Description
Nowadays, cancer research is mainly based on drug discovery and High-Content Screening (HCS) platforms are widely used to detect drugs that decrease the tumour viability. In particular, multi-well plates with 2D cell cultures are typically used for analysing the short- and long-term effects of anti-cancer drugs. However, 2D cell cultures are just a poor representation of the complex human body. There is intense excitement in the scientific community about 3D cellular models because they promise to resemble and recapitulate the in vivo tissues more faithfully than 2D systems. HCS platform using multi-cellular spheroids would be a more reliable tool for testing anti-cancer drugs and this would also permit to limit the usage of animal models, saving time and money. The complexity connected with 3D in vitro models makes necessary a multi-level analysis, starting from the macro observation of the drug effects on the whole spheroids, to the micro analysis of the single composing cells. In fact, in vivo tumours are heterogeneous and the presence of even a few "particular" cells, for instance those typically called Cancer Stem Cells (CSCs), is one of the key determinants of tumour regeneration. Identification and analysis of the CSCs are fundamental for a more accurate evaluation of drug efficacy. A standardised procedure to first analysing the effects of drugs on the whole spheroids, and then performing single-cell screenings would give the opportunity to better understand the macro- and micro-behaviour of the different drugs. The goal of this project is to develop and validate an automated from macro to micro platform to perform 3D HCS of drugs using cancer multicellular spheroids. This includes a macro-analysis of the whole spheroids by extracting morphological features and quantifying the metabolites in the TCA cycle using LDI-mass spectrometry, and a micro-analysis of the single-cells for performing molecular and genetic profiling on specific classes.
Project Link
https://www.researchitaly.mur.gov.it/en/italy-south-korea-scientific-cooperation-the-executive-programme-for-2023-2025-approved/
Publications
- M. Bedeschi, et al. Cancer-Associated Fibroblast: Role in Prostate Cancer Progression to Metastatic Disease and Therapeutic Resistance. Cells, 12(5):802, 2023. DOI: 10.3390/cells12050802.
- M.M. Tumedei, et al. Follicular Lymphoma Microenvironment Traits Associated with Event-Free Survival. International Journal of Molecular Sciences, 24(12):9909, 2023. DOI: 10.3390/ijms24129909.
- M. Stellato, et al. Radiomic analysis of 3D spheroids using 2D brightfield images. Biomedical Signal Processing and Control, 103:107366, 2025. DOI: 10.1016/j.bspc.2024.107366.
- F. Piccinini, et al. Two-dimensional segmentation fusion tool: an extensible, free-to-use, user-friendly tool for combining different bidimensional segmentations. Frontiers in Bioengineering and Biotechnology, 12:1339723, 2024. DOI: 10.3389/fbioe.2024.1339723.
- C. Voros, et al. Correlative Fluorescence and Raman Microscopy to Define Mitotic Stages at the Single-Cell Level: Opportunities and Limitations in the AI Era. Biosensors, 13(2):187, 2023. DOI: 10.3390/bios13020187.
- F. Piccinini, et al. Deep Learning-Based Tool For Morphotypic Analysis Of 3D Multicellular Spheroids. Journal of Mechanics in Medicine and Biology, 23(6), 2023. DOI: 10.1142/S0219519423400341.
- J.S. Sung, et al. Monocarboxylate transporter-1 (MCT-1) inhibitors screened from autodisplayed FV-antibody library. International Journal of Biological Macromolecules, 265(1):130854, 2024. DOI: 10.1016/j.ijbiomac.2024.130854.
- A. Diosdi, et al. HCS-3DX, a next-generation AI-driven automated 3D high-content screening system. bioRxiv, 2024. DOI: 10.1101/2024.07.15.603536.
- I. Ferrero, et al., State of the Art and New Trends from the Second International StemNet Meeting. International Journal of Molecular Sciences, 25(4):2221, 2024. DOI: 10.3390/ijms25042221.
- J. Lee, et al. Optimization of Tumor Spheroid Preparation and Morphological Analysis for Drug Evaluation. Biochip Journal. 18:160–169, 2024. DOI: 10.1007/s13206-024-00143-5.