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* These authors contributed equally
This article demonstrates a standardized method for constructing three-dimensional tumor spheroids. A strategy for spheroid observation and image-based deep-learning analysis using an automated imaging system is also described.
In recent decades, in addition to monolayer-cultured cells, three-dimensional tumor spheroids have been developed as a potentially powerful tool for the evaluation of anticancer drugs. However, the conventional culture methods lack the ability to manipulate the tumor spheroids in a homogeneous manner at the three-dimensional level. To address this limitation, in this paper, we present a convenient and effective method of constructing average-sized tumor spheroids. Additionally, we describe a method of image-based analysis using artificial intelligence-based analysis software that can scan the whole plate and obtain data on three-dimensional spheroids. Several parameters were studied. By using a standard method of tumor spheroid construction and a high-throughput imaging and analysis system, the effectiveness and accuracy of drug tests performed on three-dimensional spheroids can be dramatically increased.
Cancer is one of the diseases most feared by human beings, not least because of its high mortality rate1. In recent years, the possibility of treating cancer has increased as new therapies have been introduced2,3,4,5. Two-dimensional (2D) and three-dimensional (3D) in vitro models are used to study cancer in a laboratory setting. However, 2D models cannot immediately and accurately assess all of the important parameters that indicate antitumor sensitivity; therefore, they fail to fully represent in vivo interactions in drug therapy testing6.
Since 2020, the global three-dimensional (3D) culture market has been greatly boosted. According to one report from NASDAQ OMX, the global value of the 3D cell culture market will exceed USD 2.7 billion by the end of 2025. Compared with 2D culture methods, 3D cell culturing exhibits advantageous properties, which can be optimized not only for proliferation and differentiation but also for long-term survival7,8. By such means, in vivo cellular microenvironments can be simulated to obtain more accurate tumor characterization, as well as metabolic profiling, so that genomic and protein alterations can be better understood. Due to this, 3D test systems should now be included in mainstream drug development operations, especially those with a focus on screening and evaluating novel antitumor drugs. Three-dimensional growths of immortalized established cell lines or primary cell cultures in spheroid structures possess in vivo features of tumors such as hypoxia and drug penetration, as well as cell interaction, response, and resistance, and can be regarded as a stringent and representative model for performing in vitro drug screening9,10,11.
However, these 3D culture models also suffer from several problems that may take some time to solve. Cell spheroids can be formed using these protocols, but they differ in certain details, such as culture time or embedding gels12, so these constructed cell spheroids cannot be well controlled under a restricted size range. The size of the spheroids may influence the consistency of the viability test and imaging analysis. The growth microenvironments and growth factors also vary, which may lead to different morphologies due to differences in the differentiation among cells13. There is now an obvious need for a standard, simple, and cost-effective method for constructing all types of tumors with controlled sizes.
From another perspective, although homogeneous assays and high-content imaging approaches have been developed to evaluate morphology, viability, and growth rate, the high-throughput screening of 3D models remains a challenge for various reasons reported in the literature, such as the lack of uniformity in the position, size, and morphology of tumor spheroids14,15,16.
In the protocol presented here, we list each step in the construction of 3D tumor spheroids and describe a method for spheroid observation and analysis using a high-throughput, high-content imaging system that involves auto-focus, auto-imaging, and analysis, among other advantageous characteristics. We show how this method can produce 3D tumor spheroids of uniform size that are suitable for high-throughput imaging. These spheroids also demonstrate a high sensitivity to cancer drug treatment, and morphological changes in the spheroids can be monitored using high-content imaging. In summary, we demonstrate the robustness of this methodology as a means to generate 3D tumor constructs for drug evaluation purposes.
1. Spheroid construction
2. Drug treatment
3. Spheroid viability
4. Spheroid observation and deep learning analysis through images in the drug test
Figure 1A,B shows the process used for constructing tumor spheroids in this study. We first seeded the cells in a 48-well U-bottom plate. This step is almost the same as that used in 2D cell culture. We kept the plate in a common incubator with water surrounding the wells so that the deposited cells started to form spheroids in a self-assembly process. Under normal operational conditions, most types of tumor spheroids were completely formed after 5 days when a targeted mediu...
The microenvironment plays an important role in tumor growth. It may affect the provision of extracellular matrices, oxygen gradients, nutrition, and mechanical interaction and, thus, affect gene expression, signal pathways, and many functions of tumor cells19,20,21. In many cases, 2D cells do not produce such effects or even produce opposite effects, thus affecting the evaluation of drug treatments. However, the emergence of 3D...
The authors have nothing to disclose.
We thank all the members of our laboratories for their critical input and suggestions. This research was supported by the Key Project of Jiangsu Commission of Health (K2019030). Conceptualization was conducted by C.W. and Z.C., the methodology was performed by W.H. and M.L., the investigation was performed by W.H. and M.L., the data curation was performed by W.H., Z.Z., S.X., and M.L., the original draft preparation was performed by Z.Z., J.Z., S.X., W.H., and X.L., the review and editing was performed by Z.C., project administration was performed by C.W. and Z.C., and funding acquisition was conducted by C.W. All the authors have read and agreed to the published version of the manuscript.
Name | Company | Catalog Number | Comments |
0.5-10 μL Pipette tips | AXYGEN | T-300 | |
1.5 mL Boil proof microtubes | Axygen | MCT-150-C | |
100-1000μL Pipette tips | KIRGEN | KG1313 | |
15 mL Centrifuge Tube | Nest | 601052 | |
200 μL Pipette tips | AXYGEN | T-200-Y | |
3D gel | Avatarget | MA02 | |
48-well U bottom Plate | Avatarget | P02-48UWP | |
50 mL Centrifuge Tube | Nest | 602052 | |
Alamar Blue | Thermo | DAL1100 | |
Anti-Adherence Rinsing Solution | STEMCELL | #07010 | |
Certified FBS | BI | 04-001-1ACS | |
Deionized water | aladdin | W433884-500ml | |
DMEM (Dulbecco's Modified Eagle Medium) | Gibco | 11965-092 | |
DMSO | sigma | D2650-100ML | |
Excel sofware | Microsoft office | ||
Graphpad prism sofware | GraphPad software | ||
High Content Microscope and SMART system | Avatarget | 1-I01 | |
Image J software | National Institutes of Health | ||
Insulin-Transferrin-Selenium-A Supplement (100X) | Gibco | 51300-044 | |
Parafilm | Bemis | PM-996 | |
PBS | Solarbio | P1020 | |
Penicillin/streptomycin Sol | Gibco | 15140-122 | |
RPMI 1640 | Gibco | 11875-093 | |
Scientific Fluoroskan Ascent | Thermo | Fluoroskan Ascent | |
T25 Flask | JET Biofil | TCF012050 | |
Trypsin, 0.25% (1X) | Hyclone | SH30042.01 |
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