Biology subjects

Haertter, D.

Publications and source records attributed to Haertter, D..

5 recordsLinked to original sources

bioRxiv2026

Hierarchical TPR:ZC3HC1 interactions drive stepwise assembly of nuclear basket extensions projecting into vertebrate cell nuclei

The nuclear basket (NB) is a fibrillar scaffold anchored to the nuclear pore complex (NPC), with protein TPR constituting its structural backbone. Recently, ZC3HC1 was identified as an additional structural NB component interconnecting TPR subpopulations. However, it had remained unclear how ZC3HC1 and TPR engage each other and how the resulting TPR:ZC3HC1 ensembles are arranged relative to the NB. Here, we uncover a hierarchy of sequential interactions between distinct ZC3HC1 and TPR domains, resulting in NB-appended ZC3HC1:TPR ensembles that extend beyond the NB dimensions traditionally considered prototypic. Consistent with a modular organisational principle, we furthermore find copy numbers of NPC/NB-associated TPR to be far higher than commonly anticipated. Together with sufficient ZC3HC1 copy numbers, our data define a model in which alternating recruitment of ZC3HC1 and TPR extends the NB's spatial and functional reach into the nuclear interior.

cell biology↗

bioRxiv2025

SarcAsM: AI-based multiscale analysis of sarcomere organization and contractility in cardiomyocytes

Cardiomyocyte function critically depends on sarcomere dynamics and their organization in myofibrils. To uncover how cardiomyocyte function emerges from individual sarcomere dynamics, comprehensive analysis of Z-bands (nanometer scale), sarcomeres ([~]2 {micro}m scale), and myofibrils ([~]10 to 100 {micro}m scale) is required. Tools for such multiscale analyses are presently lacking. Here we introduce the Sarcomere Analysis Multitool (SarcAsM), which combines deep learning and graph-based methods for automated, fast, and unbiased structural assessment of sarcomeric Z-bands, sarcomeres, and myofibrils as well as their organization in larger myofibril domains. SarcAsM features a generalist deep learning model, pre-trained on a broad range of experimental and published images, ensuring its versatile application and immediate usability across diverse datasets. Finally, we demonstrate the versatile utility of SarcAsM in analyzing sarcomere structure and dynamics under acute and chronic drug exposure in hiPSC-derived cardiomyocytes with fluorescently tagged Z-bands. SarcAsM is available as open-source Python package and stand-alone application.

bioinformatics↗

bioRxiv2024

Stochastic tug-of-war among sarcomeres mediates cardiomyocyte response to environmental stiffness

Cardiac contraction is driven by the collective action of cardiomyocytes, that contain parallel bundles of myofibrils consisting of linear chains of sarcomeres, the basic force-generating units. The dynamics of individual sarcomeres within intact cardiomyocytes remain incompletely understood. While most models assume uniform, synchronized contractions, recent studies hint at unexpected heterogeneity whose origins and significance are not yet clear. By combining the culture of fluorescent sarcomere-reporter hiPSC-derived cardiomyocytes on micropatterned soft gels of different stiffness (5 - 85 kPa) with AI-based tracking of sarcomere motion, we found that increasingly stiff substrates inhibited overall cardiomyocyte contraction, but, surprisingly, did not diminish individual sarcomere dynamics. Instead, sarcomeres competed in a tug-of-war causing increasing heterogeneity, including rapid length oscillations and overextensions (popping). Statistical analysis showed that the heterogeneous dynamics were not caused by static structural differences but were largely stochastic. Stochastic heterogeneity is thus an intrinsic property of cardiac sarcomeres and likely mediates the adaptation of cardiomyocyte contractility to mechanical constraints. A mesoscopic model of coupled sarcomeres shows that these phenomena can be explained by a non-monotonic force-velocity relationship and stochastic fluctuations, where dynamic instability at a critical yielding force creates heterogeneity. Stochastic heterogeneity compensates for structural disorder by randomizing yield events beat-to-beat, preventing damage to specific sarcomeres. Our findings recast cardiac sarcomeres as active, dynamically unstable, and stochastic units engaged in a stochastic tug-of-war, where transient, velocity-dependent forces dominate. We propose that pathological disorder in cardiomyopathy drives a transition from protective stochastic fluctuations to more deterministic, persistently overloaded sarcomeres.

biophysics↗

bioRxiv2023

Minimal vertex model explains how the amnioserosa avoids fluidization during Drosophila dorsal closure

Dorsal closure is a process that occurs during embryogenesis of Drosophila melanogaster. During dorsal closure, the amnioserosa (AS), a one-cell thick epithelial tissue that fills the dorsal opening, shrinks as the lateral epidermis sheets converge and eventually merge. During this process, both shape index and aspect ratio of amnioserosa cells increase markedly. The standard 2-dimensional vertex model, which successfully describes tissue sheet mechanics in multiple contexts, would in this case predict that the tissue should fluidize via cell neighbor changes. Surprisingly, however, the amnioserosa remains an elastic solid with no such events. We here present a minimal extension to the vertex model that explains how the amnioserosa can achieve this unexpected behavior. We show that continuous shrinkage of the preferred cell perimeter and cell perimeter polydispersity lead to the retention of the solid state of the amnioserosa. Our model accurately captures measured cell shape and orientation changes and predicts non-monotonic junction tension that we confirm with laser ablation experiments. Significance StatementDuring embryogenesis, cells in tissues can undergo significant shape changes. Many epithelial tissues fluidize, i.e. cells exchange neighbors, when the average cell shape index increases above a threshold value, consistent with the standard vertex model. During dorsal closure in Drosophila melanogaster, however, the amnioserosa tissue remains solid even as the average cell shape index increases well above threshold. We introduce perimeter polydispersity and allow the preferred cell perimeters, usually held fixed in vertex models, to decrease linearly with time as seen experimentally. With these extensions to the standard vertex model, we capture experimental observations quantitatively. Our results demonstrate that vertex models can describe the behavior of the amnioserosa in dorsal closure by allowing normally fixed parameters to vary with time.

biophysics↗

bioRxiv2021

DeepProjection: Rapid and structure-specific projections of tissue sheets embedded in 3D microscopy stacks using deep learning

The efficient extraction of local high-resolution content from massive amounts of imaging data remains a serious and unsolved problem in studies of complex biological tissues. Here we present DeepProjection, a trainable projection algorithm based on deep learning. This algorithm rapidly and robustly extracts image content contained in curved manifolds from time-lapse recorded 3D image stacks by binary masking of background content, stack by stack. The masks calculated for a given movie, when predicted, e.g., on fluorescent cell boundaries on one channel, can subsequently be applied to project other fluorescent channels from the same manifold. We apply DeepProjection to follow the dynamic movements of 2D-tissue sheets in embryonic development. We show that we can selectively project the amnioserosa cell sheet during dorsal closure in Drosophila melanogaster embryos and the periderm layer in the elongating zebrafish embryo while masking highly fluorescent out-of-plane artifacts.

developmental biology↗