Category: Article

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

Bridging scientific research and innovation to strengthen Europe’s competitiveness and societal impact. (Image generated by ChatGPT from A. Ciarlo.)
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.

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.

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.

SmartTrap: automated precision experiments with optical tweezers published in Nature Methods

Illustration of three different experiments autonomously performed by the SmartTrap system: DNA pulling experiments (top), red blood cell stretching (bottom left), and particle-particle interaction measurements (bottom right). (Image by M. Selin.)
SmartTrap: automated precision experiments with optical tweezers
Martin Selin, Antonio Ciarlo, Giuseppe Pesce, Lars Bengtsson, Joan Camunas-Soler, Vinoth Sundar Rajan, Fredrik Westerlund, L. Marcus Wilhelmsson, Isabel Pastor, Felix Ritort, Steven B. Smith, Carlos Bustamante, Giovanni Volpe
Nature Methods (2026)
arXiv: 2505.05290
doi: 10.1038/s41592-026-03129-3

Optical tweezers are widely used in single-molecule biophysics, cell biomechanics and soft matter physics, but require a human operator, limiting throughput and repeatability. Here we present a smart optical tweezers platform, named SmartTrap, capable of performing complex experiments autonomously by integrating real-time three-dimensional particle tracking, custom electronics and a microfluidics system. Through a series of experiments, we demonstrate it can operate continuously, acquiring high-precision data over extended periods of time. By bridging the gap between manual experimentation and autonomous operation, SmartTrap establishes a robust and open-source framework for the next generation of optical tweezers research, capable of performing large-scale studies in single-molecule biophysics, cell mechanics and colloidal science with minimal experimental overhead and operator bias.

Protein Dynamics Beyond Structure Prediction on ArXiv

Computational advances in protein folding studies. Current approaches address multiple levels of resolution and methodological frameworks, however, none of the existing methods provides quantitative and dynamic information of the relationship between protein sequence and folding mechanism at all-atom resolution and at scale. (Graphics by J. Sacquegno.)
Protein Dynamics Beyond Structure Prediction
Juliette Griffié, Sviatlana Shashkova, Antonio Ciarlo, Sreekanth K. Manikandan, Claes Andréasson, Malin Bäckström, Tristan Bereau, Hjalmar Brismar, Carlos Bustamante, Marta Carroni, Roberto Covino, Andreas Dahlin, Sebastian Deindl, Lucie Delemotte, Arne Elofsson, John Eriksson, Giovanna Fragneto, Anders Gunnarsson, Per Hammarström, Caroline Ingre, Christian Kaiser, Petronella Kettunen, Mark C. Leake, Benjamin Loos, Anna Månberg, Antonia S. J. S. Mey, Richard Neutze, Thomas Nyström, Karl Palmås, Charley Schaefer, Markus J. Tamás, Nicola Ticozzi, Tomás S. Pilvelic, Jacopo Sacquegno, B.M. (Betty)Tijms, Gunnar von Heijne, Björn Wallner, Vitali Zhaunerchyk, Simon Olsson, Joana B. Pereira, Julia Fernandez-Rodriguez, Fredrik Westerlund, Giovanni Volpe
arXiv: 2606.08647

How microorganisms respond to and interact with their environment can vary significantly from individual to individual, which can have important microbiological and ecological implications. However, most microscopy techniques can only observe motile microorganisms for short times because of their limited fields of view. Using Lagrangian tracking, a single microorganism can be followed in 3D, potentially indefinitely, allowing to decipher individual phenotypical traits. Current Lagrangian tracking methods use the fluorescence signal emitted by the microorganism as feedback to keep it in focus. However, over long times, epifluorescent imaging can induce photobleaching and photodamage, and importantly, not all microorganisms can easily be made fluorescent. Additionally, traditional algorithms used in feedback loops to determine microorganism position are prone to errors, especially in optically complex media. Here, we present a faster, more reliable, and versatile Lagrangian tracking method that uses deep learning to determine the 3D position of the microorganism. This new method demonstrates enhanced accuracy and speed in tracking fluorescent bacteria with fluorescence microscopy also in optically complex media. Furthermore, we track bacteria with other microscopy modalities, such as brightfield microscopy — for example, this enables us to track magnetotactic bacteria, which cannot be made fluorescent without degrading their magnetotactic properties. These novel capabilities allow to extract previously inaccessible quantitative information, significantly advancing the study of microorganism behavior — and thus opening new avenues for research in complex biological and ecological systems.

Beyond the black box: towards an open and citable software ecosystem in photonics published in Journal of Physics: Photonics

A centralized, peer-reviewed ecosystem in J. Phys. Photonics where human agents and/or AI agents co-develop validated, documented software. (Image from the manuscript.)
Beyond the black box: towards an open and citable software ecosystem in photonics
Anoop C Patil, Maciej Trusiak, Fei Xia, Liangcai Cao, Carlo Manzo and Giovanni Volpe
Journal of Physics: Photonics 8, 020201 (2026)
doi: 10.1088/2515-7647/ae68cd

This editorial draws attention to a major issue in photonics: important research software is often hidden, undocumented, and lost over time, making results hard to reproduce. The rise of AI-generated code increases this problem by adding more ‘black box’ systems. To address this, J. Phys. Photonics is introducing Software Articles, a new article type allowing researchers to publish, validate, and share their code as formal scientific outputs. This initiative aims to promote transparency, reproducibility, and proper credit for developers. Open and peer-reviewed software helps the community verify results, reduce duplication of effort, and build lasting tools, ensuring that computational methods become reliable, accessible, and integral to scientific progress.

Delayed Active Swimmer in a Velocity Landscape published in Physical Review of Research

Experimental setup. (Top) Thermophoretic microswimmer undergoes active Brownian motion in a spatially-varying laser intensity profile that controls the self-thermophoretic propulsion of the swimmer using a feedback loop. (Bottom) Sample trajectory of the microswimmer over 15 minutes in a chamber. Colors indicate instantaneous velocity. (Image from the manuscript.)
Delayed Active Swimmer in a Velocity Landscape
Viktor Holubec, Alexander Fischer, Giovanni Volpe, Frank Cichos
Physical Review Research 8, L022017 (2026)
arXiv: 2505.11042
doi: 10.1103/xn9x-ppjx

Active systems in nature and synthetic environments commonly exhibit spatially heterogeneous activity patterns and time-delayed responses from internal feedback mechanisms, exemplified by bacterial chemotaxis. We study an idealized active gas where particles modulate their self-propulsion based on local environmental conditions with such delays. Through integrated theoretical, computational, and experimental approaches, we demonstrate that steady-state density distributions and collective polarization exhibit characteristic peaks and valleys as functions of response delay time. We find that delays can amplify polarization by nearly an order of magnitude and trigger complete polarization reversal when particle displacement during the delay period surpasses the persistence length. Multiparticle simulations incorporating interparticle interactions validate that these phenomena remain robust in sufficiently dilute collective systems. Since density and polarization determine the current in active matter, our findings show that temporally programming the delay time allows control over both static and dynamic states in active systems, with implications for biological microswimmers and engineered microrobots.

Three-dimensional quantitative tissue clearing reveals differences in osteovascular niche of aged and young human mesenchymal stromal cells published in Nature Biomedical Engineering

Visualization of the vasculature within human bone from a 75-year-old patient by immunostaining with antibodies against CD31. (Image from the manuscript.)
Three-dimensional quantitative tissue clearing reveals differences in osteovascular niche of aged and young human mesenchymal stromal cells
Nelson Tsz Long Chu, Ostap Dregval, Yu-Wei Chang, Emil Kriukov, Xin Tian, Xin Liu, Dana Trompet, Misty Shuo Zhang, Lei Li, Zhong Li, Emiliano Gomez Ruiz, Joana B. Pereira, Mats Brittberg, Björn Barenius, Lars Sävendahl, Ralf H. Adams, Inger Gjertsson, Claes Ohlsson, Giovanni Volpe & Andrei S. Chagin
Nature Biomedical Engineering (2026)
bioRxiv: 10.1101/2025.10.07.680053
doi: 10.1038/s41551-026-01645-3

Human bone marrow mesenchymal stromal/stem cells (BM-MSCs) are widely used in clinical trials and tissue engineering, yet their native microenvironment remains poorly understood. Here we introduce a tissue-clearing protocol, DeepBone, for human bones and integrate it with simultaneous mRNA and protein detection. Using this protocol, we spatially map BM-MSCs relative to key bone microenvironment components, including human blood capillaries, adipocytes, sinusoids and bony trabeculae. Quantitative analysis reveals that the native microenvironment of human BM-MSCs in young bone is enriched in vasculature, sinusoids, bone matrix and adipocytes. In contrast, in aged bone, BM-MSCs show no preferential association with bone or adipocytes. Proliferative BM-MSCs are predominantly found along blood vessels. Moreover, we identify a specialized microenvironment for BM-MSCs in young bone, characterized by sinusoids coiled around trabeculae and enriched by R-type vessels. These findings provide insights into the native niches of BM-MSCs, offering a foundation for the development of tissue engineering strategies that mimic their physiological context.