Mapping Local Conformational Landscapes of Proteins in Solution.

The ability of proteins to adopt multiple conformational states is essential to their function, and elucidating the details of such diversity under physiological conditions has been a major challenge. Here we present a generalized method for mapping protein population landscapes by NMR spectroscopy. Experimental NOESY spectra are directly compared with a set of expectation spectra back-calculated across an arbitrary conformational space. Signal decomposition of the experimental spectrum then directly yields the relative populations of local conformational microstates. In this way, averaged descriptions of conformation can be eliminated. As the method quantitatively compares experimental and expectation spectra, it inherently delivers an R factor expressing how well structural models explain the input data. We demonstrate that our method extracts sufficient information from a single 3D NOESY experiment to perform initial model building, refinement, and validation, thus offering a complete de novo structure determination protocol.

FTSPlot: fast time series visualization for large datasets.

The analysis of electrophysiological recordings often involves visual inspection of time series data to locate specific experiment epochs, mask artifacts, and verify the results of signal processing steps, such as filtering or spike detection. Long-term experiments with continuous data acquisition generate large amounts of data. Rapid browsing through these massive datasets poses a challenge to conventional data plotting software because the plotting time increases proportionately to the increase in the volume of data. This paper presents FTSPlot, which is a visualization concept for large-scale time series datasets using techniques from the field of high performance computer graphics, such as hierarchic level of detail and out-of-core data handling. In a preprocessing step, time series data, event, and interval annotations are converted into an optimized data format, which then permits fast, interactive visualization. The preprocessing step has a computational complexity of O(n x log(N)); the visualization itself can be done with a complexity of O(1) and is therefore independent of the amount of data. A demonstration prototype has been implemented and benchmarks show that the technology is capable of displaying large amounts of time series data, event, and interval annotations lag-free with < 20 ms ms. The current 64-bit implementation theoretically supports datasets with up to 2(64) bytes, on the x86_64 architecture currently up to 2(48) bytes are supported, and benchmarks have been conducted with 2(40) bytes/1 TiB or 1.3 x 10(11) double precision samples. The presented software is freely available and can be included as a Qt GUI component in future software projects, providing a standard visualization method for long-term electrophysiological experiments.

Biophysics of microchannel-enabled neuron-electrode interfaces.

We have previously described the use of microchannels (µChannels) as substrate-integrated equivalents of micropipettes and advantageous neuron-electrode interface enhancers. The use of µChannels to establish stable recording and stimulation of threading axons results in a high signal-to-noise ratio (SNR), potentially high-throughput and low-cost alternative to conventional substrate-embedded microelectrodes. Here we confirm the consistent achievement of high SNRs with µChannels and systematically characterize the impact of µChannel geometry on the measured signals via numerical simulations and in vitro experiments. We demonstrate and rationalize how channels with a length of ≤300 µm and channel cross section of ≤12 µm(2) support spontaneous formation of seals and yield spike sizes in the millivolt range. Despite the low degree of complexity involved in their fabrication and use, µChannel devices provide a single-unit mean SNR of 101 ± 76, which compares favourably with the SNR obtained from typical microelectrode arrays.

Integrating multi-unit electrophysiology and plastic culture dishes for network neuroscience.

The electrophysiological characterisation of cultured neurons is of paramount importance for drug discovery, safety pharmacology and basic research in the neurosciences. Technologies offering low cost, low technical complexity and potential for scalability towards high-throughput electrophysiology on in vitro neurons would be advantageous, in particular for screening purposes. Here we describe a plastic culture substrate supporting low-complexity multi-unit loose-patch recording and stimulation of developing networks while retaining manufacturability compatible with low-cost and large-scale production. Our hybrid polydimethylsilane (PDMS)-on-polystyrene structures include chambers (6 mm in diameter) and microchannels (25 microm x 3.7 microm x 1 mm) serving as substrate-embedded recording pipettes. Somas are plated and retained in the chambers due to geometrical constraints and their processes grow along the microchannels, effectively establishing a loose-patch configuration without human intervention. We demonstrate that off-the-shelf voltage-clamp, current-clamp and extracellular amplifiers can be used to record and stimulate multi-unit activity with the aid of our dishes. Spikes up to 50 pA in voltage-clamp and 300 microV in current-clamp modes are recorded in sparse and bursting activity patterns characteristic of 1 week-old hippocampal cultures. Moreover, spike sorting employing principal component analysis (PCA) confirms that single microchannels support the recording of multiple neurons. Overall, this work suggests a strategy to endow conventional culture plasticware with added functionality to enable cost-efficient network electrophysiology.