Polymorphism in the structure of amyloid fibrils suggests the existence of many different assembly pathways. Characterization of this heterogeneity is the key to understanding the aggregation mechanism and toxicity, but in practice it is extremely difficult to probe individual aggregation pathways in a mixture. Here, we present development of a method combining single-molecule fluorescence lifetime imaging and deep learning for monitoring individual fibril formation in real time and their high-throughput analysis. A deep neural network (FNet) separates an image of highly overlapping fibrils into single fibril images, which allows for tracking the growth and changes in characteristics of individual fibrils. Using this method, we investigated aggregation of the 42-residue amyloid-β peptide (Aβ42). We demonstrate that highly heterogeneous fibril formation can be quantitatively characterized in terms of the number of cross-β subunits, elongation speed, growth polarity, and conformation of fibrils. Tracking individual fibril formation and growth also leads to the discovery of a general nucleation mechanism (termed heterogeneous secondary nucleation), where a fibril is formed on the surface of an oligomer with a different structure. Our development will be broadly applicable to characterization of heterogeneous aggregation processes of other proteins.
Keywords: amyloid; deep neural network; fluorescence lifetime imaging; protein aggregation; single-molecule spectroscopy.