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Chiang CC, Anne R, Chawla P, Shaw RM, He S, Rock EC, Zhou M, Cheng J, Gong YN, Chen YC. Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics. LAB ON A CHIP 2024; 24:3169-3182. [PMID: 38804084 PMCID: PMC11165951 DOI: 10.1039/d4lc00197d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 05/22/2024] [Indexed: 05/29/2024]
Abstract
Despite recent advances in cancer treatment, refining therapeutic agents remains a critical task for oncologists. Precise evaluation of drug effectiveness necessitates the use of 3D cell culture instead of traditional 2D monolayers. Microfluidic platforms have enabled high-throughput drug screening with 3D models, but current viability assays for 3D cancer spheroids have limitations in reliability and cytotoxicity. This study introduces a deep learning model for non-destructive, label-free viability estimation based on phase-contrast images, providing a cost-effective, high-throughput solution for continuous spheroid monitoring in microfluidics. Microfluidic technology facilitated the creation of a high-throughput cancer spheroid platform with approximately 12 000 spheroids per chip for drug screening. Validation involved tests with eight conventional chemotherapeutic drugs, revealing a strong correlation between viability assessed via LIVE/DEAD staining and phase-contrast morphology. Extending the model's application to novel compounds and cell lines not in the training dataset yielded promising results, implying the potential for a universal viability estimation model. Experiments with an alternative microscopy setup supported the model's transferability across different laboratories. Using this method, we also tracked the dynamic changes in spheroid viability during the course of drug administration. In summary, this research integrates a robust platform with high-throughput microfluidic cancer spheroid assays and deep learning-based viability estimation, with broad applicability to various cell lines, compounds, and research settings.
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Affiliation(s)
- Chun-Cheng Chiang
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA.
- Department of Computational and Systems Biology, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15260, USA
| | - Rajiv Anne
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA.
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA 15260, USA
| | - Pooja Chawla
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA 15260, USA
| | - Rachel M Shaw
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA 15260, USA
| | - Sarah He
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA.
- Carnegie Mellon University, Department of Biological Sciences, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA
| | - Edwin C Rock
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA 15260, USA
| | - Mengli Zhou
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA.
- Department of Computational and Systems Biology, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15260, USA
- Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Jinxiong Cheng
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA.
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA 15260, USA
| | - Yi-Nan Gong
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA.
- Department of Immunology, University of Pittsburgh School of Medicine, 3420 Forbes Avenue, Pittsburgh, PA, 15260, USA
| | - Yu-Chih Chen
- UPMC Hillman Cancer Center, University of Pittsburgh, 5115 Centre Ave, Pittsburgh, PA 15232, USA.
- Department of Computational and Systems Biology, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15260, USA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 3700 O'Hara Street, Pittsburgh, PA 15260, USA
- CMU-Pitt Ph.D. Program in Computational Biology, University of Pittsburgh, 3420 Forbes Avenue, Pittsburgh, PA 15260, USA
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Chen Z, Han S, Kim S, Lee C, Sanny A, Tan AHM, Park S. A 3D hanging spheroid-filter plate for high-throughput drug testing and CAR T cell cytotoxicity assay. Analyst 2024; 149:475-481. [PMID: 38050728 DOI: 10.1039/d3an01904g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/06/2023]
Abstract
Tumour spheroids are widely used in immune cell cytotoxicity assays and anticancer drug testing, providing a physiologically relevant model replicating the tumour microenvironment. However, co-culture of immune and tumour cells complicates quantification of immune cell killing efficiency. We present a novel 3D hanging spheroid-filter plate that efficiently facilitates spheroid formation and separates unbound/dead cells during cytotoxicity assays. Optical imaging directly measures the cytotoxic effects of anti-cancer drugs on tumour spheroids, eliminating the need for live/dead fluorescent staining. This approach enables cost-effective evaluation of T-cell cytotoxicity with specific chimeric antigen receptors (CARs), enhancing immune cell-based assays and drug testing in three-dimensional tumour models.
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Affiliation(s)
- Zhenzhong Chen
- School of Mechanical Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Seokgyu Han
- School of Mechanical Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Sein Kim
- Department of Biomedical Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Chanyang Lee
- School of Mechanical Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
| | - Arleen Sanny
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, Centros, Singapore 138668, Republic of Singapore
| | - Andy Hee-Meng Tan
- Bioprocessing Technology Institute (BTI), Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, Centros, Singapore 138668, Republic of Singapore
| | - Sungsu Park
- School of Mechanical Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
- Department of Biomedical Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Korea
- Department of Biophysics, Institute of Quantum Biophysics (IQB), Sungkyunkwan University (SKKU), Suwon, 16419, Korea.
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