ArticleMethods in molecular biology (Clifton, N.J.)2025
3D Tissue Culture Model for Virology Studies.
Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Infectious diseases, including recent outbreaks of H7N9 influenza, Ebola, Zika, and SARS-CoV-2, remain significant global health threats. While traditional two-dimensional (2D) cell cultures have long been the cornerstone of virology research, their inability to replicate complex in vivo microenvironments, such as cell-cell interactions, apical-basal polarity, and extracellular signaling gradients, limits their utility for studying viral pathogenesis and drug responses. Three-dimensional (3D) culture systems overcome these limitations by providing physiologically relevant platforms that better mimic native tissue environments. Among the commonly used methods, the hanging-drop method enables spheroid formation by suspending cell droplets, allowing natural cell aggregation at the liquid-air interface. The pHEMA method creates a nonadhesive surface through a poly-2-hydroxyethyl methacrylate coating, ensuring uniform spheroid sizes and minimal cell-matrix interactions. The Matrigel embedding method embeds cells in a growth factor-reduced extracellular matrix, supporting cell-matrix interactions, tissue morphogenesis, and differentiation. Finally, the inlet-well-based hanging-drop method employs a specialized inlet-well system, combining hanging-drop formation with controlled transfer to receiving wells while maintaining humidity and minimizing evaporation. These versatile methods facilitate studies on viral replication, infectivity, and antiviral screening, offering reproducible, high-fidelity models for understanding host-virus interactions and advancing therapeutic development.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.