Publications

Vorticity, a measure of the local rate of rotation of a fluid element, is the driver of incompressible flow. In viscous fluids, powering bulk flows requires the continuous injection of vorticity from boundaries to counteract the diffusive effects of viscosity. Here we power a flow from within by suspending approximately cylindrical particles and magnetically driving them to rotate at Reynolds numbers in the intermediate range. We find that a single particle generates a localized three-dimensional region of vorticity around it—which we call a vortlet—that drives a number of remarkable behaviours. Slight asymmetries in the particle shape can deform the vortlet and cause the particle to self-propel. Interactions between vortlets are similarly rich, generating bound dynamical states. When a large number of vortlets interact, they spontaneously form collectively moving flocks. These flocks remain coherent while propelling, splitting and merging. If enough particles are added so as to saturate the flow chamber, a homogeneous three-dimensional active chiral fluid of vortlets is formed, which can be manipulated with gravity or flow chamber boundaries, leading to lively collective dynamics. Our findings demonstrate an inertial regime for synthetic active matter, provide a controlled physical system for the quantitative study of three-dimensional flocking in non-sentient systems and establish a platform for the study of three-dimensional active chiral fluids.

Nucleocytoplasmic transport is essential for cellular function, presenting a canonical example of rapid molecular sorting inside cells. It consists of a coordinated interplay between import/export of molecules in/out the cell nucleus. Here, we investigate the role of spatiotemporal dynamics of the nucleocytoplasmic transport and its regulation. We develop a biophysical model that captures the main features of the nucleocytoplasmic transport, in particular, its regulation through the Ran cycle. Our model yields steady-state profiles for the molecular components of the Ran cycle, their relaxation times, as well as the nuclear-to-cytoplasmic molecule ratio. We show that these quantities are affected by their spatial dynamics and heterogeneity within the nucleus. Specifically, we find that the spatial nonuniformity of Ran guanine exchange factor (RanGEF)—particularly its proximity to the nuclear envelope—increases the Ran content in the nucleus. We further show that RanGEF's accumulation near the nuclear envelope results from its intrinsic dynamics as a nuclear cargo, transported by the Ran cycle itself. Overall, our work highlights the critical role of molecular spatial dynamics in cellular processes and proposes new avenues for theoretical and experimental inquiries into the nucleocytoplasmic transport.

Extracting consistent statistics between relevant free energy minima of a molecular system is essential for physics, chemistry, and biology. Molecular dynamics (MD) simulations can aid in this task but are computationally expensive, especially for systems that require quantum accuracy. To overcome this challenge, we developed an approach combining enhanced sampling with deep generative models and active learning of a machine learning potential (MLP). We introduce an adaptive Markov chain Monte Carlo framework that enables the training of one normalizing flow (NF) and one MLP per state, achieving rapid convergence toward the Boltzmann distribution. Leveraging the trained NF and MLP models, we compute thermodynamic observables such as free energy differences and optical spectra. We apply this method to study the isomerization of an ultrasmall silver nanocluster belonging to a set of systems with diverse applications in the fields of medicine and catalysis.

Pituitary adenomas cause hormonal dysregulation and severe morbidity. The absence of clear biomarkers for prognosis/treatment and the high risk of recurrence make management challenging. Non-functioning gonadotroph pituitary adenomas (NFPAs) represent proliferation of gonadotroph lineage cells without an increase in secretion of gonadotropins. Detection is often delayed until mass effects cause visual defects. Higher resolution molecular study may improve classification, diagnosis, treatment, and provide insight into adenoma biology. We analyzed four NFPAs using unbiased genome-wide same-cell single nucleus 10X Genomics multiome assay (transcriptome and chromatin accessibility) to define individual cell identity/states in comparison with normal gonadotrope cell references from twelve age/sex-matched human post-mortem pituitaries. In total, we generated high quality multiomics single-cell data for 33,832 adenoma cells and 5,451 normal gonadotropes. We identified tumor, immune, vascular, and proliferative cells confirming cellular heterogeneity. Multidimensional scaling analysis using Manhattan distances between cell types showed that NFPA gonadotropes were closer in gene expression to healthy gonadotropes among all pituitary cell types. In addition, we observed differences in the macrophage and endothelial cell populations between tumors and healthy pituitaries. Next, we studied gene regulatory circuitry (regulated gene, modulating transcription factor and its chromatin interaction site) using the new Control of Regulation Extracted from Multiomics Assays (CREMA, [1]) multiome analysis method which showed high variation between normal and tumor gonadotropes. Within each adenoma, the NFPA tumor cells encompassed two main subgroups of cells, one group (GT+ marker tumor cells) expressing established gonadotrope markers and another group not (GT- marker tumor cells). GT+ and GT- tumor cells formed a cluster distinct from healthy gonadotropes. GT+ and GT- tumor cells differed by their level of expression of mitochondrial genes, suggesting decreased aerobic capacity associated with the GT- tumor subgroup. Interestingly, differential analyses between the healthy and the NFPA gonadotropes from same-cell multiome data highlighted dissimilar gene expression and chromatin accessibility patterns.Our newly collected single cell data characterize the difference between NFPA tumor cells and normal gonadotrope cells. Our results suggest a Warburg-effect like difference in aerobic respiratory capacity among tumor cells within the same pituitary adenoma. Overall, this study brings new insights into the molecular characteristics of NFPAs, including tumor cell heterogeneity, and represents a new resource for the field.

Habituation—a phenomenon in which a dynamical system exhibits a diminishing response to repeated stimulations that eventually recovers when the stimulus is withheld—is universally observed in living systems from animals to unicellular organisms. Despite its prevalence, generic mechanisms for this fundamental form of learning remain poorly defined. Drawing inspiration from prior work on systems that respond adaptively to step inputs, we study habituation from a nonlinear dynamics perspective. This approach enables us to formalize classical hallmarks of habituation that have been experimentally identified in diverse organisms and stimulus scenarios. We use this framework to investigate distinct dynamical circuits capable of habituation. In particular, we show that driven linear dynamics of a memory variable with static nonlinearities acting at the input and output can implement numerous hallmarks in a mathematically interpretable manner. This work establishes a foundation for understanding the dynamical substrates of this primitive learning behavior and offers a blueprint for the identification of habituating circuits in biological systems.

2D template matching (2DTM) can be used to detect molecules and their assemblies in cellular cryo-EM images with high positional and orientational accuracy. While 2DTM successfully detects spherical targets such as large ribosomal subunits, challenges remain in detecting smaller and more aspherical targets in various environments. In this work, a novel 2DTM metric, referred to as the 2DTM p-value, is developed to extend the 2DTM framework to more complex applications. The 2DTM p-value combines information from two previously used 2DTM metrics, namely the 2DTM signal-to-noise ratio (SNR) and z-score, which are derived from the cross-correlation coefficient between the target and the template. The 2DTM p-value demonstrates robust detection accuracies under various imaging and sample conditions and outperforms the 2DTM SNR and z-score alone. Specifically, the 2DTM p-value improves the detection of aspherical targets such as a modified artificial tubulin patch particle (500 kDa) and a much smaller clathrin monomer (193 kDa) in simulated data. It also accurately recovers mature 60S ribosomes in yeast lamellae samples, even under conditions of increased Gaussian noise. The new metric will enable the detection of a wider variety of targets in both purified and cellular samples through 2DTM.

The terminal cells of the Drosophila larval tracheal system are perhaps the simplest delivery networks, providing an analogue for mammalian vascular growth and function in a system with many fewer components. These cells are a prime example of single-cell morphogenesis, branching significantly over time to adapt to the needs of the growing tissue they supply. While the genetic mechanisms governing local branching decisions have been studied extensively, an understanding of the emergence of a global network architecture is still lacking. Mapping out the full network architecture of populations of terminal cells at different developmental times of Drosophila larvae, we find that cell growth follows scaling laws relating the total edge length, supply area, and branch density. Using time-lapse imaging of individual terminal cells, we identify that the cells grow in three ways: by extending branches, by the side budding of new branches, and by internally growing existing branches. A generative model based on these modes of growth recapitulates statistical properties of the terminal cell network data. These results suggest that the scaling laws arise from the coupled contributions of branching and internal growth. This study establishes the terminal cell as a uniquely tractable model system for further studies of transportation and distribution networks.

In vivo molecular imaging tools hold immense potential to drive transformative breakthroughs by enabling researchers to visualize cellular and molecular interactions in real-time and/or at high resolution. These advancements will facilitate a deeper understanding of fundamental biological processes and their dysregulation in disease states. Here, we develop and characterize a self-assembling protein nanomicelle called collagen type I binding – thermoresponsive assembled protein (Col1-TRAP) that binds tightly to type I collagen in vitro with nanomolar affinity. For ex vivo visualization, Col1-TRAP is labeled with a near-infrared fluorescent dye (NIR-Col1-TRAP). Both Col1-TRAP and NIR-Col1-TRAP display approximately a 3.8-fold greater binding to type I collagen compared to TRAP when measured by surface plasmon resonance (SPR). We present a proof-of-concept study using NIR-Col1-TRAP to detect fibrotic type I collagen deposition ex vivo in the livers of mice with non-alcoholic steatohepatitis (NASH). We show that NIR-Col1-TRAP demonstrates significantly decreased plasma recirculation time as well as increased liver accumulation in the NASH mice compared to mice without disease over 4 hours. As a result, NIR-Col1-TRAP shows potential as an imaging probe for NASH with in vivo targeting performance after injection in mice.

Multiomic studies at a single cell and spatial resolution are powerful approaches to define molecular and cellular landscape of the human kidney and understand etiology of failed or successful repair in acute or chronic injury. We expand KPMP AtlasV1 with clinicopathological correlations and maps of immune-fibroblast-vascular niches with insights into AKI-CKD transition.

From the Golgi apparatus to endosomes, organelles in the endomembrane system exhibit complex and varied morphologies that are often related to their function. Such membrane-bound organelles operate far from equilibrium due to directed fluxes of smaller trafficking vesicles; the physical principles governing the emergence and maintenance of these structures have thus remained elusive. By understanding individual fission and fusion events in terms of active mechano-chemical cycles, we show how such trafficking manifests at the hydrodynamic scale, resulting not only in fluxes of material -- such as membrane area and encapsulated volume -- but also in active stresses that drive momentum transfer between an organelle and its cytosolic environment. Due to the fluid and deformable nature of the bounding membrane, this gives rise to novel physics, coupling nonequilibrium forces to organelle composition, morphology and hydrodynamic flows. We demonstrate how both stable compartment drift and ramified sac-like morphologies, each reminiscent of Golgi-cisternae, emerge naturally from the same underlying nonequilibrium dynamics of fission and fusion.