美国休斯敦卫理公会研究所Guangyu Wang团队近期取得重要工作进展，他们通过开发出一种中继速率模型工具，来推断细胞依赖性RNA速率。相关研究成果2023年4月3日在线发表于《自然—生物技术》杂志上。

据介绍，RNA速率提供了一种从单细胞RNA测序（scRNA-seq）数据推断细胞状态转变的方法。在scRNA-seq实验中，传统的RNA速率模型从所有细胞推断出普遍的动力学，导致在细胞状态的多阶段和/或多谱系转变的实验中出现不可预测的性能，其中所有细胞的相同动力学速率的假设不再成立。

研究人员开发了cellDancer工具，这是一种可扩展的深度神经网络，可从其相邻局部推断每个细胞的速率，然后中继一系列局部速度以提供速度动力学的单细胞分辨率推断。在模拟基准测试中，cellDancer 在多动力学机制、高丢失率数据集和稀疏数据集上表现出稳健的性能。研究人员发现，cellDancer在红系成熟和海马发育建模方面克服了现有RNA速率模型的局限性。

此外，cellDancer提供了转录、剪接和降解速率的细胞特异性预测，研究人员将其确定为小鼠胰腺中细胞命运的潜在指标。

附：英文原文

Title: A relay velocity model infers cell-dependent RNA velocity

Author: Li, Shengyu, Zhang, Pengzhi, Chen, Weiqing, Ye, Lingqun, Brannan, Kristopher W., Le, Nhat-Tu, Abe, Jun-ichi, Cooke, John P., Wang, Guangyu

Issue&Volume: 2023-04-03

Abstract: RNA velocity provides an approach for inferring cellular state transitions from single-cell RNA sequencing (scRNA-seq) data. Conventional RNA velocity models infer universal kinetics from all cells in an scRNA-seq experiment, resulting in unpredictable performance in experiments with multi-stage and/or multi-lineage transition of cell states where the assumption of the same kinetic rates for all cells no longer holds. Here we present cellDancer, a scalable deep neural network that locally infers velocity for each cell from its neighbors and then relays a series of local velocities to provide single-cell resolution inference of velocity kinetics. In the simulation benchmark, cellDancer shows robust performance in multiple kinetic regimes, high dropout ratio datasets and sparse datasets. We show that cellDancer overcomes the limitations of existing RNA velocity models in modeling erythroid maturation and hippocampus development. Moreover, cellDancer provides cell-specific predictions of transcription, splicing and degradation rates, which we identify as potential indicators of cell fate in the mouse pancreas.

DOI: 10.1038/s41587-023-01728-5

Source: https://www.nature.com/articles/s41587-023-01728-5