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(Peer-Reviewed) Holotomography-driven learning unlocks in-silico staining of single cells in flow cytometry by avoiding fluorescence co-registration
Daniele Pirone ¹, Giusy Giugliano ¹ ², Michela Schiavo ¹ ² ³ ⁴, Annalaura Montella ⁵ ⁶, Martina Mugnano ⁷, Vincenza Cerbone ⁵, Maddalena Raia ⁵, Giulia Scalia ⁵, Ivana Kurelac ⁸ ⁹, Diego Luis Medina ³ ¹⁰, Lisa Miccio ¹, Mario Capasso ⁵ ⁶, Achille Iolascon ⁵ ⁶, Pasquale Memmolo ¹, Pietro Ferraro ¹
¹ CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "E. Caianiello", Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
² Department of Mathematics and Physics, University of Campania "Luigi Vanvitelli", Viale Abramo Lincoln 5, 81100 Caserta, Italy
³ TIGEM, Telethon Institute of Genetics and Medicine, Via Campi Flegrei 34, 80078 Pozzuoli, Napoli, Italy
⁴ Department of Advanced Biomedical Science, University of Naples "Federico II", Via Sergio Pansini 5, 80131 Napoli, Italy
⁵ CEINGE - Advanced Biotechnologies, Via Gaetano Salvatore 486, 80131 Napoli, Italy
⁶ DMMBM, Department of Molecular Medicine and Medical Biotechnology, University of Naples "Federico II", Via Pansini 5, 80131 Napoli, Italy
⁷ DICMaPI, Department of Chemical, Materials and Production Engineering, University of Naples "Federico II", Piazzale Tecchio 80, 80125 Napoli, Italy
⁸ DIMEC, Department of Medical and Surgical Sciences, Alma Mater Studiorum-University of Bologna, Via Irnerio 49, 40126 Bologna, Italy
⁹ IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy
¹⁰ Medical Genetics Unit, Department of Medical and Translational Science, University of Naples "Federico II", Via Sergio Pansini 5, 80131 Napoli, Italy
Opto-Electronic Science , 2026-02-25
https://doi.org/10.29026/oes.2026.260003
https://www.oejournal.org/oes/article/doi/10.29026/oes.2026.260003
Abstract

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.
Holotomography-driven learning unlocks in-silico staining of single cells in flow cytometry by avoiding fluorescence co-registration_1
Holotomography-driven learning unlocks in-silico staining of single cells in flow cytometry by avoiding fluorescence co-registration_2
Holotomography-driven learning unlocks in-silico staining of single cells in flow cytometry by avoiding fluorescence co-registration_3
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