以色列耶路撒冷希伯来大学Mor Nitzan课题组研发出使用光谱模板匹配推断、过滤和增强单细胞数据中拓扑信号的软件scPrisma。相关论文于2023年2月27日发表在《自然—生物技术》杂志上。
研究人员研发了scPrisma，这是一种光谱计算方法，它使用拓扑先验来解耦，增强和过滤单细胞数据中不同类别的生物过程，例如周期性和线性信号。研究人员用scPrisma分析了HeLa细胞中细胞周期、肝小叶的昼夜节律和空间分区，衣藻的昼夜周期以及大脑视交叉上核的昼夜节律。scPrisma可利用特定特征（例如细胞类型）来区分混合细胞群，并揭示特定生物信号（例如昼夜节律）的调节网络和细胞间相互作用。
研究人员展示了scPrisma在结合已知的、拓扑信息基因推断以及可推广至其他多样化模板和系统方面的灵活性。scPrisma可用作信号分析的独立工作流程，也可用于单细胞下游分析的先前步骤。
据介绍，单细胞RNA测序有助于揭示细胞时空环境。这项任务具有挑战性，因为细胞同时编码多个潜在的交叉干扰生物信号。
附：英文原文
Title: scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching
Author: Karin, Jonathan, Bornfeld, Yonathan, Nitzan, Mor
Issue&Volume: 2023-02-27
Abstract: Single-cell RNA sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is challenging as cells simultaneously encode multiple, potentially cross-interfering, biological signals. Here we propose scPrisma, a spectral computational method that uses topological priors to decouple, enhance and filter different classes of biological processes in single-cell data, such as periodic and linear signals. We apply scPrisma to the analysis of the cell cycle in HeLa cells, circadian rhythm and spatial zonation in liver lobules, diurnal cycle in Chlamydomonas and circadian rhythm in the suprachiasmatic nucleus in the brain. scPrisma can be used to distinguish mixed cellular populations by specific characteristics such as cell type and uncover regulatory networks and cell–cell interactions specific to predefined biological signals, such as the circadian rhythm. We show scPrisma’s flexibility in incorporating prior knowledge, inference of topologically informative genes and generalization to additional diverse templates and systems. scPrisma can be used as a stand-alone workflow for signal analysis and as a prior step for downstream single-cell analysis.
DOI: 10.1038/s41587-023-01663-5
Source: https://www.nature.com/articles/s41587-023-01663-5
Nature Biotechnology：《自然—生物技术》，创刊于1996年。隶属于施普林格·自然出版集团，最新IF：68.164
官方网址：https://www.nature.com/nbt/
投稿链接：https://mts-nbt.nature.com/cgi-bin/main.plex
本期文章：《自然—生物技术》：Online/在线发表
