The environment topography alters the transition from single-cell populations to multicellular structures in Myxococcus xanthus on bioRxiv

M. xanthus cell-cell and cell-particle local interactions during cellular aggregation.
The environment topography alters the transition from single-cell populations to multicellular structures in Myxococcus xanthus
Karla C. Hernández Ramos, Edna Rodríguez-Sánchez, Juan Antonio Arias del Angel, Alejandro V. Arzola, Mariana Benítez, Ana E. Escalante, Alessio Franci, Giovanni Volpe, Natsuko Rivera-Yoshida
bioRxiv: 10.1101/2021.01.27.428527

The social soil-dwelling bacteria Myxococcus xanthus can form multicellular structures, known as fruiting bodies. Experiments in homogeneous environments have shown that this process is affected by the physico-chemical properties of the substrate, but they have largely neglected the role of complex topographies. We experimentally demonstrate that the topography alters single-cell motility and multicellular organization in M. xanthus. In topographies realized by randomly placing silica particles over agar plates, we observe that the cells’ interaction with particles drastically modifies the dynamics of cellular aggregation, leading to changes in the number, size and shape of the fruiting bodies, and even to arresting their formation in certain conditions. We further explore this type of cell-particle interaction in a minimal computational model. These results provide fundamental insights into how the environment topography influences the emergence of complex multicellular structures from single cells, which is a fundamental problem of biological, ecological and medical relevance.

Online seminar by G. Volpe at Indian Institute of Science Education and Research (IISER), Pune, India

Deep learning for microscopy and optical trapping
Giovanni Volpe
21 January 2021, 16:30 CEST
Invited seminar for Indian Institute of Science Education and Research (IISER), Pune, India

After a brief overview of artificial intelligence, machine learning and deep learning, I will present a series of recent works in which we have employed deep learning for applications in photonics and active matter. In particular, I will explain how we employed deep learning to enhance digital video microscopy, to estimate the properties of anomalous diffusion, to characterize microscopic force fields, to improve the calculation of optical forces, and to characterize nanoparticles. Finally, I will provide an outlook for the application of deep learning in photonics and active matter.

Falko Schmidt defended his PhD Thesis in Physics on 15 January 2021. Congrats!

Falko Schmidt defended his PhD Thesis in Physics on Friday, 15 January 2021. Congrats!

The disputation took place at 9 a.m., in PJ salen, Fysikgården.
Falko Schmidt’s opponent, Peer Fischer, gave an introductory presentation with title “Microswimmers and motile active matter”.


From Falko Schmidt’s PhD Thesis.
Title: Active Matter in a Critical State: From passive building blocks to active molecules, engines and droplets

The motion of microscopic objects is strongly affected by their surrounding environment. In quiescent liquids, motion is reduced to random fluctuations known as Brownian motion. Nevertheless, microorganisms have been able to develop mechanisms to generate active motion. This has inspired researchers to understand and artificially replicate active motion. Now, the field of active matter has developed into a multi-disciplinary field, with researchers developing artificial microswimmers, producing miniaturized versions of heat engines and showing that individual colloids self-assemble into larger microstructures. This thesis taps into the development of artificial microscopic and nanoscopic systems and demonstrates that passive building blocks such as colloids are transformed into active molecules, engines and active droplets that display a rich set of motions. This is achieved by combining optical manipulation with a phase-separating environment consisting of a critical binary mixture. I first show how simple absorbing particles are transformed into fast rotating microengines using optical tweezers, and how this principle can be scaled down to nanoscopic particles. Transitioning then from single particles to self-assembled modular swimmers, such colloidal molecules exhibit diverse behaviour such as propulsion, orbital rotation and spinning, and whose formation process I can control with periodic illumination. To characterize the molecules dynamics better, I introduce a machine-learning algorithm to determine the anomalous exponent of trajectories and to identify changes in a trajectory’s behaviour. Towards understanding the behaviour of larger microstructures, I then investigate the interaction of colloidal molecules with their phase-separating environment and observe a two-fold coupling between the induced liquid droplets and their immersed colloids. With the help of simulations I gain a better physical picture and can further analyse the molecules’ and droplets’ emergence and growth dynamics. At last, I show that fluctuation-induced forces can solve current limitations in microfabrication due to stiction, enabling a further development of the field towards smaller and more stable nanostructures required for nowadays adaptive functional materials. The insights gained from this research mark the path towards a new generation of design principles, e.g., for the construction of flexible micromotors, tunable micromembranes and drug delivery in health care applications.

Fast and Accurate Nanoparticle Characterization Using Deep-Learning-Enhanced Off-Axis Holography published in ACS Nano

Phase and amplitude signals from representative particles for testing the performance of the Deep-learning approach

Fast and Accurate Nanoparticle Characterization Using Deep-Learning-Enhanced Off-Axis Holography
Benjamin Midtvedt, Erik Olsén, Fredrik Eklund, Fredrik Höök, Caroline Beck Adiels, Giovanni Volpe, Daniel Midtvedt
ACS Nano 15(2), 2240–2250 (2021)
doi: 10.1021/acsnano.0c06902
arXiv: 2006.11154

The characterisation of the physical properties of nanoparticles in their native environment plays a central role in a wide range of fields, from nanoparticle-enhanced drug delivery to environmental nanopollution assessment. Standard optical approaches require long trajectories of nanoparticles dispersed in a medium with known viscosity to characterise their diffusion constant and, thus, their size. However, often only short trajectories are available, while the medium viscosity is unknown, e.g., in most biomedical applications. In this work, we demonstrate a label-free method to quantify size and refractive index of individual subwavelength particles using two orders of magnitude shorter trajectories than required by standard methods, and without assumptions about the physicochemical properties of the medium. We achieve this by developing a weighted average convolutional neural network to analyse the holographic images of the particles. As a proof of principle, we distinguish and quantify size and refractive index of silica and polystyrene particles without prior knowledge of solute viscosity or refractive index. As an example of an application beyond the state of the art, we demonstrate how this technique can monitor the aggregation of polystyrene nanoparticles, revealing the time-resolved dynamics of the monomer number and fractal dimension of individual subwavelength aggregates. This technique opens new possibilities for nanoparticle characterisation with a broad range of applications from biomedicine to environmental monitoring.