Publications

Signaling pathways induce stereotyped transcriptional changes as stem cells progress into mature cell types during embryogenesis. Signaling perturbations are necessary to discover which genes are responsive or insensitive to pathway activity. However, gene regulation is additionally dependent on cell state-specific factors like chromatin modifications or transcription factor binding. Thus, transcriptional profiles need to be assayed in single cells to identify potentially multiple, distinct perturbation responses among heterogeneous cell states in an embryo. In perturbation studies, comparing heterogeneous transcriptional states among experimental conditions often requires samples to be collected over multiple independent experiments, which can introduce confounding batch effects. We present Design-Aware Integration of Single Cell ExpEriments (DAISEE), a new algorithm that models perturbation responses in single-cell datasets collected according to complex experimental designs. We demonstrate that DAISEE improves upon a previously available integrative nonnegative matrix factorization framework, more efficiently separating perturbation responses from confounding variation. We use DAISEE to integrate newly collected single-cell RNA sequencing datasets from 5-h-old zebrafish embryos expressing optimized photoswitchable MEK (psMEK), which globally activates the extracellular signal-regulated kinase (ERK), a signaling molecule involved in many cell specification events. psMEK drives some cells that are normally not exposed to ERK signals toward other wild type states and induces novel states expressing early-acting endothelial genes. Overactive signaling is therefore capable of producing unexpected gene expression states in developing embryos.

How embryos adapt their internal cellular machinery to reductions in cell size during development remains a fundamental question in cell biology1–11. Here, we use high-resolution lattice light-sheet fluorescence microscopy and automated image analysis to quantify lineage-resolved mitotic spindle and chromosome segregation dynamics from the 2-to 64-cell stages in Caenorhabditis elegans embryos. While spindle length scales with cell size across both wild-type and size-perturbed embryos, chromosome segregation dynamics remain largely invariant, suggesting that distinct mechanisms govern these mitotic processes. Combining femtosecond laser ablation12,13 with large-scale electron tomography14, we find that central spindle microtubules mediate chromosome segregation dynamics and remain uncoupled from cell size across all stages of early development. In contrast, spindle elongation is driven by cortically anchored motor proteins and astral microtubules, rendering it sensitive to cell size12,13,15–17. Incorporating these experimental results into an extended stoichiometric model for both the spindle and chromosomes, we find that allowing only cell size and microtubule catastrophe rates to vary reproduces elongation dynamics across development. The same model also accounts for centrosome separation and pronuclear positioning in the one-cell C. elegans embryo18, spindle-length scaling across nematode species spanning ~100 million years of divergence17, and spindle rotation in human cells19. Thus, a unified stoichiometric framework provides a predictive, mechanistic account of spindle and nuclear dynamics across scales and species.

How embryos adapt their internal cellular machinery to reductions in cell size during development remains a fundamental question in cell biology1–11. Here, we use high-resolution lattice light-sheet fluorescence microscopy and automated image analysis to quantify lineage-resolved mitotic spindle and chromosome segregation dynamics from the 2- to 64-cell stages in Caenorhabditis elegans embryos. While spindle length scales with cell size across both wild-type and size-perturbed embryos, chromosome segregation dynamics remain largely invariant, suggesting that distinct mechanisms govern these mitotic processes. Combining femtosecond laser ablation12,13 with large-scale electron tomography14, we find that central spindle microtubules mediate chromosome segregation dynamics and remain uncoupled from cell size across all stages of early development. In contrast, spindle elongation is driven by cortically anchored motor proteins and astral microtubules, rendering it sensitive to cell size12,13,15–17. Incorporating these experimental results into an extended stoichiometric model for both the spindle and chromosomes, we find that allowing only cell size and microtubule catastrophe rates to vary reproduces elongation dynamics across development. The same model also accounts for centrosome separation and pronuclear positioning in the one-cell C. elegans embryo18, spindle-length scaling across nematode species spanning ~100 million years of divergence17, and spindle rotation in human cells19. Thus, a unified stoichiometric framework provides a predictive, mechanistic account of spindle and nuclear dynamics across scales and species.

Having an accurate reaction coordinate (RC) is essential for reliable kinetic characterization of molecular processes, but there are few quantitative metrics to evaluate RC quality. In this study, we consider the dimensionless γ metric from the Exponential Average Time-dependent Rate (EATR) method, which represents the fraction of a biasing potential along the RC that contributes to increasing the rate constant. We demonstrate that γ can be used to test whether the utility of a RC for predicting kinetics with a Metadynamics bias improves as the coordinate is iteratively updated to include new data. We evaluate RCs approximated via the iterative State Predictive Information Bottleneck (SPIB) approach, which was previously shown to be accurate across six protein–ligand dissociation systems. For these same systems, we compute γ values and mean accelerated times τ̅accel. After systematically scanning over fitting parameters, the results show that γ increases closer to 1, while τ̅accel decreases, revealing a consistent inverse correlation. These results demonstrate that γ serves as a practical criterion for RC evaluation and offers guidance for selecting SPIB–derived coordinates yielding quantitative kinetic predictions.

Having an accurate reaction coordinate (RC) is essential for reliable kinetic characterization of molecular processes, but there are few quantitative metrics to evaluate RC quality. In this study, we consider the dimensionless γ metric from the Exponential Average Time-dependent Rate (EATR) method, which represents the fraction of a biasing potential along the RC that contributes to increasing the rate constant. We demonstrate that γ can be used to test whether the utility of a RC for predicting kinetics with a Metadynamics bias improves as the coordinate is iteratively updated to include new data. We evaluate RCs approximated via the iterative State Predictive Information Bottleneck (SPIB) approach, which was previously shown to be accurate across six protein–ligand dissociation systems. For these same systems, we compute γ values and mean accelerated times τ̅accel. After systematically scanning over fitting parameters, the results show that γ increases closer to 1, while τ̅accel decreases, revealing a consistent inverse correlation. These results demonstrate that γ serves as a practical criterion for RC evaluation and offers guidance for selecting SPIB–derived coordinates yielding quantitative kinetic predictions.

The squeezing of blood cells and vesicles through narrow constrictions, such as splenic slits, pulmonary capillaries, vascular endothelial gaps, and microfluidic channels, is crucial in physiology and biotechnology, with fluid mechanics playing a central role. The diverse geometries of these constrictions, the associated flow conditions, and the unique mechanical properties of cells and vesicles create a rich subject in fluid mechanics emerging from nonlinear dynamics of fluid–structure interactions involving both lubrication and Marangoni flows. Advances in microfluidics, video microscopy, and computational modeling have enabled investigations into these complex processes. This review surveys the key features and approaches, recent prominent studies, and unresolved challenges related to these processes, offering insights for researchers across biomechanics, biomedical engineering, biological physics, hematology, physiology, and applied mathematics.

IgA nephropathy (IgAN), focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), and minimal change disease (MCD) account for the majority of idiopathic glomerulopathies (GN). However, there are no powered transcriptomic datasets coupled to genetic data to investigate the genetic mechanisms underlying gene regulation in the context of GN.

The highly conserved extracellular signal–regulated kinase (ERK) regulates diverse cellular processes by phosphorylating a wide range of intracellular substrates. Its catalytic activity relies on phosphorylation by a single upstream kinase, mitogen-activated protein kinase kinase (MEK), which interacts with only a few binding partners. Here, we test whether the asymmetry in protein–protein interaction network architecture influences the coevolution of the MEK–ERK complex. Phylogenetic sequence analysis across metazoan species revealed accelerated divergence in MEK’s intrinsically disordered N-terminal docking motif (docking site [D-site]), whereas ERK remained highly conserved. Structure prediction with AlphaFold2 and extensive molecular dynamics simulations showed that five conserved D-site residues form stable hydrophobic and electrostatic contacts with ERK’s D-recruitment site. Functional assays in Drosophila melanogaster confirmed that these D-site interactions are essential for proper downstream signaling and support an allosteric role for this motif. Our results demonstrate that MEK uses a structurally simple yet evolutionarily adaptable motif to regulate MEK–ERK complex stability and binding dynamics. The D-site is strongly conserved within phylogenetic groups such as insects or terrestrial vertebrates, yet diverges across them, reflecting evolutionary pressures that balance functional conservation with signaling adaptability. The presented approach illustrates how the combined approach using sequencing data, molecular simulations, and targeted perturbations can be used to address fundamental questions about the evolution of protein–protein interaction networks.

Spontaneous pattern formation in homogeneous systems is ubiquitous in nature. Although Turing demonstrated that spatial patterns can emerge in reaction-diffusion (RD) systems when the homogeneous state becomes linearly unstable, it remains unclear whether Turing mechanism is the only route for pattern formation. Here, we develop an efficient algorithm to systematically construct the solution landscape to find all steady-state solutions connected to a homogeneous state. By applying our method to generic RD models, we find that stable spatial patterns can emerge via saddle-node bifurcations before the onset of Turing instability, and reveal a general nonlinear mechanism that increases the parameter space over which pattern formation occurs in the RD systems. Furthermore, by using a generalized action in the functional space based on large deviation theory, our method is extended to evaluate stability of spatial patterns against noise. Applying this general approach in a three-species RD model, we show that though formation of Turing patterns only requires two chemical species, the third species is critical for stabilizing patterns against strong intrinsic noise in small biochemical systems.

We explore a quaternion adjugate matrix-based representation for rotational motion in the Perspective-n-Point (PnP) problem. Leveraging quadratic quaternion terms within a Determinant Ratio Matrix (DRaM) estimation framework, we extend its application to perspective scenarios, providing a robust and efficient initialization for iterative PnP pose estimation. Notably, by solving the orthographic projection least-squares problem, DRaM provides a reliable initialization that enhances the accuracy and stability of iterative PnP solvers. Experiments on synthetic and real data demonstrate its efficiency, accuracy, and robustness, particularly under high noise conditions. Furthermore, our non-minimal formulation ensures numerical stability, making it effective for real-world applications.