Structured illumination to spatially map chromatin motions

et al. 2020

Preprint

Self Cite

Image-based particle tracking is an essential tool to answer research questions in cell biology and beyond. A major challenge of particle tracking in living systems is that low light exposure is required to avoid phototoxicity and photobleaching. In addition, high-speed imaging used to fully capture particle motion dictates fast image acquisition rates. Short exposure times come at the expense of tracking accuracy. This is generally true for quantitative microscopy approaches and particularly relevant to single molecule tracking where the number of photons emitted from a single chromophore is limited. Image restoration methods based on deep learning dramatically improve the signal-to-noise ratio in low-exposure datasets. 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 datasets 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 datasets, whereas artifacts were introduced by the denoisers in 3D datasets. Experimentally, we found that, while both supervised and unsupervised approaches improved the number of trackable particles and tracking accuracy, supervised learning generally outperformed the unsupervised approach, as expected. We also highlight that with extremely noisy image sequences, deep learning algorithms produce deceiving artifacts, which underscores the need to carefully evaluate the results. Finally, we address the challenge of selecting hyper-parameters to train convolutional neural networks by implementing a frugal Bayesian optimizer that rapidly explores multidimensional parameter spaces, identifying networks yielding optional particle tracking accuracy. Our study provides quantitative outcome measures of image restoration using deep learning. We anticipate broad application of the approaches presented here to critically evaluate artificial intelligence solutions for quantitative microscopy.

Section: Resultssupporting

confidence: 85%

Section: Methodsmentioning

confidence: 99%

Section: Content-aware Denoising For Tracking Chromatin Microdomains mentioning

confidence: 99%

Section: Content-aware Denoising For Tracking Chromatin Microdomains mentioning

confidence: 99%

See 2 more Smart Citations

Performance of deep learning restoration methods for the extraction of particle dynamics in noisy microscopy image sequences

Kefer

Iqbal

et al. 2020

Preprint

Self Cite

“…extent CNN-based denoising can restore kinetic information in a biological data set. We used a custom optical setup based on diffractive optics to photoactivate 7 × 7 lattices of chromatin microdomains in U2OS cells stably expressing histone H2A tagged with photoactivatable green fluorescent protein (PAGFP-H2A) (Bonin et al, 2018) (Figure 2A). Interlaced movies were collected from the photoactivated lattices, alternating short and long exposure times.…”

Section: Content-aware Denoising For Tracking Chromatin Microdomains In Live Cellsmentioning

confidence: 99%

Performance of deep learning restoration methods for the extraction of particle dynamics in noisy microscopy image sequences

Kefer

Iqbal

et al. 2021

MBoC

Self Cite

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 datasets, 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 datasets 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 datasets, whereas artifacts were introduced by the denoisers in 3D datasets. Experimentally, we found that, while both supervised and unsupervised approaches improved tracking results compared to 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.

DNA damage reduces heterogeneity and coherence of chromatin motions

Proc. Natl. Acad. Sci. U.S.A.

Lawrimore

Lin

et al. 2022

Chromatin motions depend on and may regulate genome functions, in particular the DNA damage response. In yeast, DNA double-strand breaks (DSBs) globally increase chromatin diffusion, whereas in higher eukaryotes the impact of DSBs on chromatin dynamics is more nuanced. We mapped the motions of chromatin microdomains in mammalian cells using diffractive optics and photoactivatable chromatin probes and found a high level of spatial heterogeneity. DNA damage reduces heterogeneity and imposes spatially defined shifts in motions: Distal to DNA breaks, chromatin motions are globally reduced, whereas chromatin retains higher mobility at break sites. These effects are driven by context-dependent changes in chromatin compaction. Photoactivated lattices of chromatin microdomains are ideal to quantify microscale coupling of chromatin motion. We measured correlation distances up to 2 µm in the cell nucleus, spanning chromosome territories, and speculate that this correlation distance between chromatin microdomains corresponds to the physical separation of A and B compartments identified in chromosome conformation capture experiments. After DNA damage, chromatin motions become less correlated, a phenomenon driven by phase separation at DSBs. Our data indicate tight spatial control of chromatin motions after genomic insults, which may facilitate repair at the break sites and prevent deleterious contacts of DSBs, thereby reducing the risk of genomic rearrangements.