Biology subjects

Schiavo, M.

Publications and source records attributed to Schiavo, M..

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

Quantitative profiling of lysosomal accumulation through label-free biomarkers via High-Content Holo-Tomographic Flow Cytometry

Lysosomal storage diseases (LSDs) are genetic disorders caused by enzyme deficiencies that lead to lysosomal dysfunction and progressive cell damage. Accurate visualization and quantification of lysosomes are essential for understanding disease progression and developing effective therapies. Here, for the first time, we successfully identified and characterized lysosomes using an innovative Holo-Tomographic Flow Cytometry (HTFC) technique, which allows label-free, high-content, and high-throughput 3D imaging of lysosomal compartments in single live cells. This breakthrough could revolutionize traditional gold-standard methods overcoming the actual limitations. Leveraging this technology, we discovered novel biomarkers of lysosomal accumulation in LSD-affected cells. In fact, by generating refractive index tomograms, we achieved accurate measurement and comprehensive 3D visualization of cytoplasmic lysosomal aggregation in suspended single cells. Through experimental validation and advanced computational analyses, we identified a quantitative correlation between the 3D lysosomal architecture and the efficacy of various therapeutic strategies, including genetic and pharmacological interventions. This work represents a significant advance in lysosomal research, paving the way for improved diagnostics and the development of targeted therapies for LSDs.

cell biology↗