Biology subjects

Spahn, C.

Publications and source records attributed to Spahn, C..

10 recordsLinked to original sources

bioRxiv2026

A simple platform for in situ DNA-PAINT optimization enables improved multiplexed super-resolution imaging in Gram-negative bacteria

DNA-PAINT enables super-resolution imaging with high multiplexing capacity, yet it is rarely used in nanoscale bacteriology. A likely reason is the lack of straightforward protocols and of systematic characterization of imager-docking strand pairs inside intact bacterial cells, whose envelope and dense cytoplasm differ markedly from both the DNA origami in vitro systems used to develop them, and eukaryotic cells. In this work, we establish a screening platform based on RNA-FISH, in which we extend the 16S rRNA-directed EUB338 probe with docking sequences of choice. This allows imager-docking pairs to be benchmarked directly inside Escherichia coli cells and characterized for parameters such as specificity, binding kinetics, and localization precision. We screened 12 different DNA-PAINT sequences and used an optimized set to perform four-target 3D Exchange-PAINT in single E. coli cells. We further applied our strategy to image three effector mRNAs in the Gram-negative pathogen Yersinia enterocolitica and quantified RNA clusters and their 3D distribution. Our work provides a framework and practical guidelines for high-quality 3D DNA-PAINT imaging in Gram-negative bacteria and should facilitate its broader adoption in nanoscale bacteriology.

microbiology↗

bioRxiv2025

Activation of the Yersinia type III secretion system induces large-scale chromosomal and virulence plasmid DNA rearrangements

The type III secretion system (T3SS) is used by Gram-negative bacteria, including important pathogens, to manipulate eukaryotic target cells by injecting effector proteins. Type III secretion and bacterial physiology are known to be tightly interconnected and influence each other. Most notably, secreting cells undergo growth arrest in the T3SS model organisms Yersinia, Salmonella and Shigella. The molecular basis of this phenotype, referred to as secretion-associated growth inhibition, is debated. In Yersinia, T3SS genes are encoded extra-chromosomally in the plasmid of Yersinia virulence (pYV), whose copy number increases upon induction of T3SS secretion. In this study, we characterize the link between T3SS activity and subcellular organization by localizing and quantifying the pYV and chromosomal DNA in Yersinia enterocolitica. We find that activation of secretion not only increases the number of pYV plasmids per bacterium, but that the plasmids also move towards the membrane and poles. This relocalization is not caused by transertion (coupled transcription, translation and translocation) of effectors, but part of a broader DNA rearrangement, leading to a distinct relocalization of chromosomal DNA to mid-cell. We hypothesize that these striking DNA rearrangements occurring during secretion are a main factor in the secretion-associated growth inhibition of pathogenic bacteria.

microbiology↗

bioRxiv2024

Yersinia actively downregulates type III secretion and adhesion at higher cell densities

The T3SS injectisome is used by Gram-negative bacteria, including important pathogens, to manipulate eukaryotic target cells by injecting effector proteins. While in some bacterial species, T3SS-negative bacteria benefit from the activity of their T3SS-positive siblings, the T3SS model organism Yersinia enterocolitica was thought to uniformly express and assemble injectisomes. In this study, we found that Yersinia actively suppress T3SS expression, assembly and activity at higher cell densities, such as inside microcolonies. This effect is highly specific to the T3SS, reversible, and distinct from stationary phase adaptation. It is conferred by the main T3SS transcription factor VirF, which is downregulated at higher densities and whose in trans expression restores T3SS activity. Transcript analysis showed that this effect is mediated by increased levels of the regulatory RNAs CsrBC, which sequester the regulatory protein CsrA and destabilize the virF transcript. The concomitant downregulation of the VirF-dependent adhesin YadA led to a drastic reduction in bacterial cell adhesion. We propose that this active suppression of T3SS secretion and cell attachment at higher local bacterial densities promotes a switch during Yersinia infection from a T3SS-active colonization stage to a bacterial replication and dissemination phase.

microbiology↗

bioRxiv2023

Transertion and cell geometry organize the Escherichia coli nucleoid during rapid growth

AO_SCPLOWBSTRACTC_SCPLOWBacterial chromosomes are spatiotemporally organized and sensitive to environmental changes. However, the mechanisms underlying chromosome configuration and reorganization are currently not fully understood. Using 3D single-molecule localization microscopy and live-cell imaging, we show that the Escherichia coli nucleoid adopts a condensed, membrane-associated configuration during rapid growth. To study the influence of different biosynthetic processes on nucleoid morphology and positioning, we recorded multi-colour super-resolution images during drug treatment. After developing analysis routines for confocal and super-resolution images, we captured highly resolved snapshots which revealed the complete loss of the membrane-bound state of the nucleoid within 10 minutes of halting transcription and translation. This indicates an active role of transertion (coupled transcription, translation and membrane insertion) in nucleoid organization. In contrast, cell wall synthesis inhibition only affects nucleoid organization during morphological changes. Further, we provide evidence that the E. coli nucleoid spatially correlates with MreB in unperturbed E. coli cells, while this correlation diminishes in cells with changed cell geometry or upon inhibition of protein biosynthesis. Replication inhibition experiments, as well as multi-drug treatments highlight the role of entropic effects and transcription in nucleoid condensation and positioning. In summary, we provide experimental evidence for transertion as a principal organiser of the bacterial nucleoid, and show that an altered metabolic state and antibiotic treatment both lead to major changes in the degree of transertion and overall spatial organization of the nucleoid. Our high-resolution characterization reveals dynamics of antibiotic action and provides tools to quantify bacterial chromosome organization. This does not only provide valuable insights into the role of transertion, but can also be applied to study other cell-biological processes.

microbiology↗

bioRxiv2022

Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides

Bioactive peptides are key molecules in health and medicine. Deep learning holds a big promise for the discovery and design of bioactive peptides. Yet, suitable experimental approaches are required to validate candidates in high throughput and at low cost. Here, we established a cell- free protein synthesis (CFPS) pipeline for the rapid and inexpensive production of antimicrobial peptides (AMPs) directly from DNA templates. To validate our platform, we used deep learning to design thousands of AMPs de novo. Using computational methods, we prioritized 500 candidates that we produced and screened with our CFPS pipeline. We identified 30 functional AMPs, which we characterized further through molecular dynamics simulations, antimicrobial activity and toxicity. Notably, six de novo-AMPs feature broad-spectrum activity against multidrug-resistant pathogens and do not develop bacterial resistance. Our work demonstrates the potential of CFPS for production and testing of bioactive peptides within less than 24 hours and <10$ per screen.

synthetic biology↗

bioRxiv2022

Time-resolved cryo-EM reveals early ribosome assembly in action

Ribosome biogenesis is a fundamental multi-step cellular process in all domains of life that involves the production, processing, folding, and modification of ribosomal RNAs (rRNAs) and ribosomal proteins. To obtain insights into the still unexplored early assembly phase of the bacterial 50S subunit, we exploited a minimal in vitro reconstitution system using purified ribosomal components and scalable reaction conditions. Time-limited assembly assays combined with cryo-EM analysis visualizes the structurally complex assembly pathway starting with a particle consisting of ordered density for only [~]500 nucleotides of 23S rRNA domain I and three ribosomal proteins. In addition, our structural analysis reveals that early 50S assembly occurs in a domain-wise fashion, while late 50S assembly proceeds incrementally. Furthermore, we find that both ribosomal proteins and folded rRNA helices, occupying surface exposed regions on pre-50S particles, induce, or stabilize rRNA folds within adjacent regions, thereby creating cooperativity.

biophysics↗

bioRxiv2022

SARS-CoV-2 Nsp1 N-terminal and linker regions as a platform for host translational shutoff

In the early stages of SARS-CoV-2 infection, non-structural protein 1 (Nsp1) inhibits the innate immune response by inserting its C-terminal helices into the mRNA entry channel of the ribosome and promoting mRNA degradation. Nevertheless, the mechanism by which Nsp1 achieves host translational shutoff while allowing for viral protein synthesis remains elusive. We set out to characterize the interactome of full-length Nsp1 and its topology by crosslinking mass spectrometry in order to investigate the role of the N-terminal domain and linker regions in host translational shutoff. We find that these regions are in contact with 40S proteins lining the mRNA entry channel and detect a novel interaction with the G subunit of the eIF3 complex. The crosslink-derived distance restraints allowed us to derive an integrative model of full-length Nsp1 on the 40S subunit, reporting on the dynamic interface between Nsp1, the ribosome and the eIF3 complex. The significance of the Nsp1-eIF3G interaction is supported by further evidence that Nsp1 predominantly binds to 40-43S complexes. Our results point towards a mechanism by which Nsp1 is preferentially recruited to canonical initiation complexes, leading to subsequent mRNA degradation.

molecular biology↗

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↗

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↗

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↗