Presentation – Page 2 – Soft Matter Lab

Presentation by Anoop C. Patil at SPIE-ETAI, San Diego, 6 August 2025

In this work, we present an unsupervised deep learning framework using Variational Autoencoders (VAEs) to decode stress-specific biomolecular fingerprints directly from Raman spectral data across multiple plant species and genotypes. *(Image by the Authors of the manuscript. A part of the image was designed using Biorender.com.)*

From Spectra to Stress: Unsupervised Deep Learning for Plant Health Monitoring
Anoop C. Patil, Benny Jian Rong Sng, Yu-Wei Chang, Joana B. Pereira, Chua Nam-Hai, Rajani Sarojam, Gajendra Pratap Singh, In-Cheol Jang, and Giovanni Volpe
Date: 6 August 2025
Time: 10:30 AM – 11:00 AM
Place: Conv. Ctr. Room 4

Plants experience a wide variety of stresses, from light and temperature fluctuations to bacterial infections. Each stress has a biomolecular fingerprint, but detecting and interpreting these signatures across species can be challenging. This work presents a deep learning-based approach using Variational Autoencoders (VAEs) to uncover how plants respond to light stress, shade avoidance, temperature stress, and bacterial infection — all without requiring any human intervention in spectral processing. By encoding Raman spectral data into an intuitive latent space, this method automatically categorizes and visualizes stress-specific biomolecular shifts, offering a powerful, unsupervised tool for stress phenotyping in crops.

Reference:
Patil, A.C. et al. Deep-Learning Investigation of Vibrational Raman Spectra for Plant-Stress Analysis. arXiv preprint arXiv:2507.15772v1 (2025). URL https://arxiv.org/abs/2507.15772

Presentation by B. García Rodríguez at SPIE-ETAI, San Diego, 5 August 2025

Biomolecular condensates are analyzed and characterized using a holographic microscope, which utilizes a variational autoencoder (VAE) to quantify size, protein concentration, and the sharpness of the RNA-binding protein interface. *(Image by B. Garcia.)*

Structure and dynamics of biomolecular condensates revealed by deep learning enhanced interferometric microscopy
Berenice García Rodríguez, Makasewicz Katarzyna, Giovanni Volpe, Paolo Arosio, Daniel Sundås Midtvedt.
Date: 5 August 2025
Time: 5:00 PM – 5:15 PM PDT
Place: Conv. Ctr. Room 4
Presentation type: Oral

Biomolecular condensates are biological structures that form through weak, multivalent interactions primarily between low complexity domains of intrinsically disordered proteins, existing in cells as submicrometer structures. Their functions rely sensitively on physical properties such as size, internal protein concentration, and interfacial tension. However, direct measurements of these properties in submicrometer condensates remain scarce. In this work, we employ deep learning enhanced interferometric imaging to quantify size, protein concentration, and the sharpness of the interface of submicrometer condensates formed by the low complexity domain of the RNA-binding protein DDX4-LCD. We find that, within the two-phase region, DDX4-LCD forms spherical condensates with an internal protein concentration that can be slightly modulated by adding salt to the solution. Furthermore, we find that multiple populations of protein clusters coexist in the sample, some persisting even outside the two-phase region of the phase diagram, separable by their interface properties and dynamics. This hints at a more complex phase diagram of DDX4-LCD condensation than previously anticipated.

Presentation by M. Selin at SPIE-ETAI, San Diego, 5 August 2025

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.)*

Advanced autonomous optical tweezers experiments
Martin Selin, A. Ciarlo, G. Pesce, L. Bengtsson, J. Camuñas-Soler, V. Sundar Rajan, F. Westerlund, L. M. Wilhelmsson, I. Pastor, F. Ritort, S. B. Smith, C. Bustamante, G. Volpe
Date: 5 August 2025
Time: 4:30 PM – 4:45 PM PDT
Place: Conv. Ctr. Room 4

Single-molecule studies are vital for elucidating fundamental biological processes, including protein folding, DNA transcription, and replication. However, performing these experiments manually on individual molecules is notoriously time-consuming and costly. To address this challenge, we have developed a fully autonomous single-molecule force spectroscopy platform by integrating a custom-built optical tweezers instrument with real-time deep-learning-based image analysis and adaptive control protocols. Our system achieves human-level throughput in terms of experiments per hour while remaining robust enough to operate continuously for hours without intervention. We demonstrate the versatility of our platform by having it perform DNA pulling experiments on both lambda DNA and DNA hairpins fully autonomously. These results push the boundaries of high-throughput data collection in single-molecule biophysics, paving the way for merging single-molecule studies with large-scale, data-driven approaches—ultimately enabling new insights into the dynamic, transient states of complex biological systems.

Presentation by N. C. Palmero Cruz at SPIE-ETAI, San Diego, 5 August 2025

Light sheet fluorescence image of a zebrafish larva showing neuronal structures in the brain (green) and gut (purple). Schematic network representations illustrate putative neuronal connectivity within each region, with nodes representing neuronal cell bodies and edges indicating potential functional or structural links. The eye is indicated for anatomical reference. *(Image by N. C. Palmero Cruz.)*

Exploring gut-brain connectivity using zebrafish and graph theory
Norma Caridad Palmero Cruz, Sarah B. Flensburg, Hang Zhao, Antonio Ciarlo, Caroline Beck Adiels, Gilles Vanwalleghem, Giovanni Volpe
Date: 5 August 2025
Time: 3:15 – 3:30 PM
Place: Conv. Ctr. Room 4

The gut-brain axis constitutes a fundamental communication network linking the microbiome, enteric nervous system, and central nervous system, and is increasingly recognized for its role in mental health disorders. Although substantial progress has been made in characterizing this axis, the translation of research findings into clinical applications remains limited. In the present study, zebrafish are employed as a model organism due to their genetic and physiological similarities to humans, allowing the investigation of gut-brain interactions under controlled conditions. An experimental platform has been developed that integrates microfluidic, optical tweezers, and light sheet microscopy to introduce defined bacterial strains into the gut and to manipulate the local environment. This setup enables real-time, in vivo recording of neuronal activity across different stages of infection. By applying graph theoretical analysis to high-resolution imaging data, the study aims to characterize the neural connectivity of the gut-brain axis, potentially informing new strategies for understanding and treating mental and neurological disorders.

Presentation by M. Granfors at SPIE-ETAI, San Diego, 5 August 2025

DeepTrack2: physics-based microscopy simulations for deep learning
Mirja Granfors, Alex Lech, Benjamin Midtvedt, Jesús Pineda, Harshith Bachimanchi, Carlo Manzo, and Giovanni Volpe
Date: 5 August 2025
Time: 2:45 PM – 3:00 PM
Place: Conv. Ctr. Room 4

DeepTrack2 is a flexible and scalable Python library designed to generate physics-based synthetic microscopy datasets for training deep learning models. It supports a wide range of imaging modalities, including brightfield, fluorescence, darkfield, and holography, enabling the creation of synthetic samples that accurately replicate real experimental conditions. Its modular architecture empowers users to customize optical systems, incorporate optical aberrations and noise, simulate diverse objects across various imaging scenarios, and apply image augmentations. DeepTrack2 is accompanied by a dedicated GitHub page, providing extensive documentation, examples, and an active community for support and collaboration: https://github.com/DeepTrackAI/DeepTrack2.

Presentation by A. Lech at SPIE-ETAI, San Diego, 5 August 2025

Deeplay: Enhancing PyTorch with Customizable and Reusable Neural Networks
Alex Lech, Mirja Granfors, Benjamin Midtvedt, Jesús Pineda, Harshith Bachimanchi, Carlo Manzo, Giovanni Volpe
Date: 5 August 2025
Time: 12:00 – 12:15 PM
Place: Conv. Ctr. Room 4

Deeplay is a flexible Python library for deep learning that simplifies the definition and optimization of neural networks. It provides an intuitive framework that makes it easy to define and train models. With its modular design, deeplay lets users efficiently build and refine complex neural network architectures by seamlessly integrating reusable components based on PyTorch as well as adding a plethora of functionalities to alter and customize existing models without introducing boilerplate code. Deeplay is accompanied by a dedicated GitHub page, featuring extensive documentation, examples, and an active community for support and collaboration: https://github.com/DeepTrackAI/deeplay.

Invited presentation by A. Ciarlo at SPIE-MNPM, San Diego, 5 August 2025

Graphical representation of colloidal interaction measurements using the automated miniTweezer. *(Image by A. Ciarlo.)*

miniTweezer: an autonomous high-throughput optical tweezers platform for force spectroscopy
Antonio Ciarlo, Martin Selin, Giuseppe Pesce, Carlos Bustamante, and Giovanni Volpe
Date: 5 August 2025
Time: 9:45 AM – 10:15 AM
Place: Conv. Ctr. Room 4

Optical tweezers are essential for single-molecule studies, providing direct access to the forces underlying biological processes such as protein folding, DNA transcription, and replication. However, manual experiments are labor-intensive, costly, and slow, especially when large data sets are required. Here we present the miniTweezer, a fully autonomous force spectroscopy platform that integrates optical tweezers with real-time image analysis and adaptive control. Once configured, it operates independently to perform high-throughput trapping, molecular attachment, and force measurements. Its robust design allows for extended unattended operation, significantly increasing the efficiency of data acquisition. We demonstrate its capabilities through DNA pulling experiments and highlight its broader applicability to microparticle interactions, colloidal assembly, and soft matter mechanics. By automating force spectroscopy, the miniTweezer enables large-scale, high-precision studies in biophysics, materials science, and nanotechnology.

Invited Presentation by B. Yeroshenko at SPIE-ETAI, San Diego, 5 August 2025

Kymographs of DNA inside Channel II. *(Image from 10.1038/s41592-022-01491-6. by Barbora Špačková)*

Pushing the limits of label-free single-molecule characterization by nanofluidic scattering microscopy
Bohdan Yeroshenko, Henrik Klein Moberg, Leyla Beckerman, Joachim Fritzsche, David Albinsson, Barbora Špačková, Daniel Midtvedt, Giovanni Volpe, Christoph Langhammer
SPIE-ETAI, San Diego, CA, USA, 3 – 7 August 2025
Date: 5 August 2025
Time: 8:45 AM – 9:15 AM PDT
Place: Conv. Ctr. Room 4

Nanofluidic Scattering Microscopy (NSM) is a label-free characterization method that leverages the interference of light scattered by nanochannels and single molecules within them. This technique enables accurate determination of molecular weight and hydrodynamic radius based solely on scattering, without requiring prior molecular knowledge. However, standard analysis methods limit NSM’s sensitivity to 66 kDa for proteins. In this presentation, I will demonstrate how we push this detection limit an order of magnitude further by integrating ultrasmall geometry with an advanced machine learning analysis approach, all while maintaining the same input laser power intensity.

Presentation by H. P. Thanabalan at SPIE-ETAI, San Diego, 5 August 2025

Inchworm-inspired soft robot. *(Image by H. P. Thanabalan.)*

Bio-inspired soft robot for multi-directionality
Hari Prakash Thanabalan, Lars Bengtsson, Ugo Lafont, Giovanni Volpe
SPIE Optics+Photonics, San Diego, CA, USA, 3-7 August 2025
Date: 5th August 2025
Time: 8:30 AM – 8:45 AM
Place: Conv. Ctr. Room 4

Soft robotics are the forefront of robotics evolution that leverages compliant materials such as silicone elastomer to mimic biological organisms. With infinite degrees of freedom, soft robots surpass rigid robots in adaptability making them ideal for exploration and manipulation tasks. Here we focus on inchworm inspired soft robot achieving multidirectional locomotion through groove-guided movement. By manipulating the groove angles on a substrate, we demonstrate multidirectional locomotion by utilising only a single actuator.

Poster by A. Callegari at SPIE-OTOM, San Diego, 4 August 2025

One exemplar of the HEXBUGS used in the experiment. (Image by the Authors of the manuscript.)

Experimenting with macroscopic active matter
Angelo Barona Balda, Aykut Argun, Agnese Callegari, Giovanni Volpe
SPIE-OTOM, San Diego, CA, USA, 3 – 7 August 2025
Date: 4 August 2025
Time: 5:30 PM – 7:30 PM PDT
Place: Conv. Ctr. Exhibit Hall A

Presenter: Giovanni Volpe
Contribution submitted by Agnese Callegari

Active matter is based on concepts of nonequilibrium thermodynamics applied to the most diverse disciplines. A key concept is the active Brownian particle, which, unlike passive ones, extracts energy from its environment to generate complex motion and emergent behaviors. Despite its significance, active matter remains absent from standard curricula. This work presents macroscopic experiments using commercially available Hexbugs to demonstrate active matter phenomena. We show how Hexbugs can be modified to perform both regular and chiral active Brownian motion and interact with passive objects, inducing movement and rotation. By introducing obstacles, we sort Hexbugs based on motility and chirality. Finally, we demonstrate a Casimir-like attraction effect between planar objects in the presence of active particles.

Reference
Angelo Barona Balda, Aykut Argun, Agnese Callegari, Giovanni Volpe
Playing with Active Matter, Americal Journal of Physics 92, 847–858 (2024)