Deep-learning microscopy image reconstruction with quality control reveals second-scale rearrangements in RNA polymerase II clusters

• Published in: PNAS nexus Vol. 1; no. 3; p. pgac065
• Main Authors: Hajiabadi, Hamideh, Mamontova, Irina, Prizak, Roshan, Pancholi, Agnieszka, Koziolek, Anne, Hilbert, Lennart
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.07.2022
• Subjects: Biological, Health, and Medical Sciences; Embryonic development; Fluorescence microscopy; Genetic transcription; Image processing; Neural networks; Quality control; Quality management; RNA; Germany; image processing; transcription; gene regulation; fluorescence microscopy; reliable deep learning
• ISSN: 2752-6542; 2752-6542
• DOI: 10.1093/pnasnexus/pgac065

Fluorescence microscopy, a central tool of biological research, is subject to inherent trade-offs in experiment design. For instance, image acquisition speed can only be increased in exchange for a lowered signal quality, or for an increased rate of photo-damage to the specimen. Computational denoising can recover some loss of signal, extending the trade-off margin for high-speed imaging. Recently proposed denoising on the basis of neural networks shows exceptional performance but raises concerns of errors typical of neural networks. Here, we present a work-flow that supports an empirically optimized reduction of exposure times, as well as per-image quality control to exclude images with reconstruction errors. We implement this work-flow on the basis of the denoising tool Noise2Void and assess the molecular state and 3D shape of RNA polymerase II (Pol II) clusters in live zebrafish embryos. Image acquisition speed could be tripled, achieving 2-s time resolution and 350-nm lateral image resolution. The obtained data reveal stereotyped events of approximately 10 s duration: initially, the molecular mark for recruited Pol II increases, then the mark for active Pol II increases, and finally Pol II clusters take on a stretched and unfolded shape. An independent analysis based on fixed sample images reproduces this sequence of events, and suggests that they are related to the transient association of genes with Pol II clusters. Our work-flow consists of procedures that can be implemented on commercial fluorescence microscopes without any hardware or software modification, and should, therefore, be transferable to many other applications.