Equilibrium formally can be represented as an ensemble of uncoupled systems undergoing unbiased dynamics in which detailed balance is maintained. Many nonequilibrium processes can be described by suitable subsets of the equilibrium ensemble. Here, we employ the “weighted ensemble” (WE) simulation protocol [Huber and Kim, Biophys. J.1996, 70, 97–110] to generate equilibrium trajectory ensembles and extract nonequilibrium subsets for computing kinetic quantities. States do not need to be chosen in advance. The procedure formally allows estimation of kinetic rates between arbitrary states chosen after the simulation, along with their equilibrium populations. We also describe a related history-dependent matrix procedure for estimating equilibrium and nonequilibrium observables when phase space has been divided into arbitrary non-Markovian regions, whether in WE or ordinary simulation. In this proof-of-principle study, these methods are successfully applied and validated on two molecular systems: explicitly solvated methane association and the implicitly solvated Ala4 peptide. We comment on challenges remaining in WE calculations.

We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in vitro and in vivo multi-neuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data.

The endoplasmic reticulum (ER) is a structurally complex, membrane-enclosed compartment that stretches from the nuclear envelope to the extreme periphery of eukaryotic cells. The organelle is crucial for numerous distinct cellular processes, but how these processes are spatially regulated within the structure is unclear. Traditional imaging-based approaches to understanding protein dynamics within the organelle are limited by the convoluted structure and rapid movement of molecular components. Here, we introduce a combinatorial imaging and machine learning-assisted image analysis approach to track the motion of photoactivated proteins within the ER of live cells. We find that simultaneous knowledge of the underlying ER structure is required to accurately analyze fluorescently-tagged protein redistribution, and after appropriate structural calibration we see all proteins assayed show signatures of Brownian diffusion-dominated motion over micron spatial scales. Remarkably, we find that in some cells the ER structure can be explored in a highly asymmetric manner, likely as a result of uneven connectivity within the organelle. This remains true independently of the size, topology, or folding state of the fluorescently-tagged molecules, suggesting a potential role for ER connectivity in driving spatially regulated biology in eukaryotes.

MOTIVATION: Automatic recognition of cell identities is critical for quantitative measurement, targeting, and manipulation of cells of model animals at single-cell resolution. It has been shown to be a powerful tool for studying gene expression and regulation, cell lineages, and cell fates. Existing methods first segment cells, before applying a recognition algorithm in the second step. As a result, the segmentation errors in the first step directly affect and complicate the subsequent cell recognition step. Moreover, in new experimental settings, some of the image features that have been previously relied upon to recognize cells may not be easy to reproduce, due to limitations on the number of color channels available for fluorescent imaging or to the cost of building transgenic animals. An approach that is more accurate and relies on only a single signal channel is clearly desirable. RESULTS: We have developed a new method, called SRS (for Simultaneous Recognition and Segmentation of cells), and applied it to 3D image stacks of the model organism C. elegans. Given a 3D image stack of the animal and a 3D atlas of target cells, SRS is effectively an atlas-guided voxel classification process: cell recognition is realized by smoothly deforming the atlas to best fit the image, where the segmentation is obtained naturally via classification of all image voxels. The method achieved a 97.7% overall recognition accuracy in recognizing a key class of marker cells, the body wall muscle (BWM) cells, on a data set of 175 C. elegans image stacks containing 14,118 manually curated BWM cells providing the "ground-truth" for accuracy. This result was achieved without any additional fiducial image features. SRS also automatically identified 14 of the image stacks as involving ±90-degree rotations. With these stacks excluded from the data set, the recognition accuracy rose to 99.1%. We also show SRS is generally applicable to other cell-types, e.g. intestinal cells. AVAILABILITY: The supplementary movies can be downloaded from our website http://penglab.janelia.org/proj/celegans_seganno. The method has been implemented as a plug-in program within the V3D system (http://penglab.janelia.org/proj/v3d) and will be released in the V3D plugin source code repository.

Unlike the core histones, which are incorporated into nucleosomes concomitant with DNA replication, histone H3.3 is synthesized throughout the cell cycle and utilized for replication-independent (RI) chromatin assembly. The RI incorporation of H3.3 into nucleosomes is highly conserved and occurs at both euchromatin and heterochromatin. However, neither the mechanism of H3.3 recruitment nor its essential function is well understood. Several different chaperones regulate H3.3 assembly at distinct sites. The H3.3 chaperone, Daxx, and the chromatin-remodeling factor, ATRX, are required for H3.3 incorporation and heterochromatic silencing at telomeres, pericentromeres, and the cytomegalovirus (CMV) promoter. By evaluating H3.3 dynamics at a CMV promoter-regulated transcription site in a genetic background in which RI chromatin assembly is blocked, we have been able to decipher the regulatory events upstream of RI nucleosomal deposition. We find that at the activated transcription site, H3.3 accumulates with sense and antisense RNA, suggesting that it is recruited through an RNA-mediated mechanism. Sense and antisense transcription also increases after H3.3 knockdown, suggesting that the RNA signal is amplified when chromatin assembly is blocked and attenuated by nucleosomal deposition. Additionally, we find that H3.3 is still recruited after Daxx knockdown, supporting a chaperone-independent recruitment mechanism. Sequences in the H3.3 N-terminal tail and αN helix mediate both its recruitment to RNA at the activated transcription site and its interaction with double-stranded RNA in vitro. Interestingly, the H3.3 gain-of-function pediatric glioblastoma mutations, G34R and K27M, differentially affect H3.3 affinity in these assays, suggesting that disruption of an RNA-mediated regulatory event could drive malignant transformation.

CA1 pyramidal neurons are a major output of the hippocampus and encode features of experience that constitute episodic memories. Feature-selective firing of these neurons results from the dendritic integration of inputs from multiple brain regions. While it is known that synchronous activation of spatially clustered inputs can contribute to firing through the generation of dendritic spikes, there is no established mechanism for spatiotemporal synaptic clustering. Here we show that single presynaptic axons form multiple, spatially clustered inputs onto the distal, but not proximal, dendrites of CA1 pyramidal neurons. These compound connections exhibit ultrastructural features indicative of strong synapses and occur much more commonly in entorhinal than in thalamic afferents. Computational simulations revealed that compound connections depolarize dendrites in a biophysically efficient manner, owing to their inherent spatiotemporal clustering. Our results suggest that distinct afferent projections use different connectivity motifs that differentially contribute to dendritic integration.

Human immunodeficiency virus type 1 (HIV-1) assembly occurs on the inner leaflet of the host cell plasma membrane, incorporating the essential viral envelope glycoprotein (Env) within a budding lattice of HIV-1 Gag structural proteins. The mechanism by which Env incorporates into viral particles remains poorly understood. To determine the mechanism of recruitment of Env to assembly sites, we interrogate the subviral angular distribution of Env on cell-associated virus using multicolor, three-dimensional (3D) superresolution microscopy. We demonstrate that, in a manner dependent on cell type and on the long cytoplasmic tail of Env, the distribution of Env is biased toward the necks of cell-associated particles. We postulate that this neck-biased distribution is regulated by vesicular retention and steric complementarity of Env during independent Gag lattice formation.