Pirone, Daniele (2022) Tomographic phase microscopy in flow cytometry. [Tesi di dottorato]
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| Item Type: | Tesi di dottorato |
|---|---|
| Resource language: | English |
| Title: | Tomographic phase microscopy in flow cytometry |
| Creators: | Creators Email Pirone, Daniele daniele.pirone@unina.it |
| Date: | 10 December 2022 |
| Number of Pages: | 350 |
| Institution: | Università degli Studi di Napoli Federico II |
| Department: | Ingegneria Elettrica e delle Tecnologie dell'Informazione |
| Dottorato: | Information and Communication Technology for Health |
| Ciclo di dottorato: | 35 |
| Coordinatore del Corso di dottorato: | nome email Riccio, Daniele daniele.riccio@unina.it |
| Tutor: | nome email Liseno, Angelo UNSPECIFIED Capozzoli, Amedeo UNSPECIFIED Curcio, Claudio UNSPECIFIED Ferraro, Pietro UNSPECIFIED Memmolo, Pasquale UNSPECIFIED |
| Date: | 10 December 2022 |
| Number of Pages: | 350 |
| Keywords: | Single-Cell Analysis; Digital Holography; Imaging Flow Cytometry; Tomographic Phase Microscopy; Artificial Intelligence; Liquid Biopsy |
| Settori scientifico-disciplinari del MIUR: | Area 02 - Scienze fisiche > FIS/07 - Fisica applicata (a beni culturali, ambientali, biologia e medicina) Area 09 - Ingegneria industriale e dell'informazione > ING-INF/02 - Campi elettromagnetici Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 - Sistemi di elaborazione delle informazioni |
| Date Deposited: | 25 Jan 2023 01:09 |
| Last Modified: | 09 Apr 2025 14:10 |
| URI: | http://www.fedoa.unina.it/id/eprint/14685 |
Collection description
The future of early diagnosis and precision medicine will be based on the advanced single-cell analysis. To date, the gold-standard technique is Fluorescence Imaging Flow Cytometry (FIFC), which is able to quickly record 2D images of stained single cells while flowing through a measuring device. Thus, FIFC can satisfy the need for large informative datasets typical of Artificial Intelligence (AI), which has made possible a fast, automatic, and objective cell phenotyping. However, the staining process and the 2D qualitative information limit the FIFC clinical applications. Conversely, Tomographic Phase Microscopy (TPM) is a label-free optical microscopy technique that allows reconstructing the 3D spatial distribution of the refractive index (RI) at the single-cell level. The cellular RI is a key biophysical parameter proved to be an effective descriptor of cellular heterogeneity. In 2017, TPM has been proved working in Flow Cytometry (FC) mode. In TPM-FC, digital holograms of single cells are recorded in continuous flow while rotating in microfluidic environment. The TPM-FC tool is expected to create a breakthrough in the cell biology studies and in the clinical practice. Therefore, several computational strategies are developed in this Ph.D. Thesis for transferring the original proof of concept of TPM-FC into a concrete technology for the single-cell analysis. In particular, various issues to achieve the high-throughput property have been fixed and the lack of intracellular specificity, due to the label-free modality, has been filled for some organelles. Finally, the large datasets of single cells, collected through the TPM-FC system, have been used to train AI models for phenotyping cancer cells and recognizing drug resistance. In the near future, the attained results are expected to contribute in providing a solution to the challenging topic of the Liquid Biopsy (LB) technology, which aims to the early diagnosis of cancer and the development of personalized therapies by means of blood tests.
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