MCSPACE: inferring microbiome spatiotemporal dynamics from high-throughput co-localization data.

ABSTRACT:

Background

Recent advances in high-throughput approaches for estimating co-localization of microbes, such as SAMPL-seq, allow characterization of the biogeography of the gut microbiome longitudinally and at an unprecedented scale. However, these high-dimensional data are complex and have unique noise properties.

Results

To address these challenges, we developed MCSPACE, a probabilistic AI method that infers, from microbiome co-localization data, spatially coherent assemblages of taxa, their dynamics over time, and their responses to perturbations. To evaluate MCSPACE's capabilities, we generated the largest longitudinal microbiome co-localization dataset to date, profiling spatial relationships of microbes in the guts of mice subjected to serial dietary perturbations over 76

SUBMITTER: Uppal G 

PROVIDER: S-EPMC12817806 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature