Electrophysiological and morphological characterization of single neurons in intact human brain organoids.

Brain organoids represent a new model system for studying developmental human neurophysiology. Methods for studying the electrophysiology and morphology of single neurons in organoids require acute slices or dissociated cultures. While these methods have advantages (e.g., visual access, ease of experimentation), they risk damaging cells and circuits present in the intact organoid. To access single cells within intact organoid circuits, we have demonstrated a method for fixturing and performing whole cell patch clamp recording from intact brain organoids using both manual and automated tools. We demonstrate applied electrophysiology methods development followed by an integration of electrophysiology with reconstructing the morphology of the neurons within the brain organoid using dye filling and tissue clearing. We found that whole cell patch clamp recordings could be achieved both on the surface and within the interior of intact human brain organoids using both manual and automated methods. Manual experiments were higher yield (53 % whole cell success rate manual, 9 % whole cell success rate automated), but automated experiments were more efficient (30 patch attempts per day automated, 10 patch attempts per day manual). Using these methods, we performed an unbiased survey of cells within human brain organoids between 90 and 120 days in vitro (DIV) and present preliminary data on morphological and electrical diversity in human brain organoids. The further development of intact brain organoid patch clamp methods could be broadly applicable to studies of cellular, synaptic, and circuit-level function in the developing human brain.

Sensitivity optimization of a rhodopsin-based fluorescent voltage indicator.

The ability to optically image cellular transmembrane voltages at millisecond-timescale resolutions can offer unprecedented insight into the function of living brains in behaving animals. Here, we present a point mutation that increases the sensitivity of Ace2 opsin-based voltage indicators. We use the mutation to develop Voltron2, an improved chemigeneic voltage indicator that has a 65% higher sensitivity to single APs and 3-fold higher sensitivity to subthreshold potentials than Voltron. Voltron2 retained the sub-millisecond kinetics and photostability of its predecessor, although with lower baseline fluorescence. In multiple in vitro and in vivo comparisons with its predecessor across multiple species, we found Voltron2 to be more sensitive to APs and subthreshold fluctuations. Finally, we used Voltron2 to study and evaluate the possible mechanisms of interneuron synchronization in the mouse hippocampus. Overall, we have discovered a generalizable mutation that significantly increases the sensitivity of Ace2 rhodopsin-based sensors, improving their voltage reporting capability.

Extreme material parameters accessible by active acoustic metamaterials with Willis coupling.

Active acoustic metamaterials incorporate electric circuit elements that input energy into an otherwise passive medium to aptly modulate the effective material properties. Here, we propose an active acoustic metamaterial with Willis coupling to drastically extend the tunability of the effective density and bulk modulus with the accessible parameter range enlarged by at least two orders of magnitude compared to that of a non-Willis metamaterial. Traditional active metamaterial designs are based on local resonances without considering the Willis coupling that limit their accessible effective material parameter range. Our design adopts a unit cell structure with two sensor-transducer pairs coupling the acoustic response on both sides of the metamaterial by detecting incident waves and driving active signals asymmetrically superimposed onto the passive response of the material. The Willis coupling results from feedback control circuits with unequal gains. These asymmetric feedback control circuits use Willis coupling to expand the accessible range of the effective density and bulk modulus of the metamaterial. The extreme effective material parameters realizable by the metamaterials will remarkably broaden their applications in biomedical imaging, noise control, and transformation acoustics-based cloaking.

Method for Rapid Enzymatic Cleaning for Reuse of Patch Clamp Pipettes: Increasing Throughput by Eliminating Manual Pipette Replacement between Patch Clamp Attempts.

The whole-cell patch-clamp method is a gold standard for single-cell analysis of electrical activity, cellular morphology, and gene expression. Prior to our discovery that patch-clamp pipettes could be cleaned and reused, experimental throughput and automation were limited by the need to replace pipettes manually after each experiment. This article presents an optimized protocol for pipette cleaning, which enables it to be performed quickly (< 30 s), resulting in a high yield of whole-cell recording success rate (> 90%) for over 100 reuses of a single pipette. For most patch-clamp experiments (< 30 whole-cell recordings per day), this method enables a single pipette to be used for an entire day of experiments. In addition, we describe easily implementable hardware and software as well as troubleshooting tips to help other labs implement this method in their own experiments. Pipette cleaning enables patch-clamp experiments to be performed with higher throughput, whether manually or in an automated fashion, by eliminating the tedious and skillful task of replacing pipettes. From our experience with numerous electrophysiology laboratories, pipette cleaning can be integrated into existing patch-clamp setups in approximately one day using the hardware and software described in this article. Graphic abstract: Rapid enzymatic cleaning for reuse of patch-clamp pipettes.

Machine Learning-Based Pipette Positional Correction for Automatic Patch Clamp In Vitro.

Patch clamp electrophysiology is a common technique used in neuroscience to understand individual neuron behavior, allowing one to record current and voltage changes with superior spatiotemporal resolution compared with most electrophysiology methods. While patch clamp experiments produce high fidelity electrophysiology data, the technique is onerous and labor intensive. Despite the emergence of patch clamp systems that automate key stages in the typical patch clamp procedure, full automation remains elusive. Patch clamp pipettes can miss the target cell during automated experiments because of positioning errors in the robotic manipulators, which can easily exceed the diameter of a neuron. Further, when patching in acute brain slices, the inherent light scattering from non-uniform brain tissue can complicate pipette tip identification. We present a convolutional neural network (CNN), based on ResNet101, to identify and correct pipette positioning errors before each patch clamp attempt, thereby preventing the deleterious effects of and accumulation of positioning errors. This deep-learning-based pipette detection method enabled superior localization of the pipette within 0.62 ± 0.58 µm, resulting in improved cell detection success rate and whole-cell patch clamp success rates by 71% and 59%, respectively, compared with the state-of-the-art cross-correlation method. Furthermore, this technique reduced the average time for pipette correction by 81%. This technique enables real-time correction of pipette position during patch clamp experiments with similar accuracy and quality of recording to manual patch clamp, making notable progress toward full human-out-of-the-loop automation for patch clamp electrophysiology.

Automated Intracellular Pharmacological Electrophysiology for Ligand-Gated Ionotropic Receptor and Pharmacology Screening.

Communication between neuronal cells, which is central to brain function, is performed by several classes of ligand-gated ionotropic receptors. The gold-standard technique for measuring rapid receptor response to agonist is manual patch-clamp electrophysiology, capable of the highest temporal resolution of any current electrophysiology technique. We report an automated high-precision patch-clamp system that substantially improves the throughput of these time-consuming pharmacological experiments. The patcherBotPharma enables recording from cells expressing receptors of interest and manipulation of them to enable millisecond solution exchange to activate ligand-gated ionotropic receptors. The solution-handling control allows for autonomous pharmacological concentration-response experimentation on adherent cells, lifted cells, or excised outside-out patches. The system can perform typical ligand-gated ionotropic receptor experimentation protocols autonomously, possessing a high success rate in completing experiments and up to a 10-fold reduction in research effort over the duration of the experiment. Using it, we could rapidly replicate previous data sets, reducing the time it took to produce an eight-point concentration-response curve of the effect of propofol on GABA type A receptor deactivation from likely weeks of recording to ∼13 hours of recording. On average, the rate of data collection of the patcherBotPharma was a data point every 2.1 minutes that the operator spent interacting with the patcherBotPharma The patcherBotPharma provides the ability to conduct complex and comprehensive experimentation that yields data sets not normally within reach of conventional systems that rely on constant human control. This technical advance can contribute to accelerating the examination of the complex function of ion channels and the pharmacological agents that act on them. SIGNIFICANCE STATEMENT: This work presents an automated intracellular pharmacological electrophysiology robot, patcherBotPharma, that substantially improves throughput and reduces human time requirement in pharmacological patch-clamp experiments. The robotic system includes millisecond fluid exchange handling and can perform highly efficient ligand-gated ionotropic receptor experiments. The patcherBotPharma is built using a conventional patch-clamp rig, and the technical advances shown in this work greatly accelerate the ability to conduct high-fidelity pharmacological electrophysiology.

Deep learning-based real-time detection of neurons in brain slices for in vitro physiology.

A common electrophysiology technique used in neuroscience is patch clamp: a method in which a glass pipette electrode facilitates single cell electrical recordings from neurons. Typically, patch clamp is done manually in which an electrophysiologist views a brain slice under a microscope, visually selects a neuron to patch, and moves the pipette into close proximity to the cell to break through and seal its membrane. While recent advances in the field of patch clamping have enabled partial automation, the task of detecting a healthy neuronal soma in acute brain tissue slices is still a critical step that is commonly done manually, often presenting challenges for novices in electrophysiology. To overcome this obstacle and progress towards full automation of patch clamp, we combined the differential interference microscopy optical technique with an object detection-based convolutional neural network (CNN) to detect healthy neurons in acute slice. Utilizing the YOLOv3 convolutional neural network architecture, we achieved a 98% reduction in training times to 18 min, compared to previously published attempts. We also compared networks trained on unaltered and enhanced images, achieving up to 77% and 72% mean average precision, respectively. This novel, deep learning-based method accomplishes automated neuronal detection in brain slice at 18 frames per second with a small data set of 1138 annotated neurons, rapid training time, and high precision. Lastly, we verified the health of the identified neurons with a patch clamp experiment where the average access resistance was 29.25 M[Formula: see text] (n = 9). The addition of this technology during live-cell imaging for patch clamp experiments can not only improve manual patch clamping by reducing the neuroscience expertise required to select healthy cells, but also help achieve full automation of patch clamping by nominating cells without human assistance.

Capillary-Based and Stokes-Based Trapping of Serial Sections for Scalable 3D-EM Connectomics.

Serial section electron microscopy (ssEM), a technique where volumes of tissue can be anatomically reconstructed by imaging consecutive tissue slices, has proven to be a powerful tool for the investigation of brain anatomy. Between the process of cutting the slices, or "sections," and imaging them, however, handling 10°-106 delicate sections remains a bottleneck in ssEM, especially for batches in the "mesoscale" regime, i.e., 102-103 sections. We present a tissue section handling device that transports and positions sections, accurately and repeatability, for automated, robotic section pick-up and placement onto an imaging substrate. The device interfaces with a conventional ultramicrotomy diamond knife, accomplishing in-line, exact-constraint trapping of sections with 100-µm repeatability. An associated mathematical model includes capillary-based and Stokes-based forces, accurately describing observed behavior and fundamentally extends the modeling of water-air interface forces. Using the device, we demonstrate and describe the limits of reliable handling of hundreds of slices onto a variety of electron and light microscopy substrates without significant defects (n = 8 datasets composed of 126 serial sections in an automated fashion with an average loss rate and throughput of 0.50% and 63 s/section, respectively. In total, this work represents an automated mesoscale serial sectioning system for scalable 3D-EM connectomics.

PatcherBot: a single-cell electrophysiology robot for adherent cells and brain slices.

OBJECTIVE: Intracellular patch-clamp electrophysiology, one of the most ubiquitous, high-fidelity techniques in biophysics, remains laborious and low-throughput. While previous efforts have succeeded at automating some steps of the technique, here we demonstrate a robotic 'PatcherBot' system that can perform many patch-clamp recordings sequentially, fully unattended. APPROACH: Comprehensive automation is accomplished by outfitting the robot with machine vision, and cleaning pipettes instead of manually exchanging them. MAIN RESULTS: the PatcherBot can obtain data at a rate of 16 cells per hour and work with no human intervention for up to 3 h. We demonstrate the broad applicability and scalability of this system by performing hundreds of recordings in tissue culture cells and mouse brain slices with no human supervision. Using the PatcherBot, we also discovered that pipette cleaning can be improved by a factor of three. SIGNIFICANCE: The system is potentially transformative for applications that depend on many high-quality measurements of single cells, such as drug screening, protein functional characterization, and multimodal cell type investigations.

Optical stereolithography of antifouling zwitterionic hydrogels.

This paper reports the rapid 3D printing of tough (toughness, UT, up to 141.6 kJ m-3), highly solvated (φwater∼ 60 v/o), and antifouling hybrid hydrogels for potential uses in biomedical, smart materials, and sensor applications, using a zwitterionic photochemistry compatible with stereolithography (SLA). A Design of Experiments (DOE) framework was used for systematically investigating the multivariate photochemistry of SLA generally and, specifically, to determine an aqueous SLA system with an additional zwitterionic acrylate, which significantly increases the gelation rate, and the resilience of the resulting hybrid hydrogels relative to an equivalent non-ionic polyacrylamide hydrogel. Specifically, the resulting zwitterionic hybrid hydrogels (Z-gels) can be tuned over a large range of ultimate strains, ca. 0.5 < γult < 5.0, and elastic moduli, ca. 10 < E < 1000 kPa, while also demonstrating a high resilience under cyclic tensile loading. Importantly, unlike traditional chemistry, increasing the elastic modulus of the Z-gels does not necessarily reduce the ultimate strain. Moreover, the Z-gels can be rapidly printed using a desktop commercial SLA 3D printer, with relatively low photoirradiation dosages of visible light (135 to 675 mJ cm-2 per 50-100 µm layer). Compared with the counterpart polyacrylamide hydrogels, the Z-gels have greater antifouling properties and exhibit 58.2% less absorption of bovine serum albumin.

Ion sensing with thread-based potentiometric electrodes.

Potentiometric sensing of ions with ion-selective electrodes (ISEs) is a powerful technique for selective and sensitive measurement of ions in complex matrices. The application of ISEs is generally limited to laboratory settings, because most commercially available ISEs and reference electrodes are large, delicate, and expensive, and are not suitable for point-of-use or point-of-care measurements. This work utilizes cotton thread as a substrate for fabrication of robust and miniaturized ISEs that are suitable for point-of-care or point-of-use applications. Thread-based ISEs selective for Cl-, K+, Na+, and Ca2+ were developed. The cation-selective ISEs were fabricated by coating the thread with a surfactant-free conductive ink (made of carbon black) and then coating the tip of the conductive thread with the ion-selective membrane. The Cl- ISE was fabricated by coating the thread with an Ag/AgCl ink. These sensors exhibited slopes (of electrical potential vs. log concentration of target ion), close to the theoretically-expected values, over four orders of magnitude in concentrations of ions. Because thread is mechanically strong, the thread-based electrodes can be used in multiple-use applications as well as single-use applications. Multiple thread-based sensors can be easily bundled together to fabricate a customized sensor for multiplexed ion-sensing. These electrodes require volumes of sample as low as 200 µL. The application of thread-based ISEs is demonstrated in the analysis of ions in soil, food, and dietary supplements (Cl- in soil/water slurry, K+ and Na+ in coconut water, and Ca2+ in a calcium supplement), and in detection of physiological electrolytes (K+ and Na+ in blood serum and urine, with sufficient accuracy for clinical diagnostics).