Single-cell transcriptome analysis of Physcomitrella leaf cells during reprogramming using microcapillary manipulation

Background: Next-generation sequencing technologies have made it possible to carry out transcriptome analysis at the single-cell level. Single-cell RNA-sequencing (scRNA-seq) data provide insights into cellular dynamics, including intercellular heterogeneity as well as inter- and intra-cellular fluctuations in gene expression that cannot be studied using populations of cells. The utilization of scRNA-seq is, however, restricted to specific types of cells that can be isolated from their original tissues, and it can be difficult to obtain precise positional information for these cells in situ. Results: Here, we established single cell-digital gene expression (1cell-DGE), a method of scRNA-seq that uses micromanipulation to extract the contents of individual living cells in intact tissue while recording their positional information. Furthermore, we employed a unique molecular identifier to reduce amplification bias in the cDNA libraries. With 1cell-DGE, we could detect differentially expressed genes (DEGs) during the reprogramming of leaf cells into stem cells in excised tissues of the moss Physcomitrella patens, identifying 6,382 DEGs between cells at 0 h and 24 h after excision. We found substantial variations in both the transcript levels of previously reported reprogramming factors and the overall expression profiles between cells, which appeared to be related to their different reprogramming abilities or the estimated states of the cells according to the pseudotime based on the transcript profiles. Conclusions: We developed 1cell-DGE with microcapillary manipulation, a technique that can be used to analyze the gene expression of individual cells without detaching them from their tightly associated tissues, enabling us to retain positional information and investigate cell-cell interactions.