美国卡内基梅隆大学Jian Ma团队近期取得重要工作进展，他们研究开发了SPICEMIX计算方法，实现了细胞身份的一体化单细胞空间建模。相关研究成果2023年1月9日在线发表于《自然—遗传学》杂志上。
据介绍，空间转录组学可以揭示复杂组织中不同细胞的空间分辨基因表达。然而，开发能够利用空间转录组数据的独特性质来揭示细胞身份的计算方法仍然是一个挑战。
研究人员介绍了SPICEMIX，这是一种基于概率、潜在变量建模的可解释方法，用于从空间转录组数据中联合分析空间信息和基因表达。模拟和实际数据评估都表明，与现有方法相比，SPICEMIX在细胞类型及其空间模式的推断方面显著提高。通过应用seqFISH+、STARmap和Visium获得的人类和小鼠大脑区域的空间转录组数据，研究人员表明，SPICEMIX可以增强复杂细胞身份的推断，揭示可解释的空间元基因并揭示分化轨迹。
总之，SPICEMIX是空间转录组数据的通用分析框架，可用于研究复杂组织中细胞的细胞类型组成和空间组织。
附：英文原文
Title: SpiceMix enables integrative single-cell spatial modeling of cell identity
Author: Chidester, Benjamin, Zhou, Tianming, Alam, Shahul, Ma, Jian
Issue&Volume: 2023-01-09
Abstract: Spatial transcriptomics can reveal spatially resolved gene expression of diverse cells in complex tissues. However, the development of computational methods that can use the unique properties of spatial transcriptome data to unveil cell identities remains a challenge. Here we introduce SPICEMIX, an interpretable method based on probabilistic, latent variable modeling for joint analysis of spatial information and gene expression from spatial transcriptome data. Both simulation and real data evaluations demonstrate that SPICEMIX markedly improves on the inference of cell types and their spatial patterns compared with existing approaches. By applying to spatial transcriptome data of brain regions in human and mouse acquired by seqFISH+, STARmap and Visium, we show that SPICEMIX can enhance the inference of complex cell identities, reveal interpretable spatial metagenes and uncover differentiation trajectories. SPICEMIX is a generalizable analysis framework for spatial transcriptome data to investigate cell-type composition and spatial organization of cells in complex tissues.
DOI: 10.1038/s41588-022-01256-z
Source: https://www.nature.com/articles/s41588-022-01256-z
Nature Genetics：《自然—遗传学》，创刊于1992年。隶属于施普林格·自然出版集团，最新IF：41.307
官方网址：https://www.nature.com/ng/
投稿链接：https://mts-ng.nature.com/cgi-bin/main.plex
本期文章：《自然—遗传学》：Online/在线发表
