A 3D multimodal micron-scale human brain atlas bridging single cell data, neuropathology and neuroradiology

连接单细胞数据、神经病理学和神经放射学的 3D 多模态微米级人脑图谱

基本信息

批准号：
10370064
负责人：
PARTHA Pratim MITRA
金额：
$ 527.81万
依托单位：
COLD SPRING HARBOR LABORATORY
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：
2021-09-15 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10370064
关键词：
3-Dimensional Adult Algorithms Amygdaloid structure Animal Model Architecture Archives Area Atlases Autopsy BRAIN initiative Brain Brain Stem Brain imaging Brain region Callithrix Cell physiology Cells Censuses Clinical Collection Communities Complement Consent Coupled Cryoultramicrotomy Data Data Analyses Data Set Data Store Databases Deposition Exclusion Criteria Functional Magnetic Resonance Imaging Future Generations Geometry Hippocampus (Brain)Histologic Histology Human Hypothalamic structure Image Institution Laboratories Laboratory mice Letters Link Machine Learning Magnetic Resonance Imaging Measurement Medical Medicine Methodology Methods Microscope Microscopic Modality Molecular and Cellular Biology Monkeys Mus Myelin National Institute of Mental Health Neurosciences Process Research Personnel Resolution Resources Scanning Scientist Series Slide Source Stains Structure Techniques Thalamic structure Thick Tissue Banks Tissue Preservation Tissues Training United States National Institutes of Health Ursidae Family Wit annotation system base brain cell brain tissue clinically significant crosslink data centers data portal data repository data sharing digital epigenomics experience genome annotation histological image human data image archival system in vivo machine learning algorithm machine learning method multimodality neuroimaging neuropathology petabyte reference genome success tool transcriptome transcriptomics virtual water diffusion whole slide imaging

项目摘要

Digitized reference brains, also referred to as Common Coordinate Frameworks (CCFs), together with superposed atlas annotations, are of central importance to neuroscience. They bear the same relation to neuroscience as do reference genomes and genome annotations to cellular and molecular biology. Strikingly, however, such reference brains for humans lag far behind the corresponding CCFs for non-human model organisms such as the laboratory mouse. Existing data sets either have sections spaced relatively far apart or lack in-plane resolution down to the micron scale. Crucially, existing data sets are not well connected to the major areas in medicine that deal with the human brain, namely neuroradiology and neuropathology. We will meet this need by creating an unprecedented micron-scale, 3D atlas that combines multiple MRI modalities as well as continuous serial section histology. In particular, the reference atlas will consist of Nissl, Myelin and H&E stains, with 20 micron contiguous serial sections, and approximately ~8000 sections/brain. We will do so using the tape-transfer method, which preserves tissue geometry even in the presence of disconnected pieces to the brain being sectioned, and permits 3D reassembly of the sections into a 3D volume. We will utilize diffeomorphic mapping methods to co-register the MRI and histological data, and will create a human brain CCF in which single-cell transcriptomic and epigenomic data can be pinned in order to create a Human Brain Cell atlas. We will use machine learning approaches to segment cells and processes in these images and to algorithmically detect cytoarchitectonic boundaries; such machine learning methods will also be used to predict histology and cytoarchitecture from MRI data, with our collected data as a training set. We will make our data freely available to scientists as well as medical professionals through an online data portal with a multi-resolution viewer for zooming and panning through terapixel image data, and also deposit the data in a shared data repository to make it easily accessible to other researchers. We will connect our data to a unique on-line neuropathology resource containing over a petabyte of neuropathological images, including H&E stained sections from the coronal plane. We expect that the reference brain data we produce will become the de- facto standard for a high-resolution reference atlas for the human brain.

数字化参考大脑，也称为通用坐标框架（CCF）具有叠加的图谱注释，对神经科学至关重要。他们承担着与神经科学的关系与参考基因组和基因组注释与细胞和细胞的关系相同分子生物学。然而，引人注目的是，这种人类参考大脑远远落后于人类大脑。非人类模型生物（例如实验室小鼠）的相应 CCF。现存的数据集要么具有间隔相对较远的部分，要么缺乏低至微米级。至关重要的是，现有的数据集与医学的主要领域没有很好的联系研究人类大脑的学科，即神经放射学和神经病理学。我们将通过创建前所未有的微米级 3D 图集来满足这一需求，该图集结合了多种 MRI 模式以及连续连续切片组织学。特别是，参考图谱将由 Nissl、Myelin 和 H&E 染色剂组成，具有 20 微米连续连续切片，并且大约每个大脑约 8000 个切片。我们将使用磁带传输方法来做到这一点，该方法即使存在与大脑不相连的碎片，也能保留组织的几何形状切片，并允许将各部分 3D 重新组装成 3D 体积。我们将利用微分同态映射方法来共同配准 MRI 和组织学数据，并将创建一个人脑 CCF，其中单细胞转录组和表观基因组数据可以按顺序固定创建人类脑细胞图谱。我们将使用机器学习方法来分割这些图像中的细胞和过程，通过算法检测细胞结构边界；这样的机器学习方法也将可用于根据 MRI 数据预测组织学和细胞结构，我们收集的数据作为训练集。我们将向科学家和医疗专业人员免费提供我们的数据通过具有多分辨率查看器的在线数据门户进行缩放和平移太像素图像数据，并将数据存储在共享数据存储库中以使其轻松其他研究人员可以访问。我们将把我们的数据连接到独特的在线神经病理学包含超过 PB 神经病理学图像的资源，包括 H&E 染色切片从冠状面。我们期望我们产生的参考大脑数据将成为人脑高分辨率参考图谱的事实标准。