CELL-SELEX (Cell-Based Systematic Evolution of Ligands by EXponential enrichment) is a selection strategy used to discover nucleic-acid aptamers—short single-stranded DNA or RNA molecules that fold into shapes capable of binding cellular targets with high affinity and specificity. What makes CELL-SELEX AND BIOMARKER DISCOVERY such a powerful pairing is that cell-SELEX can enrich binders against native cell-surface features (often membrane proteins, glycoproteins, lipids, or complex epitopes) without needing to know the target in advance. This is especially valuable in biomarker discovery, where the “best” marker may be unknown, heterogeneous, or highly dependent on the cellular context. 1) What CELL-SELEX Is (and Why It Matters for Biomarkers) Traditional SELEX often starts with a purified target (e.g., a recombinant protein). In cell-SELEX, the “target” is a living cell population that represents a phenotype you care about—such as a disease subtype, drug-resistant cells, activated immune cells, or a specific differentiation stage. The selection process enriches aptamers that bind those cells while removing sequences that bind irrelevant or shared features. Why this matters for biomarkers: Native conformation is preserved. Cell-surface proteins keep their natural folding, post-translational modifications, and membrane context—features that can be lost in purified preparations. Unbiased discovery. You can discover binding…
Aptamers are short single-stranded DNA or RNA molecules that fold into 3D shapes capable of binding specific targets (proteins, small molecules, cells) with high affinity and selectivity. The classic way to discover them is SELEX(Systematic Evolution of Ligands by EXponential enrichment): iterative rounds of binding, partitioning, amplification, and re-selection. What changed the field is high-throughput sequencing (HT-SELEX)—sequencing pools after each round—turning SELEX into a data-rich optimization problem where bioinformatics is no longer optional but central to identifying true binders, understanding enrichment dynamics, and avoiding artifacts. This article explains how bioinformatics for aptamer selection works end-to-end, what signals to extract from sequencing data, how to connect sequence to structure and function, and where modern machine learning fits—without relying on external case studies or outbound links. 1) Why Bioinformatics Matters in Aptamer Selection Traditional SELEX often ends with testing a handful of sequences from late rounds. HT-SELEX changes the game by giving you: Population-level visibility: you can track millions of sequences across rounds, not just a few clones. Early discovery: promising families can emerge before the pool looks “clean,” enabling earlier decision-making and fewer wet-lab rounds when combined with modeling. Artifact detection: PCR bias, sequencing errors, and “sticky” motifs can…
