Protein stamping for MALDI mass spectrometry using an electrowetting-based microfluidic platform

Abstract

MALDI-MS (matrix-assisted laser desorption/ionization mass spectrometry) is one of the most commonly used techniques for protein analysis. In conventional systems sample preparation is typically done in well-plates and transferred onto a MALDI target by robotic systems, which are complex, huge, expensive and slow. In this paper, we present a droplet-based microfluidic interface to transfer protein samples from a well-plate format onto a MALDI target for MS analysis. The droplets are actuated using the electrowetting phenomenon, and are immersed in silicone oil which prevents non-specific adsorption and enables the manipulation of high concentrations of proteins. Droplet transport and droplet formation were evaluated as a function of protein concentration using bovine serum albumin (BSA) as a test system. Droplet transport was possible for BSA concentrations up to 10mg/mL which is three orders of magnitude higher than previously reported results on handling proteins by electrowetting. Droplet formation from on-chip reservoirs, using only electrowetting forces and no external pressure assistance, was possible up to concentrations of 0.01mg/mL. An interface between a well-plate format and the electrowetting chip, and a scheme to passively stamp droplets onto a target substrate was then designed and tested by stamping BSA solutions. In two separate experiments 3.6fmoles and 16fmoles of BSA were stamped onto a glass slide using 0.001mg/mL and 0.01mg/mL samples respectively. A protein mixture with known constituents (ABI 4700 proteomics analyzer calibration solution) was stamped onto a MALDI plate and the individual proteins were correctly identified in the mass spectrum obtained using MALDI-TOF MS. The preliminary results establish the feasibility of using an electrowetting-based microfluidic system to handle proteins especially for protein stamping applications. The proposed system has a small footprint, is easy to control, and is very fast compared to conventional robotic systems. In addition, there are no moving parts and the associated mechanical reliability issues. Future work involves scaling to a larger number of samples and integration of sample preparation steps on-chip.