Bayesian multivariate image analysis for studying oral microbiome biogeography

牙周炎和龋病是口腔生物被膜病的高发疾病。减轻这些项目的社会负担 多微生物疾病将需要更好地了解人类-微生物超微生物和 微生物物种间的相互作用。微生物学的一个关键障碍是几乎完全缺乏知识 以及在10到100微米尺度上检查微生物群落的空间组织的工具。 为了满足这一需求，我们最近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)。Clasi鱼的图像显示了高达 在样本的每个区域中有28个分类单元，同时还显示每个分类单元(分类单位)的细胞如何定位 相对于彼此和相对于宿主细胞。然而，已经被用来 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群的空间模式。 此外，它们缺乏应对生物膜体系结构提出的具体挑战的能力，例如如何 结合形状(推断细胞间接触所需的信息)，如何对高达 28个分类群的同时分类，以及如何结合多幅图像的数据。 我们提出了三个目标来解决这些限制，并通过这样做，促进了空间统计领域的发展 对于复杂图像数据的一般分析：1)扩展空间统计技术以说明 细菌类群的形状和丰度在模拟节理空间格局中的作用；2)建立多元贝叶斯模型 扩展到多个图像和非空间协变量的对数-高斯-考克斯过程模型，如主机 特征；以及3)开发贝叶斯范式来模拟和量化两个玉米芯样排列 分类群，占形状。 提出的核心创新是开发和应用超越分析的统计方法 测量丰度和组成，以量化生物膜图像中微生物之间的空间关系。 这一灵活的建模框架将允许测试关于微生物-微生物相互作用的假设和 与寄主特征的关联。这是分析这类图像的方式的根本转变， 潜在地为了解口腔微生物的作用提供了新的见解。 为了测试这些方法的性能，我们将进行模拟研究并比较口腔生物膜图像数据 来自患有牙周炎和不患有牙周炎的受试者。我们将为常规应用程序提供软件 这些方法是由微生物学家提出的。我们期待着这些新方法和软件的广泛使用，这将发现 广泛应用于其他人类生物膜疾病和一般的生物地理学。阐明空间 确定生物膜在人类口腔健康和疾病中的作用需要口腔微生物的分布。这个 我们开发的方法将导致识别可能作为新靶点的关键细菌相互作用 用于预防或治疗牙周炎和其他口腔疾病。

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.