小柯机器人

美国加州大学欧文分校Qing Nie小组提出单细胞的空间转换张量。2024年5月16日，《自然—方法学》杂志在线发表了这项成果。

据介绍，空间转录组学和信使RNA剪接编码了细胞状态和转换的大量时空信息。目前的谱系推断方法要么缺乏状态转换的空间动态，要么无法捕捉与多种细胞状态和转换路径相关的不同动态。

附：英文原文

Title: Spatial transition tensor of single cells

Author: Zhou, Peijie, Bocci, Federico, Li, Tiejun, Nie, Qing

Issue&Volume: 2024-05-16

Abstract: Spatial transcriptomics and messenger RNA splicing encode extensive spatiotemporal information for cell states and transitions. The current lineage-inference methods either lack spatial dynamics for state transition or cannot capture different dynamics associated with multiple cell states and transition paths. Here we present spatial transition tensor (STT), a method that uses messenger RNA splicing and spatial transcriptomes through a multiscale dynamical model to characterize multistability in space. By learning a four-dimensional transition tensor and spatial-constrained random walk, STT reconstructs cell-state-specific dynamics and spatial state transitions via both short-time local tensor streamlines between cells and long-time transition paths among attractors. Benchmarking and applications of STT on several transcriptome datasets via multiple technologies on epithelial–mesenchymal transitions, blood development, spatially resolved mouse brain and chicken heart development, indicate STT’s capability in recovering cell-state-specific dynamics and their associated genes not seen using existing methods. Overall, STT provides a consistent multiscale description of single-cell transcriptome data across multiple spatiotemporal scales.

DOI: 10.1038/s41592-024-02266-x

Source: https://www.nature.com/articles/s41592-024-02266-x

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex