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A high-dimensional microfluidic approach for selection of aptamers with programmable binding affinities

Abstract

Aptamers are being applied as affinity reagents in analytical applications owing to their high stability, compact size and amenability to chemical modification. Generating aptamers with different binding affinities is desirable, but systematic evolution of ligands by exponential enrichment (SELEX), the standard for aptamer generation, is unable to quantitatively produce aptamers with desired binding affinities and requires multiple rounds of selection to eliminate false-positive hits. Here we introduce Pro-SELEX, an approach for the rapid discovery of aptamers with precisely defined binding affinities that combines efficient particle display, high-performance microfluidic sorting and high-content bioinformatics. Using the Pro-SELEX workflow, we were able to investigate the binding performance of individual aptamer candidates under different selective pressures in a single round of selection. Using human myeloperoxidase as a target, we demonstrate that aptamers with dissociation constants spanning a 20-fold range of affinities can be identified within one round of Pro-SELEX.

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Fig. 1: Quantitative isolation of aptamers with different binding affinities via a high-dimensional microfluidic approach.
Fig. 2: Validation of the sorting performance of the Pro-SELEX chip using anti-thrombin aptamers with different binding affinities.
Fig. 3: Isolation of anti-MPO aptamers with different binding affinities via a high-dimensional microfluidic-based approach.
Fig. 4: Quantitative isolation of anti-MPO aptamers with desired affinities based on their Z score.
Fig. 5: Comparison of Pro-SELEX with current aptamer-selection technologies.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information . The sequencing data files are too large to be publicly shared, but they are available from the corresponding author upon reasonable request. The chip design (in the format of STL) and running protocol of the microfluidic chip will be available free of charge from the publisher’s website as a supplementary file. Source data are provided with this paper.

Code availability

The code corresponding to the AptaZ algorithm can be accessed at https://github.com/dwangnu/AptaZ.

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Acknowledgements

This research was supported in part by the Canadian Institutes of Health Research (grant no. FDN-148415) and the Collaborative Health Research Projects Program (CIHR/NSERC partnered). This research is part of the University of Toronto’s Medicine by Design initiative, which receives funding from the Canada First Research Excellence Fund. This research was supported in part by the McCormick Catalyst Fund at Northwestern University.

Author information

Authors and Affiliations

Authors

Contributions

D.C., Z.W. and S.O.K. conceived and designed the experiments. D.C. performed the aptamer selection and validation. Z.W. performed the chip fabrication and wrote the code for AptaZ. All authors discussed the results, analysed the data and contributed to the preparation and editing of the manuscript.

Corresponding author

Correspondence to Shana O. Kelley.

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Competing interests

S.O.K. is an inventor on a patent entitled ‘Device for capture of particles in a flow’ (US patent US10073079) that is licensed to CTRL Therapeutics. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Chemistry thanks Chunhai Fan, Yingfu Li and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supporting discussion, Tables 1–5 and Figs. 1–12.

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Supplementary Data

Source data used to generate Supplementary Figures.

Supplementary Data

Chip design.

Supplementary Data

Fabrication and running protocol of the microfluidic chip.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

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Chang, D., Wang, Z., Flynn, C.D. et al. A high-dimensional microfluidic approach for selection of aptamers with programmable binding affinities. Nat. Chem. 15, 773–780 (2023). https://doi.org/10.1038/s41557-023-01207-z

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