To gain insights into coordinated lineage-specification and morphogenetic processes during early embryogenesis, here we report a systematic identification of transcriptional programs mediated by a key developmental regulator-Brachyury. High-resolution chromosomal localization mapping of Brachyury by ChIP sequencing and ChIP-exonuclease revealed distinct sequence signatures enriched in Brachyury-bound enhancers. A combination of genome-wide in vitro and in vivo perturbation analysis and cross-species evolutionary comparison unveiled a detailed Brachyury-dependent gene-regulatory network that directly links the function of Brachyury to diverse developmental pathways and cellular housekeeping programs. We also show that Brachyury functions primarily as a transcriptional activator genome-wide and that an unexpected gene-regulatory feedback loop consisting of Brachyury, Foxa2, and Sox17 directs proper stem-cell lineage commitment during streak formation. Target gene and mRNA-sequencing correlation analysis of the T(c) mouse model supports a crucial role of Brachyury in up-regulating multiple key hematopoietic and muscle-fate regulators. Our results thus chart a comprehensive map of the Brachyury-mediated gene-regulatory network and how it influences in vivo developmental homeostasis and coordination.

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.

Recent results have shown the possibility of both reconstructing connectomes of small but biologically interesting circuits and extracting from these connectomes insights into their function. However, these reconstructions were heroic proof-of-concept experiments, requiring person-months of effort per neuron reconstructed, and will not scale to larger circuits, much less the brains of entire animals. In this paper we examine what will be required to generate and use substantially larger connectomes, finding five areas that need increased attention: firstly, imaging better suited to automatic reconstruction, with excellent z-resolution; secondly, automatic detection, validation, and measurement of synapses; thirdly, reconstruction methods that keep and use uncertainty metrics for every object, from initial images, through segmentation, reconstruction, and connectome queries; fourthly, processes that are fully incremental, so that the connectome may be used before it is fully complete; and finally, better tools for analysis of connectomes, once they are obtained.

Spinal muscular atrophy (SMA) is a lethal neurodegenerative disease specifically affecting spinal motor neurons. SMA is caused by the homozygous deletion or mutation of the survival of motor neuron 1 (SMN1) gene. The SMN protein plays an essential role in the assembly of spliceosomal ribonucleoproteins. However, it is still unclear how low levels of the ubiquitously expressed SMN protein lead to the selective degeneration of motor neurons. An additional role for SMN in the regulation of the axonal transport of mRNA-binding proteins (mRBPs) and their target mRNAs has been proposed. Indeed, several mRBPs have been shown to interact with SMN, and the axonal levels of few mRNAs, such as the β-actin mRNA, are reduced in SMA motor neurons. In this study we have identified the β-actin mRNA-binding protein IMP1/ZBP1 as a novel SMN-interacting protein. Using a combination of biochemical assays and quantitative imaging techniques in primary motor neurons, we show that IMP1 associates with SMN in individual granules that are actively transported in motor neuron axons. Furthermore, we demonstrate that IMP1 axonal localization depends on SMN levels, and that SMN deficiency in SMA motor neurons leads to a dramatic reduction of IMP1 protein levels. In contrast, no difference in IMP1 protein levels was detected in whole brain lysates from SMA mice, further suggesting neuron specific roles of SMN in IMP1 expression and localization. Taken together, our data support a role for SMN in the regulation of mRNA localization and axonal transport through its interaction with mRBPs such as IMP1.

Monitoring representative fractions of neurons from multiple brain circuits in behaving animals is necessary for understanding neuronal computation. Here, we describe a system that allows high-channel-count recordings from a small volume of neuronal tissue using a lightweight signal multiplexing headstage that permits free behavior of small rodents. The system integrates multishank, high-density recording silicon probes, ultraflexible interconnects, and a miniaturized microdrive. These improvements allowed for simultaneous recordings of local field potentials and unit activity from hundreds of sites without confining free movements of the animal. The advantages of large-scale recordings are illustrated by determining the electroanatomic boundaries of layers and regions in the hippocampus and neocortex and constructing a circuit diagram of functional connections among neurons in real anatomic space. These methods will allow the investigation of circuit operations and behavior-dependent interregional interactions for testing hypotheses of neural networks and brain function.

This protocol describes how to observe gastrulation in living mouse embryos by using light-sheet microscopy and computational tools to analyze the resulting image data at the single-cell level. We describe a series of techniques needed to image the embryos under physiological conditions, including how to hold mouse embryos without agarose embedding, how to transfer embryos without air exposure and how to construct environmental chambers for live imaging by digital scanned light-sheet microscopy (DSLM). Computational tools include manual and semiautomatic tracking programs that are developed for analyzing the large 4D data sets acquired with this system. Note that this protocol does not include details of how to build the light-sheet microscope itself. Time-lapse imaging ends within 12 h, with subsequent tracking analysis requiring 3-6 d. Other than some mouse-handling skills, this protocol requires no advanced skills or knowledge. Light-sheet microscopes are becoming more widely available, and thus the techniques outlined in this paper should be helpful for investigating mouse embryogenesis.

Recent progress in intracellular calcium sensors and other fluorophores has promoted the widespread adoption of functional optical imaging in the life sciences. Home-built multiphoton microscopes are easy to build, highly customizable, and cost effective. For many imaging applications a 3-axis motorized stage is critical, but commercially available motorization hardware (motorized translators, controller boxes, etc) are often very expensive. Furthermore, the firmware on commercial motor controllers cannot easily be altered and is not usually designed with a microscope stage in mind. Here we describe an open-source motorization solution that is simple to construct, yet far cheaper and more customizable than commercial offerings. The cost of the controller and motorization hardware are under $1000. Hardware costs are kept low by replacing linear actuators with high quality stepper motors. Electronics are assembled from commonly available hobby components, which are easy to work with. Here we describe assembly of the system and quantify the positioning accuracy of all three axes. We obtain positioning repeatability of the order of 1 μm in X/Y and 0.1 μm in Z. A hand-held control-pad allows the user to direct stage motion precisely over a wide range of speeds (10(-1) to 10(2) μm·s(-1)), rapidly store and return to different locations, and execute "jumps" of a fixed size. In addition, the system can be controlled from a PC serial port. Our "OpenStage" controller is sufficiently flexible that it could be used to drive other devices, such as micro-manipulators, with minimal modifications.

BACKGROUND: High-throughput sequencing is gradually replacing microarrays as the preferred method for studying mRNA expression levels, providing nucleotide resolution and accurately measuring absolute expression levels of almost any transcript, known or novel. However, existing microarray data from clinical, pharmaceutical, and academic settings represent valuable and often underappreciated resources, and methods for assessing and improving the quality of these data are lacking.

RESULTS: To quantitatively assess the quality of microarray probes, we directly compare RNA-Seq to Agilent microarrays by processing 231 unique samples from the Allen Human Brain Atlas using RNA-Seq. Both techniques provide highly consistent, highly reproducible gene expression measurements in adult human brain, with RNA-Seq slightly outperforming microarray results overall. We show that RNA-Seq can be used as ground truth to assess the reliability of most microarray probes, remove probes with off-target effects, and scale probe intensities to match the expression levels identified by RNA-Seq. These sequencing scaled microarray intensities (SSMIs) provide more reliable, quantitative estimates of absolute expression levels for many genes when compared with unscaled intensities. Finally, we validate this result in two human cell lines, showing that linear scaling factors can be applied across experiments using the same microarray platform.

CONCLUSIONS: Microarrays provide consistent, reproducible gene expression measurements, which are improved using RNA-Seq as ground truth. We expect that our strategy could be used to improve probe quality for many data sets from major existing repositories.

Sparse coding may be a general strategy of neural systems for augmenting memory capacity. In Drosophila melanogaster, sparse odor coding by the Kenyon cells of the mushroom body is thought to generate a large number of precisely addressable locations for the storage of odor-specific memories. However, it remains untested how sparse coding relates to behavioral performance. Here we demonstrate that sparseness is controlled by a negative feedback circuit between Kenyon cells and the GABAergic anterior paired lateral (APL) neuron. Systematic activation and blockade of each leg of this feedback circuit showed that Kenyon cells activated APL and APL inhibited Kenyon cells. Disrupting the Kenyon cell-APL feedback loop decreased the sparseness of Kenyon cell odor responses, increased inter-odor correlations and prevented flies from learning to discriminate similar, but not dissimilar, odors. These results suggest that feedback inhibition suppresses Kenyon cell activity to maintain sparse, decorrelated odor coding and thus the odor specificity of memories.