Journal
NATURE COMMUNICATIONS
Volume 13, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-022-30907-2
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Funding
- European Research Council [ERC CoG-724489]
- National Institutes of Health Common Fund 4D Nucleome Program [U01 EB021223]
- Human Frontier Science Program [RGY0065/2017]
- Engelhorn Foundation
- European Molecular Biology Laboratory
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This article presents an automated pipeline for camera characterization based on thermally generated signal, which can mitigate image artifacts caused by pixel-to-pixel variations in sCMOS cameras. It also enables high-quality super-resolution microscopy and live-cell time-lapse microscopy.
Modern implementations of widefield fluorescence microscopy often rely on sCMOS cameras, but this camera architecture inherently features pixel-to-pixel variations. Such variations lead to image artifacts and render quantitative image interpretation difficult. Although a variety of algorithmic corrections exists, they require a thorough characterization of the camera, which typically is not easy to access or perform. Here, we developed a fully automated pipeline for camera characterization based solely on thermally generated signal, and implemented it in the popular open-source software Micro-Manager and ImageJ/Fiji. Besides supplying the conventional camera maps of noise, offset and gain, our pipeline also gives access to dark current and thermal noise as functions of the exposure time. This allowed us to avoid structural bias in single-molecule localization microscopy (SMLM), which without correction is substantial even for scientific-grade, cooled cameras. In addition, our approach enables high-quality 3D super-resolution as well as live-cell time-lapse microscopy with cheap, industry-grade cameras. As our approach for camera characterization does not require any user interventions or additional hardware implementations, numerous correction algorithms that rely on camera characterization become easily applicable. Pixel-to-pixel variations in sCMOS cameras lead to image artifacts in widefield fluorescence microscopy, and algorithmic corrections require thorough camera characterization. Here, the authors present an open source automated pipeline for camera characterization based solely on thermally generated signal.
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