Biology subjects

Pearce, T. M.

Publications and source records attributed to Pearce, T. M..

4 recordsLinked to original sources

bioRxiv2026

Adenosylhomocysteinase drives epigenetic and metabolic reprogramming in ZFTA-RELA ependymoma

Supratentorial ependymomas (ST-EPNs) are aggressive pediatric brain tumors predominantly driven by the recurrent ZFTA-RELA fusion, an oncogenic chromatin regulator that establishes an aberrant transcriptional program. Although metabolic pathways modulate epigenetic regulation by controlling the availability of metabolic cofactors, how ZFTA-RELA coordinates metabolic reprogramming with epigenetic regulation is not well understood. Here, we show that ZFTA-RELA-positive ependymomas exhibit enhanced methionine dependency, elevated adenosyl homocysteinase (AHCY) expression, and epigenetically control aspartate metabolism for tumor growth. Mechanistically, ZFTA-RELA upregulates AHCY, increasing cellular methylation potential which promotes H3K4me3-dependent expression of CAD, the first enzyme complex of de novo pyrimidine biosynthesis, while simultaneously enforcing H3K27me3 mediated repression of ASNS, which encodes asparagine synthetase. Through this coordinated epigenetic program, aspartate utilization is directed toward pyrimidine biosynthesis while endogenous asparagine production is limited. Restricting S-adenosylmethionine availability or genetically or pharmacologically inhibiting AHCY reverses these epigenetic states, leading to CAD downregulation, ASNS upregulation, impaired nucleotide synthesis, and suppression of tumor growth in vitro and in vivo. Similarly, EZH2 inhibition also restores ASNS expression and suppresses tumor proliferation. These findings suggest that ZFTA-RELA EPN maintain ASNS-low phenotype which conditionally make them dependent on extracellular asparagine. We further show that enhanced de novo pyrimidine synthesis not only supports nucleotide production but also provides UDP-sugars required for N-glycosylation and stabilization of the asparagine transporter SLC1A5, thereby promoting extracellular asparagine uptake and tumor proliferation. Collectively, our findings identify AHCY as a critical metabolic regulator of epigenetic and metabolic reprogramming that drives pyrimidine synthesis and establishes extracellular asparagine dependency, revealing actionable metabolic vulnerabilities in ZFTA-RELA-positive ST-EPNs.

cancer biology↗

bioRxiv2026

Evaluating the Transferability of Pathology Foundation Models Across Cancer-related H&E Neurodegeneration-related Immunohistochemical Classification Tasks

Foundation models (FMs) have rapidly become dominant in artificial intelligence and are increasingly being adopted in computational pathology. Numerous pathology-specific FMs have been developed and evaluated for a variety of downstream tasks, most commonly using frozen image embeddings with linear probes. While a pathology FM, at least implicitly suggests broad reusability across tasks, the transferability of these models to neurodegenerative disease-related tasks remains largely unexplored. In this study, we evaluated fourteen frozen feature extractors, including general-vision and pathology FMs, across four pathology image classification datasets spanning two specialized neurodegenerative disease immuno-histochemistry (IHC) tasks (Tau neurofibrillary tangle (NFT) and amyloid-{beta} plaque classification) and two cancer-related hematoxylin and eosin (H&E) tasks (the breast tissue BACH dataset and the multi-tissue TIL dataset). The cancer-related H&E datasets represent tasks more closely aligned with the predominant pretraining domain of many current pathology FMs, whereas the neurodegenerative IHC datasets represent a more specialized domain with limited representation in existing FM pretraining cohorts. Frozen embedding linear probes were compared against a conventional supervised ResNet-50 convolutional neural network (CNN) trained directly on image tiles using identical train, validation, and test splits. Across both neurodegenerative IHC datasets, the supervised CNN substantially outperformed all frozen FM linear probes. In contrast, pathology FMs achieved performance comparable to, and in some cases exceeding, the supervised CNN across the two cancer-related H&E datasets, with UNI2-h achieving the highest performance on BACH and several pathology FMs performing on par with the CNN on the larger TIL dataset. Furthermore, a supervised CNN trained using only 1% of the Tau NFT training data (1,938 tiles) still exceeded the performance of the best frozen FM linear probe trained on the complete dataset. Together, these results suggest the transferability of frozen pathology FMs may depend strongly on how well the downstream task is represented by their pretraining domain. These findings demonstrate frozen pathology FMs transfer effectively to the cancer-related H&E tasks evaluated here but may be less effective for specialized neurodegenerative IHC tasks less well represented in current FM pretraining cohorts. Expanding pathology FM pretraining datasets to include a broader range of disease domains and staining modalities may therefore improve transferability to specialized pathology applications. Our findings also highlight the continued importance of conventional supervised learning and motivate future work investigating nonlinear probes and end-to-end FM fine-tuning.

pathology↗

bioRxiv2026

Multi-model Segmentation and Morphometric Quantification of Cerebral Amyloid Angiopathy in Alzheimer's Disease Whole Slide Histopathology Images

IntroductionCerebral amyloid angiopathy (CAA) is characterized by amyloid-beta deposition in cortical and leptomeningeal vessels and associated with cognitive impairment and hemorrhage. Current neuropathological assessments rely on semiquantitative grading and lack vessel-level resolution and scalability. Existing computational pathology approaches also fail to capture individual vessel morphology and spatial amyloid distribution across whole-slide images (WSIs). To address this gap, we developed a deep learning framework for reproducible, quantitative analysis of CAA in WSIs. MethodsWe analyzed 20 postmortem brain tissue sections from the frontal (n = 10) and occipital cortices (n = 10) of 10 individuals with Alzheimers disease pathology obtained from the University of Pittsburgh Alzheimers Disease Research Center, which served as the internal development cohort. An independent external cohort consisted of 10 sections (5 frontal and 5 occipital samples) from 5 individuals obtained from the University of Kentucky Alzheimers Disease Research Center. We trained and compared three semantic segmentation architectures, a standard U-Net, a dual-attention residual U-Net (DA-ResUNet), and a Swin Transformer-based U-Net (Swin-UNet), using the internal development cohort with slide-level five-fold cross-validation. All models were evaluated on the independent external cohort to assess generalization under domain shift. Based on segmentation performance and computational efficiency, we selected one architecture to generate whole-slide composite segmentation masks for vessel walls, amyloid deposits, and tissue compartments. These masks were subsequently used for deterministic vessel detection, morphometric measurements, and quantification of vascular and perivascular amyloid features through post-processing analysis. ResultsAll three architectures achieved high segmentation accuracy on the internal cohort, with Dice scores above 90% across vessel walls, amyloid deposits, gray matter, and leptomeninges. The Swin-UNet showed marginally higher performance for vessel segmentation, whereas the DA-ResUNet provided more balanced accuracy and computational efficiency and was selected for downstream analysis. External cohort evaluation demonstrated robust generalization, with attention-enhanced models outperforming the standard U-Net under domain shift. Using the selected model, the pipeline reliably detected valid vessels, excluded non-vascular artifacts, and enabled deterministic extraction of vessel morphometry, vascular and perivascular amyloid burden, and identification of circumferential CAA involvement at the vessel level. DiscussionThis framework provides a scalable, interpretable solution for vessel-level CAA analysis, supporting robust geometric and spatial characterization of cerebrovascular pathology and enabling future integration with clinical and genetic studies. Beyond CAA, the modular design allows extension to other vascular pathologies, including arteriolosclerosis, in WSIs, facilitating broader investigation of cerebrovascular disease mechanisms.

pathology↗

bioRxiv2025

A comparison of movement-related neuronal activities in cerebellar- and basal ganglia-recipient regions of the macaque thalamus

The ventral lateral (VL) nucleus of the thalamus relays signals from the cerebellum (Cb) and basal ganglia (BG) to primary motor cortex (M1). In primates, glutamatergic Cb efferents from the deep cerebellar nuclei and GABAergic BG efferents from the internal segment of the globus pallidus (GPi) terminate in distinct subregions of VL: the posterior (VLp) and anterior (VLa) divisions, respectively. This anatomical segregation suggests that Cb- and BG-thalamocortical circuits may play distinct roles in motor control, which could be revealed by comparing movement-related activity in VLp and VLa. Here, we recorded single-unit activity from VLp and VLa, identified via electrical stimulation of superior cerebellar peduncle and GPi, during a choice reaction time reaching task. We also recorded from M1, which maintains bidirectional connections with both VLp and VLa. Peri-movement increases in firing began earlier in VLp than in VLa whereas decrease-type responses were more prevalent and prolonged in duration in VLa neurons as compared with VLp and M1. Time-resolved general linear model analysis showed dynamic encoding of task parameters, particularly movement direction, in all three regions. Direction encoding was strongest in M1, moderate in VLp, and weakest in VLa. Direction encoding in VLa also lagged behind that in M1 and VLp. Clustering analysis of direction encoding strength and timing revealed a subpopulation of VLp neurons that encoded direction particularly strongly during the reaction time period. These results highlight a limitation of traditional assumptions that activity characteristics are distributed homogeneously across neural populations and suggest a novel functional organization within VLp neurons.

neuroscience↗