Biology subjects

Moser, F.

Publications and source records attributed to Moser, F..

3 recordsLinked to original sources

bioRxiv2025

Unmixing of Imaging Mass Spectrometry Measurements Using Microscopy-Informed Constraints

Imaging mass spectrometry (IMS) provides spatially resolved molecular information of organic tissue but can be limited by pixel signals mixing contributions from adjacent biological structures, e.g. of single cells and multicellular functional tissue units (FTUs). This paper proposes computational methods to predict mass spectral profiles of biological structures on the basis of IMS data by "unmixing" pixel-level signals, leveraging microscopy-based boundary information of these structures. By modeling each biological structure as having a unique mass spectrum, we formulate a linear mixing model and solve the corresponding inverse problem that unmixes blended signals. In particular, we cover both overdetermined and underdetermined linear system scenarios and compare ordinary least squares, nonnegative least squares, and singular value thresholding to a custom algorithm, coined Tissue-informed Unmixing of Labeled regions by Inverse Problem (TULIP), specifically tailored to IMS data. Validation on a synthetic in-situ single cell dataset and demonstration on a largescale kidney FTU dataset illustrate the potential of these methods for enhanced in-situ tissue structure analysis, e.g. in cellular and tissue studies.

bioinformatics↗

bioRxiv2025

Integration of customizable 3D printed mixing printheads for controlled and tuneable material and stiffness gradient fabrication with high cell viability in extrusion-based bioprinting

The fabrication of native tissue-like structures with gradual transitions in material properties, cell types and growth factors remains a major challenge in biofabrication. Particularly the complex hierarchical organization, present in living tissues, has to be mimicked as close as possible for the created models to fulfil the desired function. However, the fabrication of gradual structures incorporating several materials ensuring high cell survival and subsequent unaffected tissue maturation is highly challenging. To get a step closer to the goal of generating tissue models containing controlled gradient structures, here we show a novel approach combining extrusion- based 3D bioprinting with static mixing, using self-designed Digital Light Processing (DLP) printed mixing units to fabricate defined gradual structures. Two passive mixing geometries, sinusoidal and obstacle-based, are designed and fabricated and benchmarked against commercially available static mixers. The mixing performance is assessed pixelwise using dyed alginate solutions that are extruded through the mixers using an adapted 3D printer equipped with cavity pumps. The obstacle structure exhibits the highest mixing rate compared to the sinusoidal design and commercially available static mixers. Beyond mixing efficiency, the biological compatibility of the systems is assessed by evaluating the viability of U87 and NIH3T3 cells after extrusion. Two distinct polymer solutions of differing viscosities, composed of allyl-modified gelatin (gelAGE), polyethylene glycol dithiol (PEG-2-SH), and Matrigel, are extruded through the static mixer, revealing significantly higher cell viability in the self-designed printheads compared to commercial mixers. Subsequently, graded structures mimicking the mechanical profile of native brain tissue are fabricated and mechanically characterized using nanoindentation, confirming the successful generation of continuous stiffness gradients. In summary, this study demonstrates that combining extrusion-based 3D printing with customizable mixing units enables the controlled fabrication of tissue-like gradient structures with enhanced mixing rates and cell viability. The design flexibility and rapid fabrication of DLP-printed printheads enable the adaptation to specific tissue requirements, providing a robust platform for future developments in biofabrication and tissue engineering. This work serves as foundation for the creation of increasingly complex and functional tissue models.

bioengineering↗

bioRxiv2021

Subcortical Segmentation of the Fetal Brain in 3D Ultrasound using Deep Learning

The quantification of subcortical volume development from 3D fetal ultrasound can provide important diagnostic information during pregnancy monitoring. However, manual segmentation of subcortical structures in ultra-sound volumes is time-consuming and challenging due to low soft tissue contrast, speckle and shadowing artifacts. For this reason, we developed a convolutional neural network (CNN) for the automated segmentation of the choroid plexus (CP), lateral posterior ventricle horns (LPVH), cavum septum pellucidum et vergae (CSPV), and cerebellum (CB) from 3D ultrasound. As ground-truth labels are scarce and expensive to obtain, we applied few-shot learning, in which only a small number of manual annotations (n = 9) are used to train a CNN. We compared training a CNN with only a few individually annotated volumes versus many weakly labelled volumes obtained from atlas-based segmentations. This showed that segmentation performance close to intra-observer variability can be obtained with only a handful of manual annotations. Finally, the trained models were applied to a large number (n = 278) of ultrasound image volumes of a diverse, healthy population, obtaining novel US-specific growth curves of the respective structures during the second trimester of gestation.

neuroscience↗