Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
Princess Margaret Cancer Center, University Health Network, Toronto, Ontario, Canada.
Proteomics. 2022 Dec;22(23-24):e2200127. doi: 10.1002/pmic.202200127. Epub 2022 Aug 30.
The human brain represents one of the most complex biological structures with significant spatiotemporal molecular plasticity occurring through early development, learning, aging, and disease. While much progress has been made in mapping its transcriptional architecture, more downstream phenotypic readouts are relatively scarce due to limitations with tissue heterogeneity and accessibility, as well as an inability to amplify protein species prior to global -OMICS analysis. To address some of these barriers, our group has recently focused on using mass-spectrometry workflows compatible with small amounts of formalin-fixed paraffin-embedded tissue samples. This has enabled exploration into spatiotemporal proteomic signatures of the brain and disease across otherwise inaccessible neurodevelopmental timepoints and anatomical niches. Given the similar theme and approaches, we introduce an integrated online portal, "The Brain Protein Atlas (BPA)" (www.brainproteinatlas.org), representing a public resource that allows users to access and explore these amalgamated datasets. Specifically, this portal contains a growing set of peer-reviewed mass-spectrometry-based proteomic datasets, including spatiotemporal profiles of human cerebral development, diffuse gliomas, clinically aggressive meningiomas, and a detailed anatomic atlas of glioblastoma. One barrier to entry in mass spectrometry-based proteomics data analysis is the steep learning curve required to extract biologically relevant data. BPA, therefore, includes several built-in analytical tools to generate relevant plots (e.g., volcano plots, heatmaps, boxplots, and scatter plots) and evaluate the spatiotemporal patterns of proteins of interest. Future iterations aim to expand available datasets, including those generated by the community at large, and analytical tools for exploration. Ultimately, BPA aims to improve knowledge dissemination of proteomic information across the neuroscience community in hopes of accelerating the biological understanding of the brain and various maladies.
人脑是最复杂的生物结构之一,具有显著的时空分子可塑性,这种可塑性发生在早期发育、学习、衰老和疾病过程中。虽然在绘制其转录结构方面已经取得了很大进展,但由于组织异质性和可及性的限制,以及在进行全局 -OMICS 分析之前无法扩增蛋白质种类,因此相对较少的下游表型读数。为了解决其中的一些障碍,我们小组最近专注于使用与小量福尔马林固定石蜡包埋组织样本兼容的质谱工作流程。这使得我们能够探索大脑和疾病的时空蛋白质组学特征,而这些在其他情况下是无法到达的神经发育时间点和解剖位。鉴于相似的主题和方法,我们引入了一个集成的在线门户,即“大脑蛋白质图谱 (BPA)”(www.brainproteinatlas.org),它代表了一个公共资源,允许用户访问和探索这些合并数据集。具体来说,该门户包含一组不断增加的基于同行评审的质谱蛋白质组数据集,包括人类大脑发育、弥漫性神经胶质瘤、侵袭性脑膜瘤以及胶质母细胞瘤的详细解剖图谱的时空图谱。基于质谱的蛋白质组学数据分析的一个进入障碍是提取生物学相关数据所需的陡峭学习曲线。因此,BPA 包括几个内置的分析工具,用于生成相关的图(例如,火山图、热图、箱线图和散点图),并评估感兴趣蛋白质的时空模式。未来的迭代旨在扩展可用数据集,包括来自广大社区的数据集,以及用于探索的分析工具。最终,BPA 的目标是提高神经科学界对蛋白质组学信息的传播,希望加速对大脑和各种疾病的生物学理解。