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Characterize Collective Lysosome Heterogeneous Dynamics in Live Cell with a Space-and-Time-Resolved Method Hansen Zhao, Qiming Zhou, Mengchan Xia, Jiaxin Feng, Yang Chen, Si-Chun Zhang, and Xinrong Zhang Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b01563 • Publication Date (Web): 11 Jul 2018 Downloaded from http://pubs.acs.org on July 12, 2018

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Analytical Chemistry

Characterize Collective Lysosome Heterogeneous Dynamics in Live Cell with a Space-and-Time-Resolved Method Hansen Zhao1, Qiming Zhou2, Mengchan Xia1, Jiaxin Feng1, Yang Chen2,3*, Sichun Zhang1, Xinrong Zhang1* 1. Department of Chemistry, Tsinghua University, Beijing 100084, China 2. Beijing National Research Center for Information Science and Technology, BNRist; School of Medicine, Tsinghua University, Beijing 100084, China 3. Beijing National Research Center for Information Science and Technology, BNRist; Department of Automation, Tsinghua University, Beijing, 100084, China ABSTRACT: While studies of collective cell migration and bacteria swarming have tremendously promoted our fundamental knowledge of the complex systematic phenomena, the quantitative characterization of the collective organelles movement at sub-cellular level is yet to be fully explored. Here we tagged the lysosomes in live cells with fluorescent probe and imaged their spatial motion with wide field microscopy. To quantitatively characterize the collective lysosomal behavior with high spatiotemporal heterogeneity dynamics, we developed the Particle Collective Analysis (PECAN) method based on the single particle tracking techniques. Thousands of trajectories were detected and analyzed in each single cell. The reliability was validated by comparing with traditional PIV method, simulated and experimental datasets. We show that the lysosomes in live cells move collectively with spatial heterogeneous and temporal long-term correlated dynamics. Furthermore, the continuous wavelet analysis suggested the existence of collective lysosomal oscillation in mouse neural cells. Generally, our method provides a practical workflow for characterizing the collective lysosomal motions which can benefit related areas such as organelles mediated drug delivery and cell activity profiling.

Collective behavior refers to a systematic phenomenon that emerges from interactions between active entities and their local environments1. It has attracted great attentions in many fields such as animal grouping, cell migration, bacteria swarming and artificial nanomotor dynamics2-5. As the active components in nonequilibrium systems are capable of emerging behavior at a systematic level6, which cannot be interpreted individually, a fundamental challenge arises about how to characterize, describe, model and even predict this ubiquitous existing phenomenon. Furthermore, in which way our understanding of the collective behavior affects our comprehension of the biological and chemical system as a functional complex7. Local visional, physical or chemical guiding signal between neighbors have been considered as the underlying mechanism explaining most of the collective behavior we observed including cell-level systems such as cell migration and bacteria swarming8-11. External stimuli were proved to be an important origin of the collective dynamics of the micromotors 12. However, the motion dynamics of particles in a smaller scale, such as sub-micron organelles or single proteins, is usually affected by thermal-based diffusion. On the other hands, the non-equilibrium and uneven biological environment such as cell plasma also exerts complex influence on the particles inside via spatial confinement13 or protein interactions 14. Lysosomes as one of vital organelles in cells driven by motor protein are known to participate in several crucial cellular processes such as cell starvation and autophagy15. With its individual movement has been well-studied16, we asked that how lysosomes in live cells act

collectively and how can we characterize its collective behavior quantitatively as so many lysosomes move simultaneously in single cells and so heterogeneously they behave in space and time. We argue that the answers to these questions are fundamentally important for us to understand the collective behaviors in submicron scale and the biochemical process related such as lysosome-mediated drug delivery and protein degradation. Besides, the characterization of collective lysosomal dynamics can potentially serve as a new perspective to profile the cell activity. To answer these questions, the first step we need to take is to characterize the collective properties of the lysosomes movement quantitatively, which provide necessary information for further analysis and interpretation. A commonly used method to characterize the collective movement is the Particle Image Velocimetry (PIV)5,17,18, which is an excellent tool to reveal the spatial distribution of the moving direction and velocity between two frames. Based on the image sub-window pattern recognition, the PIV method can reveal the velocity flow between two successive images without extract the exact particle trajectories. This feature lowers its computational complexity but limits the information it can provide. However, the motion dynamics in nanoscale is highly uncertain due to the thermal diffusion. Constantly appearing and disappearing of the particle in 2D images19 for defocusing or out-of-view-border also brings difficulty in pattern recognition. Furthermore, more physical meaningful properties about the movement dynamics such as diffusion

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Figure 1. The general pipeline and the basic idea of the PECAN. (a) Two simple steps of performing PECAN method. In the first step, particles are tracked by corresponding SPT methods to get the individuals trajectory, the trajectories are then stored in a standard input file format, ImageJ plugin TrackMate and a homemade tool converting XML file to the input standard file that PECAN required (see Note S3). In the second step, the PECAN analyzes and visualizes the spatiotemporal information of the observed system. (b) lysosomal movement in single HeLa cell can be detected and thousands of trajectories (gray lines) were observed. The PECAN method processed these traces one by one (blue line) and finally extracted their collective properties in a given spatial region and temporal range. (c) Lysosome movement appears highly spatiotemporally heterogeneous, directed motion (purple line) and confined motion (orange line) can be observed in a single trace. (d) PECAN visualizes the spatial heterogeneous dynamic by heat maps. Local exponent α of MSD curve was mapped. The red color corresponds to directed motion (α > 1) while the blue color represents the confined motion (α < 1). (e) Exponent α (upper) and average velocity (lower) of the given trace can also be plotted. Note that b-e shows the basic idea of the PECAN method in processing a single trace for simplicity. In real-world analysis, thousands of trajectories are handled in the same way.

coefficient are essential to be characterized to reveal the underlying collective mechanism from thousands of image frames. To address these problems, we need to obtain the exact trace that each particle follows. Thanks to the great advances in experimental equipment20,21 and tracking methods22,23, we are now able to track a large number of trajectories of the lysosomes from the recorded video with the Single Particle Tracking (SPT). Various techniques such as gap linking have been developed to obtain promising accurate particle dynamics. On the other hands, the collective movement in biological systems is usually heterogeneous both spatially and temporally24. To prevent the severe information loss caused by averaging, we need to calculate local properties to tag the spatial and temporal non-stationarity commonly observed in biological systems, which is essential to reveal the systematic underlying mechanism such as group learning and intelligence evolution25, global external stimulation3, spontaneous system state change and causality relationship with time-order. Keeping that in mind, we developed the particle collective analysis (PECAN) method. The method is based on lattice spatial division13 and sliding window time series analysis 26 to characterize and visualize the spatial and temporal distribution of various local lysosome movement properties such as average velocity, net displacement and the exponent factor of the mean squared

displacement(MSD) curve. The general workflow and basic idea are shown in Figure 1. Briefly, for each observed image sequence, SPT is firstly applied to extract reliable particle trajectories. Various SPT methods were developed and validated previously27. In this work, we chose the TrackMate, a plugin for Fiji28,29, to perform the tracking step. In a typical condition, thousands of trajectories can be extracted in each cell (Figure 1b). Next, each trajectory was to be analyzed by PECAN and the results were finally assembled together to form a systematic perspective. As the heterogeneity of the single lysosome motion dynamics shown as Figure 1c commonly exists, single observation is not sufficient to provide enough information for the high randomness nature in sub-micron nanoscale. To address this problem, we introduce the sliding window operation which cuts the nonstationary time series observations by certain window length, assuming that the dynamic behavior inside the window is stationary. This operation includes several successive observations together which enable the calculation of various local physical parameter following. Another issue for collective movement characterization is spatial uneven dynamics. Here we adopted the spatial lattice method to divide the observed space into grids with certain resolution. Sub-trajectories dynamics obtained from sliding window operation will be assigned to corresponding lattice and time frame by their central spatial and


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temporal position (Figure 1d-e). More details about the algorithm can be found in Supporting Information. Comparing to the image pattern recognition based PIV method, the PECAN method is based on a down-top strategy which firstly extracts each particle trace and then assembles all the information together to provide a systematic viewpoint of collective motion. Verification by published PIVlab software30 revealed that our method can capture the essential collective pattern in standard PIV example where particles traced the liquid flow. Besides, the single particle tracking based PECAN method shown more accurate estimation on the simulated Brownian motion dataset27,31 (Figure S4, S5, Table S2). We note that our method can be a complementary method to deal with certain cases that PIV method may not fully cover. For example, systems trigger failed pattern recognition due to the low particle density or z-axis movement or the case where the research need more physical/biological information beyond local velocity vector. To evaluate the reliability of the method, a general PECAN based workflow was tested by both simulated and published experimental data3,32 (Figure S12, S15) and proved to be reliable and efficient in revealing stationary spatial heterogeneity and nonstationary spatiotemporal dynamics. The validations show that the PECAN can potentially serve as a basic and important tool for the analysis of collective particle movement and other similar systems. Finally, with the aid of the PECAN method, we investigated the collective lysosome movement in live cells with both space and time-resolved manner. Symmetry breaking phenomena such as spatially heterogeneous motion states, temporally long-term correlated diffusion, and nonstationary dynamics were characterized quantitatively. We also report the oscillation movement of the lysosomes in mouse neural cell. Characterizing the collective dynamics of lysosomes can potentially provide a new perspective for cellular state profiling as the lysosomes positioning is fine regulated by the cellular pathway.

EXPERIMENTAL SECTION Cell culture. HeLa cells were seeded in DMEM medium with 10% FBS in confocal laser dish. The medium volume was 1 mL. 100 U/mL penicillin and streptomycin were added with 1% (v/v) concentration. The cell was incubated in 37 oC and 5% CO2 for 24 h before the experiment. Hippocampal neurons isolation and culture from prenatal mice. The day before neuron isolation, coat 30 mm glass bottom cell culture dishes with Rat Tail: poly-D-Lysine solution overnight at 37 degrees in the dark. Wash the dishes three times with HBSS. Sterilize all the equipment before tissue harvest. Euthanize an approximately 19-day-pregnant mouse by decapitation. Use scissors and forceps to open the mid-ventral size of the pregnant mouse. Isolate the prenatal pups into a 100 mm cell culture dish and decapitate pups with scissors. Place the heads on sterile 60mm under a dissecting microscope. Use sterile scissors and forceps to open up cranium of pup and remove the entire brain with forceps. Remove the cerebellum and grasp a small section around the hippocampus with forceps and take it out gently. Transfer the tissue into a 60mm cell culture dish with 3 mL 37-degree-prewarmed HBSS. After collection of five to eight hippocampus tissues from multiple pups, the tissue can be mined with scalpel gently.

The minced tissues are transferred into a 15 mL conical tube with the addition of 1.5 mL HBSS and 0.5 mL 0.25% trypsin solution. Gently invert the tube 5-10 times and avoid any bubbles. Incubate the tissue at 37 degrees for 15 minutes and invert the tube every 5 minutes. Neutralize the digestion with 5 mL of DMEM medium with 10% fetal bovine serum and 1% Penicillin-Streptomycin. Gently pipette the solution for 10 times and avoid bubbles. Settle the tissue for 2-3 minutes, transfer the solution through a 70um cell strainer. Centrifuge for 5 minutes at 1000 g and remove the supernatant. Resuspend the cell pellet with 5 mL DMEM medium with 10% fetal bovine serum and 1% Penicillin-Streptomycin. Incubate the cell at 37 degrees for 2 hours and replace the DMEM with 2 mL fresh warmed (37 degrees) Neuralbasal medium containing 2% B27 Supplement, 1% Penicillin-Streptomycin and 1x GlutaMax. Half of the Neuralbasal medium should be replaced with the same volume of fresh ones every 3 days. Neurons are optimal for observation at Day 7. The validation of the neural cell by immuno-fluorescent marker TuJ1 can be found in Figure S16. Labeling and imaging. Lysosomes were stained by LysoTracker Red DND-99 (50 μL, 1 mM, Thermo Fisher Scientific, Inc.) for lysosome tracking in live cells. The regent was firstly diluted by DMSO to 100μM and then the diluted solution was added to the cell culture medium with a final concentration of 100 nM. After 30 min incubation with the LysoTracker, the cell was placed under the DeltaVision Elite (GE) wield field microscopy with UPLS Apo 100x NA 1.40 oil immersion objective. To minimize the influence of the photobleaching effect, EMCCD was used as the camera with 100x gain and the resolution of 512x512. Image frame rate was set at 10 Hz. Cell nuclei were stained with Hochest 33342. The Hochest was firstly dissolved in DMSO to 5mg/mL as the stock solution. The final concentration in the cell culture medium is 2.5μg/mL. Confocal imaging was carried out in Olympus FV1200 Confocal with UPLSAPO 100x NA 1.40 objective lens. The samples were scanned with speed of 8.0 μs/pixel. Z-slicing was performed with 5 μm/slice. Image preprocessing and tracking. Raw images were loaded by Fiji28,29. Auto-contrast adjustment was then applied to improve the image brightness and contrast. Median filter with 1-pixel radius can be optional to smooth the raw image. Higher radius will blur the raw data. After the preprocessing, the tracking step was performed by the TrackMate plugin22. With the well-designed TrackMate, the trajectories of lysosomes can be extracted step-by-step. In the particle detecting step, we set the estimate blob diameter as 0.8 μm and the detecting threshold as 300. After detecting all the particle frame-by-frame, a filter step was carried out to exclude the false detected spots. There exist large bright spots in the images due to the assembling of the lysosomes, which seldom move during the observations. However, as its radius is much larger than the estimation, random false detection can occur due to the naturally fluctuations in the pixel intensity. Maximum intensity filter can be used to exclude these bright spots in particle detection. As the maximum intensity for a 16-bit image is 65535, the excluding threshold was usually set as around 48000. Subtle adjustment should be performed manually depending on the intensity distribution of each image stack. In the next step, linking the particles detected in each frame was carried out by simple LAP tracer22. The max linking distance, gap closing max distance and gap-closing frame were set as 1.2μm, 1.2μm and 2, respectively. The



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Figure 2. Mapping spatial heterogeneity by PECAN. (a) A binary image (top) with letter pattern was used to control the local viscosity of a heterogeneous medium in which particles diffuse (bottom). The simulation was carried out by Field-2D-Simulator (FIST) implemented by Python (see Note S4). (b) Particle collective motion dynamics was analyzed by PECAN and local diffusion coefficient was mapped with color bar. The left one corresponding to the heterogeneous system shown in Figure 2a, while the right one was a typical result of the simulation carried out in a homogeneous medium. (c) Value profile of the heat map shown in Figure 2b. The black dot line corresponds to the profile of Figure 2b left, while the blue solid line corresponds to the right one. Red dot line is ground truth in the simulation of the black pixel controlled region. Tracked Lysosome motion trajectories and the heat map of the exponent α of the MSD curve were plotted in the left and middle in (d) and (e), the scale bar indicates 4μm. The dot line in heat map indicates the manual selected central region (dark blue) and the peripheral region (orange). Note that the dot line drawn here is a rough description. More specifically region marker can be found in Figure S10, S11. The right bar shows the mean and standard derivation of the α in the corresponding region.

gap closing operation is aimed to estimate the locations of transient disappearing and reappearing particles due to the defocusing. After linking step, the trajectories were extracted and visualized by TrackMate. Trajectories were filtered by their number of spots and velocity standard deviation to get effective result. Short trace with number of spots smaller than 15 or the trace with its velocity standard deviation larger than 2 was excluded because they were usually statistically insufficient or containing linking error. Finally, we exported the trajectories data as XML file in TrackMate and the

data was then converted to CSV file, which is ready to be analyzed by PECAN.

RESULTS AND DISCUSSION Collective Spatial Heterogeneity of Lysosomes. Collective Spatial Heterogeneity of Lysosomes. Spatial heterogeneity as a self-organized pattern in live cells is commonly found in the plasma13 or membrane33 where small molecules, organelles or


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particles34 interact with complex environment. As the lysosomal positioning biological mechanism driven by motor protein has been well studied in recent year, showing its close relationship with various vital cell biological process such as cell starvation and autophagy14,15,35, characterization of its collective dynamics can be a potential profiling of the cell states. Here, with the aid of PECAN method, the spatial heterogeneity of the lysosomes was studied. We firstly evaluated the efficiency of the PECAN method in tagging the spatial heterogeneity. To mimic the uneven medium properties inside cell plasma, we simulated a binary image controlled viscous medium where Brownian diffusive particles move (Figure 2a). The controlling binary image actually served as the ground truth of the system, while the simulated individual particle movement frame by frame was considered as the observed signal. Two simulated experiments were conducted. In the first simulation, particles moved through a heterogeneous medium whose local viscosity was controlled by a binary letter pattern image. The black area and white area in the image represent different local viscosity mimicking the uneven properties of the cell plasma13. In the other case, a homogeneous medium was simulated whose viscosity was equal to black area in the first case. The diffusion coefficient heat map shown in Figure 2b revealed the underlying spatial heterogeneous pattern is almost identical to the ground truth in the first case while the heat map calculated from the second one with the even solution only shown random fluctuation. The result indicated that the local diffusion coefficient, which is a metric of viscosity, revealed by PECAN matches the ground truth of the simulation. Similar validation with published experimental data32 can be found in Figure S12. With the PECAN proved to be efficient, we sought whether the lysosomes move in live cell correlatively. Although heterogeneous directed and diffusive movement can be observed individually in lysosome movement, uncorrelated moving behaviors will still result in spatial homogeneous properties in average. Here, the lysosomes movement is tracked by TrackMate22, an ImageJ28,29 plugin, and analyzed by the PECAN. We then found that the spatially heterogeneous motion dynamics can be observed collectively (Figure 2d). Local exponent α mapping in the middle further confirms that the collective heterogeneous pattern does exist, which indicates the spatial dependent lysosome movement. Furthermore, the spatial pattern suggests that the lysosomes in the peripheral region tend to show more directed motion while the central ones tend to be more confined. It should be emphasized that the peripheral part and the central part mentioned here is referring to the intracellular region that lysosome movement covered, its relationship to the cell nucleus region can be seen in Figure S7-S9. We validated the hypothesis of the spatial movement differences by manually selecting the spatial lattice in either peripheral region or central region. One-way analysis of variance (ANOVA) was applied to test the significance of the hypothesis showing that the distribution of local exponent α in these two regions are significantly different with P value smaller than 0.001. Similar results can be found in Figure S10. We were then curious about whether the spatial distribution of collective lysosome dynamics is related to the state of cells since the lysosome movement is known as motor protein driven process 36. So we stimulated the HeLa cells with widely used microtubule destabilized regent nocodazole with 20 μg/mL final concentration

to alter the cell state. The same processing workflow was applied and the lysosome directed motion is significantly depressed as expected (Figure 2e). A typical mapping of exponent factor α shows that most of the lysosomes exhibit confined movement dynamics with blue color. The difference of the lysosome dynamics between the peripheral region and the central region is not significant this time in the example in Figure 2e with its P value in one-way ANOVA test of 0.12. Similar results also show a less significance level between these two regions (Figure S11). Temporal long-term correlations in lysosome collective movement. The quantitative description of the lysosomal movement in a data-driven way helped us find the underlying spatial heterogeneity from thousands of individual trajectories. So how the lysosomes behave temporally? Before characterizing the temporal behavior of the lysosomes, we firstly validated the PECAN by a simulated system that continuously changed its underlying states, which can be a critical cases occur in biological system. Here we chose ant colony searching behavior which is widely studied37 for the model clearance. The collective ant colony behavior changes its environment by transport finite amount of food and spread information hormone38 while at the meantime, the environment local properties such as food existence and pheromone spatial concentration gradient also alter the behavior of the ant. Follow these basic rules, we here simulated a simple model shown in Figure 3a. The top panel was the underlying system dynamics which is difficult to be detected in most cases (such as directly monitoring the total pheromone concentration in a region over time). The amount of food remaining in the field (blue line) drops down at around 25 and 80 frame. Meanwhile, the sum of hormone concentration (orange line) in the field exhibits two peaks in the whole process. As expected, after the food remaining dropped down to zero, the hormone concentration decreased to zero gradually. We then analyzed the simulated collective moving trajectories of the ant colony by PECAN. Time series dynamics of the mean direction change (MDC, Note S2) shown a highly consistent shape with the underlying total hormone concentration curve, implying that by analyzing the collective ant spatial movement, the underlying dynamics of the system can be revealed. Additionally, the distribution of local MDC also agrees to the hormone spatial pattern lying behind (Figure 3b). The result shows that our method is capable of revealing the underlying time series dynamics of the complex biological system. Similar results can be found in Figure S15, in which the PECAN was validated by published nanomotor dynamics3. Like the example mentioned above, lysosomes moving through the live cell are surely affected by the local properties of the cell plasma, the lysosome movements and functions also help to maintain the intracellular environment. Observations and characterizations of lysosomal time-resolved behavior can be potentially helpful to profile the temporal cell state change during a specific event related to the lysosome function. In this paper, the temporal dynamics of collective lysosome movement is focused on the time series of the average lysosome velocity. Simulated Brownian motion trajectories 39 (see Note S4) were used for comparison as the freely thermal based diffusion model. A typical map of exponent α shown in Figure 3c indicates that the simulation result satisfies the theoretical conclusion since the local α in the whole area is nearly equal to 1. We found that the average velocity



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samples shown in Figure 3e. As the sliding window method intrinsically introduce the overlap between the successive frame, the controlling of the simulated no-memory Monte Carlo process is necessary. While the autocorrelation coefficient of average velocity of Brownian motion (yellow) series dropped down to zero in the time lag of around 1 s, the autocorrelation curve of HeLa cell without and with nocodazole stimulated dropped much mildly (Figure 3f). The result suggests that the collective movement velocity in HeLa cell is self-correlated, even after the stimulation of nocodazole which significantly depresses the directed motion. We further confirmed our conclusion by detrend fluctuation analysis 40 (DFA, Figure 3f, Figure S13) to address the nonstationary problem in the time series. The fluctuation exponent here measures the long-term correlation in the time series excluding the influence of overall tendency in the series. The magnitude of 0.5 indicates noncorrelated series while the value larger than 0.5 implies long-term correlation41. We found that the fluctuation exponent factor of the simulated Brownian motion is slightly higher than 0.5, which may result from the sliding window process. On the other hand, the time series from HeLa cell with or without nocodazole stimulation exhibits a value of much higher than 0.5, suggesting that the existence of long-term correlation behavior in the average lysosome movement. With the help of the PECAN method, we show that the lysosome in HeLa cells exhibits long-term correlated dynamics, which is significantly different from the pure thermal-based Brownian motion, even after the directed motion is depressed.

Figure 3. Temporal lysosome dynamics revealed by PECAN. (a) Ant dynamics simulated by FIST. The blue solid line stands for the food remain in the field while the orange solid line represents the time series dynamics of the amount of hormone in the field. The temporally region where the food remain drop down significantly matches to the region where the hormone amount is abundant. The yellow solid line in bottom panel stands for the collective MDC for the ant movement revealed by PECAN analysis, which is consistence to the underlying dynamics of the amount of hormone. (b) The spatial distribution of underlying hormone which is hard to be detected (left) and the local movement MDC calculated by PECAN (right). Two different region were selected, 30-50 frame where the transporting is active and 130150 frame where the transporting disappears for lack of food source. Lysosome temporal average velocity dynamics of HeLa cell with (blue) and without (orange) nocodazole stimulation were compared with simulated Brownian motion (c) and the time series of these three cases were plotted in d. (e) Autocorrelation analysis suggests that the lysosome movement is self-correlated even after the directed movement was depressed. (f) The DFA1 method was applied. Fluctuation exponent of the lysosome averaging moving velocity in each frame indicates its long-term correlation properties. The comparison to generated Gaussian distributed signal and DFA with higher order can be found in Figure S13.

dynamics of both normal and nocodazole stimulated HeLa cell behaved differently comparing to the time series dynamics of Brownian motion (Figure 3d). The observation was further confirmed by autocorrelation analysis of the randomly selected 10

Oscillation dynamic of the collective lysosomal movement in mouse neural cell. It is more interesting studying the lysosome movement in morphologically polar cells such as neurons for the nature confinement of the cell membrane. Meanwhile, the lysosome function is also considered to be important for the health of the neural system42. Figure 4a shows a typical region where axons overlap and cross with each other to form a complex spatial structure in vitro (Movie S5). The motion trajectories were extracted by the same workflow mentioned above. Like the circumstances in the HeLa cell, both directed and confinement movement can be observed. The directed motion traces (Figure 4b) indicate the active transport pathway in the observed region while the confined movement also exists in the pathway (Figure 4c, Figure S17). The MSD curve exponent factor heat map shown in Figure 4d characterizes the spatial distribution of the directed (α > 1, red) and confined (α < 1, blue) motion states during the observation. Figure 4e-f shows the motion direction along x or y axis, the positive direction was marked as red and negative direction was marked as blue while the green color indicates no obvious direction tendency in observation duration. Besides knowing “where” and “which direction” that the lysosomes movement occurs in neural cells, we also interested in “when” the events happen. We selected a small rectangle region in subcellular scale marker as the white dot frame in Figure 4b. The time series dynamics of the net displacement in y-axis from 1800 to 2300 frame was shown in Figure 4g. We found that in the most of time, the series dynamics appeared high-frequency oscillation above and under the red dot zero line, which may be brought by the


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Figure 4. Spatiotemporally collective lysosome movement analyzed of neural cell by PECAN. Mouse hippocampus tissue was cultured (see Experimental Section) and lysosome movement was recorded by wide field fluorescent microscopy in 10Hz frame rate. (a) A typical frame of the neural axons overlap to form a network in vitro. Directed trajectories (b) and confined trajectories (c) of the lysosomes were plotted with random selected color and shown with the scale bar of 10 μm. (d) The heat mapping of the exponent factor α, in which the red color indicates directed motion and blue color indicates confinement motion. Normalized net displacement along axis x (e) and axis y (f) were capable of indicating the spatial distribution of the moving direction in which red color indicates positive direction along the corresponding axis and the blue color stands for negative direction. The result shows that the motion dynamics is heterogeneous and varies with different position. (g) The time series dynamics of net displacement along y axis inside a subcellular region marked in white dot rectangle in Figure 4a was plotted from 1800 to 2300 frame. The insert figure shows the whole profile of the observation in gray line. Spark events happen and indicated by green and orange arrow. The red dot line indicates the zero level. The lysosomal net displacement along the y-axis pointed by green arrow (negative magnitude, 1976 frame) and orange arrow (positive magnitude, 2146 frame) was plotted as heat map in (h) and (i), respectively. The colorbar of these two figure are the same with those in Figure 4e-f. The green spots in the figure stands for a lysosome movement with small magnitude in y-axis, while the blue spot in (h) and the orange spots in (i) indicates the large magnitude movement, which can be considered as transient active transportation. The statements can be further confirmed by the inserted figures in (h) and (i). The insert figure shows all lysosome local trajectories in corresponding time with the beginning point of each trace moved to the origin of the coordinate (indicated by red dot line). The color of the local trajectory was selected by the direction of their net displace. Red color means positive net displacement and blue color represents negative



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confinement movement in the region (Movie S6). Meanwhile, spark events referring to a sudden fast axon lysosome transport were also observed along the temporal series, which are pointed by green (in frame 1976) and orange (in frame 2146) arrow in this example. The mapping of the net displacement along the y-axis inside a short temporal window (Figure 2h and 2i) indicated that the spark event was induced by the active transport of lysosome. The inserted figure shows the local trajectories inside the corresponding temporal window where the beginning of the local trace was moved to origin point and the trace with positive net displacement in yaxis was colored in red, blue in the opposite. The result implies that the negative direction spark event arisen in 1946 frame is induced by a directional moving lysosome pointed by a green arrow in Figure 4h while the positive spark event in 2146 frame is caused by two positive directional lysosome moving. By observing the time series dynamics of the collective net displacement of the lysosomes, we were surprised to find an oscillation phenomenon in mouse neural cell. Figure 5a shows the time series profile of the net displacement in x axis of a typical image. The blue and orange colored regions were compared to show that the oscillation dynamics was not stationary over time. The similar result cannot be found in the lysosome movement in HeLa cell with the same workflow (Figure S20). The nonstationary dynamics (blue versus orange region in Figure 5a) firstly drawn our attention. Further analysis by continue wavelet transform (CWT)24 indicates that two frequency band existed throughout the time (Figure 5b) in the scalogram, which shows the magnitude of the oscillation versus time (x-axis) and frequency (yaxis) by heat maps. The high-frequency component is around 4 Hz and the low-frequency component is around 0.25~0.5Hz. The scalogram of the time segment from 130s to 170s (blue color in Figure 5a) and the segment from 220s to 260s (orange color in Figure 5a) are different from each other. Figure 5b shows that the former one has higher high-frequency magnitude and its lowfrequency band is nearly in 0.25-0.5 Hz while the later one has lower high-frequency magnitude and its low-frequency band is nearly in 0.125-0.25 Hz. We average the magnitude scalogram in the time domain for these two segment and the result was shown in Figure 5c, which clearly shows the differences of the peak locations of these two segments. We also mapped the spatial distribution of the exponent α within these two regions. The results showed that the active transportation (high α magnitude) appeared in different positions (Figure S21). The oscillation phenomenon was confirmed to be reproducible in mouse neural cell (Figure S19). The scalogram of the samples that appears the phenomenon is also significantly different from those observed in HeLa cell or stimulated data (Figure S20). This result indicates that the phenomenon is not introduced by the microscopy recording or PECAN method. However, we believe that more deep and careful experiment and analysis should be carried out to confirm the result and provide more biological insight into this phenomenon in further work.

CONCLUSIONS Collective behaviors as a widely observed phenomenon in different scales from sub-micron controlled diffusion to

Figure 5. The oscillation-like phenomenon in neural cells. (a) The time series of normalized collective lysosomal net displacement in x axis of same region in Figure 4. Manually selected local segment from 130s to 170s (lower left) and from 220s to 260s (lower right). (b) The Continues Wavelet Analysis of the corresponding time series segment in (a). The left scalogram is corresponding to the blue region and the right one is corresponding to the orange region. The scalogram shows how the oscillation magnitude various versus time and frequency. The bright area in the figure indicates main oscillation frequency component with the red-hot colormap. Two main frequency bands were observed. High frequency band in around 4 Hz and the low frequency band in around 0.125-0.5 Hz. (c) We average the oscillation intensity along the time domain (x-axis) and obtain the oscillation magnitude versus frequency curve of the 130s to 170s (blue) and 220s260s (orange) segments. The inserted figure is the amplified figure in from 0 to 1 Hz. The curve implies that the main difference between these two region is the low frequency component. The low-frequency component of the oscillation is around 0.2 Hz between 130s and 170s while the low-frequency component is around 0.4 Hz from 220s to 260s.

macroscopic animal grouping have drawn great research attentions in recent years. It provides us with a systematic perspective to understand the biological behavior as a whole and at the same time, pay attention to individual dynamics. This down-top strategy is commonly adopted in the modeling and simulations of the collective behavior and was introduced for characterization in this paper. Here, to analyze the collective pattern of the lysosomes, we developed the single particle tracking based PECAN method and proved its efficiency. Symmetry breaking property was found in the collective lysosomal behavior characterized by spatial heterogeneity, temporal long-term correlation and non-stationary dynamics, which can be emerged from the cell regulation by motor protein. Furthermore, oscillation movement was observed in


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mouse neural cell with the aid of PECAN. Our study provides a new perspective to investigate the subcellular behavior in submicron scale and potentially contributes to the research of biochemical process mediated by organelles movements such as drug delivery and cellular activity profiling.

density or low signal-to-noise ratio images potentially lower the reliability of the tracking data and also the results of PECAN analysis. To choose a suitable tool and parameter in a certain case, the researchers can refer to several reviews 19,43 and validations 27 to get a better choice.

PECAN can offer a powerful platform to analyze collective behaviors of different observed agent-based complex systems spatiotemporally and quantitatively. Instead of identifying local velocity vector by image pattern recognition like PIV does, PECAN workflow firstly requires exact single particle trajectories that contains the records of individual spatiotemporal dynamics and then assembles all individual information as a whole to produce a perspective of the system. Comparing to the PIV method, the down-top strategy enables PECAN to perform various of calculation based on the trajectories data and thus provide with more biological/physical information of the collective motion but with higher computational costs. We introduce the temporal sliding window and spatial lattice operation to divide the observations into small fragments and assume that the motion dynamics in the small fragment is stationary, which enables our method to resolve the collective dynamics both spatially and temporally. By validating with simulated uneven Brownian motion, ant colony migration and published experimental data of nanomotor flocking, we show that PECAN is efficient in revealing the underlying spatial uneven or temporal nonstationary collective dynamics from a large amount of trajectories data detected. As the spatiotemporal heterogeneity is commonly observed in biological systems, we note that our method can be an important tool for systematic states profiling. Taking advantages of the widespread applications and still advancing single particle tracking techniques which provide the original data for our method, PECAN can play a more vital role in characterizing and visualizing the collective particle motions in the studies of organelles transportation, cell migration, nanomotor dynamics, etc. The quantitative and visualized results can aid the studies of collective behaviors by 1) serving as a routine tool to profile the systematic state. 2) giving an intuitive view of the system for further modeling and inspire new knowledge discovery. 3) supporting the Pattern-Oriented Modeling (POM) of the system with quantitative comparisons between the systematic observation and model performance. As a MATLAB software with user-friendly interface and modular design, we hope our method can contribute to the studies of complex particle movements in more general fields. There also exist several shortcomings in the current method. We now mainly focus on the analysis on 2D images to get highest temporal resolution in our imaging equipment. Note that current results are based on the 2D images of the lysosome dynamics. Slightly z-axis movement is projected to x-y plane and severe z-axis movement should be filtered out for defocusing. How the z-axis movement of the lysosomes affects the characterization needs to be further explored with high-frequency 3D images equipment and extending PECAN method. We believe that 3D images with z-axis resolution will be more widely adopted in the future with higher zaxis and temporal resolution. We note that the basic strategy and operations mentioned here still suitable for 3D collective motion dynamics characterization. With our concepts proved here, extending from 2D to 3D can be achieved in future work. Another main issue is that the efficiency of our single particle tracking based method relies on the accuracy of the tracking results. High particle

In this paper, PECAN was also applied to reveal the collective dynamics of the lysosomes as a critical organelle related to several cell behaviors that receives considerable attention, such as cell starvations, recycling, signal transducing and autophagy15,44. Lysosomes collective movement driven by motor proteins is related to the cell plasma properties and cell state such as cell skeleton distribution and stability. Characterizing the cell collective lysosomal behavior not only contributes to the research of biochemical process mediated by organelles movement such as drug delivery45, but also provides a new perspective to profile the cell states, which can be potentially helpful for drug screening or cell typing.

ASSOCIATED CONTENT Supporting Information The algorithm of PECAN was discussed in details in Note S1. The calculations of local physical properties were described in Note S2. Single Particle Tracking and the data analysis by PECAN were described in Note S3. Model simulation was described in Note S4; Nocodazole stimulation of HeLa cell and the validation of mouse neural cell by markers were described in Note S5. And the structure of the software was shown in Figure S1. Figure S2: the original bright field of the HeLa cell overlapped with probed lysosome. The particle detection process and linking process were shown aid by TrackMate. Figure S3: the zero outlier elimination process for data cleaning. Figure S4-5: The comparison between PECAN and PIV method. S6: The trajectories of the simulation shown in Figure 2. Figure S7-9: The colocalization of lysosomes and cell nucleus. The raw heat map without interpolation and similar result of the spatial heterogeneous lysosome movement without (Figure S10) and with (Figure S11) nocodazole stimulation. Figure S12: validation with Andreas’s work in which ferromagnetic particle collectively form different systematic behavior. Figure S13: DFA1 analysis of the temporal lysosomal dynamics. Figure S14: simulation details of the ant colony in Figure 3. Figure S15: validation with Tailin’s work in which nanomotor reserve flocking in response to the external acoustic stimulation. Figure S16: immunofluorescent label of the neural cell. Figure S17: bright field image of mouse neural cell overlapped with probed lysosome. Figure S18: the CWT analysis of the lysosome displacement in y axis. Figure S19: similar results of the oscillation dynamics in neural cell. Figure S20: same oscillation cannot be found in HeLa cell, simulated Brownian motion and the Gaussian noise. Figure S21: the spatial distribution of the active transportation in frame 1300-1700 and frame 2200-2600 in the sample shown in Figure 5. Table S1: the standard input format for PECAN. Table S2: The different features of the PECAN comparing to the PIV method. Movie S1: Particle Tracking in HeLa cell. Movie S2: the lysosome movement in HeLa cell after nocodazole stimulation. Movie S3: ant dynamics. Movie S4: PECAN analysis of ant dynamics. Movie S5: lysosome movement in neural cell. Movie S6: PECAN analysis of lysosome dynamics in neural cell.

AUTHOR INFORMATION Corresponding Author



* [email protected]

Author Contributions Hansen Zhao designed and implemented the PECAN method, analyzed the data and wrote the majority of the manuscript. QiMing Zhou cultured the mice hippocampal neural cells and wrote the related sections. Hansen Zhao and Qingming Zhou acquired the experimental image sequence data. Mengchan Xia and Jiaxin Feng cultured the HeLa cell and wrote the related sections.

Notes

The authors declare no competing financial interest.

ACKNOWLEDGMENT We thank Nadhir and Yan He for their helpful suggestion and discussion. We also thank Qi Pan of Tsinghua University for her discussion. This work was supported by the Nation Nature Science Foundation of China (21390410; 21727813).

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