Biology subjects

Hansen, C. G.

Publications and source records attributed to Hansen, C. G..

5 recordsLinked to original sources

bioRxiv2024

Development of a pipeline to evaluate YAP-TEAD inhibitor potential - direct TEAD inhibition represses cancerous growth in a NF2-mutant model of mesothelioma

As the core, tumorigenic downstream effectors of the Hippo signalling pathway, YAP/TAZ and the TEAD family of transcription factors represent attractive targets for drug discovery efforts within cancer research. This is particularly true within the context of pleural mesothelioma, in which there are many recent preclinical developments and clinical trials evaluating the efficacy of TEAD inhibitors. The range of inhibitors have shown great promise, but comparisons of their performances are so far limited. Here we develop a high content pipeline that enables a comparative analysis of currently developed YAP/TAZ-TEAD inhibitors. We take advantage of isogenic cellular models that enable us to examine inhibitor specificity. We identify genetic compensation of the Hippo pathway transcriptional module, with implications for therapeutic targeting, and implement Cell Painting to develop a detailed morphological profiling pipeline that enables further characterisation, quantification, and analysis of off-target effects. Our pipeline is scalable and allows us to establish specificity and comparative potency within cancer relevant assays in a clinically relevant cellular model.

cancer biology↗

bioRxiv2023

Standing wave mesoscopy

Standing wave (SW) microscopy is a method that uses an interference pattern to excite fluorescence from labelled cellular structures and produces high-resolution images of three-dimensional objects in a two-dimensional dataset. SW microscopy is performed with high magnification, high numerical aperture objective lenses, and while this results in high resolution images, the field of view is very small. Here we report upscaling of this interference imaging method from the microscale to the mesoscale using the Mesolens, which has the unusual combination of a low magnification and high numerical aperture. With this method, we produce SW images within a field of view of 4.4 mm x 3.0 mm that can readily accommodate over 16,000 cells in a single dataset. We demonstrate the method using both single-wavelength excitation and the multi-wavelength SW method TartanSW. We show application of the method for imaging of fixed and living cells specimens, with the first application of SW imaging to study cells under flow conditions.

cell biology↗

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↗

bioRxiv2021

The transcription factor EGR2 is indispensable for tissue-specific imprinting of alveolar macrophages in health and tissue repair

Alveolar macrophages are the most abundant macrophages in the healthy lung where they play key roles in homeostasis and immune surveillance against air-borne pathogens. Tissue-specific differentiation and survival of alveolar macrophages relies on niche-derived factors, such as colony stimulating factor 2 (CSF-2) and transforming growth factor beta (TGF-{beta}). However, the nature of the downstream molecular pathways that regulate the identity and function of alveolar macrophages and their response to injury remains poorly understood. Here, we identify that the transcriptional factor EGR2 is an evolutionarily conserved feature of lung alveolar macrophages and show that cell-intrinsic EGR2 is indispensable for the tissue-specific identity of alveolar macrophages. Mechanistically, we show that EGR2 is driven by TGF-{beta} and CSF-2 in a PPAR-{gamma}-dependent manner to control alveolar macrophage differentiation. Functionally, EGR2 was dispensable for lipid handling, but crucial for the effective elimination of the respiratory pathogen Streptococcus pneumoniae. Finally, we show that EGR2 is required for repopulation of the alveolar niche following sterile, bleomycin-induced lung injury and demonstrate that EGR2-dependent, monocyte-derived alveolar macrophages are vital for effective tissue repair following injury. Collectively, we demonstrate that EGR2 is an indispensable component of the transcriptional network controlling the identity and function of alveolar macrophages in health and disease. One Sentence SummaryEGR2 controls alveolar macrophage function in health and disease

immunology↗

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↗