Biology subjects

Lee, I.-B.

Publications and source records attributed to Lee, I.-B..

3 recordsLinked to original sources

bioRxiv2024

Determination of the absolute concentration of Rayleigh particles via scattering microscopy

Nanoparticles are crucial in diverse fields such as healthcare, electronics, and energy. Their small size allows them to cross biological barriers, enhancing drug delivery but also posing health risks. Accurate characterization of nanoparticles is essential for assessing their safety and efficacy, particularly in medical applications. Traditional methods such as dynamic light scattering and mass spectrometry have limitations in sensitivity and range of application. To address these challenges, we introduce the interferometric Concentration Analyzer and Ultrasmall Nanoparticle Tracker (iCAUNT), a technique for detecting and quantifying nanoparticles smaller than one-tenth of the imaging light wavelength. As a non-invasive method, iCAUNT provides precise size and concentration measurements of biological and synthetic nanoparticles, offering significant potential for diagnostics, therapeutics, and broader nanoscience applications.

biophysics↗

bioRxiv2022

Long-term cellular cargo tracking reveals intricate trafficking through active cytoskeletal networks

A eukaryotic cell is a microscopic world within which efficient material transport is essential. To examine cargo transport in a crowded cellular environment, we tracked unlabeled cargos in directional motion in a massively parallel fashion using the interferometric scattering microscopy. Our label-free, cargo-tracing method revealed not only dynamic cargo transportation but also the fine architecture of the actively used cytoskeletal highways and the long-term evolution of the associated traffic at sub-diffraction resolution via a myriad of molecular strokes. Cargos experience a traffic jam, but they have an effective strategy to circumvent it: moving together in tandem or migrating collectively. All taken together, a cell is an incredibly complex and busy space where the principle and practice in transportation intriguingly parallel those of our macroscopic world.

biophysics↗

bioRxiv2016

Decoding Single Molecule Time Traces with Dynamic Disorder

Single molecule time trajectories of biomolecules provide glimpses into complex folding landscapes that are difficult to visualize using conventional ensemble measurements. Recent experiments and theoretical analyses have highlighted dynamic disorder in certain classes of biomolecules, whose dynamic pattern of conformational transitions is affected by slower transition dynamics of internal state hidden in a low dimensional projection. A systematic means to analyze such data is, however, currently not well developed. Here we report a new algorithm - Variational Bayes-double chain Markov model (VB-DCMM) - to analyze single molecule time trajectories that display dynamic disorder. The proposed analysis employing VB-DCMM allows us to detect the presence of dynamic disorder, if any, in each trajectory, identify the number of internal states, and estimate transition rates between the internal states as well as the rates of conformational transition within each internal state. Applying VB-DCMM algorithm to single molecule FRET data of H-DNA in 100 mM-Na+ solution, followed by data clustering, we show that at least 6 kinetic paths linking 4 distinct internal states are required to correctly interpret the duplex-triplex transitions of H-DNA.\n\nAuthor SummaryWe have developed a new algorithm to better decode single molecule data with dynamic disorder. Our new algorithm, which represents a substantial improvement over other methodologies, can detect the presence of dynamic disorder in each trajectory and quantify the kinetic characteristics of underlying energy landscape. As a model system, we applied our algorithm to the single molecule FRET time traces of H-DNA. While duplex-triplex transitions of H-DNA are conventionally interpreted in terms of two-state kinetics, slowly varying dynamic patterns corresponding to hidden internal states can also be identified from the individual time traces. Our algorithm reveals that at least 4 distinct internal states are required to correctly interpret the data.

biophysics↗