Protocol for Analysis and Consolidation of TrackMate Outputs for Measuring Two-Dimensional Cell Motility using Nuclear Tracking
Summary
This protocol for high-throughput measurement of cell motility in HaCaT keratinocytes describes methods for collecting and processing images of cell nuclei and performing particle tracking using the ImageJ plugin TrackMate.
Abstract
Collective cellular migration plays a key role in many fundamental biological processes including development, wound healing, and cancer metastasis. To understand the regulation of cell motility, we must be able to measure it easily and consistently under different conditions. Here we describe a method for measuring and quantifying single-cell and bulk motility of HaCaT keratinocytes using a nuclear stain. This method includes a MATLAB script for analyzing TrackMate output files to calculate displacements, motility rates, and trajectory angles in single cells and in bulk for an imaging site. This motility analysis script allows for quick, straightforward, and scalable analysis of cell motility rates from TrackMate data and could be broadly used to identify and study the regulation of motility in epithelial cells. We also provide a MATLAB script for reorganizing microscopy videos collected on a microscope and converting them to TIF stacks, which can be analyzed using the ImageJ TrackMate plugin in bulk. Using this methodology to explore the roles of adherens junctions and actin cytoskeletal dynamics in regulating cell motility in HaCaT keratinocytes, we demonstrate evidence that Arp2/3 activity is required for the elevated motility seen after α-catenin depletion in HaCaT keratinocytes.
Introduction
Precise, responsive regulation of cellular motility in epithelial cells is crucial for wound healing and for replenishing the epithelial layer. Failure to sustain motility can lead to problems with embryonic development and wound healing1 and overactive motility signaling is a key contributor to cancer metastasis2.
Understanding cellular control of motility in HaCaT keratinocytes offers important insights into these processes. The procedures outlined here provide consistent measurements and calculations of the average magnitude of cellular motility on a single-cell or population level. We have used this method to measure motility in HaCaT keratinocytes after either genetic perturbation or treatment with small molecule inhibitors to understand the cell signaling that controls rates of motility. The provided MATLAB script measures both the average speed and average direction of motility for each cell.
Collective cell motility is crucial for epithelial-mesenchymal transition both in development and in cancer metastasis3. Cells achieve motility through coordination of adhesion, polarization, protrusion, and retraction4. These processes rely heavily on coordinated regulation of the dynamic actin cytoskeleton. Activation of Rac1 GTPase downstream of PI3K or various receptor tyrosine kinases polarizes the cell, resulting in actin polymerization5. Actin cytoskeletal dynamics are regulated by this and other Rho-mediated GTPases, which are activated by downstream of various growth factors. These Rho-mediated GTPases then activate the Arp2/3 complex that stimulates actin branching6. These actin cytoskeleton dynamics are closely related to another key regulator of motility: intercellular junctions. We are particularly interested in how the adherens junctions, which form strong adhesions between cells, coordinate with the actin cytoskeleton to maintain tissue integrity7.
We have previously shown that HaCaT keratinocytes expressing an shRNA-mediated α-catenin knockdown demonstrate higher motility rates than those expressing a non-targeting shRNA control8. We wish to use the motility analysis tools we developed to further understand the mechanism of this elevated motility upon depletion of α-catenin. α-Catenin is a required cytosolic component of adherens junctions9. It participates in adherens junctions through its interaction with ß-catenin, which it binds as a monomer10,11. However, α-catenin can also dissociate from adherens junctions to form a homodimer that binds and bundles actin filaments, which inhibits Arp2/3 activity10,11. While α-catenin does not interact directly with actin while it is bound to ß-catenin, it may still facilitate or strengthen the coordination between adherens junctions and the cytoskeleton through binding to vinculin, which stabilizes actin filaments12.
While the TrackMate plugin for ImageJ is a well-established method for particle tracking that can be used to track cell nuclei, we found that the available tools for parsing, analyzing, and consolidating TrackMate outputs to calculate cell motility for multiple imaging sites were not integrated and were challenging to use for those without expertise in multiple programming languages. Tinevez and Herbert offer a primer on using TrackMate, but the mean square displacement analysis section requires considerable MATLAB skill13. Existing methods for extracting motility rates from time-lapse microscopy videos offer more complex three-dimensional analyses but require more programming expertise14,15. Pathfinder, a cell tracking and motility analysis software previously developed in our lab, measures cell migration speed and direction but is only executable on Windows machines and requires a limiting Java Runtime environment16. Because TrackMate is such a reliable and well-documented tool, we developed a straightforward method for generating, analyzing, and organizing large two-dimensional TrackMate datasets using MATLAB. Our script also removes the repeated integer datapoints that sometimes occur in TrackMate outputs after a tracked cell crosses out of the imaging site, allowing for the inclusion of cells with high motility and/or directionality in analysis without the inclusion of these spurious datapoints. We also provide a script for reorganizing images collected on an ImageXpress Micro XL into TIFF stacks that can be analyzed in bulk using TrackMate.
In this protocol, cells are seeded in a 96-well imaging plate. After allowing enough time for the cells to adhere to the bottom of the plate, we treat them with Hoechst nuclear stain and any small molecules whose effects on motility are of interest. We collect images of the cell nuclei over the course of five or more hours, after which the image sequences are processed into background-subtracted TIFF stacks using MATLAB. These TIFF stacks are analyzed using the ImageJ TrackMate plugin, which registers and tracks each individual cell across timepoints17. After we have generated cell tracks for all imaging sites, we use a custom MATLAB script to remove spurious datapoints that occur after cells leave the imaging field and calculate average motility rates for each imaging site. The script only analyzes cells that are tracked for a user-specified minimum number of timepoints and allows the user to filter cells by overall distance and/or direction traveled. The result is the average displacement, average direction of travel, overall displacement, and overall direction of travel for each cell, which are used to calculate the bulk averages of those values for all cells in an imaging site. This protocol can be performed in bulk on large imaging experiments, which lends itself to relatively high-throughput motility analysis (Figure 1).
Protocol
Representative Results
Discussion
The methodology and analysis tools described above provide a straightforward and scalable means for measuring and quantifying the motility of HaCaT keratinocytes that uses MATLAB and requires minimal programming experience. This protocol calls for the nuclei of cells to be stained and imaged over the course of at least 5 h. While data collection can be performed on any suitable microscope, this protocol provides a script for processing images collected on an ImageXpress Micro XL microscope into a TIFF stack for each imag…
Offenlegungen
The authors have nothing to disclose.
Acknowledgements
We thank Daniel Messenger, Lewis Baker, Douglas Chapnick, Adrian Ramirez, Quanbin Xu, and other members of the Liu and Bortz labs for their insight and advice. We thank Jian Tay for sharing his MATLAB expertise and for writing the function for XML import. We thank Joseph Dragavon of the BioFrontiers Advanced Light Microscopy Core for his microscopy and imaging support. We thank Theresa Nahreini and Nicole Kethley of the Cell Culture Core Facility for their cell culture support. This work was supported by grants from the National Cancer Institute and the National Institute of Arthritis and Musculoskeletal and Skin Diseases of the National Institutes of Health (R01AR068254) to X. L. and National Institute of General Medical Sciences (NIGMS) R01GM126559 to D.B. and X. L. G.E.W. and E.N.B. were supported by a predoctoral training grant from the NIGMS (T32GM08759). The ImageXpress Micro XL was supported by the National Center for Research Resources (S10 RR026680). FACSAria was supported by the National Institutes of Health (S10OD021601).
Materials
| 96-well flat clear bottom black polystyrine TC-treated microplates, individually wrapped, with lid, sterile | Corning | 3603 | |
| Dulbecco's modified eagle medium (high D-glucose) | Life Technologies Corporation/Thermo Fisher Scientific | 12800-082 | |
| Fetal bovine serum | Sigma-Aldrich Inc | F0926 | |
| Fluorobrite Dulbecco's modified eagle medium (high D-glucose, 3.7 g/L sodium bicarbonate, no L-glutamine, no phenol red) | Gibco/Thermo Fisher Scientific | A18967-01 | |
| GlutaminePlus | R&D Systems Inc. | R90210 | |
| Hoechst 33342, trihydrochloride, trihydrate | Invitrogen/Thermo Fisher Scientific | H21492 | |
| Penicillin streptomycin | Life Technologies Corporation/Thermo Fisher Scientific | 15140-122 | |
| Phosphate buffered saline | Gibco/Thermo Fisher Scientific | 14190-144 |
Referenzen
- Li, L., He, Y., Zhao, M., Jiang, J. Collective cell migration: Implications for wound healing and cancer invasion. Burns & Trauma. 1 (1), 21-26 (2015).
- Stuelten, C. H., Parent, C. A., Montell, D. J. Cell motility in cancer invasion and metastasis: insights from simple model organisms. Nature Reviews Cancer. 18 (5), 296-312 (2018).
- Campbell, K., et al. Collective cell migration and metastases induced by an epithelial-to-mesenchymal transition in Drosophilaintestinal tumors. Nature Communications. 10 (1), 1-10 (2019).
- Devreotes, P., Horwitz, A. R. Signaling networks that regulate cell migration. Cold Spring Harbor Perspectives in Biology. 7 (8), (2015).
- Parri, M., Chiarugi, P. Rac and Rho GTPases in cancer cell motility control. Cell Communication and Signaling. 8 (1), 23 (2010).
- Sumida, G. M., Yamada, S. Rho GTPases and the downstream effectors actin- related protein 2/3 (Arp2/3) complex and myosin II induce membrane fusion at self-contacts. The Journal of Biological Chemistry. 290 (6), 3238-3247 (2015).
- Alberts, B., et al. Cell junctions. Molecular Biology of the Cell., 4th edition. , (2002).
- Bunker, E. N., Wheeler, G. E., Chapnick, D. A., Liu, X. Suppression of α-catenin and adherens junctions enhances epithelial cell proliferation and motility via TACE- mediated TGF-α autocrine/paracrine signaling. Molecular Biology of the Cell. 32 (4), 348-361 (2021).
- Niessen, C. M. Tight junctions/adherens junctions: basic structure and function. Journal of Investigative Dermatology. 127 (11), 2525-2532 (2007).
- Drees, F., Pokutta, S., Yamada, S., Nelson, W. J., Weis, W. I. α-Catenin is a molecular switch that binds E-cadherin-β-catenin and regulates actin-filament assembly. Cell. 123 (5), 903-915 (2005).
- Yamada, S., Pokutta, S., Drees, F., Weis, W. I., Nelson, W. J. Deconstructing the cadherin-catenin-actin complex. Cell. 123 (5), 889-901 (2005).
- Rangarajan, E., Izard, T. The cytoskeletal protein α-catenin unfurls upon binding to vinculin. The Journal of Biological Chemistry. 287 (22), 18492-18499 (2012).
- Tinevez, J. -. Y., Herbert, S. The NEMO dots assembly: single-particle tracking and analysis. Bioimage Data Analysis Workflows. , 67-97 (2020).
- Visweshwaran, S. P., Maritzen, T. A simple 3D cellular chemotaxis assay and analysis workflow suitable for a wide range of migrating cells. MethodsX. 6, 2807-2821 (2019).
- Wu, P. -. H., Giri, A., Wirtz, D. Statistical analysis of cell migration in 3D using the anisotropic persistent random walk model. Nature Protocols. 10, 517-527 (2015).
- Chapnick, D. A., Jacobsen, J., Liu, X. The development of a novel high throughput computational tool for studying individual and collective cellular migration. PloSOne. 8 (12), 82444 (2013).
- Tinevez, J. -. Y., et al. TrackMate: an open and extensible platform for single-particle tracking. Methods. 115, 80-90 (2017).
- Vargas Aguilera, C. A. int2strz.m. MATLAB Commons. , (2016).
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