| KEYWORD |
Multi-Modal Single-Cell Neuronal Plasticity Analysis
keywords BIOINFORMATICS, DATA ANALYSIS, NEUROSCIENCE, SINGLE-CELL DATA
Reference persons ROBERTA BARDINI, STEFANO DI CARLO, ALESSANDRO SAVINO
External reference persons Gianluca Amprimo
Lorenzo Martini
Research Groups DAUIN - GR-24 - reSilient coMputer archItectures and LIfE Sci - SMILIES
Description This thesis proposal seeks to integrate electrophysiological and transcriptomic data from single neurons obtained via patch-seq to uncover the molecular basis of neuronal plasticity. By correlating spike frequency, input resistance, and short-term plasticity parameters with gene expression profiles, we aim to identify distinct transcriptomic signatures that define varying states of intrinsic excitability and synaptic plasticity. Such an integrative analysis promises deeper insights into how specific genetic programs underpin key aspects of neuronal adaptability, advancing our understanding of how the brain dynamically modulates information processing.
The research will develop and refine bioinformatic pipelines to align, normalize, and jointly analyze electrophysiological and transcriptomic data. This project will employ multimodal integration, clustering, and differential expression techniques to pinpoint gene modules and pathways that correlate with unique plasticity states. Ultimately, this approach will yield a blueprint of candidate molecular regulators, setting the stage for future experimental validation and potential therapeutic targeting of pathways involved in modulating neuronal function and resilience.
Deadline 10/12/2025
PROPONI LA TUA CANDIDATURA