影像转录组学数据处理与应用进展

Advances in data processing and application of imaging transcriptomics

随着全脑转录组数据,即全脑基因表达图谱(如艾伦人类脑图谱)的出现,影像转录组学为理解大脑分子尺度上的空间分布与宏观神经影像表型之间的关系开辟了新的机会。越来越多的研究揭示了基因表达的特定时空模式与大脑结构和功能的不同属性之间的联系。本文介绍了影像转录组学领域广泛使用的基因表达数据集,以及转录组数据处理的基本步骤和常用的工具箱,并概述了将基因表达数据与影像数据相关联的基本工作流程和三大类分析方法。近年来,影像转录组学已被广泛应用于理解脑神经发育和各类神经精神疾病中;但该领域仍处于新兴阶段,必须克服一些方法学挑战以确保研究结果的稳健性。随着该领域的发展和现有方法的改进,未来的研究可以与越来越全面和精确的将转录组图谱数据相结合,这将会为识别体内观察到的与疾病相关的大脑变化的分子相关性提供一个强大可靠的框架。

With the advent of brain-wide transcriptomics data,i.e.brain-wide gene expression atlases such as the Allen human brain atlas,imaging transcriptomics has opened new opportunities for understanding the relationship between spatial variations on molecular scale of the brain and macroscopic neuroimaging phenotypes.A growing body of literature is demonstrating relationships between gene expression and different properties of brain structure and function.This article introduces the gene expression dataset widely used in the field of imaging transcriptomics,as well as the basic steps and the commonly used toolbox for transcriptomics data processing,and outlines the basic workflow and three kinds of analysis methods for associating gene expression data with image data.In recent years,imaging transcriptomics has been widely used to understand brain neurodevelopment and various neuropsychiatric disorders.However,the field is still nascent,and several methodological challenges must be overcome to ensure the robustness of the findings.As the filed develops and existing methodologies are refined,future studies can be combined with increasingly more comprehensive and precise transcriptional atlas data,which will offer a powerful and reliable framework for identifying the molecular correlates of disease-related brain changes observed in vivo.