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.

Inferring thalamocortical monosynaptic connectivity in vivo.

As the tools to simultaneously record electrophysiological signals from large numbers of neurons within and across brain regions become increasingly available, this opens up for the first time the possibility of establishing the details of causal relationships between monosynaptically connected neurons and the patterns of neural activation that underlie perception and behavior. Although recorded activity across synaptically connected neurons has served as the cornerstone for much of what we know about synaptic transmission and plasticity, this has largely been relegated to ex vivo preparations that enable precise targeting under relatively well-controlled conditions. Analogous studies in vivo, where image-guided targeting is often not yet possible, rely on indirect, data-driven measures, and as a result such studies have been sparse and the dependence upon important experimental parameters has not been well studied. Here, using in vivo extracellular single-unit recordings in the topographically aligned rodent thalamocortical pathway, we sought to establish a general experimental and computational framework for inferring synaptic connectivity. Specifically, attacking this problem within a statistical signal detection framework utilizing experimentally recorded data in the ventral-posterior medial (VPm) region of the thalamus and the homologous region in layer 4 of primary somatosensory cortex (S1) revealed a trade-off between network activity levels needed for the data-driven inference and synchronization of nearby neurons within the population that results in masking of synaptic relationships. Here, we provide a framework for establishing connectivity in multisite, multielectrode recordings based on statistical inference, setting the stage for large-scale assessment of synaptic connectivity within and across brain structures.NEW & NOTEWORTHY Despite the fact that all brain function relies on the long-range transfer of information across different regions, the tools enabling us to measure connectivity across brain structures are lacking. Here, we provide a statistical framework for identifying and assessing potential monosynaptic connectivity across neuronal circuits from population spiking activity that generalizes to large-scale recording technologies that will help us to better understand the signaling within networks that underlies perception and behavior.

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.

A robot for high yield electrophysiology and morphology of single neurons in vivo.

Single-cell characterization and perturbation of neurons provides knowledge critical to addressing fundamental neuroscience questions including the structure-function relationship and neuronal cell-type classification. Here we report a robot for efficiently performing in vivo single-cell experiments in deep brain tissues optically difficult to access. This robot automates blind (non-visually guided) single-cell electroporation (SCE) and extracellular electrophysiology, and can be used to characterize neuronal morphological and physiological properties of, and/or manipulate genetic/chemical contents via delivering extraneous materials (for example, genes) into single neurons in vivo. Tested in the mouse brain, our robot successfully reveals the full morphology of single-infragranular neurons recorded in multiple neocortical regions, as well as deep brain structures such as hippocampal CA3, with high efficiency. Our robot thus can greatly facilitate the study of in vivo full morphology and electrophysiology of single neurons in the brain.

Optical method for automated measurement of glass micropipette tip geometry.

Many experimental biological techniques utilize hollow glass needles called micropipettes to perform fluid extraction, cell manipulation, and electrophysiological recordings For electrophysiological recordings, micropipettes are typically fabricated immediately before use using a "pipette puller", which uses open-loop control to heat a hollow glass capillary while applying a tensile load. Variability between manufactured micropipettes requires a highly trained operator to qualitatively inspect each micropipette; typically this is achieved by viewing the pipette under 40-100x magnification in order to ensure that the tip has the correct shape (e.g., outer diameter, cone angle, taper length). Since laboratories may use hundreds of micropipettes per week, significant time demands are associated with micropipette inspection. Here, we have automated the measurement of micropipette tip outer diameter and cone angle using optical microscopy. The process features repeatable constraint of the micropipette, quickly and automatically moving the micropipette to bring its tip into the field of view, focusing on the tip, and computing tip outer diameter and cone angle measurements from the acquired images by applying a series of image processing algorithms. As implemented on a custom automated microscope, these methods achieved, with 95% confidence, ±0.38 µm repeatability in outer diameter measurement and ±5.45° repeatability in cone angle measurement, comparable to a trained human operator. Accuracy was evaluated by comparing optical pipette measurements with measurements obtained using scanning electron microscopy (SEM); optical outer diameter measurements differed from SEM by 0.35 ± 0.36 µm and optical cone angle measurements differed from SEM by -0.23 ± 2.32°. The algorithms we developed are adaptable to most commercial automated microscopes and provide a skill-free route to rapid, quantitative measurement of pipette tip geometry with high resolution, accuracy, and repeatability. Further, these methods are an important step toward a closed-loop, fully-automated micropipette fabrication system.

Disposable platform provides visual and color-based point-of-care anemia self-testing.

BACKGROUND: Anemia, or low blood hemoglobin (Hgb) levels, afflicts 2 billion people worldwide. Currently, Hgb levels are typically measured from blood samples using hematology analyzers, which are housed in hospitals, clinics, or commercial laboratories and require skilled technicians to operate. A reliable, inexpensive point-of-care (POC) Hgb test would enable cost-effective anemia screening and chronically anemic patients to self-monitor their disease. We present a rapid, stand-alone, and disposable POC anemia test that, via a single drop of blood, outputs color-based visual results that correlate with Hgb levels. METHODS: We tested blood from 238 pediatric and adult patients with anemia of varying degrees and etiologies and compared hematology analyzer Hgb levels with POC Hgb levels, which were estimated via visual interpretation using a color scale and an optional smartphone app for automated analysis. RESULTS: POC Hgb levels correlated with hematology analyzer Hgb levels (r = 0.864 and r = 0.856 for visual interpretation and smartphone app, respectively), and both POC test methods yielded comparable sensitivity and specificity for detecting any anemia (n = 178) (<11 g/dl) (sensitivity: 90.2% and 91.1%, specificity: 83.7% and 79.2%, respectively) and severe anemia (n = 10) (<7 g/dl) (sensitivity: 90.0% and 100%, specificity: 94.6% and 93.9%, respectively). CONCLUSIONS: These results demonstrate the feasibility of this POC color-based diagnostic test for self-screening/self-monitoring of anemia. TRIAL REGISTRATION: Not applicable. FUNDING: This work was funded by the FDA-funded Atlantic Pediatric Device Consortium, the Georgia Research Alliance, Children's Healthcare of Atlanta, the Georgia Center of Innovation for Manufacturing, and the InVenture Prize and Ideas to Serve competitions at the Georgia Institute of Technology.