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33 results on '"Mugnano, Martina"'

1. Towards label-free liquid biopsy: combining machine learning and tomographic phase imaging flow cytometry for the identification of tumor cells

Author

Popp, Jürgen, Gergely, Csilla, Pirone, Daniele, Montella, Annalaura, Cavina, Beatrice, Giugliano, Giusy, Schiavo, Michela, Mugnano, Martina, Cerbone, Vincenza, Scalia, Giulia, Porcelli, Anna Maria, Gasparre, Giuseppe, Kurelac, Ivana, Bianco, Vittorio, Miccio, Lisa, Capasso, Mario, Iolascon, Achille, Maffettone, Pier Luca, Memmolo, Pasquale, and Ferraro, Pietro

Published
2024
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5. Label-free 3D visualization and quantification of endogenous and exogenous intracellular particles in single cells by holographic flow tomography

Author

Shaked, Natan T., Hayden, Oliver, Pirone, Daniele, Mugnano, Martina, Sirico, Daniele, Miccio, Lisa, Bianco, Vittorio, Merola, Francesco, del Giudice, Danila, Lama, Giuseppe Cesare, Castaldo, Rachele, Grilli, Simonetta, Pasquinelli, Giannandrea, Valente, Sabrina, Lemma, Silvia, Iommarini, Luisa, Kurelac, Ivana, Memmolo, Pasquale, and Ferraro, Pietro

Published
2023
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9. Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry

Author

Pirone, Daniele, Lim, Joowon, Merola, Francesco, Miccio, Lisa, Mugnano, Martina, Bianco, Vittorio, Cimmino, Flora, Visconte, Feliciano, Montella, Annalaura, Capasso, Mario, Iolascon, Achille, Memmolo, Pasquale, Psaltis, Demetri, and Ferraro, Pietro

Abstract

Quantitative phase imaging has gained popularity in bioimaging because it can avoid the need for cell staining, which, in some cases, is difficult or impossible. However, as a result, quantitative phase imaging does not provide the labelling of various specific intracellular structures. Here we show a novel computational segmentation method based on statistical inference that makes it possible for quantitative phase imaging techniques to identify the cell nucleus. We demonstrate the approach with refractive index tomograms of stain-free cells reconstructed using tomographic phase microscopy in the flow cytometry mode. In particular, by means of numerical simulations and two cancer cell lines, we demonstrate that the nucleus can be accurately distinguished within the stain-free tomograms. We show that our experimental results are consistent with confocal fluorescence microscopy data and microfluidic cyto-fluorimeter outputs. This is a remarkable step towards directly extracting specific three-dimensional intracellular structures from the phase contrast data in a typical flow cytometry configuration.

Published
2022
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24. Toward the specificity in QPI 3D tomographic cell flow cytometry holography: recent achievements and perspectives in biomedical sciences

Author

Liu, Yang, Park, YongKeun, Pirone, Daniele, Lim, Joowon, Merola, Francesco, Miccio, Lisa, Mugnano, Martina, Bianco, Vittorio, Valentino, Marika, Giugliano, Giusy, Cimmino, Flora, Visconte, Feliciano, Montella, Annalaura, Capasso, Mario, Iolascon, Achille, Memmolo, Pasquale, Psaltis, Demetri, and Ferraro, Pietro

Published
2023
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25. A pin-based pyro-electrohydrodynamic jet sensor for tuning the accumulation of biomolecules down to sub-picogram level detection

Author

Itri, Simona, del Giudice, Danila, Mugnano, Martina, Tkachenko, Volodymyr, Uusitalo, Sanna, Kokkonen, Annukka, Päkkilä, Inka, Ottevaere, Heidi, Nie, Yunfeng, Mazzon, Emanuela, Gugliandolo, Agnese, Ferraro, Pietro, and Grilli, Simonetta

Abstract

The detection of low abundant biomarkers is of great interest in different clinical cases, such as the early diagnosis of Alzheimer's disease. Here we present what we call pin-based pyro-electrohydrodynamic jet (p-jet) sensor that can linearly tune the accumulation rate of biomolecules in tiny droplets. In this newly revealed pin-based configuration we demonstrate the ability to detect biomolecules down to sub-picogram concentration. Two different fluorescent probes at a varying number of accumulated droplets, up to 50, have been used to investigate the p-jet sensor. Moreover, a comparative study with a commercial piezo-driven bio-spotter has been performed. The spots of biomolecules exhibit high reproducibility by achieving a coefficient of variation <10%, both in diameter and fluorescence intensity, respectively. Remarkably, the spot intensity increases linearly with the number of accumulated droplets, thus showing the effective inherent additive functionality of this technique. Such accumulation effect allowed us to reach a limit of detection down to sub-picogram level. The p-jet sensor was applied to detect the Aβ1–42 protein, a typical Alzheimer's disease biomarker, through an immunoreaction protocol carried out on the p-jet spots. The results demonstrate the compatibility of the p-jet with immunoassay procedures, opening the route to the development of a highly sensitive device able to detect low abundant biomarkers for early diagnosis applications.

Published
2022
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28. SensApp: a FET-open project for developing a supersensor able to detect Alzheimer's disease biomarkers in blood

Author

Ferraro, Pietro, Grilli, Simonetta, Ritsch-Marte, Monika, Hitzenberger, Christoph K., Rega, Romina, Mugnano, Martina, del Giudice, Danila, Itri, Simona, Tkachenko, Volodymyr, Vespini, Veronica, Coppola, Sara, Ferraro, Pietro, Ottevaere, Heidi, Nie, Yunfeng, Uusitalo, Sanna, Schwoediauer, Reinhard, Kaltenbrunner, Martin, Känsäkoski, Markku, Mazzon, Emanuela, Maffettone, Pier Luca, D'Avino, Gaetano, and Grilli, Simonetta

Published
2021
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29. Three-Dimensional Quantitative Intracellular Visualization of Graphene Oxide Nanoparticles by Tomographic Flow Cytometry

Author

Pirone, Daniele, Mugnano, Martina, Memmolo, Pasquale, Merola, Francesco, Lama, Giuseppe Cesare, Castaldo, Rachele, Miccio, Lisa, Bianco, Vittorio, Grilli, Simonetta, and Ferraro, Pietro

Abstract

Interaction of nanoparticles (NPs) with cells is of fundamental importance in biology and biomedical sciences. NPs can be taken up by cells, thus interacting with their intracellular elements, modifying the life cycle pathways, and possibly inducing death. Therefore, there is a great interest in understanding and visualizing the process of cellular uptake itself or even secondary effects, for example, toxicity. Nowadays, no method is reported yet in which 3D imaging of NPs distribution can be achieved for suspended cells in flow-cytometry. Here we show that, by means of label-free tomographic flow-cytometry, it is possible to obtain full 3D quantitative spatial distribution of nanographene oxide (nGO) inside each single flowing cell. This can allow the setting of a class of biomarkers that characterize the 3D spatial intracellular deployment of nGO or other NPs clusters, thus opening the route for quantitative descriptions to discover new insights in the realm of NP–cell interactions.

Published
2021
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30. Cover Picture: Perspectives on liquid biopsy for label‐free detection of “circulating tumor cells” through intelligent lab‐on‐chips (View 3/2020)

Author

Miccio, Lisa, Cimmino, Flora, Kurelac, Ivana, Villone, Massimiliano M., Bianco, Vittorio, Memmolo, Pasquale, Merola, Francesco, Mugnano, Martina, Capasso, Mario, Iolascon, Achille, Maffettone, Pier Luca, and Ferraro, Pietro

Abstract

In article number 20200034, Pietro Ferraro and co‐workers emphasize that early detection in cancer diseases is a key issue. The current state of the art of technology could allow a great opportunity for this purpose, to develop systems for detecting circulating cancer cells (CTC) in the blood. The challenge is to recognize and classify cancer cells and detect them non‐invasively through a lab‐on‐chip device as they flow. Single‐cell holographic phase contrast tomography offers the possibility in the real world to detect CTC in a label‐free mode by means of morphological analyzes when combined with artificial intelligence (AI). The cover summarizes and depicts the main concepts and the technological ingredients necessary to achieve such an ambitious goal, namely the recognition and sorting of cells, 3D‐imaging by microscope and complete morphological analysis of 3D‐phase‐contrast tomograms by AI tools.

Published
2020
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31. Perspectives on liquid biopsy for label‐free detection of “circulating tumor cells” through intelligent lab‐on‐chips

Author

Miccio, Lisa, Cimmino, Flora, Kurelac, Ivana, Villone, Massimiliano M., Bianco, Vittorio, Memmolo, Pasquale, Merola, Francesco, Mugnano, Martina, Capasso, Mario, Iolascon, Achille, Maffettone, Pier Luca, and Ferraro, Pietro

Abstract

Circulating tumor cells (CTCs) are rare tumor cells released from primary, metastatic, or recurrent tumors in the peripheral blood of cancer patients. CTCs isolation from peripheral blood and their molecular characterization represent a new marker in cancer screening, a diagnostic tool called “liquid biopsy” (LB). Compared to traditional tissue biopsy that is invasive and does not reveal tumor heterogeneity, LB is noninvasive and reflects in “real‐time” tumor dynamism and drug sensitivity. In the frame of LB, a new paradigm based on single‐cell and label‐free analysis based on morphological analysis is emerging. Here, we review the latest research developments in this emerging vision of LB. In particular, we survey and discuss recent improvements in microfluidics, imaging label‐free diagnosis and cell classification by artificial intelligence and how to combine them to realize an intelligent platform based on lab‐on‐chip technology. This prospect appears to open up promising and intriguing new scenarios for cancer management through single‐cell analysis that will revolutionize the future of early cancer diagnosis and therapeutic choice with disruptive impact on the society. The most promising approach to liquid biopsy has its roots in the smart integration between label‐free quantitative phase microscopy, accurate manipulation of microfluidic streams, and artificial intelligence. Lab‐on‐a‐chip devices with embedded imaging functions can now rely on robust deep learning architectures to generate accurate classification results from single‐cell analysis of blood flows. Flow engineering allows sorting and controlling the rotation of blood components, thus permitting added‐value high‐throughput tomographic inspection of circulating tumor cells.

Published
2020
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