2025-10-27 マックス・プランク研究所
<関連情報>
- https://www.mpg.de/25607106/matching-gene-expression-to-metabolite-production-in-single-plant-cells
- https://www.pnas.org/doi/abs/10.1073/pnas.2512828122
単一植物細胞における単一細胞メタボロームとRNA-seqマルチプレックス Single-cell metabolome and RNA-seq multiplexing on single plant cells
Moonyoung Kang, Anh Hai Vu, Abbie L. Casper, +5 , and Sarah E. O’Connor
Proceedings of the National Academy of Sciences Published:October 24, 2025
DOI:https://doi.org/10.1073/pnas.2512828122
Significance
Plants produce valuable metabolites through the action of complex biosynthetic pathways, metabolic processes that are typically composed of many genes. Advances in single-cell omics now allow measurement of either gene expression or metabolite levels within individual cells. Here, we demonstrate that these two approaches can be applied to the same single cell. This enables the generation of matched gene–metabolite datasets, allowing rigorous correlation analyses at single-cell resolution. Such integration could facilitate the discovery of the genes involved in metabolite production, which in turn may improve access to these valuable molecules.
Abstract
Plants produce valuable natural products used for a wide variety of industrial applications. Thus, there is enormous interest in elucidating the biosynthetic pathways that are responsible for the production of these compounds. Identification of the genes that comprise these biosynthetic pathways has been enabled by gene-to-metabolite networks that are generated from transcriptomic and metabolomic datasets. Recent advances in both single-cell RNA-seq (scRNA-seq) and single-cell mass spectrometry metabolomics (scMS) have enabled the measurement of either gene expression or metabolite levels in individual cells. However, these datasets can only be used to indirectly correlate gene expression levels with metabolite concentrations at the single-cell level. In this proof-of-concept study, performed on cells derived from the leaves of the medicinal plant Catharanthus roseus, we demonstrate that both scRNA-seq and scMS can be applied to the same plant cell, thereby enabling direct comparisons between gene expression and metabolite levels. Protoplasts are sorted into 96-well plates using a microfluidics-based robot and then lysed under conditions that are suitable for both scMS and SMART-seq single-cell protocols. This multiplexing approach reveals both qualitative and quantitative correlations between metabolite levels and biosynthetic gene expression in individual cells. This integrated approach sheds light on the underlying processes driving complex plant biosynthesis.
