The promising role of proteomes and metabolomes in defining the single-cell landscapes of plants

Abstract

The plant community has a strong track record of RNA sequencing technology deployment, which combined with the recent advent of spatial platforms (e.g. 10× genomics) has resulted in an explosion of single-cell and nuclei datasets that can be positioned in an in situ context within tissues (e.g. a cell atlas). In the genomics era, application of proteomics technologies in the plant sciences has always trailed behind that of RNA sequencing technologies, largely due in part to upfront cost, ease-of-use, and access to expertise. Conversely, the use of early analytical tools for characterizing small molecules (metabolites) from plant systems predates nucleic acid sequencing and proteomics analysis, as the search for plant-based natural products has played a significant role in improving human health throughout history. As the plant sciences field now aims to fully define cell states, cell-specific regulatory networks, metabolic asymmetry and phenotypes, the measurement of proteins and metabolites at the single-cell level will be paramount. As a result of these efforts, the plant community will unlock exciting opportunities to accelerate discovery and drive toward meaningful translational outcomes.

Keywords: mass spectrometry; metabolomics; multi‐omics; proteomics; single cell.

Fig. 1

New approaches for ex situ and in situ single‐cell mass spectrometry (MS)‐based proteomics and metabolomics are being developed for plant biology. Here, cells can be extracted, protoplasted, and sorted for deep targeted and untargeted proteomics and/or metabolomics. For protein analysis, this is typically performed in a bottom‐up approach, where proteins are enzymatically digested, the resulting peptides are then analyzed, and detection of said peptide(s) encodes for detection of a respective protein. Alternatively, in situ methods using MS imaging can measure metabolites and proteins directly from tissue‐embedded cells. Either of these approaches can be integrated with transcriptomics data to provide a holistic systems biology understanding of cell phenotypes and states.