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A robust, low-cost platform for EM connectomics

强大、低成本的 EM 连接组学平台

基本信息

批准号：
10273540
负责人：
Daniel Joseph Bumbarger
金额：
$ 281.14万
依托单位：
ALLEN INSTITUTE
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-15 至 2024-07-14
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10273540
关键词：
3-Dimensional Adoption Alzheimer&apos s Disease Animal Model Archives Autopsy Biological Assay Brain Brain region Cells Collaborations Collection Communities Complex Computer software Custom Data Set Dementia Development Diagnostic Disease Electron Microscopy Electrons Epilepsy Equipment Film Functional disorder Goals Gold Human Image Industrialization Institutes Intervention Invertebrates Mammals Manuals Medical Mental Depression Mental disorders Methods Microscope Microscopy Monitor Mus Neurological Models Optics Organism Pathologic Patients Property Public Health Publishing Research Personnel Retina Risk Reduction Robot Scanning Scanning Electron Microscopy Schizophrenia Series Side Silicon Stains Stroke Structure System Techniques Technology Testing Thick Thinness Tissues Translations Transmission Electron Microscopy Work autism spectrum disorder base clinical application comparative cost cost effective cost efficient data quality design experimental study flexibility human tissue imaging modality improved industry partner instrumentation interest large datasets lens light microscopy machine vision nervous system disorder neural circuit next generation novel novel strategies open source programs prototype reconstruction routine imaging sensor silicon nitride software systems success tool transmission process

项目摘要

Project Summary/Abstract Over the past decade, serial-section electron microscopy has come into its own as a method to study the connectivity of neural circuits, from local circuits in mammals to entire invertebrate brains. Recently, the emphasis in the field has been to create increasingly large data sets, while comparatively little effort has been spent on making the tools of EM connectomics available to a large number of circuit neuroscientists. Obstacles exist at multiple levels. Manual approaches to serial sectioning are prohibitively difficult, while automated approaches require complex, expensive equipment that is difficult to deploy. High throughput scanning EM is limited to multi- beam approaches that are extremely expensive. Transmission EM is far less expensive, but automated approaches to sectioning remain challenging and require expensive substrates that are hard to manufacture and difficult to use. We propose to develop a new approach, already prototyped by our group and our industry partner, to establish a robust platform optimized to achieve the widest possible adoption. The system will center on an open source serial sectioning robot implementing a novel collection approach. The goal is to create a system that can be used at a variety of scales, from the current state of the art (1 mm3 or greater), to small volumes that can be sectioned and imaged routinely. Up to now, each published EM volume for connectomics has required a multi-year effort. Instead, our goal is to use volume reconstruction as an assay, rather than an end unto itself, in the context of other experiments. In the final year, we will create data sets that test the flexibility and robustness of the approach by creating EM volumes ranging from 50µm on a side to very large volumes encompassing >1 mm3.

项目总结/文摘