Digital Microfluidics is a technique characterized by the manipulation of discrete droplets (~nL – mL) on an array of electrodes by the application of electrical fields. It is well-suited for carrying out rapid, sequential, miniaturized automated biochemical assays. Here, we report a platform capable of automating several proteomic processing steps.
Clinical proteomics has emerged as an important new discipline, promising the discovery of biomarkers that will be useful for early diagnosis and prognosis of disease. While clinical proteomic methods vary widely, a common characteristic is the need for (i) extraction of proteins from extremely heterogeneous fluids (i.e. serum, whole blood, etc.) and (ii) extensive biochemical processing prior to analysis. Here, we report a new digital microfluidics (DMF) based method integrating several processing steps used in clinical proteomics. This includes protein extraction, resolubilization, reduction, alkylation and enzymatic digestion. Digital microfluidics is a microscale fluid-handling technique in which nanoliter-microliter sized droplets are manipulated on an open surface. Droplets are positioned on top of an array of electrodes that are coated by a dielectric layer – when an electrical potential is applied to the droplet, charges accumulate on either side of the dielectric. The charges serve as electrostatic handles that can be used to control droplet position, and by biasing a sequence of electrodes in series, droplets can be made to dispense, move, merge, mix, and split on the surface. Therefore, DMF is a natural fit for carrying rapid, sequential, multistep, miniaturized automated biochemical assays. This represents a significant advance over conventional methods (relying on manual pipetting or robots), and has the potential to be a useful new tool in clinical proteomics.
Mais J. Jebrail, Vivienne N. Luk, and Steve C. C. Shih contributed equally to this work.
Sergio L. S. Freire’s current address is at the University of the Sciences in Philadelphia located at 600 South 43rd Street, Philadelphia, PA 19104.
Part 1: Device Fabrication
Part 2: Device Set-up and Automation
Part 3: Sample and Reagent Preparation
Part 4: Digital Microfluidic Sample Processing
Part 5: Post-Processing Sample Preparation
Part 6: Mass Spectrometry
Figure 1. (a) A picture a DMF device mated to 40-pin connectors for automated droplet actuation. (b) A schematic of a device depicting the positioning of sample and reagents required for a proteomic workup.
Figure 2. Frames from a movie depicting the automated extraction and purification of BSA in 20% TCA (precipitant) and 70/30% chloroform/acetonitrile (rinse solution). In frame 6, the precipitated protein is redissolved in a droplet of 100 mM ammonium bicarbonate.
Figure 3. Frames from a movie illustrating sequential reduction, alkylation, and digestion of a droplet of resolubilized protein. In this figure, the reagents are colored with dyes for clarity; in practice, the reagents are not colored.
Figure 4. MS chromatogram of a sample of bovine serum albumin processed by digital microfluidics. 25 distinct peptides were identified (99.9% confidence interval) corresponding a sequence coverage of 44%.
The lack of standardized sample handling and processing in proteomics is a major limitation for the field. In addition, conventional macroscale sample handling involves multiple containers and solution transfers, which can lead to sample loss and contamination. A potential solution to these problems is to form integrated systems for sample processing relying on digital microfluidics1 (DMF). In previous work, DMF was shown to be useful for efficient removal of unwanted contaminants in heterogeneous protein-containing solutions.2 Likewise, DMF was shown to be compatible with integration of multistep solution-phase processing (reduction, alkylation and digestion) on an integrated device.3 Here, we have demonstrated a fully integrated system with automated droplet control for protein extraction by precipitation followed by solution-phase processing. We speculate that if methods such as these are widely adopted, the human error inherent in proteomic sample processing can be largely eliminated, resulting in analyses with better reproducibility. In short, we propose that DMF has the potential for being useful for a broad cross-section of applications, as the conditions can be precisely duplicated in any laboratory in the world.
We thank the Natural Sciences and Engineering Research Council (NSERC) and the Canadian Cancer Society for financial support. SCCS thanks NSERC and VNL thanks the Ontario Graduate Scholarship (OGS) program for graduate fellowships. ARW thanks the CRC for a Canada Research Chair.