Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq
Open accessAbstract
Despite the importance of the mammalian neocortex for complex cognitive processes, we still lack a comprehensive description of its cellular components. To improve the classification of neuronal cell types and the functional characterization of single neurons, we present Patch-seq, a method that combines whole-cell electrophysiological patch-clamp recordings, single-cell RNA-sequencing and morphological characterization. Following electrophysiological characterization, cell contents are aspirated through the patch-clamp pipette and prepared for RNA-sequencing. Using this approach, we generate electrophysiological and molecular profiles of 58 neocortical cells and show that gene expression patterns can be used to infer the morphological and physiological properties such as axonal arborization and action potential amplitude of individual neurons. Our results shed light on the molecular underpinnings of neuronal diversity and suggest that Patch-seq can facilitate the classification of cell types in the nervous system.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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 Cadwell et al., 2016
The study provides a morpho-electric annotation of almost all transcriptomically defined neural cell types.
“Here we used Patch-seq8 to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of more than 1,300 neurons in adult mouse primary motor cortex, providing a morpho-electric annotation of almost all transcriptomically defined neural cell types.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [8]
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 [8]
Patch-seq combines patch-clamp recording, biocytin staining, and single-cell RNA sequencing of more than 1,300 neurons in adult mouse primary motor cortex.
“Here we used Patch-seq8 to combine patch-clamp recording, biocytin staining, and single-cell RNA sequencing of more than 1,300 neurons in adult mouse primary motor cortex, providing a morpho-electric annotation of almost all transcriptomically defined neural cell types.”
From Phenotypic variation of transcriptomic cell types in mouse motor cortex (2021) · cites this paper as [8]
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 [20]
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 Cadwell et al., 2016
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927 · see citing works
Shrestha, S., & Offer, S. M. (2016) · Medical Epigenetics · doi:10.1159/000444713 · Cited by 8,949 · see citing works
THE USE OF CONFIDENCE OR FIDUCIAL LIMITS ILLUSTRATED IN THE CASE OF THE BINOMIAL
Clopper, C. J., & Pearson, E. S. (1934) · Biometrika · doi:10.1093/biomet/26.4.404 · Cited by 5,106 · see citing works
Full-length RNA-seq from single cells using Smart-seq2
Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.006 · Cited by 4,137 · see citing works
Self-citationmRNA-Seq whole-transcriptome analysis of a single cell
Tang, F., Barbacioru, C., Wang, Y., Nordman, E., Lee, C., Xu, N., Wang, X., Bodeau, J., Tuch, B. B., Siddiqui, A., et al. (2009) · Nature Methods · doi:10.1038/nmeth.1315 · Cited by 3,969 · see citing works
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124 · see citing works
Single-channel currents recorded from membrane of denervated frog muscle fibres
Neher, E., & Sakmann, B. J. (1976) · Nature · doi:10.1038/260799a0 · Cited by 2,577 · see citing works
Smart-seq2 for sensitive full-length transcriptome profiling in single cells
Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2639 · Cited by 2,358 · see citing works
Self-citationMassively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types
Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Elefant, N., Paul, F., Zaretsky, I., Mildner, A., Cohen, N., Jung, S., Tanay, A., et al. (2014) · Science · doi:10.1126/science.1247651 · Cited by 1,795 · see citing works
Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex
The Petilla Interneuron Nomenclature Group (PING) (2008) · Nature Reviews Neuroscience · doi:10.1038/nrn2402 · Cited by 1,491 · see citing works
Localization of the glial fibrillary acidic protein in astrocytes by immunofluorescence
Bignami, A., Eng, L. F., Dahl, D. R., & Uyeda, C. T. (1972) · Brain Research · doi:10.1016/0006-8993(72)90398-8 · Cited by 1,456 · see citing works
Intrinsic firing patterns of diverse neocortical neurons
Connors, B. W., & Gutnick, M. J. (1990) · Trends in Neurosciences · doi:10.1016/0166-2236(90)90185-d · Cited by 1,440 · see citing works
A survey of human brain transcriptome diversity at the single cell level
Darmanis, S., Sloan, S. A., Zhang, Y., Enge, M., Caneda, C., Shuer, L. M., Hayden Gephart, M. G., Barres, B. A., & Quake, S. R. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1507125112 · Cited by 1,401 · see citing works
Bayesian approach to single-cell differential expression analysis
Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348 · see citing works
The Expression of Vesicular Glutamate Transporters Defines Two Classes of Excitatory Synapse
Fremeau, R. T., Troyer, M. D., Pahner, I., Nygaard, G. O., Tran, C. H., Reimer, R. J., Bellocchio, E. E., Fortin, D. L., Storm‐Mathisen, J., & Edwards, R. H. (2001) · Neuron · doi:10.1016/s0896-6273(01)00344-0 · Cited by 1,266 · see citing works
Dysfunction in GABA signalling mediates autism-like stereotypies and Rett syndrome phenotypes
Chao, H.-T., Chen, H., Samaco, R. C., Xue, M., Chahrour, M., Yoo, J., Neul, J. L., Gong, S., Lu, H.-C., Heintz, N., et al. (2010) · Nature · doi:10.1038/nature09582 · Cited by 1,168 · see citing works
Accounting for technical noise in single-cell RNA-seq experiments
Brennecke, P., Anders, S., Kim, J. K., Kołodziejczyk, A. A., Zhang, X., Proserpio, V., Baying, B., Benes, V., Teichmann, S. A., Marioni, J. C., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2645 · Cited by 979 · see citing works
Principles of connectivity among morphologically defined cell types in adult neocortex
Jiang, X., Shen, S., Cadwell, C. R., Berens, P., Sinz, F., Ecker, A. S., Patel, S., & Tolias, A. S. (2015) · Science · doi:10.1126/science.aac9462 · Cited by 908 · see citing works
Self-citationTn5 transposase and tagmentation procedures for massively scaled sequencing projectsPreprint
Picelli, S., Björklund, Å. K., Reinius, B., Sagasser, S., Winberg, G., & Sandberg, R. (2014) · Genome Research · doi:10.1101/gr.177881.114 · Cited by 829 · see citing works
Self-citationAn Abundance of Ubiquitously Expressed Genes Revealed by Tissue Transcriptome Sequence Data
Ramsköld, D., Wang, E. T., Burge, C. B., & Sandberg, R. (2009) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000598 · Cited by 820 · see citing works
Self-citationWaking state: rapid variations modulate neural and behavioral responses
McGinley, M. J., Vinck, M., Reimer, J., Batista-Brito, R., Zagha, E., Cadwell, C. R., Tolias, A. S., Cardin, J. A., & McCormick, D. A. (2015) · Neuron · doi:10.1016/j.neuron.2015.09.012 · Cited by 801 · see citing works
Self-citationTing, J. T., Daigle, T. L., Chen, Q., & Feng, G. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-1096-0_14 · Cited by 631 · see citing works
Alcántara, S., Ruiz, M., D’Arcangelo, G., Ezan, F., de Lecea, L., Curran, T., Sotelo, C., & Soriano, E. (1998) · The Journal of Neuroscience · doi:10.1523/jneurosci.18-19-07779.1998 · Cited by 568 · see citing works
Miyoshi, G., Hjerling-Leffler, J., Karayannis, T., Sousa, V. H., Butt, S. J. B., Battiste, J., Johnson, J. E., Machold, R. P., & Fishell, G. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.4515-09.2010 · Cited by 559 · see citing works
Regulation of cortical microcircuits by unitary GABA-mediated volume transmission
Oláh, S., Füle, M., Komlósi, G., Varga, C., Báldi, R., Barzó, P., & Tamás, G. (2009) · Nature · doi:10.1038/nature08503 · Cited by 386 · see citing works
The organization of two new cortical interneuronal circuits
Jiang, X., Wang, G., Lee, A. J., Stornetta, R. L., & Zhu, J. J. (2013) · Nature Neuroscience · doi:10.1038/nn.3305 · Cited by 338 · see citing works
Self-citationEctopic expression of the Dlx genes induces glutamic acid decarboxylase and Dlx expression
Stühmer, T., Anderson, S. A., Ekker, M., & Rubenstein, J. L. R. (2002) · Development · doi:10.1242/dev.129.1.245 · Cited by 257 · see citing works
Entering the era of single-cell transcriptomics in biology and medicine
Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2764 · Cited by 242 · see citing works
Self-citationKalashnikova, E., Lorca, R. A., Kaur, I., Barisone, G. A., Li, B., Ishimaru, T., Trimmer, J. S., Mohapatra, D. P., & Dı́az, E. (2010) · Neuron · doi:10.1016/j.neuron.2009.12.021 · Cited by 149 · see citing works
Chapter 14 S100β as a neurotrophic factor
Marshak, D. R. (1990) · Progress in brain research · doi:10.1016/s0079-6123(08)63175-1 · Cited by 145 · see citing works
For Better or for Worse: Complexins Regulate SNARE Function and Vesicle Fusion
Brose, N. (2008) · Traffic · doi:10.1111/j.1600-0854.2008.00758.x · Cited by 125 · see citing works
Many specialists for suppressing cortical excitation
Burkhalter, A. (2008) · Frontiers in Neuroscience · doi:10.3389/neuro.01.026.2008 · Cited by 122 · see citing works
Emx1 is a Marker for Pyramidal Neurons of the Cerebral Cortex
Chan, C.-H. (2001) · Cerebral Cortex · doi:10.1093/cercor/11.12.1191 · Cited by 121 · see citing works
The Neuron Identity Problem: Form Meets Function
Fishell, G., & Heintz, N. (2013) · Neuron · doi:10.1016/j.neuron.2013.10.035 · Cited by 115 · see citing works
PCR and patch-clamp analysis of single neurons
Sucher, N. J., & Deitcher, D. L. (1995) · Neuron · doi:10.1016/0896-6273(95)90257-0 · Cited by 78 · see citing works
NPAS1 Represses the Generation of Specific Subtypes of Cortical Interneurons
Stanco, A., Pla, R., Vogt, D. L., Chen, Y., Mandal, S., Walker, J. M., Hunt, R. F., Lindtner, S., Erdman, C. A., Pieper, A. A., et al. (2014) · Neuron · doi:10.1016/j.neuron.2014.10.040 · Cited by 71 · see citing works
Single-neuron RNA-Seq: technical feasibility and reproducibility
Qiu, S., Luo, S. J., Evgrafov, O. V., Li, R. G., Schroth, G. P., Levitt, P. R., Knowles, J. A., & Wang, K. (2012) · Frontiers in Genetics · doi:10.3389/fgene.2012.00124 · Cited by 67 · see citing works
Genes and channels: patch/voltage-clamp analysis and single-cell RT-PCR
Sucher, N. J., Deitcher, D. L., Baro, D. J., Harris‐Warrick, R. M., & Guenther, E. (2000) · Cell and Tissue Research · doi:10.1007/s004410000289 · Cited by 57 · see citing works
Development of Layer 1 Neurons in the Mouse Neocortex
Ma, J., Yao, X.-H., Fu, Y., & Yu, Y. (2013) · Cerebral Cortex · doi:10.1093/cercor/bht114 · Cited by 57 · see citing works
Association of NPAS3 exonic variation with schizophrenia
MacIntyre, G., Alford, T., Xiong, L., Rouleau, G. A., Tibbo, P. G., & Cox, D. W. (2010) · Schizophrenia Research · doi:10.1016/j.schres.2010.04.002 · Cited by 46 · see citing works
DPP6 regulation of dendritic morphogenesis impacts hippocampal synaptic development
Lin, L., Sun, W., Throesch, B. T., Kung, F., DeCoster, J., Berner, C. J., Cheney, R. E., Rudy, B., & Hoffman, D. A. (2013) · Nature Communications · doi:10.1038/ncomms3270 · Cited by 44 · see citing works
Toledo‐Rodriguez, M., & Markram, H. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-1096-0_8 · Cited by 35 · see citing works
Subkhankulova (2010) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2010.00010 · Cited by 35 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [1]Cajal SR Pasik P Pasik T Texture of the Nervous System of Man and the Vertebrates Springer 2002
Cited by
Sorted by citations
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · see citing works
GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits
Tremblay, R., Lee, S., & Rudy, B. (2016) · Neuron · doi:10.1016/j.neuron.2016.06.033 · Cited by 2,033 · see citing works
Shared and distinct transcriptomic cell types across neocortical areas
Tasic, B., Yao, Z., Graybuck, L. T., Smith, K. A., Nguyen, T. N., Bertagnolli, D., Goldy, J., Garren, E., Economo, M. N., Viswanathan, S., et al. (2018) · Nature · doi:10.1038/s41586-018-0654-5 · Cited by 1,746 · see citing works
Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram
Biancalani, T., Scalia, G., Buffoni, L., Avasthi, R., Lu, Z., Sanger, A., Tokcan, N., Vanderburg, C. R., Segerstolpe, Å., Zhang, M., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01264-7 · Cited by 931 · see citing works
Revealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632 · see citing works
A multimodal cell census and atlas of the mammalian primary motor cortex
BRAIN Initiative Cell Census Network (BICCN), BRAIN Initiative Cell Census Network (BICCN) Corresponding authors, Callaway, E. M., Dong, H.-W., Ecker, J. R., Hawrylycz, M. J., Huang, Z. J., Lein, E. S., Ngai, J., Osten, P., et al. (2021) · Nature · doi:10.1038/s41586-021-03950-0 · Cited by 522 · see citing works
Self-citationIntegrated Morphoelectric and Transcriptomic Classification of Cortical GABAergic Cells
Gouwens, N. W., Sorensen, S. A., Baftizadeh, F., Budzillo, A., Lee, B. R., Jarsky, T., Alfiler, L., Baker, K., Barkan, E., Berry, K., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.057 · Cited by 538 · see citing works
“In particular, using a modified version of the Patch-seq technique (Cadwell et al., 2016; Fuzik et al., 2016; Földy et al., 2016), we characterized electrophysiological responses to a series of hyperpolarizing and depolarizing current injections, extracted and reverse transcribed nuclear and cytosolic mRNA, sequenced…”
What is a cell type and how to define it?
Zeng, H. (2022) · Cell · doi:10.1016/j.cell.2022.06.031 · Cited by 439 · see citing works
Development and arealization of the cerebral cortex
Cadwell, C. R., Bhaduri, A., Mostajo-Radji, M. A., Keefe, M. G., & Nowakowski, T. J. (2019) · Neuron · doi:10.1016/j.neuron.2019.07.009 · Cited by 408 · see citing works
Self-citationThe promise of spatial transcriptomics for neuroscience in the era of molecular cell typing
Lein, E., Borm, L. E., & Linnarsson, S. (2017) · Science · doi:10.1126/science.aan6827 · Cited by 383 · see citing works
Phenotypic variation of transcriptomic cell types in mouse motor cortex
Scala, F., Kobak, D., Bernabucci, M., Bernaerts, Y., Cadwell, C. R., Castro, J. R., Hartmanis, L., Jiang, X., Laturnus, S., Miranda, E., et al. (2021) · Nature · doi:10.1038/s41586-020-2907-3 · Cited by 398 · see citing works
“We used the Patch-seq technique 8,17–19 to describe the morpho-electric phenotypes for most of the t-types in MOp 20 .”
Self-citationIntegrated multimodal cell atlas of Alzheimer’s disease
Gabitto, M. I., Travaglini, K. J., Rachleff, V. M., Kaplan, E. S., Long, B., Ariza, J., Ding, Y., Mahoney, J. T., Dee, N., Goldy, J., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01774-5 · Cited by 397 · see citing works
Dulken, B. W., Leeman, D. S., Boutet, S. C., Hebestreit, K., & Brunet, A. (2017) · Cell Reports · doi:10.1016/j.celrep.2016.12.060 · Cited by 334 · see citing works
Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease
Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320 · see citing works
Disentangling neural cell diversity using single-cell transcriptomics
Poulin, J.-F., Tasic, B., Hjerling-Leffler, J., Trimarchi, J. M., & Awatramani, R. (2016) · Nature Neuroscience · doi:10.1038/nn.4366 · Cited by 293 · see citing works
Human neocortical expansion involves glutamatergic neuron diversification
Berg, J., Sorensen, S. A., Ting, J. T., Miller, J. A., Chartrand, T., Buchin, A., Bakken, T. E., Budzillo, A., Dee, N., Ding, S.-L., et al. (2021) · Nature · doi:10.1038/s41586-021-03813-8 · Cited by 285 · see citing works
Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics
Harris, K. D., Hochgerner, H., Skene, N. G., Magno, L., Katona, L., Bengtsson Gonzales, C., Somogyi, P., Kessaris, N., Linnarsson, S., & Hjerling-Leffler, J. (2018) · PLoS Biology · doi:10.1371/journal.pbio.2006387 · Cited by 276 · see citing works
Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes
Armand, E. J., Li, J., Xie, F., Luo, C., & Mukamel, E. A. (2021) · Neuron · doi:10.1016/j.neuron.2020.12.010 · Cited by 241 · see citing works
The diversity of GABAergic neurons and neural communication elements
Huang, Z. J., & Paul, A. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0195-4 · Cited by 201 · see citing works
Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex
Chen, A., Sun, Y., Lei, Y., Li, C., Liao, S., Meng, J., Bai, Y., Liu, Z., Liang, Z., Zhu, Z., et al. (2023) · Cell · doi:10.1016/j.cell.2023.06.009 · Cited by 232 · see citing works
Molecular and functional variation in iPSC-derived sensory neurons
HIPSCI Consortium, Schwartzentruber, J., Foskolou, S., Kilpinen, H., Rodrigues, J., Alasoo, K., Knights, A. J., Patel, M., Goncalves, A., Ferreira, R., et al. (2018) · Nature Genetics · doi:10.1038/s41588-017-0005-8 · Cited by 223 · see citing works
Layer 4 of mouse neocortex differs in cell types and circuit organization between sensory areas
Scala, F., Kobak, D., Shan, S., Bernaerts, Y., Laturnus, S., Cadwell, C. R., Hartmanis, L., Froudarakis, E., Castro, J. R., Tan, Z. H., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-12058-z · Cited by 193 · see citing works
Self-citationSingle-cell RNAseq reveals cell adhesion molecule profiles in electrophysiologically defined neurons
Földy, C., Darmanis, S., Aoto, J., Malenka, R. C., Quake, S. R., & Südhof, T. C. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1610155113 · Cited by 179 · see citing works
Neuronal circuits in barrel cortex for whisker sensory perception
Staiger, J. F., & Petersen, C. C. H. (2021) · Physiological Reviews · doi:10.1152/physrev.00019.2019 · Cited by 165 · see citing works
Nigro, M. J., Hashikawa-Yamasaki, Y., & Rudy, B. (2018) · The Journal of Neuroscience · doi:10.1523/jneurosci.2415-17.2017 · Cited by 149 · see citing works
“An interesting approach might be to exploit the combination of patch-clamp recordings with single cell sequencing techniques to obtain more specific molecular markers in morphologically and/or electrophysiologically identified cells (Cadwell et al., 2016; Fuzik et al., 2016).”
BackgroundCadwell, C. R., Scala, F., Li, S., Livrizzi, G., Shen, S., Sandberg, R., Jiang, X., & Tolias, A. S. (2017) · Nature Protocols · doi:10.1038/nprot.2017.120 · Cited by 147 · see citing works
Self-citationIntrinsically photosensitive retinal ganglion cells of the human retina
Mure, L. S. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.636330 · Cited by 151 · see citing works
Zhu, H., Zou, G., Wang, N., Zhuang, M., Xiong, W., & Huang, G. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1615557114 · Cited by 133 · see citing works
Temporal plasticity of apical progenitors in the developing mouse neocortex
Oberst, P., Fièvre, S., Baumann, N., Concetti, C., Bartolini, G., & Jabaudon, D. (2019) · Nature · doi:10.1038/s41586-019-1515-6 · Cited by 121 · see citing works
Origin and segmental diversity of spinal inhibitory interneurons
Sweeney, L. B., Bikoff, J. B., Gabitto, M. I., Brenner-Morton, S., Baek, M., Yang, J. H., Tabak, E. G., Dasen, J. S., Kintner, C. R., & Jessell, T. M. (2018) · Neuron · doi:10.1016/j.neuron.2017.12.029 · Cited by 116 · see citing works
Single cell transcriptomics in neuroscience: cell classification and beyond
Tasic, B. (2018) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.04.021 · Cited by 83 · see citing works
Transcriptomic correlates of neuron electrophysiological diversity
Tripathy, S. J., Toker, L., Li, B., Crichlow, C.-L., Tebaykin, D., Mancarci, B. O., & Pavlidis, P. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005814 · Cited by 69 · see citing works
Perspectives on defining cell types in the brain
Mukamel, E. A., & Ngai, J. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2018.11.007 · Cited by 56 · see citing works
Intracortical astrocyte subpopulations defined by astrocyte reporter Mice in the adult brain
Morel, L., Men, Y., Chiang, M. S. R., Tian, Y., Jin, S., Yelick, J., Higashimori, H., & Yang, Y. (2019) · Glia · doi:10.1002/glia.23545 · Cited by 59 · see citing works
Neurogliaform cortical interneurons derive from cells in the preoptic area
Niquille, M., Limoni, G., Markopoulos, F., Cadilhac, C., Prados, J., Holtmaat, A., & Dayer, A. (2018) · eLife · doi:10.7554/elife.32017 · Cited by 55 · see citing works
Peng, 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
A community-based transcriptomics classification and nomenclature of neocortical cell types
Yuste, R., Hawrylycz, M., Aalling, N., Aguilar-Valles, A., Arendt, D., Armañanzas, R., Ascoli, G. A., Bielza, C., Bokharaie, V., Bergmann, T. B., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0685-8 · Cited by 32 · see citing works
Self-citationSparse 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
Self-citationJiang, X., Shen, S., Sinz, F., Reimer, J., Cadwell, C. R., Berens, P., Ecker, A. S., Patel, S., Denfield, G. H., Froudarakis, E., et al. (2016) · Science · doi:10.1126/science.aaf6102 · Cited by 13 · see citing works
Self-citationLinking neuronal structure to function in rodent hippocampus: a methodological prospective
Preston-Ferrer, P., & Burgalossi, A. (2018) · Cell and Tissue Research · doi:10.1007/s00441-017-2732-7 · Cited by 11 · see citing works
- Authors
- Cadwell, C. R., Palasantza, A., Jiang, X., Berens, P., Deng, Q., Yilmaz, M., Reimer, J., Shen, S., Bethge, M., Tolias, K. F., Sandberg, R., & Tolias, A. S.
- Affiliations
- Baylor College of Medicine (US)
- Karolinska Institutet (SE)
- Max Planck Institute for Biological Cybernetics (DE)
- Corresponding author
- Sandberg, Rickard
- Tolias, Andreas S
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2015-12-21
- Volume
- 34
- Issue
- 2
- Pages
- 199-203
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC4840019
- OpenAlex
- W2267554996
- Cited by
- 581
- References
- 44
- Citations by year
- 2026: 47 · 2025: 56 · 2024: 57 · 2023: 61 · 2022: 60 · 2021: 75 · 2020: 67 · 2019: 57 · 2018: 49 · 2017: 37 · 2016: 13 · 2014: 1
- FWCI
- 14.32
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Funders
- National Institutes of Health (1dp1od008301-01)
- National Institute of Mental Health (F30MH095440, R01MH103108)
- National Eye Institute (DP1EY023176, T32 EY07001, P30EY002520, T32EY007001)
- National Institute of General Medical Sciences (T32GM007330)
- National Institute of Neurological Disorders and Stroke (R01NS062829)
- National Institute of Biomedical Imaging and Bioengineering (T32EB006350)
- National Center for Chronic Disease Prevention and Health Promotion (DP1EY023176)
- 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, neocortex, neuronal cell types, single-cell transcriptomics, axonal arborization, action potential amplitude, neuronal heterogeneity, cell type classification
- MeSH terms
- Animals, Female, Male, Neurons, Sequence Analysis, RNA, Patch-Clamp Techniques, Neocortex, Gene Expression Profiling, Cell Shape, Mice, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
