Optimizing Spatial Transcriptomics in Plants: Developing an Analysis Pipeline for STOmics-Based Data in Polyploid Crops

• Grant number: 25/10055-4
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: September 01, 2025
• End date: February 28, 2026
• Field of knowledge: Biological Sciences - Genetics - Plant Genetics
• Principal Investigator: Antonio Vargas de Oliveira Figueira
• Grantee: Pedro Cristovão Carvalho
• Supervisor: Robert James Henry
• Host Institution: Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil
• Institution abroad: University of Queensland, Brisbane (UQ), Australia
• Associated to the scholarship: 23/10829-4 - Integration of functional genomics and systems biology in transcriptomic analysis associated with the water deficit tolerance and its inference in rice (Oryza sativa) and sugarcane (Saccharum spp.) through network analysis, BP.DR

Abstract

Sugarcane (Saccharum spp.) represents an important grass crop used for sugar and biofuel production. The modern commercial cultivars originally derived from interspecific and back-crosses between the high sugar content S. officinarum and the more robust S. spontaneum. The hybrid origin of the sugarcane cultivars led to a highly complex polyploid genome, with frequent aneuploidy and high degree of heterozygosity. A few approaches have been used to sequence and resolve the genomic complexity of sugarcane. Systems biology associated with multiomics approaches aggregates important tools to simplify the genomic complexity. Spatial Transcriptomics (ST) analysis is a technology that is capable of providing information regarding heterogeneity and cellular localization of gene differential expression. The SpaTial Enhanced REsolution Omics-sequencing (STOmics) approach is capable of capturing spatial information regarding differential gene expression. The high-throughput and large dataset produced by this technology presents challenges for analysis and extraction of relevant information. The available analysis pipelines were developed for human and animal models, with few adapted for plants. The present project intends to develop data analysis pipelines focused on complex polyploid plants, such as sugarcane and wheat. The analysis pipeline will address precise computational and technical caveats, highlighting the potential application of spatial transcriptomics in plants, and its use in the selection of candidate genes for the improvement of complex genome plants by simplifying its complexity.