Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing
Abstract
The primary sensory system requires the integrated function of multiple cell types, although its full complexity remains unclear. We used comprehensive transcriptome analysis of 622 single mouse neurons to classify them in an unbiased manner, independent of any a priori knowledge of sensory subtypes. Our results reveal eleven types: three distinct low-threshold mechanoreceptive neurons, two proprioceptive, and six principal types of thermosensitive, itch sensitive, type C low-threshold mechanosensitive and nociceptive neurons with markedly different molecular and operational properties. Confirming previously anticipated major neuronal types, our results also classify and provide markers for new, functionally distinct subtypes. For example, our results suggest that itching during inflammatory skin diseases such as atopic dermatitis is linked to a distinct itch-generating type. We demonstrate single-cell RNA-seq as an effective strategy for dissecting sensory responsive cells into distinct neuronal types. The resulting catalog illustrates the diversity of sensory types and the cellular complexity underlying somatic sensation.
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The division of peripheral sensory neurons suggests refinements to previous classifications.
“Within the peripheral sensory neurons (of the dorsal root ganglia), cell types were divided into three main branches: peptidergic (eight types), non-peptidergic (six types), and neurofilament (three types), which suggests refinements to previous classifications (Li et al., 2016, Usoskin et al., 2015) (Figure S4 and Table S2).”
From Molecular Architecture of the Mouse Nervous System (2018) · cites this paper as Usoskin et al., 2015
Within the peripheral sensory neurons (of the dorsal root ganglia), cell types were divided into three main branches: peptidergic (eight types), non-peptidergic (six types), and neurofilament (three types).
“Within the peripheral sensory neurons (of the dorsal root ganglia), cell types were divided into three main branches: peptidergic (eight types), non-peptidergic (six types), and neurofilament (three types), which suggests refinements to previous classifications (Li et al., 2016, Usoskin et al., 2015) (Figure S4 and Table S2).”
From Molecular Architecture of the Mouse Nervous System (2018) · cites this paper as Usoskin et al., 2015
t-SNE is a powerful nonlinear dimensionality-reduction alternative, especially when computed with an efficient approximation that scales to large genomic data sets.
“A powerful, nonlinear alternative5,105–107 is t-distributed stochastic neighbor embedding108 (t-SNE; introduced for single-cell analysis as viSNE105), especially when computed with an efficient approximation109 compatible with the scale of large genomic data sets110.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [107]
Cites
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888 · see citing works
Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq
Treutlein, B., Brownfield, D. G., Wu, A. R., Neff, N. F., Mantalas, G. L., Espinoza, F. H., Desai, T. J., Krasnow, M. A., & Quake, S. R. (2014) · Nature · doi:10.1038/nature13173 · Cited by 1,342 · see citing works
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Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
The Tabula Muris Consortium, coordination, O., coordination, L., processing, O. C. A., sequencing, L. P. A., analysis, C. D., annotation, C. T., Writing group, Supplemental text writing group, & Principal investigators (2018) · Nature · doi:10.1038/s41586-018-0590-4 · Cited by 3,326 · see citing works
Molecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,995 · see citing works
Self-citationAdult mouse cortical cell taxonomy revealed by single cell transcriptomics
Tasic, B., Menon, V., Nguyen, T. N., Kim, T. K., Jarsky, T., Yao, Z., Levi, B., Gray, L. T., Sorensen, S. A., Dolbeare, T., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4216 · Cited by 1,714 · see citing works
Brann, D. H., Tsukahara, T., Weinreb, C., Lipovsek, M., Van den Berge, K., Gong, B., Chance, R., Macaulay, I. C., Chou, H.-J., Fletcher, R. B., et al. (2020) · Science Advances · doi:10.1126/sciadv.abc5801 · Cited by 1,198 · see citing works
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071 · see citing works
Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics
Shekhar, K., Lapan, S. W., Whitney, I. E., Tran, N. M., Macosko, E. Z., Kowalczyk, M., Adiconis, X., Levin, J. Z., Nemesh, J., Goldman, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.054 · Cited by 1,176 · see citing works
Sensory Neurons Co-opt Classical Immune Signaling Pathways to Mediate Chronic Itch
Oetjen, L. K., Mack, M. R., Feng, J., Whelan, T. M., Niu, H., Guo, C. J., Chen, S., Trier, A. M., Xu, A. Z., Tripathi, S. V., et al. (2017) · Cell · doi:10.1016/j.cell.2017.08.006 · Cited by 1,092 · see citing works
Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain
Lake, B. B., Ai, R., Kaeser, G. E., Salathia, N. S., Yung, Y. C., Liu, R., Wildberg, A., Gao, D., Fung, H.-L., Chen, S., et al. (2016) · Science · doi:10.1126/science.aaf1204 · Cited by 957 · see citing works
Spatial Transcriptomics and In Situ Sequencing to Study Alzheimer’s Disease
Chen, W.-T., Lu, A., Craessaerts, K., Pavie, B., Sala Frigerio, C., Corthout, N., Qian, X., Laláková, J., Kühnemund, M., Voytyuk, I., et al. (2020) · Cell · doi:10.1016/j.cell.2020.06.038 · Cited by 949 · see citing works
Bias, robustness and scalability in single-cell differential expression analysis
Soneson, C., & Robinson, M. D. (2018) · Nature Methods · doi:10.1038/nmeth.4612 · Cited by 770 · see citing works
A molecular census of arcuate hypothalamus and median eminence cell types
Campbell, J. N., Macosko, E. Z., Fenselau, H., Pers, T. H., Lyubetskaya, A., Tenen, D., Goldman, M., Verstegen, A. M. J., Resch, J. M., McCarroll, S. A., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4495 · Cited by 817 · see citing works
Classification of low quality cells from single-cell RNA-seq data
Ilicic, T., Kim, J. K., Kolodziejczyk, A. A., Bagger, F. O., McCarthy, D. J., Marioni, J. C., & Teichmann, S. A. (2016) · Genome Biology · doi:10.1186/s13059-016-0888-1 · Cited by 693 · see citing works
The Role of Voltage-Gated Sodium Channels in Pain Signaling
Bennett, D. L., Clark, A. J., Huang, J., Waxman, S. G., & Dib-Hajj, S. D. (2019) · Physiological Reviews · doi:10.1152/physrev.00052.2017 · Cited by 730 · 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
Structure, Function, and Pharmacology of Glutamate Receptor Ion Channels
Hansen, K. B., Wollmuth, L. P., Bowie, D., Furukawa, H., Menniti, F. S., Sobolevsky, A. I., Swanson, G. T., Swanger, S. A., Greger, I. H., Nakagawa, T., et al. (2021) · Pharmacological Reviews · doi:10.1124/pharmrev.120.000131 · Cited by 686 · see citing works
Missing data and technical variability in single-cell RNA-sequencing experiments
Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A. (2018) · Biostatistics · doi:10.1093/biostatistics/kxx053 · Cited by 635 · see citing works
Häring, M., Zeisel, A., Hochgerner, H., Rinwa, P., Jakobsson, J. E. T., Lönnerberg, P., La Manno, G., Sharma, N., Borgius, L., Kiehn, O., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0141-1 · Cited by 470 · see citing works
Self-citationCellular Taxonomy of the Mouse Striatum as Revealed by Single-Cell RNA-Seq
Gokce, O., Stanley, G. M., Treutlein, B., Neff, N. F., Camp, J. G., Malenka, R. C., Rothwell, P. E., Fuccillo, M. V., Südhof, T. C., & Quake, S. R. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.06.059 · Cited by 430 · see citing works
Nuclear RNA-seq of single neurons reveals molecular signatures of activation
Lacar, B., Linker, S. B., Jaeger, B. N., Krishnaswami, S. R., Barron, J. J., Kelder, M. J. E., Parylak, S. L., Paquola, A. C. M., Venepally, P., Novotny, M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11022 · Cited by 405 · see citing works
Subchondral bone osteoclasts induce sensory innervation and osteoarthritis pain
Zhu, S., Zhu, J., Zhen, G., Hu, Y., An, S., Li, Y., Zheng, Q., Chen, Z., Yang, Y., Wan, M., et al. (2019) · Journal of Clinical Investigation · doi:10.1172/jci121561 · Cited by 456 · see citing works
Crow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J. (2018) · Nature Communications · doi:10.1038/s41467-018-03282-0 · Cited by 365 · see citing works
Fuzik, J., Zeisel, A., Máté, Z., Calvigioni, D., Yanagawa, Y., Szabó, G., Linnarsson, S., & Harkany, T. (2016) · Nature Biotechnology · doi:10.1038/nbt.3443 · Cited by 386 · see citing works
Self-citationSathyamurthy, A., Johnson, K. R., Matson, K. J. E., Dobrott, C. I., Li, L., Ryba, A. R., Bergman, T. B., Kelly, M. C., Kelley, M. W., & Levine, A. J. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.02.003 · Cited by 370 · see citing works
Spatial Gene-Expression Gradients Underlie Prominent Heterogeneity of CA1 Pyramidal Neurons
Cembrowski, M. S., Bachman, J. L., Wang, L., Sugino, K., Shields, B. C., & Spruston, N. (2016) · Neuron · doi:10.1016/j.neuron.2015.12.013 · Cited by 328 · see citing works
Could a neuroscientist understand a microprocessor?
Jonas, E., & Paul Kording, K. (2016) · doi:10.1101/055624 · Cited by 329 · see citing works
Simultaneous profiling of transcriptome and DNA methylome from a single cell
Hu, Y., Huang, K., An, Q., Du, G., Hu, G., Xue, J., Zhu, X., Wang, C.-Y., Xue, Z., & Fan, G. (2016) · Genome Biology · doi:10.1186/s13059-016-0950-z · Cited by 300 · 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
Self-citationNeural Subtype Specification from Human Pluripotent Stem Cells
Tao, Y., & Zhang, S.-C. (2016) · Cell Stem Cell · doi:10.1016/j.stem.2016.10.015 · Cited by 287 · 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
Self-citationCircuit dissection of the role of somatostatin in itch and pain
Huang, J., Polgár, E., Solinski, H. J., Mishra, S. K., Tseng, P.-Y., Iwagaki, N., Boyle, K. A., Dickie, A. C., Kriegbaum, M. C., Wildner, H., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0119-z · Cited by 257 · see citing works
The Itch–Scratch Cycle: A Neuroimmune Perspective
Mack, M. R., & Kim, B. S. (2018) · Trends in Immunology · doi:10.1016/j.it.2018.10.001 · Cited by 255 · see citing works
Targeting peripheral somatosensory neurons to improve tactile-related phenotypes in ASD models
Orefice, L. L., Mosko, J. R., Morency, D. T., Wells, M. F., Tasnim, A., Mozeika, S. M., Ye, M., Chirila, A. M., Emanuel, A. J., Rankin, G., et al. (2019) · Cell · doi:10.1016/j.cell.2019.07.024 · Cited by 247 · 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
A rapidly acting glutamatergic ARC→PVH satiety circuit postsynaptically regulated by α-MSH
Fenselau, H., Campbell, J. N., Verstegen, A. M. J., Madara, J. C., Xu, J., Shah, B. P., Resch, J. M., Yang, Z., Mandelblat-Cerf, Y., Livneh, Y., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4442 · Cited by 233 · see citing works
Single-cell transcriptome sequencing: recent advances and remaining challenges
Liu, S., & Trapnell, C. (2016) · F1000Research · doi:10.12688/f1000research.7223.1 · Cited by 230 · see citing works
Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
Van den Berge, K., Perraudeau, F., Soneson, C., Love, M. I., Risso, D., Vert, J.-P., Robinson, M. D., Dudoit, S., & Clement, L. (2018) · Genome Biology · doi:10.1186/s13059-018-1406-4 · Cited by 228 · see citing works
Integrative neurocircuits that control metabolism and food intake
Brüning, J. C., & Fenselau, H. (2023) · Science · doi:10.1126/science.abl7398 · Cited by 214 · see citing works
Single-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
Using neural networks for reducing the dimensions of single-cell RNA-Seq data
Lin, C., Jain, S., Kim, H., & Bar-Joseph, Z. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx681 · Cited by 197 · see citing works
“PCA has been used extensively for clustering single cells (1,10,11).”
MethodEgerod, K. L., Petersen, N., Timshel, P. N., Rekling, J. C., Wang, Y., Liu, Q., Schwartz, T. W., & Gautron, L. (2018) · Molecular Metabolism · doi:10.1016/j.molmet.2018.03.016 · Cited by 191 · see citing works
Mechanical Allodynia Circuitry in the Dorsal Horn Is Defined by the Nature of the Injury
Peirs, C., Williams, S.-P. G., Zhao, X., Arokiaraj, C. M., Ferreira, D. W., Noh, M.-C., Smith, K. M., Halder, P., Corrigan, K. A., Gedeon, J. Y., et al. (2021) · Neuron · doi:10.1016/j.neuron.2020.10.027 · Cited by 190 · see citing works
Cadwell, 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
Independent origins of neurons and synapses: insights from ctenophores
Moroz, L. L., & Kohn, A. B. (2016) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2015.0041 · Cited by 171 · see citing works
Peripheral itch sensitization in atopic dermatitis
Tominaga, M., & Takamori, K. (2022) · Allergology International · doi:10.1016/j.alit.2022.04.003 · Cited by 160 · see citing works
Beyond the brain: Optogenetic control in the spinal cord and peripheral nervous system
Montgomery, K. L., Iyer, S. M., Christensen, A. J., Deisseroth, K., & Delp, S. L. (2016) · Science Translational Medicine · doi:10.1126/scitranslmed.aad7577 · Cited by 147 · see citing works
Fast and precise single-cell data analysis using a hierarchical autoencoder
Tran, D., Nguyen, H., Tran, B., La Vecchia, C., Luu, H. N., & Nguyen, T. (2021) · Nature Communications · doi:10.1038/s41467-021-21312-2 · Cited by 145 · see citing works
The cellular and molecular basis of somatosensory neuron development
Meltzer, S., Santiago, C., Sharma, N., & Ginty, D. D. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.004 · Cited by 149 · see citing works
Identification of a Spinal Circuit for Mechanical and Persistent Spontaneous Itch
Pan, H., Fatima, M., Li, A., Lee, H., Cai, W., Horwitz, L., Hor, C. C., Zaher, N., Cin, M., Slade, H., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.06.016 · Cited by 140 · 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
An active vesicle priming machinery suppresses axon regeneration upon adult CNS injury
Hilton, B. J., Husch, A., Schaffran, B., Lin, T.-C., Burnside, E. R., Dupraz, S., Schelski, M., Kim, J., Müller, J. A., Schoch, S., et al. (2022) · Neuron · doi:10.1016/j.neuron.2021.10.007 · Cited by 116 · see citing works
Protease-activated receptors in health and disease
Peach, C. J., Edgington-Mitchell, L. E., Bunnett, N. W., & Schmidt, B. L. (2023) · Physiological Reviews · doi:10.1152/physrev.00044.2021 · Cited by 116 · see citing works
A Functional Topographic Map for Spinal Sensorimotor Reflexes
Gatto, G., Bourane, S., Ren, X., Di Costanzo, S., Fenton, P. K., Halder, P., Seal, R. P., & Goulding, M. D. (2021) · Neuron · doi:10.1016/j.neuron.2020.10.003 · Cited by 92 · 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
Detecting heterogeneity in single-cell RNA-Seq data by non-negative matrix factorization
Zhu, X., Ching, T., Pan, X., Weissman, S. M., & Garmire, L. (2017) · PeerJ · doi:10.7717/peerj.2888 · Cited by 86 · see citing works
Ma, Y., Sun, S., Shang, X., Keller, E. T., Chen, M., & Zhou, X. (2020) · Nature Communications · doi:10.1038/s41467-020-15298-6 · Cited by 81 · see citing works
Wu, H., Petitpré, C., Fontanet, P., Sharma, A., Bellardita, C., Quadros, R. M., Jannig, P. R., Wang, Y., Heimel, J. A., Cheung, K. K. Y., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21173-9 · Cited by 86 · see citing works
BTR: training asynchronous Boolean models using single-cell expression data
Lim, C. Y., Wang, H., Woodhouse, S., Piterman, N., Wernisch, L., Fisher, J., & Göttgens, B. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1235-y · Cited by 81 · see citing works
Geometry of the Gene Expression Space of Individual Cells
Korem, Y., Szekely, P., Hart, Y., Sheftel, H., Hausser, J., Mayo, A., Rothenberg, M. E., Kalisky, T., & Alon, U. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004224 · Cited by 85 · see citing works
Bayesian sparse regression analysis documents the diversity of spinal inhibitory interneurons
Gabitto, M. I., Pakman, A., Bikoff, J. B., Abbott, L. F., Jessell, T. M., & Paninski, L. (2016) · Cell · doi:10.1016/j.cell.2016.01.026 · Cited by 73 · see citing works
Jenkins, B. A., & Lumpkin, E. A. (2017) · Development · doi:10.1242/dev.120402 · Cited by 79 · see citing works
Neuronal diversity in the somatosensory system: bridging the gap between cell type and function
Gatto, G., Smith, K. M., Ross, S. E., & Goulding, M. (2019) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2019.03.002 · Cited by 74 · see citing works
Valid Post-clustering Differential Analysis for Single-Cell RNA-Seq
Zhang, J. M., Kamath, G. M., & Tse, D. N. (2019) · Cell Systems · doi:10.1016/j.cels.2019.07.012 · Cited by 72 · see citing works
Ahn, J., Ohk, K., Won, J., Choi, D.-H., Jung, Y. H., Yang, J. H., Jun, Y., Kim, J.-A., Chung, S., & Lee, S.-H. (2023) · Nature Communications · doi:10.1038/s41467-023-37187-4 · Cited by 65 · see citing works
Calretinin positive neurons form an excitatory amplifier network in the spinal cord dorsal horn
Smith, K. M., Browne, T. J., Davis, O. C., Coyle, A., Boyle, K. A., Watanabe, M., Dickinson, S. A., Iredale, J. A., Gradwell, M. A., Jobling, P., et al. (2019) · eLife · doi:10.7554/elife.49190 · Cited by 57 · see citing works
Missing Data and Technical Variability in Single-Cell RNA-Sequencing Experiments
Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A. (2015) · doi:10.1101/025528 · Cited by 625 · see citing works
Pain in the Ehlers–Danlos syndromes: Mechanisms, models, and challenges
Malfait, F., Colman, M., Vroman, R., De Wandele, I., Rombaut, L., Miller, R. E., Malfait, A., & Syx, D. (2021) · American Journal of Medical Genetics Part C: Seminars in Medical Genetics · doi:10.1002/ajmg.c.31950 · Cited by 41 · see citing works
Spinal somatostatin-positive interneurons transmit chemical itch
Fatima, M., Ren, X., Pan, H., Slade, H. F. E., Asmar, A. J., Xiong, C. M., Shi, A., Xiong, A. E., Wang, L., & Duan, B. (2019) · Pain · doi:10.1097/j.pain.0000000000001499 · Cited by 41 · see citing works
Tamoxifen induces cellular stress in the nervous system by inhibiting cholesterol synthesis
Denk, F., Ramer, L. M., Erskine, E. L. K. S., Nassar, M. A., Bogdanov, Y., Signore, M., Wood, J. N., McMahon, S. B., & Ramer, M. S. (2015) · Acta Neuropathologica Communications · doi:10.1186/s40478-015-0255-6 · Cited by 44 · see citing works
“4c), as well as costaining with markers for the four prominent mouse DRG subpopulations [25] (Fig.”
Gutierrez-Mecinas, M., Polgár, E., Bell, A. M., Herau, M., & Todd, A. J. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1629-x · Cited by 28 · see citing works
Bell, A. M., Gutierrez-Mecinas, M., Stevenson, A., Casas-Benito, A., Wildner, H., West, S. J., Watanabe, M., & Todd, A. J. (2020) · Scientific Reports · doi:10.1038/s41598-020-69711-7 · Cited by 25 · see citing works
Simultaneous Profiling of mRNA Transcriptome and DNA Methylome from a Single Cell
Hu, Y., An, Q., Guo, Y., Zhong, J., Fan, S., Rao, P., Liu, X., Liu, Y., & Fan, G. (2019) · Methods in Molecular Biology · doi:10.1007/978-1-4939-9240-9_21 · Cited by 26 · see citing works
Spinal Inhibitory Ptf1a-Derived Neurons Prevent Self-Generated Itch
Escalante, A., & Klein, R. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108422 · Cited by 17 · see citing works
Neuroimmune mechanisms of type 2 inflammation in the skin and lung
Tamari, M., & Ver Heul, A. M. (2025) · Allergology International · doi:10.1016/j.alit.2025.02.001 · Cited by 17 · see citing works
Incretin receptor agonism rapidly inhibits AgRP neurons to suppress food intake in mice
McMorrow, H. E., Cohen, A. B., Lorch, C. M., Hayes, N. W., Fleps, S. W., Frydman, J. A., Xia, J. L., Samms, R. J., & Beutler, L. R. (2025) · Journal of Clinical Investigation · doi:10.1172/jci186652 · Cited by 12 · see citing works
- Authors
- Usoskin, D., Furlan, A., Islam, S., Abdo, H., Lönnerberg, P., Lou, D., Hjerling-Leffler, J., Haeggström, J., Kharchenko, O., Kharchenko, P. V., Linnarsson, S., & Ernfors, P.
- Affiliations
- Karolinska Institutet (SE)
- Boston Children's Hospital (US)
- Corresponding author
- Linnarsson, Sten
- Ernfors, Patrik
- ORCID
- Venue
- Nature Neuroscience
- Published
- 2014-11-24
- Volume
- 18
- Issue
- 1
- Pages
- 145-153
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2033072655
- Cited by
- 2,136
- References
- 2
- Citations by year
- 2026: 181 · 2025: 168 · 2024: 183 · 2023: 212 · 2022: 206 · 2021: 250 · 2020: 247 · 2019: 186 · 2018: 182 · 2017: 145 · 2016: 106 · 2015: 63 · 2014: 3
- FWCI
- 43.08
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Stiftelsen för Strategisk Forskning
- Vetenskapsrådet
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Neuroscience › Sensory Systems › Ion Channels and Receptors
- Health Sciences › Medicine › Physiology › Pain Mechanisms and Treatments
- Keywords
- sensory neurons, primary afferent neurons, mechanoreceptors, nociceptors, thermosensitive neurons, proprioception, transcriptome, atopic dermatitis, mice
- MeSH terms
- Animals, Behavior, Animal, Female, Inflammation, Male, Mice, Inbred C57BL, Pruritus, Sensory Receptor Cells, Gene Expression, Sequence Analysis, RNA, Cell Size, Mice
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 ›
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