Professor Achillefs Kapanidis

Abstract:

The convergence of biosensing and nucleic acid (NA) nanotechnology represents an opportunity for the development of diagnostic technologies. By harnessing the programmability of nucleic acids, we can design biosensors that offer advantages in stability, scalability, versatility and sensitivity, compared to protein-based systems. In this work we introduce DNA-FLASH (DNA-based FLuorescence Amplification upon Single-target Hybridization), a DNA nanosensor concept for digital biosensing. DNA-FLASH leverages fluorescence amplification by a multicomponent NA enzyme (MNAzyme)-driven DNA walker mechanism on a DNA origami disk. Using super-resolution microscopy and single-molecule photobleaching, we demonstrate reproducible fabrication of DNA-FLASH nanosensors with 12 fluorophore-quencher substrates on a ring-shaped track, surrounding a single MNAzyme walker. This nanoarchitecture enables single-molecule detection of DNA targets down to picomolar concentrations. Through precise patterning of DNA-FLASH nanosensors in arrays on glass, we facilitate high-throughput single-molecule readout. We successfully demonstrate DNA-FLASH in human plasma samples and on an in-house developed, fully integrated, self-powered, disposable microfluidic chip, highlighting its potential use in point-of-care settings. Altogether, DNA-FLASH may support the development of next-generation biosensors capable of addressing pressing global challenges, including rapid disease detection, environmental sustainability, and personalized healthcare.

Abstract:

Single-molecule fluorescence resonance energy transfer (smFRET) is a versatile technique for studying biomolecular dynamics and function by detecting nanoscale movements as fluorescence signals. Analysing such signals is a complex exercise, which has recently been the focus of approaches relying on deep learning. Here, we survey such artificial-intelligence-based approaches and compare them with classical methods for smFRET analysis. The use of deep learning has shown potential to enhance precision, accuracy, and speed in analysing massive smFRET datasets.

Abstract:

Single-molecule imaging was developed to resolve behaviours obscured by ensemble averaging, but early tracking experiments typically captured only brief temporal windows, restricting analysis to individual states rather than the progression between them. Observation times now extend to minutes, revealing complete multi-stage biological processes that require new analytical approaches to capture sequences of events. Here we present DeepTRACE, a flexible tool for analysing single-molecule tracks in living cells that learns sequences of molecular events using past and future context from subcellular location, mobility, and photometric properties. It learns any molecular behaviour that can be annotated with natural-language labels, enabling users to tailor models themselves to specific biological questions without ML expertise. DeepTRACE generalises rapidly from very small datasets, training in minutes on a few hundred tracks, and supports extensive downstream analysis, including discovery of relationships absent from the training data. As DeepTRACE natively handles any numerical feature outside of its standard feature set, it incorporates photometric readouts, including measurements of internal conformation that reflect molecular action, alongside motion, temporal context, and subcellular location. We anticipate that researchers will use DeepTRACE to define biological states by molecular behaviour rather than mobility alone in complex multi-stage processes.

Abstract:

Biological function is fundamentally determined by nucleic acid and protein sequence. Beyond encoding genetic information, nucleic acids also display complex physicochemical parameters that shape structure, dynamics, and interactions. Understanding how sequence variation sculpts the energetic landscapes underlying these properties requires methods that capture both molecular diversity and dynamic behavior. Single-molecule techniques are ideally suited to this task, but conventional formats remain time and cost intensive. Recent breakthroughs have enabled highly multiplexed approaches for observing molecular dynamics across millions of individual molecules representing thousands of sequences or barcoded entities. Though still in development, these methods have begun to bridge sequence, structure, dynamics, and function at scale, opening new opportunities in drug discovery, molecular diagnostics, and functional genomics.

Abstract:

Conjugal transfer of plasmids between bacteria is a major route for the spread of antimicrobial resistance. Many conjugative plasmids encode exclusion systems that inhibit redundant conjugation. In incompatibility group F (IncF) plasmids surface exclusion is mediated by the outer membrane protein TraT. Here we report the cryoEM structure of the TraT exclusion protein complex from the canonical F plasmid of Escherichia coli. TraT is a hollow homodecamer shaped like a chef’s hat. In contrast to most outer membrane proteins, TraT spans the outer membrane using transmembrane a-helices. We develop a microscopy-based conjugation assay to  probe the effects of directed mutagenesis on TraT. Our analysis provides no support for the idea that TraT has specific interactions with partner proteins. Instead, we infer that TraT is most likely to function by physical interference with conjugation. This work provides structural insight into a natural inhibitor of microbial gene transfer.