetic data and lineage-resolved C. elegans datasets. The approach offers a scalable, data-efficient pathway to uncover molecular dynamics of lineage decisions in challenging organisms, with practical implications for developmental biology and regenerative medicine.
Abstract
How a single fertilized cell gives rise to a complex array of specialized cell types in development is a central question in biology. The cells grow, divide, and acquire differentiated characteristics through poorly understood molecular processes. A key approach to studying developmental processes is to infer the tree graph of cell lineage division and differentiation histories, providing an analytical framework for dissecting individual cells' molecular decisions during replication and differentiation. Although genetically engineered lineage-tracing methods have advanced the field, they are either infeasible or ethically constrained in many organisms. In contrast, modern single-cell technologies can measure high-content molecular profiles (e.g., transcriptomes) in a wide range of biological systems. Here, we introduce CellTreeQM, a novel deep learning method based on transformer architectures that learns an embedding space with geometric properties optimized for tree-graph inference. By formulating lineage reconstruction as a tree-metric learning problem, we have systematically explored supervised, weakly supervised, and unsupervised training settings and present a Lineage Reconstruction Benchmark to facilitate comprehensive evaluation of our learning method. We benchmarked the method on (1) synthetic data modeled via Brownian motion with independent noise and spurious signals and (2) lineage-resolved single-cell RNA sequencing datasets. Experimental results show that CellTreeQM recovers lineage structures with minimal supervision and limited data, offering a scalable framework for uncovering cell lineage relationships in challenging animal models. To our knowledge, this is the first method to cast cell lineage inference explicitly as a metric learning task, paving the way for future computational models aimed at uncovering the molecular dynamics of cell lineage.