Thermophoretic trap for single amyloid fibril and protein aggregation studies.

The study of the aggregation of soluble proteins into highly ordered, insoluble amyloid fibrils is fundamental for the understanding of neurodegenerative disorders. Here, we present a method for the observation of single amyloid fibrils that allows the investigation of fibril growth, secondary nucleation or fibril breakup that is typically hidden in the average ensemble. Our approach of thermophoretic trapping and rotational diffusion measurements is demonstrated for single Aß40, Aß42 and pyroglutamyl-modified amyloid-ß variant (pGlu3-Aß3-40) amyloid fibrils.

Accumulation and Stretching of DNA Molecules in Temperature-Induced Concentration Gradients.

Temperature fields provide a noninvasive approach for manipulating individual macromolecules in solution. Utilizing thermophoresis and other secondary effects resulting from the inhomogeneous distribution of crowding agents, one may gain valuable insights into the interactions of molecular mixtures. In this report, we examine the steady-state concentration distribution and dynamics of DNA molecules in a poly(ethylene glycol) (PEG)/water solution when exposed to localized temperature gradients generated by optical heating of a thin chrome layer at a liquid-solid boundary. This allowed us to experimentally investigate the interplay between DNA thermophoresis and PEG-induced entropic depletion effects. Our quantitative analysis demonstrates that the depletion effects dominate over DNA thermophoresis, causing the DNA polymers to migrate toward the heat source. Additionally, we explore the transient stretching of individual DNA molecules in thermally induced PEG gradients and estimate the contributing forces.

Roadmap on Deep Learning for Microscopy.

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences.

FMNL formins boost lamellipodial force generation.

Migration frequently involves Rac-mediated protrusion of lamellipodia, formed by Arp2/3 complex-dependent branching thought to be crucial for force generation and stability of these networks. The formins FMNL2 and FMNL3 are Cdc42 effectors targeting to the lamellipodium tip and shown here to nucleate and elongate actin filaments with complementary activities in vitro. In migrating B16-F1 melanoma cells, both formins contribute to the velocity of lamellipodium protrusion. Loss of FMNL2/3 function in melanoma cells and fibroblasts reduces lamellipodial width, actin filament density and -bundling, without changing patterns of Arp2/3 complex incorporation. Strikingly, in melanoma cells, FMNL2/3 gene inactivation almost completely abolishes protrusion forces exerted by lamellipodia and modifies their ultrastructural organization. Consistently, CRISPR/Cas-mediated depletion of FMNL2/3 in fibroblasts reduces both migration and capability of cells to move against viscous media. Together, we conclude that force generation in lamellipodia strongly depends on FMNL formin activity, operating in addition to Arp2/3 complex-dependent filament branching.