Biology subjects

Socciarelli, F.

Publications and source records attributed to Socciarelli, F..

8 recordsLinked to original sources

bioRxiv2026

Suppression of de novo lipogenesis and dietary PUFA supplementation inhibit prostate cancer progression

Prostate cancer progression is characterized by dysregulated lipid metabolism, with activation of fatty acid synthase (FASN), the rate-limiting step in de novo lipogenesis (DNL), resulting in significant accumulation of saturated lipids. Here, we show that pharmacologic FASN inhibition creates a metabolic state that increases reliance on exogenous polyunsaturated fatty acids (PUFAs). Inhibition of FASN profoundly alters membrane phospholipid composition, driving compensatory incorporation of PUFAs into membrane phospholipids, thus increasing susceptibility to lipid peroxidation and oxidative damage. Combined FASN inhibition and PUFA exposure induce mitochondrial hyperpolarization and enhance lipid peroxidation in both hormone-sensitive and castration-resistant prostate cancer models, resulting in increased reactive oxygen species production, ferroptosis, as well as apoptosis. Marked inhibition of growth in castration-resistant human and murine prostate cancer organoids is achieved ex vivo. In genetically engineered, DNL-reliant Hi-Myc mice, a diet enriched in PUFAs significantly inhibited invasive carcinoma compared to a saturated fat-enriched diet. Thus, environmental PUFAs modulate and enhance the therapeutic efficacy of FASN-targeted strategies. These findings set the stage for pharmacologic and dietary intervention in prostate cancer patients.

cancer biology↗

bioRxiv2025

Multi-omics integration and batch correction using a modality-agnostic deep learning framework

State-of-the-art biotechnologies allow the detection of different molecular species on the same biological sample, generating complex highly-dimensional multi-modal datasets. Gaining a holistic understanding of biological phenomena, such as oncogenesis or aging, requires integrating these diverse modalities into low-dimensional data representations while correcting for technical artifacts. Here we present MIMA, a modular, unsupervised AI framework for multi-omics data integration and batch correction. Applied to complex spatial and single-cell datasets, MIMA effectively removes batch effects, while preserving biologically relevant information, and learns representations predictive of expert pathologist annotations. Additionally, it enables cross-modal translation, uncovers molecular patterns not captured by manual annotations, and despite being modality-agnostic performs on par with specialized state-of-the-art tools. MIMAs flexibility and scalability make it a powerful tool for multimodal data analysis. MIMA provides a foundation for AI-based, multi-omics augmented digital pathology frameworks, offering new opportunities for improved patient stratification and precision medicine through the comprehensive integration of high-dimensional molecular data and histopathological imaging.

bioinformatics↗

bioRxiv2025

Single-cell spatial mapping reveals dynamic bone marrow microarchitectural alterations and enhances clinical diagnostics in MDS

Myelodysplastic neoplasms (MDS) disrupt bone marrow hematopoiesis, yet clinical assessment relies largely on blast enumeration and qualitative morphology, which incompletely capture marrow architecture and disease state. We applied whole-slide multiplex immunofluorescence imaging with single-cell phenotyping to map bone marrow microarchitecture in MDS. Diagnostic biopsies (n=36), longitudinal treatment samples (n=29), precursor states (n=13), and normal controls (n=21) were analyzed, comprising >5 million spatially resolved cells. MDS marrow exhibited coordinated, genotype-imprinted architectural remodeling, including altered progenitor composition and spatial patterning, disrupted erythroid island organization, and displacement of hematopoietic stem and progenitor cells from perivascular niches. Interrogation of 82 cellular and spatial features yielded a composite Microarchitectural Perturbation Score (MDS-MAPS), derived from diagnostic samples and fixed prior to longitudinal analyses. In leave-one-patient-out cross-validation, MDS-MAPS discriminated remission from active disease more accurately than blast percentage (AUC 0.883 vs 0.660) and distinguished low-blast MDS from clonal cytopenia of undetermined significance (CCUS) (AUC 0.815). Mixed-effects modeling showed MAPS decreased in remission statistically independent of blast burden, with architectural normalization during remission and re-emergence at relapse. These findings define quantitative bone marrow architecture as a dynamic tissue-state biomarker that complements molecular and blast-based assessment in MDS.

pathology↗

bioRxiv2025

Spatially organized lymphocytic microenvironments in high grade primary prostate tumors

The spatial arrangement of immune cells in the tumor microenvironment (TME) varies widely, from dispersed to clustered and tumor excluded to infiltrating. Multiplexed spatial profiling is an effective means of characterizing tumor-infiltrating lymphocytes (TILs) and immune complexes such as tertiary lymphoid structures (TLS) in the TME. However, few approaches have been described for objectively parametrizing patterns of immune organization and assessing their association with biological or clinical variables. This makes it difficult to evaluate whether a set of tumors is relatively immunologically cold or hot. Here we describe an intuitive set of statistical tools (available in the R package, tlsR) for characterizing lymphocyte patterns in the TME of solid cancers. We apply tlsR to primary prostate cancer (PCa), which is often described as immunologically cold. Using a cohort of 29 radical prostatectomy specimens stratified into low Gleason-grade (LGG; n=15) and high Gleason-grades (HGG; n =14) we show that HGG PCa is significantly more infiltrated than LGG PCa with lymphocytes organized into B cell or T cell enriched immune clusters (BICs and TICs). A subset of these ICs have the B and T cell zonation and follicular dendritic cells characteristic of a bona fide TLS. HGGs are also enriched with ICs containing precursor exhausted T cells (Tpex) and proliferating B cells and their tumor compartments harbor granzyme-B+ cytotoxic T cells in contact with cancer cells. Thus, far from being cold, a subset of HGG PCa has features associated with active immune surveillance, a finding with implications for emerging PCa immunotherapies.

cancer biology↗

bioRxiv2025

The Spatial Atlas of Human Anatomy (SAHA): A Multimodal Subcellular-Resolution Reference Across Human Organs

The Spatial Atlas of Human Anatomy (SAHA) represents the first multimodal, subcellular- resolution reference of healthy adult human tissues across multiple organ systems. Integrating spatial transcriptomics, proteomics, and histological features across over 15 million cells from more than 100 donors, SAHA maps conserved and organ-specific cellular niches in gastrointestinal and immune tissues. High-resolution profiling using CosMx SMI, 10x Xenium, RNAscope, GeoMx DSP, and single-nucleus RNA-seq reveals spatially organized cell states, rare adaptive immune populations, and tissue-specific cell-cell interactions and ligand-receptor pairs. Comparative analyses with colorectal cancer and inflammatory bowel disease demonstrate the power of SAHA to detect disease-associated spatial disruptions, including crypt dedifferentiation, perineural invasion, and therapy-resistant immune remodeling. All data are openly accessible through a FAIR-compliant interactive portal to support exploration, benchmarking, and machine learning model training. Through SAHA, we provide a foundational framework for spatial diagnostics and next-generation precision medicine grounded in a comprehensive human tissue atlas, enabling the development of context-aware models that simulate tissue behavior, decode complex pathologies, and accelerate therapeutic innovation at unprecedented scale.

systems biology↗

bioRxiv2025

KODAMA enables self-guided weakly supervised learning in spatial transcriptomics

Spatial transcriptomics provides researchers with a powerful tool to investigate gene expression patterns within their native positional context in tissues, revealing intricate cellular relationships. However, analyzing spatial transcriptomics data presents unique challenges due to their high dimensionality and complexity. Several competitive tools have emerged, each aiming to integrate spatial dependencies into their workflow. Yet, the overall performance of these approaches remains constrained by limitations in prediction accuracy and compatibility with specific data types. Here, we introduce the third version of the KODAMA algorithm, specifically tailored for spatial transcriptomics analysis. This upgraded version incorporates a novel approach that effectively reduces data dimensionality while preserving spatial information. At its core, the KODAMA algorithm employs parallel iterations that enforce spatial constraints throughout an embedded clustering process. Our method provides the flexibility to simultaneously analyze multiple samples, streamlining analysis workflows. Additionally, it extends beyond traditional 2D analysis to accommodate multidimensional datasets, including 3D spatial information. KODAMA seamlessly integrates into various pipelines, such as Seurat and Giotto. Extensive evaluations of KODAMA on 10x Visium, Visium HD, and image-based 3D datasets demonstrate its high accuracy in spatial domain prediction. Comparative analyses against alternative dimensionality reduction techniques and spatial analysis tools consistently validate and highlight KODAMAs superior performance in unraveling the spatial organization of cellular components across both single and integrated tissue samples.

bioinformatics↗

bioRxiv2023

Integration of Multiple Spatial-Omics Modalities Reveals Unique Insights into Molecular Heterogeneity of Prostate Cancer

Recent advances in spatial omics methods are revolutionising biomedical research by enabling detailed molecular analyses of cells and their interactions in their native state. As most technologies capture only a specific type of molecules, there is an unmet need to enable integration of multiple spatial-omics datasets. This, however, presents several challenges as these analyses typically operate on separate tissue sections at disparate spatial resolutions. Here, we established a spatial multi-omics integration pipeline enabling co-registration and granularity matching, and applied it to integrate spatial transcriptomics, mass spectrometry-based lipidomics, single nucleus RNA-seq and histomorphological information from human prostate cancer patient samples. This approach revealed unique correlations between lipids and gene expression profiles that are linked to distinct cell populations and histopathological disease states and uncovered molecularly different subregions not discernible by morphology alone. By its ability to correlate datasets that span across the biomolecular and spatial scale, the application of this novel spatial multi-omics integration pipeline provides unprecedented insight into the intricate interplay between different classes of molecules in a tissue context. In addition, it has unique hypothesis-generating potential, and holds promise for applications in molecular pathology, biomarker and target discovery and other tissue-based research fields.

cancer biology↗

bioRxiv2023

NSD2 maintains lineage plasticity and castration-resistance in neuroendocrine prostate cancer

The clinical use of potent androgen receptor (AR) inhibitors has promoted the emergence of novel subtypes of metastatic castration-resistant prostate cancer (mCRPC), including neuroendocrine prostate cancer (CRPC-NE), which is highly aggressive and lethal1. These mCRPC subtypes display increased lineage plasticity and often lack AR expression2-5. Here we show that neuroendocrine differentiation and castration-resistance in CRPC-NE are maintained by the activity of Nuclear Receptor Binding SET Domain Protein 2 (NSD2)6, which catalyzes histone H3 lysine 36 dimethylation (H3K36me2). We find that organoid lines established from genetically-engineered mice7 recapitulate key features of human CRPC-NE, and can display transdifferentiation to neuroendocrine states in culture. CRPC-NE organoids express elevated levels of NSD2 and H3K36me2 marks, but relatively low levels of H3K27me3, consistent with antagonism of EZH2 activity by H3K36me2. Human CRPC-NE but not primary NEPC tumors expresses high levels of NSD2, consistent with a key role for NSD2 in lineage plasticity, and high NSD2 expression in mCRPC correlates with poor survival outcomes. Notably, CRISPR/Cas9 targeting of NSD2 or expression of a dominant-negative oncohistone H3.3K36M mutant results in loss of neuroendocrine phenotypes and restores responsiveness to the AR inhibitor enzalutamide in mouse and human CRPC-NE organoids and grafts. Our findings indicate that NSD2 inhibition can reverse lineage plasticity and castration-resistance, and provide a potential new therapeutic target for CRPC-NE.

cancer biology↗