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).

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.

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.