Biology subjects

Annicchiarico, L.

Publications and source records attributed to Annicchiarico, L..

2 recordsLinked to original sources

bioRxiv2025

Unveiling Oligodendrocyte-Lineage Differentiation in the Glioblastoma Infiltrative Zone Through Spatial Transcriptomics

IDH-wildtype glioblastoma (GBM) is the most aggressive primary brain tumor, characterized by limited therapeutic options and inevitable recurrence. This recurrence is driven by the highly infiltrative behavior of GBM cells, which prevents complete resection. Here, we performed spatially resolved transcriptomic profiling of four surgically defined GBM regions to characterize the infiltrative tumor compartment. Across multiple datasets and profiling approaches, the infiltrative zone showed reduced cellular heterogeneity compared with the tumor core. By comparison with an integrated reference of single-cell RNA sequencing data, we revealed that this reduction reflects a preferential differentiation of infiltrative GBM cells toward an oligodendrocyte-like lineage, accompanied by attenuated inflammatory signaling. In contrast, necrotic and proliferative regions display pronounced astrocyte-like and mesenchymal-like programs. Combined immunohistochemistry and fluorescence in situ hybridization confirmed the enrichment of oligodendrocyte-like tumor cells in the infiltrative area. Together, these findings identify the oligodendrocyte-like state as a defining feature and potential therapeutic vulnerability of infiltrative GBM driving brain invasion and relapse.

cancer biology↗

bioRxiv2020

Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation

Virtual delineation of white matter bundles in the human brain is of paramount importance for multiple applications, such as pre-surgical planning and connectomics. A substantial body of literature is related to methods that automatically segment bundles from diffusion Magnetic Resonance Imaging (dMRI) data indirectly, by exploiting either the idea of connectivity between regions or the geometry of fiber paths obtained with tractography techniques, or, directly, through the information in volumetric data. Despite the remarkable improvement in automatic segmentation methods over the years, their segmentation quality is not yet satisfactory, especially when dealing with datasets with very diverse characteristics, such as different tracking methods, bundle sizes or data quality. In this work, we propose a novel, supervised streamline-based segmentation method, called Classifyber, which combines information from atlases, connectivity patterns, and the geometry of fiber paths into a simple linear model. With a wide range of experiments on multiple datasets that span from research to clinical domains, we show that Classifyber substantially improves the quality of segmentation as compared to other state-of-the-art methods and, more importantly, that it is robust across very diverse settings. We provide an implementation of the proposed method as open source code, as well as web service.

neuroscience↗