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April 5, 2026Science Advances2 citationsOpen Access

A microphysiologic human cervical model recapitulates microbial, immune, and pathogenic properties of sexually transmitted infections

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KNKatherine M. NelsonDMDaniel MinahanVEVonetta L. Edwards

Key Points

  • The aim is to create a model that accurately represents human cervical infections and their complexities.
  • Developed a microphysiologic system (MPS) for human cervical tissue modeling.
  • Evaluated the model's susceptibility to Chlamydia trachomatis and Neisseria gonorrhoeae.
  • Validated the system across multiple laboratories, ensuring reproducibility.
  • The MPS successfully mimics dynamic, polymicrobial, immune, and pathogenic features of STIs.
  • Demonstrated transferability and usability in nonengineering environments.
  • Offers a low-cost alternative to existing models, enhancing research into genital infections.

Abstract

Sexually transmitted infections (STIs) of the cervicovaginal mucosa are among the most common global infections. Current monolayer cell culture and animal models fail to reproduce the multilevel complexity required to investigate host-microbiota-pathogen relationships simultaneously and/or with sufficient physiological relevance. To address this limitation, we have developed a microphysiologic system (MPS) that models human cervical tissue and its microbiota and is susceptible to infection by two prominent genital pathogens, Chlamydia trachomatis and Neisseria gonorrhoeae . Notably, this MPS platform recapitulates essential dynamic, polymicrobial, immune, and pathogenic features of chlamydial and gonococcal infections as they occur in humans. The low-cost MPS device requires no specialized equipment or specific expertise and was experimentally validated across multiple nonengineering, remotely located laboratories, demonstrating its transferability and reproducibility. The platform provides a tool for research into genital infections in a system that closely mimics the cervical epithelium, an important advance over existing models.

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Cite This Study

Nelson et al. (2026) studied this question.

synapsesocial.com/papers/69d1fdd4a79560c99a0a41a0https://doi.org/10.1126/sciadv.aeb4864
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