| Title: |
Holotomography-driven learning unlocks in-silico staining of single cells in flow cytometry by avoiding fluorescence co-registration |
| Authors: |
Pirone, Daniele; Giugliano, Giusy; Schiavo, Michela; Montella, Annalaura; Mugnano, Martina; Cerbone, Vincenza; Raia, Maddalena; Scalia, Giulia; Kurelac, Ivana; Medina, Diego Luis; Miccio, Lisa; Capasso, Mario; Iolascon, Achille; Memmolo, Pasquale; Ferraro, Pietro |
| Contributors: |
Pirone, Daniele; Giugliano, Giusy; Schiavo, Michela; Montella, Annalaura; Mugnano, Martina; Cerbone, Vincenza; Raia, Maddalena; Scalia, Giulia; Kurelac, Ivana; Medina, Diego Lui; Miccio, Lisa; Capasso, Mario; Iolascon, Achille; Memmolo, Pasquale; Ferraro, Pietro |
| Publication Year: |
2026 |
| Collection: |
IRIS Università degli Studi di Bologna (CRIS - Current Research Information System) |
| Subject Terms: |
quantitative phase imaging holographic tomography flow cytometry label-free computational microscopy in-silico staining deep learning |
| Description: |
Virtual staining is the current state-of-the-art computational technique to cleverly enhance intracellular specificity in unstained biological samples by using convolutional neural networks (CNNs) trained on co-registered pairs of unstained/stained images. While effective, this approach suffers from unpredictable biases inherent to fluorescence microscopy and encounters challenges when applied to flow cytometry data as it would require accurate co-registration on a huge number of images. Here, we present a novel method that exploits for the first time a Holotomography-driven learning to completely eliminate the need for co-registration. We demonstrate that training a CNN on a stain-free dataset of 3D refractive index tomograms of flowing cells unlocks stain-free intracellular specificity for the first time in quantitative phase imaging flow cytometry. This self-supervised solution, by circumventing the critical obstacle of fluorescence co-registration, opens unprecedented perspectives for label-free, high-throughput imaging flow cytometry, offering a powerful new paradigm for advanced 2D and 3D single-cell analysis. |
| Document Type: |
article in journal/newspaper |
| File Description: |
ELETTRONICO |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/wos/WOS:001706400300001; volume:5; issue:2; firstpage:1; lastpage:15; numberofpages:15; journal:Opto-electronic science (Online); https://hdl.handle.net/11585/1049932 |
| DOI: |
10.29026/oes.2026.260003 |
| Availability: |
https://hdl.handle.net/11585/1049932; https://doi.org/10.29026/oes.2026.260003; https://www.oejournal.org/oes/article/doi/10.29026/oes.2026.260003 |
| Rights: |
info:eu-repo/semantics/openAccess ; license:Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY) ; license uri:iris.PUB15 |
| Accession Number: |
edsbas.CF0BDCD0 |
| Database: |
BASE |