ACS NanoBiophysicsPeer-reviewed journal article
High-throughput single molecule microscopy with adaptable spatial resolution using exchangeable oligonucleotide labels
Klarinda de ZwaaniD, Ran HuoiD, Myron N.F. HensgensiD, Hannah Lena Wienecke, Miyase TekpınariD, Hylkje J. GeertsemaiD, Kristin S. GrußmayeriD
Abstract
Super-resolution microscopy facilitates the visualization of cellular structures at resolutions approaching the molecular level. Especially, super-resolution techniques based on the localization of single-molecules have relatively modest instrument requirements and are thus good candidates for adoption in bioimaging. However, their low throughput nature hampers their applicability in biomolecular research and screening. Here, we propose a workflow for more efficient data collection, starting with scanning of large areas using fast fluctuation based imaging, followed by single-molecule localization microscopy of selected cells. To achieve this workflow, we exploit the versatility of DNA oligo hybridization kinetics with DNA-PAINT probes to tailor the fluorescent blinking towards high-throughput and high-resolution imaging. Additionally, we employ super-resolution optical fluctuation imaging (SOFI) to analyze statistical fluctuations in the DNA-PAINT binding kinetics, thereby tolerating much denser blinking and facilitating accelerated imaging speeds. Thus, we demonstrate 30-300-fold faster imaging of different cellular structures compared to conventional DNA-PAINT imaging, albeit at a lower resolution. Notably, by tuning image medium and data processing though, we can flexibly switch between high-throughput SOFI (scanning a FOV of 0.65mm x 0.52mm within 4 minutes of total acquisition time) and super-resolution DNA-PAINT microscopy and thereby demonstrate that combining DNA-PAINT and SOFI enables to adapt image resolution and acquisition time based to the imaging needs. We envision this approach to be especially powerful when combined with multiplexing and 3D imaging.
- assay: fluorescence / live-cell microscopy
- research focus: benchmarking
- approach: deep learning
- approach: statistical model
- Citations
- 9
- FWCI
- 3.38
- Field percentile
- top 7%
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