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Patch clamp Abstract 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.
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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.
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Our tool can multiply the number of daily measurements to help brain research. Download PDF Introduction Research of the past decade uncovered the unprecedented cellular heterogeneity of the mammalian brain.
It is well accepted now, that the complexity of the rodent and human cortex can be best resolved by classifying individual neurons into subsets by their cellular phenotypes 123. By characterizing molecular, morphological, connectional, physiological, and functional properties several neuronal subtypes have been defined 45.
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Revealing cell-type heterogeneity is still incomplete and challenging since classification based on quantitative features requires large amounts of individual cell samples, often thousands or more, encompassing a highly heterogeneous cell population. Recording morphological, electrophysiological, and transcriptional properties of neurons requires different techniques combined on the same sample such as patch clamp electrophysiology, posthoc morphological reconstruction, or single-cell transcriptomics.
The fundamental technique to achieve such trimodal characterization of neurons is the patch clamp recording, which is highly laborious and expertise intense. Therefore, there is a high demand to efficiently automate this labor intense and challenging process. Recently, the patch clamp technique has been automated and improved to a more advanced level 67.
Blind patch clamping was first done kezelése cukorbetegség hijama vitro and only later performed in vivo 89 In this case, the pipette is gradually moved forward and the brain cells are detected automatically by a resistance increase at the pipette tip.
Automated systems soon incorporated image-guidance by using multiphoton microscopy on genetically modified rodents 1112 Further improvements include the integration of tools for monitoring animal behavior 14the design of an obstacle avoidance algorithm before reaching the target cell 15 or the development of a pipette cleaning method which allows the immediate reuse of the pipettes up to ten times 16 Automated multi-pipette systems were developed to study the synaptic connections 18 It is also shown that cell morphology can be examined using automated systems One crucial step for image-guided automation is pipette ada 2021 guidelines pdf free download localization.
Different label-free pipette detection algorithms were compared previously Some automated patch clamp systems already contain pipette detection algorithms, e.
The other crucial step is the automatic detection of the cells which has only been performed in two-photon images so far. It is currently not possible to efficiently fluorescently stain human brain tissues. Alternatively, detection of cells in label-free images would open up new application possibilities in vitro 23e.
Most recently, deep learning 24 has been emerging to a level that in the case of well-defined tasks, outperforms humans, and often reaches human performance on ill-defined problems like detecting astrocyte cells In this paper, we describe a system we developed in order to overcome time-consuming and expertise-intense neuron characterization and collection. This fully automated differential interference contrast microscopy DIC, or label-free in general image-guided patch clamping system DIGAP combines 3D infrared video microscopy, cell detection using deep convolutional neural networks and a glass microelectrode guiding system to approach, attach, break-in, and record biophysical properties of the target cell.
Automatic deep learning-driven label-free image-guided patch clamp system
The steps of the visual patch clamp recording process are illustrated in Fig. Before the first use of the system, the pipette has to be calibrated, so that it can be moved relative to the field of view of the camera 1.
Thereafter, a position update is made after every pipette replacement 2 using the built-in pipette detection algorithms 3 to overcome the problem caused by pipette length differences. At this point, the system is ready to perform patch clamp recordings. We have acquired and annotated a single cell image database on label-free neocortical brain tissues, to our knowledge the largest 3D set of this kind.
A deep convolutional neural network has been trained for cell detection. The system can automatically select a detected cell for recording 4. When a cell is selected, multiple subsystems are started simultaneously that perform the patch clamping: i A subsystem controls the movement of the micropipette next to the cell. If any obstacle is found in the way, an avoidance algorithm tries to bypass it 5.
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Full size image Once the pipette touches the cell cell-attached configuration the system performs gigaseal formation 8then breaks in the cell membrane 9 and automatically starts the electrophysiological measurements When the recording is completed, the operator can decide either to start over the process on a new target cell or continue with one or both of the following manual steps. Ada 2021 guidelines pdf free download nucleus or the cytoplasm of the patched cell can be harvested 11or the recorded cells can be anatomically reconstructed in the tissue At the end of the measurements, the implemented pipette cleaning method can be performed or the next patch clamp recording can be started after pipette replacement and from the pipette tip position update step 3.
An event logging system collects information during the patch clamp process, including the target locations and the outcome success, and report files can be generated at the end. The report files are compatible with the Allen Cell Types Database Our system was tested on rodent and human samples in vitro. The quality of the electrophysiological measurements strongly correlates to that made by a trained experimenter.
We have used the system for harvesting cytoplasm and nucleus from the recorded cells and performed anatomical reconstruction on the samples. Our system can operate on unstained tissues using deep learning, that reaches the cell detection accuracy of human experts, and that enables the multiplication of the number of recordings while preserving high-quality measurements.
Results Here, we introduce an automated seek-and-patch system that performs electrophysiological recordings and sample harvesting for molecular biological analysis from single cells on unlabeled neocortical brain slices.
Automatic deep learning-driven label-free image-guided patch clamp system | Nature Communications
Using deep learning, trained on a previously built database of single neurons acquired in 3D, our system can detect most of the healthy neuronal somata in a Z-stack recorded by DIC microscopy from a living neocortical slice.
Components of the system are a typical electrophysiological setup: IR video microscopy imaging system, motorized microelectrode manipulators, XY shifting table, electrical amplifier, and a custom-designed pressure controller.
The system was successfully applied to perform patch clamp recordings on a large set of rodent and human cells and 74, respectively. The automatically collected cells well represent the wide-range phenotypic heterogeneity of the brain cortex.
Subsequent transcriptome profiling and whole-cell anatomical reconstruction confirmed the usefulness and applicability of the proposed system. Hardware development and control The hardware setup of the proposed system is shown in Fig. The software system we developed controls each hardware using their drivers on application programming interface API level, which makes the system modular and different types of hardware components e.
The classes which control hardware elements are inherited from abstract classes. Thus, if the software is to be used with a different hardware element then only a few methods should be implemented in a child class that sends commands to that specific ada 2021 guidelines pdf free download e.
Full size image The electrophysiological signal from the current monitor output of the amplifier is transferred to the DIGAP software via the analog input channel of the USB digitizer board National Instruments, USBwhich enables real-time resistance measurement. To apply different air pressure on the pipette in distinct phases of the patching procedure we built a custom pressure controller detailed in Supplementary Information: Pressure Regulator.
Analog pressure sensors are used for monitoring the actual air pressure on the pipette and voltage signals of the sensors were connected in the input channels of the USB digitizer board.
The solenoid valves of the regulator are controlled with TTL signals of the digital output channels of the digitizer. Pipette calibration and automatic detection Pipette calibration is a one-time process which determines the coordinate system transformation between the pipette and the stage axes. The calibration consists of moving the pipette along its axes with known distances, finding it with the stage and detecting the exact pipette tip position in the camera image.
Calibration allows the pipette to be moved at any position of the microscope stage space.
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Note that no assumptions are made on the orientation or the tilt angles of the pipette. The glass pipettes usually differ in length, thus the tip position should be updated after a pipette change.
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To automate this step we have developed algorithms for pipette detection in DIC images. First, we use a fast initialization heuristic and then refine the detection. The refinement step is the extension of our previous differential geometry-based method to three dimensions The pipette is modeled as two cylinders that have a common reference point and an orientation.
The model is updated by the gradient descent method such that it covers dark regions introduced by the pipette in the image. Figure 3a shows the starting and final state of the algorithm from different projections in gradient images for visualization purposes.
The detailed description of the algorithms and the equation derivations can be found in Supplementary Information: Pipette Detection System. The algorithm has an accuracy of 0.
Initial state blue contour and the result green contour of our pipette localization algorithm are shown. DIC images of the targeted in blue box and patched cell in green box.
The cell drifted from its initial location arrows in the right panel during the pipette maneuver. The template image was captured at the optimal focal depth in red boxes before starting the tracking.
During the pipette movement, image stacks were captured from the targeted cell upper panels such that the middle slice was taken of the most recent focus position.