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Single Molecule Imaging and Machine Learning Analysis: Estimation of diffusive states hidden by Ensemble-average

Title
Single Molecule Imaging and Machine Learning Analysis: Estimation of diffusive states hidden by Ensemble-average
Authors
Seo, Daeha
DGIST Authors
Seo, Daeha
Issue Date
2020-07-06
Citation
제125회 대한화학회 학술발표회
Type
Conference
Abstract
Monitoring the dynamics of both of receptor protein and cell signaling in live cell on appropriate spatiotemporal scales may provide key information on long-standing questions in molecular and cellular regulatory mechanisms. However, tools capable of imaging, tracking, and data processing the data over time have been elusive. We, therefore are developing microscopy technologies (i.e. super-resolution fluorescence and dark-field) and tracking and machine learning algorithms to understand single protein diffusive dynamics. Monitoring the dynamics of proteins in live cell on appropriate spatiotemporal scales may provide key information on long-standing questions in molecular and cellular regulatory mechanisms. However, tools capable of imaging the conformational changes over time have been elusive. Here, we present a single-molecule stroboscopic imaging probes by developing gyroscopic plasmonic nanoparticles, allowing for replication of protein-protein interactions and the conformational dynamics based on rotational and lateral velocities. This study is the first to monitor the rotational motion of membrane protein, epidermal growth factor receptor (EGFR), to decipher undiscovered structural dynamics in live cells without any molecular perturbations. Such a method not only offers a strategy to visualize assemblies and conformational changes, but also provides unique insights into the mechanism underlying the molecular dynamics for receptors. Our results show that rotational diffusion dynamics of EGFR was separated into three states through hidden Markov model (HMM), demonstrating that the states are good index for estimating interaction between intracellular domain of EGFR with lipid membrane.
URI
http://hdl.handle.net/20.500.11750/14047
Publisher
대한화학회
Related Researcher
  • Author Seo, Daeha SMALL LAB(Single Molecule Approaches to ceLL Lab)
  • Research Interests Synthetic Chemistry of Nanomaterials; Biophysics; Cell biology
Files:
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Collection:
Department of Physics and ChemistrySMALL LAB(Single Molecule Approaches to ceLL Lab)2. Conference Papers


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