A fast and robust method for automated analysis of axonal transport

Prior-Apprised Unsupervised Learning of Subpixel Curvilinear Features in Low Signal/Noise Images

Many biophysical problems involve molecular and nanoscale targets moving next to a curvilinear track, e.g., a cytosolic cargo transported by motor proteins moving along a microtubule. For this type of problem, fluorescence imaging is usually the primary tool of choice. There is, however, an ∼20-fold mismatch between target-localization precision and track-imaging resolution such that questions requiring high-fidelity definition of the target's track remain inaccessible. On the other hand, if the contextual image of the tracks can be refined to a level comparable to that of the target, many intuitive yet mechanistically important issues can begin to be addressed. This work demonstrates that it is possible to statistically infer, to subpixel precision, curvilinear features in a low signal/noise image. This is achieved by a framework that consists of three stages: the Hessian-based feature enhancement, the subimage feature sampling and registration, and the statistical learning of the underlying curvilinear structure using a new, to our knowledge, method developed here for inferring the principal curves. In each stage, the descriptive prior information that the features come from curvilinear elements is explicitly taken into account. It is fully automated without user supervision, which is distinctly different from approaches that require user seeding or well-defined training data sets. Computer simulations of realistic images are used to investigate the performance of the framework and its implementation. The characterization results suggest that curvilinear features are refined to the same order of precision as that of the target and that the bootstrap confidence intervals from the analysis allow an estimate for the statistical bounds of the simulated "true" curve. Also shown are analyses of experimental images from three different microscopy modalities: two-photon laser-scanning microscopy, epifluorescence microscopy, and total internal reflection fluorescence microscopy. The practical application of this prior-apprised unsupervised learning framework as well as its potential outlook are discussed.

Analyzing Mitochondrial Transport and Morphology in Human Induced Pluripotent Stem Cell-Derived Neurons in Hereditary Spastic Paraplegia

Neurons have intense demands for high energy in order to support their functions. Impaired mitochondrial transport along axons has been observed in human neurons, which may contribute to neurodegeneration in various disease states. Although it is challenging to examine mitochondrial dynamics in live human nerves, such paradigms are critical for studying the role of mitochondria in neurodegeneration. Described here is a protocol for analyzing mitochondrial transport and mitochondrial morphology in forebrain neuron axons derived from human induced pluripotent stem cells (iPSCs). The iPSCs are differentiated into telencephalic glutamatergic neurons using well-established methods. Mitochondria of the neurons are stained with MitoTracker CMXRos, and mitochondrial movement within the axons are captured using a live-cell imaging microscope equipped with an incubator for cell culture. Time-lapse images are analyzed using software with "MultiKymograph", "Bioformat importer", and "Macros" plugins. Kymographs of mitochondrial transport are generated, and average mitochondrial velocity in the anterograde and retrograde directions is read from the kymograph. Regarding mitochondrial morphology analysis, mitochondrial length, area, and aspect ratio are obtained using the ImageJ. In summary, this protocol allows characterization of mitochondrial trafficking along axons and analysis of their morphology to facilitate studies of neurodegenerative diseases.

Automated Multi-Peak Tracking Kymography (AMTraK): A Tool to Quantify Sub-Cellular Dynamics with Sub-Pixel Accuracy

Kymographs or space-time plots are widely used in cell biology to reduce the dimensions of a time-series in microscopy for both qualitative and quantitative insight into spatio-temporal dynamics. While multiple tools for image kymography have been described before, quantification remains largely manual. Here, we describe a novel software tool for automated multi-peak tracking kymography (AMTraK), which uses peak information and distance minimization to track and automatically quantify kymographs, integrated in a GUI. The program takes fluorescence time-series data as an input and tracks contours in the kymographs based on intensity and gradient peaks. By integrating a branch-point detection method, it can be used to identify merging and splitting events of tracks, important in separation and coalescence events. In tests with synthetic images, we demonstrate sub-pixel positional accuracy of the program. We test the program by quantifying sub-cellular dynamics in rod-shaped bacteria, microtubule (MT) transport and vesicle dynamics. A time-series of E. coli cell division with labeled nucleoid DNA is used to identify the time-point and rate at which the nucleoid segregates. The mean velocity of microtubule (MT) gliding motility due to a recombinant kinesin motor is estimated as 0.5 μm/s, in agreement with published values, and comparable to estimates using software for nanometer precision filament-tracking. We proceed to employ AMTraK to analyze previously published time-series microscopy data where kymographs had been manually quantified: clathrin polymerization kinetics during vesicle formation and anterograde and retrograde transport in axons. AMTraK analysis not only reproduces the reported parameters, it also provides an objective and automated method for reproducible analysis of kymographs from in vitro and in vivo fluorescence microscopy time-series of sub-cellular dynamics.

KymoAnalyzer: a software tool for the quantitative analysis of intracellular transport in neurons

In axons, proper localization of proteins, vesicles, organelles, and other cargoes is accomplished by the highly regulated coordination of kinesins and dyneins, molecular motors that bind to cargoes and translocate them along microtubule (MT) tracks. Impairment of axonal transport is implicated in the pathogenesis of multiple neurodegenerative disorders including Alzheimer's and Huntington's diseases. To understand how MT-based cargo motility is regulated and to delineate its role in neurodegeneration, it is critical to analyze the detailed dynamics of moving cargoes inside axons. Here, we present KymoAnalyzer, a software tool that facilitates the robust analysis of axonal transport from time-lapse live-imaging sequences. KymoAnalyzer is an open-source software that automatically classifies particle trajectories and systematically calculates velocities, run lengths, pauses, and a wealth of other parameters that are characteristic of motor-based transport. We anticipate that laboratories will easily use this package to unveil previously uncovered intracellular transport details of individually-moving cargoes inside neurons.

Axonal transport velocity estimation from kymographs based on curvilinear feature extraction and spline fitting

Axonal transport velocities are obtained from spatio-temporal maps called kymographs developed from time-lapse confocal microscopy movies of neurons. The kymographs of axonal transport of C.elegans worms are much noisier due to in vivo nature of imaging. Existing methodologies for velocity measurement include laborious manual delineation of axonal movement ridges on the kymographs and thereby determining particle velocities from the slopes of ridges marked. Manual kymograph analysis is not only time consuming but also prone to human errors in marking the ridges. An automated algorithm to extract all the ridges and determine the velocities without significant manual efforts is highly preferred. Not many methods are currently available for such biological studies. We present an almost automated method based on information fusion using LDA classifier, morphological image processing and spline fitting for determining axonal transport velocities. Experimental analysis of 50 kymographs shows considerable reduction of 89% in time taken with manual intervention of 10.83%. Comparitive study with the results of two of the previous literatures shows that our algorithm performs better.

The Ca2+ sensor protein swiprosin-1/EFhd2 is present in neurites and involved in kinesin-mediated transport in neurons

Swiprosin-1/EFhd2 (EFhd2) is a cytoskeletal Ca²⁺ sensor protein strongly expressed in the brain. It has been shown to interact with mutant tau, which can promote neurodegeneration, but nothing is known about the physiological function of EFhd2 in the nervous system. To elucidate this question, we analyzed EFhd2^−/−/lacZ reporter mice and showed that lacZ was strongly expressed in the cortex, the dentate gyrus, the CA1 and CA2 regions of the hippocampus, the thalamus, and the olfactory bulb. Immunohistochemistry and western blotting confirmed this pattern and revealed expression of EFhd2 during neuronal maturation. In cortical neurons, EFhd2 was detected in neurites marked by MAP2 and co-localized with pre- and post-synaptic markers. Approximately one third of EFhd2 associated with a biochemically isolated synaptosome preparation. There, EFhd2 was mostly confined to the cytosolic and plasma membrane fractions. Both synaptic endocytosis and exocytosis in primary hippocampal EFhd2^−/− neurons were unaltered but transport of synaptophysin-GFP containing vesicles was enhanced in EFhd2^−/− primary hippocampal neurons, and notably, EFhd2 inhibited kinesin mediated microtubule gliding. Therefore, we found that EFhd2 is a neuronal protein that interferes with kinesin-mediated transport.

Simple and direct assembly of kymographs from movies using KYMOMAKER

In tracking analysis, the movement of cargos by motor proteins in axons is often represented by a time-space plot termed a 'kymograph'. Manual creation of kymographs is time-consuming and complicated for cell biologists. Therefore, we developed KYMOMAKER, a simple system that automatically creates a kymograph from a movie without generating multiple time-dissected movie stacks. In addition, KYMOMAKER can automatically extract faint vesicle traces, and can thereby effectively analyze cargos expressed at low levels in axons. A filter can be applied to remove traces of non-physiological movements and to extract meaningful traces of anterograde or retrograde cargo transport. For example, only cargos that move at a speed of >0.4 µm/second for a distance of >1 µm can be included. Another function of KYMOMAKER is to create a color kymograph in which the color of the trace varies according to the position of the fluorescent particle in the axis perpendicular to the long axis of the axon. Such positional information is completely lost in conventional kymographs. KYMOMAKER is an open access program that can be easily used to analyze vesicle transport in axons by cell biologists who do not have specific knowledge of bioimage informatics.

Computational analysis of axonal transport: a novel assessment of neurotoxicity, neuronal development and functions

Axonal transport plays a crucial role in neuronal morphogenesis, survival and function. Despite its importance, however, the molecular mechanisms of axonal transport remain mostly unknown because a simple and quantitative assay system for monitoring this cellular process has been lacking. In order to better characterize the mechanisms involved in axonal transport, we formulate a novel computer-assisted monitoring system of axonal transport. Potential uses of this system and implications for future studies will be discussed.