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

## TL;DR

MCSPACE is a new AI tool that helps understand how gut microbes are arranged in space and time, using high-throughput data to track changes in their locations and responses to diet.

## Contribution

MCSPACE introduces a probabilistic AI method for inferring spatially coherent microbial assemblages and their dynamics from co-localization data.

## Key findings

- MCSPACE outperformed existing methods in benchmarking on longitudinal microbiome co-localization datasets.
- The method identified temporally persistent and dynamic microbial assemblages in the human gut.
- Dietary perturbations in mice induced shifts in gut microbial assemblages, revealed by MCSPACE analysis.

## Abstract

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.

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 days. Analyses of these data and two existing human longitudinal datasets demonstrated superior benchmarking performance of MCSPACE over existing methods and moreover yielded insights into the spatiotemporal structuring of the gut microbiome, including identifying temporally persistent and dynamic microbial assemblages in the human gut, and shifts in assemblages in the murine gut induced by specific dietary components.

Our results highlight the utility of MCSPACE, which we make available to the community as an open-source software tool, for elucidating the dynamics of microbiome biogeography and gaining insights into the role of spatial relationships in host-microbial ecosystem function.

Video Abstract

Video Abstract

The online version contains supplementary material available at 10.1186/s40168-025-02279-4.