RNA-seq data of PatchSeq dataset from Pvalb-Cre positive interneurons in the mouse hippocamus CA1 region

This study takes on the problem of bridging transcriptional data to neuronal phenotype and function by using publicly available datasets characterizing distinct neuronal populations based on gene expression, electrophysiology and morphology. In addition, a non-published PatchSeq dataset of Pvalb-cre positive cells in CA1 was used, which is the dataset submitted here. Taken together, these datasets were used to identify cross-cell type correlations between these data modalities. Detected correlations were classified as “class-driven” if they could be explained by differences between excitatory and inhibitory cell classes, or “non-class driven” if they could be explained by gradient like phenotypic differences within cell classes. Some genes whose relationships to electrophysiological or morphological properties were found to to be specific to either excitatory or inhibitory cell types. The Patch Seq data specifically allowed simultaneous single-cell characterization of gene expression and electrophysiology, showing that the gene-property correlations observed across cell types were further predictive of within-cell type heterogeneity. Patchseq data was collected from single cells of the mouse hippocampus CA1 in order to investigate correlations between gene expression patterns and electrophysiological properties of various interneuron cell classes 19 individual cells Re-analysis details included in supplementary file readme.txt.

本研究聚焦于搭建转录组数据与神经元表型及功能之间的桥梁，所用方法为利用基于基因表达、电生理特性与形态学特征对不同神经元群体进行表征的公开数据集。此外，本研究还使用了一项尚未公开的CA1脑区Pvalb-cre阳性细胞的PatchSeq（PatchSeq）数据集，即本次提交的数据集。综合上述数据集，本研究旨在识别这些数据模态之间的跨细胞类型相关性。若相关性可由兴奋性与抑制性细胞类群间的差异解释，则将其归类为“类驱动型”；若可由细胞类群内部类梯度式的表型差异解释，则归类为“非类驱动型”。部分基因与电生理或形态学特性的关联被发现仅特异性存在于兴奋性或抑制性细胞类群中。PatchSeq数据可实现单细胞水平上基因表达与电生理特性的同步表征，研究显示，跨细胞类型观测到的基因-特性相关性可进一步用于预测细胞类群内部的异质性。为探究不同中间神经元类群的基因表达模式与电生理特性间的关联，研究人员从小鼠海马CA1脑区的单个细胞中采集了PatchSeq数据，共涉及19个独立细胞。详细的重分析流程已收录于补充文件readme.txt中。