Scheck, J. G. | Search bioRxiv

bioRxiv2025

Voxel-accurate MRI-microscopy correlation enables AI-powered prediction of brain disease states

Magnetic resonance imaging (MRI) is essential for visualizing the healthy and diseased brain, yet the cellular basis of MRI signal and how it changes over time remain poorly understood. Here, we present BRIDGE (Brain Radiological Imaging with Deep-learning based Ground-Truth Exploration), a platform integrating in vivo MRI with in vivo two-photon (2P) and ex vivo super-resolution microscopy using a multi-step, iterative co-registration pipeline. It enables in vivo, longitudinal, and voxel-precise mapping of MRI signals to their cellular origins for the first time. The registered overlay reveals the cellular and anatomical origins of MRI signals and enables training of convolutional neural networks to enhance the effective resolution of MRI. Using BRIDGE, we identified a microenvironmental vessel biomarker for early metastatic colonization in patient-derived xenograft models of brain metastasis. In particular we found that distinct T2*-weighted hypointense lesions correspond to reduced blood flow and erythrostasis in perimetastatic capillaries. In glioma, longitudinal intravital studies further demonstrated direct correlations between non-vasogenic T2-weighted signal changes and patient-dependent tumor growth dynamics. Taken together, BRIDGE advances radiological interpretation by establishing a microscopic ground truth for MRI signatures over time, enabling deep learning-based predictive histology, and providing cellular-level insights into tumor microenvironment features with direct clinical imaging implications. Graphical abstractBRIDGE enables longitudinal voxel-to-voxel correlation and ground truth based automatic segmentation of MR images O_FIG O_LINKSMALLFIG WIDTH=177 HEIGHT=200 SRC="FIGDIR/small/680637v1_ufig1.gif" ALT="Figure 1"> View larger version (73K): org.highwire.dtl.DTLVardef@f1f64eorg.highwire.dtl.DTLVardef@1619e3eorg.highwire.dtl.DTLVardef@1dc2e7forg.highwire.dtl.DTLVardef@7097e1_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2024

Characterizing and targeting glioblastoma neuron-tumor networks with retrograde tracing

Glioblastomas are invasive brain tumors with high therapeutic resistance. Neuron-to-glioma synapses have been shown to promote glioblastoma progression. However, a characterization of tumor-connected neurons has been hampered by a lack of technologies. Here, we adapted retrograde tracing using rabies viruses to investigate and manipulate neuron-tumor networks. Glioblastoma rapidly integrated into neural circuits across the brain engaging in widespread functional communication, with acetylcholinergic neurons driving glioblastoma invasion. We uncovered patient-specific and tumor cell state-dependent differences in synaptogenic gene expression associated with neuron-tumor connectivity and subsequent invasivity. Importantly, radiotherapy enhanced neuron-tumor connectivity by increased neuronal activity. In turn, simultaneous neuronal activity inhibition and radiotherapy showed increased therapeutic effects, indicative of a role for neuron-to-glioma synapses in contributing to therapeutic resistance. Lastly, rabies-mediated genetic ablation of tumor-connected neurons halted glioblastoma progression, offering a viral strategy to tackle glioblastoma. Together, this study provides a framework to comprehensively characterize neuron-tumor networks and target glioblastoma.

cancer biology↗