Lysosome Dynamic Properties during Neuronal Stem Cell Differentiation Studied by Spatiotemporal Fluctuation Spectroscopy and Organelle Tracking

Trajectory Analysis in Single-Particle Tracking: From Mean Squared Displacement to Machine Learning Approaches

Single-particle tracking is a powerful technique to investigate the motion of molecules or particles. Here, we review the methods for analyzing the reconstructed trajectories, a fundamental step for deciphering the underlying mechanisms driving the motion. First, we review the traditional analysis based on the mean squared displacement (MSD), highlighting the sometimes-neglected factors potentially affecting the accuracy of the results. We then report methods that exploit the distribution of parameters other than displacements, e.g., angles, velocities, and times and probabilities of reaching a target, discussing how they are more sensitive in characterizing heterogeneities and transient behaviors masked in the MSD analysis. Hidden Markov Models are also used for this purpose, and these allow for the identification of different states, their populations and the switching kinetics. Finally, we discuss a rapidly expanding field-trajectory analysis based on machine learning. Various approaches, from random forest to deep learning, are used to classify trajectory motions, which can be identified by motion models or by model-free sets of trajectory features, either previously defined or automatically identified by the algorithms. We also review free software available for some of the analysis methods. We emphasize that approaches based on a combination of the different methods, including classical statistics and machine learning, may be the way to obtain the most informative and accurate results.

Impact of temporal resolution in single particle tracking analysis

Temporal resolution is a key parameter in the observation of dynamic processes, as in the case of single molecules motions visualized in real time in two-dimensions by wide field (fluorescence) microscopy, but a systematic investigation of its effects in all the single particle tracking analysis steps is still lacking. Here we present tools to quantify its impact on the estimation of diffusivity and of its distribution using one of the most popular tracking software for biological applications on simulated data and movies. We found important shifts and different widths for diffusivity distributions, depending on the interplay of temporal sampling conditions with various parameters, such as simulated diffusivity, density of spots, signal-to-noise ratio, lengths of trajectories, and kind of boundaries in the simulation. We examined conditions starting from the ones of experiments on the fluorescently labelled receptor p75NTR, a relatively fast-diffusing membrane receptor (diffusivity around 0.5-1 µm2/s), visualized by TIRF microscopy on the basal membrane of living cells. From the analysis of the simulations, we identified the best conditions in cases similar to these ones; considering also the experiments, we could confirm a range of values of temporal resolution suitable for obtaining reliable diffusivity results. The procedure we present can be exploited in different single particle/molecule tracking applications to find an optimal temporal resolution.

Identification of nanoparticles as vesicular cargo via Airy scanning fluorescence microscopy and spatial statistics

Many biomedical applications of nanoparticles on the cellular level require a characterisation of their subcellular distribution. Depending on the nanoparticle and its preferred intracellular compartment, this may be a nontrivial task, and consequently, the available methodologies are constantly increasing. Here, we show that super-resolution microscopy in combination with spatial statistics (SMSS), comprising the pair correlation and the nearest neighbour function, is a powerful tool to identify spatial correlations between nanoparticles and moving vesicles. Furthermore, various types of motion like for example diffusive, active or Lévy flight transport can be distinguished within this concept via suitable statistical functions, which also contain information about the factors limiting the motion, as well as regarding characteristic length scales. The SMSS concept fills a methodological gap related to mobile intracellular nanoparticle hosts and its extension to further scenarios is straightforward. It is exemplified on MCF-7 cells after exposure to carbon nanodots, demonstrating that these particles are stored predominantly in the lysosomes.

Pathophysiology of Lysosomes in a Nutshell

Lysosomes are acidic organelles present in all nucleated mammalian cells [...].

Protocol and Software for Automated Detection of Lysosome Active "Runs" and "Flights" with Wavelet Transform Approach

Lysosomes are highly dynamic degradation/recycling organelles that harbor sophisticated molecular sensors and signal transduction machinery through which they control cell adaptation to environmental cues and nutrients. The movements of these signaling hubs comprise persistent, directional runs-active, ATP-dependent transport along the microtubule tracks-interspersed by short, passive movements and pauses imposed by cytoplasmic constraints. The trajectories of individual lysosomes are usually obtained by time-lapse imaging of the acidic organelles labeled with LysoTracker dyes or fluorescently-tagged lysosomal-associated membrane proteins LAMP1 and LAMP2. Subsequent particle tracking generates large data sets comprising thousands of lysosome trajectories and hundreds of thousands of data points. Analyzing such data sets requires unbiased, automated methods to handle large data sets while capturing the temporal heterogeneity of lysosome trajectory data. This chapter describes integrated and largely automated workflow from live cell imaging to lysosome trajectories to computing the parameters of lysosome dynamics. We describe an open-source code for implementing the continuous wavelet transform (CWT) to distinguish trajectory segments corresponding to active transport (i.e., "runs" and "flights") versus passive lysosome movements. Complementary cumulative distribution functions (CDFs) of the "runs/flights" are generated, and Akaike weight comparisons with several competing models (lognormal, power law, truncated power law, stretched exponential, exponential) are performed automatically. Such high-throughput analyses yield useful aggregate/ensemble metrics for lysosome active transport.

Visualizing Lysosomal Positioning with a Fluorescent Probe Reveals a New Synergistic Anticancer Effect

The observation and discovery of lysosome dynamic alterations will greatly contribute to the in-depth understanding of lysosome biology and the development of new cancer therapeutics. To visualize lysosomal dynamics, here we have developed a lysosome-targetable fluorescent probe of NIM-3 showing integrated high selectivity, high photostability, and low cytotoxicity. With the aid of the excellent spatial and temporal imaging capability of NIM-3, three different types of motion of lysosomes were defined, and perinuclear accumulation of lysosomes in response to the pro-inflammatory cytokine stimulus was observed in various cells. More importantly, through lysosomal positioning studies, a new and potential anticancer therapy, i.e., the combination treatment of TNFα (tumor necrosis factor alpha) and chloroquine (CQ, a lysosomal pH elevator), was disclosed. The efficacy of the "CQ + TNFα" treatment was verified by different types of human cancer cells, and the anticancer mechanism may be partially attributed to lysosomal dilation.

Mitochondria and Other Organelles in Neural Development and Their Potential as Therapeutic Targets in Neurodegenerative Diseases

The contribution of organelles to neural development has received increasing attention. Studies have shown that organelles such as mitochondria, endoplasmic reticulum (ER), lysosomes, and endosomes play important roles in neurogenesis. Specifically, metabolic switching, reactive oxygen species production, mitochondrial dynamics, mitophagy, mitochondria-mediated apoptosis, and the interaction between mitochondria and the ER all have roles in neurogenesis. Lysosomes and endosomes can regulate neurite growth and extension. Moreover, metabolic reprogramming represents a novel strategy for generating functional neurons. Accordingly, the exploration and application of mechanisms underlying metabolic reprogramming will be beneficial for neural conversion and regenerative medicine. There is adequate evidence implicating the dysfunction of cellular organelles—especially mitochondria—in neurodegenerative disorders, and that improvement of mitochondrial function may reverse the progression of these diseases through the reinforcement of adult neurogenesis. Therefore, these organelles have potential as therapeutic targets for the treatment of neurodegenerative diseases. In this review, we discuss the function of these organelles, especially mitochondria, in neural development, focusing on their potential as therapeutic targets in neurodegenerative disorders, including Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and amyotrophic lateral sclerosis.

Large-Scale, Wavelet-Based Analysis of Lysosomal Trajectories and Co-Movements of Lysosomes with Nanoparticle Cargos

Lysosomes—that is, acidic organelles known for degradation/recycling—move through the cytoplasm alternating between bursts of active transport and short, diffusive motions or even pauses. While their mobility is essential for lysosomes’ fusogenic and non-fusogenic interactions with target organelles, their movements have not been characterized in adequate detail. Here, large-scale statistical analysis of lysosomal movement trajectories reveals that lysosome trajectories in all examined cell types—both cancer and noncancerous ones—are superdiffusive and characterized by heavy-tailed distributions of run and flight lengths. Consideration of Akaike weights for various potential models (lognormal, power law, truncated power law, stretched exponential, and exponential) indicates that the experimental data are best described by the lognormal distribution, which, in turn, can be related to one of the space-search strategies particularly effective when “thorough” search needs to balance search for rare target(s) (organelles). In addition, automated, wavelet-based analysis allows for co-tracking the motions of lysosomes and the cargos they carry—particularly the nanoparticle aggregates known to cause selective lysosome disruption in cancerous cells. The methods we describe here could help study nanoparticle assemblies, viruses, and other objects transported inside various vesicle types, as well as coordinated movements of organelles/particles in the cytoplasm. Custom-written code that includes integrated workflow for our analyses is made available for academic use.

Spatiotemporal Correlation Spectroscopy Reveals a Protective Effect of Peptide-Based GLP-1 Receptor Agonism against Lipotoxicity on Insulin Granule Dynamics in Primary Human β-Cells

Glucagon-like peptide-1 receptor (GLP-1R) agonists are being used for the treatment of type 2 diabetes (T2D) and may have beneficial effects on the pancreatic β-cells. Here, we evaluated the effects of GLP-1R agonism on insulin secretory granule (ISG) dynamics in primary β-cells isolated from human islets exposed to palmitate-induced lipotoxic stress. Islets cells were exposed for 48 h to 0.5 mM palmitate (hereafter, ‘Palm’) with or without the addition of a GLP-1 agonist, namely 10 nM exendin-4 (hereafter, ‘Ex-4’). Dissociated cells were first transfected with syncollin-EGFP in order to fluorescently mark the ISGs. Then, by applying a recently established spatiotemporal correlation spectroscopy technique, the average structural (i.e., size) and dynamic (i.e., the local diffusivity and mode of motion) properties of ISGs are extracted from a calculated imaging-derived Mean Square Displacement (iMSD) trace. Besides defining the structural/dynamic fingerprint of ISGs in human cells for the first time, iMSD analysis allowed to probe fingerprint variations under selected conditions: namely, it was shown that Palm affects ISGs dynamics in response to acute glucose stimulation by abolishing the ISGs mobilization typically imparted by glucose and, concomitantly, by reducing the extent of ISGs active/directed intracellular movement. By contrast, co-treatment with Ex-4 normalizes ISG dynamics, i.e., re-establish ISG mobilization and ability to perform active transport in response to glucose stimulation. These observations were correlated with standard glucose-stimulated insulin secretion (GSIS), which resulted in being reduced in cells exposed to Palm but preserved in cells concomitantly exposed to 10 nM Ex-4. Our data support the idea that GLP-1R agonism may exert its beneficial effect on human β-cells under metabolic stress by maintaining ISGs’ proper intracellular dynamics.

Single-Particle Tracking with Scanning Non-Linear Microscopy

This study describes the adaptation of non-linear microscopy for single-particle tracking (SPT), a method commonly used in biology with single-photon fluorescence. Imaging moving objects with non-linear microscopy raises difficulties due to the scanning process of the acquisitions. The interest of the study is based on the balance between all the experimental parameters (objective, resolution, frame rate) which need to be optimized to record long trajectories with the best accuracy and frame rate. To evaluate the performance of the setup for SPT, several basic estimation methods are used and adapted to the new detection process. The covariance-based estimator (CVE) seems to be the best way to evaluate the diffusion coefficient from trajectories using the specific factors of motion blur and localization error.