Biology subjects

Arzt, M.

Publications and source records attributed to Arzt, M..

4 recordsLinked to original sources

bioRxiv2026

Expanded Proteome Coverage Powered by Advanced Ion Processing Enables Deep Single-Cell Drug Response Subtyping in Human Stem Cell Derived Cardiomyocytes

Single-cell proteomics (SCP) enables the study of cellular heterogeneity at the functional level but remains limited by incomplete proteome coverage and high data missingness. Here, we present an enhanced label-free SCP workflow that leverages the timsUltra AIP mass spectrometry platform equipped with the Athena Ion Processor (AIP). Across a controlled dilution series of human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs), AIP-enabled acquisition consistently increased proteome depth and detection consistency across cells at all input levels. In single iPSC-CMs, the timsUltra AIP quantified up to 3,858 protein groups, averaging [~]1,300 proteins per cell, enabling robust proteome-level classification of cardiomyocyte subtypes. Using a reference-based protein classifier, cells were stratified into mature cardiomyocytes and less differentiated cell states, revealing substantial baseline heterogeneity. Importantly, increased single-cell sensitivity translated directly into biological insight, as approximately 30% of differentially expressed proteins associated with subtype-specific drug responses were detected exclusively by timsUltra AIP. Application of this workflow to PR-364 (a mitophagy boosting drug) dose-response experiment uncovered distinct, subtype-dependent pathway adaptations. Mature cardiomyocytes exhibited dose-dependent increases in mitochondrial and metabolic pathway activity, while immature cells showed enrichment of cytoskeletal and developmental programs. These effects were partially obscured in simulated bulk analyses, highlighting the value of single-cell resolution. Together, these results demonstrate that improved fragment ion transmission and utilization translate directly into enhanced biological insight, enabling more comprehensive and functionally relevant single-cell proteomics.

molecular biology↗

bioRxiv2026

Embryo-eggshell interaction counteracts chiral bias in early Drosophila morphogenesis

Morphogenetic processes during animal development are remarkably invariant (Duboule, 1994; Hall, 1997; Kalinka et al., 2010; Raff, 1996). This stability is established by the interaction between genetic determination of developmental progression and the constraints imposed by the surrounding embryonic environment (Busby and Steventon, 2021; Gilmour et al., 2017; Gorfinkiel and Martinez Arias, 2021). We discovered that the germ band extension process in Drosophila is rather variable: instead of extending straight towards the head, the germ band tends to twist to the side. Through a combination of experiments and theory, we demonstrated that Scab integrin-mediated attachment to the vitelline envelope stabilizes the germ band and supports its straight extension. Our quantification of germ band extension dynamics also revealed a consistent handedness to the twist of the germ band. We showed that this left-right asymmetry can be altered by manipulating the expression of Myo1D, the molecular determinant of chirality in Drosophila (Lebreton et al., 2018). Our data thus suggest that Myo1D expression causes the early gastrulating blastoderm epithelium to already exhibit inherent chirality and that the resulting destabilization of germ band extension is suppressed by Scab-mediated friction between the blastoderm and the vitelline envelope.

developmental biology↗

bioRxiv2025

Mastodon: the Command Center for Large-Scale Lineage-Tracing Microscopy Datasets

Understanding development in living organisms requires following the divisions, movements, and fates of cells across developing systems. While advances in microscopy have enabled whole-embryo imaging at the cellular level, extracting and analyzing cell lineages from these massive datasets remains a significant computational challenge. We present Mastodon, a scalable, extensible software platform for manual, semi-automated, and automated cell tracking in large images. A purpose-built graph model supports responsive performance for datasets with millions of annotations, making Mastodon a future-proof platform for cell lineage analysis. Built as a Fiji plugin, Mastodon enables interactive visualization, editing, and analysis of complex lineage trees, seamlessly integrated with the raw image data. Comprehension of cell lineages in complex three-dimensional geometries is facilitated by interoperability with the powerful open-source render engine Blender. In three distinct developmental contexts, we demonstrate how Mastodon will accelerate biological insights by providing user-friendly navigation and explorative analysis in complex lineage datasets.

bioinformatics↗

bioRxiv2021

Labkit: Labeling and Segmentation Toolkit for Big Image Data

We present LO_SCPLOWABKITC_SCPLOW, a user-friendly Fiji plugin for the segmentation of microscopy image data. It offers easy to use manual and automated image segmentation routines that can be rapidly applied to single- and multi-channel images as well as to timelapse movies in 2D or 3D. LO_SCPLOWABKITC_SCPLOW is specifically designed to work efficiently on big image data and enables users of consumer laptops to conveniently work with multiple-terabyte images. This efficiency is achieved by using ImgLib2 and BigDataViewer as the foundation of our software. Furthermore, memory efficient and fast random forest based pixel classification inspired by the Waikato Environment for Knowledge Analysis (Weka) is implemented. Optionally we harness the power of graphics processing units (GPU) to gain additional runtime performance. LO_SCPLOWABKITC_SCPLOW is easy to install on virtually all laptops and workstations. Additionally, LO_SCPLOWABKITC_SCPLOW is compatible with high performance computing (HPC) clusters for distributed processing of big image data. The ability to use pixel classifiers trained in LO_SCPLOWABKITC_SCPLOW via the ImageJ macro language enables our users to integrate this functionality as a processing step in automated image processing workflows. Last but not least, LO_SCPLOWABKITC_SCPLOW comes with rich online resources such as tutorials and examples that will help users to familiarize themselves with available features and how to best use LO_SCPLOWABKITC_SCPLOW in a number of practical real-world use-cases.

bioinformatics↗