JEONG @ NIH - Technology

Imaging Signaling Molecules

Monitoring intracellular signaling dynamics in specified neural circuits is essential for understanding the molecular mechanisms by which neuromodulators, drugs, and disease states influence gene regulation, physiological changes in the brain, and behavior. To date, Förster resonance energy transfer (FRET)–based biosensors have been widely used to monitor second-messenger signaling in cultured cells; however, their translation to in vivo application has remained restricted to only highly specialized designs. To address this challenge, our team is developing a novel cAMP biosensor optimized for in vivo use, enabling the measurement of sustained cAMP dynamics in freely behaving animals.

Fluorescence Lifetime Imaging (FLIM) Toolkits

My research leverages advanced optical imaging and engineered protein technologies to detect signaling molecules in real time. I develop fluorescence lifetime–based imaging approaches to monitor in vivo G protein–coupled receptor (GPCR) signaling cascades using FRET-based biosensors. Specifically, I quantify intracellular second messengers such as cAMP using genetically encoded fluorescent proteins in combination with fluorescence lifetime imaging microscopy (FLIM). These approaches provide robust and quantitative measurements of signaling dynamics independent of intensity-based artifacts. Together, these tools enable the dissection of molecular and circuit-level mechanisms underlying neuronal plasticity and gene expression, with potential implications for understanding and treating neurological and psychiatric disorders.

In-vivo optical imaging for large-scale neural dynamics

To study neural activity in behaving animals, I employ miniaturized head-mounted microscopes that enable longitudinal imaging of large neuronal populations during naturalistic behaviors. I develop custom signal processing algorithms to extract, denoise, and analyze neural signals from high-dimensional imaging datasets. These data are integrated with behavioral measurements to identify relationships between neural activity patterns and behavioral states. My work focuses on cortical and basal ganglia circuits, with the goal of understanding how distributed neural networks encode action selection, learning, and adaptive behaviors. To manage the scale and complexity of these datasets, I incorporate machine learning–based approaches for feature extraction, pattern recognition, and predictive modeling of neural dynamics.

High Performance Computation (HPC)

At the NIH, I utilize the Biowulf high-performance computing (HPC) cluster to accelerate computationally intensive workflows related to FLIM image analysis, behavioral video processing, and in vivo fluorescence imaging. These large-scale datasets require efficient parallel processing and scalable analytical pipelines. We also employ AlphaFold2 and AlphaFold3 (Google DeepMind) for structure prediction and validation to inform the rational design and optimization of protein-based biosensors.

Our team is interested in developing machine learning–based algorithms to quantify and classify complex neural datasets, integrating multimodal data from imaging and behavior. In parallel, I build computational models of neural dynamics and animal behavior to investigate mechanisms of learning and adaptation, particularly in the context of acute and chronic substance exposure. These approaches enable a systems-level understanding of how molecular signaling and circuit activity give rise to behavior.

Electronics, Optics, and Implantable Technologies

With extensive engineering background, I have worked on the development of integrated electronics, implantable device technology, and bio-optical systems. The technology includes implantable physical biosensors, multifunctional biomaterials, Raman spectroscopy imaging,, signal/imaging processing algorithms, and artificial neural network (ANN) for bio applications. In my previous biomedical and electrical engineering projects, I developed on-chip CMOS biosensors, implantable optical biosensing systems, and micro and nano electromechanical system (M/NEMS). My current research interest is the integration of electrical, optical, and computational approaches to create highly sensitive and novel neural monitoring modalities that can enable innovative neuroscience research.