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Schüttpelz, M.

Publications and source records attributed to Schüttpelz, M..

2 recordsLinked to original sources

Sodium sulfite as a switching agent for single molecule based super-resolution optical microscopy

Photoinduced off-switching of organic fluorophores is routinely used in super-resolution microscopy to separate and localize single fluorescent molecules, but the method typically relies on the use of complex imaging buffers. The most common buffers use primary thiols to reversibly reduce excited fluorophores to a non-fluorescent dark state, but these thiols have a limited shelf life and additionally require high illumination intensities in order to efficiently switch the emission of fluorophores. Recently a high-index, thiol-containing imaging buffer emerged which used sodium sulfite as an oxygen scavenger, but the switching properties of sulfite was not reported on. Here, we show that sodium sulfite in common buffer solutions reacts with fluorescent dyes, such as Alexa Fluor 647 and Alexa Fluor 488 under low to medium intensity illumination to form a semi-stable dark state. The duration of this dark state can be tuned by adding glycerol to the buffer. This simplifies the realization of different super-resolution microscopy modalities such as direct Stochastic Reconstruction Microscopy (dSTORM) and Super-resolution Optical Fluctuation Microscopy (SOFI). We characterize sulfite as a switching agent and compare it to the two most common switching agents by imaging cytoskeleton structures such as microtubules and the actin cytoskeleton in human osteosarcoma cells.

biophysics

Deep-learning based denoising and reconstruction of super-resolution structured illumination microscopy images

Super-resolution structured illumination microscopy (SR-SIM) provides an up to two-fold enhanced spatial resolution of fluorescently labeled samples. The reconstruction of high quality SR-SIM images critically depends on patterned illumination with high modulation contrast. Noisy raw image data, e.g. as a result of low excitation power or low exposure times, result in reconstruction artifacts. Here, we demonstrate deep-learning based SR-SIM image denoising that results in high quality reconstructed images. A residual encoding-decoding convolution neural network (RED-Net) was used to successfully denoise computationally reconstructed noisy SR-SIM images. We also demonstrate the entirely deep-learning based denoising and reconstruction of raw SIM images into high-resolution SR-SIM images. Both image reconstruction methods prove to be very robust against image reconstruction artifacts and generalize very well over various noise levels. The combination of computational reconstruction and subsequent denoising via RED-Net shows very robust performance during inference after training even if the microscope settings change.

biophysics