Stain-free intracellular specificity via nesting-based strategy in immersive holo-tomographic flow cytometry
Year: 2025
Type: article
Abstract: Although fluorescence microscopy has greatly advanced intracellular studies, it is limited by phototoxicity and photobleaching. Instead, quantitative phase imaging (QPI) enables stain-free live cell studies by generating quantitative contrast from refractive index (RI) variations. QPI has also been demonstrated in flow cytometry (FC) microscopy modality by allowing 3D RI tomographic reconstruction of single cells. Recently, we have partially overcome the inherent lack of specificity in such 3D label-free microscopy by introducing a computational method for nucleus retrieval. However, identification of smaller intracellular structures remains a significant challenge. Here we propose nesting specificity (NS) as a novel strategy for retrieving multiple intracellular compartments in FC modality. We demonstrate an unprecedented cascade approach using a priori knowledge about the nucleus to facilitate the segmentation of smaller intracellular structures. To show NS robustness, we test it on different cell types with dissimilar complexity, thus demonstrating its efficacy in segmenting cells’ compartments such as lipid droplets, lysosomes, nucleoli, vacuoles, and substructures compatible with mitotic spindles. To explore these complex 3D arrangements, we develop a virtual reality environment that allows immersive, interactive visualization. This tool offers a novel platform for examining intracellular architecture in detail, enhancing understanding of intracellular structures and functions.
Source: Journal of Physics Photonics
Authors: Daniele Pirone, Maria Di Summa, Nicola Mosca, Giusy Giugliano, Michela Schiavo, Massimo D’ Agostino, Diego Luis Medina, Vittorio Bianco, Lisa Miccio, Pasquale Memmolo, Pietro Ferraro
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,700
OpenAlex ID: W4417514402