Aureo Lucio Melo Zanon

Advisor: Xiaowei Wang

Co-mentor: Andrei Karginov

Title: Advancing Computational Methods for Cellular and Spatial Analysis in High-Resolution Lung Transcriptomics

Abstract:
Spatially resolved transcriptomics, particularly the high-resolution Visium HD platform from 10x Genomics, has transformed biological research by enabling unprecedented mapping of cellular heterogeneity, interactions, and tissue architecture. Despite these advances, accurately assigning transcriptomic signals to cellular and subcellular sources remains challenging, especially in morphologically complex tissues like the lung. Current segmentation methods often fail to accurately delineate irregular or elongated cells, such as alveolar cell types, limiting downstream analyses such as cell typing and spatial biomarker discovery. To address these limitations, we propose a comprehensive bioinformatic solution centered around a novel cell boundary inference algorithm leveraging shrinkage-based modeling, weighted Voronoi tessellations, topological point-cloud analysis, and the Cellular Potts-based model. Our framework integrates robust segmentation with a modular pipeline supporting precise spatial analyses, including cell annotation, differential expression analysis, and intercellular interaction mapping. Validation utilizing co-registered histological staining to demonstrate superiority to existing Visium HD cell segmentation approaches. Application to lung tissue samples, both healthy and diseased (e.g., idiopathic pulmonary fibrosis), demonstrate the pipeline’s capability to uncover cell-type-specific expression patterns, immune cell localization, and fibrotic remodeling. Ultimately, this segmentation approach enhances the biological interpretability of Visium HD data and broadly advances research into lung diseases and other complex tissues.

Here is some additional insight about Aureo.

Aureo is a PhD student in bioinformatics and joined the Wang lab in August 2024. He earned a BS in Biology and a BS in Statistics at the Pennsylvania State University in 2023. Aureo’s current research interests lie in the development of methods to improve the analysis of spatially resolved transcriptomic data.