Non-Destructive Evaluation of Regional Cell Density Within Tumor Aggregates Following Drug Treatment

This protocol provides information on tumor model progression in a non-destructive manner. It also gives indications of therapeutic response through entire drug regimens within singular aggregates rather than relying on age match samples. Conventional methods for obtaining cell density and viability within aggregates require sample fixation and sectioning, while ours provides data non-destructively.

So, changes can be tracked within a single aggregate over time. This technique is expected to improve our understanding of drug response within discreet regions of aggregate models, with implications on how well these models reflect in vivo responses and drug screening applications. To begin, prepare 70 to 90%confluence cell cultures of the desired cell lines in standard conditions.

Detached cell monolayers from their culture flasks following the standard trypsinization method and add the cell suspension into a centrifuge tube. Add 10 microliters of the cell suspension to a hemocytometer and count via microscopy to determine the number of cells in the suspension. Pellet cells via centrifugation and resuspend cells in media at the desired concentration of 2.5 times 10 to the fifth cells per milliliter.

For suspensions prepared with the basement membrane matrix, remove the matrix vial from minus 20 degree Celsius storage and placed in the refrigerator to thaw overnight. Prepare a container with growth media and refrigerate for 10 minutes to chill. Using a frozen pipette tip add the matrix to chilled media such that the final concentration of this solution is 5%Add 50 microliters of this media to each well of a round bottom, non-adherent 96-well plate such that the final concentration of matrix in these wells will be 2.5%following addition of cell suspension.

For suspensions prepared without matrix, add 50 microliters of plain growth media to each well. Dispense 50 microliters of cell suspension into each well. Centrifuge the plates at 123 G for 10 minutes at room temperature immediately following seeding to ensure the collection of a cell pellet at the bottom of each well.

Utilize an OCT system for structural imaging. Set the A scan rate to 5.5 kilohertz for high resolution image collection. Set the index of refraction to 1.33 for samples in a liquid medium.

In the image parameters window on the right side of the screen, set the field of view by inputting X, Y, and Z values such that the sample is encompass within this region of interest. Click on 3D Acquisition Mode and then click on Record to collect the 3D volume scan of the sample. To create volume reconstruction, open Imaris and navigate to the converted TIF file within their arena.

Go to edit, then click Image Properties and input the voxel size from the OCT image into the corresponding XYZ boxes. Next, click on OK.Click on Add New Surfaces above the objects tree. In the menu below the tree, click on Skip Automatic Creation and edit manually.

Within the display adjustment window, manually slide the red and black arrows to enhance the contrast between the sample and the background and improve sample visualization. Adjust the slice position to the slice at one edge of the sample. Use the escape key to change the mouse from the navigation mode to the select mode and then click on Draw.

Manually trace the outline of the region displaying the signal. Advance the slice position by entering the next position into the input box. This next position should be less than or equal to 100 slices further into the sample than the prior one.

Manually trace the region displaying the signal. Repeat this step through the thickness of the sample until the opposite edge of the sample is reached. Then click on Create Surface and left menu to stitch these slices together.

Finally, click on Edit, then click Mask Selection, then click OK to complete the volume reconstruction. To obtain the sample's total cell density, select Add New Spots. In the algorithm settings menu, deselect all boxes.

Click on the blue arrow to move to the source channel screen. From the dropdown menu that appears, select the Masked Channel. Input the average cell diameter for the sample in the XY diameter box.

Ensure that background subtraction is checked. Click on the blue arrow to move to the classify spot's screen. In the graph at the bottom of the menu, click and drag the left edge of the yellow threshold to the left edge of the graph such that all objects are included in the yellow shaded threshold.

Then click on the green arrow to complete the spot creation. Obtain the number of objects identified by clicking on Statistics. Then click Overall and finally click Total Number of Spots.

Click on the surface created and navigate to the surfaces style quality tab. Change selection to Center Point and change pixel width to less than equal to 20 for best visibility. Navigate to Statistics.

Then click Detailed, followed by Position and record the location at the center spot. Select Add New Reference Frame from the menu above the object tree. Check the visible and fixed boxes next to the X, Y in the menu.

Click and drag the center of the reference frame icon such that it is in line with the center spot. Deselect the XY visible and fix boxes and select them for XZ.Once again, click and drag the center of the reference frame icon such that it is in line with the center spot. Lastly, repeat this for the YZ plane, alternating between these three fixed planes until the reference frame perfectly aligns with the center spot.

Double-click on the reference frame in the object tree and rename it center or similar. Click on the spots created and navigate to Statistics. Then click Detailed, followed by Position Reference Frame.

Click on position X reference frame until it sorts from highest to lowest value. Record the highest value to obtain the aggregates'radius. Perform manual calculations to determine additional locations of interest.

To do this, add 100 micrometers to the X center value and then use the Y and Z values of the center point to define the first location along the center axis. Select Add New Spots. Then select Skip Automatic Creation Edit Manually.

Hold the shift button on the keyboard and click anywhere on the screen to place a new spot. Input the XYZ position values for the first location of interest. Then select Add New Reference Frame and align it over the spot.

Add 100 micrometers to each sequential X location. Place the last reference frame less than equal to 50 micrometers away from the outer radius of the sample. Click on the spots created and navigate to Statistics.

In the bottom right corner of the menu, click on Export All Statistics to File and save data into a spreadsheet. Open the spreadsheet. Navigate to the distance from origin reference frame tab.

Use the mod function to numerically assign each distance to its corresponding reference frame. Filter the values in this column to work with the distances in each reference frame. For each of the reference frames, calculate the number of objects within 50 micrometers of the frame using the function COUNTIF.

The resulting value corresponds to the number of cells in that regional plug. Divide this value by the volume of the 100 micrometer plug to obtain cell density. Student's T testing revealed significantly higher cell density in the spheroid core than in the transitional and outer layers for MDA-MB-231 spheroid models.

The result indicates compaction in this spheroid core after four days. Within both AU565 and MDA-MB-231 aggregate models, the addition of the matrix does not appear to affect the volume or cell count, and thus seems to have negligible influence on cell proliferation. Rather, matrix addition appears to redistribute cell density promoting significant core compaction and decreasing cell density in the outer layers.

These findings provide valuable insight into the physical mechanisms by which the matrix enables cell aggregation in different breast cancer cell lines. Looking next at AU565 tumors spheroid prepared with matrix, matured aggregates were treated with trastuzumab and regional cell density was evaluated through a five day drug regimen. Plugs were set every 100 micrometers throughout the thickness of the aggregates.

Minor fluctuations in cell density were observed over time within the inner 500 micrometers of each aggregate indicating minimal cell death. The visualization of cell death as indicative drug response mostly in the outer layers of tumor spheroids is consistent with drug penetration issues of trastuzumab. We can now non-destructively measure cell viability in aggregates without sacrificing structure.

This enables assessment of longitudinal drug response in singular tumor aggregates to identify temporal and penetration qualities of candidate anti-cancer drugs.