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Mapping individual molecular connectomes in Alzheimer’s disease published in Alzheimer’s & Dementia

Diagnostic classification. (Image from the article.)
Mapping individual molecular connectomes in Alzheimer’s disease
Zhilei Xu, Mite Mijalkov, Jiawei Sun, Yu-Wei Chang, Arianna Sala, Giovanni Volpe, Mario Severino, Mattia Veronese, Sara Garcia-Ptacek, Joana B. Pereira, for the Alzheimer’s Disease Neuroimaging Initiative
Alzheimer’s & Dementia 22, e71310 (2026)
DOI: 10.1002/alz.71310

INTRODUCTION
Mapping individual differences is crucial to improve personalized medicine approaches in Alzheimer’s disease (AD), which is characterized by strong inter-individual variability in the accumulation patterns of tau and amyloid beta pathology.

METHODS
We assess the progression of AD across the disease continuum by building individual molecular connectomes using longitudinal positron emission tomography (PET) data.

RESULTS
We demonstrate that these connectomes constitute a unique fingerprint, capable of identifying a single individual from a large group of subjects. Alterations in the connectomes discriminate different diagnostic groups and predict cognitive decline to a higher extent than conventional PET measures. We introduce a novel gene-specific transcription network analysis that linked individual tau and amyloid connectomes to a common transcriptomic profile of apoptosis, with the tau connectome being specifically related to pyrimidine metabolism, and the amyloid connectome to histone acetylation.

DISCUSSION
Individual molecular connectome mapping provides a novel and sensitive framework to monitor AD progression.

Highlights

  • Individual molecular connectomes constitute a unique fingerprint, capable of identifying a single individual from a large group of subjects.
  • Alterations in individual molecular connectomes significantly increase both across the Alzheimer’s disease (AD) continuum and over time.
  • Alterations in individual molecular connectomes discriminate different diagnostic groups and predict cognitive decline to a higher extent than conventional positron emission tomography measures.
  • Susceptibilities of individual tau and amyloid connectomes to AD are linked to a common transcriptomic profile of apoptosis, with the tau connectome being specifically related to pyrimidine metabolism, and the amyloid connectome to histone acetylation.

Presentation by S. K. Mondal, online, 22 April, 2026

Optical Fiber Micro/Nano Axicon Tip: An Optical Imaging Platform
Samir K. Mondal
CSIR-CSIO, Chandigarh, India
Date: 22 April 2026
Time: 12:30
Place: Online on zoom

Optical fiber tip under structural modifications enhances light-matter interaction by focusing, collecting or modulating light in microscopic scale and combined with waveguide property, it emerges as a potential optical tool, especially for spectroscopic, endoscopic and imaging application. A chemical etching technique has been introduced to permanently modify the tip as Micro/Nano axicon, capable in generating structured beams. The optics of the axicons have been studied in detail and further used in optical imaging experiments, namely phase microscopy, photonic nanojet and nanoscopy. The seminar will highlight first-hand information about the probe and experiments addressing the above-mentioned application.

Short Bio

Dr. Mondal is Chief Scientist at CSIR-CSIO, Chandigarh. He earned his Ph.D. in Electronic Science and M.Sc. in Physics from the University of Calcutta. After postdoctoral research at the University of California, Irvine and the University of Minnesota, he joined Tyndall Research Institute, Ireland.

With over 25 years in optics and photonics, his work spans optical interconnects, photonic crystals, lasers, and fiber instrumentation. He leads research in optical fiber antennas, near-field optics, imaging, and plasmonics, aiming for sustainable photonics platforms.

He collaborates internationally and is known for pioneering micro/nano axicons on fiber tips. He has over 50 publications and serves as an editor and reviewer.

Hari Prakash Thanabalan defended his PhD thesis on March 23rd, 2026. Congrats!

Photograph of the soft robot, consisting of a multilayer rolled dielectric elastomer actuator integrated with a
flexible PET sheet. (Image by H. P. Thanabalan.)
Hari Prakash Thanabalan defended his PhD thesis on March 23rd, 2026. Congrats!
The defense took place in PJ Salen lecture hall, Institutionen för fysik, Johanneberg Campus, Göteborg, at 13:00.

Title: Soft Robotic Platforms for Dynamic Conditions: From Adaptive Locomotion to Space Exploration

Abstract:
Inspired by living organisms, soft robots represent a significant advancement in robotics, offering exceptional flexibility and nearly infinite degrees of freedom. These properties make them ideal for unstructured and remote environments such as planetary surfaces. However, challenges remain in developing efficient and durable soft actuators capable of withstanding complex operational conditions. This work presents two interconnected parts.

In the first part, an inchworm-inspired soft robot was developed that is capable of controlled directionality through a passive alignment mechanism integrated with a 3D-printed grooved substrate. This design enables precise locomotion control using only a single rolled dielectric elastomer actuator (RDEA), eliminating the need for multiple actuators or complex control systems. Experimental validation confirms that manipulating groove angles on the substrate reliably guides locomotion, improving energy efficiency and mechanical simplicity.

In the second part, the fabrication and resilience of fault-tolerant RDEAs were tested. RDEAs utilising Single-Walled Carbon Nanotubes (SWCNTs) as compliant electrodes were developed to withstand multiple damages where they were tested for punctures and cuts. Additionally, the radiation tolerance of these actuators was evaluated under space-like conditions, including Galactic Cosmic Rays and Solar Particle Events, which expose materials to high-energy protons and alpha particles. A computational dual-simulation framework was applied, combining the Stopping and Range of Ions in Matter (SRIM) software for alpha particle interactions and ESA’s SPENVIS Multi-Layered Shielding Simulation Software (MULASSIS) for proton radiation effects.

This framework concerns material selection for robust RDEA fabrication aimed at extraterrestrial applications. Together, these projects advance the development of bioinspired soft robots with improved directional control and environmental resilience, supporting future applications in search and rescue, pipe inspection, and planetary exploration.

Thesis: https://hdl.handle.net/2077/90552

Supervisor: Giovanni Volpe
Examiner: Bernhard Mehlig
Opponent: Maria Guix Noguera
Committee: Juliane Simmchen, Hamid Kellay, Paolo Vinai
Alternate board member: Måns Henningson

 

Tracking early cognitive decline in preclinical AD with brain MRI similarity published in Alzheimer’s & Dementia

Parcellation of the brain cortex. (Image from the article.)
Tracking early cognitive decline in preclinical AD with brain MRI similarity
Jiawei Sun, Blanca Zufiria-Gerbolés, Massimiliano Passaretti, Giovanni Volpe, Mite Mijalkov, Joana B. Pereira, for the Alzheimer’s Disease Neuroimaging Initiative
Alzheimer’s & Dementia 22, e71170 (2026)
DOI: 10.1002/alz.71170

INTRODUCTION
Early detection of neuroanatomical changes in preclinical Alzheimer’s disease (AD) is critical for timely intervention. However, conventional magnetic resonance imaging (MRI) and fluid biomarkers often lack sensitivity to subtle structural alterations in early disease stages.

METHODS
To identify early brain alterations, we applied a perturbation-based brain similarity approach to cognitively normal participants from Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Open Access Series of Imaging Studies (OASIS), stratified by amyloid status. We evaluated its predictive performance for cognition and diagnostic conversion against cortical thickness, volumetric MRI, and fluid biomarkers.

RESULTS
In both cohorts, brain similarity consistently outperformed other biomarkers across cognitive domains and amyloid groups. It also achieved superior accuracy in predicting clinical conversion and exhibited associations with cytoarchitectural organization.

DISCUSSION
These findings highlight brain similarity as a sensitive marker of early neuroanatomical disruption in AD. Its ability to detect subtle structural changes before overt atrophy underscores its potential for early disease monitoring and treatment assessment in preclinical AD trials.

Highlights

  • Brain similarity captures early brain changes in preclinical Alzheimer’s disease (AD).
  • Brain similarity outperforms conventional biomarkers such as cortical thickness, volume measures, and fluid biomarkers in predicting cognitive decline.
  • Brain similarity predicts conversion to mild cognitive impairment and AD more accurately than traditional imaging markers, and its predictive performance is further improved when combined with fluid biomarkers.
  • Brain similarity captures structural disruptions associated with cortical layer II of the cytoarchitectonic lamina of human neocortex.

Label-free mass and size characterization of few-kDa biomolecules by hierarchical vision transformer augmented nanofluidic scattering microscopy published in Nature Communications

The principle of differential imaging in NSM, in which we subtract the light scattered (yellow arrows indicate the scattered-light direction) by an empty nanochannel from the light scattered by the same channel with a molecule inside. A sequence of differential images of a nanochannel containing a diffusing single molecule obtained in this way is combined into a kymograph, which then contains the full molecular trajectory. (Image from the article.)
Label-free mass and size characterization of few-kDa biomolecules by hierarchical vision transformer augmented nanofluidic scattering microscopy
Henrik K. Moberg, Bohdan Yeroshenko, Joachim Fritzsche, David Albinsson, Barbora Spackova, Daniel Midtvedt, Giovanni Volpe, Christoph Langhammer
Nature Communications 17, 2533 (2026)
DOI: 10.1038/s41467-026-70514-z

Nanofluidic scattering microscopy characterizes single molecules in subwavelength nanofluidic channels label-free, using the interference of visible light scattered by the molecule and nanochannel. It determines a molecule’s hydrodynamic radius by tracking its diffusion trajectory and its molecular weight by analyzing its scattering intensity along that trajectory. However, using standard analysis algorithms, it is limited to characterization of proteins larger than ≈ 60 kDa. Here, we push this limit by one order of magnitude to below ≈ 6 kDa molecular weight and ≈ 1.5 nm hydrodynamic radius — as we exemplify on the peptide hormone insulin — by using ultrasmall nanofluidic channels and by analyzing the data with a hierarchical vision transformer. When we benchmark this approach against the theoretical limit set by the Cramér–Rao Lower Bound, we find that it can be approached with sufficiently long molecular trajectories. This enables quantitative label-free single-molecule microscopy for biologically relevant families of sub-10-kDa molecules, such as cytokines, chemokines and peptide hormones.

Poster by P. Dutta at the Protein Folding in Real Time Conference, Stockholm, 11th March 2026

A coarse-grained molecular dynamics framework used to simulate plasmid DNA analyzed via atomic force microscopy (AFM). The resulting images are used to train a U-Net for DNA chain and crossing segmentation and classification. (Image by P. Dutta.)
ASAP (AFM Simulation and Analysis Pipeline)
Prakhar Dutta, Jiacheng Huang, Nazli Demirpehlivan, Thomas Catley, Sylvia Whittle, Carlo Manzo, Rahul Nagshi, Rachel Owen, Giovanni Volpe
Date: 11th March 2026
Time: 18:00 – 20:00
Place: Aula Medica, Karolinska Institute, Solna
Conference Protein Folding in Real Time, 11-13 March 2026, Stockholm, Sweden

Abstract: Atomic force microscopy (AFM) resolves biological structure and mechanics at high resolution, but produces vast, heterogeneous datasets that are often noisy and very time-consuming to analyse. Although deep learning could automate quality control, segmentation and feature extraction, adoption is limited by scarce ground-truth training data and high technical barriers for experimentalists. Here we present ASAP, an open-source tutorial and pipeline implemented in DeepTrack to provide a reproducible foundation for AI-enabled AFM. At the protein folding conference, a dual-pathway simulation for DNA, offering both molecular dynamics and rapid, non-MD geometries to generate perfect ground truth for segmentation training was presented. By consolidating simulation and learning into a single modular ecosystem, this work enables users to build upon our pipeline to optimize AFM workflows for more efficient data acquisition and robust processing.

Poster by Anqi Lyu at the Protein Folding in Real Time conference, Stockholm, 11 March 2026

Opposing age trajectories and late-life divergence in protein abundance between conditions. (Image by A. Lyu.)
Age-Dependent Plasma Protein Dynamics in Health and Disease
Anqi Lyu, Maria Jesus Iglesias, Jochen Schwenk, Mathias Uhlén, Jacob Odeberg, Caroline Adiels, Lynn Butler
Date: 11 March 2026
Time: 18:00-20:00
Place: Aula Medica, Karolinska Institute, Solna
Conference Protein Folding in Real Time, 11-13 March 2026, Stockholm, Sweden

Aging is biologically heterogeneous, and chronological age alone cannot explain molecular variability across individuals. Previous studies have shown that disease states can reshape age-associated proteomic trajectories, leading to divergent molecular patterns over time. Here, we explore these dynamics by analyzing age-dependent changes in plasma protein abundance, focusing on differences between case and control conditions during aging. We identify opposing trends and nonlinear transitions, particularly in later life, highlighting critical periods of accelerated molecular change. Beyond descriptive patterns, our analysis emphasizes how disease modifies the underlying structure of aging trajectories, providing insights into the mechanisms of age-related divergence.

Poster by M. Granfors at the Protein Folding in Real Time conference, Stockholm, 11 March 2026

Fluorescence microscopy image of yeast cells, with Hsp104-GFP marking protein aggregates, making them visible as bright spots. (Image by J. Masaryk.)
Machine learning based tracking of protein aggregates in yeast
Mirja Granfors, Jakub Masaryk, Carlo Manzo, Markus Tamas, Giovanni Volpe
Date: 11th March 2026
Time: 18:00 – 20:00
Place: Aula Medica, Karolinska Institute, Solna
Conference Protein Folding in Real Time, 11-13 March 2026, Stockholm, Sweden

Arsenic is a toxic metal linked to serious diseases like cancer and neurodegeneration. One proposed mechanism of toxicity is that arsenic causes proteins to misfold and aggregate inside cells, but the dynamics and regulation of this process remain poorly understood. Using fluorescence microscopy data from living yeast cells, we are developing a machine learning approach to automatically detect, track, and analyze protein aggregate movement over time.

Poster by X. Zhang at at the Protein Folding in Real Time conference, Stockholm, 11 March 2026

Reconstructed field of LCD2–CTPR4-Func1 condensates with LC3 at the sample plane (shown here as the imaginary component of the complex field). The condensates increase in size through Ostwald ripening and recruitment of LC3. (Image by X. Zhang.)
Quantitative Characterization of Biomolecular Condensates Using Off-Axis Holographic Microscopy
Xinwen Zhang, Nora Haanaes, Berenice García Rodríguez, Giovanni Volpe,  Janet Kumita and Daniel Midtvedt
Date: 11 March 2026
Time: 18:00-20:00
Place: Aula Medica, Karolinska Institute, Solna
Conference Protein Folding in Real Time, 11-13 March 2026, Stockholm, Sweden

Biomolecular condensates formed through liquid–liquid phase separation (LLPS) play important roles in cellular organization, yet quantitative and label-free characterization of their physical properties remains challenging. In this work, we apply off-axis holographic microscopy to study a synthetic biomolecular condensate platform based on the LCD2-CTPR protein system. These proteins, composed of modular consensus-designed tetratricopeptide repeat (CTPR) domains fused to intrinsically disordered regions, undergo phase separation under varying salt concentrations. By incorporating short binding motifs such as ATG13 or Func1, the condensates can specifically recruit the autophagy-related protein LC3. Using label-free quantitative phase measurements, we analyze changes in condensate optical radius and refractive index during LC3 recruitment and over time. Our results show measurable variations in condensate size and optical properties, highlighting the sensitivity of these systems to compositional changes. This work demonstrates the applicability of holographic microscopy for quantitative characterization of synthetic biomolecular condensates and provides a framework for studying protein phase separation in a non-invasive manner.

Poster by E. A. Duta Costache at the Protein Folding in Real Time conference, Stockholm, 11 March 2026

Pointwise absolute error plots for the heat equation tested across five architectures. The plots show the mean absolute error achieved by each architecture on a periodic-mode initial condition. Errors are shown on a logarithmic scale. Blue colors indicate smaller errors. (Image by E. A. Duta Costache.)
The optimization autopsy of PINNs
Eduard Duta Costache, Benjamin Girault
Date: 11 March 2026
Time: 18:00-20:00
Place: Aula Medica, Karolinska Institute, Solna
Conference Protein Folding in Real Time, 11-13 March 2026, Stockholm, Sweden

Physics-Informed Neural Networks (PINNs) have emerged as a promising method for solving Partial Differential Equations (PDEs) by combining data-driven learning with physical laws. However, the spectral bias and optimization challenges limit their efficacy. This work investigates these issues and whether the advantages of classical spectral methods translate to the non-convex neural network optimization landscape. We show that gradient imbalance greatly affect learning and we study the Hessian conditioning under different settings. Our results indicate that spectral priors stabilize training, reduce error, and improve parameter efficiency. We also identify that learnable-basis models act as implicit regularizers under sparse sampling.