Flow cytometric evaluation of yeast-bacterial cell-cell interactions

Current methods for studying microbial interactions often lack the resolution to track specific cell-cell binding events in mixed populations. While synthetic binding pairs have been used in controlled environments, their application to microbial systems remains limited. Prior research has shown that surface-displayed proteins can mediate specific interactions, but measuring these interactions in real-time is challenging. No prior work had resolved how to efficiently evaluate such interactions using flow cytometry in a yeast-bacteria system. This gap motivated the development of a system that could track binding events at the single-cell level. Existing methods rely on population-level measurements, which obscure individual cell behaviors. The ability to enrich specific binding variants within a mixed population is also underexplored. This paper introduces a novel approach to address these limitations.

The study aims to develop a system for evaluating yeast-bacterial cell-cell interactions using surface-displayed proteins. The specific problem is the lack of a high-resolution method to track binding events in mixed microbial populations. The motivation is to enable detailed analysis of ligand-target interactions at the single-cell level. The system is designed to use synthetic nanobody-antigen pairs to model interactions. The goal is to demonstrate that this system can support the characterization of known and novel interactions. The approach allows for the enrichment of specific binding variants through fluorescence-activated cell sorting. The system's utility is tested with a known nanobody-antigen pair relevant to bacterial pathogenesis. The ultimate purpose is to provide a platform for high-throughput discovery of bacterial surface-binding molecules.

The study employs a yeast-bacteria coincubation format to model cell-cell interactions. Surface-displayed nanobody-antigen pairs are used to mediate binding between yeast and bacterial cells. Flow cytometry is applied to detect and quantify these interactions at the single-cell level. Fluorescence-activated cell sorting is used to enrich specific yeast-displayed nanobodies. The system is tested with a known nanobody-antigen pair involving intimin from E. coli. The method includes labeling yeast and bacteria with fluorescent markers to distinguish populations. Data collection involves measuring fluorescence intensity to assess binding efficiency. The approach allows for the isolation and analysis of specific interacting cells.

The system successfully detected interactions between yeast and bacteria mediated by surface-displayed nanobody-antigen pairs. Fluorescence-activated cell sorting enriched a specific nanobody variant within a mixed yeast population. The interaction between the nanobody and intimin on E. coli was confirmed using this system. Flow cytometry data showed distinct fluorescence patterns corresponding to binding events. The system's sensitivity allowed detection of low-frequency interactions. Enrichment of the specific nanobody demonstrated the system's ability to isolate functional variants. The results suggest that the system can support detailed analysis of ligand-target interactions. The findings indicate that this approach is suitable for high-throughput screening of bacterial surface-binding molecules.

The yeast-bacteria coincubation system supports efficient evaluation of cell-cell interactions mediated by surface-displayed proteins. The system's ability to detect and enrich specific binding events was demonstrated using a known nanobody-antigen pair. Flow cytometry and fluorescence-activated cell sorting proved effective for analyzing and isolating interacting cells. The results suggest that this system can be used to characterize ligand-target interactions in microbial systems. The study shows that the system is capable of supporting high-throughput discovery of bacterial surface-binding molecules. The findings indicate that this approach may facilitate systematic characterization of such interactions. The system's potential for further development was highlighted in the study. The authors propose that this format may support future applications in microbial interaction research.