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SELEX: Critical factors and optimization strategies for successful aptamer selection
Open accessAbstract
Within the last decade, the application range of aptamers in biochemistry and medicine has expanded rapidly. More than just a replacement for antibodies, these intrinsically structured RNA‐ or DNA‐oligonucleotides show great potential for utilization in diagnostics, specific drug delivery, and treatment of certain medical conditions. However, what is analyzed less frequently is the process of aptamer identification known as systematic evolution of ligands by exponential enrichment (SELEX) and the functional mechanisms that lie at its core. SELEX involves numerous singular processes, each of which contributes to the success or failure of aptamer generation. In this review, critical steps during aptamer selection are discussed in‐depth, and specific problems are presented along with potential solutions. The discussed aspects include the size and molecule type of the selected target, the nature and stringency of the selection process, the amplification step with its possible PCR bias, the efficient regeneration of RNA or single‐stranded DNA, and the different sequencing procedures and screening assays currently available. Finally, useful quality control steps and their role within SELEX are presented. By understanding the mechanisms through which aptamer selection is influenced, the design of more efficient SELEX procedures leading to a higher success rate in aptamer identification is enabled.
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Aita, T., Nishigaki, K., & Husimi, Y. (2012) · Mathematical Biosciences · doi:10.1016/j.mbs.2012.07.006 · Cited by 12
Selection of aptamers for AMACR detection from DNA libraries with different primers
Yang, D., Chou, C., & Chen, L. (2018) · RSC Advances · doi:10.1039/c8ra01808a · Cited by 10
McGivney, J. B., Csordas, A. T., Walker, F. M., Bagley, E. R., Gruber, E. M., Mage, P. L., Casas‐Finet, J. R., Nakamoto, M. A., Eisenstein, M. S., Larkin, C. J., et al. (2018) · Analytical Chemistry · doi:10.1021/acs.analchem.7b04775 · Cited by 9
Dickey, D. D., Giangrande, P. H., & Thiel, W. H. (2016) · Molecular Therapy · doi:10.1016/s1525-0016(16)33552-3 · Cited by 6
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Puumala, L. S., Grist, S. M., Morales, J. M., Bickford, J. R., Chrostowski, L., Shekhar, S., & Cheung, K. C. (2022) · Biosensors · doi:10.3390/bios13010053 · Cited by 55
Metadata
- Authors
- Kohlberger, M., & Gadermaier, G.
- Venue
- Biotechnology and Applied Biochemistry
- Publisher
- Wiley
- Published
- 2021-09-03
- Volume
- 69
- Issue
- 5
- Pages
- 1771-1792
- ISSN
- 0885-4513 (ISSN-L)
- 1470-8744
- OpenAlex
- W3195771950
- Cited by
- 341
- References
- 117
- FWCI
- 11.14
- Type
- journal article
- Language
- en
- Open access
- hybrid
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 ›
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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.
