formation-biomembrane-microarrays-with-squeegee-based-assembly

Formation of Biomembrane Microarrays with a Squeegee-based Assembly Method

Lipid bilayer membranes form the plasma membranes of cells and define the boundaries of subcellular organelles. In nature, these membranes are ...

University of Minnesota

A growing number of brain-machine interfaces have now been developed that allow movements of an external device to be controlled using recordings from the brain. This work has been undertaken with a number of different animal models, as well as several human patients with quadriplegia. The resulting movements, whether of computer cursors or robotic limbs, remain quite slow and unstable compared to normal limb movements. It is an open question, how much of this instability is the result of the limited forward control path, and how much has to do with the total lack of normal proprioceptive feedback. We have begun preliminary studies of the effectiveness of electrical stimulation in the proprioceptive area of the primary somatosensory cortex (area 3a) as a potential means to deliver an artificial sense of proprioception to a monkey. We have demonstrated that it is possible for the monkey to detect brief stimulus trains at relatively low current levels, and to discriminate between trains of different frequencies. These observations need to be expanded to include more complex, time-varying waveforms that could potentially convey information about the state of the limb.

Electrical stimulation of the proprioceptive cortex (area 3a) used to instruct a behaving monkey.

Brain-machine interfaces (BMIs) have the potential to improve quality of life for thousands of motor-impaired individuals. Many different signal sources have been investigated for use in controlling a BMI, including scalp EEG, field potentials from inside and the surface of the cerebral cortex, and single-neuron action potentials. A relatively unexplored region for recording signals is the epidural space. This study attempts to help determine the optimal spatial resolution of epidural and subdural electrode arrays using both a mathematical model and spatial spectral analysis. For rats, optimal spacing for both epidural and subdural electrodes was approximately 0.7 mm.

Optimal spatial resolution of epidural and subdural electrode arrays for brain-machine interface applications.

Brain-machine interfaces (BMIs) use signals recorded directly from the brain to control an external device, such as a computer cursor or a prosthetic limb. These control signals have been recorded from different levels of the brain, from field potentials at the scalp or cortical surface to single neuron action potentials. At present, the more invasive recordings have better signal quality, but also lower stability over time. Recently, subdural field potentials have been proposed as a stable, good quality source of control signals, with the potential for higher spatial and temporal bandwidth than EEG. Here we used finite element modeling in rats and humans and spatial spectral analysis in rats to compare the spatial resolution of signals recorded epidurally (outside the dura), with those recorded from subdural and scalp locations. Resolution of epidural and subdural signals was very similar in rats and somewhat less so in human models. Both were substantially better than signals recorded at the scalp. Resolution of epidural and subdural signals in humans was much more similar when the cerebrospinal fluid layer thickness was reduced. This suggests that the less invasive epidural recordings may yield signals of similar quality to subdural recordings, and hence may be more attractive as a source of control signals for BMIs.

Optimal spacing of surface electrode arrays for brain-machine interface applications.

The center-out task is a standard paradigm often used to study the neural control of reaching movements in human and non-human primates. However, there are several disadvantages to the use of monkeys, notably costs, infrastructural requirements, and ethical considerations. Here we describe a similar task designed to examine forelimb movements in rats. Rats were trained to grasp a joystick with their forepaw and use it to control the movements of a sipper tube in two dimensions. The rats learned to move the joystick in four directions with at least 70% accuracy after about 45 days of training. In addition, rats were able to learn a reversed mapping between joystick and sipper tube movement. This is a more complicated behavior than has been previously demonstrated for rats, and it could allow more motor behavior studies to be conducted in rodents instead of monkeys. We currently are using this behavior to decode the rats' forelimb movements from their brain signals.

A new rodent behavioral paradigm for studying forelimb movement.

Brain-machine interfaces (BMIs) use signals from the brain to control a device such as a computer cursor. Various types of signals have been used as BMI inputs, from single-unit action potentials to scalp potentials. Recently, intermediate-level signals such as subdural field potentials have also shown promise. These different signal types are likely to provide different amounts of information, but we do not yet know what signal types are necessary to enable a particular BMI function, such as identification of reach target location, control of a two-dimensional cursor or the dynamics of limb movement. Here we evaluated the performance of field potentials, measured either intracortically (local field potentials, LFPs) or epidurally (epidural field potential, EFPs), in terms of the ability to decode reach direction. We trained rats to move a joystick with their forepaw to control the motion of a sipper tube to one of the four targets in two dimensions. We decoded the forelimb reach direction from the field potentials using linear discriminant analysis. We achieved a mean accuracy of 69 ± 3% with EFPs and 57 ± 2% with LFPs, both much better than chance. Signal quality remained good up to 13 months after implantation. This suggests that using epidural signals could provide BMI inputs of high quality with less risk to the patient than using intracortical recordings.

Decoding the rat forelimb movement direction from epidural and intracortical field potentials.

The recent explosion of interest in brain-machine interfaces (BMIs) has spurred research into choosing the optimal input signal source for a desired application. The signals with highest bandwidth--single neuron action potentials or spikes--typically are difficult to record for more than a few years after implantation of intracortical electrodes. Fortunately, field potentials recorded within the cortex (local field potentials, LFPs), at its surface (electrocorticograms, ECoG) and at the dural surface (epidural, EFPs) have also been shown to contain significant information about movement. However, the relative performance of these signals has not yet been directly compared. Furthermore, while it is widely postulated, it has not yet been demonstrated that these field potential signals are more durable than spike recordings. The aim of this study was to address both of these questions. We assessed the offline decoding performance of EFPs, LFPs and spikes, recorded sequentially, in primary motor cortex (M1) in terms of their ability to decode the target of reaching movements, as well as the endpoint trajectory. We also examined the decoding performance of LFPs on electrodes that are not recording spikes, compared with the performance when they did record spikes. Spikes were still present on some of the other electrodes throughout this study. We showed that LFPs performed nearly as well as spikes in decoding velocity, and slightly worse in decoding position and in target classification. EFP performance was slightly inferior to that reported for ECoG in humans. We also provided evidence demonstrating that movement-related information in the LFP remains high regardless of the ability to record spikes concurrently on the same electrodes. This is the first study to provide evidence that LFPs retain information about movement in the absence of spikes on the same electrodes. These results suggest that LFPs may indeed remain informative after spike recordings are lost, thereby providing a robust, accurate signal source for BMIs.

Accurate decoding of reaching movements from field potentials in the absence of spikes.

With recent advances in high-throughput proteomics and systems biology, there is a growing demand for new instruments that can precisely quantify a wide range of receptor-ligand binding kinetics in a high-throughput fashion. Here we demonstrate a surface plasmon resonance (SPR) imaging spectroscopy instrument capable of simultaneously extracting binding kinetics and affinities from 50 parallel microfluidic channels. The instrument utilizes large-area (~ cm(2)) metallic nanohole arrays as SPR sensing substrates and combines a broadband light source, a high-resolution imaging spectrometer and a low-noise CCD camera to extract spectral information from every channel in real time with a refractive index resolution of 7.7 × 10(-6) refractive index units. To demonstrate the utility of our instrument for quantifying a wide range of biomolecular interactions, each parallel microfluidic channel is coated with a biomimetic supported lipid membrane containing ganglioside (GM1) receptors. The binding kinetics of cholera toxin b (CTX-b) to GM1 are then measured in a single experiment from 50 channels. By combining the highly parallel microfluidic device with large-area periodic nanohole array chips, our SPR imaging spectrometer system enables high-throughput, label-free, real-time SPR biosensing, and its full-spectral imaging capability combined with nanohole arrays could enable integration of SPR imaging with concurrent surface-enhanced Raman spectroscopy.

Real-time full-spectral imaging and affinity measurements from 50 microfluidic channels using nanohole surface plasmon resonance.

Matrix molecules convey biochemical and physical guiding signals to neurons in the central nervous system (CNS) and shape the trajectory of neuronal fibers that constitute neural networks. We have developed recombinant human IgMs that bind to epitopes on neural cells, with the aim of treating neurological diseases. Here we test the hypothesis that recombinant human IgMs (rHIgM) can guide neurite outgrowth of CNS neurons. Microcontact printing was employed to pattern rHIgM12 and rHIgM22, antibodies that were bioengineered to have variable regions capable of binding to neurons or oligodendrocytes, respectively. rHIgM12 promoted neuronal attachment and guided outgrowth of neurites from hippocampal neurons. Processes from spinal neurons followed grid patterns of rHIgM12 and formed a physical network. Comparison between rHIgM12 and rHIgM22 suggested the biochemistry that facilitates anchoring the neuronal surfaces is a prerequisite for the function of IgM, and spatial properties cooperate in guiding the assembly of neuronal networks.

A patterned recombinant human IgM guides neurite outgrowth of CNS neurons.

Characterization of binding kinetics and affinity between a potential drug and its receptor are key steps in the development of new drugs. Among the techniques available to determine binding affinities, surface plasmon resonance has emerged as the gold standard because it can measure binding and dissociation rates in real-time in a label-free fashion. Surface plasmon resonance is now finding applications in the characterization of molecules for treatment of neurodegenerative diseases, characterization of molecules associated with pathogenesis of neurodegenerative diseases and detection of neurodegenerative disease biomarkers. In addition it has been used in the characterization of a new class of natural autoantibodies that have therapeutic potential in a number of neurologic diseases. In this review we will introduce surface plasmon resonance and describe some applications of the technique that pertain to neurodegenerative disorders and their treatment.

Luke R. Jordan

Department of Biomedical Engineering

University of Minnesota

Formation of Biomembrane Microarrays with a Squeegee-based Assembly Method