Localizing Genes to Cerebellar Layers by Classifying ISH Images

Gene expression controls how the brain develops and functions. Understanding control processes in the brain is particularly hard since they involve numerous types of neurons and glia, and very little is known about which genes are expressed in which cells and brain layers. Here we describe an approach to detect genes whose expression is primarily localized to a specific brain layer and apply it to the mouse cerebellum. We learn typical spatial patterns of expression from a few markers that are known to be localized to specific layers, and use these patterns to predict localization for new genes. We analyze images of in-situ hybridization (ISH) experiments, which we represent using histograms of local binary patterns (LBP) and train image classifiers and gene classifiers for four layers of the cerebellum: the Purkinje, granular, molecular and white matter layer. On held-out data, the layer classifiers achieve accuracy above 94% (AUC) by representing each image at multiple scales and by combining multiple image scores into a single gene-level decision. When applied to the full mouse genome, the classifiers predict specific layer localization for hundreds of new genes in the Purkinje and granular layers. Many genes localized to the Purkinje layer are likely to be expressed in astrocytes, and many others are involved in lipid metabolism, possibly due to the unusual size of Purkinje cells.基因表达调控大脑的发育与功能运作。解析大脑的基因调控机制尤为困难，这是因为其涉及种类繁多的神经元与神经胶质细胞，且目前对于哪些基因会在特定细胞及脑区层中表达的认知仍十分有限。本研究提出一种可检测表达主要定位于特定脑区层的基因的方法，并将其应用于小鼠小脑。我们从已知定位于特定脑层的少数标记基因中学习典型的表达空间模式，并利用这些模式对新基因的定位进行预测。我们对原位杂交（in-situ hybridization, ISH）实验的图像进行分析，采用局部二值模式（local binary patterns, LBP）直方图对图像进行表征，并针对小鼠小脑的四层结构——浦肯野（Purkinje）层、颗粒层、分子层以及白质层——训练图像分类器与基因分类器。在预留测试数据集上，通过对图像进行多尺度表征，并将多张图像的评分整合为单基因水平的决策结果，脑层分类器的AUC（曲线下面积）准确率可达94%以上。当将该分类器应用于完整小鼠基因组时，其可预测浦肯野层与颗粒层中数百个新基因的特异性脑层定位。许多定位于浦肯野层的基因极有可能在星形胶质细胞中表达，而其余诸多基因则参与脂质代谢过程，这或许与浦肯野细胞异常庞大的体积存在关联。