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Griddient: a microfluidic array to generate reconfigurable gradients on-demand for spatial biology applications

Biological tissues are highly organized structures where spatial-temporal gradients (e.g., nutrients, hypoxia, cytokines) modulate multiple physiological and pathological processes including inflammation, tissue regeneration, embryogenesis, and cancer progression. Current in vitro technologies strug...

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Detalles Bibliográficos
Autores principales: Sanchez-de-Diego, Cristina, Virumbrales-Muñoz, María, Hermes, Brock, Juang, Terry D., Juang, Duane S., Riendeau, Jeremiah, Guzman, Emmanuel Contreras, Reed-McBain, Catherine A., Abizanda-Campo, Sara, Patel, Janmesh, Hess, Nicholas J., Skala, Melissa C., Beebe, David J., Ayuso, Jose M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10492845/
https://www.ncbi.nlm.nih.gov/pubmed/37689746
http://dx.doi.org/10.1038/s42003-023-05282-3
Descripción
Sumario:Biological tissues are highly organized structures where spatial-temporal gradients (e.g., nutrients, hypoxia, cytokines) modulate multiple physiological and pathological processes including inflammation, tissue regeneration, embryogenesis, and cancer progression. Current in vitro technologies struggle to capture the complexity of these transient microenvironmental gradients, do not provide dynamic control over the gradient profile, are complex and poorly suited for high throughput applications. Therefore, we have designed Griddent, a user-friendly platform with the capability of generating controllable and reversible gradients in a 3D microenvironment. Our platform consists of an array of 32 microfluidic chambers connected to a 384 well-array through a diffusion port at the bottom of each reservoir well. The diffusion ports are optimized to ensure gradient stability and facilitate manual micropipette loading. This platform is compatible with molecular and functional spatial biology as well as optical and fluorescence microscopy. In this work, we have used this platform to study cancer progression.