The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Ultrahigh-throughput screening in drop-based microfluidics for directed evolution
Open accessAbstract
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 ∼100 variants comparable in activity to the parent from an initial population of ∼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 ∼10 8 individual enzyme reactions in only 10 h, using < 150 μL 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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Blount, Z. D., Borland, C. Z., & Lenski, R. E. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0803151105 · Cited by 840
Cited by
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356
Self-citationSchneider, G. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.232 · Cited by 910
Organic Reactions in Microdroplets: Reaction Acceleration Revealed by Mass Spectrometry
Yan, X., Bain, R. M., & Cooks, R. G. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201602270 · Cited by 583
Chip in a lab: microfluidics for next generation life science research
Streets, A. M., & Huang, Y. (2013) · Biomicrofluidics · doi:10.1063/1.4789751 · Cited by 180
Nanotechnology: emerging tools for biology and medicine
Wong, I. Y., Bhatia, S. N., & Toner, M. (2013) · Genes & Development · doi:10.1101/gad.226837.113 · Cited by 132
Metadata
- Authors
- Agresti, J. J., Antipov, E., Abate, A. R., Ahn, K., Rowat, A. C., Baret, J.-C., Marquez, M., Klibanov, A. M., Griffiths, A. D., & Weitz, D. A.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2010-02-08
- Volume
- 107
- Issue
- 9
- Pages
- 4004-4009
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2118131251
- Cited by
- 1,149
- References
- 1
- FWCI
- 42.78
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Ultrahigh-throughput screening in drop-based microfluidics for directed evolution. https://app.cochat.ai/papers/10.1073/pnas.0910781107
- The paper
- Agresti, J. J., Antipov, E., Abate, A. R., Ahn, K., Rowat, A. C., Baret, J.-C., Marquez, M., Klibanov, A. M., Griffiths, A. D., & Weitz, D. A. (2010). Ultrahigh-throughput screening in drop-based microfluidics for directed evolution. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.0910781107
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.