Biology subjects

Giugliano, G.

Publications and source records attributed to Giugliano, G..

7 recordsLinked to original sources

bioRxiv2026

Impact of Image Representation on Deep Learning-Based Single-Cell Classification by Holographic Imaging Flow Cytometry

Accurate cell type classification is essential for a wide range of biomedical applications, including disease diagnosis, drug discovery, and the study of cellular processes. Holographic imaging flow cytometry (HIFC) provides label-free quantitative phase imaging (QPI) of individual cells, enabling classification based on phase images. However, reconstructing holograms into phase images involves multi-step image processing, which introduces substantial computational overhead. The availability of diverse image representations across holographic reconstruction stages allows for flexible analytical strategies, enabling the optimization of trade-off between classification accuracy and computational efficiency. Moreover, deep learning offers an efficient alternative, accelerating the reconstruction process while performing accurate classification. However, despite its importance, this optimization challenge remains largely unexplored in the current literature. Here, we present the first systematic evaluation aimed at balancing classification accuracy with computational efficiency, highlighting how different image representations affect overall performance. We focus on a binary classification task discriminating natural killer cells from breast cancer cells. Six distinct classification pipelines are evaluated: direct processing of raw holograms, analysis of demodulated complex fields (CFs), refocused CFs, unwrapped phase images, and two deep learning-based methods that either replace the automatic refocusing stage or perform end-to-end hologram-to-phase reconstruction. For each strategy, we assess both computational cost and classification performance. Our results reveal a clear trade-off: reconstructed phase images provide the highest accuracy, whereas simpler representations or accelerated reconstruction methods significantly reduce processing time with minimal loss of accuracy. A Pareto analysis identifies the optimal set of strategies, offering practical guidelines for selecting image representations and processing pipelines based on available hardware and desired performance. Thus, this work offers a systematic framework for high-throughput deep learning classification in HIFC, serving as a potential reference for future biomedical applications.

biophysics↗

bioRxiv2026

Profiling co-occurrent morphological phenotypes and their degree of expression severity in vacuolated cells by holo-tomographic flow cytometry and fractal analysis

Cells are complex systems characterized by large phenotype heterogeneity. Conventional single cell classification approaches usually separate cells expressing a certain phenotype (e.g. associated to a disease condition) from the healthy control. However, multiple phenotypes typically coexist within the same cell as a result of complex intracellular interactions, machineries, functioning and external stimuli. Here we use label-free optical microscopy, powered by AI, to investigate how morphological phenotypes co-occur within vacuolated cells. Cytoplasmic vacuoles are important hallmarks of several pathological states (e.g. lysosomal storage diseases, viral infections, cancer). We rely on Holo-Tomographic Flow Cytometry (HTFC) to obtain 3D refractive index tomograms of vacuolated cells in continuous flow. Then, we propose a strategy to reduce the dimensionality of the tomogram using cross-sectioning and minimum intensity projection maps. We extract a set of morphological, refractive index-based, and fractal parameters demonstrating that the complex heterogeneity of vacuole patterns can be captured and can foster classification based on interpretable features. For training the AI, biologist domain-experts provided annotation of the different morphological phenotypes expressed and ranked them in terms of expression severity from the tomographic observations. Thus, we introduce a pipeline for morphometric phenotype profiling, in which each cell is associated with a 7-digits classification code representing the combination of coexisting phenotypes it expresses and their expression severity levels.

bioengineering↗

bioRxiv2026

Against the current: upstream behavior in diatoms

Diatoms significantly contribute to aquatic primary productivity and biogeochemical cycles, with motility playing a crucial role in their ecological success. While several factors influence their motility, the effect of water flow remains poorly understood. This study used a digital holographic microscope to investigate the locomotion of the pennate diatom Navicula cf parapontica under varying flow rates. It demonstrates, for the first time, that Navicula perceives and actively counteracts water flows. As flow rates increased up to 500 nL/s, cells consistently moved against the current and frequently adjusted their orientation to maximize resistance. This behaviour allowed the diatoms to maintain a stable locomotion velocity despite a 6.7-fold increase in flow rate. This active rheotaxis likely serves as a strategy to resist resuspension and passive dispersal. These findings reveal a behavioural trait that might play significant role in the way benthic diatom communities maintain their position in the sediments, influencing bentho-pelagic coupling and biogeochemical processes.

ecology↗

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↗

bioRxiv2026

Establishing label-free quantitative X-ray dose-response profiling by Holo-Tomographic Flow Cytometry

Flow cytometry (FC) offers multiparametric analysis capabilities that can quantify cellular damage after exposure to cytotoxic agents. Here, we present a comprehensive study establishing a label-free quantitative X-ray dose-response profiling using a novel FC modality based on 3D Quantitative Phase Imaging, termed Holo-Tomographic Flow Cytometry (HTFC). This approach enables fully label-free 3D refractive index (RI) measurements, allowing detailed and quantitative characterization of the biophysical properties and morphology of living cells exposed to X-rays. By analyzing datasets of 3D RI tomograms from cells irradiated at graded doses, we identify intracellular biophysical markers that define a robust X-ray dose-response curve. Validation against standard clonogenic survival assays on three model cancer cell lines reveal a high correlation (>90%). HTFC not only eliminates labeling and operator bias but also markedly reduces experimental time from 1-2 weeks to 24 hours, offering a fully automated and objective readout. While clonogenic survival remains the benchmark for radiosensitivity assessment, our findings establish HTFC as a powerful label-free platform for fast assessment of radiation damage. This technology paves the way for predictive biosensors that can capture patient-specific responses, thereby supporting the transition from conventional, uniform radiotherapy protocols to personalized treatment strategies.

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↗