Quantification of cancer cell migration with an integrated experimental-computational pipeline

Cell culture

MDA-MB-231 cell line was obtained from the American Type Culture Collection (ATCC, No. HTB-26) and tested for mycoplasma contamination before usage. Cells were maintained in DMEM media (Thermo Fisher, No. 21063045), supplemented with 10% fetal bovine serum (FBS), they were kept at 37°C in a humidified incubator with 5% CO₂. Cells were used within 6 months of purchase.

Cell migration assay

The Oris™ migration assay (No. CMA1.101, Platypus Technologies, Madison, WI, USA) uses a physical barrier “stopper” to create a defined circular region that is intended to prevent cell adhesion at the start of the assay. This central cell-free detection zone is in the center of each well of a 96-well plate. As the cells migrate to the cell-free zone over 24–48 hours, real-time assessment of migratory cells allows acquisition of richer data sets. Since there are no artificial membranes or inserts in the light path through which cells must pass, this assay is amenable to quantification with microscopy. We used the Oris cell migration assay from Platypus Technologies to create migration regions by inserting stoppers in each of the 96 wells on a plate. Shortly after inserting the stoppers, we seeded MDA-MB-231 cells and waited until they reached 80% confluent (approximately 24 hours). We then fed cells with either treated or untreated media. Treated media included Taxol (5 nM), mouse anti-AGR2 antibody (1:50 of 2 μg/ml) (Santa Cruz Biotechnology, No. sc-101211), and mouse-IgG (Santa Cruz Biotechnology, No. sc-2005), while the untreated media was Dimethyl sulfoxide (DMSO) (VWR, No. 97061-250). Next we removed the stoppers and allowed the cells to move into the migration region. 48 hours after removal of the stoppers, we imaged each well using a fluorescence microscope (Zeoiss Observer.Z1 microscope at 2.5x objective with AxioCam MRm camera and Axio Vision 4.8 software).

Implementation

This tool reads microscopy images which are placed in the same folder as the main code. First, the script first trains using the negative control (i.e., the image of a well where the stopper was not removed) to find the optimal disk which represents the migration region. Next, the script finds all the images present in the same folder as the code (by default it looks for TIF files) and applies the migration quantification metric to each of them, recording its output in a text file called output_c3.txt for easy access and plotting (a Python 3 script which creates a publication-quality plot based on this output is provided as well for any user who wishes to use it).

Operation

Typically a user only needs to make one modification the main script called code.m, make one minor modification to the img_name variable (on the third line of the script), and run the script. The change to the img_name variable needs to reflect the name of the image which contains the negative control.

If the user desires change the format of the images the script uses for the quantification, the line file_list= dir('* .tif'); needs to change to reflect the desired format. The images need to be in the same directory as the script.

This tool has been tested in multiple laptops running Matlab spanning releases 2015a–2018a.

Automatic selection of migration region

We first created a mask corresponding to the area covered by cells using standard deviation filtering and applying a series of morphological operations in Matlab R2016a (MathWorks) as shown in Figure 1.

Figure 1. Sample images immediately after the stopper was removed.

In order to identify the migration region, we took images of each well (left), then we select a mask that covers the area utilized by cells, highlighted in green (right).

Note that these images are gray scale (green is used throughout to highlight software outputs as is shown in the right panel of Figure 1), hence every pixel’s value belongs to the interval [0,1] where a completely black pixel has value 0 and a completely white pixel has value 1. Also note that a mask is a binary matrix that indicate which pixels belong to the mask (with value of 1, these pixels are referred to as “cell pixels”) and which pixels do not belong to the mask (with value 0).

We then used a genetic algorithm to determine the coordinates of the center and the radius of a circle according to Equation (1). This optimal circle determines the migration region.

where $c_x^{*},c_y^{*},$ and r^* are the optimal parameters of the migration region, M is the Matlab mask we are evaluating (i.e., a circle with center at coordinates (c_x, c_y) and radius r), so ${\displaystyle {\sum }_{m_{i,j}\in \mathit{M}}{m}_{i,j}}$ is the sum of all the pixel intensities (m_i,j) which belong to the mask M, #M is the cardinality of M (i.e., the number of pixels which belong to M), and p is a penalty parameter. If p = 1, we have:

Hence, the maximization problem from Equation (1) is equivalent to the minimization represented in Equation (2) (when p = 1). From Equation (2), we can interpret the optimization performed by the genetic algorithm as finding “the largest circle which contains the least number of cell pixels.” Figure 2 shows the optimal circular region selected by the genetic algorithm when the input is the image from Figure 1.

Figure 2. Automatically-selected migration region.

The genetic algorithm selects the largest circle which contains the least number of masked pixels from the cell area mask. This optimal circle is the migration region.

Percent of migration region covered by cells: We defined a metric to quantify the migration of MDA-MB-231 cells. This metric is Q, the percentage of migration pixels inside the migration region. We define a migration pixel as any pixel whose intensity value is greater than or equal to a threshold T. We chose T equal to 1.25 times the median pixel intensity of the migration region immediately after the stopper was removed (i.e., the green region in Figure 2). This is:

Where M^* is the optimal circle defined by the three parameters ($\left[c_x^{*},c_y^{*},r^{*}\right]$ from Equation (1) and Equation (2)) and the set {M^* ≥ T} includes all of the pixels inside M^* with intensities higher than T .

Preprint

A previous version of this manuscript is available from bioRxiv: https://doi.org/10.1101/130526⁷