This study was supported by NIH R01 BRG CA207725 (MB/DTC) and NIH R01 CA233188 (MB). We would like to thank AMC Pharmacy for the Trastuzumab provided for these experiments.

AU565 (HER2+) and MDA-MB-231 breast cancer cell lines were used for the present study (see Table of Materials).
1. Preparing tumor aggregates
<ol>
	<li>Prepare AU565 (HER2+) breast cancer cell growth media using Roswell Park Memorial Institute (RPMI) 1640 basal medium (+) in L-glutamine supplemented with 10% (v/v) fetal bovine serum and 1% penicillin/streptomycin (see Table of Materials).</li>
	<li>Prepare MDA-MB-231 triple-negative breast c...

<ol>
	<li>Sutherland, R., JA, M., Inch, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Growth+of+multicell+spheroids+in+tissue+culture+as+a+model+of+nodular+carcinomas.">Growth of multicell spheroids in tissue culture as a model of nodular carcinomas.</a> Journal of the National Cancer Institute. 46 (1), 113-120 (1971).</li><li>Sachs, N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+living+biobank+of+breast+cancer+organoids+captures+disease+heterogeneity.">A living biobank of breast cancer organoids captures disease heterogeneity.</a> Cell. 172 (1-2), 373-386 (2018).</li><li>Nagelkerke, A., Bussink, J., Sweep, F. C. G. J., Span, P. 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Q., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Enhancement+of+permeability+of+glycerol+with+ultrasound+in+human+normal+and+cancer+breast+tissues+in+vitro+using+optical+coherence+tomography.">Enhancement of permeability of glycerol with ultrasound in human normal and cancer breast tissues in vitro using optical coherence tomography.</a> Laser Physics Letters. 7 (5), 388-395 (2010).</li><li>Fujimoto, J., Swanson, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+development,+commercialization,+and+impact+of+optical+coherence+tomography.">The development, commercialization, and impact of optical coherence tomography.</a> Investigative Ophthalmology &amp; Visual Science. 57 (9), (2016).</li><li>Huang, Y., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optical+coherence+tomography+detects+necrotic+regions+and+volumetrically+quantifies+multicellular+tumor+spheroids.">Optical coherence tomography detects necrotic regions and volumetrically quantifies multicellular tumor spheroids.</a> Cancer Research. 77 (21), 6011-6020 (2017).</li><li>Hari, N., Patel, P., Ross, J., Hicks, K., Vanholsbeeck, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Optical+coherence+tomography+complements+confocal+microscopy+for+investigation+of+multicellular+tumour+spheroids.">Optical coherence tomography complements confocal microscopy for investigation of multicellular tumour spheroids.</a> Scientific Reports. 9 (1), 1-11 (2019).</li><li>El-Sadek, I. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Three-dimensional+dynamics+optical+coherence+tomography+for+tumor+spheroid+evaluation.">Three-dimensional dynamics optical coherence tomography for tumor spheroid evaluation.</a> Biomedical Optics Express. 12 (11), 6844 (2021).</li><li>Kingsley, D. 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High throughput screening format identifies synthetic mimics of matrigel for tubulogenesis screening Available from: <a target="_blank" href="https://abstracts.biomaterials.org/data/papers/2015/abstracts/547.pdf">https://abstracts.biomaterials.org/data/papers/2015/abstracts/547.pdf</a> (2015)</li><li>Duchnowska, R., Szczylik, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Central+nervous+system+metastases+in+breast+cancer+patients+administered+trastuzumab.">Central nervous system metastases in breast cancer patients administered trastuzumab.</a> Cancer Treatment Reviews. 31 (4), 312-318 (2005).</li><li>Zazo, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Generation,+characterization,+and+maintenance+of+trastuzumab-resistant+HER2++breast+cancer+cell+lines.">Generation, characterization, and maintenance of trastuzumab-resistant HER2+ breast cancer cell lines.</a> American Journal of Cancer Research. 6 (11), 2661-2678 (2016).</li></ol>

The authors have nothing to disclose.

Significance 
Multicellular tumor spheroids (MCTSs) are powerful 3D in vitro models for studying tumor progression and drug screening1,2,3. Advancing the utility of these relatively simple aggregate models relies heavily on the characterization of their key features, such as morphology and cell density, which are known to influence both tumor model progression and therapeutic response. However, the...

Researchers have largely turned to benchtop 3D culture in vitro systems to study some of the key features of tumor progression. Much of this research has been led by the re-emergence of multicellular tumor spheroids (MCTSs) and more complex organoids1,2. Although these models are avascular, they provide a powerful tool for recapitulating physiological and pathological processes that occur in vivo3,4,5. In particular, medium-sized models (300-500 &#181;m diameter) can mimic key tumor features such as 3D structure, pathophysiological gradients, and metastatic signaling due to hypoxia within the core. It is well documented that these models display the characteristic concentric layers seen in vascularized in vivo tumors, namely, an outer layer of proliferative cells, a transitional layer of senescent/quiescent cells, and cells experiencing hypoxia in the core3,6,7,8,9.&#160;Unique insight can be gained from these models by characterizing cell behavior within these layers, during development and in response to the drug. However, the requisite MCTS size, necessary to develop the gradients that make them such powerful in vitro models, drastically limits the tools used for non-destructive assessment. Indeed, one of the biggest challenges with non-destructive analysis of MTCSs is quantifying cell-scale details. Bright-field and phase-contrast microscopy are routinely utilized to assess 3D MCTSs growth and development non-destructively. However, these modalities are limited to 2D projections, lacking the capacity to visualize the crucial 3D structure of these models10,11,12,13. Information on cytotoxicity and cell proliferation is typically collected through fluorescent imaging (i.e., light-sheet microscopy, confocal microscopy) or ex vivo immunohistological staining14,15,16. While these approaches provide valuable, high-resolution information on tissue structure, cellular density, and cellular function, they often require sample preparation such as optical clearing, fixing/staining, or embedding that prevents longitudinal analyses.
Optical Coherence Tomography (OCT) is a non-destructive structural imaging modality that has the potential to overcome some of the challenges mentioned above. It boasts cellular resolution and a sufficiently wide field of view (up to 10 mm x 10 mm) capable of visualizing entire multicellular aggregates17,18,19. Importantly, due to the visible nature of the light used, this technique is completely non-destructive and label-free17. Also, samples can be imaged in situ without requiring sample preparation, such that samples can be taken straight from the incubator, quickly scanned with OCT (scan duration ~5-10 min), then returned to the incubator, enabling longitudinal characterization. Many studies seeking to use OCT to analyze tumor spheroid behavior have recently emerged. In one of the most exciting demonstrations, Huang et al. used OCT to non-destructively detect necrotic cores within large tumor spheroid models, noting that live and dead cell regions possess discernible differences in optical attenuation, which may be utilized for label-free viability monitoring20. Similarly, Hari et al. conducted refractive index (RI) measurements of human colon cancer (HCT116) spheroids imaged with OCT to study the presence of hypoxia within the samples21. Their measurements were not sufficient for direct inferences, though they did observe lower RI in locations that correlated with the site, though not size, of necrotic cores, later identified via confocal microscopy. Abd El-Sadek et al. used OCT to visualize and quantify regional tissue viability of breast cancer tumor models22. They reported two OCT-based methods for visualizing tissue dynamics and showed a moderate correlation between differences in these metrics and microscopy-identified regions of live/dead cells.
Our published work using OCT built upon this prior literature to establish a quantitative, non-destructive approach to measure the 3D morphology and cell count within MCTSs breast cancer models during development10,23. Using Imaris 3D rendering image analysis software to count the number of cell-sized objects (i.e., spots) imaged within the OCT volume scans, the cell counts were non-destructively measured in MCTSs that were statistically similar to those determined via hemocytometer upon aggregate dissociation. However, due to the structural nature of OCT, cell membranes still present after cell death by necrosis may be erroneously counted as live cells. Furthermore, this characterization was extended to non-destructively track cell viability within individual aggregates subjected to a drug regimen with promising success10. Importantly, it was noted that similar cell viability was reported from our OCT-Imaris approach with what was benchmarked within these samples upon dissociation. This non-destructive and label-free cell approach enables cells to be counted within 3D constructs and dense aggregates longitudinally without sacrificing the construct/aggregate structure.
The present work reports an improved approach to directly quantify regional cell density within dense aggregates by leveraging the ability of OCT-Imaris to measure both 3D aggregate morphology and cell number. This methodological advancement provides a more detailed picture of cells&#39; spatial distribution and proliferation within the characteristic concentric layers of MCTSs models. Rather than simply calculating an overall average aggregate cell density, such local density measurements can reveal cell density gradients, such as those associated with compaction. This regional assessment is also applied to aggregates treated with a chemotherapeutic to assess regional drug response, as measured by changes in local cell density. This combination of OCT and advanced imaging analysis methods provide quantification of regional cell viability, which may be used to explore drug penetration based on which regions experience decreases in cell density. This is the first report to non-destructively quantify regional cell density and viability in response to the drug within dense cellular tissues and measure it longitudinally. Such characterization of three-dimensional cell density and spatial distribution throughout entire MCTSs may help optimize drug delivery in cancer treatment and improve the understanding of cancer model progression.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>96 well plates</td>
			<td>Greiner Bio-One&#160;</td>
			<td>650970</td>
			<td>CellStar Cell-Repellent Surface, https://shop.gbo.com/en/usa/products/bioscience/cell-culture-products/cellstar-cell-repellent-surface/</td>
		</tr>
		<tr>
			<td>0.25% trypsin, 2.21 mM EDTA</td>
			<td>Corning</td>
			<td>25-053-CI</td>
			<td></td>
		</tr>
		<tr>
			<td>AU565 breast cancer cells</td>
			<td>ATCC</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Dulbecco&#39;s Modified Eagle&#39;s Medium</td>
			<td>Corning</td>
			<td>10-013-CV</td>
			<td></td>
		</tr>
		<tr>
			<td>Fetal Bovine Serum</td>
			<td>ATCC</td>
			<td>30-2020</td>
			<td></td>
		</tr>
		<tr>
			<td>FIJI software</td>
			<td>open-source</td>
			<td>(Fiji Is Just) ImageJ v2.1/1.5.3j</td>
			<td>Downloaded from https://imagej.net/software/fiji/</td>
		</tr>
		<tr>
			<td>Hemocytometer</td>
			<td>Fisher Scientific</td>
			<td>0267151B</td>
			<td></td>
		</tr>
		<tr>
			<td>Imaris image analysis software</td>
			<td>Bitplane</td>
			<td></td>
			<td>Current version 9.8</td>
		</tr>
		<tr>
			<td>L-glutamine</td>
			<td>Lonza</td>
			<td>17-605E</td>
			<td></td>
		</tr>
		<tr>
			<td>Matrigel</td>
			<td>Corning</td>
			<td>354263</td>
			<td></td>
		</tr>
		<tr>
			<td>MDA-MB-231 breast cancer cells</td>
			<td>ATCC</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Microscope</td>
			<td>Zeiss</td>
			<td>Z1 AxioVision</td>
			<td></td>
		</tr>
		<tr>
			<td>Penicilin streptomycin</td>
			<td>Corning</td>
			<td>30-0002CI</td>
			<td></td>
		</tr>
		<tr>
			<td>Plate centrifuge</td>
			<td>Eppendorf</td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>RPMI medium 1640</td>
			<td>Gibco</td>
			<td>11875-085</td>
			<td></td>
		</tr>
		<tr>
			<td>Spectral Domain Optical Coherence Tomography</td>
			<td>ThorLabs</td>
			<td>TEL220C1</td>
			<td></td>
		</tr>
		<tr>
			<td>T75 cell culture flasks</td>
			<td>Greiner Bio-One&#160;</td>
			<td>658175</td>
			<td></td>
		</tr>
		<tr>
			<td>Trastuzumab</td>
			<td></td>
			<td></td>
			<td>Remnant clinical samples of Trastuzumab were used in this study, generously gifted by the Albany Medical College Pharmacy.&#160;</td>
		</tr>
	</tbody>
</table>

non-destructive evaluation of regional cell density within tumor aggregates following drug treatment

Multicellular tumor spheroid (MCTSs) models have demonstrated increasing utility for in vitro study of cancer progression and drug discovery. These relatively simple avascular constructs mimic key aspects of in vivo tumors, such as 3D structure and pathophysiological gradients. MCTSs models can provide insights into cancer cell behavior during spheroid development and in response to drugs; however, their requisite size drastically limits the tools used for non-destructive assessment. Optical Coherence Tomography structural imaging and Imaris 3D analysis software are explored for rapid, non-destructive, and label-free measurement of regional cell density within MCTSs. This approach is utilized to assess MCTSs over a 4-day maturation period and throughout an extended 5-day treatment with Trastuzumab, a clinically relevant anti-HER2 drug. Briefly, AU565 HER2+ breast cancer MCTSs were created via liquid overlay with or without the addition of Matrigel (a basement membrane matrix) to explore aggregates of different morphologies (thicker, disk-like 2.5D aggregates or flat 2D aggregates, respectively). Cell density within the outer region, transitional region, and inner core was characterized in matured MCTSs, revealing a cell-density gradient with higher cell densities in core regions compared to outer layers. The matrix addition redistributed cell density and enhanced this gradient, decreasing outer zone density and increasing cell compaction in the cores. Cell density was quantified following drug treatment (0 h, 24 h, 5 days) within progressively deeper 100 &#181;m zones to assess potential regional differences in drug response. By the final timepoint, nearly all cell death appeared to be constrained to the outer 200 &#181;m of each aggregate, while cells deeper in the aggregate appeared largely unaffected, illustrating regional differences in the drug response, possibly due to limitations in drug penetration. The current protocol provides a unique technique to non-destructively quantify regional cell density within dense cellular tissues and measure it longitudinally.

In a prior publication, a method was established for the non-destructive measurement of global cell density within cellular aggregates using OCT10. Herein, this technique is extended to assess the regional cell density of developing cell aggregates. Figure 1 shows a schematic of this extension, where cell density can be evaluated in concentric layers of a spheroid or more locally by looking instead at small (100 &#181;m diameter) spherical plugs, denoted by the yellow...

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

The present protocol develops an image-based technique for rapid, non-destructive, and label-free regional cell density and viability measurement within 3D tumor aggregates. Findings revealed a cell-density gradient, with higher cell densities in core regions than outer layers in developing aggregates and predominantly peripheral cell death in HER2+ aggregates treated with Trastuzumab.

Multicellular tumor spheroid (MCTSs) models have demonstrated increasing utility for in vitro study of cancer progression and drug discovery. These ...

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.

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Cancer Research

2.1K 조회수.  Rensselaer Polytechnic Institute. 이 프로토콜은 비파괴적인 방식으로 종양 모델 진행에 대한 정보를 제공한다. 또한 연령 일치 샘플에 의존하기보다는 단일 집계 내에서 전체 약물 요법을 통해 치료 반응의 징후를 제공합니다. 응집체 내에서 세포 밀도와 생존력을 얻는 기존의 방법은 샘플 고정 및 절편이 필요하지만 비파괴적으로 데이터를 제공합니다.따라서 변경 사항은 시간이 지남에 따라 단일 집?...