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. 2010 Mar 2;107(9):4004-9.
doi: 10.1073/pnas.0910781107. Epub 2010 Feb 8.

Ultrahigh-throughput screening in drop-based microfluidics for directed evolution

Jeremy J Agresti  1 , Eugene Antipov, Adam R Abate, Keunho Ahn, Amy C Rowat, Jean-Christophe Baret, Manuel Marquez, Alexander M Klibanov, Andrew D Griffiths, David A Weitz
Affiliations

Ultrahigh-throughput screening in drop-based microfluidics for directed evolution

Jeremy J Agresti et al. Proc Natl Acad Sci U S A. .

Erratum in

  • Proc Natl Acad Sci U S A. 2010 Apr 6;107(14):6550

Abstract

The explosive growth in our knowledge of genomes, proteomes, and metabolomes is driving ever-increasing fundamental understanding of the biochemistry of life, enabling qualitatively new studies of complex biological systems and their evolution. This knowledge also drives modern biotechnologies, such as molecular engineering and synthetic biology, which have enormous potential to address urgent problems, including developing potent new drugs and providing environmentally friendly energy. Many of these studies, however, are ultimately limited by their need for even-higher-throughput measurements of biochemical reactions. We present a general ultrahigh-throughput screening platform using drop-based microfluidics that overcomes these limitations and revolutionizes both the scale and speed of screening. We use aqueous drops dispersed in oil as picoliter-volume reaction vessels and screen them at rates of thousands per second. To demonstrate its power, we apply the system to directed evolution, identifying new mutants of the enzyme horseradish peroxidase exhibiting catalytic rates more than 10 times faster than their parent, which is already a very efficient enzyme. We exploit the ultrahigh throughput to use an initial purifying selection that removes inactive mutants; we identify approximately 100 variants comparable in activity to the parent from an initial population of approximately 10(7). After a second generation of mutagenesis and high-stringency screening, we identify several significantly improved mutants, some approaching diffusion-limited efficiency. In total, we screen approximately 10(8) individual enzyme reactions in only 10 h, using < 150 microL of total reagent volume; compared to state-of-the-art robotic screening systems, we perform the entire assay with a 1,000-fold increase in speed and a 1-million-fold reduction in cost.

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Conflict of interest statement

Patent applications that include some of the ideas described in this manuscript have been filed by Harvard University, the Medical Research Council, UK, and the University of Strasbourg. Should these institutions receive revenues as a result of licensing these patents, the authors are entitled to receive payments through the respective Inventor’s Rewards Schemes. J.J.A. is founder of Fluid Discovery, which uses ultrahigh-throughput screening methods derived from ideas described herein. A.D.G. and D.A.W. are also founders of Raindance Technologies, which has licensed some of these patent applications.

Figures

Fig. 1.
Fig. 1.
Modules of the ultrahigh-throughput microfluidic screening platform. (A) A low-magnification image of the entire drop-making device. (B) A suspension of yeast cells displaying the HRP on their surface (aq1) is combined with a second aqueous stream containing the fluorogenic substrate AUR (aq2). The yeast are at a concentration of 1 × 108 cells per milliliter, which gives an average of 0.3 cells per 6-pL drop after being diluted by half by the substrate stream. The aqueous drops are formed at a flow-focusing junction (33) in a fluorocarbon oil at a rate of 2 kHz, and the number of cells per drop follows a Poisson distribution: ∼22% have a single cell (16) (SI Text). (C) The drops flow out of this device into a tube that acts as an incubation line where they incubate for ∼5 min. We use a fluorosurfactant (15) to prevent coalescence and to give the drops a biocompatible interface. (D) A single layer of drops after incubation showing the fluorescence developing from the active HRP displayed on the surface of the cells. (E) From the delay line, the drops flow as a solid plug to a junction where oil is added to separate the drops. To visually demonstrate the sorting process, we sorted an emulsion containing light and dark drops; the light drops contain 1 mM fluorescein, and the dark contain 1% bromophenol blue. Fluorescence levels are detected as the drops pass a laser focused on the channel at the gap between two electrodes. When sorting is on, the light drops, which are brighter than the threshold level, are sorted by dielectrophoresis (17, 18) into the bottom channel (SI Text). (Scale bar, 80 μm.)
Fig. 2.
Fig. 2.
Schematic of the directed evolution experiment. (A) The wild-type HRP gene is encoded on a plasmid as a C-terminal fusion to the Aga2 gene to allow surface display, and expression is driven by an inducible (10) promoter. (B) We create two libraries for each generation. Each library has ∼107 variants. For the first generation, we use one epPCR and one active-site-targeted saturation mutagenesis library. For the second generation, we make both a high- and a low-mutation-rate epPCR library after recombination of the fastest first-generation sequences (SI Text). (C) The libraries are transformed into yeast strain EBY100. Upon induction with galactose, each cell displays on its surface ∼10,000 copies of a single mutant HRP protein (μHRP). (D) The yeast and nonfluorescent substrate are coencapsulated into drops on the microfluidic platform (Fig. 1). In the first round of each generation, we maximize the number of mutants screened by using a higher loading, ∼1 cell per drop. In subsequent rounds, to mininimize coencapsulation and ensure the highest enrichment possible, we load cells at 0.3 cells per drop (17). (E) Active mutants convert the AUR (gray) to its fluorescent oxidation product (pink) in an incubation line (F), and then flow into the sorter (G), where the brightest drops are sorted. We break the emulsion to release the cells from the drops, allow the cells to replicate, and then repeat the growth, induction, and sorting process.
Fig. 3.
Fig. 3.
Results of screening. (A) Enrichment of library pools. The activities are normalized relative to wild-type HRP. The first-generation epPCR and saturation mutagenesis libraries (10) (SI Text) (dashed red and orange, respectively) enrich to a level of ∼2 times the activity of the wild type after four sorting rounds. The second-generation low- and high-mutation rate libraries (solid blue and cyan, respectively) enrich to ∼8 times the wild type. The right panel shows a dot plot of the activities of the 50 unique first-generation (g1) mutants and 31 second-generation (g2) mutants. Red circles denote g1 mutants that were chosen as founders for g2, and blue circles denote g2 mutants that were chosen for detailed kinetic characterization. Amino acid substitutions and kinetics are detailed in the SI Text. (B) Schematic of the protein showing residues with substitutions only in the first or second generation shown in green or red, respectively. Yellow residues are inherited between rounds. Note the clusters of inherited residues around the two structural pink calcium ions and at the opening to the active site in front of the brown heme group. (C) Residues are color coded highlighting the frequency with which a substitution was found at that residue in the g2 mutants. The residues most frequently found mutated tend to cluster around the calcium ions and the opening to the active site; the site of substrate binding.
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