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DOI 10.64898/2026.07.21.739887

Медицинская библиотека · bioRxiv : the preprint server for biology · 2026

Bacterial surface display enables lysis-independent joint host-pathogen single-cell profiling.

Gunnarsson Cal, Hsiao Jeff C, Hochfelder Jacob B, Solomon Sydney L, Lepe Bianca A, Zhu Sharon, Xu Kelly, Chung Hattie A, Bryson Bryan D

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Аннотация

Heterogeneity in host-pathogen interactions arises from variation in both host cell state and pathogen state, yet most single-cell methods capture these features separately. Joint profiling is limited by fundamental technical mismatches between host- and pathogen-derived material, particularly differences in cell wall structure, lysis requirements, and molecular abundance. Here we introduce a lysis-independent strategy that enables unified measurement of intracellular bacterial presence and state alongside host single-cell profiles. We repurpose bacterial surface display to encode promoter activity and bacterial identity as antibody-detectable signals, rendering bacterial features compatible with existing antibody-based single-cell assays. This approach is modular across promoters, epitope tags, display scaffolds, and bacterial species, including Escherichia coli and Mycobacterium tuberculosis . Surface-displayed reporters are detectable during intracellular infection and can be read out by flow cytometry and droplet-based single-cell RNA sequencing without pathogen-specific lysis optimization or pre-sorting on pathogen signal. Applying this method to infected macrophages, we link heterogeneous bacterial uptake to heterogeneous expression of phagocytosis-associated host programs. This strategy enables scalable, joint host-pathogen single-cell measurements and expands the range of pathogens and states accessible to high-throughput single-cell analysis.

Для цитирования: Gunnarsson Cal, Hsiao Jeff C, Hochfelder Jacob B, Solomon Sydney L, Lepe Bianca A, Zhu Sharon, Xu Kelly, Chung Hattie A, Bryson Bryan D. Bacterial surface display enables lysis-independent joint host-pathogen single-cell profiling.. bioRxiv : the preprint server for biology. 2026. DOI: 10.64898/2026.07.21.739887.
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