Characterize Collective Lysosome Heterogeneous Dynamics in Live Cell with a Space- and Time-Resolved Method

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.

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.

Metabolomic profiling of single enlarged lysosomes

Lysosomes are critical for cellular metabolism and are heterogeneously involved in various cellular processes. The ability to measure lysosomal metabolic heterogeneity is essential for understanding their physiological roles. We therefore built a single-lysosome mass spectrometry (SLMS) platform integrating lysosomal patch-clamp recording and induced nano-electrospray ionization (nanoESI)/mass spectrometry (MS) that enables concurrent metabolic and electrophysiological profiling of individual enlarged lysosomes. The accuracy and reliability of this technique were validated by supporting previous findings, such as the transportability of lysosomal cationic amino acids transporters such as PQLC2 and the lysosomal trapping of lysosomotropic, hydrophobic weak base drugs such as lidocaine. We derived metabolites from single lysosomes in various cell types and classified lysosomes into five major subpopulations based on their chemical and biological divergence. Senescence and carcinoma altered metabolic profiles of lysosomes in a type-specific manner. Thus, SLMS can open more avenues for investigating heterogeneous lysosomal metabolic changes during physiological and pathological processes.

Real-Time Study of Protein Phase Separation with Spatiotemporal Analysis of Single-Nanoparticle Trajectories

Liquid-liquid phase separation (LLPS) underlies the formation mechanism of membraneless biomolecular condensates locally to perform important physiological functions such as selective autophagy, but little is known about the relationship between their dynamic structural organization and biophysical properties. Here, a dark-field microscopy based single plasmonic nanoparticle tracking (DFSPT) technique was introduced to simultaneously monitor the diffusion dynamics of multiple gold nanorod (AuNR) probes in a protein LLPS system and to quantitatively characterize the spatiotemporal heterogeneity of the LLPS condensates during their phase transformation. Based on spatially and temporally resolved analysis of the diffusional behavior of the AuNRs, structure and material properties of p62 condensates, such as the viscoelasticity, the compartmentalization, and the recruitment of protein-covered nanoparticles into the large droplet, have been observed. Moreover, the nonsmooth droplet interface, its solidification after further phase transition or maturation, and the size effect of the inner vacuoles have also been revealed. Our method can be potentially applied to in vitro investigation of different reconstituted membrane-free biomolecular condensates and in vivo study of their dynamic evolution.