CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
School of Mathematical Sciences, Xiamen University, Xiamen, China.
Nat Biotechnol. 2024 May;42(5):778-789. doi: 10.1038/s41587-023-01887-5. Epub 2023 Jul 31.
Single-cell RNA sequencing (scRNA-seq) is a powerful approach for studying cellular differentiation, but accurately tracking cell fate transitions can be challenging, especially in disease conditions. Here we introduce PhyloVelo, a computational framework that estimates the velocity of transcriptomic dynamics by using monotonically expressed genes (MEGs) or genes with expression patterns that either increase or decrease, but do not cycle, through phylogenetic time. Through integration of scRNA-seq data with lineage information, PhyloVelo identifies MEGs and reconstructs a transcriptomic velocity field. We validate PhyloVelo using simulated data and Caenorhabditis elegans ground truth data, successfully recovering linear, bifurcated and convergent differentiations. Applying PhyloVelo to seven lineage-traced scRNA-seq datasets, generated using CRISPR-Cas9 editing, lentiviral barcoding or immune repertoire profiling, demonstrates its high accuracy and robustness in inferring complex lineage trajectories while outperforming RNA velocity. Additionally, we discovered that MEGs across tissues and organisms share similar functions in translation and ribosome biogenesis.
单细胞 RNA 测序（scRNA-seq）是研究细胞分化的强大方法，但准确跟踪细胞命运转变可能具有挑战性，特别是在疾病情况下。在这里，我们介绍了 PhyloVelo，这是一种计算框架，通过使用单调表达基因（MEGs）或表达模式要么增加要么减少但不循环通过系统发育时间的基因来估计转录组动力学的速度。通过将 scRNA-seq 数据与谱系信息集成，PhyloVelo 可以识别 MEGs 并重建转录组速度场。我们使用模拟数据和秀丽隐杆线虫的真实数据验证了 PhyloVelo 的有效性，成功地恢复了线性、分叉和收敛的分化。将 PhyloVelo 应用于使用 CRISPR-Cas9 编辑、慢病毒条形码或免疫受体谱分析生成的七个谱系追踪 scRNA-seq 数据集，表明其在推断复杂谱系轨迹时具有很高的准确性和鲁棒性，并且优于 RNA 速度。此外，我们发现不同组织和生物体的 MEGs 在翻译和核糖体生物发生中具有相似的功能。
