Cellular heterogeneity within plant tissues is based on differences in cellular transcriptional states, and the specificity of transcriptional states is determined and maintained stable by gene regulatory networks (GRNs) dominated by transcription factors. Significant advances in global GRNs prediction methods have been made in the last few decades, and there is ample evidence that GRNs function as plant recovery mechanisms by directly measuring transcription factor binding and target gene regulation. As single-cell sequencing technologies continue to improve in resolution, capture rates, and available assays, the possibility of studying plant single-cell gene regulatory networks (scGRNs) has opened up.

Fig.1. General workflow for using single-cell gene regulatory networks for enhancing crop resilience. (Tripathi RK et al., 2021)

What We Offer

Lifeasible provides professional single-cell solutions for plant gene regulatory network analysis to help clients study the regulatory mechanisms of plant development. We aim to determine the regulatory mechanisms that generate the transcriptional or genomic data provided by scRNA-seq.

We start our analysis with the regulatory networks of transcription factors in different cell types. Based on scRNA-seq, we provide promising computational methods to predict plant scGRNs, allowing the identification of genes co-expressed with transcription factors and potential biophysical parameters of transcription factor activity.

GRNs are key mechanisms that confer resilience to stress. Our technical team uses single-cell sequencing technology to analyze plant GRNs to enhance crop resilience by predicting and targeting the power of genome editing through scGRNs. Lifeasible provides a framework for using scGRNs predictions to guide crop stress recovery research.

• Designing experiments for scGRNs prediction
  (1) Tissue selection. We can isolate sufficient numbers of high-quality protoplasts or nuclei for scRNA-seq analysis.
  (2) Selection of the duration, timing, and intensity of stress treatments will elicit different physiological responses in plants and tissues and will expose other aspects of the stress-responsive gene regulatory network.
• Target selection for genome editing
  After predicting the GRNs, the basic steps to design improved crops are prioritizing regulatory interactions, using genome editing to alter regulatory interactions, and testing plants to improve resistance to stressors.

Information that scGRNs Can Provide:

• Identify sub-networks enriched for cell types of interest.
• Identify biological processes due to interest using gene set enrichment analysis.
• Identify regulatory interactions.
• Identify co-regulatory genes strongly differentially expressed in response to stress treatment.
• Characterize transcription factors that regulate many target genes.

Lifeasible aims to help clients analyze gene interactions during cell development by constructing fine-grained GRNs from dynamic expression data of scRNA-seq transcription factors. Our experts are also committed to using scGRNs to enhance plant recovery from high temperatures and other abiotic stresses. If you are interested in our services or have some questions, please feel free to contact us or make an online inquiry.

Reference

1. Tripathi RK, Wilkins O. Single cell gene regulatory networks in plants: Opportunities for enhancing climate change stress resilience. Plant Cell Environ. 2021 Jul;44(7):2006-2017.

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