2 research outputs found
From Images to Insights: Machine Learning Enhanced Spectral Imaging for Dissecting Microbiome Spatial Structure
No full textAdvances in imaging technologies and computational tools are transforming our understanding of the spatial organization of microbial communities within the host. Here, we investigated the spatial structure of the gut microbiome and its functional relationship with host mucus phenotypes by integrating high-resolution imaging, machine learning-based spectral unmixing, and gnotobiotic zebrafish models to uncover the mechanistic principles underlying host–microbe interactions. We first reviewed recent developments in microbiome spatial ecology, highlighting two distinct yet complementary dimensions of spatial structure: biogeography, the distribution of microbes across anatomical regions, and architecture, their fine-scale organization within a niche. This framework underscores how spatial context governs microbial community function and host interaction.
Using axenic, conventional, and gnotobiotic zebrafish models, we found that specific microbial taxa, rather than total bacterial biomass, drive mucus abundance and spatial patterning in a tissue- and context-dependent manner. For example, colonization with a defined bacterial consortium was sufficient to restore mucus production and gut architecture in axenic fish, recapitulating conventional phenotypes despite variation in overall microbial load—supporting the concept of microbial functional sufficiency. However, we also observed inherent variability in colonization outcomes and host responses across gnotobiotic individuals, highlighting a limitation of current re-conventionalization approaches. These findings reinforce the need to validate microbial colonization in gnotobiotic experiments to improve reproducibility and biological interpretation in host–microbiota studies.
Building on this framework, we addressed a major technical challenge in microbial imaging: resolving species-level identity in densely labeled, multiplexed communities. To overcome the resolution limitations of conventional fluorescence imaging and 16S rRNA-based methods, we developed, Cross-hybridization Inference and Phylogenetic Resolution fluorescence in-situ hybridization (CIPHR-FISH), a novel machine learning-based spectral classification framework for species-level identification in multiplex-labeled microbial communities. This approach was developed to test our functional sufficiency hypothesis more directly by enabling species-level resolution of microbial spatial patterns and identifying core microbiota configurations associated with differential host outcomes. This method generates and captures ‘cross hybridization inference’ that capture excitation/emission properties, spectral cross talk and probe cross-reactivity, enabling pixel-level classification of microbial species. Applied to a synthetic zebrafish gut consortium, the framework achieved 100% specificity and high sensitivity, surpassing traditional linear unmixing techniques and setting a new benchmark for microbial imaging. We then applied this high-resolution image analysis pipeline to examine microbial identity and spatial organization in vivo in the zebrafish gut and identified Aeromonas sp. as the dominant taxon in the presence of 5 other co-colonizing taxa—consistent with 16S rRNA sequencing data from these communities.
Altogether, this work integrates microbiome imaging, machine learning, and host physiology and development to reveal how microbial spatial structure shapes host mucosal environments, advancing tools our ability to map, resolve, and interpret microbiota–host relationships with unprecedented resolution
Hydrogeochemical evolution and assessment of groundwater quality for drinking and irrigation purposes in the Gushegu Municipality and some parts of East Mamprusi District, Ghana
No full textThe Gushegu Municipality and the East Mamprusi District in Ghana are dominated by the Oti/Pendjari Group within the Voltaian Supergroup. The major rock types found in the area are quartzites, siltstones, conglomerates, and shales with minor occurrences of tillites, silexites, limestones, and barite-rich dolomites. The inhabitants of the area are mainly peasant farmers, and their activities might be influencing the groundwater chemistry, but little is known about the quality of the groundwater. Therefore, this study evaluated the suitability of groundwater resources in the Gushegu Municipality, and some parts of the East Mamprusi District in Ghana for domestic and irrigation uses, employing hydrogeochemical graphing, geochemical modelling, multivariate statistical analysis, and computation of water quality indices. Sodium (Na+), with concentrations ranging from 4.93 to 323 mg/L and a mean of 169 mg/L, is found to be the major cation in the groundwater, while bicarbonate (HCO3-), with concentrations ranging from 19.9 to 685 mg/L and a mean of 397 mg/L, is the major anion in the area. The dominant hydrochemical facies is the Na-HCO3 type, accounting for about 72.7% of the study area's groundwater and is influenced by silicate weathering, carbonate mineral dissolution, and ion exchange reactions. Other factors accounting for this dominance may be anthropogenic activities, including the dissolution and leaching of fertilizers from farmlands. Overall, this study reveals that the groundwater in the area is suitable for drinking based on the WQI classification. There are localized contaminations with respect to B and F-, making the water in those areas unsuitable for drinking. Also, the groundwater in the area is unsuitable for irrigation purposes due to the high Na% values (43 to 99% with a mean of 86%), magnesium hazard values (1 to 312 with a mean of 88), and sodium adsorption ratio (0.57 to 42.4 with a mean of 12.7). All these indices exceed their respective standards for irrigation purposes.Department of Geological Engineering of UMaTThis study is part of the Bachelor of Science project work/thesis of the first author, which was supervised by the first author at the University of Mines and Technology (UMaT), Tarkwa, Ghana. Therefore, the authors appreciate the support from the Department of Geological Engineering of UMaT. The authors also thank everyone who has contributed to improving the quality of this study. The two anonymous Reviewers and Editors are greatly acknowledged for the valuable contribution they provided
