Selected Presentations & Appearances
The mechanical properties of soft materials can be probed on small length scales by microrheology, commonly done by tracking embedded micrometer-sized beads. We here introduce filament-based microrheology (FMR) using high-aspect-ratio semi-flexible filaments as probes. Such quasi-1D probes are much less invasive than beads due to their small cross sections. By imaging transverse bending modes, we can simultaneously probe multiple length scales. As a proof of principle, we use semiflexible single-walled carbon nanotubes (SWNTs) as probes that can be accurately and rapidly imaged based on their stable near-IR fluorescence. We find that the viscoelastic properties of sucrose, polyethylene oxide, and hyaluronic acid solutions measured in this way are in good agreement with those measured by conventional micro- and macrorheology.
When probing cells, it can be advantageous to avoid introducing probe particles altogether. We show that one can directly use fluctuation analysis of native cytoskeletal filaments, in this case microtubules, to perform intracellular microrheology when filament properties are known. Alternatively, one can probe filament mechanics when the response properties of the embedding cytoplasm are known. The latter approach is useful because microtubule mechanics in living cells are believed to be regulated by post-translational modifications, but are extremely difficult to probe directly, while fluctuations are difficult to interpret because they are generated by active forces in a surrounding cytoplasm with poorly understood material properties. We discovered that polyglutamylation, a post-translational modification enriched on microtubule networks that need to withstand large mechanical forces such as those in axons or cilia, significantly increases microtubule stiffness in living cells.
Cardiomyopathies, diseases of the heart muscle, affect 1 in 500 adults in Western countries. Nevertheless, reliable knowledge about disease onset and pathogenesis is lacking. To develop effective treatment options for patients, a dynamic and quantitative understanding of cardiomyopathies is needed. We developed an assay in which individual stem-cell derived heart cells of a fluorescent sarcomere reporter cell line grow in a heart-like environment - while allowing for automated high-resolution and high-framerate imaging - using micropatterned polyacrylamide acid (PAA) gels. We analyze the time-course of cell morphology and function upon drug-induced or genetic interventions with our deep-learning-based SarcAsM (Sarcomere Analysis Multitool) software. The resulting multiparametric functional and structural trajectories of cardiac muscle cells can be used to gain novel dynamical perspectives on the time-course and interplay of structure and function in health and disease and might contribute to the discovery of novel treatments.
Dorsal closure in Drosophila melanogaster embryos is a key model system for cell sheet morphogenesis and wound healing. We pursue a data-driven approach to understand the emergence of organized behavior on tissue level from the stochastic dynamics of single cells across scales. We developed DeepTissue, a deep-learning-based algorithm to automatically and robustly detect and temporally track various single cell features: cell shapes, cell junction lengths, myosin intensities, and tissue topology. Epithelial cells in dorsal closure exhibit oscillations and contribute to progressive cell sheet movements, while showing a large variability in individual shapes, dynamics, and fates. Based on high-quality multi-parametric trajectories of 1000s of single cells, we use unsupervised machine learning techniques to detect and classify behavioral and structural phenotypes. Further we study how the behavior of single cells throughout closure is driven by deterministic and/or stochastic factors, with the aim to predict singular cell ingression events.
The ability to maintain turgor pressure, i.e. osmotic imbalance, across the cell envelope, is a requirement for
proper metabolism and growth in walled microbial cells. To date, turgor pressure has only been estimated or
indirectly measured, because direct access has been difficult due to small cell sizes. How turgor pressure
depends on external conditions such as osmolarity and nutrient content remains largely unknown. Here we
present a micromanipulation technique using an atomic force microscope (AFM) that allows us to directly
measure and track turgor pressure on a living bacterium. We compress single cells between a large bead and a
flat surface using an AFM cantilever. Measured forces and indentation depths are then used to determine turgor
pressure with the help of a mechanical model that describes the deformation of the cell. We report dependencies
on external osmolarities for E. coli and B. subtilis.
Bacterial cell walls have to contain high internal turgor pressures of ~1atm in gram-negative bacteria and >10 atm in gram-positive bacteria. At the same time the wall has to be continuously expanded while a bacterium grows. For most bacterial cell walls a covalently crosslinked polymer network, the peptidoglycan (PG) layer, provides mechanical toughness. The PG layer is a thin porous polymer network - made of rigid glycan strands crosslinked by flexible oligopeptides. Bacteria achieve mechanical toughness while the wall is growing by careful control of defect generation, material insertion and network repair mechanisms. Many antibiotics act by interfering with these mechanisms. In order to understand the mechanisms of mechanical wall failure under extreme challenges, we model the PG layer as an anisotropic elastic network composed of two types of nonlinear springs (glycans and oligopeptides) using parameters from E-coli. The model assigns different structural, linear and non-linear elastic properties to the network constituents: Glycan strands are rigid and long, while peptides are flexible and short. We characterize stress-strain relationships, anisotropy, pore size distributions, and failure susceptibility and geometry as a function of the crosslink density, length distribution of glycan strands and angular alignment.
Sarcomeres are the basic contractile units of cardiac muscle
cells. We cultured individual hiPSC-derived cardiomyocytes on
biomimetic patterned substrates. We automatically tracked
single sarcomere dynamics from high-speed confocal
recordings with a custom machine-learning tool. While
emergent cell-level contractions were smooth, we found highly
stochastic and heterogeneous motions of single sarcomeres.
Rigid mechanical constraints force sarcomeres into a tug-ofwar
like competition driving dynamic heterogeneity. Analysis of
a large data set (>1200 cells) indicates that sarcomere
heterogeneity is not caused by static non-uniformity among
sarcomeres (e.g., strong/weak units), but can be primarily
attributed to the stochastic and non-linear nature of sarcomere
dynamics. We show that a simple dynamic model reproduces
crucial experimental findings by assuming non-monotonic
force-velocity relations for single sarcomeres, as previously
predicted for ensembles of motor proteins. This led us to a
novel, active matter perspective on sarcomere motion, with
sarcomeres as interacting non-linear, stochastic agents.
Presentation of new research on mechanosensitivity in beacteria and fruit flies to biology faculty and students
Presentation of research on Drosophila mechanosensory organs to a worldwide audience in the APS/BPPB online seminar series
Report on recent applications of advanced imaging methods at the ALIS center
Presentation of research results on Drosophila mechanosensory organs