Abdelaal Tamim, Grossouw Laurens M, Pasterkamp R Jeroen, Lelieveldt Boudewijn P F, Reinders Marcel J T, Mahfouz Ahmed
Department of Radiology, Leiden University Medical Center, 2333ZC Leiden, The Netherlands.
Systems and Biomedical Engineering Department, Faculty of Engineering Cairo University, 12613 Giza, Egypt.
NAR Genom Bioinform. 2024 Aug 6;6(3):lqae100. doi: 10.1093/nargab/lqae100. eCollection 2024 Sep.
RNA Velocity allows the inference of cellular differentiation trajectories from single-cell RNA sequencing (scRNA-seq) data. It would be highly interesting to study these differentiation dynamics in the spatial context of tissues. Estimating spatial RNA velocities is, however, limited by the inability to spatially capture spliced and unspliced mRNA molecules in high-resolution spatial transcriptomics. We present SIRV, a method to spatially infer RNA velocities at the single-cell resolution by enriching spatial transcriptomics data with the expression of spliced and unspliced mRNA from reference scRNA-seq data. We used SIRV to infer spatial differentiation trajectories in the developing mouse brain, including the differentiation of midbrain-hindbrain boundary cells and marking the forebrain origin of the cortical hem and diencephalon cells. Our results show that SIRV reveals spatial differentiation patterns not identifiable with scRNA-seq data alone. Additionally, we applied SIRV to mouse organogenesis data and obtained robust spatial differentiation trajectories. Finally, we verified the spatial RNA velocities obtained by SIRV using 10x Visium data of the developing chicken heart and MERFISH data from human osteosarcoma cells. Altogether, SIRV allows the inference of spatial RNA velocities at the single-cell resolution to facilitate studying tissue development.
RNA速度分析能够从单细胞RNA测序（scRNA-seq）数据中推断细胞分化轨迹。在组织的空间背景下研究这些分化动态将非常有趣。然而，由于无法在高分辨率空间转录组学中对剪接和未剪接的mRNA分子进行空间捕获，估计空间RNA速度受到限制。我们提出了SIRV，这是一种通过用来自参考scRNA-seq数据的剪接和未剪接mRNA的表达来丰富空间转录组学数据，从而在单细胞分辨率下空间推断RNA速度的方法。我们使用SIRV推断发育中的小鼠大脑中的空间分化轨迹，包括中脑-后脑边界细胞的分化以及标记皮质下托和间脑细胞的前脑起源。我们的结果表明，SIRV揭示了仅靠scRNA-seq数据无法识别的空间分化模式。此外，我们将SIRV应用于小鼠器官发生数据，并获得了稳健的空间分化轨迹。最后，我们使用发育中的鸡心脏的10x Visium数据和来自人骨肉瘤细胞的MERFISH数据验证了SIRV获得的空间RNA速度。总之，SIRV能够在单细胞分辨率下推断空间RNA速度，以促进对组织发育的研究。
