Neighbor-specific gene expression revealed from physically interacting cells during mouse embryonic development
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 RNAseq (scRNAseq) 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 novel 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 overexpress Lhx5 and Nkx2-1 genes, when exclusively interacting with the definitive endoderm (DE) cell. Moreover, there was a reciprocal impact on the transcriptome of the DE cells, as they tend to overexpress Rax and Gsc genes when in contact with the 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-tSNE to display the pseudo-spatial distribution of cells in a 2-dimensional space. In summary, we describe an innovative approach to study contact-specific gene regulation during embryogenesis with potential broader implication in other physiologically relevant processes. SignificancePhysical contact between neighboring cells is known to induce transcriptional changes in the interacting partners. Accurate measurement of these cell-cell contact based influences on the transcriptome is a very difficult experimental task. However, determining such transcriptional changes will highly enhance our understanding for the developmental processes. Current scRNAseq technology isolates the tissue into individual cells, making it hard to determine the potential transcriptomic changes due to its interacting partners. Here, we combined PIC-seq and computational algorithms to identify cell-type contact dependent transcriptional profiles focusing on endoderm development. We have computationally identified and experimentally validated specific gene expression patterns depending upon the presence of specific neighboring cell types. Our study suggests a new way to study cell-cell interactions for embryogenesis.