Ren Liu

This paper introduces in situ graphene-seq, a platform integrating chronic electrophysiology with spatial transcriptomics to analyze tissue microenvironments, showing how spatial heterogeneity affects cell function and gene expression.

This paper introduces growth-adaptive spring electronics and a spike processing pipeline to enable long-term, same-neuron mapping in developing rat brains, revealing neuron-specific developmental decorrelation.

Agentic Lab integrates AI agents with physical lab operations to improve reproducibility in cell and organoid research by autonomously generating protocols and analyzing experimental data.

DeviceAgent is an AI agent that integrates LLMs and VLMs to automate bioelectronics research tasks, demonstrating capabilities in design, fabrication, defect inspection, and electrophysiological analysis.

BehaveAgent automates cross-species behavior analysis from video input using multimodal AI without manual intervention or retraining, enabling zero-shot reasoning and end-to-end analysis.

This paper introduces an AI-cyborg system that uses adaptive intelligent modulation to enhance cardiac organoid maturation through real-time feedback.

This paper introduces AutoSort, a deep learning method for real-time decoding of neural activity from the same cells over months, showing improved efficiency and accuracy compared to existing methods.

This paper introduces in situ electro-sequencing to map gene expression and electrophysiology in 3D tissues, improving cell type analysis and developmental trajectory reconstruction.

This study develops a stable neural recording method for mice, enabling long-term tracking of single-neuron activity and analysis of aging effects using machine learning.