Biology subjects

Kölln, L. S.

Publications and source records attributed to Kölln, L. S..

3 recordsLinked to original sources

bioRxiv2022

MesoTIRF: a Total Internal Reflection Fluorescence illuminator for axial super-resolution membrane imaging at the mesoscale

Total Internal Reflection Fluorescence (TIRF) illumination bypasses the axial diffraction limit of light by using an evanescent field to excite fluorophores close to a sample substrate. TIRF illumination significantly improves image contrast, allowing researchers to study membrane structure and dynamics with localized reductions in photobleaching. However, a significant limitation of most TIRF microscopes is the relatively small field of view (FOV). TIRF objectives require a high numerical aperture (NA) to generate the evanescent wave. Such lenses invariably have a high magnification and result in a [~] 50 {micro}m diameter imaging field, requiring many subsequent images for accurate statistical analysis. Waveguide and prism-based TIRF systems are, in principle, compatible with lower magnification lenses to widen the FOV but these have a correspondingly low NA and lateral resolution. To overcome these limitations, we present a prism-based TIRF illuminator for the Mesolens - a specialist objective lens with the unusual combination of low magnification and high NA. This new imaging mode - MesoTIRF - enables TIRF imaging across a 4.4 mm x 3.0 mm FOV. We demonstrate evanescent wave illumination of cell specimens, and show the multi-wavelength capability of the modality across more than 700 cells in a single image. MesoTIRF images have up to a 6-fold improvement in signal-to-background ratio compared to widefield epi-fluorescence illumination, and we illustrate the benefit of this improved contrast for the detection and quantification of focal adhesions in fixed cells. Fluorescence intensities and resolvable structural detail do not vary considerably in homogeneity across the MesoTIRF FOV.

biophysics↗

bioRxiv2020

Label2label: Using deep learning and dual-labelling to retrieve cellular structures in fluorescence images

Fluorescence microscopy is an essential tool in cell biology to visualise the spatial distribution of proteins that dictates their role in cellular homeostasis, dynamic cellular processes, and dysfunction during disease. However, unspecific binding of the antibodies that are used to label a cellular target often leads to high background signals in the images, decreasing the contrast of a cellular structure of interest. Recently, convolutional neural networks (CNNs) have been successfully employed for denoising and upsampling in fluorescence microscopy, but current image restoration methods cannot correct for background signals originating from the label. Here, we report a new method to train a CNN as content filter for non-specific signals in fluorescence images that does not require a clean benchmark, using dual-labelling to generate the training data. We name this method label2label (L2L). In L2L, a CNN is trained with image pairs of two non-identical labels that target the same cellular structure of interest. We show that after L2L training a network restores images not only with reduced image noise but also label-induced unspecific fluorescence signal in images of a variety of cellular structures, resulting in images with enhanced structural contrast. By implementing a multi-scale structural similarity loss function, the performance of the CNN as a content filter is further enhanced, for example, in STED images of caveolae. We show evidence that, for this loss function, sample differences in the training data significantly decrease so-called hallucination effects in the restorations that we otherwise observe when training the CNN with images of the same label. We also assess the performance of a cycle generative adversarial network as a content filter after L2L training with unpaired image data. Lastly, we show that a CNN can be trained to separate structures in superposed fluorescence images of two different cellular targets, allowing multiplex imaging with microscopy setups where the number of excitation sources or detectors is limited.

bioinformatics↗

bioRxiv2019

Three-dimensional observations of an aperiodic oscillatory gliding motility behaviour in Myxococcus xanthus using confocal interference reflection microscopy

The Delta-proteobacterium, Myxococcus xanthus, has been used as a model for bacterial motility and to provide insights of bacterial swarming behaviours. Fluorescence microscopy techniques have shown that various mechanisms are involved in gliding motility, but these have almost entirely been limited to 2D studies and there is currently no understanding of gliding motility in a 3D context. We present here the first use of confocal interference reflection microscopy (IRM) to study gliding bacteria, and we reveal aperiodic oscillatory behaviour with changes in the position of the basal membrane relative to the coverglass on the order of 90 nm in vitro. Firstly, we use a model plano-convex lens specimen to show how topological information can be obtained from the wavelength-dependent interference pattern in IRM. We then use IRM to observe gliding M. xanthus and show that cells undergo previously unobserved changes in their height as they glide. We compare the wild-type with mutants of reduced motility, which also exhibit the same changes in adhesion profile during gliding. We find that the general gliding behaviour is independent of the proton motive force-generating complex, AglRQS, and suggest that the novel behaviour we present here may be a result of recoil and force transmission along the length of the cell body following firing of the Type IV pili.

microbiology↗