Biomedical Image Informatics, VRVis Research Center, Vienna, Austria.
Global Computational Biology and Digital Sciences, Boehringer Ingelheim Pharma, Biberach an der Riss, Germany.
Commun Biol. 2024 Jun 14;7(1):730. doi: 10.1038/s42003-024-06355-7.
Exploring the relationships between genes and brain circuitry can be accelerated by joint analysis of heterogeneous datasets from 3D imaging data, anatomical data, as well as brain networks at varying scales, resolutions, and modalities. Generating an integrated view, beyond the individual resources' original purpose, requires the fusion of these data to a common space, and a visualization that bridges the gap across scales. However, despite ever expanding datasets, few platforms for integration and exploration of this heterogeneous data exist. To this end, we present the BrainTACO (Brain Transcriptomic And Connectivity Data) resource, a selection of heterogeneous, and multi-scale neurobiological data spatially mapped onto a common, hierarchical reference space, combined via a holistic data integration scheme. To access BrainTACO, we extended BrainTrawler, a web-based visual analytics framework for spatial neurobiological data, with comparative visualizations of multiple resources. This enables gene expression dissection of brain networks with, to the best of our knowledge, an unprecedented coverage and allows for the identification of potential genetic drivers of connectivity in both mice and humans that may contribute to the discovery of dysconnectivity phenotypes. Hence, BrainTACO reduces the need for time-consuming manual data aggregation often required for computational analyses in script-based toolboxes, and supports neuroscientists by directly leveraging the data instead of preparing it.
通过联合分析来自 3D 成像数据、解剖数据以及不同尺度、分辨率和模态的脑网络的异构数据集,可以加速基因与大脑回路之间关系的研究。生成超越单个资源原始目的的综合视图,需要将这些数据融合到一个公共空间中,并使用可视化技术在不同尺度之间架起桥梁。然而,尽管数据集不断扩大,但用于集成和探索这种异构数据的平台却很少。为此,我们提出了 BrainTACO(脑转录组和连接数据)资源,这是一组经过空间映射到共同的层次参考空间的异构和多尺度神经生物学数据的选择,并通过整体数据集成方案进行了组合。为了访问 BrainTACO,我们扩展了 BrainTrawler,这是一个用于空间神经生物学数据的基于网络的可视化分析框架,具有多种资源的比较可视化功能。这使得可以对脑网络进行基因表达剖析,据我们所知,其涵盖范围前所未有,并可以识别出小鼠和人类连接的潜在遗传驱动因素,这可能有助于发现连接异常表型。因此,BrainTACO 减少了在基于脚本的工具包中进行计算分析时通常需要的耗时的手动数据聚合的需求,并通过直接利用数据而不是准备数据来支持神经科学家。