Zuwan Lin

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 develops Deep-STARmap and Deep-RIBOmap for 3D spatial transcriptomics and translatomics in thick tissue blocks, enabling analysis of gene expression and immune interactions in human skin cancer.

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.

This paper introduces BCI-Agent, an AI framework that classifies neuronal cell types from electrophysiological data without fine-tuning, validated in rodent and human datasets.

This paper introduces a stretchable microelectrode array that integrates into the developing brain, enabling long-term, stable neural activity tracking without disrupting development.

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.

The paper introduces STAgent, an AI tool that automates spatial transcriptomics analysis, revealing in vivo maturation of human stem cell-derived pancreatic cells.

SpikeAgent automates spike sorting with a multimodal LLM-based AI agent, achieving curation consistency equal to or exceeding human experts and drastically reducing expertise barriers.

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

The paper uses flexible electronics to study electrical maturation of human pancreatic islets over time, identifying key electrical states and their role in hormone secretion.

The paper creates a high-resolution spatial atlas of the mouse CNS by combining in situ sequencing with scRNA-seq data to map cell types and gene expression across the brain and spinal cord.

This paper introduces TEMPOmap, a method to profile subcellular RNA kinetics in single cells, revealing kinetic gene clusters with distinct functional roles.

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.

This study uses machine learning to optimize organoid morphogenesis, revealing an excitable system driven by WNT and FGF signaling that governs human axial elongation.

This paper introduces UnitedNet, a multi-task deep learning model for analyzing single-cell multi-modality data, showing improved performance in joint group identification and cross-modal prediction.

ClusterMap is a framework for analyzing spatial gene expression data, clustering RNAs into subcellular structures and tissue regions across multiple dimensions.