26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Control of Bionanoparticles with Electric Fields

Single-particle imaging (SPI) is a method that promises high-resolution structure determination of artificial or biological nanoparticles, including proteins. In a thin stream, these particles are guided into the brilliant flashes of free-electron lasers. Upon interception, incoming photons diffract off randomly-oriented individual nanoparticles in the gas phase. The low-signal snapshots are then classified and combined to retrieve the real-space structure of the investigated molecules. Since this is the result from averaging over hundreds of thousands of individual images, in order to achieve atomic resolution with SPI, the nanoparticles need to be identical on the same length scales. For various reasons, biological molecules, like proteins, have structural variability. Different oligomeric or conformational states may co-exist already in solution and multiple charges, for example acquired in the process of aerosolization, deform soft proteins due to Coulomb stretching. When not accounted for, these and other morphologic deviations introduce positional ambiguity and effectively reduce the overall achievable experimental resolution. In light of these challenges, methods to characterize and control the particles to deliver high-purity particle beams in SPI experiments need to be developed.Here, experimental results on the production of beams of aerosolized nanoparticles with well-characterized charge- and oligomeric states and ways to modulate their charge-state distributions are presented. Furthermore, based on computational modeling, an electrostatic deflection setup to enable the spatial separation of conformers is proposed, in which charge-neutral biological macromolecules can be separated according to their conformational states. These findings are crucial steps toward atomic-resolution imaging of identical macromolecules in the gas phase, which can be directly applied in SPI experiments

Optimizing the geometry of aerodynamic lens injectors for single-particle coherent diffractive imaging of gold nanoparticles

Single-particle x-ray diffractive imaging (SPI) of small (bio-)nanoparticles (NPs) requires optimized injectors to collect sufficient diffraction patterns to reconstruct the NP structure with high resolution. Typically, aerodynamic-lens-stack injectors are used for single NP injection. However, current injectors were developed for larger NPs (100 nm) and their ability to generate high-density NP beams suffers with decreasing NP size. Here, an aerodynamic-lens-stack injector with variable geometry and the geometry-optimization procedure are presented. The optimization for 50 nmgold NP (AuNP) injection using a numerical simulation infrastructure capable of calculating the carrier gas flow and the particle trajectories through the injector is introduced. The simulations are experimentally validated using spherical AuNPs and sucrose NPs. In addition, the optimized injector is compared to the standard-installation “Uppsala-injector” for AuNPs and results for these heavy particles show a shift in the particle-beam focus position rather than a change in beam size, which results in a lower gas background for the optimized injector. Optimized aerodynamic-lens stack injectors will allow to increase NP beam density, reduce the gas background, discover the limits of current injectors, and contribute to structure determination of small NPs using SPI

Optimizing the geometry of aerodynamic lens injectors for single-particle coherent diffractive imaging of gold nanoparticles

Single-particle x-ray diffractive imaging (SPI) of small (bio-)nanoparticles (NPs) requires optimized injectors to collect sufficient diffraction patterns to reconstruct the NP structure with high resolution. Typically, aerodynamic-lens-stack injectors are used for single NP injection. However, current injectors were developed for larger NPs (100 nm) and their ability to generate high-density NP beams suffers with decreasing NP size. Here, an aerodynamic-lens-stack injector with variable geometry and the geometry-optimization procedure are presented. The optimization for 50 nm gold NP (AuNP) injection using a numerical simulation infrastructure capable of calculating the carrier gas flow and the particle trajectories through the injector is introduced. The simulations are experimentally validated using spherical AuNPs and sucrose NPs. In addition, the optimized injector is compared to the standard-installation “Uppsala-injector” for AuNPs and results for these heavy particles show a shift in the particle-beam focus position rather than a change in beam size, which results in a lower gas background for the optimized injector. Optimized aerodynamic-lens stack injectors will allow to increase NP beam density, reduce the gas background, discover the limits of current injectors, and contribute to structure determination of small NPs using SPI

Time-resolved single-particle x-ray scattering reveals electron-density gradients as coherent plasmonic-nanoparticle-oscillation source

Dynamics of optically excited plasmonic nanoparticles are presently understood as a series of scattering events involving the initiation of nanoparticle breathing oscillations. According to established models, these are caused by statistical heat transfer from thermalized electrons to the lattice. An additional contribution by hot-electron pressure accounts for phase mismatches between theory and experimental observations. However, direct experimental studies resolving the breathing-oscillation excitation are still missing. We used optical transient-absorption spectroscopy and time-resolved single-particle X-ray diffractive imaging to access the electron system and lattice. The time-resolved single-particle imaging data provided structural information directly on the onset of the breathing oscillation and confirmed the need for an additional excitation mechanism for thermal expansion. We developed a new model that reproduces all of our experimental observations. We identified optically induced electron density gradients as the initial driving source

Unsupervised learning approaches to characterizing heterogeneous samples using X-ray single-particle imaging

One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. Proposed here are two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA), provides a rough classification which is essentially parameter free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs), can generate 3D structures of the objects at any point in the structural landscape. Both these methods are implemented in combination with the noise-tolerant expand-maximizecompress (EMC) algorithm and its utility is demonstrated by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern. Both discrete structural classes and continuous deformations are recovered. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility of moving beyond the study of homogeneous sample sets to addressing open questions on topics such as nanocrystal growth and dynamics, as well as phase transitions which have not been externally triggered