Single-particle fluorescence tracking combined with TrackMate assay reveals highly heterogeneous and discontinuous lysosomal transport in freely orientated axons

Axonal transport plays a significant role in the establishment of neuronal polarity, axon growth, and synapse formation during neuronal development. The axon of a naturally growing neuron is a highly complex and multifurcated structure with a large number of bends and branches. Nowadays, the study of dynamic axonal transport in morphologically complex neurons is greatly limited by the technological barrier. Here, a sparse gene transfection strategy was developed to locate fluorescent mCherry in the lysosome of primary neurons, thus enabling us to track the lysosome-based axonal transport with a single-particle resolution. Thereby, several axonal transport models were observed, including forward or backward transport model, stop-and-go model, repeated back-and-forth transport model, and cross-branch transport model. Then, the accurate single-particle velocity quantification by TrackMate revealed a highly heterogeneous and discontinuous transportation process of lysosome-based axonal transport in freely orientated axons. And, multiple physical factors, such as the axonal structure and the size of particles, were disclosed to affect the velocity of particle transporting in freely orientated axons. The combined single-particle fluorescence tracking and TrackMate assay can be served as a facile tool for evaluating axonal transport in neuronal development and axonal transport-related diseases.

Unsupervised learning approaches to characterizing heterogeneous samples using X-ray single-particle imaging

One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. Proposed here are two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA), provides a rough classification which is essentially parameter free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs), can generate 3D structures of the objects at any point in the structural landscape. Both these methods are implemented in combination with the noise-tolerant expand–maximize–compress (EMC) algorithm and its utility is demonstrated by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern. Both discrete structural classes and continuous deformations are recovered. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility of moving beyond the study of homogeneous sample sets to addressing open questions on topics such as nanocrystal growth and dynamics, as well as phase transitions which have not been externally triggered.

Heterogeneous Effects on Chemo-Mechanical Coupling Behaviors at the Single-Particle Level

Understanding and alleviating the chemo-mechanical degradation of silicon anodes is a formidable challenge due to the large volume change during operations. Here, for a comprehensive understanding of heterogeneous effects on chemo-mechanical behaviors at the single-particle level, in-situ observation of single-crystalline silicon micropillar electrodes under the inhomogeneous extrinsic conditions, taken as an example, was made. The observation shows that the anisotropic deformation patterns and fracture starting sites are reshaped with the combination of the inhomogeneous electrochemical driving force for charge transfer at the interface between the silicon micropillar and the electrolyte, and crystal orientation-dependent lithiation dynamics. Also, the numerical simulation unravels the underlying mechanisms of deformation and fracture behaviors, and well predicts the relative depth of lithiation at the time of crack initiation under heterogeneous conditions. The results show that heterogeneities arising from extrinsic conditions may induce inhomogeneous mechanical damage and tailor lithiation degree at an active particle level, offering insights into designing large-volume-change battery particles with good mechanical integrity and electrochemical performance under heterogeneous impacts.

Single Particle Hopping as an Indicator for Evaluating Electrocatalysts

Design and screening electrocatalysts for gas evolution reactions suffer from scanty understanding of multi-phase processes at the electrode-electrolyte interface. Due to the complexity of multi-phase interface, it is still a great challenge to capture gas evolution dynamics under operando condition to precisely portray the intrinsic catalytic performance of interface. Here, we establish a single particle imaging method to real time monitor a potential-dependent vertical motion or hopping of electrocatalysts induced by electrogenerated gas nanobubbles. The hopping feature of single particle is closely correlated with intrinsic activities of electrocatalysts, thus is developed to be an indicator to evaluate gas evolution performance of various electrocatalysts. This optical indicator diminishes interferences from heterogeneous morphologies, non-Faradaic processes and parasitic side reactions that are unavoidable in conventional electrochemical measurements, therefore enables precise evaluation and high-throughput screening of catalysts for gas evolution systems.