PDF(Pubmed)
Abstract：
Understanding how different neuronal types connect and communicate is critical to interpreting brain function and behavior. However, it has remained a formidable challenge to decipher the genetic underpinnings that dictate the specific connections formed between neuronal types. To address this, we propose a novel bilinear modeling approach that leverages the architecture similar to that of recommendation systems. Our model transforms the gene expressions of presynaptic and postsynaptic neuronal types, obtained from single-cell transcriptomics, into a covariance matrix. The objective is to construct this covariance matrix that closely mirrors a connectivity matrix, derived from connectomic data, reflecting the known anatomical connections between these neuronal types. When tested on a dataset of Caenorhabditis elegans, our model achieved a performance comparable to, if slightly better than, the previously proposed spatial connectome model (SCM) in reconstructing electrical synaptic connectivity based on gene expressions. Through a comparative analysis, our model not only captured all genetic interactions identified by the SCM but also inferred additional ones. Applied to a mouse retinal neuronal dataset, the bilinear model successfully recapitulated recognized connectivity motifs between bipolar cells and retinal ganglion cells, and provided interpretable insights into genetic interactions shaping the connectivity. Specifically, it identified unique genetic signatures associated with different connectivity motifs, including genes important to cell-cell adhesion and synapse formation, highlighting their role in orchestrating specific synaptic connections between these neurons. Our work establishes an innovative computational strategy for decoding the genetic programming of neuronal type connectivity. It not only sets a new benchmark for single-cell transcriptomic analysis of synaptic connections but also paves the way for mechanistic studies of neural circuit assembly and genetic manipulation of circuit wiring.
摘要：
了解不同类型的神经元如何连接和交流对于解释大脑功能和行为至关重要。然而,破译决定神经元类型之间形成的特定连接的遗传基础仍然是一个巨大的挑战。为了解决这个问题，我们提出了一种新颖的双线性建模方法，该方法利用了类似于推荐系统的体系结构。我们的模型转换了突触前和突触后神经元类型的基因表达，从单细胞转录组学获得，成协方差矩阵。目的是构造紧密反映连通性矩阵的协方差矩阵，来自连接体数据，反映了这些神经元类型之间已知的解剖学联系。当在秀丽隐杆线虫的数据集上测试时，我们的模型取得了与，如果比，先前提出的基于基因表达重建电突触连接的空间连接体模型(SCM)。通过比较分析,我们的模型不仅捕获了SCM确定的所有遗传相互作用，而且还推断了其他遗传相互作用。应用于小鼠视网膜神经元数据集，双线性模型成功地概括了双极细胞和视网膜神经节细胞之间公认的连接基序，并提供了对塑造连通性的遗传相互作用的可解释见解。具体来说,它确定了与不同连接基序相关的独特遗传特征，包括对细胞粘附和突触形成重要的基因，强调它们在协调这些神经元之间特定突触连接中的作用。我们的工作建立了一种创新的计算策略，用于解码神经元类型连通性的遗传编程。它不仅为突触连接的单细胞转录组学分析树立了新的基准，而且为神经电路组装的机理研究和电路布线的遗传操纵铺平了道路。
