Alzheimer's β-amyloid precursor protein (APP) associates with kinesin-1 via JNK-interacting protein 1 (JIP1); however, the role of JIP1 in APP transport by kinesin-1 in neurons remains unclear. We performed a quantitative analysis to understand the role of JIP1 in APP axonal transport. In JIP1-deficient neurons, we find that both the fast velocity (∼2.7 μm/s) and high frequency (66%) of anterograde transport of APP cargo are impaired to a reduced velocity (∼1.83 μm/s) and a lower frequency (45%). We identified two novel elements linked to JIP1 function, located in the central region of JIP1b, that interact with the coiled-coil domain of kinesin light chain 1 (KLC1), in addition to the conventional interaction of the JIP1b 11-amino acid C-terminal (C11) region with the tetratricopeptide repeat of KLC1. High frequency of APP anterograde transport is dependent on one of the novel elements in JIP1b. Fast velocity of APP cargo transport requires the C11 domain, which is regulated by the second novel region of JIP1b. Furthermore, efficient APP axonal transport is not influenced by phosphorylation of APP at Thr-668, a site known to be phosphorylated by JNK. Our quantitative analysis indicates that enhanced fast-velocity and efficient high-frequency APP anterograde transport observed in neurons are mediated by novel roles of JIP1b.

Most current superresolution (SR) microscope techniques surpass the diffraction limit at the expense of temporal resolution, compromising their applications to live-cell imaging. Here we describe a new SR fluorescence microscope based on confocal microscope optics, which we name the spinning disk superresolution microscope (SDSRM). Theoretically, the SDSRM is equivalent to a structured illumination microscope (SIM) and achieves a spatial resolution of 120 nm, double that of the diffraction limit of wide-field fluorescence microscopy. However, the SDSRM is 10 times faster than a conventional SIM because SR signals are recovered by optical demodulation through the stripe pattern of the disk. Therefore a single SR image requires only a single averaged image through the rotating disk. On the basis of this theory, we modified a commercial spinning disk confocal microscope. The improved resolution around 120 nm was confirmed with biological samples. The rapid dynamics of micro-tubules, mitochondria, lysosomes, and endosomes were observed with temporal resolutions of 30-100 frames/s. Because our method requires only small optical modifications, it will enable an easy upgrade from an existing spinning disk confocal to a SR microscope for live-cell imaging.

In neurons, amyloid β-protein precursor (APP) is transported by binding to kinesin-1, mediated by JNK-interacting protein 1b (JIP1b), which generates the enhanced fast velocity (EFV) and efficient high frequency (EHF) of APP anterograde transport. Previously, we showed that EFV requires conventional interaction between the JIP1b C-terminal region and the kinesin light chain 1 (KLC1) tetratricopeptide repeat, whereas EHF requires a novel interaction between the central region of JIP1b and the coiled-coil domain of KLC1. We found that phosphorylatable Thr466 of KLC1 regulates the conventional interaction with JIP1b. Substitution of Glu for Thr466 abolished this interaction and EFV, but did not impair the novel interaction responsible for EHF. Phosphorylation of KLC1 at Thr466 increased in aged brains, and JIP1 binding to kinesin-1 decreased, suggesting that APP transport is impaired by aging. We conclude that phosphorylation of KLC1 at Thr466 regulates the velocity of transport of APP by kinesin-1 by modulating its interaction with JIP1b.

Across the cell cycle, the subcellular organization undergoes major spatiotemporal changes that could in principle contain biological features that could potentially represent cell cycle phase. We applied convolutional neural network-based classifiers to extract such putative features from the fluorescence microscope images of cells stained for the nucleus, the Golgi apparatus, and the microtubule cytoskeleton. We demonstrate that cell images can be robustly classified according to G1/S and G2 cell cycle phases without the need for specific cell cycle markers. Grad-CAM analysis of the classification models enabled us to extract several pairs of quantitative parameters of specific subcellular features as good classifiers for the cell cycle phase. These results collectively demonstrate that machine learning-based image processing is useful to extract biological features underlying cellular phenomena of interest in an unbiased and data-driven manner.