SPARKing aptamer discovery
Summary
Qin Wu, Weihong Tan, and their team have developed SPARK-seq, a high-throughput platform designed for aptamer discovery and kinetic profiling within native cellular environments. Published in Nature Methods in February 2026, this innovation addresses the significant challenge of identifying aptamers for cell surface proteins, which are crucial drug targets. Existing methods like Cell-SELEX are low-throughput and often compromise the proteins' native conformations, failing to identify exact molecular targets despite yielding aptamers for conditions like triple-negative breast cancer. SPARK-seq aims to overcome these limitations, laying the groundwork for "aptomics," a high-throughput strategy for aptamer discovery and optimization, particularly relevant for advancing precision medicine.
Key takeaway
For researchers focused on developing precision medicine therapies, SPARK-seq offers a significant advancement by enabling high-throughput aptamer discovery against cell surface proteins in their native state. This method could accelerate the identification of novel therapeutic targets, particularly for challenging diseases like triple-negative breast cancer, by providing precise molecular target information previously unavailable with techniques like Cell-SELEX.
Key insights
SPARK-seq enables high-throughput aptamer discovery and kinetic profiling in native cellular contexts.
Principles
- Cell surface proteins are valuable drug targets.
- Native conformation preservation is critical for aptamer discovery.
Method
SPARK-seq is a high-throughput platform for aptamer discovery and kinetic profiling, designed to operate in native cellular contexts, overcoming limitations of methods like Cell-SELEX.
In practice
- Discover aptamers for cell surface proteins.
- Profile aptamer kinetics in live cells.
Topics
- Aptamer Discovery
- SPARK-seq Platform
- Cell Surface Proteins
- Precision Medicine
- Triple-Negative Breast Cancer
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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine learning : nature.com subject feeds.