参见作者原研究论文

本实验方案简略版
Aug 2019

本文章节


 

Method for Rapid Enzymatic Cleaning for Reuse of Patch Clamp Pipettes: Increasing Throughput by Eliminating Manual Pipette Replacement between Patch Clamp Attempts
膜片钳移液管重复使用的快速酶清洗方法:通过消除膜片钳尝试之间的手动移液管更换来提高吞吐量   

引用 收藏 提问与回复 分享您的反馈 Cited by

Abstract

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


Keywords: Patch-clamp (膜片钳), Automation (自动化), Enzymatic detergent (酶清洗剂), High-throughput (高通量), Electrophysiology (电生理学)

Background

Whole-cell patch-clamp recordings allow unprecedented access to electrical activity, neuronal morphology, and gene expression at the single-cell level (Jiang et al., 2015; Gouwens et al., 2019). However, because this method requires a great amount of skill and care to perform correctly, it remains one of the most difficult in neuroscience. A crucial step in this method is the formation of a tight, high resistance (e.g., >1 GΩ) connection between the cell membrane and the glass pipette known as a gigaseal. Gigaseal formation requires a clean pipette surface, and even small contaminants (e.g., cell debris or dust) can disrupt this process (Hamill et al., 1981). For this reason, patch-clamp experimenters need to replace glass pipettes after each recording attempt (Figure 1A), requiring additional time and attention (e.g., removal, fabrication, filling, and installation of pipettes). We have previously found that contrary to decades of this ubiquitous practice in the field, patch-clamp pipettes can be cleaned and reused, enabling many patch clamp recordings with a single pipette (Kolb et al., 2016).


Pipette cleaning is a robust, simple process involving the following steps: (1) attempt whole-cell patch-clamp recording; (2) retract patch-clamp pipette and move towards bath containing cleaning solution; (3) with tip submerged in cleaning solution, cycle positive and negative pressures to remove cell debris from pipette tip; (4) position pipette over new target cell for second patch-clamp attempt; and (5) repeat steps 1-4 until the experiment is completed or the pipette fails (e.g., tip breakage, clog, evaporation of cleaning solution, or user error). In this protocol, we will describe the use of simple hardware and software to automate pipette cleaning, which will assist other labs in implementing pipette cleaning in either fully automated or “push-to-clean” methods (Figure 1B).



Figure 1. Pipette cleaning methods. A. Process flow chart for traditional manual patch clamping without pipette cleaning. Removing, filling, and installing fresh pipettes takes between 60-120 s. B. Process flow chart for manual patching with automated cleaning (“push-to-clean”). Automated cleaning can be run in as little as 30 s. C. Close up images of pipette being moved from the experimental chamber (left) to the cleaning bath (middle) and to the rinse bath (right) before returning to the experimental bath to patch another cell. Scale bar is 25 mm. D. Custom experimental chamber for pipette cleaning featuring fluid inlet and outlet, inset for ground wire, and external baths for cleaning and rinsing solutions. Scale bar is 1 cm.


We published our initial discovery of this method in 2016 and used it to develop the first fully autonomous patch-clamp robot capable of recording dozens of cells with no human supervision in 2019 (Kolb et al., 2016 and 2019). This pipette cleaning method has been used by us, our collaborators, and other groups to make large-scale patch-clamp studies (i.e., single-cell electrophysiology and connectomics in rodents and humans and high throughput screening) more efficient (Peng et al., 2019; Koos et al., 2020) and to make complex experiments (i.e., in vivo patch-clamp) simpler (Suk et al., 2017; Stoy et al., 2020). In addition, since our initial report, we have discovered that 2% w/v Tergazyme is a superior cleaning solution (Figure 2A) and that the rinsing step from the original method is unnecessary (Figure 5). These improvements to the method have increased the whole-cell recording yield ~20%, increased the number of total cleans with a single pipette by a factor of 10 in HEK 293 cells (Figure 4), and decreased the time needed for each round of cleaning to <30 s, faster than manual pipette replacement (~1-2 min). Building on these gains in efficiency, we improved whole-cell yield to ~90% in HEK 293 cells by optimizing the position of the pipette tip relative to the cell membrane (Figure 2A-C) (Stoy et al., 2020). Briefly, by varying the distance the pipette was advanced into the cell, we found a strong relationship between distance and gigaseal probability, which reached ~100% at a range of 1-2 µm below the cell surface (defined as the z-axis point where pipette resistance increased 0.1 MΩ from initial resistance) (Stoy et al., 2020). When the patcherBot was programmed to attempt gigasealing at this position, the whole-cell recording yield increased significantly, as shown in the “Optimized” trace of Figure 2A (P = 0.044, Kolmorogov-Smirnov test). This method is used by the patcherBot in Figure 3 and Figure 4. Overall, this improved pipette cleaning method enables automated patch-clamp experiments to operate completely unattended for 3-4 h at yields of up to 90% and throughputs of up to 15 whole-cell recordings per hour, surpassing the output of highly skilled human experimenters (Figure 2, Figure 3). We were able to achieve up to 100 whole-cell recordings in ~13 h with a single pipette (Figure 4). We have also developed a simple hardware and software package for “push-to-clean” semi-automated patch-clamp experiments, enabling electrophysiologists to easily integrate this method into their hardware setup (Figure 1B-D, Figure 6, and Supplemental Information).



Figure 2. Improvements to pipette cleaning. A. Each trace represents the number of whole-cell recordings in HEK 293 cells as a function of the number of recording attempts with a single pipette. Each trace is the average of at least three pipettes. The “Saline” trace is a negative control (i.e., cleaning solution replaced with extracellular solution), and a 100% theoretical maximum is included for reference. The “Alconox” trace shows the performance of 2% w/v Alconox cleaning, which decreases as a function of the number of attempts. The “Tergazyme” trace shows no decrease in yield for 30 attempts with 2% w/v Tergazyme. The “Optimized” trace represents 2% w/v Tergazyme cleaning with optimized pipette positioning relative to the cell surface for gigasealing. The “Tergazyme” performance is superior to that of Alconox (*, P = 1.375E-5, Kolmogorov-Smirnov test). The “Optimized” performance is superior to that of “Tergazyme” (**, P = 0.04368, Kolmogorov-Smirnov test). B. Success rate of whole-cell patch-clamp as a function of the number of cleans using 2% w/v Tergazyme shows no significant decrease in likelihood of subsequent whole-cell recording (Odds ratio (OR) = 1.0067, CI: 0.97-1.04, P = 0.69, n = 215 attempts, each attempt is for n = 7 pipettes, except attempts 29 and 30, which are for n = 6). C. Optimized indentation with Tergazyme cleaning shows no significant decrease in likelihood of subsequent whole-cell recording (OR = 1.00, CI: 0.94-1.06, P = 0.95, n = 124 attempts, each attempt is for n = 4 pipettes).



Figure 3. High-throughput opsin screening with pipette cleaning. A. Yield curve for a single pipette channelrhodopsin-2 (ChR-2) characterization experiment (46/51 attempts, 90% yield). B. Representative photocurrent trace (voltage clamp) in response to an initial pulse of 500 ms 480 nm LED pulse recorded from transiently transfected HEK 293 cell showing a large peak photocurrent response. C. Photocurrent traces (voltage clamp) from cells recorded in the middle of a series of 500 ms light pulses showing steady-state photocurrents over many pipette cleans. Large initial photocurrent responses are typical of ChR-2 (B) and are reduced in subsequent stimulation pulses (C) (Lin et al., 2009).



Figure 4. Upper limits of pipette cleaning with 2% w/v Tergazyme. A. Yield curves for individual pipettes showing cleaning for over 90 recording attempts with associated failure modes. The “success” trace shows effective pipette cleaning, the “clog” trace shows reversible pipette tip clogs that cause low yield over time, and the “break” trace shows experiments terminated by broken pipette tips. Theoretical maximum (100% yield) included for reference. B. Representative pipette images taken at 40× magnification for each failure mode in (A). Scale bar is 1 µm. C. Individual gigaseal resistance traces from the “success” trace (n = 122 gigaseal attempts). D. Access resistance of cells recorded in the “success” trace (n = 101 whole-cell recordings).



Figure 5. Tergazyme cleaning is effective without a rinsing step in acute mouse brain slices. A. The success rate of patching without a rinse step does not decrease significantly with the number of cleans (OR = 1.14, CI: 0.87-1.41, P = 0.34, n = 36 attempts). The number of pipettes used for each experiment is noted above each number of reuses. B. Yield plot for a single pipette using 2% w/v Tergazyme without rinsing. C. Representative recordings of evoked action potential firing in current clamp from three neurons recorded with a single pipette cleaned in 2% w/v Tergazyme without rinsing. D. Enlarged single evoked action potentials from the neurons in (C).


A robust and easy-to-implement method for automated pipette cleaning is of interest to all laboratories using the patch-clamp method. Although no negative side effects have been measured in cleaning experiments to date (Kolb et al., 2016 and 2019; Peng et al., 2019), for experiments where the possibility of exposing sensitive cells to cleaning residues is of particular concern (e.g., pharmacology or single-channel recordings), the method can be easily adapted to further minimize this risk (see Notes). We believe that this improved pipette cleaning method will be especially useful to labs working in the areas of multi-pipette patch clamping and high-throughput screening (see Notes, Figure 3).


The pipette cleaning method described in this protocol enables pipette reuse for patch clamping. By eliminating the need to fabricate, fill, install, and remove pipettes throughout experiments, experimenters can save valuable time and attention from these labor-intensive tasks. We show that 2% w/v Tergazyme enables up to and over 100 cells to be recorded with a single pipette (5-8 MΩ, Warner Instruments), eliminating the need for experimenters to replace pipettes over the course of an experimental day.

Materials and reagents

  1. Borosilicate pipette glass with filament (Warner Instruments, catalog number: 64-0793)

  2. Syringe, 5 ml (VWR, catalog number: BD309646)

  3. Syringe filter, 0.2 µm (VWR, catalog number: 10218-486)

  4. 23G needle (VWR, catalog number: 89134-098)

  5. Tergazyme (Alconox, catalog number: 1304-1)

Equipment

The equipment listed is in addition to standard patch-clamp electrophysiology equipment (e.g., amplifier, digitizer, headstage, micromanipulator, microscope, and pipette puller). Specific details of the patch-clamp rig used in this paper are described in detail elsewhere (Kolb et al., 2016 and 2019).

  1. Cleaning dish (3D print or mill according to CAD files in SI, with appropriate changes for microscope stage)

  2. Pressure control box (detailed plans and parts list on autopatcher.org, direct order from Neuromatic Devices, neuromaticdevices.com)

Software

Depending on the level of automation desired, download either (1) and (2) for full automation or only (2) to enable “push-to-clean” for manual patch clamping with cleaning.

  1. Autopatcher software (downloadable at autopatcher.org). This software enables full automation of the cell detection, gigasealing, and break-in functions.

  2. Push-to-clean software (downloadable at Github, https://github.com/mightenyip/Pipette-Cleaning-Software). The terminology “push-to-clean” is defined as an otherwise manual electrophysiology rig that includes a button-actuated pipette cleaning function. The button initiates a series of pipette position and pressure changes to clean the pipette for reuse.

Procedure

Before the first experiment

  1. Manufacture cleaning dish according to plans and microscope stage dimensions (Figure 1C, CAD files in Supplementary Information Appendix B) in-house, using an on-demand production service (e.g., Protolabs, protolabs.com), or purchase from a commercial supplier (e.g., Neuromatic Devices).


  2. Install pressure control box on existing patch-clamp electrophysiology rig

    1. The pressure control box can be built from scratch according to plans presented by Kodandaramaiah et al. (2016). Schematics, instructions, and parts lists are also available for download at autopatcher.org.

    2. A cleaning-compatible pressure control box can be purchased directly from Neuromatic Devices (neuromaticdevices.com).


  3. Download and install software from autopatcher.org. Perform initial software setup. Detailed instructions are provided in Supplementary Information Appendix A.

    1. Register the manipulator according to the manufacturer and COM port (see Supplementary Information Figures S1-S2).

    2. Register the pressure control box to the specified COM port (see Supplementary Information Figures S1-S2).


Before each experiment

  1. Prepare biological samples for patch-clamp recording. Methods are referenced for experimental preparations in which pipette cleaning has been validated by us or in other published reports.

    1. For in vitro HEK 293 cells, follow Kolb et al. (2016). Pipette cleaning works well with wild-type cells, stably transfected lines, and transient transfections (Kolb et al., 2016 and 2019)

    2. For rodent neuron culture recording, follow Kaech and Banker (2006) and Kolb et al. (2016). Cleaning for this preparation is verified in Kolb et al. (2016 and 2019).

    3. For acute brain tissue slices recording, follow Jiang et al. (2015). Cleaning for this preparation is verified in the following reports Kolb et al. (2016 and 2019).

    4. For in vivo mouse recording in anesthetized preparations, follow Bagal et al. (2013). Cleaning for this preparation is verified in Kolb et al. (2016 and 2019) and Stoy et al. (2020).

    5. For acute human brain tissue slices, follow Ting et al. (2018) and Peng et al. (2019). Cleaning for this preparation is verified in Peng et al. (2019).

    6. For human cerebral organoids, follow Mariani et al. (2015) and Qian et al. (2016). We have verified the cleaning in this preparation in unpublished experiments.


  2. Prepare electrophysiology rig for the patch-clamp experiment and prepare pipettes as appropriate for experiment. Detailed guides for patch-clamp rig setup, denoising, and troubleshooting are provided elsewhere (Perin and Markram, 2013; Wang et al., 2015; Kodandaramaiah et al., 2016).


  3. Load software for a push-to-clean patch-clamp experiment (Supplementary Information Appendix C).


  4. Make 2% w/v Tergazyme cleaning solution.

    1. Prepare 2% w/v Tergazyme solution in room temperature deionized water.

    2. Mix solution until all Tergazyme powder is dissolved.

      Note: Because Tergazyme is an enzymatic detergent, the enzymatic component degrades over time. The manufacturer recommends making fresh solutions and using them within 8 h for maximum efficacy.


  5. Fill cleaning and rinsing bath reservoirs with filtered solutions

    1. Using a syringe with a 0.2 µm filter and 23G needle, fill the appropriate bath reservoir with freshly made 2% w/v Tergazyme (or extracellular solution for rinsing).

    2. Be careful not to overfill the cleaning bath reservoir, as this can cause Tergazyme solution to flow into the experimental chamber, potentially damaging the cells.

      Note: To ensure there is no fluid exchange between the cleaning bath and the experimental bath, insert the tip of the pipette into the cleaning bath and monitor the square wave pulse in the voltage clamp. If there is no electrical contact between the ungrounded cleaning bath and the grounded experimental bath, you will see capacitive transients, similar to when the tip of the pipette is in the air. If there is electrical contact, you will see a square wave pulse, similar to when the tip is submerged in the experimental bath. To resolve this, use a task wipe to remove a small amount of fluid from the cleaning bath until electrical contact is eliminated.


  6. In the software interface, calibrate manipulators in reference to cells and cleaning baths (Figure 6).

    1. Select and save “exp bath location” position above target cell.

    2. Select and save “location above baths” position directly above the cleaning bath reservoir.

    3. Select and save “cleaning bath location” position with tip safely submerged in cleaning solution.

    4. Select and save “wash bath” position with tip safely submerged in rinsing solution (i.e., extracellular solution) if desired.



      Figure 6. Calibration of pipettes for pipette cleaning. Positions used by the push-to-clean software for each cleaning attempt. Images in (A) refer to saved position values in (B). Briefly, (1) refers to position directly above target cells or tissue, (2) refers to a z-location above the clean and rinse baths, (3) refers to the position where the tip is submerged in cleaning solution, and (4) refers to the position where the pipette tip is submerged in the rinsing solution.


Patch-clamp experiment

  1. Attempt patch-clamp recording on target cell


  2. Initiate pipette cleaning using the software interface by clicking the “clean” button. The functions performed by the software are as follows:

    1. Pipette retracts from cell to “location above baths” position under light positive pressure (+50 mbar).

    2. Pipette moves from “location above baths” position to “cleaning bath location” position until contact is made between the pipette tip and the cleaning solution.

      Note: Touching the pipette to the surface of the cleaning solution can be detected as a change in the capacitance of a square wave pulse at the pipette tip. This can often be observed prior to the pipette visibly touching the surface. Visible confirmation of pipette-fluid contact is also sufficient to begin cleaning.

    3. With pipette tip in cleaning bath, suction is applied (-345 mbar) for 5 s.

    4. Five rounds of alternating pulses of suction (-345 mbar for 1 s) and positive pressure (+700 mbar for 1 s) are applied.

    5. Positive pressure is applied (+700 mbar) for 5 s to expel any residual cleaning solution from pipette tip.

    6. Pipette is retracted from cleaning bath to “location above baths” position and then to either “exp bath location” position or “wash bath” position (optional).


  3. Rinse the pipette (optional)

    1. Pipette is moved from “cleaning bath location” position to “location above baths” position under positive pressure (+ 200 mbar).

    2. Pipette is moved down from “location above baths” position toward “wash bath” position until contact is made between the pipette tip and the cleaning solution.

    3. With pipette tip in rinse bath, apply 3 s of suction (-345 mbar) followed by 10 s of positive pressure (+700 mbar).

    4. Retract pipette from “wash bath” position to “location above baths” position under positive pressure (+200 mbar).


  4. Attempt patch-clamp recording on next target cell


  5. Repeat steps A-D until the end of the experiment or failure of the pipette (e.g., tip breakage, clog, evaporation of cleaning solution, or user error).

    Note: If the pipette appears to be clogged (i.e., visible internal clog observed in pipette tip or increase in resistance) or broken (i.e., visible broken tip or decrease in resistance), then replace it and repeat calibration.

Data analysis

Data from patch-clamp experiments using pipette cleaning can be processed in the same way as traditional patch-clamp experiments using software tools like pClamp (Molecular Devices) or Matlab (Mathworks). A useful analysis to characterize the efficacy of pipette cleaning is a yield curve, with the number of attempts on the x-axis and the gigaseal or whole-cell yield on the y-axis (Figure 2A, Figure 3A, Figure 4A, and Figure 5B). By comparing yield curves to ideal yields in which 100% of cells one attempts to patch result in a whole-cell patch-clamp configuration, it is possible to diagnose problems with cleaning yield, clogs, breaks, or other failure modes (Figure 4A). Methods and experiments can be compared from yield curve data using the Kolmogorov-Smirnov test. In addition, it is important to verify that cleaning does not cause a decrease in patching yield. Success rate plots that show the probability of obtaining a whole-cell for a defined number of cleaning attempts are also useful (Figure 1B-C, Figure 5A). Data from these plots can be modeled using linear regression (e.g., the mnrfit function in MATLAB). Odds ratios, 95% confidence intervals, and p-values test for deviations from initial performance.

Notes

  1. How can pipette cleaning be used for multi-pipette experiments? Peng et al. (2019) recently demonstrated that multi-pipette connectivity studies in both rodent and human brain slices can be greatly accelerated by using pipette cleaning to both increase yield and extend the number of connections tested per tissue [see Figures 3 and 5 of Peng et al. (2019)]. These systems rely on routinely achieving simultaneous whole-cell recordings with all available pipettes to efficiently test for inter-neuronal connections, but obtaining simultaneous whole-cell recordings on all available pipettes is difficult and dependent on experimenter skill (n.b., a study by Perin and Markram (2013) found that with a 12 pipette rig, novice users achieved an average of 4.8 ± 1.7 out of 12 possible whole-cell recordings per attempt, whereas experts achieved 9.6 ± 1.4 out of 12 possible whole-cell recordings per attempt). Implementing a single round of pipette cleaning with 2% w/v Alconox and no rinsing resulted in significant improvements in the success rate (i.e., the ratio of actual to possible number of simultaneous whole-cell recordings) relative to no cleaning for both 8- and 10-manipulator patch-clamp recording setups, from 85 ± 13% to 97 ± 5% and 79 ± 11% to 92 ± 6%, respectively (Peng et al., 2019). Furthermore, once the first round of simultaneous whole-cell recordings was obtained, pipette cleaning allowed for additional surrounding neurons to be patched, increasing the total number of connections that can be tested in a single sample from 140 ± 24% to 244 ± 52% with an 8 pipette rig (Peng et al., 2019). Using this method could enable faster, more efficient collection of large datasets required for understanding neuronal connectivity (Goriounova et al., 2018; Gouwens et al., 2019).

  2. How can pipette cleaning be used for high throughput screening? Large-scale efforts are also underway to discover and characterize drug candidates (Dunlop et al., 2008; Bagal et al., 2013), engineer improved genetic tools for neuroscience (Piatkevich et al., 2018; Yang et al., 2019), and understand human mutations in ion channel proteins (Swanger et al., 2016; Ogden et al., 2017). However, many of these projects are limited by the low throughput of traditional patch-clamp experiments (Park et al., 2013; Cho et al., 2019). For most screening experiments, manual patch clamping without cleaning has a throughput of 8-10 whole-cell recordings per experimenter per day (Milligan et al., 2009). Using our automated patch clamp system with 2% w/v Tergazyme, we have demonstrated routine throughputs of 10 whole-cell recordings per pipette per hour and daily throughputs of up to 100 whole-cell recordings (Figure 5) (Kolb et al., 2019). To further show the utility of pipette cleaning for functional screening, we performed a pilot experiment using HEK 293 cells transiently transfected with channelrhodopsin-2 (ChR-2). In this experiment, 46 whole-cell recordings were obtained from 51 patch-clamp attempts with a single pipette cleaned using 2% w/v Tergazyme (Figure 3).

  3. What are expected yields using this method? Our yield and recording quality of patch-clamp recordings were comparable to manual pipette replacement. For experiments with HEK 293 cells, whole-cell recording yield was consistently 70-90% using the patcherBot. The ability to perform high-yield, high-throughput experiments in a fully automated Tergazyme cleaning system has also enabled us to iteratively improve our automated method in HEK 293 cells. For example, we compared 2% w/v Tergazyme solution to 2% w/v Alconox and saline in a randomized experiment where the operator was blinded to the identity of the cleaning solution (Figure 2). Following that experiment, we randomly varied the depth of pipette indentation into the cell membrane and found an optimal range that increased whole-cell recording yield to ~90% (Figure 2).

  4. How long can cleaned pipettes be used? We have also found that Tergazyme cleaning is effective on single pipettes reused over 100 times, using the patcherBot (Figure 5) (Kolb et al., 2019). Because typical throughput for patch-clamp electrophysiology experiments is in the range of 10-30 recordings per day, it is likely that pipettes only need to be replaced once per day, except in cases where pipettes are broken or clogged.

  5. How does the cleaning method fail? Using a single pipette cleaned with our improved 2% w/v Tergazyme cleaning solution, we achieved 102 whole-cell recordings in 122 patch-clamp attempts over a 13 h automated experiment. In our attempts to find the failure point of 2% w/v Tergazyme cleaning, pipette breakage or internal clogs were more likely to cause failure than an inability to clean the pipette. Internal pipette clogs are thought to form from environmental dust of particulates in the pipette solution. Clogs tended to form as a function of the duration of positive pressure applied and were more likely to occur over long experiments. Clogs can be diagnosed from flat portions in the yield curve that are unlikely to result from chance. Some clogs are reversible (see a representative trace in Figure 4). Pipettes can also fail after a tip breakage, which typically occurs if a target cell is missed. To determine an approximate failure point of cleaning, consider each patch-clamp attempt as an independent event with a probability equal to the gigaseal recording failure rate and determine the number of cleaning attempts until the probability is less than or equal to 0.01. For example, with a gigaseal failure rate of 30% (i.e., gigaseal success rate = 70%), the likelihood of a sequence of four failures to gigaseal has a probability of <1%.

  6. Does cleaning transfer residual enzyme or detergent to the cells? One concern of patch-clamp experimenters interested in implementing cleaning is the possibility of contamination from residual cleaning solution in the pipette after cleaning. Our initial study addressed this concern with two types of experiments (Kolb et al., 2016). First, we performed electrospray ionization mass spectrometry (ESI-MS) on fresh pipettes and pipettes cleaned with 2% w/v Alconox and found that no Alconox residues were detectable. For this experiment, we used 2% w/v Alconox, and the limit of detection was 147 ng/ml. Secondly, we performed patch-clamp experiments on HEK 293 cells expressing the γ-aminobutyric acid type A Receptor (GABAAR), which is known to be sensitive to extracellular application of detergents. In these experiments, we did not find any statistically significant differences in GABAAR electrophysiology between cleaned and fresh pipettes.

  7. Is the rinsing step (Procedure Step C. Rinse the pipette) required? Interestingly, recent experiments by Peng et al. (2019) (using human brain slices) and by our lab (using mouse brain slices, Figure 5) have provided evidence that the rinse step (Procedure, Step C. Rinse the pipette (optional) ) is unnecessary. Thus, in this method description, we label it as “optional.” However, if the possibility of contamination is still a concern in a particular experiment, we suggest rinsing as described in Procedure Step C. Rinse the pipette (optional) with the following modifications as needed to minimize risk:

    1. Add time and cycles to the rinsing step to remove residual Tergazyme by dilution.

    2. Reduce the concentration of Tergazyme in the cleaning solution. A 2% w/v Tergazyme solution is effective at cleaning pipettes up to 100 times with no measurable degradation in yield (Figure 4). This suggests that lower concentrations of Tergazyme will still be effective for pipette cleaning, with a potential trade-off in the maximum number of cleans per pipette.

    3. Increase the perfusion rate of the external solution so that any residual Tergazyme is removed from the experimental chamber quickly.

Acknowledgments

This work was supported by the National Institutes of Health (NIH) grants R01NS102727, R01DA029639, U01MH106027, and R01 EY023173. We also acknowledge the original research papers from which this protocol is derived, Kolb et al. (2016 and 2019).

Competing interests

MCY and IK have consulting agreements with Neuromatic Devices, which manufactures pipette pressure control systems. IK, WAS, and CRF are inventors on U.S. Patent 10,830,758 related to pipette cleaning technology and licensed to Sensapex.

Ethics

For the representative work of cleaning using acute rodent brain slices, all animal procedures were in accordance with the US National Institutes of Health Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee at the Georgia Institute of Technology (A100359, Exp: Dec, 2022). When using cleaning methods for experiments requiring ethics committee approval, please follow the appropriate legal and institutional guidance.

References

  1. Bagal, S. K., Brown, A. D., Cox, P. J., Omoto, K., Owen, R. M., Pryde, D. C., Slidders, B., Skerratt, S. E., Stevens, E. B., Storer, R. I. and Swain, N. A. (2013). Ion channels as therapeutic targets: A drug discovery perspective. J Med Chem 56 (3): 593-624.
  2. Cho, Y. K., Park, D., Yang, A., Chen, F., Chuong, A. S., Klapoetke, N. C. and Boyden, E. S. (2019). Multidimensional screening yields channelrhodopsin variants having improved photocurrent and order-of-magnitude reductions in calcium and proton currents. J Biol Chem 294 (11): 3806-3821.
  3. Dunlop, J., Bowlby, M., Peri, R., Vasilyev, D. and Arias, R. (2008). High-throughput electrophysiology: an emerging paradigm for ion-channel screening and physiology. Nat Rev Drug Discov 7(4): 358-368.
  4. Goriounova, N. A., Heyer, D. B., Wilbers, R., Verhoog, M. B., Giugliano, M., Verbist, C., Obermayer, J., Kerkhofs, A., Smeding, H., Verberne, M., Idema, S., Baayen, J. C., Pieneman, A. W., de Kock, C. P., Klein, M. and Mansvelder, H. D. (2018). Large and fast human pyramidal neurons associate with intelligence. Elife 7: e41714.
  5. Gouwens, N. W., Sorensen, S. A., Berg, J., Lee, C., Jarsky, T., Ting, J., Sunkin, S. M., Feng, D., Anastassiou, C. A., Barkan, E., et al. (2019). Classification of electrophysiological and morphological neuron types in the mouse visual cortex. Nat Neurosci 22(7): 1182-1195.
  6. Hamill, O.P., Marty, A., Neher, E., Sakmann, B. and Sigworth, F.J. (1981). Improved patch-clamp techniques for high-resolution current recording from cells and cell-free membrane patches. Pflugers Arch Eur J Physiol 391: 85-100.
  7. Jiang, X., Shen, S., Cadwell, C. R., Berens, P., Sinz, F., Ecker, A.S., Patel, S.and Tolias, A. S. (2015). Principles of connectivity among morphologically defined cell types in adult neocortex. Science 350 (6264): aac9462-aac9462.
  8. Kaech, S. and Banker, G. (2006). Culturing hippocampal neurons. Nat Protoc 1(5): 2406-2415.
  9. Kodandaramaiah, S. B., Holst, G. L., Wickersham, I. R., Singer, A. C., Franzesi, G. T., McKinnon, M. L., Forest, C. R. and Boyden, E. S. (2016). Assembly and operation of the autopatcher for automated intracellular neural recording in vivo. Nat Protoc 11(4): 634-654.
  10. Kolb, I., Landry, C. R., Yip, M. C., Lewallen, C. F., Stoy, W. A., Lee, J., Felouzis, A., Yang, B., Boyden, E. S., Rozell, C. J. and Forest, C. R. (2019). PatcherBot: a single-cell electrophysiology robot for adherent cells and brain slices. J Neural Eng 16(4): 046003.
  11. Kolb, I., Stoy, W. A., Rousseau, E. B., Moody, O. A., Jenkins, A. and Forest, C. R. (2016). Cleaning patch-clamp pipettes for immediate reuse. Sci Rep 6: 35001.
  12. Koos, K., Oláh, G., Balassa, T., Mihut, N., Rózsa, M., Qzsvar, A., Tasnadi, E., Barzó, P., Faragó, N., Puskás, L., Molnár, G., Molnár, J., Tamás, G. and Horvath, P. (2020). Automatic deep learning driven label-free image guided patch clamp system for human and rodent in vitro slice physiology. bioRxiv 2020.05.05.078162.
  13. Lin, J. Y., Lin, M. Z., Steinbach, P. and Tsien, R. Y. (2009). Characterization of engineered channelrhodopsin variants with improved properties and kinetics. Biophys J 96: 1803-1814.
  14. Mariani, J., Coppola, G., Zhang, P., Abyzov, A., Provini, L., Tomasini, L., Amenduni, M., Szekely, A., Palejev, D., Wilson, M., Gerstein, M., Grigorenko, E. L., Chawarska, K., Pelphrey, K. A., Howe, J. R. and Vaccarino, F. M. (2015). FOXG1-Dependent dysregulation of GABA/Glutamate neuron differentiation in autism spectrum disorders. Cell 162(2): 375-390.
  15. Milligan, C. J., Li, J., Sukumar, P., Majeed, Y., Dallas, M. L., English, A., Emery, P., Porter, K. E., Smith, A. M., McFadzean, I., Beccano-Kelly, D., Bahnasi, Y., Cheong, A., Naylor, J., Zeng, F., Liu, X., Gamper, N., Jiang, L. H., Pearson, H. A., Peers, C., Robertson, B. and Beech, D. J. (2009). Robotic multiwell planar patch-clamp for native and primary mammalian cells. Nat Protoc 4(2): 244-255.
  16. Ogden, K. K., Chen, W., Swanger, S. A., McDaniel, M. J., Fan, L. Z., Hu, C., Tankovic, A., Kusumoto, H., Kosobucki, G. J., Schulien, A. J., Su, Z., Pecha, J., Bhattacharya, S., Petrovski, S., Cohen, A. E., Aizenman, E., Traynelis, S. F. and Yuan, H. (2017). Molecular Mechanism of Disease-Associated Mutations in the Pre-M1 Helix of NMDA Receptors and Potential Rescue Pharmacology. PLoS Genet 13(1): e1006536.
  17. Park, J., Werley, C. A., Venkatachalam, V., Kralj, J. M., Dib-Hajj, S. D., Waxman, S. G. and Cohen, A. E. (2013). Screening fluorescent voltage indicators with spontaneously spiking HEK cells. PLoS One 8(12): e85221.
  18. Peng, Y., Mittermaier, F. X., Planert, H., Schneider, U. C., Alle, H. and Geiger, J. R. P. (2019). High-throughput microcircuit analysis of individual human brains through next-generation multineuron patch-clamp. Elife 8: e48178.
  19. Perin, R. and Markram, H. (2013). A computer-assisted multi-electrode patch-clamp system. J Vis Exp 80: e50630.
  20. Piatkevich, K. D., Jung, E. E., Straub, C., Linghu, C., Park, D., Suk, H. J., Hochbaum, D. R., Goodwin, D., Pnevmatikakis, E., Pak, N., Kawashima, T., Yang, C. T., Rhoades, J. L., Shemesh, O., Asano, S., Yoon, Y. G., Freifeld, L., Saulnier, J. L., Riegler, C., Engert, F., Hughes, T., Drobizhev, M., Szabo, B., Ahrens, M. B., Flavell, S. W., Sabatini, B. L. and Boyden, E. S. (2018). A robotic multidimensional directed evolution approach applied to fluorescent voltage reporters. Nat Chem Biol 14(4): 352-360.
  21. Qian, X., Nguyen, H. N., Song, M. M., Hadiono, C., Ogden, S. C., Hammack, C., Yao, B., Hamersky, G. R., Jacob, F., Zhong, C., et al.(2016). Brain-Region-Specific Organoids Using Mini-bioreactors for Modeling ZIKV Exposure. Cell 165 (5): 1238-1254.
  22. Stoy, W. M., Yang, B., Kight, A., Wright, N. C., Borden, P. Y., Stanley, G. B. and Forest, C. R. (2020). Compensation of physiological motion enables high-yield whole-cell recording in vivo. J Neurosci Methods 348: 109008.
  23. Suk, H. J., van Welie, I., Kodandaramaiah, S. B., Allen, B., Forest, C. R. and Boyden, E. S. (2017). Closed-Loop real-time imaging enables fully automated cell-targeted patch-clamp neural recording In vivo. Neuron 95(5): 1037-1047 e1011.
  24. Swanger, S. A., Chen, W., Well, G., Burger, P., Tankovic, A., Bhattacharya, S., Strong, K . L., Hu, C., Kusumoto, H., Zhang, J., Adams, D. R., Millichap, J. J., Petrovski, S., Traynelis, S. F. and Yuan, H. (2016). Mechanistic Insight into NMDA Receptor Dysregulation by Rare Variants in the GluN2A and GluN2B Agonist Binding Domains. Am J Hum Genet 99 (6): 1261-1280.
  25. Ting, J. T., Kalmbach, B., Chong, P., de Frates, R., Keene, C. D., Gwinn, R. P., Cobbs, C., Ko, A. L., Ojemann, J. G., Ellenbogen, R. G., Koch, C. and Lein, E. (2018). A robust ex vivo experimental platform for molecular-genetic dissection of adult human neocortical cell types and circuits. Sci Rep 8(1): 8407.
  26. Wang, G., Wyskiel, D. R., Yang, W., Wang, Y., Milbern, L. C., Lalanne, T., Jiang, X., Shen, Y., Sun, Q. Q. and Zhu, J. J. (2015). An optogenetics- and imaging-assisted simultaneous multiple patch-clamp recording system for decoding complex neural circuits. Nat Protoc 10(3): 397-412.
  27. Yang, K. K., Wu, Z. and Arnold, F. H. (2019). Machine-learning-guided directed evolution for protein engineering. Nat Methods 16(8): 687-694.

简介

[摘要]全-细胞膜片-钳法是金标准单-电活性,细胞形态和基因表达的细胞分析。在此之前我们发现补丁-钳吸管可以清洗和再利用,实验通量和自动化是由需要更换吸管限制手动每次实验后。本文提出了一种优化的协议为吸移管清洗,这使得它可以快速(执行<30秒),产生一个高产量的全-细胞记录的成功率(> 90%)用于单个吸液管的100重新使用。对于大多数补丁-钳实验(< 30个全-每天细胞记录),这个方法使得能够使用用于实验一整天的单个移液管。此外,我们还介绍了易于实施的硬件和软件以及故障排除技巧,以帮助其他实验室在他们自己的实验中实施此方法。吸移管清洗使补丁-钳实验进行是否手动或以自动化的方式,通过消除繁琐和SKIL与更高的吞吐量进行升更换移液器的FUL任务。˚F ROM我们与众多的电生理实验室的经验,吸管清洗可以集成到现有的补丁-钳设置在大约一天ü唱本文中介绍的硬件和软件。



图文摘要:


快速酶清洁补丁的再利用-钳移液器


[背景]全-细胞膜片-钳记录允许在单个电活性,神经元形态和基因表达前所未有的访问-细胞水平(姜等人。,20 15; Gouwens等人,20 19 )。然而,由于这种方法需要大量的技巧和细心才能正确执行,因此它仍然是神经科学中最困难的方法之一。该方法的一个关键步骤是在细胞膜和称为gigaseal的玻璃吸管之间形成紧密的高电阻(例如,> 1 G Ω)连接。千兆欧密封的形成需要一个清洁吸液管的表面,以及即使是小的污染物(例如,细胞碎片或灰尘),可以破坏这种处理(哈米尔等人,1981)。出于这个原因,补丁-钳实验者需要每个记录的尝试(图后,以取代玻璃吸管URE 1A),这需要额外的时间和精力(例如,除去,制造,填充,和安装移液器)。之前我们已经发现,违背几十年来在该领域这种无处不在的实践,补丁-钳移液器可清洗和再利用,使许多膜片钳记录与单一吸管(科尔布。等,20 16) 。

吸移管清洗是涉及以下步骤的健壮的,简单的过程:(1)尝试全-细胞膜片-钳记录; (2)缩回补丁-钳吸管并朝向含有清洗液浴移动; (3) 吸头浸没在清洗液中,循环正负压以清除移液器吸头上的细胞碎片;(4)在用于第二贴片新目标小区的位置移液管-钳尝试; 和(5)重复步骤1 -直到4的实验完成或移液管失败(例如,尖端破损,堵塞,清洗液的蒸发,或用户错误)。在这个协议中,我们将描述使用简单的硬件和软件来自动移液管清洁,这将有助于其他实验室中在任一完全自动化的或“推到干净”的方法(图执行移液管清洁URE 1B)。





图1 。移液器清洁方法。一个。无需移液器清洁的传统手动膜片钳工艺流程图。移除、填充和安装新的移液器需要 60 - 120秒。B. 带有自动清洁(“一键清洁”)的手动修补工艺流程图。自动清洁可在短短 30 秒内运行。C.关闭了移液管的图像从中移出的实验室(左)至所述清洗槽(中间)和到所述漂洗浴(右)返回到实验浴修补另一个小区之前。比例尺为 25 毫米。D. 用于移液器清洁的定制实验室,具有流体入口和出口、用于接地线的插入件以及用于清洁和冲洗溶液的外部浴槽。比例尺为 1 厘米。



我们发表在2016年这种方法的初步发现,并用它来开发第一个完全自主的补丁-能够在2019年没有人监督记录几十个细胞的钳机器人(科尔布。等人,20 16和20 19 )。这种吸管清洗方法已经使用了我们,我们的合作者和其他团体进行大规模的补丁-钳研究(即,单-细胞电生理和连接组学在啮齿类动物和人类和高通量筛选)更有效的(蓬等。,20 19;和信。等人,20 20 ),并使复杂的实验(即,在体内补丁-钳)更简单(淑等人。,20 17;斯托伊。等人,20 20 )。另外,由于我们的初始报告,我们已经发现,2%w / v的Tergazyme是一个优异的清洁溶液(图URE 2A)和从原始方法的漂洗步骤是不必要(图URE 5)。这些改进的方法已经增加了全细胞记录收率〜20% ,与单个移液管通过10倍于HEK 293个细胞(图增加的总清除的数量URE 4) ,并降低所需的每一轮的时间清洁时间 <30 秒,比手动更换移液器更快(约 1-2 分钟)。在效率这些成果的基础上,我们提高的全-通过优化相对于所述细胞膜的移液管尖端的(图1中的位置细胞产量以〜90%在HEK 293个细胞URE 2A-C) (斯托伊等人,20 20). 简单地说,通过改变移液管前进到细胞的距离,我们发现距离和之间有很强的关系千兆欧密封概率,其在范围为1达到〜100%- 2微米的细胞表面下方(定义为z轴点其中移液器电阻从初始电阻增加了 0.1 MΩ)(Stoy等人,20 20)。当patcherBot被编程以尝试gigasealing在该位置,所述全-细胞记录产量显著增加,如图中的“优化”跟踪URE 2A(P = 0.044,Kolmorogov -Smirnov检验)。该方法用于由patcherBot图URE 3和图URE 4.总体而言,这种改进的吸移管的清洗方法能够自动膜片-钳实验为3操作完全自动-在高达90%的收率和最多的吞吐量4小时,以15条全-每小时细胞记录,超过高度熟练的人实验者(图的输出URE 2,图URE 3)。我们能够实现高达100条整体-在〜13小时细胞记录与单一移液管(图URE 4)。我们还开发了“推到干净的”半自动化补丁简单的硬件和软件组件-钳实验,使电生理学家容易地该方法集成到他们的硬件设置(图URE 1B-d,图6,和补充信息)。





图2 。移液器清洁的改进。A.每个迹线代表整个的数量-细胞记录在HEK 293细胞的功能的记录与单个吸液管的尝试次数。每个痕迹是至少三个移液器的平均值。的“盐水”跟踪是阴性对照(即,清洗液与细胞外溶液替换),以及一个100%的理论最大包含以供参考。所述“的Alconox ”跟踪显示的2%性能w / v的的Alconox清洁,其增减ES作为的函数的尝试次数。所述“ Tergazyme ”跟踪显示收率不降低为用2%30次尝试w / v的Tergazyme 。的“优化的”迹线代表2%w / v的Tergazyme清洗吸管定位优化相对于细胞面F或gigasealing 。所述“ Tergazyme ”性能优于该的的Alconox (*,P = 1.375E-5,Kolmogorov-Smirnov检验)。的“优化的”性能要优于其的“ Tergazyme ” (**,P = 0.04368,Kolmogorov-Smirnov检验)。整体的B.成功率-细胞膜片-钳作为的函数的用2%w / v的清洗次数Tergazyme示出了随后的整个的可能性没有显著下降-细胞记录(比值比(OR)= 1.0067,CI:0.97 -1.04,P = 0.69,n = 215 次尝试,每次尝试用于 n = 7 个移液器,除了尝试 29 和 30,它们用于n = 6) 。C.优化压痕与Tergazyme清洁的节目在整个随后的可能性没有显著下降-细胞记录(OR = 1.00,CI:0.94-1.06,P = 0.95,N = 124次的尝试中,每次尝试是对于n = 4次的移液管)。





图3 。使用移液器清洁进行高通量视蛋白筛选。A. 单个移液管 channelrhodopsin-2 (ChR-2) 表征实验的产量曲线(46/51 次尝试,90% 产量)。B. 响应从瞬时转染的 HEK 293 细胞记录的 500 ms 480 nm LED 脉冲的初始脉冲的代表性光电流迹线(电压钳),显示出大峰值光电流响应。C.光电流从记录在一系列500的中间细胞迹线(电压钳)毫秒光脉冲呈现稳定-在许多吸管清除状态的光电流。大的初始光电流响应是 ChR-2 的典型特征 (B),并在随后的刺激脉冲 (C) 中降低(Lin等人,2009 年)。





图4 。使用 2% w/v Tergazyme清洁移液器的上限。A. 单个移液器的产量曲线显示了超过 90 次与相关故障模式的记录尝试的清洁。的“小号uccess”跟踪显示有效吸移管清洗,在“木屐”跟踪显示可逆的移液器吸头堵塞原因在于低收率随着时间的推移,并且该“中断”跟踪显示实验小号通过破碎移液管尖端终止小号。包括理论最大值(100% 产率)以供参考。B.对于 (A) 中的每种故障模式,在 40倍放大倍数下拍摄的代表性移液器图像。比例尺为 1 µm。C.个人千兆欧密封从电阻迹线的“成功”迹(N = 122千兆欧密封尝试)。记录在细胞D.获得电阻的(N = 101整体“成功”痕量-细胞记录)。





图5 。Tergazyme清洗是没有有效的一个在急性小鼠脑切片冲洗步骤。A.在S没有修补的uccess率一个冲洗步骤不与显著降低的清洗次数(= 1.14 OR,CI:0.87-1.41,P = 0.34,N = 36次)。所述n用于每个实验移液器的棕土是上述再利用每个数字指出。B. 使用 2% w/v Tergazyme无需冲洗的单个移液器的产量图。C. 电流钳中诱发动作电位放电的代表性录音,来自三个神经元,用单个移液器记录,在 2% w/v Tergazyme 中清洗,无需冲洗。D. 来自(C)中神经元的放大的单个诱发动作电位。



对于自动吸清洁一个强大和易于实施的方法感兴趣的是使用补丁中所有的实验室-钳方法。尽管迄今为止在清洁实验中没有测量到负面副作用(Kolb等人,20 16 和20 19;Peng等人,20 19 ),但对于将敏感细胞暴露于清洁残留物的可能性特别关注的实验(例如,药理学或单-声道录音),该方法可以容易地适应以进一步最小化这种风险(见注释)。我们认为,这种改进的吸管清洗方法将是多吸管膜片钳和领域工作的高通量筛选实验室中特别有用(小号EE注意,图3)。

本协议中描述的移液器清洁方法使移液器可重复用于膜片钳。通过消除整个实验过程中制造、填充、安装和移除移液器的需要,实验者可以从这些劳动密集型任务中节省宝贵的时间和注意力。我们表明,2% w/v Tergazyme可以使用单个移液器(5-8 M Ω ,Warner Instruments)记录多达 100 个以上的细胞,无需实验者在整个实验过程中更换移液器。

关键字:膜片钳, 自动化, 酶清洗剂, 高通量, 电生理学


材料和试剂

1.带灯丝的硼硅酸盐移液管玻璃(Warner Instruments,目录号:64-0793)     
2.注射器,5毫升(VWR,目录号:BD309646)     
3.注射器过滤器,0.2 µm(VWR,目录号:10218-486)     
4. 23G针(VWR,目录号:89134-098)     
5. Tergazyme (Alconox ,目录号:1304-1)     

设备
                                                                   
电子列出quipment是除了标准膜片-钳电设备(例如,放大器,数字转换器,探头,显微操纵,显微镜,以及移液管牵拉)。贴剂的具体细节-在本文中使用的夹具钻机在别处详细描述(Kolb的等人,20 16和20 19 )。
清洁盘(根据 SI 中的 CAD 文件进行 3D 打印或铣削,并适当更改显微镜载物台)
压力控制箱(d上autopatcher.org etailed计划,零件清单,从直接命令Neuromatic设备,neuromaticdevices.com)

软件

根据所需的自动化水平,下载 (1) 和 (2) 以实现完全自动化,或仅下载 (2) 以启用“一键清洁”以进行手动贴片钳和清洁。
Autopatcher软件(可从 autopatcher.org 下载)。钍是软件使细胞检测,全自动化gigasealing和磨合功能。
Push-to-clean 软件(可在Github下载,https://github.com/mightenyip/Pipette-Cleaning-Software)。的术语“推到干净”被定义为以其他方式手动钻机电生理学,包括一个按钮-致动移液管清洁功能。该按钮启动一系列移液器位置和压力变化,以清洁移液器以供重复使用。


程序

之前的第一个实验
制造根据计划和显微镜载物台的尺寸(清洁盘图URE 1C,CAD文件中的补充信息附录B的内部),使用按需生产服务(例如,Protolabs从,protolabs.com),或购买一个商业供应商(例如,神经设备)。

在现有的补丁安装压力控制箱-钳电钻机
该p ressure控制箱可以从头开始按照由提出的计划来构建Kodandaramaiah等。( 20 16 ) 。原理图、说明和零件清单也可从autopatcher.org下载。
可直接从Neuromatic Devices ( neuromaticdevices.com )购买与清洁兼容的压力控制盒。

从 autopatcher.org 下载并安装软件。执行初始软件设置。补充信息附录 A中提供了详细说明。
寄存器的根据操纵器的制造商和COM端口(小号EE补充信息图URE小号小号1-小号2 )。
注册该压力控制箱到指定的COM端口(小号EE补充信息图URE小号小号1-小号2 )。

每次实验前
准备生物样品小号的补丁-钳记录。我们或其他已发表的报告中已对移液器清洁进行了验证的实验准备方法作为参考。
对于体外HEK 293 细胞,请遵循 Kolb等人。( 20 16 ) 。移液器清洗适用于野生型细胞、稳定转染细胞系和瞬时转染(Kolb等人,20 16 和20 19 )
对于啮齿动物神经元培养记录,请遵循Kaech和 Banker ( 2006 )以及Kolb等人。( 20 16 ) 。Kolb等人验证了此准备工作的清洁。(2016 年和 2019 年)。
对于急性脑组织切片记录,请遵循姜等人。( 20 15 ) 。Kolb等人的以下报告证实了此准备工作的清洁。(2016 年和 2019 年)。
对于麻醉制剂中的体内小鼠记录,请遵循Bagal等人的方法。(2013 年)。Kolb等人验证了此准备工作的清洁。(2016 年和2019 年)和Stoy等人。(2020) 。
对于急性人脑组织切片,请遵循 Ting等人的方法。( 20 18 )和彭等人。(2019 年)。Peng等人验证了此制剂的清洁。(2019) 。
对于人类大脑类器官,请遵循Mariani等人。( 2015 )和钱等人。( 20 16 ) 。我们已经在未发表的实验中验证了该制剂中的清洁。

准备电钻机的补丁-钳实验,并在适当的实验用吸管。补丁详细指南-钳钻机设置,去噪,和故障诊断中别处提供(PERIN和马克拉姆,2013;王等人,2015; Kodandaramaiah等人,2016)。

加载软件的一推清洁的补丁-钳实验(补充资料附录C)。

配制2% w/v Tergazyme清洁液。
在室温去离子水中制备 2% w/v Tergazyme溶液。
混合溶液直至所有Tergazyme粉末溶解。
注意:因为Tergazyme是一种酶洗涤剂,酶成分会随着时间的推移而降解。制造商建议制作新鲜溶液并在 8 小时内使用它们以获得最大功效。

填写清洗和冲洗沐浴水库小号与过滤解决方案
使用带有 0.2 µm 过滤器和 23G 针头的注射器,用新鲜制作的 2% w/v Tergazyme (或用于冲洗的细胞外溶液)填充适当的浴槽。
注意不要到溢出清洗槽贮存器,因为这可能会导致Tergazyme溶液流入实验室中,潜在地损坏的细胞。
注意:为确保有清洗槽和实验浴之间没有流体交换,插入吸移管到清洁槽的前端和监测方波脉冲的电压钳位。如果不接地清洗槽接地实验洗澡间没有电接触,你会看到电容瞬态,类似于当吸管尖是在空气中。如果有电接触,您将看到一个方波脉冲,类似于当尖端浸入实验浴中时。要解决此问题,使用任务擦拭以去除一个,直到电接触被消除从清洗槽的液量小。

在软件界面中,参考细胞和清洁浴校准操纵器(图 6)。
选择并保存目标单元上方的“exp Bath location”位置。
选择并保存“浴槽上方的位置”位置,直接位于清洁浴槽的上方。
选择并保存“清洗槽位置”位置,吸头安全地浸入清洗液中。
如果需要,选择并保存“洗涤浴”位置,将尖端安全地浸入冲洗溶液(即细胞外溶液)中。
图 6. 用于移液器清洁的移液器校准。push-to-clean 软件用于每次清洁尝试的位置。(A) 中的图像是指 (B) 中保存的位置值。简而言之,(1) 是指直接位于目标细胞或组织上方的位置,(2) 是指清洁和冲洗槽上方的 z 位置,(3) 是指尖端浸没在清洁溶液中的位置,以及 (4) ) 指的是移液器吸头浸没在冲洗液中的位置。

补丁-钳实验
尝试补丁-对靶细胞钳

启动使用吸管清洗的通过点击“清除”按钮软件界面。软件实现的功能如下:
在轻微正压 (+50 mbar) 下,移液器从细胞缩回至“浴槽上方位置”位置。
移液器从“浴槽上方位置”位置移动到“清洗槽位置”位置,直到移液器吸头与清洗液接触。
注意:将移液器接触到清洁溶液的表面可以检测到移液器尖端方波脉冲电容的变化。这通常可以在移液器明显接触表面之前观察到。移液器与流体接触的可见确认也足以开始清洁。
将移液器吸头置于清洁槽中,吸力 (-345 mbar) 5 秒。
施加五轮交替抽吸脉冲(-345 毫巴,持续 1 秒)和正压(+700 毫巴,持续 1 秒)。
P ositive压力施加(700毫巴)5秒吨ö排液任何残留的清洁从移液管尖端的解决方案。
移液器从清洗槽缩回到“槽上方位置”位置,然后到“exp 槽位置”位置或“洗涤槽”位置(可选)。

冲洗移液器(可选)
在正压 (+ 200 mbar) 下,移液器从“清洗槽位置”位置移动到“槽上方位置”位置。
移液器从“浴槽上方的位置”位置朝“洗涤浴槽”位置向下移动,直到移液器吸头与清洁溶液接触。
在冲洗浴中使用移液器尖端,施加 3 秒的吸力(-345 毫巴),然后施加 10 秒的正压(+700 毫巴)。
在正压 (+200 毫巴) 下,将移液器从“洗涤浴”位置缩回到“浴槽上方位置”位置。

尝试补丁-钳记录上的下一个目标细胞

重复步骤甲- d直到所述的端部的的实验或失败的移液管(例如,尖端破损,堵塞,清洗溶液,或用户错误的蒸发)。
注意:如果该移液管似乎被堵塞(即,在移液管尖端或在电阻的增加观察到可见的内部堵塞)或破裂(即,可见破碎尖端或降低电阻),然后替换它并重复校准。

数据分析

从补丁数据-用吸管清洁钳实验可以以同样的方式作为传统的补丁来处理-使用软件工具,如钳实验pClamp (Molecular Devices公司)或Matlab的(Mathworks公司)。一个有用的分析来表征吸移管清洗的效力是一个产量的曲线,与该尝试的在x轴数和千兆欧密封或全-在y轴的细胞产量(图2A,图3A,图4A,以及图5B)。通过将产量曲线与理想产量进行比较,其中 100% 的细胞试图修补导致整体-细胞膜片-钳配置,可以诊断清洁产量、堵塞、断裂或其他故障模式的问题(图 4A) . 可以使用 Kolmogorov-Smirnov 检验从收益率曲线数据中比较方法和实验。此外,重要的是要验证清洁不会导致修补产量下降。成功率曲线,显示获得的整体概率-细胞在限定清洁尝试次数也是有用的(图1B-C,图5A)。可以使用线性回归(例如,MATLAB 中的mnrfit函数)对来自这些图的数据进行建模。优势比、95% 置信区间和p 值测试与初始性能的偏差。             

笔记

如何将移液器清洁用于多移液器实验?彭等人。(2019)最近表明,在啮齿类和人脑切片的多移液管连通的研究可通过使用吸移管清洗,以既增加产量和延长每组织测试连接的数量大大加快[见˚F igures 3和5彭等人. ( 20 19 ) ] 。这些系统依赖于常规实现同步全-与所有可用的移液器细胞记录有效测试间的神经连接,但获取的同时整体-在所有可用的移液器细胞记录是困难的,依赖于实验者技能(NB的,由研究PERIN和马克拉姆(20 13)发现,具有12移液管钻机,新手用户实现平均4.8± 1.7的12条可能整个-每尝试细胞记录,而专家实现9.6± 1.4的12个可能整个-每尝试细胞记录) . 实施单轮移液管清洗用2%w / v的的Alconox和没有漂洗导致显著改进的成功率(即,所述的实际同时的整体的可能数目的比率-细胞记录)相对于没有清洁两个8-和 10 机械手膜片钳记录设置,分别从 85 ± 13%到 97 ± 5%和 79 ± 11%到 92 ± 6% ( Peng et al. , 20 19 ) 。进一步更,一旦第一轮同时整体的-获得细胞记录,将吸移管清洗允许额外周围神经元进行修补,从而增加可在单个样品中的140±被测试的连接的总数目为24%至244± 52 %使用 8 移液器装置(Peng等人,20 19 )。我们荷兰国际集团该方法能实现更快,对于理解神经元连接所需的大数据集的更有效的收集(Goriounova等人,20 18; Gouwens 。等人,20 19 )。
移液器清洁如何用于高通量筛选?发现和表征候选药物的大规模努力也在进行中(Dunlop等人,20 08;Bagal等人,20 13 ),为神经科学设计改进的遗传工具(Piatkevich等人,20 18;Yang等人,20 18 年)。, 20 19 ) ,并了解离子通道蛋白中的人类突变(Swanger等人,20 16;Ogden等人,20 17 )。然而,许多这些项目是由传统的贴片的吞吐量低限于-钳实验(公园。等人,20 13 ;卓等人。,20 19 )。对于大多数的筛选实验中,不清洗手动膜片钳具有吞吐量8 - 10全-每实验者细胞记录每天(米利等人,20 09 )。使用我们的自动膜片钳系统用2%w / v的Tergazyme ,我们已经证明的10个整个例行吞吐量-每吸管细胞记录每小时和高达100个整体的每日吞吐量-细胞记录(图URE 5) (Kolb的等。, 20 19 ) 。为了进一步展示移液器清洁在功能筛选中的效用,我们使用瞬时转染了通道视紫红质-2 (ChR-2) 的 HEK 293 细胞进行了试点实验。在该实验中,46个整体-从51次获得的贴片细胞记录-钳尝试与使用2%w / v的清洗单个移液管Tergazyme (图URE 3)。
使用这种方法的预期收益是多少?我们的补丁的产量和录音质量-钳记录媲美手动移液器更换。对于HEK 293细胞的实验,全-细胞记录收率为70一致地-使用90%patcherBot 。在全自动Tergazyme清洗系统中执行高产量、高通量实验的能力也使我们能够迭代改进我们在 HEK 293 细胞中的自动化方法。例如,我们在随机实验中比较了 2% w/v Tergazyme溶液与 2% w/v Alconox和盐水,其中操作员对清洁溶液的身份不知情(图 2)。以下该实验中,我们随机改变移液管压痕的深度到细胞膜,发现增加整个的最佳范围-细胞记录收率〜90%(图2)。
清洗过的移液器可以使用多久?我们还发现,使用patcherBot (图 5)(Kolb等人,20 19 ),Tergazyme清洁对重复使用 100 次以上的单个移液器是有效的。由于补丁典型的吞吐量-钳电生理学实验是在10个范围-每天30录音,很可能只吸液管需要更换每天一次,除非在移液管破损或堵塞的情况。
清洗方法如何失效?使用我们的改进的2%w / v的清洗单个移液管Tergazyme清洁溶液,我们实现了102个全-在122次补丁细胞记录-钳尝试在13小时自动化实验。在我们试图找到 2% w/v Tergazyme清洁的故障点时,移液器破损或内部堵塞比无法清洁移液器更有可能导致故障。内部移液器堵塞被认为是由移液器溶液中颗粒的环境灰尘形成的。木屐趋于形成作为的函数的施加和更可能发生在长的实验正压的持续时间。可以从收益率曲线的平坦部分诊断堵塞,这不太可能是偶然的。一些木屐是可逆的(参见图 4 中的代表性迹线)。移液器在吸头破损后也可能出现故障,这通常发生在靶细胞丢失的情况下。要确定清洗的近似故障点,考虑每个补丁-钳尝试为具有概率的独立事件等于千兆欧密封记录失败率,并确定清洁尝试直到数量的概率小于或等于0.01。例如,对于一千兆欧密封的30%(失败率即,千兆欧密封成功率= 70%),的序列的似然4点的故障,以千兆欧密封具有<1%的概率。
清洁是否会将残留的酶或清洁剂转移到细胞中?补丁的一个担忧-兴趣实施清洁钳实验者是从清洗后的吸管残留的清洗液污染的可能性。我们最初的研究通过两种类型的实验解决了这个问题(Kolb等人,20 16 )。首先,我们对新鲜的移液器和用 2% w/v Alconox清洁的移液器进行了电喷雾电离质谱 (ESI-MS) ,发现没有检测到Alconox残留物。对于本实验,我们使用了 2% w/v Alconox ,检测限为 147 ng/ ml 。其次,我们进行补丁-对HEK钳实验293个细胞中表达γ氨基丁酸A型受体(GABA甲R),这是已知的洗涤剂的细胞外应用敏感。在这些实验中,我们没有发现清洁的和新鲜的移液器之间GABA A R 电生理学有任何统计学上的显着差异。
是否需要冲洗步骤(程序步骤 C.冲洗移液器)?有趣的是,彭等人最近的实验。(2019) (使用人脑切片)和由我们的实验室(使用小鼠脑切片,图5)提供了证据,该漂洗步骤(操作步骤,步骤C.冲洗移液管(可选))是不必要的。因此,在此方法描述中,我们将其标记为“可选” 。” 但是,如果在特定实验中仍然存在污染的可能性,我们建议按照程序步骤 C 中所述进行冲洗。根据需要使用以下修改冲洗移液器(可选)以最大程度地降低风险:              
在冲洗步骤中增加时间和周期,以通过稀释去除残留的Tergazyme 。
降低清洗液中Tergazyme的浓度。2% w/v Tergazyme溶液可有效清洁移液器多达 100 次,而产量没有可测量的降低(图 4)。这表明较低浓度的Tergazyme仍然对移液器清洁有效,但可能会在每个移液器的最大清洁次数方面进行权衡。
增加的灌注率的外部溶液,使任何残留的Tergazyme从试验室中迅速地除去。

致谢

这项工作得到了美国国立卫生研究院 (NIH) 赠款 R01NS102727、R01DA029639、U01MH106027 和 R01 EY023173 的支持。我们也承认该协议源自的原始研究论文,Kolb等人。(20 16 和20 19 )。

利益争夺

MCY 和 IK 与制造移液管压力控制系统的Neuromatic Devices签订了咨询协议。IK,当时,和CRF是在美国专利10830758发明涉及移液管清洗技术和授权给Sensapex 。

伦理

对于使用急性啮齿动物脑切片进行清洁的代表性工作,所有动物程序均符合美国国立卫生研究院实验室动物护理和使用指南,并获得乔治亚研究所动物护理和使用机构委员会的批准。技术(A100359,Exp:2022 年 12 月)。当使用清洁方法小号的要求伦理委员会批准的实验,请遵循的适当的法律和制度的指导。

参考
Bagal, SK, Brown, AD, Cox, PJ, Omoto, K., Owen, RM, Pryde, DC, Slidders, B., Skerratt, SE, Stevens, EB, Storer, RI 和 Swain, NA (2013)离子通道作为治疗目标:药物发现的观点。J Med Chem 56 (3): 593 - 624。
Cho, YK, Park, D., Yang, A., Chen, F., Chuong, AS, Klapoetke, NC 和 Boyden, ES (2019)。多维筛选产生具有改进的光电流和钙和质子电流的数量级减少的视紫红质通道变体。J Biol Chem 294 (11): 3806-3821。
Dunlop, J.、Bowlby, M.、Peri, R.、Vasilyev, D. 和 Arias, R. (2008)。高通量电生理学:离子通道筛选和生理学的新兴范例。Nat Rev Drug Discov 7(4): 358-368。
Goriounova, NA, Heyer, DB, Wilbers, R., Verhoog, MB, Giugliano, M., Verbist, C., Obermayer, J., Kerkhofs, A., Smeding, H., Verberne, M., Idema, S ., Baayen, JC, Pieneman, AW, de Kock, CP, Klein, M. 和 Mansvelder, HD (2018)。大而快的人类锥体神经元与智力有关。Elife 7 :e41714。
Gouwens, NW, Sorensen, SA, Berg, J., Lee, C., Jarsky, T., Ting, J., Sunkin, SM, Feng, D., Anastassiou, CA, Barkan, E.等。(2019)。小鼠视觉皮层中电生理学和形态学神经元类型的分类。Nat Neurosci 22(7):1182-1195。
Hamill, OP, Marty, A., Neher, E., Sakmann, B.和Sigworth, FJ (1981)。改进的膜片钳技术,用于从细胞和无细胞膜片进行高分辨率电流记录。Pflugers Arch Eur J Physiol 391 :85 - 100。
Jiang, X.、Shen, S.、Cadwell, CR、Berens, P.、Sinz, F.、Ecker, AS、Patel, S. 和 Tolias, AS (2015)。成人新皮层中形态学定义的细胞类型之间的连接原理。科学350 (6264):aac9462 - aac9462。
Kaech, S. 和 Banker, G. (2006)。培养海马神经元。国家协议1(5):2406-2415。
Kodandaramaiah, SB, Holst, GL, Wickersham, IR, Singer, AC, Franzesi, GT, McKinnon, ML, Forest, CR 和 Boyden, ES (2016)。用于体内自动细胞内神经记录的 autopatcher 的组装和操作。国家议定书 11(4): 634-654。
Kolb, I., Landry, CR, Yip, MC, Lewallen, CF, Stoy, WA, Lee, J., Felouzis, A., Yang, B., Boyden, ES, Rozell, CJ 和 Forest, CR (2019) . PatcherBot:用于贴壁细胞和脑切片的单细胞电生理机器人。J 神经工程16(4):046003。
Kolb, I., Stoy, WA, Rousseau, EB, Moody, OA, Jenkins, A. 和 Forest, CR (2016)。清洁膜片钳移液器以便立即重复使用。科学报告6:35001。
和信,K.,01 á小时,G.,巴拉萨,T.,Mihut,N.,R ó ZSA,M.,Qzsvar,A.,Tasnadi,E.,Barz ó ,P.,法拉格ó ,N. , Pusk á s, L., Moln á r, G., Moln á r, J., Tam á s, G. 和 Horvath, P. (2020)。用于人和啮齿动物体外切片生理学的自动深度学习驱动的无标签图像引导膜片钳系统。bioRxiv 2020.05.05.078162。
Lin, J. Y.、Lin, M. Z.、Steinbach, P.和Tsien, R. Y. ( 2009 ) 。具有改进的特性和动力学的工程化视紫红质变体的表征。生物物理学 J 96 :1803-1814。
Mariani, J., Coppola, G., Zhang, P., Abyzov, A., Provini, L., Tomasini, L., Amenduni, M., Szekely, A., Palejev, D., Wilson, M., Gerstein, M., Grigorenko, EL, Chawarska, K., Pelphrey, KA, Howe, JR 和 Vaccarino, FM (2015)。自闭症谱系障碍中 GABA/谷氨酸神经元分化的 FOXG1 依赖性失调。单元格162(2):375-390。
Milligan, CJ, Li, J., Sukumar, P., Majeed, Y., Dallas, ML, English, A., Emery, P., Porter, KE, Smith, AM, McFadzean, I., Beccano-Kelly, D., Bahnasi, Y., Cheong, A., Naylor, J., Zeng, F., Liu, X., Gamper, N., Jiang, LH, Pearson, HA, Peers, C., Robertson, B.和比奇,DJ(2009 年)。用于天然和原代哺乳动物细胞的机器人多孔平面膜片钳。国家议定书4(2):244-255。
Ogden, KK, Chen, W., Swanger, SA, McDaniel, MJ, Fan, LZ, Hu, C., Tankovic, A., Kusumoto, H., Kosobucki, GJ, Schulien, AJ, Su, Z., Pecha , J., Bhattacharya, S., Petrovski, S., Cohen, AE, Aizenman, E., Traynelis, SF and Yuan, H. (2017)。NMDA 受体前 M1 螺旋中疾病相关突变的分子机制和潜在的拯救药理学。PLoS 基因13(1):e1006536。
Park, J., Werley, CA, Venkatachalam, V., Kralj, JM, Dib-Hajj, SD, Waxman, SG 和 Cohen, AE (2013)。筛选具有自发尖峰 HEK 细胞的荧光电压指示剂。PLoS 一号8(12):e85221。
Peng, Y., Mittermaier, FX, Planert, H., Schneider, UC, Alle, H. 和 Geiger, JRP (2019)。通过下一代多神经元膜片钳对单个人脑进行高通量微电路分析。Elife 8:e48178 。
Perin, R. 和 Markram, H.(2013 年)。计算机辅助多电极膜片钳系统。J Vis Exp 80:e50630。
Piatkevich, KD, Jung, EE, Straub, C., Linghu, C., Park, D., Suk, HJ, Hochbaum, DR, Goodwin, D., Pnevmatikakis, E., Pak, N., Kawashima, T. , Yang, CT, Rhoades, JL, Shemesh, O., Asano, S., Yoon, YG, Freifeld, L., Saulnier, JL, Riegler, C., Engert, F., Hughes, T., Drobishev, M ., Szabo, B., Ahrens, MB, Flavell, SW, Sabatini, BL 和 Boyden, ES (2018)。应用于荧光电压报告器的机器人多维定向进化方法。Nat Chem Biol 14(4): 352-360。
Qian, X., Nguyen, HN, Song, MM, Hadiono, C., Ogden, SC, Hammack, C., Yao, B., Hamersky, GR, Jacob, F., Zhong, C.,等。(2016)。使用微型生物反应器模拟 ZIKV 暴露的脑区域特异性类器官。单元格165 (5):1238 - 1254。
Stoy, WM, Yang, B., Kight, A., Wright, NC, Borden, PY, Stanley, GB 和 Forest, CR (2020)。生理运动的补偿能够在体内进行高产全细胞记录。J Neurosci 方法348:109008。
Suk, HJ, van Welie, I., Kodandaramaiah, SB, Allen, B., Forest, CR 和 Boyden, ES (2017)。闭环实时成像可实现全自动细胞靶向膜片钳神经记录体内。神经元95(5):1037-1047 e1011。
Swanger, SA, Chen, W., Well, G., Burger, P., Tankovic, A., Bhattacharya, S., Strong, K. L., Hu, C., Kusumoto, H., Zhang, J., Adams, DR, Millichap, JJ, Petrovski, S., Traynelis, SF 和 Yuan, H. (2016)。GluN2A 和 GluN2B 激动剂结合域中稀有变体对 NMDA 受体失调的机制洞察。Am J Hum Genet 99 (6): 1261 - 1280。
Ting, JT, Kalmbach, B., Chong, P., de Frates, R., Keene, CD, Gwinn, RP, Cobbs, C., Ko, AL, Ojemann, JG, Ellenbogen, RG, Koch, C. 和莱恩 E.(2018 年)。一个强大的体外实验平台,用于成人新皮质细胞类型和回路的分子遗传解剖。科学报告8(1): 8407。
Wang, G., Wyskiel, DR, Yang, W., Wang, Y., Milbern, LC, Lalanne, T., Jiang, X., Shen, Y., Sun, QQ 和 Zhu, JJ (2015)。用于解码复杂神经回路的光遗传学和成像辅助同步多膜片钳记录系统。国家议定书 10(3): 397-412。
Yang, KK, Wu, Z. 和 Arnold, FH(2019 年)。机器学习引导的蛋白质工程定向进化。Nat 方法16(8): 687-694。
登录/注册账号可免费阅读全文
  • English
  • 中文翻译
免责声明 × 为了向广大用户提供经翻译的内容,www.bio-protocol.org 采用人工翻译与计算机翻译结合的技术翻译了本文章。基于计算机的翻译质量再高,也不及 100% 的人工翻译的质量。为此,我们始终建议用户参考原始英文版本。 Bio-protocol., LLC对翻译版本的准确性不承担任何责任。
Copyright: © 2021 The Authors; exclusive licensee Bio-protocol LLC.
引用:Landry, C. R., Yip, M. C., Kolb, I., Stoy, W. M., Gonzalez, M. M. and Forest, C. R. (2021). Method for Rapid Enzymatic Cleaning for Reuse of Patch Clamp Pipettes: Increasing Throughput by Eliminating Manual Pipette Replacement between Patch Clamp Attempts. Bio-protocol 11(14): e4085. DOI: 10.21769/BioProtoc.4085.
提问与回复
提交问题/评论即表示您同意遵守我们的服务条款。如果您发现恶意或不符合我们的条款的言论,请联系我们:eb@bio-protocol.org。

如果您对本实验方案有任何疑问/意见, 强烈建议您发布在此处。我们将邀请本文作者以及部分用户回答您的问题/意见。为了作者与用户间沟通流畅(作者能准确理解您所遇到的问题并给与正确的建议),我们鼓励用户用图片的形式来说明遇到的问题。

如果您对本实验方案有任何疑问/意见, 强烈建议您发布在此处。我们将邀请本文作者以及部分用户回答您的问题/意见。为了作者与用户间沟通流畅(作者能准确理解您所遇到的问题并给与正确的建议),我们鼓励用户用图片的形式来说明遇到的问题。