Browsing by Author "Zhang, Mengdi"

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    A guide for single-particle chromatin tracking in live cell nuclei
    (Wiley, 2022) Zhang, Mengdi; Seitz, Clayton; Chang, Garrick; Iqbal, Fadil; Lin, Hua; Liu, Jing; Physics, School of Science
    The emergence of labeling strategies and live cell imaging methods enables the imaging of chromatin in living cells at single digit nanometer resolution as well as milliseconds temporal resolution. These technical breakthroughs revolutionize our understanding of chromatin structure, dynamics and functions. Single molecule tracking algorithms are usually preferred to quantify the movement of these intranucleus elements to interpret the spatiotemporal evolution of the chromatin. In this review, we will first summarize the fluorescent labeling strategy of chromatin in live cells which will be followed by a systematic comparison of live cell imaging instrumentation. With the proper microscope, we will discuss the image analysis pipelines to extract the biophysical properties of the chromatin. Finally, we expect to give practical suggestions to broad biologists on how to select methods and link to the model properly according to different investigation purposes. This article is protected by copyright. All rights reserved.
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    Monitoring Dark-State Dynamics of a Single Nitrogen-Vacancy Center in Nanodiamond by Auto-Correlation Spectroscopy: Photonionization and Recharging
    (MDPI, 2021-04-10) Zhang, Mengdi; Li, Bai-Yan; Liu, Jing; Physics, School of Science
    In this letter, the photon-induced charge conversion dynamics of a single Nitrogen-Vacancy (NV) center in nanodiamond between two charge states, negative (NV−) and neutral (NV0), is studied by the auto-correlation function. It is observed that the ionization of NV− converts to NV0, which is regarded as the dark state of the NV−, leading to fluorescence intermittency in single NV centers. A new method, based on the auto-correlation calculation of the time-course fluorescence intensity from NV centers, was developed to quantify the transition kinetics and yielded the calculation of transition rates from NV− to NV0 (ionization) and from NV0 to NV− (recharging). Based on our experimental investigation, we found that the NV−-NV0 transition is wavelength-dependent, and more frequent transitions were observed when short-wavelength illumination was used. From the analysis of the auto-correlation curve, it is found that the transition time of NV− to NV0 (ionization) is around 0.1 μs, but the transition time of NV0 to NV− (recharging) is around 20 ms. Power-dependent measurements reveal that the ionization rate increases linearly with the laser power, while the recharging rate has a quadratic increase with the laser power. This difference suggests that the ionization in the NV center is a one-photon process, while the recharging of NV0 to NV− is a two-photon process. This work, which offers theoretical and experimental explanations of the emission property of a single NV center, is expected to help the utilization of the NV center for quantum information science, quantum communication, and quantum bioimaging.
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    Performance of deep learning restoration methods for the extraction of particle dynamics in noisy microscopy image sequences
    (American Society for Cell Biology, 2021-04-19) Kefer, Paul; Iqbal, Fadil; Locatelli, Maelle; Lawrimore, Josh; Zhang, Mengdi; Bloom, Kerry; Bonin, Keith; Vidi, Pierre-Alexandre; Liu, Jing; Physics, School of Science
    Particle tracking in living systems requires low light exposure and short exposure times to avoid phototoxicity and photobleaching and to fully capture particle motion with high-speed imaging. Low-excitation light comes at the expense of tracking accuracy. Image restoration methods based on deep learning dramatically improve the signal-to-noise ratio in low-exposure data sets, qualitatively improving the images. However, it is not clear whether images generated by these methods yield accurate quantitative measurements such as diffusion parameters in (single) particle tracking experiments. Here, we evaluate the performance of two popular deep learning denoising software packages for particle tracking, using synthetic data sets and movies of diffusing chromatin as biological examples. With synthetic data, both supervised and unsupervised deep learning restored particle motions with high accuracy in two-dimensional data sets, whereas artifacts were introduced by the denoisers in three-dimensional data sets. Experimentally, we found that, while both supervised and unsupervised approaches improved tracking results compared with the original noisy images, supervised learning generally outperformed the unsupervised approach. We find that nicer-looking image sequences are not synonymous with more precise tracking results and highlight that deep learning algorithms can produce deceiving artifacts with extremely noisy images. Finally, we address the challenge of selecting parameters to train convolutional neural networks by implementing a frugal Bayesian optimizer that rapidly explores multidimensional parameter spaces, identifying networks yielding optimal particle tracking accuracy. Our study provides quantitative outcome measures of image restoration using deep learning. We anticipate broad application of this approach to critically evaluate artificial intelligence solutions for quantitative microscopy.