6 January 2026

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Single nuclei RNA sequencing delivers high resolution insights allowing for lead gene discovery

KeyGene to deploy Chromium X for single-nuclei transcriptomics

Recently, KeyGene has developed a single-nuclei transcriptomics technology based on the Chromium X platform from 10 X Genomics. Single-cell transcriptomics is a cutting-edge functional genomics technology used to analyze the gene expression profiles of individual cells. KeyGene will utilize this new capability to study, among others, plant-pathogen interactions and plant development.

Recent KeyGene results to be presented at PAG33

Alexander Wittenberg, genomics scientist at KeyGene, will present recent results in this field during the Industry Workshop by Oxford Nanopore Technologies at PAG33 in San Diego on January 12: ‘Single-Nucleus Transcriptomics and Comparative Sequencing in tomato under Drought’

Studying gene activity at high resolution

Unlike traditional “bulk” RNA sequencing (RNA-seq), which averages gene expression across millions of cells, single-cell RNA-seq (scRNA-seq) enables researchers to understand cellular heterogeneity by examining the RNA transcripts in each distinct cell. Single-cell RNA-seq is transforming crop breeding by providing a high-resolution view of gene activity within individual plant cells. This enables:

  • Improved understanding of complex traits/pathways that are often regulated at a cell type-specific level.
  • Identification of new lead genes/novel breeding targets.
  • Enhanced capacity to develop crops that are more resilient, productive, and adaptable to changing environments.

Development of innovative single-nucleus RNA-seq

Single-cell RNA-seq in plants faces unique challenges compared to animal systems. Plant cells have rigid cell walls, making enzymatic digestion to isolate viable whole cells (i.e. protoplasts) difficult. Therefore, KeyGene developed an innovative single-nucleus RNA sequencing (snRNA-seq) workflow to circumvent scRNA-seq with protoplasts. This universal approach preserves RNA integrity and offers better representation of cell types, especially for challenging tissues and species.

While short-read sequencing is widely used in single-cell research (due to its high accuracy and cost-effectiveness), it provides only fragmented transcripts and offers limited resolution of transcript isoforms. To overcome this, KeyGene also applies long-read sequencing technologies to read out snRNA-seq libraries.  This provides additional information on potential transcript isoforms and their potentially cell-type-specific functional relevance.

Proof of application

As a proof of application, KeyGene scientists studied suberin formation in tomato root tissue. Suberin deposition can be activated in the root exodermis by drought or mimicked by treatment with the hormone ABA that that initiates a drought response. The induction of suberin occurs in the exodermis and can function as a defense layer against (a)biotic stresses (Canto-Pastor et al., Nature Plants 2024).

KeyGene scientists isolated tomato nuclei and used the Chromium X platform in combination with the GEM-X Universal 3’ Gene Expression (V4) assay to generate cDNA. The cDNA was sequenced on the short-read platform MGI DNBSEQ-G400 as well as on the Oxford Nanopore Technologies PromethION in order to generate comparable datasets for Mock and ABA treated samples.

Image describing cell types in a root tip.

The KeyGene team analyzed both data sets and generated so-called UMAPs (Uniform Manifold Approximation and Projection) to visualize and interpret the high-dimensional snRNA-seq data. Specific cell types could be identified for almost all clusters (see figure). Moreover, all genes involved in suberin biosynthesis were identified in the exodermis cell cluster, which confirmed recently published findings on ABA-induced suberin formation (Canto-Pastor et al. 2024).  Moreover, the KeyGene dataset provides up to 8 times higher resolution in detecting the number of exoderm nuclei expressing suberin biosynthesis genes.

KeyGene’s single-cell transcriptomics specialist in Wageningen, Petra van Bergeijk, is enthusiastic about using this new technology in crop research. “Single-cell analysis of tomato roots is shedding light on the diverse cell types and dynamic gene networks that underpin stress resilience, offering promising targets for breeding more resilient crops.”

Image on the left: source: Kajala et al., Cell, 2021.

Comparison of short versus long-read data

The two generated snRNA-seq datasets showed differences and similarities. The long-read data showed a substantially higher mapping accuracy, while the short-read snRNA-seq dataset showed higher percentages of cell barcodes and UMI counts.

The UMAPs and differential analysis showed similar results between the two datasets. Moreover, both datasets were of high resolution, which allowed the identification of additional cell types (e.g. cortex cells and atrichoblast cells in the root epidermis) and genes involved in ABA-induced stress response.

The current long-read snRNA-seq dataset did not allow for accurate isoform analysis, due to the nature of the 10X Genomics 3’-generated libraries.

Annotated single-cell clusters from tomato root tip displayed by an integrated UMAP generated from the short-read data.

Annotated single-cell clusters from tomato root tip displayed by an integrated UMAP generated from the short-read data.

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