Analysis of the tumor microenvironment metabolic state using spatial transcriptomic data

• Grant number: 24/07593-1
• Support Opportunities: Scholarships abroad - Research Internship - Scientific Initiation
• Start date: November 01, 2024
• End date: February 28, 2025
• Field of knowledge: Health Sciences - Medicine - Medical Clinics
• Principal Investigator: Tathiane Maistro Malta Pereira
• Grantee: Felipe Pimenta Carcanholo
• Supervisor: Jasmine Plummer
• Host Institution: Faculdade de Ciências Farmacêuticas de Ribeirão Preto (FCFRP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Institution abroad: St. Jude Children's Research Hospital, United States
• Associated to the scholarship: 22/16458-5 - Assessing the metabolic status of glioma microenvironment using single cell transcriptomic data, BP.IC

Abstract

Gliomas are brain heterogeneous tumors with low therapeutic response and are classified based on molecular characteristics and their histological degree of malignancy. A hallmark of cancer is the reprogramming of energy metabolism to meet the high energy demands of rapid tumor growth, allowing for the uncontrolled proliferation of cells. The tumor microenvironment is highly heterogeneous, composed of various distinct cell types. This complexity poses a challenge for studies that rely solely on mass profiling, as they might miss critical information about cell-to-cell variability. It's necessary to analyze the transcriptome at the single-cell level to better understand cellular functions and behavior. To advance even more, it's important to consider not only every cell individually but also their spatial location. Spatial omics technology combines next-generation sequencing and high-level multiplexing with advanced imaging techniques, providing a deeper and clearer understanding of the spatial distribution of gene expression, cell-state transitions, and cell-cell interactions. This project aims to investigate the metabolic reprogramming of cells in the intratumoral microenvironment by analyzing spatial transcriptomic data. We will use spatial molecular data and apply the algorithm we have established during this scientific initiation project to quantify the activity of distinct metabolic pathways. Such research holds the potential to unveil intricate cellular interactions and organizational patterns across multiple dimensions, thereby expediting discoveries and enhancing our understanding of interactions among different cellular subtypes.