Gene Activity Matched to Metabolite Production in Single Plant Cells
A pioneering method enables simultaneous RNA and metabolite analysis in the same cell.
The Department of Natural Product Biosynthesis, headed by Sarah O'Connor, focuses its research on understanding the biosynthesis of complex chemical compounds in medicinal plants. These compounds are produced in several steps through a sequence of enzyme reactions known as biosynthetic pathways. However, biosynthesis is often not completed in a single cell. Instead, the individual steps are distributed across different cell types. Each cell specializes in specific reactions, and the intermediate products must be transported to other cells for further processing. Additionally, metabolites can accumulate in high concentrations either for storage or to balance supply and demand between interconnected metabolic pathways. "These processes form a complex metabolic network that can only be fully understood by integrating gene expression and metabolic data from the same cell," explains Moonyoung Kang, one of the first authors.
The cell factory: blueprints versus stock inventory
In biology, genes are often described as the blueprints of a cell factory. Using single-cell RNA sequencing (scRNA-seq), researchers can "read" these blueprints to identify the potential products of a cell. In contrast, single-cell mass spectrometry (scMS) measures the quantities of the final products, i.e., the "end products," present in a cell at a given time.
If we compare the cell to a clothing factory, scRNA-seq shows which garments the factory can produce and how many blueprints are available for them. scMS, on the other hand, shows how many items are currently in stock.
A large inventory indicates high production, but it may also indicate a delay in transport. Conversely, if products are shipped immediately, the factory may appear nearly empty despite operating at full capacity.
"To truly understand the entire logistics process — from production and storage to distribution — it is crucial to know the genetic blueprints and actual quantities of products in cells," says Lorenzo Caputi, head of the project group Natural Product Pathways in Plants and Single Cells.
A new approach: combining gene and metabolic data from the same cell
In collaboration with colleagues from the Max Planck Institute of Biochemistry in Munich, the research team led by Lorenzo Caputi and Sarah O'Connor successfully integrated single-cell RNA sequencing (scRNA-seq) and single-cell mass spectrometry (scMS) within the same plant cell. This enables scientists to directly correlate gene expression with metabolite abundance for the first time.
The approach begins by trapping individual cells in microwells. Then, a robot transfers each cell individually into a 96-well plate. The cell is lysed, or broken open, releasing its contents — genes, proteins, metabolites, and organelles — into a water-based solution. The lysate is divided into two samples: one for gene analysis (scRNA-seq) and one for metabolic analysis (scMS).
"Because each cell is in a specific position on the 96-well plate, each data point can be accurately identified later. This allows us to directly match the gene activity of a cell to its metabolic profile," emphasizes Anh Hai Vu, the second lead author.
Cell types as specialists in metabolism
The research team tested the new method on Madagascar periwinkle (Catharanthus roseus), a medicinal plant from which the cancer drugs vinblastine and vincristine are derived. The biosynthesis of these medicinal plant products is complex and has already been intensively researched. At least three different cell types are involved.
"We believe that our new method will accelerate the elucidation of important plant natural product biosynthetic pathways by providing clear insights into the cell types involved. It will also enable the identification of previously uncharacterized specialized cell types in other medicinal plants, and facilitate comparisons of the diverse logistic strategies that different species have evolved.," summarizes Sarah O'Connor.
The researchers are currently optimizing several steps in the workflow and automating parts of the protocol to enhance reproducibility and reduce experimental time. They are also applying the combined analysis approach of sequence and metabolite data in a cell to other plant species and tissues — such as leaves, roots, and stems — to identify potential bottlenecks and develop more robust sample preparation procedures. They are also working to reduce the cost per experiment and make the method accessible to a wider range of users. This method improves our understanding of plant metabolic processes and could accelerate the sustainable production of plant-based medicines in the future.
Reference: Kang M, Vu AH, Casper AL, et al. Single-cell metabolome and RNA-seq multiplexing on single plant cells. PNAS. 2025;122(43):e2512828122. doi: 10.1073/pnas.2512828122
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