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A central amygdala-globus pallidus circuit conveys unconditioned stimulus information and controls fear learning
Get PDFThe central amygdala (CeA) is critically involved in a range of adaptive behaviors. In particular, the somatostatin-expressing (Sst + ) neurons in the CeA are essential for classic fear conditioning. These neurons send long-range projections to several extra-amygdala targets, but the functions of these projections remain elusive. Here, we found in mice that a subset of Sst + CeA neurons send projections to the globus pallidus external segment (GPe), and constitute essentially the entire GPe-projecting CeA population. Notably, chronic inhibition of GPe-projecting CeA neurons completely blocks auditory fear conditioning. These neurons are selectively excited by the unconditioned stimulus (US) during fear conditioning, and transient inactivation or activation of these neurons during US presentation impairs or promotes, respectively, fear learning. Our results suggest that a major function of Sst + CeA neurons is to represent and convey US information through the CeA-GPe circuit, thereby instructing learning in fear conditioning
The entry of nanoparticles into solid tumours.
No full textThe concept of nanoparticle transport through gaps between endothelial cells (inter-endothelial gaps) in the tumour blood vessel is a central paradigm in cancer nanomedicine. The size of these gaps was found to be up to 2,000 nm. This justified the development of nanoparticles to treat solid tumours as their size is small enough to extravasate and access the tumour microenvironment. Here we show that these inter-endothelial gaps are not responsible for the transport of nanoparticles into solid tumours. Instead, we found that up to 97% of nanoparticles enter tumours using an active process through endothelial cells. This result is derived from analysis of four different mouse models, three different types of human tumours, mathematical simulation and modelling, and two different types of imaging techniques. These results challenge our current rationale for developing cancer nanomedicine and suggest that understanding these active pathways will unlock strategies to enhance tumour accumulation
The evolution of brain structure captured in stereotyped cell count and cell type distributions
No full textThe stereotyped features of brain structure, such as the distribution, morphology and connectivity of neuronal cell types across brain areas, are those most likely to explain the remarkable capacity of the brain to process information and govern behaviors. Recent advances in anatomical methods, including the simple but versatile isotropic fractionator and several whole-brain labeling, clearing and microscopy methods, have opened the door to an exciting new era in comparative brain anatomy, one that has the potential to transform our understanding of the brain structure-function relationship by representing the evolution of brain complexity in quantitative anatomical features shared across species and species-specific or clade-specific. Here we discuss these methods and their application to mapping brain cell count and cell type distributions-two particularly powerful neural correlates of vertebrate cognitive and behavioral capabilities
SYNPLA, a method to identify synapses displaying plasticity after learning
No full textWhich neural circuits undergo synaptic changes when an animal learns? Although it is widely accepted that changes in synaptic strength underlie many forms of learning and memory, it remains challenging to connect changes in synaptic strength at specific neural pathways to specific behaviors and memories. Here we introduce SYNPLA (synaptic proximity ligation assay), a synapse-specific, high-throughput, and potentially brain-wide method capable of detecting circuit-specific learning-induced synaptic plasticity
Non-parametric Bayesian density estimation for biological sequence space with applications to pre-mRNA splicing and the karyotypic diversity of human cancer
No full textDensity estimation in sequence space is a fundamental problem in machine learning that is of great importance in computational biology. Due to the discrete nature and large dimensionality of sequence space, how best to estimate such probability distributions from a sample of observed sequences remains unclear. One common strategy for addressing this problem is to estimate the probability distribution using maximum entropy, i.e. calculating point estimates for some set of correlations based on the observed sequences and predicting the probability distribution that is as uniform as possible while still matching these point estimates. Building on recent advances in Bayesian field-theoretic density estimation, we present a generalization of this maximum entropy approach that provides greater expressivity in regions of sequence space where data is plentiful while still maintaining a conservative maximum entropy char-acter in regions of sequence space where data is sparse or absent. In particular, we define a family of priors for probability distributions over sequence space with a single hyper-parameter that controls the expected magnitude of higher-order correlations. This family of priors then results in a corresponding one-dimensional family of maximum a posteriori estimates that interpolate smoothly between the maximum entropy estimate and the observed sample frequencies. To demonstrate the power of this method, we use it to explore the high-dimensional geometry of the distribution of 5′ splice sites found in the human genome and to understand the accumulation of chromosomal abnormalities during cancer progression
Structural mechanism of helicase loading onto replication origin DNA by ORC-Cdc6
No full textDNA replication origins serve as sites of replicative helicase loading. In all eukaryotes, the six-subunit origin recognition complex (Orc1-6; ORC) recognizes the replication origin. During late M-phase of the cell-cycle, Cdc6 binds to ORC and the ORC-Cdc6 complex loads in a multistep reaction and, with the help of Cdt1, the core Mcm2-7 helicase onto DNA. A key intermediate is the ORC-Cdc6-Cdt1-Mcm2-7 (OCCM) complex in which DNA has been already inserted into the central channel of Mcm2-7. Until now, it has been unclear how the origin DNA is guided by ORC-Cdc6 and inserted into the Mcm2-7 hexamer. Here, we truncated the C-terminal winged-helix-domain (WHD) of Mcm6 to slow down the loading reaction, thereby capturing two loading intermediates prior to DNA insertion in budding yeast. In "semi-attached OCCM," the Mcm3 and Mcm7 WHDs latch onto ORC-Cdc6 while the main body of the Mcm2-7 hexamer is not connected. In "pre-insertion OCCM," the main body of Mcm2-7 docks onto ORC-Cdc6, and the origin DNA is bent and positioned adjacent to the open DNA entry gate, poised for insertion, at the Mcm2-Mcm5 interface. We used molecular simulations to reveal the dynamic transition from preloading conformers to the loaded conformers in which the loading of Mcm2-7 on DNA is complete and the DNA entry gate is fully closed. Our work provides multiple molecular insights into a key event of eukaryotic DNA replication
Out-of-focus brain image detection in serial tissue sections
No full textBACKGROUND: A large part of image processing workflow in brain imaging is quality control which is typically done visually. One of the most time consuming steps of the quality control process is classifying an image as in-focus or out-of-focus (OOF). NEW METHOD: In this paper we introduce an automated way of identifying OOF brain images from serial tissue sections in large datasets (>1.5 PB). The method utilizes steerable filters (STF) to derive a focus value (FV) for each image. The FV combined with an outlier detection that applies a dynamic threshold allows for the focus classification of the images. RESULTS: The method was tested by comparing the results of our algorithm with a visual inspection of the same images. The results support that the method works extremely well by successfully identifying OOF images within serial tissue sections with a minimal number of false positives. COMPARISON WITH EXISTING METHODS: Our algorithm was also compared to other methods and metrics and successfully tested in different stacks of images consisting solely of simulated OOF images in order to demonstrate the applicability of the method to other large datasets. CONCLUSIONS: We have presented a practical method to distinguish OOF images from large datasets that include serial tissue sections that can be included in an automated pre-processing image analysis pipeline
Expanded encyclopaedias of DNA elements in the human and mouse genomes
No full textThe human and mouse genomes contain instructions that specify RNAs and proteins and govern the timing, magnitude, and cellular context of their production. To better delineate these elements, phase III of the Encyclopedia of DNA Elements (ENCODE) Project has expanded analysis of the cell and tissue repertoires of RNA transcription, chromatin structure and modification, DNA methylation, chromatin looping, and occupancy by transcription factors and RNA-binding proteins. Here we summarize these efforts, which have produced 5,992 new experimental datasets, including systematic determinations across mouse fetal development. All data are available through the ENCODE data portal (https://www.encodeproject.org), including phase II ENCODE(1) and Roadmap Epigenomics(2) data. We have developed a registry of 926,535 human and 339,815 mouse candidate cis-regulatory elements, covering 7.9 and 3.4% of their respective genomes, by integrating selected datatypes associated with gene regulation, and constructed a web-based server (SCREEN; http://screen.encodeproject.org) to provide flexible, user-defined access to this resource. Collectively, the ENCODE data and registry provide an expansive resource for the scientific community to build a better understanding of the organization and function of the human and mouse genomes
Hierarchical Representation for Chromatic Processing across Macaque V1, V2, and V4
No full textThe perception of color is an internal label for the inferred spectral reflectance of visible surfaces. To study how spectral representation is transformed through modular subsystems of successive cortical areas, we undertook simultaneous optical imaging of intrinsic signals in macaque V1, V2, and V4, supplemented by higher-resolution electrophysiology and two-photon imaging in awake macaques. We find a progressive evolution in the scale and precision of chromotopic maps, expressed by a uniform blob-like architecture of hue responses within each area. Two-photon imaging reveals enhanced hue-specific cell clustering in V2 compared with V1. A phenomenon of endspectral (red and blue) responses that is clear in V1, recedes in V2, and is virtually absent in V4. The increase in mid- and extra-spectral hue representations through V2 and V4 reflects the nature of hierarchical processing as higher areas read out locations in chromatic space from progressive integration of signals relayed by V1