Publications

Although membranous nephropathy (MN) is one of the most common causes of nephrotic syndrome, the molecular characteristics of the kidney damage in MN remain poorly defined. In this study, the authors applied a machine-learning framework to predict diagnosis on the basis of gene expression in microdissected kidney tissue from patients with glomerulonephropathies. They found that MN has a glomerular transcriptional signature that distinguishes it from other glomerulonephropathies and identified a set of MN-specific genes differentially expressed across two independent cohorts and robustly recovered in an additional validation cohort. They also found the MN-specific genes are enriched in targets of transcription factor NF-κB and are predominantly expressed in podocytes. This work provides a molecular snapshot of MN and elucidates transcriptional alterations specific to this disease.

The cytoskeleton -- a collection of polymeric filaments, molecular motors, and crosslinkers -- is a foundational example of active matter, and in the cell assembles into organelles that guide basic biological functions. Simulation of cytoskeletal assemblies is an important tool for modeling cellular processes and understanding their surprising material properties. Here we present aLENS, a novel computational framework to surmount the limits of conventional simulation methods. We model molecular motors with crosslinking kinetics that adhere to a thermodynamic energy landscape, and integrate the system dynamics while efficiently and stably enforcing hard-body repulsion between filaments -- molecular potentials are entirely avoided in imposing steric constraints. Utilizing parallel computing, we simulate different mixtures of tens to hundreds of thousands of cytoskeletal filaments and crosslinking motors, recapitulating self-emergent phenomena such as bundle formation and buckling, and elucidating how motor type, thermal fluctuations, internal stresses, and confinement determine the evolution of active matter aggregates.

A conserved phase of gametogenesis is the development of oocytes and sperm within cell clusters (germline cysts) that arise through serial divisions of a founder cell. The resulting cell lineage trees (CLTs) exhibit diverse topologies across animals and can give rise to numerous emergent behaviors. Despite their centrality, sex-specific differences underlying the evolution and patterning of these cell trees are unknown. In Drosophila melanogaster, oocytes develop within a highly invariant and maximally branched 16-cell tree whose topology is constrained by the fusome – a branched membranous organelle critical for proper mitosis in females; the same division pattern and topology are widely thought to occur during spermatogenesis. Using highly-resolved three-dimensional reconstructions based on a supervised learning algorithm, we show that cell divisions in male cysts can deviate from the maximally branched pattern, leading to greater topological variability. Furthermore, in contrast to females, fusome fragmentation is common, suggesting germ cell mitoses can occur in its absence. These findings thus add to the repertoire of CLT formation strategies, highlighting the diversity of mechanisms employed during gametogenesis in the animal kingdom.

Quaternions are important for a wide variety of rotation-related problems in computer graphics, machine vision, and robotics. We study the nontrivial geometry of the relationship between quaternions and rotation matrices by exploiting the adjugate matrix of the characteristic equation of a related eigenvalue problem to obtain the manifold of the space of a quaternion eigenvector. We argue that quaternions parameterized by their corresponding rotation matrices cannot be expressed, for example, in machine learning tasks, as single-valued functions: the quaternion solution must instead be treated as a manifold, with different algebraic solutions for each of several single-valued sectors represented by the adjugate matrix. We conclude with novel constructions exploiting the quaternion adjugate variables to revisit several classic pose estimation applications: 2D point-cloud matching, 2D point-cloud-to-projection matching, 3D point-cloud matching, 3D orthographic point-cloud-to-projection matching, and 3D perspective point-cloud-to-projection matching. We find an exact solution to the 3D orthographic least squares pose extraction problem, and apply it successfully also to the perspective pose extraction problem with results that improve on existing methods.

Continuum kinetic theories provide an important tool for the analysis and simulation of particle suspensions. When those particles are anisotropic, the addition of a particle orientation vector to the kinetic description yields a dimensional theory which becomes intractable to simulate, especially in three dimensions or near states where the particles are highly aligned. Coarse-grained theories that track only moments of the particle distribution functions provide a more efficient simulation framework, but require closure assumptions. For the particular case where the particles are apolar, the Bingham closure has been found to agree well with the underlying kinetic theory; yet the closure is non-trivial to compute, requiring the solution of an often nearly-singular nonlinear equation at every spatial discretization point at every timestep. In this paper, we present a robust, accurate, and efficient numerical scheme for evaluating the Bingham closure, with a controllable error/efficiency tradeoff. To demonstrate the utility of the method, we carry out high-resolution simulations of a coarse-grained continuum model for a suspension of active particles in parameter regimes inaccessible to kinetic theories. Analysis of these simulations reveals that inaccurately computing the closure can act to effectively limit spatial resolution in the coarse-grained fields. Pushing these simulations to the high spatial resolutions enabled by our method reveals a coupling between vorticity and topological defects in the suspension director field, as well as signatures of energy transfer between scales in this active fluid model.

Neurons from layer II of the entorhinal cortex (ECII) are the first to accumulate tau protein aggregates and degenerate during prodromal Alzheimer’s disease. Here, we use a data-driven functional genomics approach to model ECII neurons in silico and identify the proto-oncogene DEK as a potential driver of tau pathology. By modulating DEK levels in EC neurons in vitro and in vivo, we first validate the accuracy and cell-type specificity of our network predictions. We then show that Dek silencing changes the inducibility of immediate early genes and alters neuron excitability, leading to dysregulation of neuronal plasticity genes. We further find that loss of function of DEK leads to tau accumulation in the soma of ECII neurons, reactivity of surrounding microglia, and eventually microglia-mediated neuron loss. This study validates a pathological gene discovery tool that opens new therapeutic avenues and sheds light on a novel pathway driving tau pathology in vulnerable neurons.

Labeled protein-based biomaterials have become popular for various biomedical applications such as tissue-engineered, therapeutic, and diagnostic scaffolds. Labeling of protein biomaterials, including with ultrasmall superparamagnetic iron oxide (USPIO) nanoparticles, has enabled a wide variety of imaging and therapeutic techniques. These USPIO-based biomaterials are widely studied in magnetic resonance imaging (MRI), thermotherapy, and magnetically-driven drug delivery, which provide a method for direct and non-invasive monitoring of implants or drug delivery agents. Where most developments have been made using polymers or collagen hydrogels, shown here is the use of a rationally designed protein as the building block for a meso-scale fiber. While USPIOs have been chemically conjugated to antibodies, glycoproteins, and tissue-engineered scaffolds for targeting or improved biocompatibility and stability, these constructs have predominantly served as diagnostic agents and often involve harsh conditions for USPIO synthesis. Here, we present an engineered protein–iron oxide hybrid material comprised of an azide-functionalized coiled-coil protein with small molecule binding capacity conjugated via bioorthogonal azide–alkyne cycloaddition to an alkyne-bearing iron oxide templating peptide, CMms6, for USPIO biomineralization under mild conditions. The coiled-coil protein, dubbed Q, has been previously shown to form nanofibers and, upon small molecule binding, further assembles into mesofibers via encapsulation and aggregation. The resulting hybrid material is capable of doxorubicin encapsulation as well as sensitive weighted MRI darkening for strong imaging capability that is uniquely derived from a coiled-coil protein.

We present a spectrally accurate embedded boundary method for solving linear, inhomogeneous, elliptic partial differential equations (PDE) in general smooth geometries, focusing in this manuscript on the Poisson, modified Helmholtz, and Stokes equations. Unlike several recently proposed methods which rely on function extension, we propose a method which instead utilizes function `intension', or the smooth truncation of known function values. Similar to those methods based on extension, once the inhomogeneity is truncated we may solve the PDE using any of the many simple, fast, and robust solvers that have been developed for regular grids on simple domains. Function intension is inherently stable, as are all steps in the proposed solution method, and can be used on domains which do not readily admit extensions. We pay a price in exchange for improved stability and flexibility: in addition to solving the PDE on the regular domain, we must additionally (1) solve the PDE on a small auxiliary domain that is fitted to the boundary, and (2) ensure consistency of the solution across the interface between this auxiliary domain and the rest of the physical domain. We show how these tasks may be accomplished efficiently (in both the asymptotic and practical sense), and compare convergence to several recent high-order embedded boundary schemes.

There is a need for better classification and understanding of tumor-infiltrating lymphocytes (TILs). Here, we applied advanced functional genomics to interrogate 9,000 human tumors and multiple single-cell sequencing sets using benchmarked T cell states, comprehensive T cell differentiation trajectories, human and mouse vaccine responses, and other human TILs. Compared with other T cell states, enrichment of T memory/resident memory programs was observed across solid tumors. Trajectory analysis of single-cell melanoma CD8+ TILs also identified a high fraction of memory/resident memory-scoring TILs in anti-PD-1 responders, which expanded post therapy. In contrast, TILs scoring highly for early T cell activation, but not exhaustion, associated with non-response. Late/persistent, but not early activation signatures, prognosticate melanoma survival, and co-express with dendritic cell and IFN-γ response programs. These data identify an activation-like state associated to poor response and suggest successful memory conversion, above resuscitation of exhaustion, is an under-appreciated aspect of successful anti-tumoral immunity.

During early Drosophila embryogenesis, a network of gene regulatory interactions orchestrates terminal patterning, playing a critical role in the subsequent formation of the gut. We utilized CRISPR gene editing at endogenous loci to create live reporters of transcription and light-sheet microscopy to monitor the individual components of the posterior gut patterning network across 90 min prior to gastrulation. We developed a computational approach for fusing imaging datasets of the individual components into a common multivariable trajectory. Data fusion revealed low intrinsic dimensionality of posterior patterning and cell fate specification in wild-type embryos. The simple structure that we uncovered allowed us to construct a model of interactions within the posterior patterning regulatory network and make testable predictions about its dynamics at the protein level. The presented data fusion strategy is a step toward establishing a unified framework that would explore how stochastic spatiotemporal signals give rise to highly reproducible morphogenetic outcomes.