Autonomous patch-clamp robot for functional characterization of neurons in vivo: development and application to mouse visual cortex

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.

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains

Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic ground-truth and in vitro data sets, where the connectivity labels were obtained from simultaneous high-density microelectrode array (HD-MEA) and patch-clamp recordings. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike-train data. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms and show how it improves network reconstruction accuracy and robustness. Overall, the eANN demonstrated strong performance across different dynamical regimes, worked well on smaller datasets, and improved the detection of synaptic connectivity, especially inhibitory connections. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity.

Immediate reuse of patch-clamp pipettes after ultrasonic cleaning

The patch-clamp technique has revolutionized neurophysiology by allowing to study single neuronal excitability, synaptic connectivity, morphology, and the transcriptomic profile. However, the throughput in recordings is limited because of the manual replacement of patch-pipettes after each attempt which are often also unsuccessful. This has been overcome by automated cleaning the tips in detergent solutions, allowing to reuse the pipette for further recordings. Here, we developed a novel method of automated cleaning by sonicating the tips within the bath solution wherein the cells are placed, reducing the risk of contaminating the bath solution or internal solution of the recording pipette by any detergent and avoiding the necessity of a separate chamber for cleaning. We showed that the patch-pipettes can be used consecutively at least ten times and that the cleaning process does not negatively impact neither the brain slices nor other patched neurons. This method, combined with automated patch-clamp, highly improves the throughput for single and especially multiple recordings.

A biologically validated mathematical model for decoding epithelial apical, basolateral, and paracellular electrical properties

Epithelial tissues form selective barriers to ions, nutrients, waste products, and infectious agents throughout the body. Damage to these barriers is associated with conditions such as celiac disease, cystic fibrosis, diabetes, and age-related macular degeneration. Conventional electrophysiology measurements like transepithelial resistance can quantify epithelial tissue maturity and barrier integrity but are limited in differentiating between apical, basolateral, and paracellular transport pathways. To overcome this limitation, a combination of mathematical modeling, stem cell biology, and cell physiology led to the development of 3 P-EIS, a novel mathematical model and measurement technique. 3 P-EIS employs an intracellular pipette and extracellular electrochemical impedance spectroscopy to accurately measure membrane-specific properties of epithelia, without the constraints of prior models. 3 P-EIS was validated using electronic circuit models of epithelia with known resistances and capacitances, confirming a median error of 19% (interquartile range: 14%-26%) for paracellular and transcellular resistances and capacitances (n = 5). Patient stem cell-derived retinal pigment epithelium tissues were measured using 3 P-EIS, successfully isolating the cellular responses to adenosine triphosphate. 3 P-EIS enhances quality control in epithelial cell therapies and has extensive applicability in drug testing and disease modeling, marking a significant advance in epithelial physiology.NEW & NOTEWORTHY This interdisciplinary paper integrates mathematics, biology, and physiology to measure epithelial tissue's apical, basolateral, and paracellular transport pathways. A key advancement is the inclusion of intracellular voltage recordings using a sharp pipette, enabling precise quantification of relative impedance changes between apical and basolateral membranes. This enhanced electrochemical impedance spectroscopy technique offers insights into epithelial transport dynamics, advancing disease understanding, drug interactions, and cell therapies. Its broad applicability contributes significantly to epithelial physiology research.

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.

Translational opportunities and challenges of invasive electrodes for neural interfaces

Invasive brain-machine interfaces can restore motor, sensory and cognitive functions. However, their clinical adoption has been hindered by the surgical risk of implantation and by suboptimal long-term reliability. In this Review, we highlight the opportunities and challenges of invasive technology for clinically relevant electrophysiology. Specifically, we discuss the characteristics of neural probes that are most likely to facilitate the clinical translation of invasive neural interfaces, describe the neural signals that can be acquired or produced by intracranial electrodes, the abiotic and biotic factors that contribute to their failure, and emerging neural-interface architectures.

Smart imaging to empower brain-wide neuroscience at single-cell levels

A deep understanding of the neuronal connectivity and networks with detailed cell typing across brain regions is necessary to unravel the mechanisms behind the emotional and memorial functions as well as to find the treatment of brain impairment. Brain-wide imaging with single-cell resolution provides unique advantages to access morphological features of a neuron and to investigate the connectivity of neuron networks, which has led to exciting discoveries over the past years based on animal models, such as rodents. Nonetheless, high-throughput systems are in urgent demand to support studies of neural morphologies at larger scale and more detailed level, as well as to enable research on non-human primates (NHP) and human brains. The advances in artificial intelligence (AI) and computational resources bring great opportunity to 'smart' imaging systems, i.e., to automate, speed up, optimize and upgrade the imaging systems with AI and computational strategies. In this light, we review the important computational techniques that can support smart systems in brain-wide imaging at single-cell resolution.

Patch Clamp: The First Four Decades of a Technique That Revolutionized Electrophysiology and Beyond

Forty years ago, the introduction of a new electrophysiological technique, the patch clamp, revolutionized the fields of Cellular Physiology and Biophysics, providing for the first time the possibility of describing the behavior of a single protein, an ion-permeable channel of the cell plasma membrane, in its physiological environment. The new approach was actually much more potent and versatile than initially envisaged, and it has evolved into several different modalities that have radically changed our knowledge of how cells (not only the classical "electrically excitable "ones, such as nerves and muscles) use electrical signaling to modulate and organize their activity. This review aims at telling the history of the background from which the new technique evolved and at analyzing some of its more recent developments.

Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization

The Patch-seq approach is a powerful variation of the patch-clamp technique that allows for the combined electrophysiological, morphological, and transcriptomic characterization of individual neurons. To generate Patch-seq datasets at scale, we identified and refined key factors that contribute to the efficient collection of high-quality data. We developed patch-clamp electrophysiology software with analysis functions specifically designed to automate acquisition with online quality control. We recognized the importance of extracting the nucleus for transcriptomic success and maximizing membrane integrity during nucleus extraction for morphology success. The protocol is generalizable to different species and brain regions, as demonstrated by capturing multimodal data from human and macaque brain slices. The protocol, analysis and acquisition software are compiled at https://githubcom/AllenInstitute/patchseqtools. This resource can be used by individual labs to generate data across diverse mammalian species and that is compatible with large publicly available Patch-seq datasets.

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.

Measuring Absolute Membrane Potential Across Space and Time

Membrane potential (Vmem) is a fundamental biophysical signal present in all cells. Vmem signals range in time from milliseconds to days, and they span lengths from microns to centimeters. Vmem affects many cellular processes, ranging from neurotransmitter release to cell cycle control to tissue patterning. However, existing tools are not suitable for Vmem quantification in many of these areas. In this review, we outline the diverse biology of Vmem, drafting a wish list of features for a Vmem sensing platform. We then use these guidelines to discuss electrode-based and optical platforms for interrogating Vmem. On the one hand, electrode-based strategies exhibit excellent quantification but are most effective in short-term, cellular recordings. On the other hand, optical strategies provide easier access to diverse samples but generally only detect relative changes in Vmem. By combining the respective strengths of these technologies, recent advances in optical quantification of absolute Vmem enable new inquiries into Vmem biology.

In Vivo Whole-Cell Patch-Clamp Methods: Recent Technical Progress and Future Perspectives

Brain functions are fundamental for the survival of organisms, and they are supported by neural circuits consisting of a variety of neurons. To investigate the function of neurons at the single-cell level, researchers often use whole-cell patch-clamp recording techniques. These techniques enable us to record membrane potentials (including action potentials) of individual neurons of not only anesthetized but also actively behaving animals. This whole-cell recording method enables us to reveal how neuronal activities support brain function at the single-cell level. In this review, we introduce previous studies using in vivo patch-clamp recording techniques and recent findings primarily regarding neuronal activities in the hippocampus for behavioral function. We further discuss how we can bridge the gap between electrophysiology and biochemistry.

Advances in 3D neuronal microphysiological systems: towards a functional nervous system on a chip

Microphysiological systems (MPS) designed to study the complexities of the peripheral and central nervous systems have made marked improvements over the years and have allowed researchers to assess in two and three dimensions the functional interconnectivity of neuronal tissues. The recent generation of brain organoids has further propelled the field into the nascent recapitulation of structural, functional, and effective connectivities which are found within the native human nervous system. Herein, we will review advances in culture methodologies, focused especially on those of human tissues, which seek to bridge the gap from 2D cultures to hierarchical and defined 3D MPS with the end goal of developing a robust nervous system-on-a-chip platform. These advances have far-reaching implications within basic science, pharmaceutical development, and translational medicine disciplines.

In Vivo Patch-Clamp Studies

Intact and functioning brain enables quantification of neural activities directly associated with real world such as visual and auditory information. In vivo patch clamp can record different types of neuronal activity, such as spiking responses, membrane potential dynamics, and synaptic currents (e.g., EPSC, IPSC) in either anesthetized or awake or even free moving animals. Researchers can not only directly measure these neuronal activities but also quantify and unravel synaptic contribution from excitatory and inhibitory circuits. Here, we describe the requirements and standard protocols to perform in vivo patch clamp recording. The key factors of successful recording based on references and our experiences are also provided.

Electrophysiology prediction of single neurons based on their morphology.. [DOI: 10.1101/2020.02.04.933697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Electrophysiology data acquisition of single neurons represents a key factor for the understanding of neuronal dynamics. However, the traditional method to acquire this data is through patch-clamp technology, which presents serious scalability flaws due to its slowness and complexity to record at fine-grained spatial precision (dendrites and axon).

In silico biophysical models are therefore created for simulating hundreds of experiments that would be impractical to recreate in vitro. The optimal way to create these models is based on the knowledge of the morphological and electrical features for each neuron. Since large-scale data acquisition is often unfeasible for electrical data, previous expert knowledge can be used but it must have an acceptable degree of similarity with the type of neurons that we are trying to model.

Here, we present a data-driven machine learning approach to predict the electrophysiological features of single neurons in case of only having their morphology available. To solve this multi-output regression problem, we use an artificial neural network that has the particularity of providing a probability distribution for every output feature, to incorporate uncertainty. Input data to train the model is obtained from from the Allen Cell Types database. The electrical properties can depend on the morphology, whose acquisition technology is highly automated and scalable so there exist large data sets of them. We also provide integrations with the BluePyOpt library to create a biophysical model using the original morphology and the predicted electrical features. Finally, we connect the resulting biophysical model with the Geppetto UI software to run all the simulations in a sophisticated user interface.

High-yield, automated intracellular electrophysiology in retinal pigment epithelia

BACKGROUND

Recent advancements with induced pluripotent stem cell-derived (iPSC) retinal pigment epithelium (RPE) have made disease modeling and cell therapy for macular degeneration feasible. However, current techniques for intracellular electrophysiology - used to validate epithelial function - are painstaking and require manual skill; limiting experimental throughput.

NEW METHOD

A five-stage algorithm, leveraging advances in automated patch clamping, systematically derived and optimized, improves yield and reduces skill when compared to conventional, manual techniques.

RESULTS

The automated algorithm improves yield per attempt from 17% (manually, n = 23) to 22% (automated, n = 120) (chi-squared, p = 0.004). Specifically for RPE, depressing the local cell membrane by 6 μm and electroporating (buzzing) just prior to this depth (5 μm) maximized yield.

COMPARISON WITH EXISTING METHOD

Conventionally, intracellular epithelial electrophysiology is performed by manually lowering a pipette with a micromanipulator, blindly, towards a monolayer of cells and spontaneously stopping when the magnitude of the instantaneous measured membrane potential decreased below a predetermined threshold. The new method automatically measures the pipette tip resistance during the descent, detects the cell surface, indents the cell membrane, and briefly buzzes to electroporate the membrane while descending, overall achieving a higher yield than conventional methods.

CONCLUSIONS

This paper presents an algorithm for high-yield, automated intracellular electrophysiology in epithelia; optimized for human RPE. Automation reduces required user skill and training while, simultaneously, improving yield. This algorithm could enable large-scale exploration of drug toxicity and physiological function verification for numerous kinds of epithelia.

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.