Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Cold Spring Harbor School of Biological Sciences, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Stanley Institute for Cognitive Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
Cell Syst. 2021 Jul 21;12(7):748-756.e3. doi: 10.1016/j.cels.2021.04.010. Epub 2021 May 19.
Gene-gene relationships are commonly measured via the co-variation of gene expression across samples, also known as gene co-expression. Because shared expression patterns are thought to reflect shared function, co-expression networks describe functional relationships between genes, including co-regulation. However, the heterogeneity of cell types in bulk RNA-seq samples creates connections in co-expression networks that potentially obscure co-regulatory modules. The brain initiative cell census network (BICCN) single-cell RNA sequencing (scRNA-seq) datasets provide an unparalleled opportunity to understand how gene-gene relationships shape cell identity. Comparison of the BICCN data (500,000 cells/nuclei across 7 BICCN datasets) with that of bulk RNA-seq networks (2,000 mouse brain samples across 52 studies) reveals a consistent topology reflecting a shared co-regulatory signal. Differential signals between broad cell classes persist in driving variation at finer levels, indicating that convergent regulatory processes affect cell phenotype at multiple scales.
基因-基因关系通常通过跨样本的基因表达变化来衡量，也称为基因共表达。由于共享的表达模式被认为反映了共同的功能，因此共表达网络描述了基因之间的功能关系，包括共调控。然而，批量 RNA-seq 样本中细胞类型的异质性在共表达网络中创建了连接，这些连接可能会掩盖共调控模块。大脑倡议细胞普查网络 (BICCN) 单细胞 RNA 测序 (scRNA-seq) 数据集提供了一个无与伦比的机会来了解基因-基因关系如何塑造细胞身份。将 BICCN 数据（7 个 BICCN 数据集的 50 万个细胞/核）与批量 RNA-seq 网络（52 项研究中的 2000 个小鼠大脑样本）进行比较，揭示了一致的拓扑结构，反映了共同的共调控信号。在更精细的水平上，广泛的细胞类型之间的差异信号仍然在驱动变化，这表明趋同的调控过程在多个尺度上影响细胞表型。
