Browsing by Author "Iqbal, Fadil"

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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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    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.
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    RAD51 regulates eukaryotic chromatin motions in the absence of DNA damage
    (American Society for Cell Biology, 2024) Maarouf, Amine; Iqbal, Fadil; Sanaullah, Sarvath; Locatelli, Maëlle; Atanasiu, Andrew T.; Kolbin, Daniel; Hommais, Chloé; Mühlemann, Joëlle K.; Bonin, Keith; Bloom, Kerry; Liu, Jing; Vidi, Pierre-Alexandre; Physics, School of Science
    In yeasts and higher eukaryotes, chromatin motions may be tuned to genomic functions, with transcriptional activation and the DNA damage response both leading to profound changes in chromatin dynamics. The RAD51 recombinase is a key mediator of chromatin mobility following DNA damage. As functions of RAD51 beyond DNA repair are being discovered, we asked whether RAD51 modulates chromatin dynamics in the absence of DNA damage and found that inhibition or depletion of RAD51 alters chromatin motions in undamaged cells. Inhibition of RAD51 increased nucleosome clustering. Predictions from polymer models are that chromatin clusters reduce chain mobility and, indeed, we measured reduced motion of individual chromatin loci in cells treated with a RAD51 inhibitor. This effect was conserved in mammalian cells, yeasts, and plant cells. In contrast, RAD51 depletion or inhibition increased global chromatin motions at the microscale. The results uncover a role for RAD51 in regulating local and global chromatin dynamics independently from DNA damage and highlight the importance of considering different physical scales when studying chromatin dynamics.