We introduce an efficient search strategy to substantially accelerate feature based registration. Previous feature based registration algorithms often use truncated search strategies in order to achieve small computation times. Our new accelerated search strategy is based on the realization that the search for corresponding features can be dramatically accelerated by utilizing Johnson-Lindenstrauss dimension reduction. Order of magnitude calculations for the search strategy we propose here indicate that the algorithm proposed is more than a million times faster than previously utilized naive search strategies, and this advantage in speed is directly translated into an advantage in accuracy as the fast speed enables more comparisons to be made in the same amount of time. We describe the accelerated scheme together with a full complexity analysis. The registration algorithm was applied to large transmission electron microscopy (TEM) images of neural ultrastructure. Our experiments demonstrate that our algorithm enables alignment of TEM images with increased accuracy and efficiency compared to previous algorithms.

Only a few years after its inception, localization-based super-resolution microscopy has become widely employed in biological studies. Yet, it is primarily used in two-dimensional imaging and accessing the organization of cellular structures at the nanoscale in three dimensions (3D) still poses important challenges. Here, we review optical and computational techniques that enable the 3D localization of individual emitters and the reconstruction of 3D super-resolution images. These techniques are grouped into three main categories: PSF engineering, multiple plane imaging and interferometric approaches. We provide an overview of their technical implementation as well as commentary on their applicability. Finally, we discuss future trends in 3D localization-based super-resolution microscopy.

Transcription factors (TFs) control gene expression by binding to genomic DNA in a sequence-specific manner. Mutations in TF binding sites are increasingly found to be associated with human disease, yet we currently lack robust methods to predict these sites. Here, we developed a versatile maximum likelihood framework named No Read Left Behind (NRLB) that infers a biophysical model of protein-DNA recognition across the full affinity range from a library of in vitro selected DNA binding sites. NRLB predicts human Max homodimer binding in near-perfect agreement with existing low-throughput measurements. It can capture the specificity of the p53 tetramer and distinguish multiple binding modes within a single sample. Additionally, we confirm that newly identified low-affinity enhancer binding sites are functional in vivo, and that their contribution to gene expression matches their predicted affinity. Our results establish a powerful paradigm for identifying protein binding sites and interpreting gene regulatory sequences in eukaryotic genomes.

To flexibly navigate, many animals rely on internal spatial representations that persist when the animal is standing still in darkness, and update accurately by integrating the animal's movements in the absence of localizing sensory cues. Theories of mammalian head direction cells have proposed that these dynamics can be realized in a special class of networks that maintain a localized bump of activity via structured recurrent connectivity, and that shift this bump of activity via angular velocity input. Although there are many different variants of these so-called ring attractor networks, they all rely on large numbers of neurons to generate representations that persist in the absence of input and accurately integrate angular velocity input. Surprisingly, in the fly, Drosophila melanogaster, a head direction representation is maintained by a much smaller number of neurons whose dynamics and connectivity resemble those of a ring attractor network. These findings challenge our understanding of ring attractors and their putative implementation in neural circuits. Here, we analyzed failures of angular velocity integration that emerge in small attractor networks with only a few computational units. Motivated by the peak performance of the fly head direction system in darkness, we mathematically derived conditions under which small networks, even with as few as 4 neurons, achieve the performance of much larger networks. The resulting description reveals that by appropriately tuning the network connectivity, the network can maintain persistent representations over the continuum of head directions, and it can accurately integrate angular velocity inputs. We then analytically determined how performance degrades as the connectivity deviates from this optimally-tuned setting, and we find a trade-off between network size and the tuning precision needed to achieve persistence and accurate integration. This work shows how even small networks can accurately track an animal's movements to guide navigation, and it informs our understanding of the functional capabilities of discrete systems more broadly.

The self-assembly of proteins into highly ordered nanoscale architectures is a hallmark of biological systems. The sophisticated functions of these molecular machines have inspired the development of methods to engineer self-assembling protein nanostructures; however, the design of multi-component protein nanomaterials with high accuracy remains an outstanding challenge. Here we report a computational method for designing protein nanomaterials in which multiple copies of two distinct subunits co-assemble into a specific architecture. We use the method to design five 24-subunit cage-like protein nanomaterials in two distinct symmetric architectures and experimentally demonstrate that their structures are in close agreement with the computational design models. The accuracy of the method and the number and variety of two-component materials that it makes accessible suggest a route to the construction of functional protein nanomaterials tailored to specific applications.

Nature provides many examples of self- and co-assembling protein-based molecular machines, including icosahedral protein cages that serve as scaffolds, enzymes, and compartments for essential biochemical reactions and icosahedral virus capsids, which encapsidate and protect viral genomes and mediate entry into host cells. Inspired by these natural materials, we report the computational design and experimental characterization of co-assembling, two-component, 120-subunit icosahedral protein nanostructures with molecular weights (1.8 to 2.8 megadaltons) and dimensions (24 to 40 nanometers in diameter) comparable to those of small viral capsids. Electron microscopy, small-angle x-ray scattering, and x-ray crystallography show that 10 designs spanning three distinct icosahedral architectures form materials closely matching the design models. In vitro assembly of icosahedral complexes from independently purified components occurs rapidly, at rates comparable to those of viral capsids, and enables controlled packaging of molecular cargo through charge complementarity. The ability to design megadalton-scale materials with atomic-level accuracy and controllable assembly opens the door to a new generation of genetically programmable protein-based molecular machines.

The fast turnover of membrane components through endocytosis and recycling allows precise control of the composition of the plasma membrane. Endocytic recycling can be rapid with some molecules returning to the plasma membrane with a <5 minutes. Existing methods to study these trafficking pathways utilize chemical, radioactive, or fluorescent labeling of cell surface receptors in pulse-chase experiments, which require tedious washing steps and manual collection of samples. Here, we introduce a live-cell endocytic recycling assay, based on a newly designed cell-impermeable, fluorogenic ligand for HaloTag: 'Janelia Fluor 635i' (JFi; i=impermeant) which allows real-time detection of membrane receptor recycling at steady state. We used this method to study the effect of iron depletion on transferrin receptor (TfR) recycling using the chelator desferrioxamine. We found this perturbation significantly increases the TfR recycling rate. The high temporal resolution and simplicity of this assay provides a clear advantage over extant methods and makes it ideal for large scale cellular imaging studies. This assay can be adapted to examine other cellular kinetic parameters such as protein turnover and biosynthetic trafficking.

To study the complex synaptic interactions underpinning dendritic information processing in single neurons, experimenters require methods to mimic presynaptic neurotransmitter release at multiple sites with no physiological damage. We show that laser scanning systems built around large-aperture acousto-optic deflectors and high numerical aperture objective lenses provide the sub-millisecond, sub-micron precision necessary to achieve physiological, exogenous synaptic stimulation. Our laser scanning systems can produce the sophisticated spatio-temporal patterns of synaptic input that are necessary to investigate single-neuron dendritic physiology.