Integration of electrophysiological recordings with single-cell RNA-seq data identifies neuronal subtypes
Open accessAbstract
Traditionally, neuroscientists have defined the identity of neurons by the cells' location, morphology, connectivity and excitability. However, the direct relationship between these parameters and the molecular phenotypes has remained largely unexplored. Here, we present a method for obtaining full transcriptome data from single neocortical pyramidal cells and interneurons after whole-cell patch-clamp recordings in mouse brain slices. In our approach, termed Patch-seq, a patch-clamp stimulus protocol is followed by the aspiration of the entire somatic compartment into the recording pipette, reverse transcription of RNA including addition of unique molecular identifiers, cDNA amplification, Illumina library preparation and sequencing. We show that Patch-seq reveals a close link between electrophysiological characteristics, responses to acute chemical challenges and RNA expression of neurotransmitter receptors and channels. Moreover, it distinguishes neuronal subpopulations that correspond to both well-established and, to our knowledge, hitherto undescribed neuronal subtypes. Our findings demonstrate the ability of Patch-seq to precisely map neuronal subtypes and predict their network contributions in the brain.
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Single-cell mRNA-sequencing methods enable unbiased, high-resolution transcriptomic analysis of individual neurons.
“recent technical advances in single-cell mRNA-sequencing methods are now enabling unbiased, high-resolution transcriptomic analysis of individual neurons”
From GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits (2016) · cites this paper as Fuzik et al., 2016
The study used Patch-seq to describe the morpho-electric phenotypes for most of the t-types in MOp.
“We used the Patch-seq technique8,17–19 to describe the morpho-electric phenotypes for most of the t-types in MOp20.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [18]
Imaging-based single-cell transcriptomics and integration with functional imaging and electrophysiology enable mapping spatial organization and key phenotypic properties of molecularly defined cell types.
“Imaging-based single-cell transcriptomics and its combination with functional imaging, and integration of electrophysiology and single-cell sequencing, enable mapping of the spatial organization and key phenotypic properties of molecularly defined cell types20–24.”
From A multimodal cell census and atlas of the mammalian primary motor cortex (2021) · cites this paper as [21]
Patch-seq connects single-cell sequencing to neuronal physiology by recording whole-cell patch in slices and then extracting cytosol for transcriptome sequencing.
“Single cell sequencing and neuronal physiology can be directly linked by performing whole-cell patch recordings in brain slices, followed by cytosol extraction through the patch pipette for transcriptome sequencing (patch-seq) (Cadwell et al., 2016; Fuzik et al., 2016).”
From Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes (2021) · cites this paper as Fuzik et al., 2016
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Self-citationPeng, H., Xie, P., Liu, L., Kuang, X., Wang, Y., Qu, L., Gong, H., Jiang, S., Li, A., Ruan, Z., et al. (2019) · doi:10.1101/675280 · Cited by 47 · see citing works
Assessing Transcriptome Quality in Patch-Seq Datasets
Tripathy, S. J., Toker, L., Bomkamp, C., Mancarci, B. O., Belmadani, M., & Pavlidis, P. (2018) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2018.00363 · Cited by 39 · see citing works
Emmenegger, V., Qi, G., Wang, H., & Feldmeyer, D. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhx352 · Cited by 26 · see citing works
Sparse Reduced-Rank Regression for Exploratory Visualisation of Paired Multivariate Data
Kobak, D., Bernaerts, Y., Weis, M. A., Scala, F., Tolias, A. S., & Berens, P. (2021) · Journal of the Royal Statistical Society Series C: Applied Statistics · doi:10.1111/rssc.12494 · Cited by 24 · see citing works
- Authors
- Fuzik, J., Zeisel, A., Máté, Z., Calvigioni, D., Yanagawa, Y., Szabó, G., Linnarsson, S., & Harkany, T.
- Affiliations
- Karolinska Institutet (SE)
- HUN-REN Institute of Experimental Medicine (HU)
- Gunma University (JP)
- Corresponding author
- Linnarsson, Sten
- Harkany, Tibor
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2015-12-21
- Volume
- 34
- Issue
- 2
- Pages
- 175-183
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4745137
- OpenAlex
- W2268971953
- Cited by
- 386
- References
- 50
- Citations by year
- 2026: 19 · 2025: 28 · 2024: 18 · 2023: 26 · 2022: 49 · 2021: 47 · 2020: 58 · 2019: 45 · 2018: 43 · 2017: 31 · 2016: 21
- FWCI
- 13.76
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Karolinska Institutet
- Novo Nordisk Fonden (NNF15OC0015964, NNF14OC0010695)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- Patch-seq, neuronal cell types, single-cell transcriptomics, electrophysiology, whole-cell patch-clamp recording, neocortical pyramidal cells, interneurons, neurotransmitter receptors, ion channels, mouse brain slices
- MeSH terms
- Animals, Electrophysiology, Neurons, RNA, Messenger, Sequence Analysis, RNA, Patch-Clamp Techniques, Gene Expression Profiling, Mice, Single-Cell Analysis
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