Biology subjects

Moliterno, J.

Publications and source records attributed to Moliterno, J..

5 recordsLinked to original sources

bioRxiv2026

Decoding Spatial Programs in Human Glioblastoma Through Surprisal Information-Theoretical Analysis

Glioblastoma (GBM) is a highly heterogeneous and invasive brain tumor in which complex interactions among tumor cells, immune cells, and neurons shape disease progression and therapeutic resistance. Resolving spatial patterns programs in glioblastoma (GBM) requires analytical approaches that go beyond variance-driven embeddings. Here, we applied thermodynamic surprisal analysis, an information-theoretic decomposition framework, to spatial transcriptomic sequencing from human GBM specimens to identify orthogonal constraint modes that capture dominant and previously hidden spatial programs. Surprisal analysis revealed structured patterns in the data that are not highlighted by standard approaches such as PCA or cell type deconvolution, including immune-activation-associated signatures as well as spatial programs consistent with synapse remodeling. Coupling surprisal decomposition with spatial ligand-receptor interaction analysis, NICHES, along with multiplexed protein imaging connected these spatial hidden modes to reveal potential communication networks. Together, these results position surprisal analysis as a powerful, complementary strategy for interrogating spatial tumor architecture, enabling discovery of non-obvious spatial programs and interactions that are obscured by variance-based methods.

systems biology↗

bioRxiv2026

Multicellular Spatial Programs Define the Histopathological Architecture of Meningioma

Tumors are often described by cell types or gradients, but the organizing units of tumor tissue remain unclear. Using meningiomas, which show marked morphologic diversity despite constrained and recurrent genetics, we identify reproducible multicellular spatial molecular programs (SMPs) characterized by recurring cell-type mixtures that combine in different proportions across tumors. We built a multi-omic atlas of 147 human meningiomas (1.6 million cells/spots), integrating scRNA-seq, Visium, CosMx-RNA, and CosMx-Protein. Across platforms, SMPs mapped onto canonical whorl-lobule architecture and defined a structured ecological landscape linking hypoxic, immune-evasive states to vascularized, matrix-remodeling, and mineralization-rich states. For translation, we developed MeningNet, a hybrid ConvNeXt-Vision Transformer that infers SMPs directly from hematoxylin-and-eosin sections. MeningNet generalized across an internal replication cohort and 465 external whole-slide images, recovering cross-platform inference and showing significant association with CNS-WHO grade. These findings establish meningioma architecture as a reproducible histopathological framework for inferring spatial molecular state from routine pathology.

Cancer Biology↗

bioRxiv2025

Analysis of the phosphorylation networks characterizing distinct phenotypic states in glioblastoma cell Populations

Cancer cells, including the most aggressive brain cancer, glioblastoma, can exhibit a high degree of phenotypic plasticity. However, it is not well understood whether and how distinct phenotypic states might be associated with or driven by specific signaling processes. Here, we use a novel approach to the identification of phenotype-specific signaling networks in cells undergoing the Go-or-Grow switch in populations of GBM cell lines. We find that the transition to invasive spread may be associated with the onset of DNA damage response, cell cycle arrest, and activation of the AKT-mTOR signaling. We reconstructed the large-scale signaling networks, revealing integration of diverse pathways and possible feedback interactions mediating phenotypic stability. We further show that phosphorylation outcomes may be preferentially mediated by 14-3-3 proteins, previously implicated in stress response and control of the cell cycle. Finally, we demonstrate that the phenotype-specific phospho-site signatures have predictive power for disease-free patient survival. This study paves the way for a more comprehensive understanding of phenotypic plasticity in GBM and other aggressive cancers.

systems biology↗

bioRxiv2025

Spatial epigenomic niches underlie glioblastoma cell state plasticity

IDH-wildtype glioblastoma (GBM) is an aggressive brain tumor with poor survival and few therapeutic options. Transcriptionally-defined cell states coexist in GBM and occupy defined regions of the tumor. Evidence indicates that GBM cell states are plastic, but it remains unclear if they are determined by the underlying epigenetic state and/or by microenvironmental factors. Here, we present spatially-resolved epigenomic profiling of human GBM tissues that implicate chromatin structure as a key enabler of cell plasticity. We report two epigenetically-defined and spatially-nested tumor niches. Each niche activates short-range molecular signals to maintain its own state and, surprisingly, long-range signals to reinforce the state of the neighboring niche. The position of a cell along this gradient-like system of opposing signals determines its likelihood to be in one state or the other. Our results reveal an intrinsic system for cell plasticity that is encoded in the chromatin profiles of two adjacent niches that dot the topological architecture of GBM in cartesian space.

genomics↗

bioRxiv2022

spARC Recovers Human Glioma Spatial SignalingNetworks with Graph Filtering

Biological networks operate within architectural frameworks that influence the state and function of cells through niche-specific factors such as exposure to nutrients and metabolites, soluble signaling molecules, and direct cognate cell-cell communication. Spatial omics technologies incorporate environmental information into the study of biological systems, where the spatial coordinates of cells may directly or indirectly encode these micro-anatomical features. However, they suffer from technical artifacts, such as dropout, that impede biological discovery. Current methods that attempt to correct for this fail to adequately integrate highly informative spatial information when recovering gene expression and modelling cell-cell dynamics in situ. To address this oversight, we developed spatial Affinity-graph Recovery of Counts (spARC), a data diffusion-based filtration method that shares information between neighboring cells in tissue and related cells in expression space, to recover gene dynamics and simulate signalling interactions in spatial transcriptomics data. Following validation, we applied spARC to 10 IDH-mutant surgically resected human gliomas across WHO grades II-IV in order to study signaling networks across disease progression. This analysis revealed co-expressed genes that border the interface between tumor and tumor-infiltrated brain, allowing us to characterize global and local structure of glioma. By simulating paracrine signaling in silico, we identified an Osteopontin-CD44 interaction enriched in grade IV relative to grade II and grade III astrocytomas, and validated the clinical relevance of this signaling axis using TCGA.

bioinformatics↗