英国博士招聘-爱丁堡大学-自闭症关联疗法

发布时间：2020-12-18

浏览次数：2040次

如有招聘信息发布(博士/博后/访学)可联系

邮箱：cxw@med.tj.cn

微信：shelver888

公众号链接：https://mp.weixin.qq.com/s?__biz=MzU5NTk2OTE2Mw==&mid=2247485902&idx=1&sn=364c36079b02e38ab2302fa30ca8713b&chksm=fe689c2ac91f153cad09ffdacbae14ddb8f6f606d696f2c7c0e68b19aa96d9daa73f845c15bf&token=1651122633&lang=zh_CN#rd

学校：爱丁堡大学 University of Edinburgh

院系：Centre for Discovery Brain Sciences

教授：Dr Gulsen Surmeli, Dr Matthias Hennig & Dr Ian Simpson

申请网址： http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

截止日期：January 7th, 2021

介绍

背景

自闭症是分散式脑神经网络疾病，而非局部大脑区域的问题。人类大脑成像研究为分散式脑神经网络的核心节点之间的长程连通性异常和协调活动丧失提供了证据。这些结果引发了新的假设，即自闭症（ASD）是由长程低连通性和局部超连通性（https://paperpile.com/c/33oswX/m05Do）引起的。然而，由于利用传统解剖学方法研究多种途径中的长程连通性方面存在技术困难，因此在啮齿动物ASD模型中突触连通性与该假设相符的程度仍不清楚。 

高通量的全脑范围的长程连接结构变化的定量分析可通过MAPSeq（Multiplex Analysis of Projections by Sequencing）（https://paperpile.com/c/33oswX/4LI7v+Scrs）途径实现。MAPSeq用唯一的分子标识符（条形码RNA）标记单个神经元，该标识符被传输到神经元的轴突末端。量化目标区域条形码RNA水平揭示了标记神经元的靶标和投射强度。在实验中，可以研究成千上万个神经元的单个投影轮廓，从而使MAPSeq成为前所未有的工具，可以对大脑范围的连通性进行高通量，高分辨率的评估。在我们的实验室中，我们已经成功地将该技术应用于脑部记忆网络的研究。

我们可以设想多种自闭症结构化关联性的表现方式。例如，轴突可能误入异常目标。或者，到目标或在目标部位的轴突分支可能被破坏。区分这些模型和其他模型将极大地帮助理解ASD的机理基础。这些问题非常适合MAPseq方法，但是使用当前的功能成像或解剖学标记技术解决这些问题将非常困难且耗时。

目的

在这个项目中，内部区域连接处将采用MAPseq和3D成像，以定量方式研究ASD啮齿动物模型中的交感神经。我们的目标是从记忆，注意力和社交行为网络的三个关键节点（前额叶皮层mPFC，后壁顶区PPA和扁桃体AMY）中识别出改变结构连通性的通信路径。我们将专注于与Fmr1，Syngap和Neurexin基因相关的自闭症小鼠模型。报告指出FMRP- / y小鼠视觉区域传入输入的结构变化，表明是系统性连接问题（https://paperpile.com/c/33oswX/zu8Z），但这些缺陷的确切性质尚不清楚，该问题也未受到关注。 

获得的数据将直接检验长程的低连通性假设，并将揭示特定连通性缺陷的性质。

培训成果

1.掌握用于大型数据集分析的数学和统计方法。

2.掌握计算建模方法。

3.掌握转录组谱分析的高通量RNA测序方法。

这些技能适用于科学，医学和工业领域的广泛研究学科。

Surmeli实验室的实践经验：

-使用光片显微镜进行组织制备和成像。

-啮齿类动物脑部手术。

-病毒干预方法。

-组织切片的制备和解剖。

Surmeli和Hennig实验室的分析类经验，其中包含辛普森博士实验室的分析类经验：

-使用当前可用的和正在开发的用于光片显微镜图像的计算工具，以3D方式分析轴突束。

-使用统计建模工具处理深度测序数据。

申请

点击以下网址立即申请：http://www.ed.ac.uk/studying/postgraduate/degrees/index.php?r=site/view&id=919

21/22申请的截止日期为 2021年1月7日（星期四）。

申请人必须申请一个特定的项目，请确保在申请的第4部分中包含要申请的项目的详细信息。提出申请之前，您应该联系项目负责导师。 

在申请特定项目时，您无需在申请时提交研究建议书。 

请确保在提交申请时上传尽可能多的所需文件，包括简历。  

Job description

Background

Autism is a disorder of dispersed brain networks rather than a problem localized to an individual brain area. Human brain imaging studies provide evidence for abnormal long-range connectivity and a lack of coordinated activity between key nodes of dispersed brain networks. These findings lead to the hypothesis that autism spectrum disorders (ASDs) result from long range hypo-connectivity and local hyper-connectivity1. However, because of technical challenges in quantitatively applying classical anatomical methods to investigate long-range connectivity in multiple pathways, the extent to which synaptic connectivity in rodent ASD models is consistent with this hypothesis remains unclear. 

High-throughput quantitative brain-wide analysis of structural changes in long-range connections has recently become feasible using an approach that goes with the acronym MAPSeq (Multiplex Analysis of Projections by Sequencing)2,3. MAPSeq relies on tagging individual neurons with a unique molecular identifier, a barcode RNA, that is transported to the neuron’s axon terminals. Quantifying the levels of barcode RNA in areas of interest reveals the targets of the labelled neurons and the strength of projections. In one experiment, the individual projection profile of thousands of neurons can be investigated, making MAPSeq an unprecedented tool for high-throughput, high-resolution assessment of brain-wide connectivity. We have already successfully applied this technique to the study of memory networks in my lab.

We can envision a number of ways in which structural connectophies could manifest in ASD. For example, axons might be misrouted to abnormal targets. Alternatively, axonal branching en route to targets or at the target site might be disrupted. Distinguishing between these and other models would greatly help understand the mechanistic basis of ASDs. These problems are well suited to MAPseq approaches, but would be difficult and unfeasibly time consuming to address using current functional imaging or anatomical labeling techniques.

Aims

In this project we will apply MAPseq and 3D imaging on inter-area connections to investigate connectopathies in rodent models of ASD in a quantitative manner. Our goal is to identify communication pathways with altered structural connectivity from three key nodes of memory, attention and social behaviour networks (Prefrontal cortex mPFC, Posterior parietal area PPA and amygdala AMY). We will focus on the mouse models of autism spectrum disorders associated with Fmr1, Syngap and Neurexin genes. Structural changes in afferent inputs to visual areas were reported in FMRP-/y mice suggesting a systemic connectivity problem4, but the precise nature of these deficits is unclear and the issue has otherwise received very little attention. 

The data obtained will directly test long range hypo-connectivity hypotheses and will reveal the nature of specific connectivity deficits involved.

Training Outcomes

1. Expertise in mathematical and statistical methods for analysis of large datasets.

2. Expertise in methods for computational modeling.

3. Expertise in high-throughput RNA sequencing methods for transcriptomic profiling.

These skills are applicable in a broad range of research disciplines in science, medicine and industry.

Practical lab experience in the Surmeli Lab:

-Tissue preparation and imaging using light sheet microscopy.

-Rodent brain surgery.

-Viral intervention approaches.

-Tissue slice preparation and dissection.

Analytical lab experience in the Surmeli and Hennig Labs with input from Dr. Simpson:

-Analysis of axonal tracts in 3D using currently available computational tools available and in development for light sheet microscopy images.

-Implementation of statistical modelling tools for processing of deep sequencing data.

References

1. Menon, V. Developmental pathways to functional brain networks: emerging principles. Trends Cogn. Sci. 17, 627–640 (2013).

2. Kebschull, J. M. et al. High-Throughput Mapping of Single-Neuron Projections by Sequencing of Barcoded RNA. Neuron 91, 975–987 (2016).

3. Han, Y. et al. The logic of single-cell projections from visual cortex. Nature 556, 51–56 (2018).

4. Haberl, M. G. et al. Structural-functional connectivity deficits of neocortical circuits in the Fmr1−/y mouse model of autism. Science Advances 1, e1500775 (2015).

如需申请博士留学，请联系好予博士留学。

我们为您提供更专业的出国方案—免费获取定制出国方案