Biology subjects

Heilemann, M.

Publications and source records attributed to Heilemann, M..

36 records · Page 2Linked to original sources

bioRxiv2023

Revealing the Oligomerization of Channelrhodopsin-2 in the Cell Membrane using Photo-Activated Localization Microscopy

Microbial rhodopsins are retinal membrane proteins that found a broad application in optogenetics. The oligomeric state of rhodopsins is important for their functionality and stability. Of particular interest is the oligomeric state in the cellular native membrane environment. Fluorescence microscopy provides powerful tools to determine the oligomeric state of membrane proteins directly in cells. Among these methods is quantitative photoactivated localization microscopy (qPALM) allowing the investigation of molecular organization at the level of single protein clusters. Here, we apply qPALM to investigate the oligomeric state of the first and most used optogenetic tool Channelrhodopsin-2 (ChR2) in the plasma membrane of eukaryotic cells. ChR2 appeared predominantly as a dimer in the cell membrane and did not form higher oligomers. The disulfide bonds between Cys34 and Cys36 of adjacent ChR2 monomers were not required for dimer formation and mutations disrupting these bonds resulted in only partial monomerization of ChR2. The monomeric fraction increased when the total concentration of mutant ChR2 in the membrane was low. The dissociation constant was estimated for this partially monomerized mutant ChR2 as 2.2{+/-}0.9 proteins/m2. Our findings are important for understanding the mechanistic basis of ChR2 activity as well as for improving existing and developing future optogenetic tools.

biophysics↗

bioRxiv2023

Function of ERphagy receptors is regulated via phosphorylation-dependent ubiquitination pathways

Selective autophagy of the endoplasmic reticulum (ER), known as ER-phagy, is an important regulator of ER remodeling and essential to maintain cellular homeostasis during environmental changes. We recently showed that members of the FAM134 family play a critical role during stress-induced ER-phagy. However, the mechanisms on how they are activated remain largely unknown. In this study, we analyzed phosphorylation of FAM134 as a trigger of FAM134-driven ER-phagy upon mTOR (mechanistic target of rapamycin) inhibition. An unbiased screen of kinase inhibitors revealed that CK2 is essential for FAM134B- and FAM134C-driven ER-phagy after mTOR inhibition. Furthermore, we provide evidence that ER-phagy receptors are regulated by ubiquitination events and that treatment with E1 inhibitor suppresses Torin1-induced ER-phagy flux. Using super-resolution microscopy, we show that CK2 activity is essential for the formation of high-density FAM134B and FAM134C clusters. In addition, dense clustering of FAM134B and FAM134C requires phosphorylation-dependent ubiquitination of FAM134B and FAM134C. Treatment with the CK2 inhibitor SGC-CK2-1 or mutation of FAM134B and FAM134C phosphosites prevents ubiquitination of FAM134 proteins, formation of high-density clusters, as well as Torin1-induced ER-phagy flux. Therefore, we propose that CK2-dependent phosphorylation of ER-phagy receptors precedes ubiquitin-dependent activation of ER-phagy flux.

cell biology↗

bioRxiv2022

Biased activation of the receptor tyrosine kinase HER2

HER2 belongs to the ErbB sub-family of receptor tyrosine kinases and regulates cellular proliferation and growth. Different from other ErbB receptors, HER2 has no known ligand. Activation occurs through heterodimerization with other ErbB receptors and their cognate ligands. This suggests several possible activation paths of HER2 with ligand-specific, differential response, which so far remained unexplored. Using single-molecule tracking and the diffusion profile of HER2 as a proxy for activity, we measured the activation strength and temporal profile in live cells. We found that HER2 is strongly activated by EGFR-targeting ligands EGF and TGF, yet with a distinguishable temporal fingerprint. The HER4-targeting ligands EREG and NRG{beta}1 showed weaker activation of HER2, a preference for EREG, and a delayed response to NRG{beta}1. Our results indicate a selective ligand response of HER2 that may serve as a regulatory element. Our experimental approach is easily transferable to other membrane receptors targeted by multiple ligands. HighlightsO_LIHER2 exhibits heterogeneous motion in the plasma membrane C_LIO_LIThe fraction of immobile HER2 correlates with phosphorylation levels C_LIO_LIDiffusion properties serve as proxies for HER2 activation C_LIO_LIHER2 exhibits ligand-specific activation strength and temporal profiles C_LI Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/519064v2_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@172415eorg.highwire.dtl.DTLVardef@21bf95org.highwire.dtl.DTLVardef@1a907aforg.highwire.dtl.DTLVardef@37a85a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2022

DBlink: Dynamic localization microscopy in super spatiotemporal resolution via deep learning

Single molecule localization microscopy (SMLM) has revolutionized biological imaging, improving the spatial resolution of traditional microscopes by an order of magnitude. However, SMLM techniques depend on accumulation of many localizations over thousands of recorded frames to yield a single super-resolved image, which is time consuming. Hence, the capability of SMLM to observe dynamics has always been limited. Typically, a few minutes of data acquisition are needed to reconstruct a single super-resolved frame. In this work, we present DBlink, a novel deep-learning-based algorithm for super spatiotemporal resolution reconstruction from SMLM data. The input to DBlink is a recorded video of single molecule localization microscopy data and the output is a super spatiotemporal resolution video reconstruction. We use bi-directional long short term memory (LSTM) network architecture, designed for capturing long term dependencies between different input frames. We demonstrate DBlink performance on simulated data of random filaments and mitochondria-like structures, on experimental SMLM data in controlled motion conditions, and finally on live cell dynamic SMLM. Our neural network based spatiotemporal interpolation method constitutes a significant advance in super-resolution imaging of dynamic processes in live cells.

bioengineering↗

bioRxiv2022

Exchangeable HaloTag Ligands (xHTLs) for multi-modal super-resolution fluorescence microscopy

We introduce exchangeable ligands for fluorescence labeling of HaloTag7 as an alternative to covalently bound probes. The exchangeable ligands open up new possibilities in imaging for a widely used labeling approach, including applications in points accumulation for imaging in nanoscale topography (PAINT), MINFLUX and live-cell, multi-frame stimulated emission depletion (STED) microscopy. We furthermore introduce orthogonal pairs of exchangeable ligands and HaloTags for dual-color PAINT and STED microscopy. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/496706v1_ufig1.gif" ALT="Figure 1"> View larger version (63K): org.highwire.dtl.DTLVardef@1e7d689org.highwire.dtl.DTLVardef@1b5db18org.highwire.dtl.DTLVardef@433efaorg.highwire.dtl.DTLVardef@dbbd61_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2021

Dynamic in situ confinement triggers ligand-free neuropeptide receptor signaling

Membrane receptors are central to cell-cell communication. Receptor clustering at the plasma membrane modulates physiological responses, and mesoscale receptor organization is critical for downstream signaling. Spatially restricted cluster formation of the neuropeptide Y2 hormone receptor (Y2R) was observed in vivo; however, the relevance of this confinement is not fully understood. Here, we controlled Y2R clustering in situ by a chelator nanotool. Due to the multivalent interaction, we observed a dynamic exchange in the microscale confined regions. Fast Y2R enrichment in clustered areas triggered a ligand-independent downstream signaling determined by an increase in cytosolic calcium, cell spreading, and migration. We revealed that the cell response to ligand-induced activation was amplified when cells were pre-clustered by the nanotool. Ligand-independent signaling by clustering differed from ligand-induced activation in the binding of arrestin-3 as downstream effector, which was recruited to the confined regions only in the presence of the ligand. This approach enables in situ clustering of membrane receptors and raises the possibility to explore different modalities of receptor activation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/472742v2_ufig1.gif" ALT="Figure 1"> View larger version (43K): org.highwire.dtl.DTLVardef@9c7823org.highwire.dtl.DTLVardef@93660corg.highwire.dtl.DTLVardef@110c414org.highwire.dtl.DTLVardef@1b906d3_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2021

Fast DNA-PAINT imaging using a deep neural network

DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) is a super-resolution technique with relatively easy-to-implement multi-target imaging. However, image acquisition is slow as sufficient statistical data has to be generated from spatio-temporally isolated single emitters. Here, we trained the neural network (NN) DeepSTORM to predict fluorophore positions from high emitter density DNA-PAINT data. This achieves image acquisition in one minute. We demonstrate multi-color super-resolution imaging of structure-conserved semi-thin neuronal tissue and imaging of large samples. This improvement can be integrated into any single-molecule microscope and enables fast single-molecule super-resolution microscopy.

biophysics↗

bioRxiv2021

DeepBacs: Bacterial image analysis using open-source deep learning approaches

Deep Learning (DL) is rapidly changing the field of microscopy, allowing for efficient analysis of complex data while often out-performing classical algorithms. This revolution has led to a significant effort to create user-friendly tools allowing biomedical researchers with little background in computer sciences to use this technology effectively. Thus far, these approaches have mainly focused on analysing microscopy images from eukaryotic samples and are still underused in microbiology. In this work, we demonstrate how to use a range of state-of-the-art artificial neural-networks particularly suited for the analysis of bacterial microscopy images, using our recently developed ZeroCostDL4Mic platform. We showcase different DL approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth stages in time-lapse imaging data, and carry out DL-assisted phenotypic profiling of antibiotic-treated cells. To also demonstrate the DL capacity to enhance low-phototoxicity live-cell microscopy, we showcase how image denoising can allow researchers to attain high-fidelity data in faster and longer imaging. Finally, artificial labelling of cell membranes and predictions of super-resolution images allow for accurate mapping of cell shape and intracellular targets. To aid in the training of novice users, we provide a purposefully-built database of training and testing data, enabling bacteriologists to quickly explore how to analyse their data through DL. We hope this lays a fertile ground for the efficient application of DL in microbiology and fosters the creation of novel tools for bacterial cell biology and antibiotic research.

microbiology↗

bioRxiv2021

A framework for evaluating the performance of SMLM cluster analysis algorithms

Single molecule localisation microscopy (SMLM) generates data in the form of Cartesian coordinates of localised fluorophores. Cluster analysis is an attractive route for extracting biologically meaningful information from such data and has been widely applied. Despite the range of developed cluster analysis algorithms, there exists no consensus framework for the evaluation of their performance. Here, we use a systematic approach based on two metrics, the Adjusted Rand Index (ARI) and Intersection over Union (IoU), to score the success of clustering algorithms in diverse simulated clustering scenarios mimicking experimental data. We demonstrate the framework using three analysis algorithms: DBSCAN, ToMATo and KDE, show how to deduce optimal analysis parameters and how they are affected by fluorophore multiple blinking. We propose that these standard conditions and metrics become the basis for future analysis algorithm development and evaluation.

biophysics↗

bioRxiv2021

KAHRP dynamically relocalizes to remodeled actin junctions and associates with knob spirals in P. falciparum-infected erythrocytes

The knob-associated histidine-rich protein (KAHRP) plays a pivotal role in the pathophysiology of Plasmodium falciparum malaria by forming membrane protrusions in infected erythrocytes, which anchor parasite-encoded adhesins to the membrane skeleton. The resulting sequestration of parasitized erythrocytes in the microvasculature leads to severe disease. Despite KAHRP being an important virulence factor, its physical location within the membrane skeleton is still debated, as is its function in knob formation. Here, we show by super-resolution microscopy that KAHRP initially associates with various skeletal components, including ankyrin bridges, but eventually co-localizes with remnant actin junctions. We further present a 35[A] map of the spiral scaffold underlying knobs and show that a KAHRP-targeting nanoprobe binds close to the spiral scaffold. Single-molecule localization microscopy detected [~]60 KAHRP molecules per knob. We propose a dynamic model of KAHRP organization and a function of KAHRP in attaching other factors to the spiral scaffold.

microbiology↗

bioRxiv2021

Visualizing multi-protein patterns at the synapse of neuronal tissue with DNA-assisted single-molecule localization microscopy

The development of super-resolution microscopy (SRM) has widened our understanding of biomolecular structure and function in biological materials. Imaging multiple targets within a single area would elucidate their spatial localization relative to the cell matrix and neighboring biomolecules, revealing multi-protein macromolecular structures and their functional co-dependencies. SRM methods are, however, limited to the number of suitable fluorophores that can be imaged during a single acquisition as well as the loss of antigens during antibody washing and restaining for organic dye multiplexing. We report the visualization of multiple protein targets within the pre- and postsynapse in 350-400 nm thick neuronal tissue sections using DNA-assisted single-molecule localization microscopy. Using antibodies labeled with short DNA oligonucleotides, multiple targets are visualized successively by sequential exchange of fluorophore-labeled complementary oligonucleotides present in the imaging buffer. The structural integrity of the tissue is maintained owing to only a single labelling step during sample preparation. Multiple targets are imaged using a single laser wavelength, minimizing chromatic aberration. This method proved robust for multi-target imaging in semi-thin tissue sections, paving the way towards structural cell biology with single-molecule super-resolution microscopy.

biophysics↗

bioRxiv2021

Unbiased choice of global clustering parameters in single-molecule localization microscopy

Single-molecule localization microscopy resolves objects below the diffraction limit of light via sparse, stochastic detection of target molecules. Single molecules appear as clustered detection events after image reconstruction. However, identification of clusters of localizations is often complicated by the spatial proximity of target molecules and by background noise. Clustering results of existing algorithms often depend on user-generated training data or user-selected parameters, which can lead to unintentional clustering errors. Here we suggest an unbiased algorithm (FINDER) based on adaptive global parameter selection and demonstrate that the algorithm is robust to noise inclusion and target molecule density. We benchmarked FINDER against the most common density based clustering algorithms in test scenarios based on experimental datasets. We show that FINDER can keep the number of false positive inclusions low while also maintaining a low number of false negative detections in densely populated regions.

bioinformatics↗

bioRxiv2020

The Spatial Scale of Synaptic Protein Allocation during Homeostatic Plasticity

An individual neuron hosts up to 10,000 individual synapses that can be made stronger or weaker by local and cell-wide plasticity mechanisms, both of which require protein synthesis. To address over what spatial scale a neuron allocates synaptic resources, we quantified the distribution of newly synthesized proteins after global homeostatic upscaling using metabolic labeling and single-molecule localization (DNA-PAINT). Following upscaling, we observed a global increase in locally synthesized nascent protein in synapses and at dendrites, with a high degree of variability between individual synapses. We determined the smallest spatial scale over which nascent proteins were evenly distributed and found that it is best described by synaptic neighborhoods (~ 10 microns in length)-smaller than a dendritic branch and larger than an individual synapse. Protein allocation at the level of neighborhoods thus represents a solution to the problem of protein allocation within a neuron that balances local autonomy and global homeostasis.

neuroscience↗

bioRxiv2020

ZeroCostDL4Mic: an open platform to simplify access and use of Deep-Learning in Microscopy

The resources and expertise needed to use Deep Learning (DL) in bioimaging remain significant barriers for most laboratories. We present https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki, a platform simplifying access to DL by exploiting the free, cloud-based computational resources of Google Colab. https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki allows researchers to train, evaluate, and apply key DL networks to perform tasks including segmentation, detection, denoising, restoration, resolution enhancement and image-to-image translation. We demonstrate the application of the platform to study multiple biological processes.

bioinformatics↗

bioRxiv2019

Automated highly multiplexed super-resolution imaging of protein nano-architecture in cells and tissues

Understanding the nano-architecture of protein machines in diverse sub-cellular compartments remains a challenge despite rapid progress in super-resolution microscopy. While singlemolecule localization microscopy techniques allow the visualization and identification of cellular structures with near-molecular resolution, multiplex-labeling of tens of target proteins within the same sample has not yet been achieved routinely. However, single sample multiplexing is essential to detect patterns that threaten to get lost in multi-sample averaging. Here, we report maS3TORM (multiplexed automated serial staining stochastic optical reconstruction microscopy), a microscopy approach capable of fully automated 3D dSTORM imaging and solution exchange employing a re-staining protocol to achieve highly multiplexed protein localization within individual biological samples. We demonstrate 3D super-resolution images of 15 target proteins in single cultured cells and 16 targets in individual neuronal tissue samples with <10 nm localization precision. This allowed us to define novel nano-architectural features of protein distribution within the presynaptic nerve terminal.

neuroscience↗

bioRxiv2018

Bleaching-independent, whole-cell, 3D and multi-color STED imaging with exchangeable fluorophores

We demonstrate bleaching-independent STED microscopy using fluorogenic labels that reversibly bind to their target structure. A constant exchange of labels guarantees the removal of photobleached fluorophores and their replacement by intact fluorophores, thereby circumventing bleaching-related limitations of STED super-resolution imaging in fixed and living cells. Foremost, we achieve a constant labeling density and demonstrate a fluorescence signal for long and theoretically unlimited acquisition times. Using this concept, we demonstrate whole-cell, 3D, multi-color and live cell STED microscopy with up to 100 min acquisition time.

biophysics↗

bioRxiv2018

CD95 receptor activation by ligand-induced trimerization is independent of its partial pre-ligand assembly

CD95 (Fas, APO-1, TNFRSF6) is a widely expressed single-pass transmembrane protein that is implicated in cell death, inflammatory response, proliferation and cell migration. CD95 ligand (CD95L, FasL, TNFSF6), is a potent apoptotic inducer in the membrane form but not when cleaved into soluble CD95L (sCD95L). Here, we aimed at understanding the relation between ligand-receptor multimerization and receptor activation by correlating the kinetics of ligand binding, receptor oligomerization, FADD (FAS-Associated via Death Domain) recruitment and caspase-8 activation inside living cells. Using single molecule localization microscopy and Forster resonance energy transfer imaging we show that the majority of CD95 receptors on the plasma membrane are monomeric at rest. This was confirmed functionally as the wild-type receptor is not blocked by a receptor mutant that cannot bind ligand. Moreover, using time-resolved fluorescence imaging approaches we demonstrated that receptor multimerization follows instantaneously ligand binding, whereas FADD recruitment is delayed. This process can explain the typical delay time seen with caspase-8 activity reporters. Finally, the low activity of sCD95L, which was caused by inefficient FADD recruitment, was not explained by the low avidity for the receptor but by a receptor clustering mechanism that was different from the one induced by the strong apoptosis inducer IZ-sCD95L. Our results reveal that receptor activation is modulated by the capacity of its ligand to trimerize it.\n\nHighlightsO_LIAt a density of less than 10 receptors per {micro}m2 CD95 exists as monomer (58%) and dimer (42%)\nC_LIO_LIPre-formed dimers do not contribute to ligand-induced CD95 apoptotic signaling\nC_LIO_LIThe PLAD of CD95 attenuates overexpression-induced, ligand-independent cell death\nC_LIO_LIsoluble CD95L can rapidly multimerize CD95 after binding but it is still a poor inducer of apoptosis through inefficient FADD recruitment\nC_LIO_LIFADD recruitment kinetics but not ligand binding kinetics correlates with caspase-8 onset of activity\nC_LI

cell biology↗

bioRxiv2017

Multiplexed Live-Cell Visualization Of Endogenous Proteins With Nanometer Precision By Fluorobodies

The visualization of endogenous proteins in living cells is a major challenge. A fundamental requirement for spatiotemporally precise imaging is a minimal disturbance of protein function at high signal-to-background ratio. Current approaches for visualization of native proteins in living cells are limited by dark emitting, bulky fluorescent proteins and uncontrollable expression levels. Here, we demonstrate the labeling of endogenous proteins using nanobodies with site-specifically engineered bright organic fluorophores, named fluorobodies. Their fast and fine-tuned intracellular transfer by microfluidic cell squeezing allowed for low background, low toxicity, and high-throughput. Multiplexed imaging of distinct cellular structures was facilitated by specific protein targeting, culminating in live-cell super-resolution imaging of protein networks. The high-throughput delivery of engineered nanobodies will open new avenues in visualizing native cellular structures with unprecedented accuracy in cell-based screens.

cell biology↗