Bayesian multivariate 3D spatial modeling for microbiome image analysis

用于微生物组图像分析的贝叶斯多元 3D 空间建模

• 批准号: 10586135
• 负责人: KYU HA LEE
• 金额: $ 61.99万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-04 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10586135
• 关键词: 3-Dimensional; Address; Affect; Analysis of Variance; Architecture; Area; Astronomy; Bacteria; Biological; Calibration; Cells; Characteristics; Clinical; Communities; Complex; Computer software; Confocal Microscopy; Data; Data Set; Dependence; Development; Dimensions; Disease; Ecology; Environment; Epithelium; Evaluation; Exhibits; Fluorescent in Situ Hybridization; Forestry; Free Will; Goals; Health; Hour; Human; Image; Image Analysis; Imaging Techniques; Joints; Knowledge; Label; Location; Maps; Mathematics; Measures; Medical Imaging; Methods; Microbe; Microbial Biofilms; Microbiology; Modeling; Multivariate Analysis; Nutrient; Oral; Oral cavity; Organelles; Pathogenicity; Pattern; Performance; Physiology; Play; Process; Radial; Recording of previous events; Role; Running; Salivary; Sampling; Site; Slice; Spatial Distribution; Statistical Methods; Structure; Surface; System; Taxon; Taxonomy; Techniques; Technology; Testing; Three-Dimensional Image; Tongue; Visualization; Work; biomedical imaging; cell type; community organizations; computerized tools; data integration; disorder control; disorder prevention; experience; flexibility; high dimensionality; improved; innovation; insight; microbial; microbial community; microbiome; microbiome research; microorganism interaction; novel; oral biofilm; organizational structure; scale up; skills; software development; spectrograph; statistics; three-dimensional modeling; tool; two-dimensional; user-friendly; virtual

Bacteria play critical beneficial and harmful roles in human health. Living in biofilm communities, one species may attack, protect, or provide nutrients for neighboring species. These interactions determine the community's net effects. Clarifying community organization is needed to understand how biofilm affects health. To begin to meet this need, we developed an imaging technique, Combinatory Labeling and Spectral Imaging Fluorescence in Situ Hybridization (CLASI-FISH), which displays how taxa's cells are located relative to each other and to host cells. Yet biofilm's complex, three-dimensional (3D) architecture is poorly captured by commonly used measures, such as intercellular distances or global biofilm volume for one or two taxa. Here, we propose to extend Log Gaussian Cox process models (LGCP) to describe and test hypotheses about human biofilm architecture, a novel application. Computational burden limits existing LGCP models for geostatistical data to datasets with thousands of observations. These methods cannot be applied to biofilm image data typically containing millions of observations. In preliminary work on two-dimensional (2D) biofilm images, we have successfully scaled up multivariate LGCPs for six taxa. Estimated pairwise cross-correlation functions differ in univariate analyses, which ignore other taxa's locations, versus multivariate analyses, which leverage taxa's joint spatial distribution. We propose statistical innovations to address challenges raised by, but not unique to, 3D biofilm images. Comparing biofilm across sample groups defined experimentally or based on exposure history requires integrating data across subjects' images that lack true spatial correspondence. Further, 3D spatial analyses have not been applied to multivariate data with millions of observations. The goal of this proposal is therefore to build a Bayesian multivariate 3D LGCP that incorporates different images—thereby allowing for non-spatial covariate factors—by applying a separate coordinate system to each image. This proposal has three parts: (a) the development of novel multivariate 3D spatial analysis methods (aims 1-3), (b) evaluation of a hypothesis regarding the spatial structure of human tongue microbiome (aim 4), and (c) software development and dissemination, based on best practices (aim 5). The interdisciplinary team has a deep skill set and experience developing Bayesian high-dimensional multivariate analysis methods. The core innovation proposed is to integrate non-spatial covariates with multivariate spatial data across 3D images lacking a common coordinate system. Sample accessibility and prior biological knowledge make the oral cavity the best starting point to develop a flexible modeling framework that will allow testing of hypotheses regarding microbial interactions and associations with host characteristics. This is a fundamental shift for how such images will be analyzed, potentially providing new insight into the role of oral microbes. In advancing capabilities for studying multivariate 3D spatial patterns across images, the mathematical adaptations and software we develop will have the potential to yield a breakthrough technology.

细菌在人类健康中发挥着重要的有益和有害作用。生活在生物膜群落中， 物种可以攻击、保护或为邻近物种提供营养。这些相互作用决定了 社区的净效应。需要澄清社区组织，以了解生物膜如何影响健康。 为了开始满足这一需求，我们开发了一种成像技术，组合标记和光谱 成像荧光原位杂交（CLASI-FISH），显示分类群的细胞如何相对定位 以及宿主细胞之间的相互作用。然而，生物膜的复杂的三维（3D）结构很难被捕获， 通常使用的措施，如细胞间的距离或全球生物膜体积的一个或两个分类群。 在这里，我们建议扩展对数高斯考克斯过程模型（LGCP）来描述和检验假设 关于人类生物膜结构的新应用。计算负担限制了现有的LGCP模型， 地统计数据到具有数千个观测值的数据集。这些方法不能应用于生物膜 图像数据通常包含数百万个观测值。在二维（2D）生物膜的初步工作中， 图像中，我们已经成功地扩大了六个分类群的多元LGCP。估计成对互相关 函数在单变量分析中不同，单变量分析忽略了其他分类群的位置，而多变量分析 利用分类群的联合空间分布。我们提出统计创新，以应对挑战，但 这并不是3D生物膜图像所独有的。比较实验定义或基于以下的样品组中的生物膜 曝光历史需要整合缺乏真实空间对应性的受试者图像之间的数据。 此外，3D空间分析尚未应用于具有数百万观测值的多变量数据。 因此，本提案的目标是构建贝叶斯多变量3D LGCP， 图像-从而允许非空间协变量因子-通过将单独的坐标系应用于每个 形象该建议有三个部分：（a）开发新的多元3D空间分析方法 (aims（B）评价关于人舌微生物组的空间结构的假设（目的4）， 以及（c）根据最佳做法开发和传播软件（目标5）。跨学科团队 拥有深厚的技能和开发贝叶斯高维多变量分析方法的经验。 提出的核心创新是将非空间协变量与3D中的多变量空间数据集成 缺乏共同坐标系的图像。样本可及性和先前的生物学知识使得 口腔是开发一个灵活的建模框架的最佳起点，该框架将允许对假设进行测试 关于微生物与宿主特征的相互作用和关联。这是一个根本性的转变， 这些图像将被分析，可能为口腔微生物的作用提供新的见解。在推进 跨图像研究多变量3D空间模式的能力，数学适应性， 我们开发的软件将有可能产生突破性的技术。