Bayesian multivariate image analysis for studying oral microbiome biogeography

用于研究口腔微生物组生物地理学的贝叶斯多元图像分析

• 批准号: 10336589
• 负责人: KYU HA LEE
• 金额: $ 16.54万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10336589
• 关键词: Bayesian; multivariate; image; analysis; studying

Periodontitis and caries are highly prevalent oral biofilm diseases. Reducing the societal burden of these polymicrobial diseases will require a better understanding of the human-microbe superorganism and interactions among microbial species. A critical barrier in microbiology has been a near total lack of knowledge and tools to examine the spatial organization of microbial communities at the ten- to 100-micron scale. To meet this need, we recently developed an imaging technique, Combinatory Labeling and Spectral Imaging Fluorescence in Situ Hybridization (CLASI-FISH). CLASI-FISH images display the abundance of up to 28 taxa in each region of a sample while also displaying how cells of each taxon (taxonomic unit) are located relative to each other and relative to host cells. However, the quantitative methods that have been used to analyze spectral imaging data thus far are limited to describing spatial patterns of one or two taxa at a time. Moreover, they lack the ability to address challenges raised specifically by biofilm architecture, such as how to incorporate shapes (information needed to infer cell-to-cell contact), how to model spatial distributions of up to 28 taxa simultaneously, and how to combine data from multiple images. We propose three aims that address these limitations and, in doing so, advance the field of spatial statistics for the analysis of complex image data in general: 1) extend spatial statistics techniques to account for bacterial taxa’s shape and abundance in modeling joint spatial patterns; 2) develop a multivariate Bayesian log-Gaussian Cox process model that extends to multiple images and non-spatial covariates, such as host characteristics; and 3) develop a Bayesian paradigm to model and quantify corncob-like arrangements of two taxa, accounting for shapes. The core innovation proposed is to develop and apply statistical methods that go beyond analyzing measures of abundance and composition to quantify spatial relationships among microbes in biofilm images. This flexible modeling framework will allow testing of hypotheses regarding microbe-microbe interactions and associations with host characteristics. This is a fundamental shift for how such images will be analyzed, potentially providing new insights into the role of microbes in the oral cavity. To test the methods’ performance, we will perform simulation studies and compare oral biofilm image data from subjects with and without periodontitis. We will make software available for the routine application of these methods by microbiologists. We anticipate wide use of these novel methods and software, which will find broad application to other human biofilm diseases and to biogeography in general. Elucidating the spatial distribution of oral microbes is required to determine the role of biofilm in human oral health and disease. The methods we develop will lead to the identification of key bacterial interactions that may serve as novel targets for the prevention or treatment of periodontitis and other oral diseases.

牙周炎和汽车是高度普遍的口腔生物膜疾病。减少这些社会燃烧 多数疾病将需要更好地理解人类微生物超生物和 微生物物种之间的相互作用。微生物学的关键障碍几乎完全缺乏知识 以及以十至100微米量表检查微生物群落空间组织的工具。 为了满足这种需求，我们最近开发了一种成像技术，组合标签和光谱 成像荧光原位杂交（CLASI-FISH）。 clasi-fish图像显示了最高的抽象 28样品的每个区域中的分类单元，同时还显示每个分类单元的细胞（分类单元）的位置 相对于彼此，相对于宿主细胞。但是，已用于的定量方法 到目前为止，分析光谱成像数据仅限于一次描述一个或两个分类单元的空间模式。 此外，他们缺乏解决生物膜建筑专门提出的挑战的能力，例如 合并形状（推断细胞间接触所需的信息），如何建模到最高的空间分布 28分类单元，以及如何结合来自多个图像的数据。 我们提出了三个解决这些限制的目标，并在此限制中提高了空间统计的领域 对于一般的复杂图像数据的分析：1）扩展空间统计技术以说明 细菌分类群的形状和在建模关节空间模式中的丰富度； 2）开发多元贝叶斯人 扩展到多个图像和非空间协变量的log-gaussian Cox过程模型，例如主机 特征; 3）开发一个贝叶斯范式，以建模和量化两个玉米科目的排列 分类单元，核算形状。 提出的核心创新是开发和应用超出分析的统计方法 量化生物膜图像中微生物之间空间关系的抽象和组成量度。 这个灵活的建模框架将允许测试有关微生物相互作用和的假设 与宿主特征的关联。对于如何分析此类图像，这是一个基本转变， 有可能提供有关微生物在口腔中的作用的新见解。 为了测试该方法的性能，我们将进行仿真研究并比较口头生物膜图像数据 来自有或没有牙周炎的受试者。我们将使软件可用于常规应用 这些方法由微生物学家。我们预计这些新颖的方法和软件会广泛使用，这将发现 对其他人类生物膜疾病和生物地理学的广泛应用。阐明空间 需要口服微生物的分布来确定生物膜在人口腔健康和疾病中的作用。这 我们开发的方法将导致鉴定关键细菌相互作用，这些相互作用可能是新目标 用于预防或治疗牙周炎和其他口腔疾病。