Xian Wang, Yonit Tsatskis, Sevan Hopyan, Helen McNeill, Yu Sun
“Robotic Intracellular Manipulation and Measurement with Multi-Pole Magnetic Tweezers”
The capability to directly interrogate intracellular structures inside a single cell for measurement and manipulation has significant implications in the understanding of subcellular and sub-organelle activities, diagnosing diseases, and developing new therapeutic approaches. Compared to measurements of single cells, physical measurement and manipulation of sub-cellular structures and organelles remain underexplored. To spearhead an exciting new era of intracellular physical measurement and manipulation, we have developed a multi-pole magnetic tweezers system for micromanipulation involving sub-micrometer position control and picoNewton force control of a sub-micron magnetic bead inside a single cell for measurement on different locations (spatial) and different time points (temporal). The bead was three-dimensionally positioned in the cell using a generalized predictive controller that tackles the control challenge caused by the low bandwidth of visual feedback from high-resolution confocal imaging. The average positioning error was quantified to be 0.4 µm, slightly larger than Brownian motion-imposed constraint (0.31 µm, 1 µm = 〖10〗^(-6) m). The system is also capable of applying a force up to 60 pN with a resolution of 4 pN (1 pN = 〖10〗^(-12) N) for a period of time longer than 30 mins. The measurement results revealed significantly higher stiffness exists in the nucleus’ major axis than in the minor axis. This stiffness polarity is likely attributed to the aligned actin filament. We also proved that the nucleus stiffens upon the application of an intracellularly applied force, which can be attributed to the response of structural protein lamin A/C and the intracellular stress fiber actin filaments.
Ryan Koh, Adrian Nachman, Jose Zariffa
“Classification of naturally evoked compound action potentials in peripheral nerve recordings via convolutional neural networks”
Objective: Recording and stimulation from the peripheral nervous system are becoming important components in a new generation of bioelectronics systems. Neurostimulation in humans using implanted peripheral neural interfaces has seen a long history of success, including in applications such as reducing phantom pain in amputees, treatment for overactive bladder, and implanted functional electrical stimulation for movement. Unfortunately, recording applications using implanted peripheral neural interfaces has not been as successful and remains a challenge. Improvements to recording devices and signal processing techniques to extract useful information from those devices are needed. With the objective of recording selectively and reliably from different neural pathways in a peripheral nerve, we propose to use a convolutional neural network to exploit the spatiotemporal structure of compound action potentials (CAPs) recorded from a 56-channel nerve cuff.
Approach: 9 Long-Evan rats were implanted with a 56-channel spiral nerve cuff electrode on the sciatic nerve. Afferent activity was selectively evoked in three fascicles of the sciatic nerve (tibial, peroneal, sural) using mechanical stimuli. Spatiotemporal signatures of recorded CAPs were used to train the CNN. A recurrent neural network was then trained to predict the joint angle based on predicted firing patterns from the CNN. Performance was measured based on the CNN’s classification accuracy and F1-score, and correlation between the ground truth and predicted joint angle of the rat’s ankle.
Main Results: Our novel technique using CNNs yielded a mean classification accuracy of 0.808 ±0.104 with corresponding mean F1-score of 0.747 ±0.114. In contrast, the mean classification accuracy and F1-score for the previous state-of-the-art were 0.686 ±0.126 and 0.605 ±0.212, respectively. Using the CNN classification results, the mean Pearson correlation coefficient was 0.826 ± 0.176 for the ankle angle predicted using the estimated firing rate vs the manually labelled ankle angle.
Significance: The proposed method demonstrates that CAP-based classification can be used to track a physiological meaningful measure (e.g. joint angle) and will allow for more precise control signals in neuroprosthetic systems.
Eric Ho, Jaclyn Obermeyer, Anup Tuladhar, Samantha Payne, Molly Shoichet
“Non-Invasive, Epicortical Delivery of Brain-Derived Neurotrophic Factor for Recovery after Stroke”
Stroke affects over 15 million people worldwide, and despite significant research patients are faced with limited treatment options. This is due, in part, to the blood brain barrier (BBB). To address this roadblock, our group has developed a non-invasive, epicortical drug delivery vehicle that circumvents the BBB to provide therapeutic effects to the central nervous system. The system was used to deliver brain-derived neurotrophic factor (BDNF), a promising protein therapeutic for stroke therapy that does not readily cross the BBB. We have shown that BDNF can be electrostatically adsorbed onto the negative surface of poly(lactic co-glycolic acid) (PLGA) nanoparticles dispersed in a hyaluronan-methylcellulose hydrogel, limiting protein denaturation while achieving a similar release profile to encapsulation in vitro. We hypothesized that the vehicle could be applied in vivo to deliver BDNF in an endothelin-1 rat model of stroke injury.
Release from the vehicle in vitro resulted in a sustained, burst free release for 30 days, with the discharged BDNF bioactive when released from the vehicle. In vivo, significant BDNF diffusion into the tissue was observed, with the protein detected up to a depth 3000 µm up to 21 days post treatment. BDNF delivery augmented plasticity after stroke, as evidenced by increased synaptophysin staining in the contralesional hemisphere of BDNF-treated rats, as well as reduced lesion volume, indicating a neuroprotective effect. When assessing behavioural recovery, hindlimb function was significantly enhanced at 7 weeks with local BDNF delivery.
This vehicle is highly tunable for the delivery of many therapeutic proteins. The controlled release is encapsulation free and governed by electrostatic interactions between PLGA and charged proteins, enabling significantly higher protein loading and lower loss of bioactivity. Release rate and dose can be controlled through modification of the PLGA nanoparticles. In vivo, a therapeutically relevant concentration of BDNF was delivered to the brains of stroke injured rats with an epicortical hydrogel-nanoparticle composite. With local, sustained delivery directly to the brain, we demonstrate the benefit of BNDF and the potential for use of this platform strategy with other biotherapeutics.
Tianhao Chen, Zia Saadatnia, Hani Naguib
“A novel, flexible and ultra-thin pressure sensor for concentric tube manipulators in intra-ventricular neurosurgery robotic tools”
Minimally invasive endoscopic intraventricular surgery is a robot-assisted technique that has improved patient outcomes with less wound healing time due to small size of incisions. Small and dexterous surgical tool can be designed and miniaturized to a size of 2 mm while maintaining its dexterity and force required to resect brain tumors without open-skull surgery. To provide instrument-tissue interaction information for this tool, force feedback is required to ensure safety and effective operation. In this study, a small and highly sensitive smart material-based sensor was designed and integrated to the tool shaft, known as the concentric tube manipulators. A 200 um- ultrathin layer of micropatterned resistive carbon-filled polyvinylidene fluoride (PVDF) conductive polymer was wrapped spirally around the 2 mm-diameter concentric tube for static and quasi-static force sensing. A layer of interdigitated electrodes was designed to achieve pressure readings with both directional and locational information. Optimizations were performed on the size, pitch and shape of the microstructures as well as the width and spacing of the electrodes to improve sensitivity with reduced hysteresis. The finalized design can sense a pressure down to 0.55 kPa while retaining its flexibility, biocompatibility and sterilizability. The sensor will also enhance more intuitive force feedback for surgeons to use the dexterous neurosurgical tool, which will have a significant impact on brain tumor and epilepsy practice.