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Assembly and operation of the autopatcher for automated intracellular neural recording in vivo

Nature Protocols volume 11, pages 634–654 (2016)Cite this article

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Abstract

Whole-cell patch clamping in vivo is an important neuroscience technique that uniquely provides access to both suprathreshold spiking and subthreshold synaptic events of single neurons in the brain. This article describes how to set up and use the autopatcher, which is a robot for automatically obtaining high-yield and high-quality whole-cell patch clamp recordings in vivo. By following this protocol, a functional experimental rig for automated whole-cell patch clamping can be set up in 1 week. High-quality surgical preparation of mice takes ∼1 h, and each autopatching experiment can be carried out over periods lasting several hours. Autopatching should enable in vivo intracellular investigations to be accessible by a substantial number of neuroscience laboratories, and it enables labs that are already doing in vivo patch clamping to scale up their efforts by reducing training time for new lab members and increasing experimental durations by handling mentally intensive tasks automatically.

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Figure 1: The autopatcher—a robot for automated whole-cell patch clamp recordings in vivo: overview of the algorithm and schematic.
Figure 2: The autopatcher: equipment photographs.
Figure 3: Optimum pipettes used for autopatching.
Figure 4: Surgical procedure for headplate implantation.
Figure 5: Autopatcher software GUI.
Figure 6: Autopatcher software graphical user interface: neuron hunting.
Figure 7: Autopatcher software graphical user interface: gigasealing and break-in.
Figure 8: Autopatcher software graphical user interface: recording.
Figure 9: Example data acquired by the autopatcher.

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Acknowledgements

We thank B.D. Allen and H.-J. Suk for feedback on the manuscript. C.R.F. acknowledges the National Institutes of Health (NIH) BRAIN Initiative (National Eye Institute (NEI) and National Institute of Mental Health (NIMH) 1-U01-MH106027-01), an NIH Single Cell Grant 1 R01 EY023173, the National Science Foundation (NSF) (Education and Human Resources (Her) 0965945 and Computer and Information Science and Engineering (CISE) 1110947), an NIH Computational Neuroscience Training grant (no. 5T90DA032466), the Georgia Tech Translational Research Institute for Biomedical Engineering & Science (TRIBES) Seed Grant Awards Program, the Georgia Tech Fund for Innovation in Research and Education (GT-FIRE), the Wallace H. Coulter Translational/Clinical Research Grant Program and support from Georgia Tech through the Institute for Bioengineering and Biosciences Junior Faculty Award, the Technology Fee Fund, Invention Studio, and the George W. Woodruff School of Mechanical Engineering. E.S.B. acknowledges NIH 1R01EY023173, the New York Stem Cell Foundation-Robertson Award, a NIH Director's Pioneer Award 1DP1NS087724, an NIH Director's Transformative Award (NIH 1R01MH103910) and an NIH BRAIN initiative grant (NIH 1R24MH106075). G.T.F. acknowledges a Friends of the McGovern Institute Fellowship.

Author information

Authors and Affiliations

  1. Media Lab, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Suhasa B Kodandaramaiah, Ian R Wickersham, Annabelle C Singer, Giovanni Talei Franzesi & Edward S Boyden

  2. McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

    Suhasa B Kodandaramaiah, Ian R Wickersham, Annabelle C Singer & Edward S Boyden

  3. Departments of Biological Engineering and Brain and Cognitive Sciences, MIT, Cambridge, Massachusetts, USA

    Suhasa B Kodandaramaiah, Ian R Wickersham, Annabelle C Singer & Edward S Boyden

  4. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA

    Gregory L Holst & Craig R Forest

  5. Department of Physiology, School of Medicine, Emory University, Atlanta, Georgia, USA

    Michael L McKinnon

Authors
  1. Suhasa B Kodandaramaiah
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  7. Craig R Forest
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Contributions

S.B.K., I.R.W., G.L.H., C.R.F. and E.S.B. designed, built and tested the autopatcher system. A.C.S. and G.T.F. assisted with experiments. M.L.M. developed the software included with the manuscript. S.B.K., I.R.W., G.L.H., A.C.S., G.T.F., C.R.F. and E.S.B. wrote the manuscript.

Corresponding authors

Correspondence to Craig R Forest or Edward S Boyden.

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Competing interests

A.C.S., G.T.F., M.L.M., C.R.F. and E.S.B. declare no competing interests. I.R.W., S.B.K. and G.L.H. received financial remuneration from Neuromatic Devices for technical consulting services provided in 2012, 2013 and 2012–2015, respectively.

Supplementary information

Supplementary Text and Figures

Supplementary Methods (PDF 176 kb)

Supplementary Data 1: File archive consisting of software required for running the Autopatcher.

Includes two Labview library files – ‘Autopatcher 2000.llb’ and ‘Hardware.llb’ that can be opened using Labview installed in Step 8 of the protocol. Also included is a corresponding ‘Autopatcher software configuration manual.pdf’ that provides detailed instructions on installation of software and configuring the software settings to control the autopatcher control box. (ZIP 2066 kb)

Supplementary Data 2: File archive consisting of mechanical drawings and computer aided design (CAD) files for making the custom head fixation base and headplate.

‘Headfixation fixation base CAD.pdf’ is a mechanical drawing of the headfixation base, while ‘Head fixation base CAD.SLDPRT’ is the 3D drawing that can be opened in Solidworks software. ‘Head Plate CAD.pdf’ is a mechanical drawing of headplate implant, and ‘Head Plate CAD.SLDPRT’ is the 3D drawing that can be opened in Solidworks software. (ZIP 356 kb)

Supplementary Data 3: File archive consisting of mechanical drawings and computer aided design (CAD) files and instructions for assembling the autopatcher pipette actuator assembly.

Assembly instructions are provided in ‘Autopatcher Robotic Arm Assembly Manual.pdf’. ‘adapter plate 1.PDF’ and ‘adapter plate 1.SLDDRW’ are mechanical drawings of the adaptor plate used for mounting programmable linear stage onto Sutter manipulator. ‘adapter plate 1.SLDPRT’ is the corresponding 3D CAD file that can be opened in Solidworks. ‘adapter plate 2.PDF’ and ‘adapter plate 2.SLDDRW’ are mechanical drawings of the adaptor plate used to mount the amplifier headstage onto the programmable linear stage. ‘adapter plate 2.SLDPRT’ is the corresponding 3D CAD file that can be opened in Solidworks. (ZIP 2951 kb)

Supplementary Data 4: File archive consisting of mechanical drawings, computer aided design (CAD) files and instructions for assembling the autopatcher control box.

Assembly instructions are provided in the ‘Autopatcher control box assembly manual.pdf’ while ‘Autopatcher control box parts list.xlsx’ provides complete list of parts required for assembling the control box. Details of each part include description, name of vendor, catalog number, price/unit (as on Aug 2015), and quantity of each part. The sub-folder ‘Laser cutter files’ contains the ‘Autopatcher panels front & back.ai’ and ‘Autopatcher structural base, platform, & manometer clamp.ai’ files which can be used to cut two structural elements used for control box assembly (See the ‘Autopatcher control box assembly manual.pdf’). The sub-folder ‘Circuit board files’ contains: ‘Autopatcher PCB parts list.xlsx’ – a full parts list of all components on the pressure control printed circuit board (PCB) and valve relay PCB. ‘pressure_board.brd’ and ‘pressure_board.sch’ are the pressure control PCB CAD files, while ‘valve-relay_board.brd’ and ‘valve-relay_board.sch’ are the valve relay PCB CAD files. (ZIP 9892 kb)

Computer screen broadcast of the autopatcher software GUI during a representative autopatching trial. (MP4 19838 kb)

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Kodandaramaiah, S., Holst, G., Wickersham, I. et al. Assembly and operation of the autopatcher for automated intracellular neural recording in vivo. Nat Protoc 11, 634–654 (2016). https://doi.org/10.1038/nprot.2016.007

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