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Job Title
Postdoctoral Researcher
Academic Rank
Fellow or Postdoc
Department
Pathology
Authors
Gurdip Uppal and Georg K. Gerber
Principal Investigator
Georg K. Gerber
Categories
Tags
The microbiome, or collection of commensal micro-organisms that live on and within us, is extremely complex and plays key roles in many prevalent human diseases. Spatial associations between microbes influence interactions and underlying community functions, which are critical to understand for purposes of altering the microbiome to treat and prevent diseases. However, little is known about the in vivo biogeography of the microbiome. Metagenomic plot sampling by sequencing (MaPS-seq) is a novel culture-independent method that can characterize the spatial organization of an entire microbiome at micron-scale resolution but produces data that is noisy and can be difficult to interpret. To address the challenges of analyzing MaPS-seq data, we developed MC-SPACE, a Bayesian model that infers mixtures of spatially coherent microbial community subtypes and alterations in their prevalence due to perturbations. We find, through MC-SPACE’s noise model and explicit modeling of perturbation effects, we recover underlying community subtypes, detect perturbation effects, and recover pairwise microbial associations significantly better than current methods. We apply MC-SPACE to a fecal microbiota transplantation (FMT) mouse study and find distinct microbial communities from donor mice that are spatially coherent and engraft into recipient mice, causing significant spatial restructuring of gut microbiomes.
Our microbiomes, or communities of trillions of beneficial micro-organisms that live on and within us, have been shown to play key roles in human health and disease. These microbial communities exhibit intricate spatial organization, which influences how the microbes interact with each other and with us, their host. Understanding this spatial organization is an important step to unlocking the potential of the microbiome to improve human health, because it can allow us, for instance, to predict which microbes can effectively colonize our bodies and prevent pathogenic bacteria such as C. difficile from infecting us. However, little is known about the spatial organization of the microbiome. In this work, we present a new computer-based method that analyzes data from an experimental method that measures detailed information about the spatial structure of microbiomes. Our method addresses challenges in analyzing this data, which is noisy and can be difficult to interpret, by automatically deducing the spatial relationships between microbes and finding sub-communities of microbes, as well as how these communities change with perturbations. We demonstrate our method’s superior performance to established models and its ability to provide new insights into the dynamics of spatial organization of the microbiome.
