Biology subjects

Cerbone, V.

Publications and source records attributed to Cerbone, V..

3 recordsLinked to original sources

bioRxiv2026

Label-Free Nucleoli Measurement by 3D Holo-Tomographic Flow Cytometry Using Biolens Phase Compensation

Holo-Tomographic Flow Cytometry (HTFC) holds the potential to transform cellular research and clinical screening through 3D label-free quantitative phase imaging (QPI) of flowing single cells. However, it has been limited by insufficient intracellular specificity in 3D refractive index (RI) distributions, since suspended cells act as highly aberrating spherical biolenses obscuring internal structures. Here, we show the Biolens Phase Compensation (BPC), a method that corrects phase aberrations in 2D QPI projections to transform the 3D RI tomogram. Working within this new 3D pseudo-RI space demonstrates for the first time the extraction of nucleoli in HTFC. Extensive validation against 2D fluorescence flow cytometry and 3D confocal microscopy demonstrates that BPC achieves reliable intranuclear specificity. Using statistically significant single-cell analysis, we provide multiplexed quantitative 3D measurements of nested intracellular compartments (cytoplasm, nucleoplasm, nucleoli). This approach extends label-free HTFC toward capabilities of gold-standard Fluorescence Microscopy, overcoming its well-known drawbacks in intracellular and intranuclear staining.

biophysics↗

bioRxiv2025

Holotomography-driven learning for in-silico staining of single cells in flow cytometry avoiding co-registration

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 elegantly unlocks stain-free intracellular specificity in quantitative phase imaging flow cytometry. This breakthrough, by circumventing the critical co-registration bottleneck, opens unprecedented perspectives for label-free, high-throughput imaging flow cytometry, offering a powerful new paradigm for advanced 2D and 3D single-cell analysis.

biophysics↗

bioRxiv2025

Type 2 Deiodinase in Cancer-Associated Fibroblasts is required to sustain growth of poorly and undifferentiated thyroid cancer

Poorly differentiated (PDTC) and anaplastic thyroid carcinomas (ATC) are characterized by rapid progression and poor patient survival. While the tumor microenvironment (TME) - particularly cancer-associated bibroblasts (CAFs)- plays a crucial role in supporting tumor growth, its metabolic contribution remains poorly understood. Here, we identify a critical role for type 2 deiodinase (D2), the enzyme that activates the thyroid hormone (TH) thyroxine (T4) into the biologically active triiodothyronine (T3), in sustaining a pro-tumorigenic TME in PDTC and ATC. We show that D2 is expressed not only in thyroid epithelial cancer cells, but at even higher levels in CAFs, especially inblammatory CAFs (iCAFs). In in vivo mouse models, pharmacological inhibition of D2 reduces tumor growth and alters composition of CAFs. In 3D co-culture spheroids, D2 activity proves essential for supporting tumor cell proliferation by establishing a paracrine loop between stromal and epithelial cancer cells that amplibies local TH signaling. Notably, human PDTC organoids expressing D2 respond to modulation of TH levels, conbirming the functional relevance of this metabolic axis in human tumors. In conclusion, these bindings identify D2 as a key mediator of stromal-epithelial crosstalk in PDTC and ATC, and highlight local TH metabolism as a potential therapeutic target in these lethal cancers.

cancer biology↗