Elena Eremeeva group
Aptamer Facility
We develop next-generation molecular recognition systems based on DNA, RNA and chemically modified nucleic acids (including XNAs) for applications in biosensing, diagnostics and environmental monitoring. Our research combines directed evolution (SELEX), high-throughput sequencing and AI-driven analysis to discover high-affinity and highly selective aptamers against challenging targets, including small molecules, peptides and clinically relevant biomarkers. We aim to establish programmable, robust and scalable recognition elements that outperform traditional antibodies in stability, versatility and cost-effectiveness.
We translate these discoveries into functional sensing platforms, including electrochemical, optical and hybrid assay formats, enabling rapid, sensitive and portable detection at the point of need. By integrating advanced selection strategies (e.g., Capture-SELEX), real-time sequencing technologies and data-driven design, we accelerate aptamer discovery and tailor specificity in complex biological and environmental matrices. Our work spans applications in healthcare diagnostics, food safety, environmental monitoring and industrial bioprocess control, with a strong focus on delivering practical, deployable biosensor technologies.
Research areas
We develop high-affinity and highly selective aptamers against a wide range of targets, including small molecules, peptides, proteins and emerging biomarkers. Our work integrates advanced SELEX methodologies, including Capture-SELEX and hybrid selection strategies, to enable the discovery of binders even for targets lacking known protein partners or structural information.
A key focus is the use of chemically modified nucleic acids (XNAs) to enhance stability, binding performance and functionality in complex environments. By applying stringent selection pressures—such as counter-selection, serum conditions and dynamic buffer systems—we engineer aptamers with real-world applicability across diagnostics, environmental monitoring and industrial bioprocessing.
We develop data-driven approaches to accelerate aptamer discovery by integrating next-generation sequencing (NGS) with machine learning and AI. Through real-time monitoring of selection campaigns using nanopore and Illumina sequencing, we track sequence enrichment, identify functional motifs and reduce the number of selection rounds required.
Our goal is to establish predictive models that link sequence to structure and function, enabling early identification of high-performing aptamers. By leveraging large historical datasets and ongoing selection campaigns, we aim to transform aptamer discovery from an empirical process into a rational, predictive and scalable technology.
We translate aptamer discovery into functional sensing technologies across electrochemical, optical and hybrid assay formats. This includes the development of aptamer-based ELISA, biolayer interferometry (BLI) assays, dye-displacement screening systems and electrochemical sensors for rapid and sensitive detection.
We are particularly interested in hybrid systems combining aptamers with antibodies, proteins or nanomaterials to enhance performance. These platforms are designed for real-world applications, delivering high sensitivity, specificity and fast response times in complex matrices such as serum, saliva and environmental samples.