DNA aptamers against bacterial cells can be efficiently selected by a SELEX process using state-of-the art qPCR and ultra-deep sequencing
Bibliographic record
Abstract
Abstract DNA aptamers generated by cell-SELEX against bacterial cells have gained increased interest as novel and cost-effective affinity reagents for cell labelling, imaging and biosensing. Here we describe the selection and identification of DNA aptamers for bacterial cells using a combined approach based on cell-SELEX, state-of-the-art applications of quantitative real-time PCR (qPCR), next-generation sequencing (NGS) and bioinformatic data analysis. This approach is demonstrated on Enterococcus faecalis ( E. faecalis ), which served as target in eleven rounds of cell-SELEX with multiple subtractive counter-selections against non-target species. During the selection, we applied qPCR-based analyses to evaluate the ssDNA pool size and remelting curve analysis of qPCR amplicons to monitor changes in pool diversity and sequence enrichment. Based on NGS-derived data, we identified 16 aptamer candidates. Among these, aptamer EF508 exhibited high binding affinity to E. faecalis cells (K D -value: 37 nM) and successfully discriminated E. faecalis from 20 different Enterococcus and non- Enterococcus spp. Our results demonstrate that this combined approach enabled the rapid and efficient identification of an aptamer with both high affinity and high specificity. Furthermore, the applied monitoring and assessment techniques provide insight into the selection process and can be highly useful to study and improve experimental cell-SELEX designs to increase selection efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".