Biotech Research and Innovation Centre, University of Copenhagen, Copenhagen N 2200, Denmark.
School of Systems Biomedical Science, Soongsil University, Dongjak-Gu, Seoul 06978, Republic of Korea.
Proc Natl Acad Sci U S A. 2023 Jan 10;120(2):e2205371120. doi: 10.1073/pnas.2205371120. Epub 2023 Jan 3.
Development of multicellular organisms is orchestrated by persistent cell-cell communication between neighboring partners. Direct interaction between different cell types can induce molecular signals that dictate lineage specification and cell fate decisions. Current single-cell RNA-seq technology cannot adequately analyze cell-cell contact-dependent gene expression, mainly due to the loss of spatial information. To overcome this obstacle and resolve cell-cell contact-specific gene expression during embryogenesis, we performed RNA sequencing of physically interacting cells (PIC-seq) and assessed them alongside similar single-cell transcriptomes derived from developing mouse embryos between embryonic day (E) 7.5 and E9.5. Analysis of the PIC-seq data identified gene expression signatures that were dependent on the presence of specific neighboring cell types. Our computational predictions, validated experimentally, demonstrated that neural progenitor (NP) cells upregulate and genes, when exclusively interacting with definitive endoderm (DE) cells. Moreover, there was a reciprocal impact on the transcriptome of DE cells, as they tend to upregulate and when in contact with NP cells. Using individual cell transcriptome data, we formulated a means of computationally predicting the impact of one cell type on the transcriptome of its neighboring cell types. We have further developed a distinctive spatial-t-distributed stochastic neighboring embedding to display the pseudospatial distribution of cells in a 2-dimensional space. In summary, we describe an innovative approach to study contact-specific gene regulation during embryogenesis.
多细胞生物的发育是由相邻细胞之间持续的细胞间通讯来协调的。不同类型的细胞之间的直接相互作用可以诱导决定谱系特化和细胞命运决定的分子信号。目前的单细胞 RNA-seq 技术不能充分分析细胞间接触依赖性的基因表达,主要是因为空间信息的丢失。为了克服这一障碍,并在胚胎发生过程中解析细胞间接触特异性基因表达,我们进行了物理相互作用细胞的 RNA 测序(PIC-seq),并将其与来自胚胎发育第 7.5 天(E)至第 9.5 天(E)的小鼠胚胎的类似单细胞转录组进行了评估。对 PIC-seq 数据的分析确定了依赖于特定相邻细胞类型存在的基因表达特征。我们的计算预测,经实验验证,表明神经祖细胞(NP)细胞在与限定内胚层(DE)细胞特异性相互作用时上调了 和 基因。此外,DE 细胞的转录组也会受到反向影响,因为当与 NP 细胞接触时,它们倾向于上调 和 基因。利用单个细胞转录组数据,我们提出了一种计算预测一种细胞类型对其相邻细胞类型转录组影响的方法。我们进一步开发了一种独特的空间 t 分布随机邻域嵌入,以在 2 维空间中显示细胞的伪空间分布。总之,我们描述了一种研究胚胎发生过程中接触特异性基因调控的创新方法。