Biology subjects

Neumann, O.

Publications and source records attributed to Neumann, O..

5 recordsLinked to original sources

bioRxiv2026

Comprehensive ctDNA Profiling Enables Tissue-of-Origin Prediction and Actionable Biomarker Detection in Cancer of Unknown Primary

Most patients with cancer of unknown primary (CUP) still receive platinum-based chemotherapy and have a poor prognosis, with overall survival of less than one year. Recent studies suggest improved outcomes with molecularly guided or site-specific therapies informed by molecular tissue profiling. Here, we analyzed ctDNA from 190 CUP patients using an integrated genomic and epigenomic assay to identify actionable alterations and predict tissue-of-origin (ToO). Integration of actionable biomarkers, ToO prediction and clinical data yielded diagnostic, prognostic or therapeutic information in 90% of unfavorable CUP cases and 88% of patients analyzed at first diagnosis. High ctDNA tumor fraction was associated with poorer prognosis in both favorable and unfavorable CUP. These findings highlight the clinical utility of ctDNA analysis for therapeutic decision-making in CUP and support its incorporation into the diagnostic work-up, particularly when tissue samples are unavailable or insufficient for molecular testing.

Cancer Biology↗

bioRxiv2026

Spatial scale of indentation explains shift in ratio between spinal cord gray and white matter stiffness

The structural integrity of spinal cord tissue and the transmission of mechanical stimuli across the different levels of tissue microarchitecture and varying spatial scales of mechanical loading challenge experimental and computational efforts to accurately model, simulate and interpret tissue mechanics, leading to conflicting findings in existing literature. Here, we demonstrate that the bead size used in spherical indentation tests significantly affects the stiffness ratio of spinal cord gray to white matter, a dependence which we only observe on the transverse plane and not the coronal plane of the tissue. Our study reveals a shift in stiffness ratio such that for smaller spherical indenters gray matter is stiffer than white matter, while for larger indenters, white matter is stiffer than gray matter. The mean relative change from the 100 {micro}m bead to the 500 {micro}m bead differed between anatomical planes, with transverse sections showing a decrease in gray matter (-13.3%) and an increase in white matter stiffness (+26.9%), accompanied by a reduction in the gray-to-white matter stiffness ratio from 1.07 to 0.76, whereas coronal sections exhibited increases in both gray (+21.0%) and white matter (+33.8%), along with a change in the ratio from 0.99 to 1.14. These findings contribute to explaining previously contradictory results in the literature and underscore the relevance of spatial scales in mechanical characterization studies.

biophysics↗

bioRxiv2022

ObiWan-Microbi: OMERO-based integrated workflow for annotating microbes in the cloud

SummaryReliable deep learning segmentation for microfluidic live-cell imaging requires comprehensive ground truth data. ObiWan-Microbi is a microservice platform combining the strength of state-of-the-art technologies into a unique integrated workflow for data management and efficient ground truth generation for instance segmentation, empowering collaborative semi-automated image annotation in the cloud. Availability and ImplementationObiWan-Microbi is open-source and available under the MIT license at https://github.com/hip-satomi/ObiWan-Microbi, along documentation and usage examples. Contactk.noeh@fz-juelich.de Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2022

microbeSEG: Accurate Cell Segmentation with OMERO Data Management

In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level. Therefore, we present microbeSEG, a user-friendly Python-based cell segmentation tool with a graphical user interface and OMERO data management. microbeSEG utilizes a state-of-the-art deep learning-based segmentation method and can be used for instance segmentation of a wide range of cell morphologies and imaging techniques, e.g., phase contrast or fluorescence microscopy. The main focus of microbeSEG is a comprehensible, easy, efficient, and complete workflow from the creation of training data to the final application of the trained segmentation model. We demonstrate that accurate cell segmentation results can be obtained within 45 minutes of user time. Utilizing public segmentation datasets or pre-labeling further accelerates the microbeSEG workflow. This opens the door for accurate and efficient data analysis of microbial cultures.

bioinformatics↗

bioRxiv2021

On Improving an Already Competitive Segmentation Algorithm for the Cell Tracking Challenge - Lessons Learned

The virtually error-free segmentation and tracking of densely packed cells and cell nuclei is still a challenging task. Especially in low-resolution and low signal-to-noise-ratio microscopy images erroneously merged and missing cells are common segmentation errors making the subsequent cell tracking even more difficult. In 2020, we successfully participated as team KIT-Sch-GE (1) in the 5th edition of the ISBI Cell Tracking Challenge. With our deep learning-based distance map regression segmentation and our graph-based cell tracking, we achieved multiple top 3 rankings on the diverse data sets. In this manuscript, we show how our approach can be further improved by using another optimizer and by fine-tuning training data augmentation parameters, learning rate schedules, and the training data representation. The fine-tuned segmentation in combination with an improved tracking enabled to further improve our performance in the 6th edition of the Cell Tracking Challenge 2021 as team KIT-Sch-GE (2).

bioinformatics↗