Efficient Strategy to Discover DNA Aptamers Against Low Abundance Cell Surface Proteins in Scarce Samples.
Xiaoqiu WuYuqing LiuDengwei ZhangJingjing YuMingxin ZhangShuwei FengLifei ZhangTing FuYamin TanTao BingWeihong TanPublished in: Journal of the American Chemical Society (2024)
Molecular recognition probes targeting cell surface proteins such as aptamers play crucial roles in precise diagnostics and therapy. However, the selection of aptamers against low-abundance proteins in situ on the cell surface, especially in scarce samples, remains an unmet challenge. In this study, we present a single-round, single-cell aptamer selection method by employing a digital DNA sequencing strategy, termed DiDS selection, to address this dilemma. This approach incorporates a molecular identification card for each DNA template, thereby mitigating biases introduced by multiple PCR amplifications and ensuring the accurate identification of aptamer candidates. Through DiDS selection, we successfully obtained a series of high-quality aptamers against cell lines, clinical specimens, and neurons. Subsequent analyses for target identification revealed that aptamers derived from DiDS selection exhibit recognition capabilities for proteins with varying abundance levels. In contrast, multiple rounds of selection resulted in the enrichment of only one aptamer targeting a high-abundance target. Moreover, the comprehensive profiling of cell surfaces at the single-cell level, utilizing an enriched aptamer pool, revealed unique molecular patterns for each cell line. This streamlined approach holds promise for the rapid generation of specific recognition molecules targeting cell surface proteins across a broad range of expression levels and expands its applications in cell profiling, specific probe identification, biomarker discovery, etc.
Keyphrases
- cell surface
- single cell
- rna seq
- nucleic acid
- single molecule
- high throughput
- gold nanoparticles
- sensitive detection
- circulating tumor
- small molecule
- antibiotic resistance genes
- cell free
- bioinformatics analysis
- magnetic resonance
- magnetic nanoparticles
- cancer therapy
- poor prognosis
- living cells
- magnetic resonance imaging
- stem cells
- spinal cord injury
- cystic fibrosis
- drug delivery
- pseudomonas aeruginosa
- big data
- long non coding rna
- machine learning
- liquid chromatography
- wastewater treatment