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: 10401247
• 负责人: KYU HA LEE
• 金额: $ 55.11万
• 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/10401247
• 关键词: 3-Dimensional; Address; Affect; Analysis of Variance; Architecture; Area; Astronomy; Bacteria; Biological; Cells; Characteristics; Clinical; Communities; Complex; Computer software; Confocal Microscopy; Data; Data Set; Dependence; Development; Dimensions; Disease; Ecology; Environment; Epithelial; Evaluation; Exhibits; Fluorescent in Situ Hybridization; Forestry; Free Will; Gaussian model; Goals; Health; Hour; Human; Image; Image Analysis; Imaging Techniques; Joints; Knowledge; Label; Location; 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; Work; base; biomedical imaging; cell type; community organizations; computerized tools; disorder control; disorder prevention; experience; flexibility; high dimensionality; improved; innovation; insight; microbial; microbial community; microbiome; microbiome research; microorganism interaction; novel; oral biofilm; 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.

细菌生活在生物膜群落中，对人类健康起着至关重要的有益和有害作用。 物种可能会攻击、保护或为邻近物种提供营养，这些相互作用决定了物种之间的关系。 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)，显示类群细胞的相对位置 然而，生物膜复杂的三维 (3D) 结构很难被捕获。 常用的测量方法，例如一两个类群的细胞间距离或全局生物膜体积。 在这里，我们建议扩展对数高斯 Cox 过程模型（LGCP）来描述和测试假设 关于人类生物膜结构，一种新颖的应用限制了现有的 LGCP 模型。 这些方法不能应用于生物膜。 在二维 (2D) 生物膜的初步工作中，图像数据通常包含数百万个观察结果。 图像中，我们成功地扩展了六个分类群的多元 LGCP 的估计成对互相关性。 单变量分析中的功能有所不同，单变量分析忽略了其他类群的位置，而多变量分析则忽略了其他分类群的位置 我们提出统计创新来解决但提出的挑战。 并非 3D 生物膜图像所独有，比较通过实验或基于实验定义的样本组的生物膜。 暴露历史需要整合受试者图像中缺乏真实空间对应性的数据。 此外，3D 空间分析尚未应用于具有数百万个观测值的多元数据。 因此，该提案的目标是构建一个贝叶斯多元 3D LGCP，其中包含不同的 图像 - 从而允许非空间协变量因素 - 通过对每个图像应用单独的坐标系 该提案分为三个部分：(a) 开发新颖的多元 3D 空间分析方法。 （目标 1-3），（b）评估有关人类舌头微生物组空间结构的假设（目标 4）， (c) 基于最佳实践的软件开发和传播（目标 5）。 拥有深厚的技能和开发贝叶斯高维多元分析方法的经验。 提出的核心创新是将非空间协变量与跨 3D 的多元空间数据集成 图像缺乏共同的坐标系，样本的可访问性和先验的生物学知识使得 口腔是开发灵活的建模框架的最佳起点，该框架将允许检验假设 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。 对这些图像进行分析，可能会为口腔微生物在进步中的作用提供新的见解。 研究跨图像的多元 3D 空间模式、数学适应和 我们开发的软件将有潜力产生突破性的技术。