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Trackosome: a computational toolbox to study the spatiotemporal dynamics of centrosomes, nuclear envelope and cellular membrane

Domingos Castro, Vanessa Nunes, Joana T. Lima, Jorge G. Ferreira, Paulo Aguiar

Preprint posted on April 28, 2020 https://www.biorxiv.org/content/10.1101/2020.04.27.064204v1

Expanding the cell biology toolkit: Trackosome

Selected by Mariana De Niz

Categories: cell biology

Background

Mitosis is a highly regulated stage of the cell cycle where multiple subcellular structures take part in a complex chain of events that culminate in chromosome segregation. This involves, among various steps, disassembly of adhesion complexes, reorganization of the cytoskeleton, and migration of duplicated centrosomes along the nuclear envelope so that a bipolar spindle can form. Players involved in this process include microtubule-associated motors kinesin 5, dynein, actin, and myosin II. How the dynamic changes in all these events are coordinated in space and time to ensure efficient centrosome separation and spindle assembly, remains unknown.

Recent advances in live-cell imaging and image analysis techniques made it possible to access the subcellular environment and quantitatively examine its underlying mechanisms. However, available tracking tools are not fine-tuned for the constrains and motion dynamics of centrosome pairs. This limits their tracking performance, and often demands for exhaustive parameter optimization. Moreover, when studying the dynamics of spindle formation, it is often necessary to analyze centrosomes movement in reference to the cellular and nuclear membrane; however, the available computational tools do not directly allow the analysis of the coordinated changes between different structures, in specific subcellular frames of reference. Driven by these computational limitations and the need to better characterize the crosstalk between subcellular structures during mitotic entry, Castro et al developed the open-source software Trackosome [1]. This novel computational tool enables a quantitative analysis of the spatiotemporal dynamics of three cellular components: centrosomes, nuclear envelope and cellular membrane.

Figure 1. Trackosome is a computational toolbox to study the spatiotemporal dynamics of centrosomes, nuclear envelope and cellular membrane (Figure from Ref1).

Key findings and developments

Tool description

Trackosome is a freely available open-source computational tool to track the centrosomes and reconstruct the nuclear and cellular membranes, based on live imaging datasets, where the structures of interest are independently tagged. The toolbox runs in MATLAB and provides a graphical user interface for easy and efficient access to the tracking and analysis algorithms. The tool has two modules: “centrosome dynamics”, used for tracking centrosomes or other subcellular organelles in 3D and studying their spatiotemporal relations with the nucleus and cell membrane; and “nuclear envelope fluctuations”, used to reconstruct, measure and analyse the dynamic fluctuations of the nuclear membrane (or others) in 2D. Trackosome measures membrane movement in a model-free condition, making it viable for irregularly shaped nuclei. Trackosome can be downloaded from https://github.com/Trackosome

Key findings

Tracking and trajectory analysis of centrosomes is performed in the Trackosome toolbox through the «centrosomes dynamics»module. In a test setup, this module allowed tracking centrosomes with high fidelity even in highly noisy environments.

The authors went on to explore the potential of the tool for following dynamics of cellular organization during early spindle assembly. Trackosome allowed quantification of specific spatiotemporal relations between the centrosome pairs and nuclear and cell membranes, during mitotic entry. Trackosome was able to reconstruct the membranes’ surface, together with the centrosomes’ trajectories in 3D. Moreover it also allowed obtaining quantitative metrics of the intracellular reorganization that occurs in cells as they enter mitosis, namely the distance and angles between centrosomes, the eccentricity of the nuclear and cellular membranes, and the angles between the major axis of the nucleus, cell and centrosomes.

Using Trackosome, the authors were able to study centrosome trajectories, and show that they are not independent. The authors followed cell development in two cell lines, until nuclear breakdown. They found that during this stage, nuclear shape remained approximately constant and correlated with the label of chromatin, while the centrosomes exhibited complex trajectories resembling a search/adaptive path around the nucleus. To infer about the coordination of movement between the centrosomes, their trajectories were analyzed using the nucleus as a reference (which is made possible by Trackosome algorithms). The results indicated a considerable degree of coordination and synchrony among trajectory pairs. The authors discuss the hypothesis of synchronous variation of the forces applied to both centrosomes, probably driven by kinesin-5 or dynein.

The authors then used Trackosome to analyse the dynamic morphology of the nuclear envelope, using Trackosome’s “membrane fluctuations” module. The membrane oscillations are determined by calculating the orthogonal displacement of each point of the membrane with respect to its medial position. The authors were able to quantify and compare the nuclear deformations for cells in interphase and mitosis. They were able to confirm that in interphase, cells present subtle but measurable nuclear membrane movements. This behavior changes in prophase, where there is an increase of the fluctuations amplitude, reflecting the occurrence of nucleus-wide deformations. The authors explored whether Trackosome would be able to detect fluctuations of the nuclear envelope based on pharmacological alterations of the cytoskeleton and the microtubule networks. They found that disruption of the microtubule cytoskeleton significantly reduces the large scale deformations of the nucleus during this stage. The authors conclude that overall, Trackosome is a powerful tool to help unravel new elements in the spatiotemporal dynamics of subcellular structures.

What I like about this preprint

I like this preprint because in it the authors identified a gap in the currently available tools for image analysis of sub-cellular structures during the process of mitosis, and went on to create their own toolbox, and validate it in different settings. I think method development and validation is extremely important in science, and it helps not only the developers, but is useful to the scientific community in general. I also like that the authors made the tool freely available, in the spirit of open science.

References

  1. Castro D., et al, Trackosome: a computational toolbox to study the spatiotemporal dynamics of centrosomes, nuclear envelope and cellular membrane, bioRxiv, 2020.
  2. https://github.com/Trackosome/

 

 

Posted on: 2nd July 2020

doi: https://doi.org/10.1242/prelights.22554

Read preprint (1 votes)




  • Author's response

    Paulo Aguiar shared

    Open questions 

    1.Firstly, congratulations on this exciting work. My first question to you is more general: can Trackosome be used to investigate other sub-cellular structures/organelles in a variety of conditions? For instance following up the Golgi, mitochondria, ER, during homeostatic conditions including cell development, or disease, such as cancer or infection? And if so, are the current two main modules already adaptable for this type of measurements, or are you further developing the tool?

    Short answer: Although Trackosome was originally developed to track centrosomes, nucleus and cell membrane, its algorithms can be used to track other subcellular structures (as long as these structures can be adequately segmented from 3D live imaging data). There are, however, some limitations such as the maximum number of simultaneously tracked subcellular structures (2 particles and 1 membrane, in the current version of Trackosome).

    Longer answer: In the “centrosomes dynamics” module, the preprocessing stage allows the user to define the approximate size of the particles of interest, therefore expanding its tracking capabilities to other organelles. However, in the current version of Trackosome, this module tracks only up to two particles and requires 3D data (live imaging with image stacks). As for the “membrane fluctuations” module, the current version is also expected to work for different membrane structures, as long as the full perimeter of membrane is captured inside the 2D frames (e.g. the target structure has to be fully framed). Also, even though it was designed to segment a relatively smooth structure (the nucleus), it can still work for more intricate membranes as the software easily allows manual corrections or readjustment of filtering conditions. We cannot guarantee that the current version of Trackosome is suitable for all the above mentioned conditions; nonetheless, at this point, we are still developing the tool so that additional metrics can be extracted from live-cell imaging datasets.

    2.You discuss in your results, mechanical properties of the nucleus. To what resolution can you study mechanical changes in the nucleus throughout mitosis, as well as other conditions, using Trackosome?

    Answer: The tests performed showed that the resolution of the results follows nicely the spatial and temporal resolutions of the input videos. In fact, from the validations with synthetic data, we show that the spatial resolution of Trackosome (for both modules) can be sub-pixel. It should be noted however, that if the videos are too noisy, they may require stronger filtering, both in space and time, which may compromise the resolution of the tracking/segmentations. In the experiments reported in our study, we have used Trackosome in conditions of high spatiotemporal resolution (for both interphase and prophase cells): 100 msec timelapse and pixel size of 106 nm for nuclear fluctuations analysis.

     

    3.Are there important limitations in the current version of Trackosome, when applied to the original questions you wished to address, namely in the context of mitosis?

    Answer: Regarding the “centrosome dynamics” module, while the algorithm can detect centrosomes even in conditions of low SNR, it may sometimes require manual adjustments. The algorithm may “lose” a centrosome if it is closely surrounded by another high intensity particle with similar dimensions. Nonetheless, this can be easily corrected taking advantage of the developed user-friendly interface. The reconstruction of the cellular membrane may also be compromised if there are neighboring cells attached to the cell of interest. Regarding the “membrane fluctuations” module, the algorithm is not able to cope with highly invaginated nucleus, or nucleus where a significant portion of the membrane is not visible. Also, it is important to keep in mind that the algorithm may provide unreliable results if a portion of the membrane deviates too much from its median position, where our orthogonal movement assumption may no longer hold. This can be qualitatively evaluated through the developed user-interface.

     

    4.You mentioned in your introduction and at other points, the relevance of molecules such as kinesin-5, dynein, myosin II, etc. Can you measure their dynamics in parallel, using trackosome, to have a full picture of mitosis as a whole?

    Answer: Using the appropriate experimental conditions, the quantitative analysis provided by Trackosome (targeting centrosomes, nucleus and cell membranes) can be combined with analysis from the labelling of other molecules (e.g. kinesin-5, dynein). Trackosome will need, however, to be combined with other analysis software as to provide such holistic characterizations.

     

    5.Can other authors add to Trackosome, depending on their needs? i.e. is it an open platform which you envisage can be increased to cover multiple needs in the field of cell biology/image analysis?

    Answer: The idea of Trackosome as an open-source tool is central in the work we developed. Given the diversity of scientific questions, it is not realistic to wish for a “swiss-army knife” toolbox that fits all needs. Instead we made an effort on creating a solid platform which is easy-to-use and open. Moreover, is was specifically developed in MATLAB, a widely used programming language in research. This allows other researchers to effectively modify the toolbox or add content to suit their needs. At the moment we are still in a prepublication stage, so even though the toolbox can be downloaded from GitHub [2], the source code will only be made available after publication.

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