Lee, H.-P. | Search bioRxiv

bioRxiv2025

Tumor cell villages define the co-dependency of tumor and microenvironment in liver cancer

Spatial cellular context is crucial in shaping intratumor heterogeneity. However, understanding how each tumor establishes its unique spatial landscape and what factors drive the landscape for tumor fitness remains significantly challenging. Here, we analyzed over 2 million cells from 50 tumor biospecimens using spatial single-cell imaging and single-cell RNA sequencing. We developed a deep learning-based strategy to spatially map tumor cell states and the architecture surrounding them, which we referred to as Spatial Dynamics Network (SDN). We found that different tumor cell states may be organized into distinct clusters, or villages, each supported by unique SDNs. Notably, tumor cell villages exhibited village-specific molecular co-dependencies between tumor cells and their microenvironment and were associated with patient outcomes. Perturbation of molecular co-dependencies via random spatial shuffling of the microenvironment resulted in destabilization of the corresponding villages. This study provides new insights into understanding tumor spatial landscape and its impact on tumor aggressiveness.

cancer biology↗

bioRxiv2024

Phase-Dependent Stimulation of the Hippocampus: A Computational Modeling Approach

Phase-amplitude coupling (PAC) between brain oscillations of different frequencies plays a fundamental role in neural processing, and phase-dependent neuromodulation has emerged as a promising strategy to modulate PAC. In the hippocampus, theta-gamma PAC is critically involved in memory-related functions and information propagation. Computational models provide a valuable platform for investigating the neurobiological mechanisms underlying phase- dependent effects, bypassing the limitations of in vivo and in vitro experiments. In this study, we extended a previously published computational model of the hippocampal CA3 region using the NEURON and Python environments. A closed-loop autoregressive (AR) forward prediction model was employed to sample the networks local field potential (LFP) in real time, enabling the precise calculation of phase-locked stimulus time points. Our results demonstrated the successful delivery of phase-locked current injections to all neuronal populations at both the peak and trough of theta oscillations. Phase-specific alterations in the theta band were observed during stimulation, along with enhanced theta-gamma coupling induced by peak-phase stimulation. Single neuron activity analysis highlighted the critical role of oriens lacunosum- moleculare (OLM) cells in modulating phase-dependent network dynamics. These findings underscore the potential of closed-loop stimulation systems to modulate PAC, with significant implications for the treatment of neurological disorders characterized by abnormal oscillatory activity, such as Alzheimers disease and other memory-related disorders. Significance StatementBy employing a computational model of the hippocampal CA3 region, we reveal the ability of the phase-dependent stimulation technique to modulate phase-amplitude coupling, a critical mechanism in memory and information processing. Our findings highlight the importance of precise phase-locked stimulation and the key role of specific interneurons in regulating network dynamics. These insights lay the groundwork for developing targeted neuromodulation therapies to restore normal oscillatory patterns in the brain, with promising implications for treating memory-related neurological disorders.

neuroscience↗