Cell lineage tracing is a crucial technique for understanding cell fate and lineage relationships, with wide applications in developmental biology, tissue regeneration, and disease progression studies. Over the years, experimental cell lineage tracing methods have advanced from early labeling techniques to modern genetic tools such as CRISPR‐Cas9‐based barcoding, whereas computational methods have emerged to analyze high‐dimensional data from single‐cell sequencing and other omics technologies. This paper reviews both experimental and computational methods, highlighting their respective strengths, limitations, and synergies. Experimental techniques focus on labeling and tracking cells, whereas computational approaches reconstruct lineage relationships and model cellular dynamics. Despite significant progress, challenges remain, including issues with accuracy, resolution, multi‐omics integration, and scalability. Future directions involve improvements in experimental techniques and the development of computational methods enhanced by advancements in artificial intelligence. These innovations are expected to drive the field forward, offering potential applications in uncovering the mysteries of life.

Motivation: Many ordinary differential equation (ODE) models have been introduced to replace linear regression models for inferring gene regulatory relationships from time-course gene expression data. But, since the observed data are usually not direct measurements of the gene products or there is an unknown time lag in gene regulation, it is problematic to directly apply traditional ODE models or linear regression models.Results: We introduce a lagged ODE model to infer lagged gene regulatory relationships from time-course measurements, which are modeled as linear transformation of the gene products. A time-course microarray dataset from a yeast cell-cycle study is used for simulation assessment of the methods and real data analysis. The results show that our method, by considering both time lag and measurement scaling, performs much better than other linear and ODE models. It indicates the necessity of explicitly modeling the time lag and measurement scaling in ODE gene regulatory models.Availability and implementation: R code is available at https://www.sta.cuhk.edu.hk/xfan/share/lagODE.zip.

Cell division timing is critical for cell fate specification and morphogenesis during embryogenesis, but how division timings are regulated among cells during development is poorly understood. In this article, we focus on the comparison of asynchrony of division, that is, difference of lifetime, between sister cells (ADS) among wild-type and mutant individuals of Caenorhabditis elegans . On the one hand, due to extreme imbalance between wild-type individuals and mutant-type samples, direct comparison of two distributions of ADS between wild type and mutant type is not feasible. On the other hand, we originally found that the ADS is correlated with the lifespan of the corresponding mother cell in wild type. Hence, a semiparametric Bayesian quantile regression method where lifetime of the mother cell is taken as covariate is developed to estimate the 95% confidence curve of ADS in wild type and then ADS of mutant type is classified as abnormal if outside the corresponding confidence interval. A high accuracy of our method is demonstrated by a large-scale simulation study. Real data analysis shows that ADS is related to gene function and expression quantitatively.