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1.
Nature ; 598(7879): 167-173, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34616065

RESUMO

Neuronal cell types are classically defined by their molecular properties, anatomy and functions. Although recent advances in single-cell genomics have led to high-resolution molecular characterization of cell type diversity in the brain1, neuronal cell types are often studied out of the context of their anatomical properties. To improve our understanding of the relationship between molecular and anatomical features that define cortical neurons, here we combined retrograde labelling with single-nucleus DNA methylation sequencing to link neural epigenomic properties to projections. We examined 11,827 single neocortical neurons from 63 cortico-cortical and cortico-subcortical long-distance projections. Our results showed unique epigenetic signatures of projection neurons that correspond to their laminar and regional location and projection patterns. On the basis of their epigenomes, intra-telencephalic cells that project to different cortical targets could be further distinguished, and some layer 5 neurons that project to extra-telencephalic targets (L5 ET) formed separate clusters that aligned with their axonal projections. Such separation varied between cortical areas, which suggests that there are area-specific differences in L5 ET subtypes, which were further validated by anatomical studies. Notably, a population of cortico-cortical projection neurons clustered with L5 ET rather than intra-telencephalic neurons, which suggests that a population of L5 ET cortical neurons projects to both targets. We verified the existence of these neurons by dual retrograde labelling and anterograde tracing of cortico-cortical projection neurons, which revealed axon terminals in extra-telencephalic targets including the thalamus, superior colliculus and pons. These findings highlight the power of single-cell epigenomic approaches to connect the molecular properties of neurons with their anatomical and projection properties.


Assuntos
Córtex Cerebral/citologia , Córtex Cerebral/metabolismo , Epigenoma , Epigenômica , Vias Neurais , Neurônios/classificação , Neurônios/metabolismo , Animais , Mapeamento Encefálico , Feminino , Masculino , Camundongos , Neurônios/citologia
2.
Nature ; 598(7879): 120-128, 2021 10.
Artigo em Inglês | MEDLINE | ID: mdl-34616061

RESUMO

Mammalian brain cells show remarkable diversity in gene expression, anatomy and function, yet the regulatory DNA landscape underlying this extensive heterogeneity is poorly understood. Here we carry out a comprehensive assessment of the epigenomes of mouse brain cell types by applying single-nucleus DNA methylation sequencing1,2 to profile 103,982 nuclei (including 95,815 neurons and 8,167 non-neuronal cells) from 45 regions of the mouse cortex, hippocampus, striatum, pallidum and olfactory areas. We identified 161 cell clusters with distinct spatial locations and projection targets. We constructed taxonomies of these epigenetic types, annotated with signature genes, regulatory elements and transcription factors. These features indicate the potential regulatory landscape supporting the assignment of putative cell types and reveal repetitive usage of regulators in excitatory and inhibitory cells for determining subtypes. The DNA methylation landscape of excitatory neurons in the cortex and hippocampus varied continuously along spatial gradients. Using this deep dataset, we constructed an artificial neural network model that precisely predicts single neuron cell-type identity and brain area spatial location. Integration of high-resolution DNA methylomes with single-nucleus chromatin accessibility data3 enabled prediction of high-confidence enhancer-gene interactions for all identified cell types, which were subsequently validated by cell-type-specific chromatin conformation capture experiments4. By combining multi-omic datasets (DNA methylation, chromatin contacts, and open chromatin) from single nuclei and annotating the regulatory genome of hundreds of cell types in the mouse brain, our DNA methylation atlas establishes the epigenetic basis for neuronal diversity and spatial organization throughout the mouse cerebrum.


Assuntos
Encéfalo/citologia , Metilação de DNA , Epigenoma , Epigenômica , Neurônios/classificação , Neurônios/metabolismo , Análise de Célula Única , Animais , Atlas como Assunto , Encéfalo/metabolismo , Cromatina/química , Cromatina/genética , Cromatina/metabolismo , Citosina/química , Citosina/metabolismo , Conjuntos de Dados como Assunto , Giro Denteado/citologia , Elementos Facilitadores Genéticos/genética , Perfilação da Expressão Gênica , Hipocampo/citologia , Hipocampo/metabolismo , Masculino , Camundongos , Camundongos Endogâmicos C57BL , Modelos Biológicos , Vias Neurais , Neurônios/citologia
3.
iScience ; 24(7): 102734, 2021 Jul 23.
Artigo em Inglês | MEDLINE | ID: mdl-34258562

RESUMO

Electric-field (E-field) control of magnetic switching provides an energy-efficient means to toggle the magnetic states in spintronic devices. The angular tunneling magnetoresistance (TMR) of an magnetic tunnel junction (MTJ)/PMN-PT magnetoelectronic hybrid indicates that the angle-dependent switching fields of the free layer can decrease significantly subject to the application of an E-field. In particular, the switching field along the major axis is reduced by 59% from 28.0 to 11.5 Oe as the E-field increases from 0 to 6 kV/cm, while the TMR ratio remains intact. The switching boundary angle decreases (increases) for the parallel (antiparallel) to antiparallel (parallel) state switch, resulting in a shrunk switching window size. The non-volatile and reversible 180° magnetization switching is demonstrated by using E-fields with a smaller magnetic field bias as low as 11.5 Oe. The angular magnetic switching originates from competition among the E-field-induced magnetoelastic anisotropy, magnetic shape anisotropy, and Zeeman energy, which is confirmed by micromagnetic simulations.

4.
ACS Appl Mater Interfaces ; 11(47): 44837-44843, 2019 Nov 27.
Artigo em Inglês | MEDLINE | ID: mdl-31680512

RESUMO

Charge transfer is of particular importance in manipulating the interface physics in transition-metal oxide heterostructures. In this work, we have fabricated epitaxial bilayers composed of polar 3d LaMnO3 and nonpolar 5d SrIrO3. Systematic magnetic measurements reveal an unexpectedly large exchange bias effect in the bilayer, together with a dramatic enhancement of the coercivity of LaMnO3. Based on first-principle calculations and X-ray absorption spectroscopy measurements, such a strong interfacial magnetic coupling is found closely associated with the polar nature of LaMnO3 and the strong spin-orbit interaction in SrIrO3, which collectively drive an asymmetric interfacial charge transfer and lead to the emergence of an interfacial reentrant spin/superspin glass state. Our study provides a new insight into the charge transfer in transition-metal oxide heterostructures and offers a novel means to tune the interfacial exchange coupling for a variety of device applications.

5.
Nat Commun ; 10(1): 5129, 2019 11 12.
Artigo em Inglês | MEDLINE | ID: mdl-31719520

RESUMO

Owing to the significant difference in the numbers of simulated and experimentally feasible zeolite structures, several alternative strategies have been developed for zeolite synthesis. Despite their rationality and originality, most of these techniques are based on trial-and-error, which makes it difficult to predict the structure of new materials. Assembly-Disassembly-Organization-Reassembly (ADOR) method overcoming this limitation was successfully applied to a limited number of structures with relatively stable crystalline layers (UTL, UOV, *CTH). Here, we report a straightforward, vapour-phase-transport strategy for the transformation of IWW zeolite with low-density silica layers connected by labile Ge-rich units into material with new topology. In situ XRD and XANES studies on the mechanism of IWW rearrangement reveal an unusual structural distortion-reconstruction of the framework throughout the process. Therefore, our findings provide a step forward towards engineering nanoporous materials and increasing the number of zeolites available for future applications.

6.
Nat Methods ; 16(10): 999-1006, 2019 10.
Artigo em Inglês | MEDLINE | ID: mdl-31501549

RESUMO

Dynamic three-dimensional chromatin conformation is a critical mechanism for gene regulation during development and disease. Despite this, profiling of three-dimensional genome structure from complex tissues with cell-type specific resolution remains challenging. Recent efforts have demonstrated that cell-type specific epigenomic features can be resolved in complex tissues using single-cell assays. However, it remains unclear whether single-cell chromatin conformation capture (3C) or Hi-C profiles can effectively identify cell types and reconstruct cell-type specific chromatin conformation maps. To address these challenges, we have developed single-nucleus methyl-3C sequencing to capture chromatin organization and DNA methylation information and robustly separate heterogeneous cell types. Applying this method to >4,200 single human brain prefrontal cortex cells, we reconstruct cell-type specific chromatin conformation maps from 14 cortical cell types. These datasets reveal the genome-wide association between cell-type specific chromatin conformation and differential DNA methylation, suggesting pervasive interactions between epigenetic processes regulating gene expression.


Assuntos
Metilação de DNA , Genoma Humano , Análise de Célula Única , Algoritmos , Cromatina/metabolismo , Conjuntos de Dados como Assunto , Epigênese Genética , Regulação da Expressão Gênica , Estudo de Associação Genômica Ampla , Humanos
7.
Proc Natl Acad Sci U S A ; 116(28): 14011-14018, 2019 07 09.
Artigo em Inglês | MEDLINE | ID: mdl-31235599

RESUMO

Three-dimensional genome structure plays a pivotal role in gene regulation and cellular function. Single-cell analysis of genome architecture has been achieved using imaging and chromatin conformation capture methods such as Hi-C. To study variation in chromosome structure between different cell types, computational approaches are needed that can utilize sparse and heterogeneous single-cell Hi-C data. However, few methods exist that are able to accurately and efficiently cluster such data into constituent cell types. Here, we describe scHiCluster, a single-cell clustering algorithm for Hi-C contact matrices that is based on imputations using linear convolution and random walk. Using both simulated and real single-cell Hi-C data as benchmarks, scHiCluster significantly improves clustering accuracy when applied to low coverage datasets compared with existing methods. After imputation by scHiCluster, topologically associating domain (TAD)-like structures (TLSs) can be identified within single cells, and their consensus boundaries were enriched at the TAD boundaries observed in bulk cell Hi-C samples. In summary, scHiCluster facilitates visualization and comparison of single-cell 3D genomes.


Assuntos
Cromatina/ultraestrutura , Estruturas Cromossômicas/ultraestrutura , Biologia Computacional , Análise de Célula Única , Algoritmos , Análise por Conglomerados , Genoma/genética , Humanos , Conformação Molecular
8.
Nanoscale ; 11(17): 8110-8118, 2019 Apr 25.
Artigo em Inglês | MEDLINE | ID: mdl-30984948

RESUMO

Defect engineering has been a powerful tool to enable the creation of exotic phases and the discovery of intriguing phenomena in ferroelectric oxides. However, the accurate control of the concentration of defects remains a big challenge. In this work, ion implantation, which can provide controllable point defects, allows us to produce a controlled defect driven true super-tetragonal (T) phase with a single-domain-state in ferroelectric BiFeO3 thin films. This point-defect engineering is found to drive the phase transition from the as-grown mixed rhombohedral-like (R) and tetragonal-like (MC) phase to true tetragonal (T) symmetry and induce the stripe multi-nanodomains to a single domain state. By further increasing the injected dose of the He ion, we demonstrate an enhanced tetragonality super-tetragonal (super-T) phase with the largest c/a ratio of ∼1.3 that has ever been experimentally achieved in BiFeO3. A combination of the morphology change and domain evolution further confirms that the mixed R/MC phase structure transforms to the single-domain-state true tetragonal phase. Moreover, the re-emergence of the R phase and in-plane nanoscale multi-domains after heat treatment reveal the memory effect and reversible phase transition and domain evolution. Our findings demonstrate the reversible control of R-Mc-T-super T symmetry changes (leading to the creation of true T phase BiFeO3 with enhanced tetragonality) and multidomain-single domain structure evolution through controllable defect engineering. This work also provides a pathway to generate large tetragonality (or c/a ratio) that could be extended to other ferroelectric material systems (such as PbTiO3, BaTiO3 and HfO2) which might lead to strong polarization enhancement.

9.
Nat Commun ; 9(1): 3824, 2018 09 20.
Artigo em Inglês | MEDLINE | ID: mdl-30237449

RESUMO

Single-cell DNA methylome profiling has enabled the study of epigenomic heterogeneity in complex tissues and during cellular reprogramming. However, broader applications of the method have been impeded by the modest quality of sequencing libraries. Here we report snmC-seq2, which provides improved read mapping, reduced artifactual reads, enhanced throughput, as well as increased library complexity and coverage uniformity compared to snmC-seq. snmC-seq2 is an efficient strategy suited for large-scale single-cell epigenomic studies.


Assuntos
Metilação de DNA/genética , Análise de Sequência de DNA , Análise de Célula Única/métodos , Adulto , Animais , Dimerização , Biblioteca Gênica , Humanos , Masculino , Camundongos Endogâmicos C57BL , Pessoa de Meia-Idade
10.
Nat Commun ; 8(1): 573, 2017 09 18.
Artigo em Inglês | MEDLINE | ID: mdl-28924171

RESUMO

The emergence of large-scale genomic, chemical and pharmacological data provides new opportunities for drug discovery and repositioning. In this work, we develop a computational pipeline, called DTINet, to predict novel drug-target interactions from a constructed heterogeneous network, which integrates diverse drug-related information. DTINet focuses on learning a low-dimensional vector representation of features, which accurately explains the topological properties of individual nodes in the heterogeneous network, and then makes prediction based on these representations via a vector space projection scheme. DTINet achieves substantial performance improvement over other state-of-the-art methods for drug-target interaction prediction. Moreover, we experimentally validate the novel interactions between three drugs and the cyclooxygenase proteins predicted by DTINet, and demonstrate the new potential applications of these identified cyclooxygenase inhibitors in preventing inflammatory diseases. These results indicate that DTINet can provide a practically useful tool for integrating heterogeneous information to predict new drug-target interactions and repurpose existing drugs.Network-based data integration for drug-target prediction is a promising avenue for drug repositioning, but performance is wanting. Here, the authors introduce DTINet, whose performance is enhanced in the face of noisy, incomplete and high-dimensional biological data by learning low-dimensional vector representations.


Assuntos
Algoritmos , Biologia Computacional/métodos , Reposicionamento de Medicamentos/métodos , Preparações Farmacêuticas/metabolismo , Proteínas/metabolismo , Animais , Celecoxib/química , Celecoxib/metabolismo , Células Cultivadas , Ciclo-Oxigenase 2/química , Ciclo-Oxigenase 2/metabolismo , Humanos , Camundongos Endogâmicos C57BL , Modelos Moleculares , Preparações Farmacêuticas/química , Ligação Proteica , Domínios Proteicos , Proteínas/química
11.
Cell Syst ; 5(3): 212-220.e6, 2017 09 27.
Artigo em Inglês | MEDLINE | ID: mdl-28957655

RESUMO

Ribosome stalling is manifested by the local accumulation of ribosomes at specific codon positions of mRNAs. Here, we present ROSE, a deep learning framework to analyze high-throughput ribosome profiling data and estimate the probability of a ribosome stalling event occurring at each genomic location. Extensive validation tests on independent data demonstrated that ROSE possessed higher prediction accuracy than conventional prediction models, with an increase in the area under the receiver operating characteristic curve by up to 18.4%. In addition, genome-wide statistical analyses showed that ROSE predictions can be well correlated with diverse putative regulatory factors of ribosome stalling. Moreover, the genome-wide ribosome stalling landscapes of both human and yeast computed by ROSE recovered the functional interplays between ribosome stalling and cotranslational events in protein biogenesis, including protein targeting by the signal recognition particles and protein secondary structure formation. Overall, our study provides a novel method to complement the ribosome profiling techniques and further decipher the complex regulatory mechanisms underlying translation elongation dynamics encoded in the mRNA sequence.


Assuntos
Códon/análise , Biologia Computacional/métodos , Elongação Traducional da Cadeia Peptídica/genética , Algoritmos , Sequência de Aminoácidos , Aprendizado Profundo , Genômica , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Humanos , Elongação Traducional da Cadeia Peptídica/fisiologia , Biossíntese de Proteínas/fisiologia , RNA Mensageiro/análise , Ribossomos/química , Saccharomyces cerevisiae/genética
12.
Science ; 357(6351): 600-604, 2017 08 11.
Artigo em Inglês | MEDLINE | ID: mdl-28798132

RESUMO

The mammalian brain contains diverse neuronal types, yet we lack single-cell epigenomic assays that are able to identify and characterize them. DNA methylation is a stable epigenetic mark that distinguishes cell types and marks regulatory elements. We generated >6000 methylomes from single neuronal nuclei and used them to identify 16 mouse and 21 human neuronal subpopulations in the frontal cortex. CG and non-CG methylation exhibited cell type-specific distributions, and we identified regulatory elements with differential methylation across neuron types. Methylation signatures identified a layer 6 excitatory neuron subtype and a unique human parvalbumin-expressing inhibitory neuron subtype. We observed stronger cross-species conservation of regulatory elements in inhibitory neurons than in excitatory neurons. Single-nucleus methylomes expand the atlas of brain cell types and identify regulatory elements that drive conserved brain cell diversity.


Assuntos
Metilação de DNA , Epigênese Genética , Lobo Frontal/metabolismo , Neurônios/metabolismo , Sequências Reguladoras de Ácido Nucleico , 5-Metilcitosina/química , Adulto , Animais , Sequência de Bases , Núcleo Celular/metabolismo , Sequência Conservada , Citosina/química , Lobo Frontal/citologia , Humanos , Masculino , Camundongos , Camundongos Endogâmicos C57BL , Análise de Sequência de DNA , Análise de Célula Única
13.
J Phys Chem Lett ; 8(13): 3115-3121, 2017 Jul 06.
Artigo em Inglês | MEDLINE | ID: mdl-28631471

RESUMO

Understanding the big discrepancy in the photoluminesence quantum yields (PLQYs) of nanoscale colloidal materials with varied morphologies is of great significance to its property optimization and functional application. Using different shaped CsPbBr3 nanocrystals with the same fabrication processes as model, quantitative synchrotron radiation X-ray diffraction analysis reveals the increasing trend in lattice strain values of the nanocrystals: nanocube, nanoplate, nanowire. Furthermore, transient spectroscopic measurements reveal the same trend in the defect quantities of these nanocrystals. These experimental results unambiguously point out that large lattice strain existing in CsPbBr3 nanoparticles induces more crystal defects and thus decreases the PLQY, implying that lattice strain is a key factor other than the surface defect to dominate the PLQY of colloidal photoluminesence materials.

14.
J Comput Biol ; 23(9): 737-49, 2016 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-27154509

RESUMO

Finding the global minimum energy conformation (GMEC) of a huge combinatorial search space is the key challenge in computational protein design (CPD) problems. Traditional algorithms lack a scalable and efficient distributed design scheme, preventing researchers from taking full advantage of current cloud infrastructures. We design cloud OSPREY (cOSPREY), an extension to a widely used protein design software OSPREY, to allow the original design framework to scale to the commercial cloud infrastructures. We propose several novel designs to integrate both algorithm and system optimizations, such as GMEC-specific pruning, state search partitioning, asynchronous algorithm state sharing, and fault tolerance. We evaluate cOSPREY on three different cloud platforms using different technologies and show that it can solve a number of large-scale protein design problems that have not been possible with previous approaches.


Assuntos
Algoritmos , Biologia Computacional/métodos , Conformação Proteica , Software , Proteína gp120 do Envelope de HIV/química , Humanos , Modelos Moleculares
15.
Nucleic Acids Res ; 44(4): e32, 2016 Feb 29.
Artigo em Inglês | MEDLINE | ID: mdl-26467480

RESUMO

RNA-binding proteins (RBPs) play important roles in the post-transcriptional control of RNAs. Identifying RBP binding sites and characterizing RBP binding preferences are key steps toward understanding the basic mechanisms of the post-transcriptional gene regulation. Though numerous computational methods have been developed for modeling RBP binding preferences, discovering a complete structural representation of the RBP targets by integrating their available structural features in all three dimensions is still a challenging task. In this paper, we develop a general and flexible deep learning framework for modeling structural binding preferences and predicting binding sites of RBPs, which takes (predicted) RNA tertiary structural information into account for the first time. Our framework constructs a unified representation that characterizes the structural specificities of RBP targets in all three dimensions, which can be further used to predict novel candidate binding sites and discover potential binding motifs. Through testing on the real CLIP-seq datasets, we have demonstrated that our deep learning framework can automatically extract effective hidden structural features from the encoded raw sequence and structural profiles, and predict accurate RBP binding sites. In addition, we have conducted the first study to show that integrating the additional RNA tertiary structural features can improve the model performance in predicting RBP binding sites, especially for the polypyrimidine tract-binding protein (PTB), which also provides a new evidence to support the view that RBPs may own specific tertiary structural binding preferences. In particular, the tests on the internal ribosome entry site (IRES) segments yield satisfiable results with experimental support from the literature and further demonstrate the necessity of incorporating RNA tertiary structural information into the prediction model. The source code of our approach can be found in https://github.com/thucombio/deepnet-rbp.


Assuntos
Proteína de Ligação a Regiões Ricas em Polipirimidinas/química , RNA Mensageiro/química , Proteínas de Ligação a RNA/química , Ribossomos/química , Sítios de Ligação , Biologia Computacional , Regulação da Expressão Gênica , Conformação de Ácido Nucleico , Proteína de Ligação a Regiões Ricas em Polipirimidinas/genética , Processamento Pós-Transcricional do RNA/genética , RNA Mensageiro/metabolismo , Proteínas de Ligação a RNA/genética , Ribossomos/genética
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