Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry
Abstract Image-based identification of circulating tumor cells in microfluidic cytometry condition is one of the most challenging perspectives in the Liquid Biopsy scenario. Here we show a machine learning-powered tomographic phase imaging flow cytometry system capable to provide high-throughput 3D...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-32110-9 |
| _version_ | 1827966346089988096 |
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| author | Daniele Pirone Annalaura Montella Daniele G. Sirico Martina Mugnano Massimiliano M. Villone Vittorio Bianco Lisa Miccio Anna Maria Porcelli Ivana Kurelac Mario Capasso Achille Iolascon Pier Luca Maffettone Pasquale Memmolo Pietro Ferraro |
| author_facet | Daniele Pirone Annalaura Montella Daniele G. Sirico Martina Mugnano Massimiliano M. Villone Vittorio Bianco Lisa Miccio Anna Maria Porcelli Ivana Kurelac Mario Capasso Achille Iolascon Pier Luca Maffettone Pasquale Memmolo Pietro Ferraro |
| author_sort | Daniele Pirone |
| collection | DOAJ |
| description | Abstract Image-based identification of circulating tumor cells in microfluidic cytometry condition is one of the most challenging perspectives in the Liquid Biopsy scenario. Here we show a machine learning-powered tomographic phase imaging flow cytometry system capable to provide high-throughput 3D phase-contrast tomograms of each single cell. In fact, we show that discrimination of tumor cells against white blood cells is potentially achievable with the aid of artificial intelligence in a label-free flow-cyto-tomography method. We propose a hierarchical machine learning decision-maker, working on a set of features calculated from the 3D tomograms of the cells’ refractive index. We prove that 3D morphological features are adequately distinctive to identify tumor cells versus the white blood cell background in the first stage and, moreover, in recognizing the tumor type at the second decision step. Proof-of-concept experiments are shown, in which two different tumor cell lines, namely neuroblastoma cancer cells and ovarian cancer cells, are used against monocytes. The reported results allow claiming the identification of tumor cells with a success rate higher than 97% and with an accuracy over 97% in discriminating between the two cancer cell types, thus opening in a near future the route to a new Liquid Biopsy tool for detecting and classifying circulating tumor cells in blood by stain-free method. |
| first_indexed | 2024-04-09T17:48:29Z |
| format | Article |
| id | doaj.art-b26503fa8e634fc5822b55c9fa8f60cc |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T17:48:29Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-b26503fa8e634fc5822b55c9fa8f60cc2023-04-16T11:11:53ZengNature PortfolioScientific Reports2045-23222023-04-0113111310.1038/s41598-023-32110-9Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometryDaniele Pirone0Annalaura Montella1Daniele G. Sirico2Martina Mugnano3Massimiliano M. Villone4Vittorio Bianco5Lisa Miccio6Anna Maria Porcelli7Ivana Kurelac8Mario Capasso9Achille Iolascon10Pier Luca Maffettone11Pasquale Memmolo12Pietro Ferraro13CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “Eduardo Caianiello”CEINGE Advanced BiotechnologiesCNR-ISASI, Institute of Applied Sciences and Intelligent Systems “Eduardo Caianiello”Department of Chemical, Materials and Production Engineering, DICMaPI, University of Naples “Federico II”Department of Chemical, Materials and Production Engineering, DICMaPI, University of Naples “Federico II”CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “Eduardo Caianiello”CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “Eduardo Caianiello”Department of Pharmacy and Biotechnology (FABIT), University of BolognaCentre for Applied Biomedical Research (CRBA), University of BolognaCEINGE Advanced BiotechnologiesCEINGE Advanced BiotechnologiesDepartment of Chemical, Materials and Production Engineering, DICMaPI, University of Naples “Federico II”CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “Eduardo Caianiello”CNR-ISASI, Institute of Applied Sciences and Intelligent Systems “Eduardo Caianiello”Abstract Image-based identification of circulating tumor cells in microfluidic cytometry condition is one of the most challenging perspectives in the Liquid Biopsy scenario. Here we show a machine learning-powered tomographic phase imaging flow cytometry system capable to provide high-throughput 3D phase-contrast tomograms of each single cell. In fact, we show that discrimination of tumor cells against white blood cells is potentially achievable with the aid of artificial intelligence in a label-free flow-cyto-tomography method. We propose a hierarchical machine learning decision-maker, working on a set of features calculated from the 3D tomograms of the cells’ refractive index. We prove that 3D morphological features are adequately distinctive to identify tumor cells versus the white blood cell background in the first stage and, moreover, in recognizing the tumor type at the second decision step. Proof-of-concept experiments are shown, in which two different tumor cell lines, namely neuroblastoma cancer cells and ovarian cancer cells, are used against monocytes. The reported results allow claiming the identification of tumor cells with a success rate higher than 97% and with an accuracy over 97% in discriminating between the two cancer cell types, thus opening in a near future the route to a new Liquid Biopsy tool for detecting and classifying circulating tumor cells in blood by stain-free method.https://doi.org/10.1038/s41598-023-32110-9 |
| spellingShingle | Daniele Pirone Annalaura Montella Daniele G. Sirico Martina Mugnano Massimiliano M. Villone Vittorio Bianco Lisa Miccio Anna Maria Porcelli Ivana Kurelac Mario Capasso Achille Iolascon Pier Luca Maffettone Pasquale Memmolo Pietro Ferraro Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry Scientific Reports |
| title | Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry |
| title_full | Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry |
| title_fullStr | Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry |
| title_full_unstemmed | Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry |
| title_short | Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry |
| title_sort | label free liquid biopsy through the identification of tumor cells by machine learning powered tomographic phase imaging flow cytometry |
| url | https://doi.org/10.1038/s41598-023-32110-9 |
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