Tag: Giovanni Volpe

How morphology affects optical trapping of red blood cells in health and disease published in Biomedical Optics Express

 Left: trapping of a healthy (biconcave) red blood cell. Right: trapping of an echinocyte-like cell with membrane protrusions. In the representation of the trapping (focused) beam, only rays originating from points close enough to the propagation axis are drawn. More peripheral rays are omitted. (Image from the manuscript.)
How morphology affects optical trapping of red blood cells in health and disease
Emir Erdem, Gökberk Kabacaoğlu, Giovanni Volpe, Agnese Callegari, and Luca Biancofiore
Biomedical Optics Express 17, 5355-5372 (2026)
doi: 10.1364/BOE.609102

Optical tweezers are widely used to manipulate microscopic particles and biological cells through optical forces and torques, yet how cell shape controls trapping response has not been fully elucidated. Here, we develop a computational framework to investigate single-beam optical trapping of red blood cells with physiologically and pathologically relevant deformed morphologies. Representative cell shapes are generated parametrically and analyzed using static displacement and rotation tests, together with Brownian dynamics simulations. We show that all considered morphologies remain laterally and orientationally confined in a single-beam trap, while axial localization is weaker by pure optical means and is experimentally realized by the balance between the optical force and the buoyancy-corrected cell weight. Cell geometry strongly affects translational and rotational stiffness, preferred orientation, and Brownian confinement. In particular, shape-induced anisotropy produces distinct dynamical signatures, indicating that optical trapping measurements may be sensitive to morphological alterations beyond simple changes in cell size or volume. These results establish a quantitative connection between red-blood-cell morphology, trap stiffness, and dynamics, providing a computational framework for interpreting morphology-dependent optical trapping experiments.

Enhancing Europe’s competitiveness by empowering researchers as innovators published in Science

Enhancing Europe’s competitiveness by empowering researchers as innovators
Amanda K. A. Silva Brun, Giorgio Presidente, Mangala Srinivas, Valentina Cauda, Alberto Fernandez-Tejada, Christine L. Mummery, Rami I. Aqeilan, Thomas Thum, Mehmet C. Onbasli, Robert Colebunders, Gianni Ciofani, Davide Iannuzzi, Marios Avraamides, João T. Barata, Romana Schirhagl, Ana Cecília A. Roque, Luana Dessbesell, Chloé Dupuis, Marco Paggi, Kerstin Kinkelin, Gerard J. J. Boink, Antonella Bongiovanni, Kevin Braeckmans, Kerem Pekkan, Lorenzo Moroni, Twan Lammers, Xiao Xiang Zhu, Adriele Prina-Mello, Carlo S. Casari, Max C. Lemme, Thomas Wolbers, Achillefs N. Kapanidis, Erdem Yörk, Jan S. Kirschke, Philipp-Leo Mengel, Giulio Cerullo, Volker Hessel, Víctor A. de la Peña O’Shea, Aitziber L. Cortajarena, Frank Kirchhoff, Harald H. H. W. Schmidt, Imre Berger, Anne Houdusse, Edgar R. Gomes, Frederic Amant, Tambet Teesalu, Joan Ballester, Juan José Vilatela, Raymond M. Schiffelers, Fabrizio d’Adda di Fagagna, Roman Jerala, Cristina Canal, Sophie Demolombe, Gaetano Gargiulo, Giovanni Volpe, Andrea C. Ferrari, Daniel Gros, Clemens Fuest, and Dario Polli
Science 393, 1302-1304 (2026)
doi: 10.1126/science.ady2388

Europe has a strong scientific research base, yet many promising discoveries still face barriers on the path from academia to technologies, products, and companies. In this Policy Forum, a broad group of European researchers, innovators, and economists examines how these barriers could be reduced to strengthen Europe’s capacity for breakthrough innovation and socioeconomic impact. Focusing particularly on Horizon Europe, the authors propose a more continuous pathway from fundamental research to commercialization, including closer integration between European Research Council Proof of Concept grants and European Innovation Council programs, stronger and more flexible support for early-stage high-risk innovation, and simpler funding procedures. They also highlight the importance of recognizing entrepreneurship within academic careers, improving intellectual-property management, and providing researchers with clearer access to mentoring, industrial partnerships, and spin-off creation. Together, these measures could help translate more of Europe’s scientific excellence into new technologies, businesses, and solutions for society, strengthening Europe’s long-term competitiveness.

Invited Talk by G. Volpe at the TR+ Conference 2026: From Static to Dynamics, LINXS, Lund, 21 September 2026

Conceptual models of protein folding: funnel versus foldon. (Figure from the Authors of the manuscript.).

How do proteins fold?
Date: 21 September 2026
Time: 13:15
Place: LINXS at The Loop, Lund

Giovanni Volpe will give the invited talk “How do proteins fold?” at the TR+ Conference 2026: From Static to Dynamics, organized by LINXS and MAX IV in Lund from 21 to 23 September 2026. The conference brings together researchers working on time-resolved methods to investigate the structural and functional dynamics of biomolecules.

How proteins fold remains a central unsolved problem in biology. While the idea of a folding code embedded in the amino acid sequence was introduced more than six decades ago, this code remains undefined. Although we now have powerful predictive tools to predict the final native structure of proteins, we still lack a predictive framework for how sequences dictate folding pathways.

Two main conceptual models dominate as explanations of folding mechanism: the funnel model, in which folding proceeds through many alternative routes on a rugged, hyperdimensional energy landscape; and the foldon model, which proposes a hierarchical sequence of discrete intermediates.

Recent advances on two fronts are now enabling folding studies in unprecedented ways. Powerful experimental approaches—in particular, single-molecule force spectroscopy and hydrogen–deuterium exchange assays—allow time-resolved tracking of the folding process at high resolution. At the same time, computational breakthroughs culminating in algorithms such as AlphaFold have revolutionized static structure prediction, opening opportunities to extend machine learning toward dynamics.

Together, these developments mark a turning point: for the first time, we are positioned to resolve how proteins fold, why they misfold, and how this knowledge can be harnessed for biology and medicine.

Reference: Bustamante, C., Kaiser, C. M., Lindahl, E., Sosa, R., & Volpe, G. (2026). “How proteins fold.” Nature Reviews Molecular Cell Biology. Advance online publication.

Soft Matter Lab members present at SPIE Optics+Photonics conference in San Diego, 23-27 August 2026

The Soft Matter Lab participates to the SPIE Optics+Photonics conference in San Diego, CA, USA, 23-27 August 2026, with the presentations listed below.

Giovanni Volpe is a coauthor of the following presentations:

  • Mite Mijalkov In-silico cognition signals aging and cognitive decline
    23 August 2026 • 3:15 PM – 3:30 PM PDT | Conv. Ctr. Room 2
    [link on SPIE]
  • Iok-ui Tiunn Unsupervised analysis of 3D human bone reveals age-sensitive niches for CXCL12+ stromal cells
    24 August 2026 • 2:15 PM – 2:30 PM PDT | Conv. Ctr. Room 2
    [link on SPIE]
  • Alexandra Badea Graph-Based Embeddings of Laminar Cortical Columns Reveal Interpretable Microstructural Patterns Associated with Alzheimer’s Disease Risk (Invited Paper)
    26 August 2026 • 10:30 AM – 11:00 AM PDT | Conv. Ctr. Room 2
    [link on SPIE]
  • Massimiliano Passaretti Clinical progression and genetic pathways in body-first and brain-first Parkinson’s disease
    26 August 2026 • 2:15 PM – 2:30 PM PDT | Conv. Ctr. Room 2
    [link on SPIE]
  • Mite Mijalkov The locus coeruleus gap: a novel marker of neurodegenerative resilience
    27 August 2026 • 8:45 AM – 9:00 AM PDT | Conv. Ctr. Room 2
    [link on SPIE]

Invited Presentation by G. Volpe at SPIE-Ultrafast Nonlinear Imaging and Spectroscopy XIV, 24 August 2026

DeepTrack 2 Logo. (Image from DeepTrack 2 Project)
Artificial Intelligence for Microscopy: From Pixels to Physical Insight
Giovanni Volpe
(Invited Paper)
Date: 24 August 2026
Time: 9:45 AM – 10:10 AM PDT
Place: Conv. Ctr. Room 15B

Advances in artificial intelligence are transforming microscopy from a tool of observation into a tool of discovery. Modern machine learning algorithms—especially deep neural networks—enable us to reconstruct, segment, and interpret microscopic images with unprecedented speed and precision. In this talk, I will discuss how AI can enhance every step of the microscopy pipeline, from data acquisition and noise reduction to virtual staining and quantitative analysis. Drawing on examples from my group’s work—including the DeepTrack framework for physics-aware deep learning and AI-driven virtual microscopy—I will show how we can train models to reveal hidden structures, track dynamic processes, and even uncover underlying physical laws directly from images. Beyond technical performance, I will also address the broader implications of this transformation: how AI reshapes the role of the experimenter, the reproducibility of quantitative microscopy, and the future of scientific understanding in an era of data-driven discovery.

EquiNET: A new technique for estimating Equilibrium free-energy differences from Non-Equilibrium Trajectories on arXiv

EquiNET uses a graph neural network to infer entropy production from non-equilibrium molecular trajectories and uses this information to estimate equilibrium free-energy differences. (Image by S. K. Manikandan.)
Free-Energy Differences from Nonequilibrium Fluctuations in High Dissipation
Sreekanth K Manikandan and Giovanni Volpe
arXiv: 2608.23394

Equilibrium free-energy differences can be measured from nonequilibrium work fluctuations using fluctuation theorems, such as the Jarzynski equality and the Crooks fluctuation theorem. However, these approaches become statistically inefficient in high-dissipation regimes because their convergence requires trajectories with negative entropy production, events that occur exponentially rarely. Here, we demonstrate that repeated measurements of trajectory fluctuations are sufficient to determine entropy production without relying on such rare events, or to determine a lower bound on it when only partial measurements are available. This information then yields exact estimates of free-energy differences, or rigorous bounds. We validate this approach, named EquiNET, with numerical simulations, including one of biomolecular folding and unfolding, showing that it recovers accurate free-energy estimates even in regimes where conventional approaches fail.

Poster presentation by G. Volpe at SPIE-ETAI, San Diego, 24 August 2026

An energy- and data-efficient foundation model for high-content phenotypic screening
Benjamin Midtvedt, Jesús D. Pineda Castro, Henrik Klein Moberg, Jim Denholm, Mattias Goksör, Christos Matsoukas, Bjarki Johannesson, Giovanni Volpe
Date: 24 August 2026
Time: 5:30 PM – 7:30 PM PDT
Place: Conv. Ctr. Exhibit Hall B1

High-content screening (HCS) enables large-scale characterization of cellular responses to chemical and genetic perturbations, yet current deep learning approaches remain limited by label scarcity, batch effects, and high computational cost. We present an energy- and data-efficient foundation model for high-content phenotypic screening that unifies these challenges within a single architecture. Trained on the full JUMP Cell Painting dataset, the model combines active learning with contrastive supervision to focus representation learning on biologically informative variation while reducing sensitivity to technical artifacts. By supporting multimodal integration of molecular metadata alongside fluorescence morphology, the model learns generalizable cellular representations that transfer across experimental conditions and downstream phenotypic tasks.

Keynote presentation by G. Volpe at SPIE-MNM, San Diego, 23 August 2026

Top: single gear; Bottom: the second gear from the right has an optical metamaterial that react to laserlight and makes the gear move. All gears are made in silica directly on a chip. Each gear is about 0.016 mm in diameter. (Image by G. Wang)
Light-driven metamachines: From metarotors to integrated microscopic gear trains
(Keynote Presentation)
Giovanni Volpe
Date: 23 August 2026
Time: 8:30 AM – 9:05 AM PDT
Place: Conv. Ctr. Room 11A

Microscopic geared metamachines provide a powerful platform to control motion and mechanical work at the microscale. While the miniaturization of mechanical systems has been pursued for decades, integrating drives and gear trains at micrometer scales has remained a major challenge. Recent advances in optical metasurfaces and on-chip fabrication enable light-driven metarotors that transfer torque to passive gears under uniform illumination. These systems have been explored under various conditions by varying light polarization, intensity, and structural symmetry to achieve directional control, tunable rotation rates, and efficient torque transmission. Integrated designs such as rack-and-pinion configurations further demonstrate conversion of rotational motion into linear actuation within compact microsystems. More broadly, geared metamachines extend the landscape of light-powered micro- and nanomachines. In this presentation, I’ll give an overview of this field with a focus on metarotor design, torque transfer across microscopic gear trains, and integrated light-programmable micromechanical systems.

Global graph features unveiled by unsupervised deep learning published in Machine Learning: Science and Technology

GAUDI leverages a hierarchical graph-convolutional variational autoencoder architecture, where an encoder progressively compresses the graph into a low-dimensional latent space, and a decoder reconstructs the graph from the latent embedding. (Image by M. Granfors and J. Pineda.)
Global graph features unveiled by unsupervised deep learning
Mirja Granfors, Jesús Pineda, Blanca Zufiria Gerbolés, Joana B. Pereira, Carlo Manzo, Giovanni Volpe
Machine Learning: Science and Technology, 7, 045054 (2026)
arXiv: 2503.05560
doi: 10.1088/2632-2153/ae8d7f

Graphs provide a powerful framework for modeling complex systems, but their structural variability poses significant challenges for analysis and classification. To address these challenges, we introduce Graph Autoencoder Uncovering Descriptive Information (GAUDI), an unsupervised graph deep learning framework designed to capture both local details and global structure. GAUDI employs an hourglass architecture with hierarchical pooling and upsampling layers linked through skip connections, which preserve essential connectivity information throughout the encoding–decoding process. Even though identical or highly similar underlying parameters describing a system’s state can lead to significant variability in graph realizations, GAUDI consistently maps them into nearby regions of a structured and continuous latent space, effectively disentangling invariant process-level features from stochastic noise. We demonstrate GAUDI’s versatility across multiple applications, including small-world networks modeling, characterization of protein assemblies from super-resolution microscopy, analysis of collective motion in the Vicsek model, and identification of age-related changes in brain connectivity. Comparison with related approaches highlights GAUDI’s superior performance in analyzing complex graphs, providing new insights into emergent phenomena across diverse scientific domains.

Vulnerability of locus coeruleus connections to aging and Alzheimer’s disease published in Alzheimer’s & Dementia

Anatomical gradients of the right (R) and left (L) LC in the AD continuum cohort (ADNI). (Image from the article.)
Vulnerability of locus coeruleus connections to aging and Alzheimer’s disease
Blanca Zufiria-Gerbolés, Daniel Vereb, Mite Mijalkov, Massimiliano Passaretti, Giovanni Volpe, Zhilei Xu, Thomas Hinault, Sara Garcia-Ptacek, Joana B. Pereira, for the Alzheimer’s Disease Neuroimaging Initiative
Alzheimer’s & Dementia 22, e71737 (2026)
doi: 10.1002/alz.71737

INTRODUCTION
The locus coeruleus (LC) is important in coordinating communication between brain regions through its widespread connections. However, the organization of its connections, and its changes with aging and Alzheimer’s disease (AD), remains unclear.

METHODS
We mapped whole-brain white matter connections from the LC in two independent cohorts: one spanning the adult lifespan and another covering the AD continuum.

RESULTS
We identified a novel dorsal–ventral organization of LC connectivity that showed changes common to aging and AD or specific to AD, was linked to gene expression patterns, and was associated with cognitive performance and tau pathology in the entorhinal cortex. We also developed an imaging marker, LC gap, capturing LC connectivity deviations associated with slower tau accumulation in cognitively normal individuals at high risk for AD.

DISCUSSION
These findings provide new insights into LC connectivity in aging and AD, highlighting its potential role as a resilience marker against AD neurodegeneration.

Highlights

  • For the first time, we mapped the whole-brain white matter connections from the Locus Coeruleus (LC) to the rest of the brain across aging and Alzheimer’s disease (AD) continuum.
  • Revealed a novel dorsal–ventral organization LC connectivity that presents changes common to aging and AD or specific to AD.
  • LC connectivity patterns are associated with cognitive performance and tau pathology in the entorhinal cortex.
  • We introduced a novel marker capturing LC connectivity deviations and that is associated with tau accumulation in cognitively normal individuals at high risk for AD.