Illustration of polymer detachments. At low pH polymers attach weakly to liquid-liquid interfaces. Having the polymer attached also to a colloidal particle allows for an optical tweezers to pull the polymer loose and to detect single detachments. (Image by M. Selin.)Optical Tweezers applications: From particle adsorption to single molecules.
Martin Selin Date: 11 April 2025 Time: 10:30 Place: University of Münster, Germany
Optical tweezers are powerful tools for probing microscale forces in systems ranging from colloidal particles to single molecules. Here, we demonstrate their use in two different fields. First, by trapping individual colloidal particles, we study their adsorption dynamics at liquid–liquid interfaces, highlighting the critical role of surface chemistry and the presence of polymer shells. We also observe reversible polymer attachments and stretching. Second, we apply tweezers to study single-molecule mechanics. By automating these complex biophysical experiments, we enable high-throughput measurements of molecular dynamics. Our results suggest that, like DNA, synthetic polymers can be effectively described by the worm-like chain model.
Illustration of adsorption process of a polymer coated particle. A single particle is brought to a liquid-liquid interface using an optical tweezers and once the polymer shell makes contact with the interface the particle immediately jumps into the interface. (Image by M. Selin.)Optical tweezers reveal how polymer coated particles jump into liquid-liquid interfaces
Martin Selin Date: 8 April 2025 Time: 17:40 Place: Center for Interdisciplinary Research, Bielefeld University, Germany
Colloidal particles typically require salt to overcome electrostatic barriers and adsorb to liquid-liquid interfaces. Here, we show that coating particles with polymers enables spontaneous adsorption without salt. Our model system consists of silica cores coated with poly(2-(dimethylamino)ethyl methacrylate) (PDMAEMA). Using optical tweezers, we track individual particles showing that the polymer shell makes particles jump into a dodecane–water interface. This behavior extends to other polymers. By tuning pH, we control polymer swelling and adsorption distance. At very low pH, the attachment to the interface is weak enough that the optical tweezers can pull particles out from the interface. During this desorption process we observe single polymers detaching. These findings offer new approaches for designing responsive emulsions.
Recent advances in nonequilibrium physics allow extracting thermodynamic quantities, such as entropy production, directly from dynamical information in microscopic movies. (Image by S. Manikandan.)Localizing entropy production in non-equilibrium processes
Sreekanth Manikandan Date: 5 Mar 2025 Time: 14:45 Place: Nordita
Part of the 14th Nordic Workshop on Statistical Physics
Quantifying the spatiotemporal forces, affinities, and dissipative costs of cellular-scale non-equilibrium processes from experimental data and localizing it in space and time remain a significant open challenge. Here, I explore how principles from stochastic thermodynamics, combined with machine learning techniques, offer a promising approach to addressing this issue. I will present preliminary results from experiments on fluctuating cell membranes and simulations of non-equilibrium systems in stationary and time-dependently driven states. These studies reveal potential strategies for localizing entropy production in experimental biophysical contexts while also highlighting key challenges and limitations that must be addressed.
MIRO employs a recurrent graph neural network to refine SMLM point clouds by compressing clusters around their center, enhancing inter-cluster distinction and background separation for efficient clustering. (Image by J. Pineda.)Relational Inductive Biases as a Key to Smarter Deep Learning Microscopy Jesús Pineda Learning on graphs and geometry meetup at Uppsala University Date: 12 February 2025 Time: 11:15 Place: Lecture hall 4101, Ångströmlaboratoriet, Uppsala, Sweden
Geometric deep learning has revolutionized fields like social network analysis, molecular chemistry, and neuroscience, but its application to microscopy data analysis remains a significant challenge. The hurdles stem not only from the scarcity of high-quality data but also from the intrinsic complexity and variability of microscopy datasets. This presentation introduces two groundbreaking geometric deep-learning frameworks designed to overcome these barriers, advancing the integration of graph neural networks (GNNs) into microscopy and unlocking their full potential. First, we present MAGIK, a cutting-edge framework for analyzing biological system dynamics through time-lapse microscopy. Leveraging a graph neural network augmented with attention-based mechanisms, MAGIK processes object features using geometric priors. This enables it to perform a range of tasks, from linking coordinates into trajectories to uncovering local and global dynamic properties with unprecedented precision. Remarkably, MAGIK excels under minimal data conditions, maintaining exceptional performance and robust generalization across diverse scenarios. Next, we introduce MIRO, a novel algorithm powered by recurrent graph neural networks. MIRO pre-processes Single Molecule Localization (SML) datasets to enhance the efficiency of conventional clustering methods. Its ability to handle clusters of varying shapes and scales enables more accurate and consistent analyses across complex datasets. Furthermore, MIRO’s single- and few-shot learning capabilities allow it to generalize effortlessly across scenarios, making it an efficient, scalable, and versatile tool for microscopy data analysis. Together, MAGIK and MIRO address critical limitations in microscopy data analysis, offering innovative solutions for multi-scale data analysis and advancing the boundaries of what is currently achievable with geometric deep learning in the field.
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 geometric deep learning
Mirja Granfors, Jesús Pineda, Blanca Zufiria Gerbolés, Daniel Vereb, Joana B. Pereira, Carlo Manzo, and Giovanni Volpe Learning on graphs and geometry meetup at Uppsala University Date: 11 February 2025 Place: Uppsala university
Graphs are used to model complex relationships, such as interactions between particles or connections between brain regions. The structural complexity and variability of graphs pose challenges to their efficient analysis and classification. Here, we propose GAUDI (Graph Autoencoder Uncovering Descriptive Information), a graph autoencoder that addresses these challenges. GAUDI’s encoder progressively reduces the size of the graph using multi-step hierarchical pooling, while its decoder incrementally increases the graph size until the original dimensions are restored, focusing on the node and edge features while preserving the graph structure through skip-connections. By training GAUDI to minimize the difference between the node and edge features of the input graph and those of the output graph, it is compelled to capture the most critical parameters describing these features in the latent space, thereby enabling the extraction of essential parameters characterizing the graphs. We demonstrate the performance of GAUDI across diverse graph data originating from complex systems, including the estimation of the parameters of Watts-Strogatz graphs, the classification of protein assembly structures from single-molecule localization microscopy data, the analysis of collective behaviors, and correlations between brain connections and age. This approach offers a robust framework for efficiently analyzing and interpreting complex graph data, facilitating the extraction of meaningful patterns and insights across a wide range of applications.
MIRO employs a recurrent graph neural network to refine SMLM point clouds by compressing clusters around their center, enhancing inter-cluster distinction and background separation for efficient clustering. (Image by J. Pineda.)Spatial clustering of molecular localizations with graph neural networks
Jesús Pineda Date: 27 January 2025 Time: 10:00 Place: SciLifeLab Campus Solna, Sweden
Single-molecule localization microscopy (SMLM) generates point clouds corresponding to fluorophore localizations. Spatial cluster identification and analysis of these point clouds are crucial for extracting insights about molecular organization. However, this task becomes challenging in the presence of localization noise, high point density, or complex biological structures. Here, we introduce MIRO (Multimodal Integration through Relational Optimization), an algorithm that uses recurrent graph neural networks to transform the point clouds in order to improve clustering efficiency when applying conventional clustering techniques. We show that MIRO supports simultaneous processing of clusters of different shapes and at multiple scales, demonstrating improved performance across varied datasets. Our comprehensive evaluation demonstrates MIRO’s transformative potential for single-molecule localization applications, showcasing its capability to revolutionize cluster analysis and provide accurate, reliable details of molecular architecture. In addition, MIRO’s robust clustering capabilities hold promise for applications in various fields such as neuroscience, for the analysis of neural connectivity patterns, and environmental science, for studying spatial distributions of ecological data.
Ivo Sbalzarini, talk. (Photo by Y.-W. Chang.)Content-adaptive deep learning for large-scale
fluorescence microscopy imaging
Ivo Sbalzarini
Max Planck Institute of Molecular Cell Biology and Genetics
Center for Systems Biology Dresden https://sbalzarini-lab.org/
Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions.
To overcome this issue, we have introduced a software, currently at version DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.1 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.
DeepTrack 2.1 Logo. (Image from DeepTrack 2.1 Project)How can deep learning enhance microscopy?
Giovanni Volpe SPAOM 2024 Date: 22 November 2024 Time: 10:15-10:45 Place: Toledo, Spain
Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we have introduced a software, currently at version DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy.
The three RMS Early Career Award speakers (l to r) Harshith Bachimanchi, Akaash Kumar and Liam Rooney. (Image by RMS.)Harshith Bachimanchi is shortlisted as one of the RMS (Royal Microscopical Society) early-career award speakers at RMS AMG 2024 (RMS Annual General Meeting 2024) held in London, UK, on 2 October 2024.
In this meeting, Harshith presented his work on leveraging deep learning as a powerful tool to enhance the microscopic data analysis pipelines, to study microorganisms in unprecedented detail. Taking holographic microscopy as an example, he demonstrated that combining holography with deep learning can be used to follow marine micro-organisms through out their lifespan, continuously measuring their three-dimensional positions and dry mass. He also presented some recent results on using deep learning to transform microscopy images from one modality to another (For eg., from Holography to Bright-field and vice versa).
The annual Early Career Award—for which Harshith is shortlisted as one of the potential candidates—recognises the achievements of an outstanding early career imaging scientist in their contribution to microscopy, image analysis, or cytometry.
From RMS:
We heard some fantastic talks earlier from the RMS #EarlyCareer Award speakers at our AGM.
