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Model-controlled hydrodynamic focusing to generate multiple overlapping gradients of surface-immobilized proteins in microfluidic devices.


ABSTRACT: Historically, it has been difficult to generate accurate and reproducible protein gradients for studies of interactions between cells and extracellular matrix. Here we demonstrate a method for rapid patterning of protein gradients using computer-driven hydrodynamic focusing in a simple microfluidic device. In contrast to published work, we are moving the complexity of gradient creation from the microfluidic hardware to dynamic computer control. Using our method, switching from one gradient profile to another requires only a few hours to devise a new control file, not days or weeks to design and build a new microfluidic device. Fitting existing protein deposition models to our data, we can extract key parameters needed for controlling protein deposition. Several protein deposition models were evaluated under microfluidic flow conditions. A mathematical model for our deposition method allows us to determine the parameters for a protein adsorption model and then predict the final shape of the surface density gradient. Simple and non-monotonic single and multi-protein gradient profiles were designed and deposited using the same device.

SUBMITTER: Georgescu W 

PROVIDER: S-EPMC4357342 | biostudies-literature | 2008 Feb

REPOSITORIES: biostudies-literature

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Model-controlled hydrodynamic focusing to generate multiple overlapping gradients of surface-immobilized proteins in microfluidic devices.

Georgescu Walter W   Jourquin Jerome J   Estrada Lourdes L   Anderson Alexander R A AR   Quaranta Vito V   Wikswo John P JP  

Lab on a chip 20071221 2


Historically, it has been difficult to generate accurate and reproducible protein gradients for studies of interactions between cells and extracellular matrix. Here we demonstrate a method for rapid patterning of protein gradients using computer-driven hydrodynamic focusing in a simple microfluidic device. In contrast to published work, we are moving the complexity of gradient creation from the microfluidic hardware to dynamic computer control. Using our method, switching from one gradient profi  ...[more]

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