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

• Published in: Nucleic acids research Vol. 47; no. 9; pp. 4539 - 4553
• Main Authors: Kubo, Minoru, Nishiyama, Tomoaki, Tamada, Yosuke, Sano, Ryosuke, Ishikawa, Masaki, Murata, Takashi, Imai, Akihiro, Lang, Daniel, Demura, Taku, Reski, Ralf, Hasebe, Mitsuyasu
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 21.05.2019
• Subjects: Bryopsida - genetics; Cellular Reprogramming - genetics; Gene Expression Profiling - methods; Gene regulation, Chromatin and Epigenetics; Plant Leaves - genetics; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Software; Transcriptome - genetics
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkz181

Abstract 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 cell types that can be isolated from their original tissues, and it can be difficult to obtain precise positional information for these cells in situ. 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. With 1cell-DGE, we could detect differentially expressed genes (DEGs) during the reprogramming of leaf cells of the moss Physcomitrella patens, identifying 6382 DEGs between cells at 0 and 24 h after excision. Furthermore, we identified a subpopulation of reprogramming cells based on their pseudotimes, which were calculated using transcriptome profiles at 24 h. 1cell-DGE with microcapillary manipulation 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.