Biology subjects

Hennies, J.

Publications and source records attributed to Hennies, J..

3 recordsLinked to original sources

bioRxiv2026

Intracellular genomic variability driven by cellular compartmentalization in the giant bacterium Achromatium spp

Bacteria of the genus Achromatium harbor hundreds of chromosomes that were previously suggested to be genetically diverse. By sequencing multiple regions from individual cells, we demonstrate that chromosomes within a single cell differ in nucleotide and amino acid sequence, reaching levels of divergence well below accepted bacterial species boundaries and below the range associated with homologous recombination. Sequencing of dividing cells further revealed that daughter cells inherit distinct chromosome populations, a mode of inheritance previously associated with sexual reproduction in eukaryotes. Combining subcellular sequencing with high-resolution microscopy, we reconstruct the three-dimensional cellular architecture of Achromatium and show that extensive intracellular heterogeneity arises from the spatial segregation of chromosome populations by the cells internal structure, which limits genome-wide recombination. Our findings establish cellular architecture as a determinant of genome evolution in giant polyploid bacteria and identify spatial genome segregation as a mechanism enabling the maintenance and inheritance of divergent chromosome populations in bacteria.

microbiology↗

bioRxiv2026

A high-performance end-to-end 3D CLEM processing workflow for facilities

Correlative Light and Electron Microscopy (CLEM) integrates the molecular specificity of light microscopy (LM) with the ultrastructural detail of electron microscopy (EM), enabling comprehensive spatial analysis of biological samples. Despite growing demand, processing 3D CLEM datasets remains challenging, specifically for service provision in facilities, due to their multimodal nature and the lack of unified approaches. Typical steps include EM slice alignment, LM-EM registration, segmentation, and 3D visualization. We present a modular, end-to-end pipeline that consolidates existing and newly developed tools into a coherent workflow for 3D CLEM analysis and allows railroading the approach. Designed as interoperable modules accessible through a user-friendly interface, the pipeline is fully open-source and scales from standard workstations to high-performance computing environments to address the need for analysis of growing datasets. While some steps still require manual input, individual components can be automated to increase throughput and reproducibility. Together, this integrated solution lowers technical barriers and supports broader adoption of 3D CLEM methodologies.

cell biology↗

bioRxiv2023

CebraEM: A practical workflow to segmentcellular organelles in volume SEM datasetsusing a transferable CNN-based membraneprediction

Segmentation of large-volume datasets obtained by volume SEM techniques is a challenging task that generally requires a considerable amount of human effort. Despite recent advances in deep learning leading to the successful segmentation of cellular organelles in a variety of datasets, it is still challenging and time-consuming to produce the necessary data for training a convolutional neural network as well as to set up targeted post-processing pipelines to obtain a good quality full-volume semantic instance segmentation. We present CebraEM, a software package that uses a novel workflow for the segmentation of organelles in volume EM datasets, which helps to minimize the annotation time for the generation of training data. It relies on a generic CNN-based membrane prediction, followed by a well-established machine-learning pipeline that includes over-segmentation before random forest classification and graph multi-cut grouping. The workflow was tested for the segmentation of organelles on different datasets originating from various sample preparations and imaging modalities in volume SEM, in each case resulting in state-of-the-art semantic instance segmentations without additional post-processing. Importantly, by considerably simplifying the segmentation problem, CebraEM empowers single users with the ability to efficiently segment hundreds of gigabytes of data.

cell biology↗