Rodriguez Fraticelli Alejo E, Sánchez Pedro Sánchez
Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain.
ICREA, Catalan Institution for Research and Advanced Studies, Barcelona, Catalonia, Spain.
Methods Mol Biol. 2025;2886:65-84. doi: 10.1007/978-1-0716-4310-5_4.
Lineage tracing has significantly advanced our comprehension in many areas of biology, such as development or immunity, by precisely measuring cellular processes like migration, division, or differentiation across labeled cells and their progeny. Traditional recombinase-based prospective lineage tracing is limited by the need for a priori cell type information and is constrained in the numbers of clones it can simultaneously track. In this sense, clonal lineage tracing with integrated random barcodes offers a robust alternative, enabling researchers to label and track a vast array of cells and their progeny over time. Moreover, clonal lineage tracing can be combined with single-cell omics technologies to study cell states and their maintenance over time. Key steps in these protocols include stable barcode integration, cell division to expand clones, and simultaneous capture of cellular properties with barcode information. Here, we comment on those steps and summarize important parameters to take into account during the design of single-cell lineage tracing experiments. Also, we present the main features for various available lentiviral libraries of expressed barcodes than can be captured alongside the transcriptome of individual cells. We cover other crucial aspects of experimental design, such as the optimization of cellular sampling, library diversity, and the minimization of clonal dropouts. Regarding sequencing data analysis, we provide some tips based on our experience, as well as available computational tools for the assignment of clonal identities and the identification of fate determinants. We finally discuss limitations of current methodologies and use an example step-by-step protocol to illustrate key points during the process. In sum, we provide a roadmap for considering and implementing single-cell lineage tracing studies to comprehensively explore fate determinants and their mechanisms.
谱系追踪通过精确测量标记细胞及其后代的迁移、分裂或分化等细胞过程，在生物学的许多领域，如发育或免疫方面，极大地推进了我们的理解。传统的基于重组酶的前瞻性谱系追踪受到先验细胞类型信息需求的限制，并且在其能够同时追踪的克隆数量上也受到限制。从这个意义上说，使用整合随机条形码的克隆谱系追踪提供了一种强大的替代方法，使研究人员能够随着时间推移标记和追踪大量细胞及其后代。此外，克隆谱系追踪可以与单细胞组学技术相结合，以研究细胞状态及其随时间的维持情况。这些方案中的关键步骤包括稳定的条形码整合、细胞分裂以扩大克隆，以及同时捕获带有条形码信息的细胞特性。在这里，我们对这些步骤进行评论，并总结在设计单细胞谱系追踪实验时需要考虑的重要参数。此外，我们介绍了各种可用的表达条形码慢病毒文库的主要特征，这些条形码可以与单个细胞的转录组一起捕获。我们涵盖了实验设计的其他关键方面，如细胞采样的优化、文库多样性以及克隆丢失的最小化。关于测序数据分析，我们根据经验提供了一些提示，以及用于克隆身份分配和命运决定因素识别的可用计算工具。我们最后讨论了当前方法的局限性，并使用一个示例分步方案来说明过程中的关键点。总之，我们提供了一个路线图，用于考虑和实施单细胞谱系追踪研究，以全面探索命运决定因素及其机制。
