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Single-particle tracking (SPT) is a key tool for quantitative analysis of dynamic biological processes and has provided unprecedented insights into a wide range of systems such as receptor localization, enzyme propulsion, bacteria motility, and drug nanocarrier delivery. The inherently complex diffusion in such biological systems can vary drastically both in time and across systems, consequently imposing considerable analytical challenges, and currently requires an a priori knowledge of the system. Here we introduce a method for SPT data analysis, processing, and classification, which we term “diffusional fingerprinting.” This method allows for dissecting the features that underlie diffusional behavior and establishing molecular identity, regardless of the underlying diffusion type. The method operates by isolating 17 descriptive features for each observed motion trajectory and generating a diffusional map of all features for each type of particle. Precise classification of the diffusing particle identity is then obtained by training a simple logistic regression model. A linear discriminant analysis generates a feature ranking that outputs the main differences among diffusional features, providing key mechanistic insights. Fingerprinting operates by both training on and predicting experimental data, without the need for pretraining on simulated data. We found this approach to work across a wide range of simulated and experimentally diverse systems, such as tracked lipases on fat substrates, transcription factors diffusing in cells, and nanoparticles diffusing in mucus. This flexibility ultimately supports diffusional fingerprinting’s utility as a universal paradigm for SPT diffusional analysis and prediction.

The function of most lipases is controlled by the lid, which undergoes conformational changes at a water–lipid interface to expose the active site, thus activating catalysis. Understanding how lid mutations affect lipases’ function is important for designing improved variants. Lipases’ function has been found to correlate with their diffusion on the substrate surface. Here, we used single-particle tracking (SPT), a powerful tool for deciphering enzymes’ diffusional behavior, to study Thermomyces lanuginosus lipase (TLL) variants with different lid structures in a laundry-like application condition. Thousands of parallelized recorded trajectories and hidden Markov modeling (HMM) analysis allowed us to extract three interconverting diffusional states and quantify their abundance, microscopic transition rates, and the energy barriers for sampling them. Combining those findings with ensemble measurements, we determined that the overall activity variation in the application condition is dependent on surface binding and lipase mobility when bound. Specifically, the L4 variant with a TLL-like lid and wild-type (WT) TLL displayed similar ensemble activity, but WT bound stronger to the surface than L4, while L4 had a higher diffusion coefficient and thus activity when bound to the surface. These mechanistic elements can only be de-convoluted by our combined assays. Our findings offer fresh perspectives on the development of the next iteration of enzyme-based detergent.

Protein misfolding in the form of fibrils or spherulites is involved in a spectrum of pathological abnormalities. Our current understanding of protein aggregation mechanisms has primarily relied on the use of spectrometric methods to determine the average growth rates and diffraction-limited microscopes with low temporal resolution to observe the large-scale morphologies of intermediates. We developed a REal-time kinetics via binding and Photobleaching LOcalization Microscopy (REPLOM) super-resolution method to directly observe and quantify the existence and abundance of diverse aggregate morphologies of human insulin, below the diffraction limit and extract their heterogeneous growth kinetics. Our results revealed that even the growth of microscopically identical aggregates, e.g., amyloid spherulites, may follow distinct pathways. Specifically, spherulites do not exclusively grow isotropically but, surprisingly, may also grow anisotropically, following similar pathways as reported for minerals and polymers. Combining our technique with machine learning approaches, we associated growth rates to specific morphological transitions and provided energy barriers and the energy landscape at the level of single aggregate morphology. Our unifying framework for the detection and analysis of spherulite growth can be extended to other self-assembled systems characterized by a high degree of heterogeneity, disentangling the broad spectrum of diverse morphologies at the single-molecule level. Real-time super-resolution microscopy analysis reveals the growth kinetics, morphology, and abundance of human insulin amyloid spherulites with different growth pathways.

Insulin formulations with diverse oligomerization states are the hallmark of interventions for the treatment of diabetes. Here using single-molecule recordings we firstly reveal that insulin oligomerization can operate via monomeric additions and secondly quantify the existence, abundance and kinetic characterization of diverse insulin assembly and disassembly pathways involving addition of monomeric, dimeric or tetrameric insulin species. We propose and experimentally validate a model where the insulin self-assembly pathway is rerouted, favoring monomeric or oligomeric assembly, by solution concentration, additives and formulations. Combining our practically complete kinetic characterization with rate simulations, we calculate the abundance of each oligomeric species from nM to mM offering mechanistic insights and the relative abundance of all oligomeric forms at concentrations relevant both for secreted and administrated insulin. These reveal a high abundance of all oligomers and a significant fraction of hexamer resulting in practically halved bioavailable monomer concentration. In addition to providing fundamental new insights, the results and toolbox presented here can be universally applied, contributing to the development of optimal insulin formulations and the deciphering of oligomerization mechanisms for additional proteins. Single-molecule studies reveal the pathway organization of self-assembly and disassembly of insulin hexamers and how this is remodeled by regulatory cues and formulations. The abundance of all oligomeric species as well their kinetics are assessed.

Homologous recombination (HR) is a major pathway to repair DNA double-strand breaks (DSB). HR uses an undamaged homologous DNA sequence as a template for copying the missing information, which requires identifying a homologous sequence among megabases of DNA within the crowded nucleus. In eukaryotes, the conserved Rad51–single-stranded DNA nucleoprotein filament (NPF) performs this homology search. Although NPFs have been extensively studied in vitro by molecular and genetic approaches, their in vivo formation and dynamics could not thus far be assessed due to the lack of functional tagged versions of Rad51. Here we develop and characterize in budding yeast the first fully functional, tagged version of Rad51. Following induction of a unique DSB, we observe Rad51–ssDNA forming exceedingly long filaments, spanning the whole nucleus and eventually contacting the donor sequence. Emerging filaments adopt a variety of shapes not seen in vitro and are modulated by Rad54 and Srs2, shedding new light on the function of these factors. The filaments are also dynamic, undergoing rounds of compaction and extension. Our biophysical models demonstrate that formation of extended filaments, and particularly their compaction–extension dynamics, constitute a robust search strategy, allowing DSB to rapidly explore the nuclear volume and thus enable efficient HR. Here the authors present a functional, tagged version of Rad51, which allows dynamic, in vivo studies of Rad51–ssDNA nucleoprotein filament (NPF) formation. NPFs display notable flexibility, which allows them to implement an efficient search strategy for homolog sequences amidst the crowded nucleus.

Homologous recombination (HR) is a major pathway to repair DNA double-strand breaks (DSB). HR uses an undamaged homologous DNA sequence as a template for copying the missing information, which requires identifying a homologous sequence among megabases of DNA within the crowded nucleus. In eukaryotes, the conserved Rad51-ssDNA nucleoprotein filament (NPF) performs this homology search. Although NPFs have been extensively studied in vitro by molecular and genetic approaches, their in vivo formation and dynamics could not thus far be assessed due to the lack of functional tagged versions of Rad51. Here, we develop and characterize in budding yeast the first fully functional, tagged version of Rad51. Following induction of a unique DSB, we observe Rad51-ssDNA forming exceedingly long filaments, spanning the whole nucleus and eventually contacting the donor sequence. Emerging filaments adopt a variety of shapes, not seen in vitro and are modulated by Rad54 and Srs2, shedding new light on the function of these factors. The filaments are also surprisingly dynamic, undergoing rounds of compaction and extension. Our biophysical models demonstrate that formation of extended filaments, and particularly their compaction-extension dynamics, constitute a robust search strategy, allowing DSB to rapidly explore the nuclear volume and thus enable efficient HR.

Lipases are interfacially activated enzymes that catalyze the hydrolysis of ester bonds and constitute prime candidates for industrial and biotechnological applications ranging from detergent industry, to chiral organic synthesis. As a result, there is an incentive to understand the mechanisms underlying lipase activity at the molecular level, so as to be able to design new lipase variants with tailor-made functionalities. Our understanding of lipase function primarily relies on bulk assay averaging the behavior of a high number of enzymes masking structural dynamics and functional heterogeneities. Recent advances in single molecule techniques based on fluorogenic substrate analogues revealed the existence of lipase functional states, and furthermore so how they are remodeled by regulatory cues. Single particle studies of lipases on the other hand directly observed diffusional heterogeneities and suggested lipases to operate in two different modes. Here to decipher how mutations in the lid region controls Thermomyces lanuginosus lipase (TLL) diffusion and function we employed a Single Particle Tracking (SPT) assay to directly observe the spatiotemporal localization of TLL and rationally designed mutants on native substrate surfaces. Parallel imaging of thousands of individual TLL enzymes and HMM analysis allowed us to observe and quantify the diffusion, abundance and microscopic transition rates between three linearly interconverting diffusional states for each lipase. We proposed a model that correlate diffusion with function that allowed us to predict that lipase regulation, via mutations in lid region or product inhibition, primarily operates via biasing transitions to the active states.

The function of most lipases is controlled by the lid, which undergoes conformational changes at a water–lipid interface to expose the active site, thus activating catalysis. Understanding how lid mutations affect lipases’ function is important for designing improved variants. Lipases’ function has been found to correlate with their diffusion on the substrate surface. Here, we used single-particle tracking (SPT), a powerful tool for deciphering enzymes’ diffusional behavior, to study Thermomyces lanuginosus lipase (TLL) variants with different lid structures in a laundry-like application condition. Thousands of parallelized recorded trajectories and hidden Markov modeling (HMM) analysis allowed us to extract three interconverting diffusional states and quantify their abundance, microscopic transition rates, and the energy barriers for sampling them. Combining those findings with ensemble measurements, we determined that the overall activity variation in the application condition is dependent on surface binding and lipase mobility when bound. Specifically, the L4 variant with a TLL-like lid and wild-type (WT) TLL displayed similar ensemble activity, but WT bound stronger to the surface than L4, while L4 had a higher diffusion coefficient and thus activity when bound to the surface. These mechanistic elements can only be de-convoluted by our combined assays. Our findings offer fresh perspectives on the development of the next iteration of enzyme-based detergent.