Direct digital sensing of protein biomarkers in solution.

The detection of proteins is of central importance to biomolecular analysis and diagnostics. Typical immunosensing assays rely on surface-capture of target molecules, but this constraint can limit specificity, sensitivity, and the ability to obtain information beyond simple concentration measurements. Here we present a surface-free, single-molecule microfluidic sensing platform for direct digital protein biomarker detection in solution, termed digital immunosensor assay (DigitISA). DigitISA is based on microchip electrophoretic separation combined with single-molecule detection and enables absolute number/concentration quantification of proteins in a single, solution-phase step. Applying DigitISA to a range of targets including amyloid aggregates, exosomes, and biomolecular condensates, we demonstrate that the assay provides information beyond stoichiometric interactions, and enables characterization of immunochemistry, binding affinity, and protein biomarker abundance. Taken together, our results suggest a experimental paradigm for the sensing of protein biomarkers, which enables analyses of targets that are challenging to address using conventional immunosensing approaches.

Machine learning-aided protein identification from multidimensional signatures.

The ability to determine the identity of specific proteins is a critical challenge in many areas of cellular and molecular biology, and in medical diagnostics. Here, we present a macine learning aided microfluidic protein characterisation strategy that within a few minutes generates a three-dimensional fingerprint of a protein sample indicative of its amino acid composition and size and, thereby, creates a unique signature for the protein. By acquiring such multidimensional fingerprints for a set of ten proteins and using machine learning approaches to classify the fingerprints, we demonstrate that this strategy allows proteins to be classified at a high accuracy, even though classification using a single dimension is not possible. Moreover, we show that the acquired fingerprints correlate with the amino acid content of the samples, which makes it is possible to identify proteins directly from their sequence without requiring any prior knowledge about the fingerprints. These findings suggest that such a multidimensional profiling strategy can lead to the development of a novel method for protein identification in a microfluidic format.

Label-Free Analysis of Protein Aggregation and Phase Behavior.

Phase transitions of protein molecules are central to biological function and malfunction. One such transition commonly encountered in nature is the conversion of soluble monomeric states into solid phases, which include crystals and amyloid fibrils, the latter of which are associated with the onset and development of neurodegenerative diseases. Monitoring aggregate formation and protein phase behavior is essential in gaining mechanistic insights into these fundamental processes. Fluorescence techniques have proven invaluable in observing biological molecules; yet, most such approaches rely on the use of an extrinsic fluorophore that binds to the molecule of interest, the installation of which can perturb the molecular systems under study. However, most proteins also possess aromatic amino acids within their peptide sequence and therefore exhibit intrinsic fluorescence. Here, we show that by measuring in space and time tryptophan autofluorescence for three proteins, reconstituted silk fibroin, ß-lactoglobulin, and lysozyme, fibrillar self-assembly can be monitored accurately and without the need for extrinsic dyes. When fibrillar protein self-assembly takes place, hydrophobic burial occurs, resulting in the minimization of exposed tryptophan residues to the solvent and consequently leading to an increase in protein autofluorescence. Moreover, by employing a droplet-microfluidic approach to confine protein self-assembly in space, we demonstrate that intrinsic fluorescence can be used to image protein nanofibrils in a label-free manner and that the microstructural analysis obtained from intrinsic fluorescence microscopy correlates well with that from samples treated with extrinsic dyes. Finally, our results show that protein autofluorescence is not limited to the observation of ß-sheet-rich structures, but can also be used to distinguish between different types of solid phases including spherulites and crystals, making this approach suitable for overall characterization of protein phase transition phenomena.