Zudi Lin

SynAnno is an interactive tool that streamlines synaptic annotation proofreading in connectomics by integrating guided workflows and machine learning assistance, improving efficiency and reducing errors.

This study investigates synaptic connectivity development in the mouse cerebellum, revealing sequential input-dependent wiring and individualized parallel fiber inputs to Purkinje cells.

The paper presents a connectomic dataset of a larval zebrafish brain, enabling circuit analysis through synaptic and neuronal annotations.

The paper presents a connectomic analysis of a human cerebral cortex fragment, revealing structural and connectivity features of neurons and synapses.

The paper develops an AI tool for quantifying ultrastructural traits from 3D microscopic datasets and improves fixation protocols for plants.

The paper presents a vCLEM protocol to map Aβ and pTau in AD mouse hippocampus connectomics data, revealing new molecular and structural alterations.

This paper introduces a method for saturated reconstruction of living brain tissue using advanced imaging and deep learning to achieve high-resolution, volumetric analysis of neural structures over time.

The paper introduces a vCLEM method using fluorescent scFvs to combine molecular labeling with ssEM, revealing new cell types and subcellular features in mouse cerebellar cortex.