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利用谱系条形码数据重建祖细胞状态层次结构和动态变化

Reconstructing Progenitor State Hierarchy and Dynamics Using Lineage Barcoding Data.

作者信息

Fang Weixiang, Yang Yi, Ji Hongkai, Kalhor Reza

机构信息

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

出版信息

Methods Mol Biol. 2025;2886:177-199. doi: 10.1007/978-1-0716-4310-5_9.

Abstract

Measurements of cell phylogeny based on natural or induced mutations, known as lineage barcodes, in conjunction with molecular phenotype have become increasingly feasible for a large number of single cells. In this chapter, we delve into Quantitative Fate Mapping (QFM) and its computational pipeline, which enables the interrogation of the dynamics of progenitor cells and their fate restriction during development. The methods described here include inferring cell phylogeny with the Phylotime model, and reconstructing progenitor state hierarchy, commitment time, population size, and commitment bias with the ICE-FASE algorithm. Evaluation of adequate sampling based on progenitor state coverage statistics is emphasized for interpreting the QFM results. Overall, this chapter describes a general framework for characterizing the dynamics of cell fate changes using lineage barcoding data.

摘要

基于自然或诱导突变(即谱系条形码)并结合分子表型对细胞系统发育进行测量,对于大量单细胞而言已变得越来越可行。在本章中,我们深入探讨定量命运图谱(QFM)及其计算流程,该流程能够探究祖细胞的动态变化及其在发育过程中的命运限制。这里描述的方法包括使用Phylotime模型推断细胞系统发育,以及使用ICE-FASE算法重建祖细胞状态层次结构、定向时间、群体大小和定向偏差。为了解释QFM结果,强调基于祖细胞状态覆盖统计来评估充足的采样。总体而言,本章描述了一个使用谱系条形码数据表征细胞命运变化动态的通用框架。

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