• Overcome the limitations of confluence-based scratch wound analysis
• Extract additional migration metrics in the form of cell speed and directionality
• Build comprehensive cell migration profiles through Livecyte’s Scratch Wound Dashboard
Cell migration is an essential and highly regulated process involved in embryonic development, tissue regeneration and cancer1-5.
The scratch wound assay is an established research tool and one of the most commonly studied models for the analysis of wound healing and collective migration6. The typical output from the assay is wound closure rate, which is calculated by measuring changes in wound area or cell confluence over time7,8. However, changes in confluence are highly susceptible to variations in initial assay set up, inconsistent wound area size and cell proliferation8-11. Indeed, compounds to inhibit proliferation are often introduced to minimize the effects on the wound area8,12, whilst cell density outside the wound area has been shown to have a potent effect on wound closure rates8.
Tracking and analysis of individual cells has the potential to circumvent the potential inconsistencies of measuring changes in cell confluence alone8,11. This approach provides a greater depth of information such as cell speed and directionality of migration but also monitoring of cell proliferation that collectively enhance and complement the standard scratch wound assay.
Individual cell segmentation and tracking using traditional label-free methods such as brightfield or phase contrast is challenging due to lack of inherent imaging contrast. Whilst these images can be analysed manually, this is both subjective and time consuming. Although the introduction of fluorescent labels enhances cell contrast, labels also have the potential to alter normal cell function, which by association may influence cell migration6,13-15.
Automated cell tracking algorithms applied in traditional label-free and fluorescence-based approaches have been reported elsewhere8,11, however, a complete and efficient workflow encompassing image analysis, cell tracking and data visualisation for scratch wound assays in multi-well plates has been lacking. In this study we sought to directly quantify individual cell migration using the standard scratch wound assay and Phasefocus Livecyte. Automated measurement of single cell migration was performed using Livecyte's label-free, quantitative phase imaging (QPI) mode and Scratch Wound Dashboard.
A2780 cells were maintained in RPMI 1640 + 10% FBS at 37°C with 5% CO2/95% humidity prior to experiments. Cells were harvested using standard techniques prior to seeding into a 12-well plate. Cells were grown to ~90% confluence and media was replaced with serum-free medium (Control/Untreated) or serum-free medium with a compound of interest (Cmpd 1, 2 or 3). Prior to imaging, a wound area was introduced at the centre of the well using a sterile 200 µl pipette tip.
• A2780 ovarian cancer cell line (ECACC)
• RPMI 1640 (Gibco)
• Foetal Bovine Serum (FBS; Gibco)
• Compounds of interest
• 12 well culture plate (CellVis P12-1.5H-N)
• Livecyte Kinetic Cytometer (Phasefocus)
• Livecyte Acquire & Analyse software (Phasefocus)
Label-free QPI images were generated via Livecyte’s automated Acquire software and Ptychographic reconstruction algorithm. Cells were imaged with a PLN 10X/0.25NA objective and 0.5mm x 1.5mm field of view (FOV) per well for 5 hours at 10-minute intervals. During imaging, cells were maintained in an environmental chamber at 37°C with 5% CO2 and 95% humidity.
The effects of three compounds of interest (Cmpd1, Cmpd2 and Cmpd3) on cell migration were examined over 5 hours using the Livecyte Kinetic Cytometer. Image processing, automated cell segmentation and tracking were performed fully automatically with Livecyte Analyse software. Single-cell metrics were extracted using the Scratch Wound Dashboard in Analyse. Mitotic Index was reported as the percentage of cells exhibiting a Mitotic Signature (gated on increased cell sphericity and thickness) within the FOV, over time.
Individual Cell Segmentation & Tracking
The high-contrast label-free images generated by Livecyte allows for robust individual cell segmentation (Figure 1). Livecyte’s analysis algorithms utilise multiple values from segmentation data to provide reliable individual cell tracking data.
Scratch Wound Dashboard
Livecyte automatically collates image, single cell segmentation and cell tracking data (Figure 1) into an interactive Scratch Wound Dashboard (Figure 2). Data across multiple wells are ordered by treatment and tangible cell migration metrics are generated in the form of relative change in wound area, median cell speed and directionality, amongst others. Multi-panel videos are also available to preview cell migration into the wound area under each treatment condition.
Standard Wound Closure Metrics
The Scratch Wound Dashboard provides an initial wound area value for each treatment condition and enables users to determine if the wound making process has been consistent (Figure 3). We report a difference of 33% between the largest (Cmpd3) and smallest (Cmpd2) wound areas at the commencement of the assay.
The wound closure rate is the most common metric of the scratch wound assay. One output is the t1/2 of the wound area, which is determined by applying a linear fit to the normalised wound area time dependence from t=0 up to the time at which the area drops to 50%. Whilst this is an important metric, this output is influenced by the wound starting area (Figure 2), with larger or smaller wound areas taking longer or shorter periods to reach t1/2, respectively. We observed reduced t1/2 values for Cmpd1 and 2 relative to controls, whilst the t1/2 was increased for Cmpd3 (Figure 4). Since the starting wound areas across each treatment condition were not equal the validity of outcomes requires further scrutiny.
Another common output for a scratch wound assay is to report relative change in cell confluence, and by proxy, wound closure rate, over time. Here we report that, relative to controls, Cmpd1 and Cmpd2 induce a modest increase in relative wound closure rate (Figure 5). Cmpd3 was shown to retard the relative wound closure rate relative to untreated controls (Figure 5). The wound closure rate values can be utilised to gather further cell migration information in the form of Collective Migration, which is calculated by (Wound closure rate(slope))/(2 x wound length (µm)).
Collective migration calculations do not require the starting wound area values and are therefore more informative than t1/2 if starting wound areas are not equal. Collective migration was shown to be enhanced in Cmpd1 and Cmpd2 groups, relative to untreated controls, whilst the there was a small decrease in the Cmpd3 group (Figure 6). Comparing to the t1/2 values we can now observe a subtly higher collective migration for Cmpd 1 treated cells with respective to Cmpd 2 treated group.
Single Cell Metrics
Robust cell segmentation enables single cell migration metrics to be reported, which can reveal trends that would be otherwise undetectable by examining wound closure rate alone. By tracking individual A2780 cells we observed an increase in median cell migration speed in Cmpd1 and Cmpd2 groups, relative to untreated controls (Figure 7) explaining the observed faster wound closure. Comparable median cell speed was observed between Cmpd3 and controls (Figure 7).
The direction of migration is an important metric8 that is enabled through single cell tracking. We observed that Cmpd1 treated cells migrated into the wound space more directly than untreated controls (Figure 8). Cmpd2 treated cells exhibited a similar directionality profile to controls. The increased directionality of Cmpd1 relative to Cmpd2 cells explains the relative difference in closure rates between these two treatments – the Cmpd1 treated cells move to close the wound more directly than Cmpd2 cells (Figure 7).
Although exhibiting comparable median cell migration speed, the pattern of migration was slightly more direct in the control group relative to Cmpd3 with more cells moving directly into the wound which may contribute to the difference in closure rate between these groups.
Cell proliferation may also have contributed to the differences in wound closure rate. Since quantifying cell proliferation within the wound space is challenging using existing techniques the impact of cell proliferation may be controlled through the introduction of mitotic inhibitors such as Mitomycin-C11. We sought to explore the impact of cell proliferation on wound closure rate between the treatment groups through the calculation of Mitotic Index by enumerating the number of cells exhibiting a mitotic signature, over time. It was revealed that the percentage of cells undergoing cell division was greater in the controls relative to the treatment groups (Figure 9).
Collectively, these outcomes suggest that the differences in wound closure rate between Cmpd3 and controls is likely to be attributed to both a reduced directionality and baseline proliferation.
Figure 1: Representative Quantitative Phase Images of A2780 cells with segmentation masks (Top) and individual cell tracks over 24 hours (Bottom). Scale bar = 200µm.
Click on image to expand
Figure 2: Representative image of the Scratch Wound Dashboard. Image analysis, cell segmentation and tracking data across multiple wells are automatically curated into tangible cell migration metrics.
Click on image to expand
Figure 3: Histogram representing the initial wound area for each treatment condition at time point zero.
Figure 4. Histogram representing time required to reduce the wound are by 50% or T1/2.
Figure 5: Line plot representing relative change in wound area size as a function of time.
Figure 6: Histogram representing collective migration of A2780 cells over 5 hours under each treatment condition.
Figure 7: Median migratory speed of A7280 cells over 5 hours under each treatment condition.
Figure 8: Plots depicting the directionality of A7280 cells over 5 hours under each treatment condition.
Figure 9: Histogram plot showing the mean number of mitotic events within the wound area FOV.
Monitoring changes in wound closure rate through confluence measurements alone is highly susceptible to the initial assay set up conditions. Inconsistent wound area size, cell proliferation and cell density outside the wound area have been shown to impact wound closure rate8-11.
Here we demonstrate that single cell analysis through automated tracking provides advanced scratch wound metrics in the form of cell speed, directionality and mitotic index that can address the shortcomings highlighted above and assist in creating a more comprehensive model of cell migration.
In summary, Livecyte’s QPI imaging mode and Analyse software can be used to automatically extract single cell migration and proliferation data, label-free, whilst avoiding the limitations of confluence-only measurements and data variability created by manual tracking and analysis. Livecyte provides an intuitive workflow that enables users to enhance the standard scratch wound assay to reveal subtle changes in migration patterns and to reliably examine the pathways involved in regulating cell migration.
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