Biology subjects

Zhovmer, A.

Publications and source records attributed to Zhovmer, A..

3 recordsLinked to original sources

bioRxiv2023

Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy

Optical aberrations hinder fluorescence microscopy of thick samples, reducing image signal, contrast, and resolution. Here we introduce a deep learning-based strategy for aberration compensation, improving image quality without slowing image acquisition, applying additional dose, or introducing more optics into the imaging path. Our method (i) introduces synthetic aberrations to images acquired on the shallow side of image stacks, making them resemble those acquired deeper into the volume and (ii) trains neural networks to reverse the effect of these aberrations. We use simulations and experiments to show that applying the trained de-aberration networks outperforms alternative methods, providing restoration on par with adaptive optics techniques; and subsequently apply the networks to diverse datasets captured with confocal, light-sheet, multi-photon, and super-resolution microscopy. In all cases, the improved quality of the restored data facilitates qualitative image inspection and improves downstream image quantitation, including orientational analysis of blood vessels in mouse tissue and improved membrane and nuclear segmentation in C. elegans embryos.

bioengineering↗

bioRxiv2020

Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes

We demonstrate residual channel attention networks (RCAN) for restoring and enhancing volumetric time-lapse (4D) fluorescence microscopy data. First, we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy 4D super-resolution data, enabling image capture over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables class-leading resolution enhancement, superior to other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling [~]2.5-fold lateral resolution enhancement using stimulated emission depletion (STED) microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy ground truth, achieving improvements of [~]1.4-fold laterally and [~]3.4-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluating and further enhancing network performance.

bioengineering↗

bioRxiv2020

Engineering T cells to enhance 3D migration through structurally and mechanically complex tumor microenvironments

Defining the principles of T cell migration in structurally and mechanically complex tumor microenvironments is critical to understanding sanctuaries from antitumor immunity and optimizing T cell-related therapeutic strategies. To enhance T cell migration through complex microenvironments, we engineered nanotextured platforms that allowed us to define how the balance between T cell phenotypes influences migration in response to tumor-mimetic structural and mechanical cues and characterize a mechanical optimum for migration that can be perturbed by manipulating an axis between microtubule stability and force generation. In 3D environments and live tumors, we demonstrate that microtubules instability, leading to increased Rho pathway-dependent cell contractility, promotes migration while clinically used microtubule-targeting chemotherapies profoundly decrease effective migration. Indeed, we show that rational manipulation of the microtubule-contractility axis, either pharmacologically or through genome engineering, results in engineered T cells that more effectively move through and interrogate 3D matrix and tumor volumes. This suggests that engineering cells to better navigate through 3D microenvironments could be part of an effective strategy to enhance efficacy of immune therapeutics.

cancer biology↗