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Multimodal profiling of single-cell morphology, electrophysiology, and gene expression using Patch-seq

Nature Protocols volume 12, pages 25312553 (2017) | Download Citation


Neurons exhibit a rich diversity of morphological phenotypes, electrophysiological properties, and gene-expression patterns. Understanding how these different characteristics are interrelated at the single-cell level has been difficult because of the lack of techniques for multimodal profiling of individual cells. We recently developed Patch-seq, a technique that combines whole-cell patch-clamp recording, immunohistochemistry, and single-cell RNA-sequencing (scRNA-seq) to comprehensively profile single neurons from mouse brain slices. Here, we present a detailed step-by-step protocol, including modifications to the patching mechanics and recording procedure, reagents and recipes, procedures for immunohistochemistry, and other tips to assist researchers in obtaining high-quality morphological, electrophysiological, and transcriptomic data from single neurons. Successful implementation of Patch-seq allows researchers to explore the multidimensional phenotypic variability among neurons and to correlate gene expression with phenotype at the level of single cells. The entire procedure can be completed in 2 weeks through the combined efforts of a skilled electrophysiologist, molecular biologist, and biostatistician.

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We thank P. Fahey, J. Reimer, Q. Deng, P. Johnsson, and B. Tasic for their helpful discussions and suggestions on improving the protocol and the manuscript. This study was supported by grants R01MH103108, DP1EY023176, P30EY002520, T32EY07001, and DP1OD008301 from the National Institutes of Health (NIH) to A.S.T.; grants from the Swedish Research Council and the Vallee Foundation to R.S.; the McKnight Scholar Award to A.S.T.; and the Arnold and Mabel Beckman Foundation Young Investigator Award to A.S.T. X.J. was supported by the BCM Faculty Start-up Fund. C.R.C. was supported by NIH grants F30MH095440, T32GM007330, and T32EB006350. This work was supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/Interior Business Center (DoI/IBC) contract no. D16PC00003. The U.S. government is authorized to reproduce and distribute reprints for governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/IBC, or the U.S. government.

Author information


  1. Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA.

    • Cathryn R Cadwell
    • , Federico Scala
    • , Shuang Li
    • , Giulia Livrizzi
    • , Shan Shen
    • , Xiaolong Jiang
    •  & Andreas S Tolias
  2. Ludwig Institute for Cancer Research, Stockholm, Sweden.

    • Rickard Sandberg
  3. Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.

    • Rickard Sandberg
  4. Jan and Dan Duncan Neurological Research Institute at Texas Children's Hospital, Houston, Texas, USA.

    • Xiaolong Jiang
  5. Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, Texas, USA.

    • Andreas S Tolias
  6. Department of Electrical and Computer Engineering, Rice University, Houston, Texas, USA.

    • Andreas S Tolias


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F.S., S.L., X.J., G.L., and S.S. performed electrophysiology. F.S., G.L., and X.J. performed immunohistochemistry. C.R.C. assisted with the electrophysiology experiments, generated cDNA libraries, performed the analysis, and drafted the manuscript. A.S.T., X.J., and R.S. supervised all experiments and analyses. All authors contributed to writing the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Rickard Sandberg or Xiaolong Jiang or Andreas S Tolias.

Supplementary information


  1. 1.

    Example of Patch-seq sample collection visualized using differential interference contrast (DIC).

    The entire sample-collection process, including cell approach, pipette positioning, and aspiration of cell contents into the pipette, is shown under DIC. The simultaneous electrophysiological recording is shown in the bottom left of the screen.

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