Integration of autopatching with automated pipette and cell detection in vitro

Patch-walking: Coordinated multi-pipette patch clamp for efficiently finding synaptic connections

Significant technical challenges exist when measuring synaptic connections between neurons in living brain tissue. The patch clamping technique, when used to probe for synaptic connections, is manually laborious and time-consuming. To improve its efficiency, we pursued another approach: instead of retracting all patch clamping electrodes after each recording attempt, we cleaned just one of them and reused it to obtain another recording while maintaining the others. With one new patch clamp recording attempt, many new connections can be probed. By placing one pipette in front of the others in this way, one can "walk" across the tissue, termed "patch-walking." We performed 136 patch clamp attempts for two pipettes, achieving 71 successful whole cell recordings (52.2%). Of these, we probed 29 pairs (i.e., 58 bidirectional probed connections) averaging 91 μm intersomatic distance, finding 3 connections. Patch-walking yields 80-92% more probed connections, for experiments with 10-100 cells than the traditional synaptic connection searching method.

High-throughput genetic manipulation of multicellular organisms using a machine-vision guided embryonic microinjection robot

Microinjection is a technique used for transgenesis, mutagenesis, cell labeling, cryopreservation, and in vitro fertilization in multiple single and multicellular organisms. Microinjection requires specialized skills and involves rate-limiting and labor-intensive preparatory steps. Here, we constructed a machine-vision guided generalized robot that fully automates the process of microinjection in fruit fly (Drosophila melanogaster) and zebrafish (Danio rerio) embryos. The robot uses machine learning models trained to detect embryos in images of agar plates and identify specific anatomical locations within each embryo in 3D space using dual view microscopes. The robot then serially performs a microinjection in each detected embryo. We constructed and used three such robots to automatically microinject tens of thousands of Drosophila and zebrafish embryos. We systematically optimized robotic microinjection for each species and performed routine transgenesis with proficiency comparable to highly skilled human practitioners while achieving up to 4× increases in microinjection throughput in Drosophila. The robot was utilized to microinject pools of over 20,000 uniquely barcoded plasmids into 1,713 embryos in 2 days to rapidly generate more than 400 unique transgenic Drosophila lines. This experiment enabled a novel measurement of the number of independent germline integration events per successfully injected embryo. Finally, we showed that robotic microinjection of cryoprotective agents in zebrafish embryos significantly improves vitrification rates and survival of cryopreserved embryos post-thaw as compared to manual microinjection. We anticipate that the robot can be used to carry out microinjection for genome-wide manipulation and cryopreservation at scale in a wide range of organisms.

Neuron Contact Detection Based on Pipette Precise Positioning for Robotic Brain-Slice Patch Clamps

A patch clamp is the "gold standard" method for studying ion-channel biophysics and pharmacology. Due to the complexity of the operation and the heavy reliance on experimenter experience, more and more researchers are focusing on patch-clamp automation. The existing automated patch-clamp system focuses on the process of completing the experiment; the detection method in each step is relatively simple, and the robustness of the complex brain film environment is lacking, which will increase the detection error in the microscopic environment, affecting the success rate of the automated patch clamp. To address these problems, we propose a method that is suitable for the contact between pipette tips and neuronal cells in automated patch-clamp systems. It mainly includes two key steps: precise positioning of pipettes and contact judgment. First, to obtain the precise coordinates of the tip of the pipette, we use the Mixture of Gaussian (MOG) algorithm for motion detection to focus on the tip area under the microscope. We use the object detection model to eliminate the encirclement frame of the pipette tip to reduce the influence of different shaped tips, and then use the sweeping line algorithm to accurately locate the pipette tip. We also use the object detection model to obtain a three-dimensional bounding frame of neuronal cells. When the microscope focuses on the maximum plane of the cell, which is the height in the middle of the enclosing frame, we detect the focus of the tip of the pipette to determine whether the contact between the tip and the cell is successful, because the cell and the pipette will be at the same height at this time. We propose a multitasking network CU-net that can judge the focus of pipette tips in complex contexts. Finally, we design an automated contact sensing process in combination with resistance constraints and apply it to our automated patch-clamp system. The experimental results show that our method can increase the success rate of pipette contact with cells in patch-clamp experiments.

Multiscale, multi-perspective imaging assisted robotic microinjection of 3D biological structures

Microinjection is a widely used technique employed by biologists with applications in transgenesis, cryopreservation, mutagenesis, labeling/dye injection and in-vitro fertilization. However, microinjection is an extremely laborious manual procedure, which makes it a critical bottleneck in the field and thus ripe for automation. Here, we present a computer-guided robot that automates the targeted microinjection of Drosophila melanogaster and zebrafish (Danio rerio) embryos, two important model organisms in biological research. The robot uses a series of cameras to image an agar plate containing embryos at multiple magnifications and perspectives. This imaging is combined with machine learning and computer vision algorithms to pinpoint a location on the embryo for targeted microinjection with microscale precision. We demonstrate the utility of this microinjection robot to successfully microinject Drosophila melanogaster and zebrafish embryos. Results obtained indicate that the robotic microinjection approach can significantly increase the throughput of microinjection as compared to manual microinjection while maintaining survival rates comparable to human operators. In the future, this robotic platform can be used to perform high throughput microinjection experiments and can be extended to automatically microinject a host of organisms such as roundworms (Caenorhabditis elegans), mosquito (Culicidae) embryos, sea urchins (Echinoidea) and frog (Xenopus) oocytes.

Automated microscope-independent fluorescence-guided micropipette

Glass micropipette electrodes are commonly used to provide high resolution recordings of neurons. Although it is the gold standard for single cell recordings, it is highly dependent on the skill of the electrophysiologist. Here, we demonstrate a method of guiding micropipette electrodes to neurons by collecting fluorescence at the aperture, using an intra-electrode tapered optical fiber. The use of a tapered fiber for excitation and collection of fluorescence at the micropipette tip couples the feedback mechanism directly to the distance between the target and electrode. In this study, intra-electrode tapered optical fibers provide a targeted robotic approach to labeled neurons that is independent of microscopy.

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.

Visual Familiarity Induced 5-Hz Oscillations and Improved Orientation and Direction Selectivities in V1

Neural oscillations play critical roles in information processing, communication between brain areas, learning, and memory. We have recently discovered that familiar visual stimuli can robustly induce 5-Hz oscillations in the primary visual cortex (V1) of awake mice after the visual experience. To gain more mechanistic insight into this phenomenon, we used in vivo patch-clamp recordings to monitor the subthreshold activity of individual neurons during these oscillations.

Neural oscillations play critical roles in information processing, communication between brain areas, learning, and memory. We have recently discovered that familiar visual stimuli can robustly induce 5-Hz oscillations in the primary visual cortex (V1) of awake mice after the visual experience. To gain more mechanistic insight into this phenomenon, we used in vivo patch-clamp recordings to monitor the subthreshold activity of individual neurons during these oscillations. We analyzed the visual tuning properties of V1 neurons in naive and experienced mice to assess the effect of visual experience on the orientation and direction selectivity. Using optogenetic stimulation through the patch pipette in vivo, we measured the synaptic strength of specific intracortical and thalamocortical projections in vivo in the visual cortex before and after the visual experience. We found 5-Hz oscillations in membrane potential (V_m) and firing rates evoked in single neurons in response to the familiar stimulus, consistent with previous studies. Following the visual experience, the average firing rates of visual responses were reduced while the orientation and direction selectivities were increased. Light-evoked EPSCs were significantly increased for layer 5 (L5) projections to other layers of V1 after the visual experience, while the thalamocortical synaptic strength was decreased. In addition, we developed a computational model that could reproduce 5-Hz oscillations with enhanced neuronal selectivity following synaptic plasticity within the recurrent network and decreased feedforward input.

SIGNIFICANCE STATEMENT Neural oscillations at around 5 Hz are involved in visual working memory and temporal expectations in primary visual cortex (V1). However, how the oscillations modulate the visual response properties of neurons in V1 and their underlying mechanism is poorly understood. Here, we show that these oscillations may alter the orientation and direction selectivity of the layer 2/3 (L2/3) neurons and correlate with the synaptic plasticity within V1. Our computational recurrent network model reproduces all these observations and provides a mechanistic framework for studying the role of 5-Hz oscillations in visual familiarity.

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.

Automatic deep learning-driven label-free image-guided patch clamp system

Patch clamp recording of neurons is a labor-intensive and time-consuming procedure. Here, we demonstrate a tool that fully automatically performs electrophysiological recordings in label-free tissue slices. The automation covers the detection of cells in label-free images, calibration of the micropipette movement, approach to the cell with the pipette, formation of the whole-cell configuration, and recording. The cell detection is based on deep learning. The model is trained on a new image database of neurons in unlabeled brain tissue slices. The pipette tip detection and approaching phase use image analysis techniques for precise movements. High-quality measurements are performed on hundreds of human and rodent neurons. We also demonstrate that further molecular and anatomical analysis can be performed on the recorded cells. The software has a diary module that automatically logs patch clamp events. Our tool can multiply the number of daily measurements to help brain research.

Patch clamp recording of neurons is slow and labor-intensive. Here the authors present a method for automated deep learning driven label-free image guided patch clamp physiology to perform measurements on hundreds of human and rodent neurons.

In Vivo Glutamate Sensing inside the Mouse Brain with Perovskite Nickelate-Nafion Heterostructures

Glutamate, one of the main neurotransmitters in the brain, plays a critical role in communication between neurons, neuronal development, and various neurological disorders. Extracellular measurement of neurotransmitters such as glutamate in the brain is important for understanding these processes and developing a new generation of brain-machine interfaces. Here, we demonstrate the use of a perovskite nickelate-Nafion heterostructure as a promising glutamate sensor with a low detection limit of 16 nM and a response time of 1.2 s via amperometric sensing. We have designed and successfully tested novel perovskite nickelate-Nafion electrodes for recording of glutamate release ex vivo in electrically stimulated brain slices and in vivo from the primary visual cortex (V1) of awake mice exposed to visual stimuli. These results demonstrate the potential of perovskite nickelates as sensing media for brain-machine interfaces.

Fabrication and ex vivo evaluation of activated carbon-Pt microparticle based glutamate biosensor

As one of the most abundant neurotransmitters in the brain and the spinal cord, glutamate plays many important roles in the nervous system. Precise information about the level of glutamate in the extracellular space of living brain tissue may provide new insights on fundamental understanding of the role of glutamate in neurological disorders as well as neurophysiological phenomena. Electrochemical sensor has emerged as a promising solution that can satisfy the requirement for highly reliable and continuous monitoring method with good spatiotemporal resolution for characterization of extracellular glutamate concentration. Recently, we published a method to create a simple printable glutamate biosensor using platinum nanoparticles. In this work, we introduce an even simpler and lower cost conductive polymer composite using commercially available activated carbon with platinum microparticles to easily fabricate highly sensitive glutamate biosensor using direct ink writing method. The fabricated biosensors are functionality superior than previously reported with the sensitivity of 5.73 ± 0.078 nA μM^-1 mm^-2, detection limit of 0.03 μM, response time less than or equal to 1 s, and a linear range from 1 μM up to 925 μM. In this study, we utilize astrocyte cell culture to demonstrate our biosensor's ability to monitor glutamate uptake process. We also demonstrate direct measurement of glutamate release from optogenetic stimulation in mouse primary visual cortex (V1) brain slices.

Visual Experience-Dependent Oscillations and Underlying Circuit Connectivity Changes Are Impaired in Fmr1 KO Mice

Fragile X syndrome (FX), the most common inherited form of autism and intellectual disability, is a condition associated with visual perceptual learning deficits. We recently discovered that perceptual experience can encode visual familiarity via persistent low-frequency oscillations in the mouse primary visual cortex (V1). Here, we combine this paradigm with a multifaceted experimental approach to identify neurophysiological impairments of these oscillations in FX mice. Extracellular recordings reveal shorter durations, lower power, and lower frequencies of peak oscillatory activity in FX mice. Directed information analysis of extracellularly recorded spikes reveals differences in functional connectivity from multiple layers in FX mice after the perceptual experience. Channelrhodopsin-2 assisted circuit mapping (CRACM) reveals increased synaptic strength from L5 pyramidal onto L4 fast-spiking cells after experience in wild-type (WT), but not FX, mice. These results suggest differential encoding of visual stimulus familiarity in FX via persistent oscillations and identify circuit connections that may underlie these changes.

Vision-Based Sensor for Three-Dimensional Vibrational Motion Detection in Biological Cell Injection

Intracytoplasmic sperm injection (ICSI) is an infertility treatment where a single sperm is immobilised and injected into the egg using a glass injection pipette. Minimising vibration in three orthogonal axes is essential to have precise injector motion and full control during the egg injection procedure. Vibration displacement sensing using physical sensors in ICSI operation is challenging since the sensor interfacing is not practically feasible. This study proposes a non-invasive technique to measure the three-dimensional vibrational motion of the injection pipette by a single microscope camera during egg injection. The contrast-limited adaptive histogram equalization (CHALE) method and blob analyses technique were employed to measure the vibration displacement in axial and lateral axes, while the actual dimension of the focal axis was directly measured using the Brenner gradient algorithm as a focus measurement algorithm. The proposed algorithm operates between the magnifications range of 4× to 40× with a resolution of half a pixel. Experiments using the proposed vision-based algorithm were conducted to measure and verify the vibration displacement in axial and lateral axes at various magnifications. The results were compared against manual procedures and the differences in measurements were up to 2% among all magnifications. Additionally, the effect of injection speed on lateral vibration displacement was measured experimentally and was used to determine the values for egg deformation, force fluctuation, and penetration force. It was shown that increases in injection speed significantly increases the lateral vibration displacement of the injection pipette by as much as 54%. It has been demonstrated successfully that visual sensing has played a key role in identifying the limitation of the egg injection speed created by lateral vibration displacement of the injection pipette tip.

High-throughput microcircuit analysis of individual human brains through next-generation multineuron patch-clamp

Comparing neuronal microcircuits across different brain regions, species and individuals can reveal common and divergent principles of network computation. Simultaneous patch-clamp recordings from multiple neurons offer the highest temporal and subthreshold resolution to analyse local synaptic connectivity. However, its establishment is technically complex and the experimental performance is limited by high failure rates, long experimental times and small sample sizes. We introduce an in vitro multipatch setup with an automated pipette pressure and cleaning system facilitating recordings of up to 10 neurons simultaneously and sequential patching of additional neurons. We present hardware and software solutions that increase the usability, speed and data throughput of multipatch experiments which allowed probing of 150 synaptic connections between 17 neurons in one human cortical slice and screening of over 600 connections in tissue from a single patient. This method will facilitate the systematic analysis of microcircuits and allow unprecedented assessment of inter-individual variability.

Robotic platform for microinjection into single cells in brain tissue

Microinjection into single cells in brain tissue is a powerful technique to study and manipulate neural stem cells. However, such microinjection requires expertise and is a low-throughput process. We developed the "Autoinjector", a robot that utilizes images from a microscope to guide a microinjection needle into tissue to deliver femtoliter volumes of liquids into single cells. The Autoinjector enables microinjection of hundreds of cells within a single organotypic slice, resulting in an overall yield that is an order of magnitude greater than manual microinjection. The Autoinjector successfully targets both apical progenitors (APs) and newborn neurons in the embryonic mouse and human fetal telencephalon. We used the Autoinjector to systematically study gap-junctional communication between neural progenitors in the embryonic mouse telencephalon and found that apical contact is a characteristic feature of the cells that are part of a gap junction-coupled cluster. The throughput and versatility of the Autoinjector will render microinjection an accessible high-performance single-cell manipulation technique and will provide a powerful new platform for performing single-cell analyses in tissue for bioengineering and biophysics applications.

Advances in the automation of whole-cell patch clamp technology

Electrophysiology is the study of neural activity in the form of local field potentials, current flow through ion channels, calcium spikes, back propagating action potentials and somatic action potentials, all measurable on a millisecond timescale. Despite great progress in imaging technologies and sensor proteins, none of the currently available tools allow imaging of neural activity on a millisecond timescale and beyond the first few hundreds of microns inside the brain. The patch clamp technique has been an invaluable tool since its inception several decades ago and has generated a wealth of knowledge about the nature of voltage- and ligand-gated ion channels, sub-threshold and supra-threshold activity, and characteristics of action potentials related to higher order functions. Many techniques that evolve to be standardized tools in the biological sciences go through a period of transformation in which they become, at least to some degree, automated, in order to improve reproducibility, throughput and standardization. The patch clamp technique is currently undergoing this transition, and in this review, we will discuss various aspects of this transition, covering advances in automated patch clamp technology both in vitro and in vivo.

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.

Accurate signal-source localization in brain slices by means of high-density microelectrode arrays

Extracellular recordings by means of high-density microelectrode arrays (HD-MEAs) have become a powerful tool to resolve subcellular details of single neurons in active networks grown from dissociated cells. To extend the application of this technology to slice preparations, we developed models describing how extracellular signals, produced by neuronal cells in slices, are detected by microelectrode arrays. The models help to analyze and understand the electrical-potential landscape in an in vitro HD-MEA-recording scenario based on point-current sources. We employed two modeling schemes, (i) a simple analytical approach, based on the method of images (MoI), and (ii) an approach, based on finite-element methods (FEM). We compared and validated the models with large-scale, high-spatiotemporal-resolution recordings of slice preparations by means of HD-MEAs. We then developed a model-based localization algorithm and compared the performance of MoI and FEM models. Both models provided accurate localization results and a comparable and negligible systematic error, when the point source was in saline, a condition similar to cell-culture experiments. Moreover, the relative random error in the x-y-z-localization amounted only up to 4.3% for z-distances up to 200 μm from the HD-MEA surface. In tissue, the systematic errors of both, MoI and FEM models were significantly higher, and a pre-calibration was required. Nevertheless, the FEM values proved to be closer to the tissue experimental results, yielding 5.2 μm systematic mean error, compared to 22.0 μm obtained with MoI. These results suggest that the medium volume or "saline height", the brain slice thickness and anisotropy, and the location of the reference electrode, which were included in the FEM model, considerably affect the extracellular signal and localization performance, when the signal source is at larger distance to the array. After pre-calibration, the relative random error of the z-localization in tissue was only 3% for z-distances up to 200 μm. We then applied the model and related detailed understanding of extracellular recordings to achieve an electrically-guided navigation of a stimulating micropipette, solely based on the measured HD-MEA signals, and managed to target spontaneously active neurons in an acute brain slice for electroporation.

Large-scale neuroanatomy using LASSO: Loop-based Automated Serial Sectioning Operation

Serial section transmission electron microscopy (ssTEM) is the most promising tool for investigating the three-dimensional anatomy of the brain with nanometer resolution. Yet as the field progresses to larger volumes of brain tissue, new methods for high-yield, low-cost, and high-throughput serial sectioning are required. Here, we introduce LASSO (Loop-based Automated Serial Sectioning Operation), in which serial sections are processed in “batches.” Batches are quantized groups of individual sections that, in LASSO, are cut with a diamond knife, picked up from an attached waterboat, and placed onto microfabricated TEM substrates using rapid, accurate, and repeatable robotic tools. Additionally, we introduce mathematical models for ssTEM with respect to yield, throughput, and cost to access ssTEM scalability. To validate the method experimentally, we processed 729 serial sections of human brain tissue (~40 nm x 1 mm x 1 mm). Section yield was 727/729 (99.7%). Sections were placed accurately and repeatably (x-direction: -20 ± 110 μm (1 s.d.), y-direction: 60 ± 150 μm (1 s.d.)) with a mean cycle time of 43 s ± 12 s (1 s.d.). High-magnification (2.5 nm/px) TEM imaging was conducted to measure the image quality. We report no significant distortion, information loss, or substrate-derived artifacts in the TEM images. Quantitatively, the edge spread function across vesicle edges and image contrast were comparable, suggesting that LASSO does not negatively affect image quality. In total, LASSO compares favorably with traditional serial sectioning methods with respect to throughput, yield, and cost for large-scale experiments, and represents a flexible, scalable, and accessible technology platform to enable the next generation of neuroanatomical studies.

Cell Membrane Tracking in Living Brain Tissue Using Differential Interference Contrast Microscopy

Differential interference contrast (DIC) microscopy is widely used for observing unstained biological samples that are otherwise optically transparent. Combining this optical technique with machine vision could enable the automation of many life science experiments; however, identifying relevant features under DIC is challenging. In particular, precise tracking of cell boundaries in a thick ( ) slice of tissue has not previously been accomplished. We present a novel deconvolution algorithm that achieves the state-of-the-art performance at identifying and tracking these membrane locations. Our proposed algorithm is formulated as a regularized least squares optimization that incorporates a filtering mechanism to handle organic tissue interference and a robust edge-sparsity regularizer that integrates dynamic edge tracking capabilities. As a secondary contribution, this paper also describes new community infrastructure in the form of a MATLAB toolbox for accurately simulating DIC microscopy images of in vitro brain slices. Building on existing DIC optics modeling, our simulation framework additionally contributes an accurate representation of interference from organic tissue, neuronal cell-shapes, and tissue motion due to the action of the pipette. This simulator allows us to better understand the image statistics (to improve algorithms), as well as quantitatively test cell segmentation and tracking algorithms in scenarios, where ground truth data is fully known.

Progress in automating patch clamp cellular physiology

Patch clamp electrophysiology has transformed research in the life sciences over the last few decades. Since their inception, automatic patch clamp platforms have evolved considerably, demonstrating the capability to address both voltage- and ligand-gated channels, and showing the potential to play a pivotal role in drug discovery and biomedical research. Unfortunately, the cell suspension assays to which early systems were limited cannot recreate biologically relevant cellular environments, or capture higher order aspects of synaptic physiology and network dynamics. In vivo patch clamp electrophysiology has the potential to yield more biologically complex information and be especially useful in reverse engineering the molecular and cellular mechanisms of single-cell and network neuronal computation, while capturing important aspects of human disease mechanisms and possible therapeutic strategies. Unfortunately, it is a difficult procedure with a steep learning curve, which has restricted dissemination of the technique. Luckily, in vivo patch clamp electrophysiology seems particularly amenable to robotic automation. In this review, we document the development of automated patch clamp technology, from early systems based on multi-well plates through to automated planar-array platforms, and modern robotic platforms capable of performing two-photon targeted whole-cell electrophysiological recordings in vivo.

Closed-Loop Real-Time Imaging Enables Fully Automated Cell-Targeted Patch-Clamp Neural Recording In Vivo

Targeted patch-clamp recording is a powerful method for characterizing visually identified cells in intact neural circuits, but it requires skill to perform. We previously developed an algorithm that automates "blind" patching in vivo, but full automation of visually guided, targeted in vivo patching has not been demonstrated, with currently available approaches requiring human intervention to compensate for cell movement as a patch pipette approaches a targeted neuron. Here we present a closed-loop real-time imaging strategy that automatically compensates for cell movement by tracking cell position and adjusting pipette motion while approaching a target. We demonstrate our system's ability to adaptively patch, under continuous two-photon imaging and real-time analysis, fluorophore-expressing neurons of multiple types in the living mouse cortex, without human intervention, with yields comparable to skilled human experimenters. Our "imagepatching" robot is easy to implement and will help enable scalable characterization of identified cell types in intact neural circuits.

Application of Automated Image-guided Patch Clamp for the Study of Neurons in Brain Slices

Whole-cell patch clamp is the gold-standard method to measure the electrical properties of single cells. However, the in vitro patch clamp remains a challenging and low-throughput technique due to its complexity and high reliance on user operation and control. This manuscript demonstrates an image-guided automatic patch clamp system for in vitro whole-cell patch clamp experiments in acute brain slices. Our system implements a computer vision-based algorithm to detect fluorescently labeled cells and to target them for fully automatic patching using a micromanipulator and internal pipette pressure control. The entire process is highly automated, with minimal requirements for human intervention. Real-time experimental information, including electrical resistance and internal pipette pressure, are documented electronically for future analysis and for optimization to different cell types. Although our system is described in the context of acute brain slice recordings, it can also be applied to the automated image-guided patch clamp of dissociated neurons, organotypic slice cultures, and other non-neuronal cell types.

Robotic navigation to subcortical neural tissue for intracellular electrophysiology in vivo

In vivo studies of neurophysiology using the whole cell patch-clamp technique enable exquisite access to both intracellular dynamics and cytosol of cells in the living brain but are underrepresented in deep subcortical nuclei because of fouling of the sensitive electrode tip. We have developed an autonomous method to navigate electrodes around obstacles such as blood vessels after identifying them as a source of contamination during regional pipette localization (RPL) in vivo. In mice, robotic navigation prevented fouling of the electrode tip, increasing RPL success probability 3 mm below the pial surface to 82% (n = 72/88) over traditional, linear localization (25%, n = 24/95), and resulted in high-quality thalamic whole cell recordings with average access resistance (32.0 MΩ) and resting membrane potential (-62.9 mV) similar to cortical recordings in isoflurane-anesthetized mice. Whole cell yield improved from 1% (n = 1/95) to 10% (n = 9/88) when robotic navigation was used during RPL. This method opens the door to whole cell studies in deep subcortical nuclei, including multimodal cell typing and studies of long-range circuits.NEW & NOTEWORTHY This work represents an automated method for accessing subcortical neural tissue for intracellular electrophysiology in vivo. We have implemented a novel algorithm to detect obstructions during regional pipette localization and move around them while minimizing lateral displacement within brain tissue. This approach leverages computer control of pressure, manipulator position, and impedance measurements to create a closed-loop platform for pipette navigation in vivo. This technique enables whole cell patching studies to be performed throughout the living brain.