Biology subjects

Gherardini, P. F.

Publications and source records attributed to Gherardini, P. F..

10 recordsLinked to original sources

bioRxiv2026

CellCage™ Technology Enables High-throughput Isolation of Defined Cell Combinations to Resolve the Determinants of CAR-T mediated Cytotoxicity

Understanding the cellular determinants of CAR-T cell-mediated cytotoxicity at the single-cell level remains a critical challenge in developing effective cellular immunotherapies. Conventional bulk co-culture assays provide only population-level measurements of cytotoxic activity and cannot resolve the functional heterogeneity of individual effector-target interactions. Here, we demonstrate the Cellanome platform, which enables the formation of CellCage Enclosures (CCEs) through spatially controlled photopolymerization, as a tool for high-resolution, high-throughput characterization of CAR-T cell-mediated cytotoxicity. By isolating defined effector-target combinations within individual CCEs and coupling encapsulation with longitudinal time-lapse imaging and automated image analysis, we resolve cytotoxic activity across thousands of individually tracked CCEs. Using anti-CD19 CAR-T effectors and NALM6-GFP targets, we show that individual CD8+ CAR-T effectors exhibit substantial functional heterogeneity, with serial killing capacity, effector-target contact dynamics, and intrinsic motility each independently correlating with cytotoxic potency. Cytotoxic efficacy scaled positively with effector number, and CD4+ T helper cells augmented CD8+-mediated killing in a dose-dependent manner, providing direct evidence for cooperative T cell behavior during tumor cell clearance. Extending the platform to a solid tumor model using AIC100 anti-ICAM-1 CAR-T cells and HeLa-GFP targets, we show that individual AIC100 effectors are insufficient to mediate effective killing, while multi-effector CCEs exhibit robust cytotoxicity scaling with effector abundance and CD4+:CD8+ composition. These data establish the Cellanome platform as a broadly applicable system for dissecting the determinants of CAR-T cell cytotoxicity at single-cell resolution, with direct translational relevance to the optimization of cellular immunotherapy.

immunology↗

bioRxiv2026

Scalable longitudinal imaging and transcriptomics of cells in dynamic enclosures

Dynamic transitions between cell states underlie both normal physiology and disease. However, most single-cell technologies capture only static snapshots. To address this gap, we developed a platform that integrates light-guided hydrogel polymerization with computer vision to generate on-demand compartments around live cells, enabling longitudinal imaging of cellular behavior paired with whole-transcriptome profiling of the same cells at scale. These data link dynamic phenotypes with molecular programs, enabling deeper characterization of cellular states. This approach revealed an adaptive, drug-resistant state in lung cancer cells characterized by potassium channel upregulation and p53-dependent quiescence. In models of adipogenesis and microglial phagocytosis, joint analysis of imaging and transcriptomic data identified key drivers of cellular function that were missed by transcriptomic clustering alone. These results establish the value of paired functional and transcriptomic analysis to resolve molecular drivers of complex cellular behaviors.

systems biology↗

bioRxiv2026

Linking live-cell behavior to transcriptional responses across perturbations using dynamic caging

Single-cell technologies, encompassing molecular, morphological, and functional assays, have emerged as cornerstones of modern biological research and discovery. However, current experimental methods often fail to explicitly link these omic modalities, especially in live cells or longitudinally through time, impeding the study of multi-scale interactions and mechanisms of regulation. CellCage Enclosure (CCE) technology overcomes these limitations by dynamically compartmentalizing cells, allowing for scalable, live-cell, longitudinal exploration and simultaneous analysis of transcriptomic, proteomic, and morphological profiles. Using this novel technology, we generate previously inaccessible insights across various in vitro cellular systems under a diverse set of perturbations, including the discovery of morphological and proteomic features linked to immune suppressive gene set expression in human primary regulatory T cells (Tregs), as well as direct association of morphological and proteomic features with inflammatory gene modules in human colonic fibroblasts. We then develop a novel pooled CRISPR genetic screening technology using CCEs, PERTURB-LINK (PERTURBational LINKage of transcriptomics and imaging in single cells via enclosure-based screening) and apply this approach in murine bone marrow derived macrophages (BMDMs), enabling multiomic dissection of NF-{kappa}B pathway regulation in response to lipopolysaccharide (LPS) stimulation. Together, these findings demonstrate the broad impact that advancements in live-cell, paired multimodal technologies, especially upon perturbation, may offer in deepening our understanding of cellular biology.

cell biology↗

bioRxiv2026

Cross-Attention Over RNA And Protein Sequences Enables Generalizable Interaction Prediction

Computational predictions are essential to characterize the RNA-protein interaction landscape, yet a persistent gap between benchmark performance and practical utility suggests that current models have limited generalization capabilities. To address this issue, we present CORAL (Cross-attention for RNA-protein Association Learning), a deep learning framework for the prediction of RNA-protein interactions that integrates pretrained protein (ESM-2) and RNA (DNABERT2) language models through bidirectional cross-attention with Low-Rank Adaptation fine-tuning. We also introduce a benchmarking framework that rigorously addresses the problem of data redundancy between training and test sets, which greatly inflates model performances reported in the literature. To this end we adopt three partitioning strategies of increasing stringency: conventional random splits, pairwise non-redundant splits, and component-wise non-redundant splits. CORAL maintains an F1 score of 0.65 under the most stringent component-wise evaluation, compared to 0.47 for the next-best method retaining discriminative behavior. Interpretability analyses further reveal that the cross-attention mechanism captures biologically meaningful features of molecular recognition at two complementary structural scales: at atomic resolution, specific attention heads systematically attend to structurally defined contact positions, showing 26% elevated attention at interface residues across 309 experimentally resolved complexes (p < 0.001); at the domain level, protein-side attention localizes to annotated RNA-binding domains across 94 % of 462 proteins examined (median within-protein Cohens d {approx} 0.94). Together, these findings establish that current RPI prediction benchmarks substantially inflate performance estimates and demonstrate that cross-modal attention architectures yield improved generalization alongside mechanistically interpretable representations.

bioinformatics↗

bioRxiv2026

A tumor metabolism-angiogenesis-immune axis governs immunotherapy responses

Despite the clinical success of immunotherapy, long-lasting benefit remains restricted to a subset of patients. Tumor metabolic adaptation is emerging as a key factor limiting immunotherapy efficacy. We previously found that glycolysis-low tumor variants, compared to the parental glycolytic tumors, better respond to neoadjuvant CTLA-4 immune checkpoint blockade (ICB) therapy. Here, we investigated new rational modalities to restore immune sensitivity of glycolytic tumors by studying how lowering the tumor-cell glycolytic capacity reshapes the tumor microenvironment (TME) to favor long-lasting systemic anti-tumor responses upon immunotherapy. We found that lowering glycolysis in cancer cells through LDHA-knock down (KD) results in TME displaying normalized vasculature, reduced angiogenic markers, increased high endothelial venules (HEVs), and enhanced recirculation of CD8+ T cells both in and out of the tumor. By leveraging public transcriptomic data sets from human solid cancers, we confirmed that glycolysis positively correlates with neo-angiogenesis and inversely correlates with features of vascular normalization and immune cytolytic activity. Moreover, a tumor signature that incorporates glycolysis- and angiogenesis-related genes as positive features, and normal vasculature, HEV, and immune cytolytic activity genes as negative features predicted poor outcomes better than the individual features across most human solid tumor types in the TCGA. To determine the therapeutic implication of these interrelated processes, we asked if targeting the vasculature would restore immunotherapy responses in glycolytic tumors. We found that combining low-dose anti-VEGFR2 with CTLA-4 blockade induces tumor regressions and protection from metastases in glycolytic tumors. These therapeutic effects were associated with vasculature normalization and increased abundance of HEVs and concentrations of lymphangiogenic factors in the TME of glycolytic tumors. Moreover, anti-VEGFR2 with anti-CTLA-4 restored recirculation of anti-tumor CD8+ T cells in and out of the TME in glycolytic tumors, with specific increases in intratumoral recruitment and activation of cytolytic CD62LCD44CD8 T cells expressing VEGFR2 and low levels of CTLA-4, suggesting potential novel direct synergistic effects of anti-VEGFR2 and anti-CTLA-4 on CD8+ T cells. Conversely, this combination opposed the beneficial immune and vascular TME features of LDHA-KD tumors, indicating tumor-metabolic-dependent effects. Accordingly, we found that standard combined regimens of anti-VEGF and ICB therapy improve survival with respect to ICB alone in patients with glycolysis-high but not glycolysis-low tumors. Together, these findings indicate that tumor cell glycolysis "primes" the TME for aberrant vascular architecture and T-cell exclusion, and that modulating the tumor vasculature can unravel these mechanisms restoring immune responsiveness. This suggests that tailoring anti-angiogenic and immunotherapy combinations to the tumor glycolytic state and associated vasculature profiles may restore immune surveillance and overcome therapy resistance.

immunology↗

bioRxiv2025

The Integrated Cellular and Molecular Landscape of Autoimmunity

We performed a large-scale immunogenomic analysis of [~]13,000 transcriptomic profiles from 10 autoimmune diseases, integrating publicly available datasets from both blood and tissue. Using meta-analysis, we identified core immune mechanisms underlying autoimmunity, including strong interferon responses, inflammation, and adaptive immune suppression, alongside disease-specific signatures. To enhance biological interpretability, we derived higher-order immune features - such as cell type proportions, cytokine levels, pathway activity, transcription factor regulation, and miRNA activity - and organized them into 15 coherent immune modules. These modules enabled systematic cross-disease comparisons, revealing shared and distinct immunopathological patterns. The inflammation module, in particular, was associated with disease severity and predicted treatment response across multiple conditions. This modular framework offers a powerful tool for understanding immune dysregulation and advancing precision medicine in autoimmune diseases. To support reproducibility and enable others to build upon this work, we developed an interactive app to explore and download the complete dataset and associated results.

bioinformatics↗

bioRxiv2025

Multi-omic landscape of human gliomas from diagnosis to treatment and recurrence

Gliomas are among the most lethal cancers, with limited treatment options. To uncover hallmarks of therapeutic escape and tumor microenvironment (TME) evolution, we applied spatial proteomics, transcriptomics, and glycomics to 670 lesions from 310 adult and pediatric patients. Single-cell analysis shows high B7H3+ tumor cell prevalence in glioblastoma (GBM) and pleomorphic xanthoastrocytoma (PXA), while most gliomas, including pediatric cases, express targetable tumor antigens in less than 50% of tumor cells, potentially explaining trial failures. Longitudinal samples of isocitrate dehydrogenase (IDH)-mutant gliomas reveal recurrence driven by tumor-immune spatial reorganization, shifting from T-cell and vasculature-associated myeloid cell-enriched niches to microglia and CD206+ macrophage-dominated tumors. Multi-omic integration identified N-glycosylation as the best classifier of grade, while the immune transcriptome best predicted GBM survival. Provided as a community resource, this study opens new avenues for glioma targeting, classification, outcome prediction, and a baseline of TME composition across all stages.

cancer biology↗

bioRxiv2023

Human lncRNAs harbor conserved modules embedded in different sequence contexts

We analyzed the structure of human long non-coding RNA (lncRNAs) genes to investigate whether the non-coding transcriptome is organized in modular domains, as is the case for protein-coding genes. To this aim, we compared all known human lncRNA exons and identified 340 pairs of exons with high sequence and/or secondary structure similarity but embedded in a dissimilar sequence context. We grouped these pairs in 106 clusters based on their reciprocal similarities. These shared modules are highly conserved between humans and the four great ape species, display evidence of purifying selection and likely arose as a result of recent segmental duplications. Our analysis contributes to the understanding of the mechanisms driving the evolution of the non-coding genome and suggests additional strategies towards deciphering the functional complexity of this class of molecules. Author summaryThe Human genome includes more than 18,000 genes coding for RNAs that are not translated into proteins, called long non-coding RNAs (lncRNA). Mounting evolutionary and experimental evidence shows that a large amount of these RNAs have a specific function, mainly as regulators of a diverse set of biological processes. Here we set out to investigate whether these genes have a modular organization similar to that of protein-coding genes. Accordingly, we compared the sequence of all the exonic regions of human lncRNAs and identified 106 clusters of non-repetitive exonic modules shared between this class of genes. These modules display evidence of purifying selection, are highly conserved between humans and the four great ape species, and may represent distinct functional units that have been shuffled among multiple lncRNA genes, in a manner similar to the exon-shuffling process that is observed in the coding genome.

evolutionary biology↗

bioRxiv2018

Comprehensive Immune Monitoring of Clinical Trials to Advance Human Immunotherapy

The success of immunotherapy has led to a myriad of new clinical trials. Connected to these trials are efforts to discover biomarkers providing mechanistic insight and predictive signatures for personalization. Still, the plethora of immune monitoring technologies can face investigator bias, missing unanticipated cellular responses in limited clinical material. We here present a mass cytometry workflow for standardized, systems-level biomarker discovery in immunotherapy trials. To broadly enumerate human immune cell identity and activity, we established and extensively assessed a reference panel of 33 antibodies to cover major cell subsets, simultaneously quantifying activation and immune checkpoint molecules in a single assay. The resulting assay enumerated [≥] 98% of peripheral immune cells with [≥] 4 positively identifying antigens. Robustness and reproducibility were demonstrated on multiple samples types, across research centers and by orthogonal measurements. Using automated analysis, we monitored complex immune dynamics, identifying signatures in bone-marrow transplantation associated graft-versus-host disease. This validated and available workflow ensures comprehensive immunophenotypic analysis, data comparability and will accelerate biomarker discovery in immunomodulatory therapeutics.

immunology↗

bioRxiv2017

Meta-analysis of Cytometry Data Reveals Racial Differences in Immune Cells

While meta-analysis has demonstrated increased statistical power and more robust estimations in studies, the application of this commonly accepted methodology to cytometry data has been challenging. Different cytometry studies often involve diverse sets of markers. Moreover, the detected values of the same marker are inconsistent between studies due to different experimental designs and cytometer configurations. As a result, the cell subsets identified by existing auto-gating methods cannot be directly compared across studies. We developed MetaCyto for automated meta-analysis of both flow and mass cytometry (CyTOF) data. By combining clustering methods with a silhouette scanning method, MetaCyto is able to identify commonly labeled cell subsets across studies, thus enabling meta-analysis. Applying MetaCyto across a set of 10 heterogeneous cytometry studies totaling 2926 samples enabled us to identify multiple cell populations exhibiting differences in abundance between White and Asian adults. Software is released to the public through GitHub (github.com/hzc363/MetaCyto).

bioinformatics↗