Biology subjects

Tay, N.

Publications and source records attributed to Tay, N..

4 recordsLinked to original sources

bioRxiv2026

E-Selectin Orchestrates IL-1B-Dependent Neuroinflammation via NLRP3 in Vincristine-Induced Neuropathy

Vincristine-induced peripheral neuropathy (VIPN) is a frequent and dose-limiting complication of cancer therapy, yet the upstream mechanisms coupling vascular activation to neuroinflammation remain poorly defined. Here we identify E-selectin as a critical orchestrator of vincristine-induced neuropathy. Systematic interrogation of endothelial adhesion molecules in a murine model of VIPN revealed that blockade of E-selectin, but not ICAM-1, PECAM-1 or P-selectin, completely prevented mechanical hypersensitivity and markedly reduced F4/80 immune cell accumulation in dorsal root ganglia and peripheral nerves. Genetic deletion of E-selectin conferred equivalent protection, despite the absence of structural loss of intraepidermal or myelinated fibres, indicating a predominantly functional neuroimmune pathology. Spatial transcriptomics demonstrated that vincristine induces a conserved stress and neuroinflammation-associated transcriptional programme in dorsal root ganglia, with immune and stromal populations acting as dominant signalling hubs. Genetic or pharmacological perturbation of E-selectin did not abolish injury-associated pathways but redistributed cell-cell communication networks, reducing immune-cell dominance and reshaping interferon and metabolic signalling states without inducing Sele expression. Mechanistically, E-selectin exerted non-canonical effects beyond endothelial adhesion. Local E-selectin administration was sufficient to induce macrophage-dependent mechanical hypersensitivity that was abolished in Fut4/7-deficient mice and following phagocyte depletion. In macrophages, E-selectin enhanced vincristine-driven NF-{kappa}B activation, NLRP3 inflammasome assembly and IL-1{beta} release. Together, these findings position E-selectin as an upstream regulator of IL-1{beta}-dependent neuroinflammation in VIPN and identify selective targeting of E-selectin-mediated immune-neuron interactions as a therapeutic strategy for chemotherapy-induced neuropathy.

neuroscience↗

bioRxiv2025

RainBar: Optical Barcoding for Pooled Live-Cell Imaging with Single-Cell Resolution

High-throughput pooled screening has advanced functional genomics, but most existing methods rely on endpoint sequencing and are blind to dynamic, time-resolved phenotypes. We developed RainBar (Rainbow Barcodes), an optical barcoding system that supports pooled live-cell imaging with single-cell resolution. RainBar uses lentiviral co-delivery of spectrally distinct nuclear and cytoplasmic fluorescent proteins to encode up to 64 unique perturbations per well. To mitigate barcode recombination and improve decoding accuracy, we employed single-template viral production, codon diversification, and a ratio-based spectral unmixing pipeline tailored to overlapping fluorophores. An inverted cytoplasmic segmentation approach and multilayer perceptron classifier enabled accurate barcode identification in both arrayed and pooled formats. As a proof of concept, we applied RainBar to dissect NF-{kappa}B signaling dynamics in epithelial cells. Live imaging of RelA translocation uncovered stimulus-specific kinetics: IL-1{beta} triggered rapid recovery, while TNF induced prolonged nuclear localization. In pooled CRISPRi screens, RainBar recovered known NF-{kappa}B regulators (e.g., IL1R1, MYD88, TNFRSF1A) and highlighted additional modulators, including the Ino80 chromatin remodeling complex subunits and KAT2A acetyltransferase. Together, these results position RainBar as a flexible platform for multiplexed, image-based functional genomics, with potential to reveal dynamic signaling architectures across diverse cellular contexts in live cells.

genomics↗

bioRxiv2025

Multiple instance learning with spatial transcriptomics for interpretable patient-level predictions: application in glioblastoma

Accurate prediction of patient outcomes remains a major challenge in oncology. While recent machine learning (ML) approaches often rely on bulk omics lacking spatial resolution or histology-based multiple instance learning (MIL), spatial transcriptomics (SpT) provides a unique opportunity to capture both molecular content and tissue architecture. However, no generalizable ML framework has yet been established to exploit SpT for patient-level outcomes. We present SpaMIL, a flexible and interpretable MIL framework designed for SpT, with a distillation strategy that enables deployment for hematoxylin and eosin (H&E) slides alone. We evaluate the framework by predicting survival from glioblastoma (GBM) patients, a clinically compelling setting given its aggressiveness with a median survival of only 15 months and the lack of prognostic clinical variables. We analyzed 76 GBM cases from the MOSAIC dataset: 43 with matched SpT, H&E, single-nucleus RNA-seq (scRNA-seq), bulk RNA-seq, and clinical variables, and 33 with H&E for external validation. We developed two main architectures: abMIL, tailored to SpTs spatial molecular structure, and MabMIL, which distills SpT-derived representations into H&E. Model interpretability was achieved through a Shapley-based framework linking prognostic predictions to cell-type compositions via SpT deconvolution. In benchmarking across the five GBM MOSAIC modalities, SpT-based abMIL achieved unprecedented prognostic accuracy (median C-index: 0.72, standard deviation: 0.04), outperforming all other modalities, including established clinical predictors. PCA and deconvolution-based SpT representations surpassed recent foundation models, suggesting the need for further research on SpT foundation models. Our interpretability analysis highlighted malignant and non-malignant cell subpopulations associated with favorable or poor prognosis, consistent with recent reports. Finally, MabMIL maintained strong performance while enabling H&E-only deployment, with improved condorance index over H&E-only baselines in both internal (0.59 vs. 0.57) and external (0.62 vs. 0.55) cohorts.

cancer biology↗

bioRxiv2019

Neurobehavioural characterisation and stratification of reinforcement-related behaviour

AbstractReinforcement-related cognitive processes, such as reward processing, impulsiveness and emotional processing are critical components of externalising and internalising behaviours. It is unclear to what extent each of these processes contributes to individual behavioural symptoms, how their neural substrates give rise to distinct behavioural outcomes, and if neural profiles across different reinforcement-related processes might differentiate individual behaviours. We created a statistical framework that enabled us to directly compare functional brain activation during reward anticipation, motor inhibition and viewing of emotional faces in the European IMAGEN cohort of 2000 14-year-old adolescents. We observe significant correlations and modulation of reward anticipation and motor inhibition networks in hyperactivity, impulsivity, inattention and conduct symptoms, and describe neural signatures across neuroimaging tasks that differentiate these behaviours. We thus characterise shared and distinct functional brain activation patterns that underlie different externalising symptoms and identify neural stratification markers, while accounting for clinically observed co-morbidity.

neuroscience↗