Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Open accessPublished version of a preprint
Abstract
To facilitate scalable profiling of single cells, we developed split-pool ligation-based transcriptome sequencing (SPLiT-seq), a single-cell RNA-seq (scRNA-seq) method that labels the cellular origin of RNA through combinatorial barcoding. SPLiT-seq is compatible with fixed cells or nuclei, allows efficient sample multiplexing, and requires no customized equipment. We used SPLiT-seq to analyze 156,049 single-nucleus transcriptomes from postnatal day 2 and 11 mouse brains and spinal cords. More than 100 cell types were identified, with gene expression patterns corresponding to cellular function, regional specificity, and stage of differentiation. Pseudotime analysis revealed transcriptional programs driving four developmental lineages, providing a snapshot of early postnatal development in the murine central nervous system. SPLiT-seq provides a path toward comprehensive single-cell transcriptomic analysis of other similarly complex multicellular systems.
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?
A method using four rounds of combinatorial barcoding uniquely labelled over 100,000 nuclei in one experiment.
“method, uniquely labelled over 100,000 nuclei in one experiment using four rounds of combinatorial barcoding58”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [58]
SPLiT-seq, a plate-based RNA method, gave unique labels to more than 100,000 nuclei in a single run through four rounds of combinatorial barcoding.
“Split-pool ligation-based transcriptome sequencing (SPLiT-seq), a plate-based transcriptomics method, uniquely labelled over 100,000 nuclei in one experiment using four rounds of combinatorial barcoding58.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [58]
hSpS cluster annotation overall matched previously annotated mouse spinal cord clusters.
“We found that, overall, cluster annotation in hSpS matched previously annotated spinal cord clusters”
From Generation of Functional Human 3D Cortico-Motor Assembloids (2020) · cites this paper as Rosenberg et al., 2018
The merged dataset contains over one hundred thousand cells and nuclei from the first six published studies of the postnatal mouse spinal cord.
“We first created a merged dataset with over one hundred thousand cells and nuclei from the first six published studies of the postnatal mouse spinal cord1–6.”
From A harmonized atlas of mouse spinal cord cell types and their spatial organization (2021) · cites this paper as [4]
The general feature of lower robustness in mid and ventral clusters is consistent with previous observations and likely reflects similar patterns of gene expression amongst mid and ventral clusters.
“This general feature was consistent with previous observations and likely reflects similar patterns of gene expression amongst mid and ventral clusters1,4.”
From A harmonized atlas of mouse spinal cord cell types and their spatial organization (2021) · cites this paper as [4]
A similar trend of overlapping ventral cell types was observed in the neonatal spinal cord.
“We had previously noted differences in the robustness of dorsal and ventral cell type clusters1 and a similar trend of overlapping ventral cell types was observed in the neonatal spinal cord4.”
From A harmonized atlas of mouse spinal cord cell types and their spatial organization (2021) · cites this paper as [4]
Recently published transcriptomic studies showed aging-related downregulation of ribosomal genes in ASC and NSC, and upregulation in MG of aged and diseased brains.
“recently published transcriptomic studies showed an agingrelated downregulation of ribosomal genes in both ASC 14 and NSC 61 , and an upregulation in MG of both aged 62,63 and diseased brains 62,64 .”
From Single-cell transcriptomic profiling of the aging mouse brain (2019) · cites this paper as [14]
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
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
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
(1996) · Cited by 20,179 · see citing works
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · see citing works
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314 · see citing works
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · see citing works
Genome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572 · see citing works
Reversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070 · see citing works
Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., et al. (2016) · Science · doi:10.1126/science.aad0501 · Cited by 4,944 · see citing works
Zhang, Y., Chen, K., Sloan, S. A., Bennett, M. L., Scholze, A. R., O'Keeffe, S., Phatnani, H. P., Guarnieri, P., Caneda, C., Ruderisch, N., et al. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.1860-14.2014 · Cited by 4,875 · see citing works
Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356 · see citing works
Cahoy, J. D., Emery, B., Kaushal, A., Foo, L. C., Zamanian, J. L., Christopherson, K. S., Xing, Y., Lubischer, J. L., Krieg, P. A., Krupenko, S. A., et al. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4178-07.2008 · Cited by 3,129 · 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
Mechanisms and Functional Implications of Adult Neurogenesis
Zhao, C., Deng, W., & Gage, F. H. (2008) · Cell · doi:10.1016/j.cell.2008.01.033 · Cited by 3,007 · see citing works
Identification of a unique TGF-β–dependent molecular and functional signature in microglia
Butovsky, O., Jedrychowski, M. P., Moore, C. S., Cialic, R., Lanser, A. J., Gabriely, G., Koeglsperger, T., Dake, B., Wu, P. M., Doykan, C. E., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3599 · Cited by 2,543 · 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
Accelerating t-SNE using tree-based algorithms
van der Maaten, L. (2014) · Journal of machine learning research · doi:10.5555/2627435.2697068 · Cited by 2,207 · see citing works
Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing
Zheng, C., Zheng, L., Yoo, J.-K., Guo, H., Zhang, Y., Guo, X., Kang, B., Hu, R., Huang, J. Y., Zhang, Q., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.035 · Cited by 2,152 · see citing works
Massively 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
The human brain in numbers: a linearly scaled-up primate brain
Herculano-Houzel, S. (2009) · Frontiers in Human Neuroscience · doi:10.3389/neuro.09.031.2009 · Cited by 1,778 · see citing works
Adult 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
Self-citationThe microglial sensome revealed by direct RNA sequencing
Hickman, S. E., Kingery, N. D., Ohsumi, T. K., Borowsky, M. L., Wang, L.-C., Means, T. K., & El Khoury, J. (2013) · Nature Neuroscience · doi:10.1038/nn.3554 · Cited by 1,511 · 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
Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system
Marques, S., Zeisel, A., Codeluppi, S., van Bruggen, D., Mendanha Falcão, A., Xiao, L., Li, H., Häring, M., Hochgerner, H., Romanov, R. A., et al. (2016) · Science · doi:10.1126/science.aaf6463 · Cited by 1,246 · see citing works
Single-cell messenger RNA sequencing reveals rare intestinal cell types
Grün, D., Lyubimova, A., Kester, L., Wiebrands, K., Basak, O., Sasaki, N., Clevers, H., & van Oudenaarden, A. (2015) · Nature · doi:10.1038/nature14966 · Cited by 1,234 · see citing works
CEL-seq: single-cell RNA-seq by multiplexed linear amplification
Hashimshony, T., Wagner, F., Sher, N., & Yanai, I. (2012) · Cell Reports · doi:10.1016/j.celrep.2012.08.003 · Cited by 1,231 · see citing works
Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells
Shalek, A. K., Satija, R., Adiconis, X., Gertner, R. S., Gaublomme, J. T., Raychowdhury, R., Schwartz, S., Yosef, N., Malboeuf, C., Lu, D., et al. (2013) · Nature · doi:10.1038/nature12172 · Cited by 1,190 · see citing works
Microglia development follows a stepwise program to regulate brain homeostasis
Matcovitch-Natan, O., Winter, D. R., Giladi, A., Vargas Aguilar, S., Spinrad, A., Sarrazin, S., Ben-Yehuda, H., David, E., Zelada González, F., Perrin, P., et al. (2016) · Science · doi:10.1126/science.aad8670 · Cited by 1,180 · 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
Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq
Venteicher, A. S., Tirosh, I., Hebert, C. M., Yizhak, K., Neftel, C., Filbin, M. G., Hovestadt, V., Escalante, L. E., Shaw, M. L., Rodman, C., et al. (2017) · Science · doi:10.1126/science.aai8478 · Cited by 1,011 · 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
Application of a translational profiling approach for the comparative analysis of CNS cell types
Doyle, J. P., Dougherty, J. D., Heiman, M., Schmidt, E. F., Stevens, T. R., Ma, G., Bupp, S., Shrestha, P., Shah, R. D., Doughty, M. L., et al. (2008) · Cell · doi:10.1016/j.cell.2008.10.029 · Cited by 912 · see citing works
Ogawa, M., Miyata, T., Nakajimat, K., Yagyu, K., Seike, M., Ikenaka, K., Yamamoto, H., & Mikoshibat, K. (1995) · Neuron · doi:10.1016/0896-6273(95)90329-1 · Cited by 889 · see citing works
Levison, S. W., & Goldman, J. E. (1993) · Neuron · doi:10.1016/0896-6273(93)90311-e · Cited by 725 · see citing works
Turnover of resident microglia in the normal adult mouse brain
Lawson, L. J., Perry, V. H., & Gordon, S. (1992) · Neuroscience · doi:10.1016/0306-4522(92)90500-2 · Cited by 709 · see citing works
Shah, S., Lubeck, E., Zhou, W., & Cai, L. (2016) · Neuron · doi:10.1016/j.neuron.2016.10.001 · Cited by 671 · see citing works
Satb2 Regulates Callosal Projection Neuron Identity in the Developing Cerebral Cortex
Alcamo, E. A., Chirivella, L., Dautzenberg, M., Dobreva, G., Fariñas, I., Grosschedl, R., & McConnell, S. K. (2008) · Neuron · doi:10.1016/j.neuron.2007.12.012 · Cited by 658 · see citing works
Common markers of proliferation
Whitfield, M. L., George, L. K., Grant, G. D., & Perou, C. M. (2006) · Nature Reviews Cancer · doi:10.1038/nrc1802 · Cited by 650 · see citing works
Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons
Habib, N., Li, Y., Heidenreich, M., Swiech, L., Avraham-Davidi, I., Trombetta, J. J., Hession, C., Zhang, F., & Regev, A. (2016) · Science · doi:10.1126/science.aad7038 · Cited by 601 · see citing works
Dopaminergic neurons inhibit striatal output through non-canonical release of GABA
Tritsch, N. X., Ding, J. B., & Sabatini, B. L. (2012) · Nature · doi:10.1038/nature11466 · Cited by 576 · see citing works
Injury-induced mechanical hypersensitivity requires C-low threshold mechanoreceptors
Seal, R. P., Wang, X., Guan, Y., Raja, S. N., Woodbury, C. J., Basbaum, A. I., & Edwards, R. H. (2009) · Nature · doi:10.1038/nature08505 · Cited by 465 · see citing works
The structural and functional heterogeneity of glutamic acid decarboxylase: A review
Erlander, M. G., & Tobin, A. J. (1991) · Neurochemical Research · doi:10.1007/bf00966084 · Cited by 462 · see citing works
Combinatorial labeling of single cells for gene expression cytometry
Fan, H. C., Fu, G. K., & Fodor, S. P. A. (2015) · Science · doi:10.1126/science.1258367 · Cited by 455 · see citing works
Hipposeq: a comprehensive RNA-seq database of gene expression in hippocampal principal neurons
Cembrowski, M. S., Wang, L., Sugino, K., Shields, B. C., & Spruston, N. (2016) · eLife · doi:10.7554/elife.14997 · Cited by 422 · 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
Moignard, V., Woodhouse, S., Haghverdi, L., Lilly, A. J., Tanaka, Y., Wilkinson, A. C., Buettner, F., Macaulay, I. C., Jawaid, W., Diamanti, E., et al. (2015) · Nature Biotechnology · doi:10.1038/nbt.3154 · Cited by 380 · see citing works
A High-Resolution Spatiotemporal Atlas of Gene Expression of the Developing Mouse Brain
Thompson, C. L., Ng, L., Menon, V., Martı́nez, S., Lee, C., Glattfelder, K. J., Sunkin, S. M., Henry, A. M., Lau, C., Dang, C., et al. (2014) · Neuron · doi:10.1016/j.neuron.2014.05.033 · Cited by 324 · see citing works
Control of the Reversibility of Cellular Quiescence by the Transcriptional Repressor HES1
Sang, L., Coller, H. A., & Roberts, J. M. (2008) · Science · doi:10.1126/science.1155998 · Cited by 313 · see citing works
The level of sonic hedgehog signaling regulates the complexity of cerebellar foliation
Corrales, J. D., Blaess, S., Mahoney, E. M., & Joyner, A. L. (2006) · Development · doi:10.1242/dev.02351 · Cited by 302 · see citing works
Sequencing thousands of single-cell genomes with combinatorial indexing
Vitak, S. A., Torkenczy, K. A., Rosenkrantz, J. L., Fields, A. J., Christiansen, L., Wong, M. H., Carbone, L., Steemers, F. J., & Adey, A. (2017) · Nature Methods · doi:10.1038/nmeth.4154 · Cited by 294 · see citing works
A Transcriptomic Atlas of Mouse Neocortical Layers
Belgard, T. G., Marques, A. C., Oliver, P. L., Abaan, H. Ö., Sirey, T., Hoerder‐Suabedissen, A., García‐Moreno, F., Molnár, Z., Margulies, E. H., & Ponting, C. P. (2011) · Neuron · doi:10.1016/j.neuron.2011.06.039 · Cited by 273 · see citing works
Cerebellum morphogenesis: the foliation pattern is orchestrated by multi-cellular anchoring centers
Sudarov, A., & Joyner, A. L. (2007) · Neural Development · doi:10.1186/1749-8104-2-26 · Cited by 255 · see citing works
Starcode: sequence clustering based on all-pairs search
Zorita, E. V., Cuscó, P., & Filion, G. J. P. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv053 · Cited by 241 · see citing works
Lavado, A., Lagutin, O. V., Chow, L. M. L., Baker, S. J., & Oliver, G. (2010) · PLoS Biology · doi:10.1371/journal.pbio.1000460 · Cited by 238 · see citing works
Analysis of the brain mural cell transcriptome
He, L., Vanlandewijck, M., Raschperger, E., Andaloussi Mäe, M., Jung, B., Lebouvier, T., Ando, K., Hofmann, J., Keller, A., & Betsholtz, C. (2016) · Scientific Reports · doi:10.1038/srep35108 · Cited by 233 · see citing works
Fixed single-cell transcriptomic characterization of human radial glial diversity
Thomsen, E. R., Mich, J. K., Yao, Z., Hodge, R. D., Doyle, A. M., Jang, S., Shehata, S. I., Nelson, A. M., Shapovalova, N. V., Levi, B. P., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3629 · Cited by 227 · see citing works
Self-citationPrdm16 promotes stem cell maintenance in multiple tissues, partly by regulating oxidative stress
Chuikov, S., Levi, B. P., Smith, M. L., & Morrison, S. J. (2010) · Nature Cell Biology · doi:10.1038/ncb2101 · Cited by 224 · see citing works
Bergmann Glial AMPA Receptors Are Required for Fine Motor Coordination
Saab, A. S., Neumeyer, A., Jahn, H. M., Cupido, A., Šimek, A. A. M., Boele, H., Scheller, A., Meur, K. L., Götz, M., Monyer, H., et al. (2012) · Science · doi:10.1126/science.1221140 · Cited by 202 · see citing works
Layer Specification of Transplanted Interneurons in Developing Mouse Neocortex
Valcanis, H., & Tan, S.-S. (2003) · The Journal of Neuroscience · doi:10.1523/jneurosci.23-12-05113.2003 · Cited by 175 · see citing works
Heimberg, G., Bhatnagar, R., El-Samad, H., & Thomson, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.04.001 · Cited by 156 · see citing works
Iwano, T., Masuda, A., Kiyonari, H., Enomoto, H., & Matsuzaki, F. (2012) · Development · doi:10.1242/dev.080002 · Cited by 155 · see citing works
Kamphuis, W., Kooijman, L., Orre, M., Stassen, O., Pekny, M., & Hol, E. M. (2015) · Glia · doi:10.1002/glia.22800 · Cited by 155 · see citing works
Alifragis, P., Liapi, A., & Parnavelas, J. G. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.1245-04.2004 · Cited by 143 · see citing works
Weisheit, G., Gliem, M., Endl, E., Pfeffer, P., Busslinger, M., & Schilling, K. (2006) · European Journal of Neuroscience · doi:10.1111/j.1460-9568.2006.04915.x · Cited by 142 · see citing works
Differentiation of ES cells into cerebellar neurons
Salero, E., & Hatten, M. E. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0610879104 · Cited by 141 · see citing works
Enjin, A., Bernhardt, N. R., Nakanishi, S. T., Vallstedt, A., Gezelius, H., Memic, F., Lind, M., Hjalt, T. Å. H., Tourtellotte, W. G., Bruder, C. E. G., et al. (2010) · The Journal of Comparative Neurology · doi:10.1002/cne.22332 · Cited by 130 · see citing works
A Single-Cell Roadmap of Lineage Bifurcation in Human ESC Models of Embryonic Brain Development
Yao, Z., Mich, J. K., Ku, S., Menon, V., Krostag, A.-R., Martinez, R. A., Furchtgott, L., Mulholland, H., Bort, S. J., Fuqua, M. A., et al. (2016) · Cell stem cell · doi:10.1016/j.stem.2016.09.011 · Cited by 127 · see citing works
Self-citationSchilling, K., Oberdick, J., Rossi, F., & Baader, S. L. (2008) · Histochemistry and Cell Biology · doi:10.1007/s00418-008-0483-y · Cited by 121 · see citing works
Gamma motor neurons survive and exacerbate alpha motor neuron degeneration in ALS
Lalancette–Hébert, M., Sharma, A., Lyashchenko, A. K., & Shneider, N. A. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1605210113 · Cited by 113 · see citing works
Zbtb20 is essential for the specification of CA1 field identity in the developing hippocampus
Xie, Z., Ma, X., Ji, W., Zhou, G., Lu, Y., Xiang, Z., Wang, Y. X., Zhang, L., Hu, Y., Ding, Y., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0912315107 · Cited by 112 · see citing works
Moriyama, M., Fukuhara, T., Britschgi, M., He, Y., Narasimhan, R., Villeda, S., Molina, H. D., Huber, B. T., Holers, M., & Wyss‐Coray, T. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.3617-10.2011 · Cited by 93 · see citing works
The late and dual origin of cerebrospinal fluid-contacting neurons in the mouse spinal cord
Petracca, Y. L., Sartoretti, M. M., Di Bella, D. J., Marin-Burgin, A., Carcagno, A. L., Schinder, A. F., & Lanuza, G. M. (2016) · Development · doi:10.1242/dev.129254 · Cited by 90 · see citing works
Altman, J., & Bayer, S. A. (1987) · The Journal of Comparative Neurology · doi:10.1002/cne.902570402 · Cited by 88 · see citing works
Buxbaum, J. D., Georgieva, L., Young, J. J., Plescia, C., Kajiwara, Y., Jiang, Y., Moskvina, V, Norton, N., Peirce, T., Williams, H., et al. (2008) · Molecular Psychiatry · doi:10.1038/sj.mp.4001991 · Cited by 86 · see citing works
Structure and Cellular Distribution of Mouse Brain Testican
Bonnet, F., Périn, J.-P., Charbonnier, F., et al. (1996) · Journal of Biological Chemistry · doi:10.1074/jbc.271.8.4373 · Cited by 80 · see citing works
Nielsen, J. V., Blom, J. B., Noraberg, J., & Jensen, N. A. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp261 · Cited by 67 · see citing works
Molecular anatomy of the thalamic complex and the underlying transcription factors
Nagalski, A., Puelles, L., Dąbrowski, M., Węgierski, T., Kuźnicki, J., & Wisniewska, M. B. (2015) · Brain Structure and Function · doi:10.1007/s00429-015-1052-5 · Cited by 64 · see citing works
Wisniewska, M. B., Misztal, K., Michowski, W., Szczot, M., Purta, E., Lesniak, W., Klejman, M. E., Dabrowski, M., Filipkowski, R. K., Nagalski, A., et al. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.1425-09.2010 · Cited by 64 · see citing works
Foxp2 regulates neuronal differentiation and neuronal subtype specification
Chiu, Y.-C., Li, M., Liu, Y., Ding, J., Yu, J., & Wang, T. (2014) · Developmental Neurobiology · doi:10.1002/dneu.22166 · Cited by 61 · see citing works
p73 and Reelin in Cajal-Retzius Cells of the Developing Human Hippocampal Formation
Ábrahám, H. (2004) · Cerebral Cortex · doi:10.1093/cercor/bhh010 · Cited by 51 · see citing works
Ehmsen, J. T., Liu, Y. U., Wang, Y., Paladugu, N., Johnson, A. E., Rothstein, J. D., du Lac, S., Mattson, M. P., & Höke, A. (2016) · Scientific Reports · doi:10.1038/srep35592 · Cited by 51 · see citing works
Honoré, A., Corre, S. L., Derambure, C., et al. (2011) · Glia · doi:10.1002/glia.22274 · Cited by 50 · see citing works
Afsari, Z., Renno, W. M., & Abd‐El‐Basset, E. M. (2008) · The Anatomical Record · doi:10.1002/ar.20678 · Cited by 45 · see citing works
VGLUTs in Peripheral Neurons and the Spinal Cord: Time for a Review
Brumovsky, P. R. (2013) · ISRN Neurology · doi:10.1155/2013/829753 · Cited by 43 · see citing works
Nakashima, K., Umeshima, H., & Kengaku, M. (2015) · Developmental Dynamics · doi:10.1002/dvdy.24276 · Cited by 35 · see citing works
Knight, H. M., WALKER, R. E., James, R., Porteous, D. J., Muir, W. J., Blackwood, D., & Pickard, B. S. (2011) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.31248 · Cited by 33 · see citing works
AP-2δ Is a Crucial Transcriptional Regulator of the Posterior Midbrain
Hesse, K., Vaupel, K., Kurt, S., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0023483 · Cited by 28 · see citing works
Age-dependent failure of axon regeneration in organotypic culture of gerbil auditory midbrain
Hafidi, A., Lanjun, G., & Sanes, D. H. (1999) · Journal of Neurobiology · doi:10.1002/(sici)1097-4695(19991105)41:2 · Cited by 26 · see citing works
Identification of a set of genes showing regionally enriched expression in the mouse brain
D’Souza, C. A., Chopra, V. S., Varhol, R., Xie, Y., Bohacec, S., Zhao, Y., Lee, L. L. C., Bilenky, M., Portales-Casamar, É., He, A., et al. (2008) · BMC Neuroscience · doi:10.1186/1471-2202-9-66 · Cited by 25 · see citing works
Song, H., Lee, B., Pyun, D., et al. (2014) · Developmental Biology · doi:10.1016/j.ydbio.2014.12.003 · Cited by 24 · see citing works
Dynamic expression of Dab2 in the mouse embryonic central nervous system
Cheung, K., Mok, S. C., Rezaie, P., & Chan, W. Y. (2008) · BMC Developmental Biology · doi:10.1186/1471-213x-8-76 · Cited by 24 · see citing works
Miyashita, T., Wintzer, M. E., Kurotani, T., Konishi, T., Ichinohe, N., & Rockland, K. S. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp093 · Cited by 21 · see citing works
Murthy, M., Bocking, S. M., Verginelli, F., et al. (2014) · Gene · doi:10.1016/j.gene.2014.02.038 · Cited by 17 · see citing works
Yang, M. J., Sim, S., Jeon, J. H., Jeong, E., Kim, H.-C., Park, Y.-J., & Kim, I. (2013) · PLoS ONE · doi:10.1371/journal.pone.0059673 · Cited by 16 · see citing works
Chang, J. C., Leung, M., Gokozan, H. N., Gygli, P. E., Catacutan, F. P., Czeisler, C. M., & Otero, J. J. (2015) · Journal of Neuropathology & Experimental Neurology · doi:10.1097/nen.0000000000000171 · Cited by 16 · see citing works
RNA-seq analysis of developing olfactory bulb projection neurons
Kawasawa, Y. I., Salzberg, A. C., Li, M., Šestan, N., Greer, C. A., & Imamura, F. (2016) · Molecular and Cellular Neuroscience · doi:10.1016/j.mcn.2016.03.009 · Cited by 16 · see citing works
Review: Could neurotransmitters influence neurogenesis and neurorepair after stroke?
Sánchez-Mendoza, E. H., Bellver‐Landete, V., Merino, J. J., González, M. P., Martínez‐Murillo, R., & Oset‐Gasque, M. J. (2013) · Neuropathology and Applied Neurobiology · doi:10.1111/nan.12082 · Cited by 14 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
The single-cell transcriptional landscape of mammalian organogenesis
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490 · see citing works
McGinnis, C. S., Murrow, L. M., & Gartner, Z. J. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.003 · Cited by 4,521 · see citing works
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758 · see citing works
Molecular diversity and specializations among the cells of the adult mouse brain
Saunders, A., Macosko, E. Z., Wysoker, A., Goldman, M., Krienen, F. M., de Rivera, H., Bien, E., Baum, M., Bortolin, L., Wang, S., et al. (2018) · Cell · doi:10.1016/j.cell.2018.07.028 · Cited by 1,847 · see citing works
“High-throughput single-cell RNA sequencing (scRNA-seq) now enables RNA profiling in thousands of individual cells in complex tissue (Han et al., 2018; Klein et al., 2015; Macosko et al., 2015; Rosenberg et al., 2018; Zheng et al., 2017).”
BackgroundEleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327 · see citing works
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460 · see citing works
Benchmarking atlas-level data integration in single-cell genomics
Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438 · see citing works
Single-Cell Multi-omic Integration Compares and Contrasts Features of Brain Cell Identity
Welch, J. D., Kozareva, V., Ferreira, A., Vanderburg, C., Martin, C., & Macosko, E. Z. (2019) · Cell · doi:10.1016/j.cell.2019.05.006 · Cited by 1,193 · see citing works
Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin
Ma, S., Zhang, B., LaFave, L. M., Earl, A. S., Chiang, Z., Hu, Y., Ding, J., Brack, A., Kartha, V. K., Tay, T., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.056 · Cited by 1,146 · see citing works
A benchmark of batch-effect correction methods for single-cell RNA sequencing data
Tran, H. T. N., Ang, K. S., Chevrier, M., Zhang, X., Lee, N. Y. S., Goh, M., & Chen, J. (2020) · Genome Biology · doi:10.1186/s13059-019-1850-9 · Cited by 1,113 · see citing works
The technological landscape and applications of single-cell multi-omics
Baysoy, A., Bai, Z., Satija, R., & Fan, R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00615-w · Cited by 1,037 · see citing works
Systematic comparison of single-cell and single-nucleus RNA-sequencing methods
Ding, J., Adiconis, X., Simmons, S. K., Kowalczyk, M. S., Hession, C. C., Marjanovic, N. D., Hughes, T. K., Wadsworth, M. H., Burks, T., Nguyen, L. T., et al. (2020) · Nature Biotechnology · doi:10.1038/s41587-020-0465-8 · Cited by 871 · see citing works
High-throughput sequencing of the transcriptome and chromatin accessibility in the same cell
Chen, S., Lake, B. B., & Zhang, K. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0290-0 · Cited by 810 · see citing works
MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices
McGinnis, C. S., Patterson, D. M., Winkler, J., Conrad, D. N., Hein, M. Y., Srivastava, V., Hu, J. L., Murrow, L. M., Weissman, J. S., Werb, Z., et al. (2019) · Nature Methods · doi:10.1038/s41592-019-0433-8 · Cited by 643 · see citing works
Single-cell transcriptomic profiling of the aging mouse brain
Ximerakis, M., Lipnick, S. L., Innes, B. T., Simmons, S. K., Adiconis, X., Dionne, D., Mayweather, B. A., Nguyen, L., Niziolek, Z., Ozek, C., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0491-3 · Cited by 736 · see citing works
Tumour heterogeneity and metastasis at single-cell resolution
Lawson, D. A., Kessenbrock, K., Davis, R. T., Pervolarakis, N., & Werb, Z. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0236-7 · Cited by 606 · see citing works
Mapping single-cell data to reference atlases by transfer learning
Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Wagenstetter, M., Avsec, Ž., Gayoso, A., Yosef, N., Interlandi, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01001-7 · Cited by 602 · see citing works
Generation of Functional Human 3D Cortico-Motor Assembloids
Andersen, J., Revah, O., Miura, Y., Thom, N., Amin, N. D., Kelley, K. W., Singh, M., Chen, X., Thete, M. V., Walczak, E. M., et al. (2020) · Cell · doi:10.1016/j.cell.2020.11.017 · Cited by 593 · see citing works
Molecular architecture of the developing mouse brain
La Manno, G., Siletti, K., Furlan, A., Gyllborg, D., Vinsland, E., Mossi Albiach, A., Mattsson Langseth, C., Khven, I., Lederer, A. R., Dratva, L. M., et al. (2021) · Nature · doi:10.1038/s41586-021-03775-x · Cited by 563 · see citing works
Supervised classification enables rapid annotation of cell atlases
Pliner, H. A., Shendure, J., & Trapnell, C. (2019) · doi:10.1101/538652 · Cited by 498 · see citing works
Single-cell transcriptomics reveals cell-type-specific diversification in human heart failure
Koenig, A. L., Shchukina, I., Amrute, J., Andhey, P. S., Zaitsev, K., Lai, L., Bajpai, G., Bredemeyer, A., Smith, G., Jones, C., et al. (2022) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00028-6 · Cited by 482 · see citing works
Computational principles and challenges in single-cell data integration
Argelaguet, R., Cuomo, A. S. E., Stegle, O., & Marioni, J. C. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00895-7 · Cited by 472 · see citing works
Comprehensive analysis of single cell ATAC-seq data with SnapATAC
Fang, R., Preissl, S., Li, Y., Hou, X., Lucero, J., Wang, X., Motamedi, A., Shiau, A. K., Zhou, X., Xie, F., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21583-9 · Cited by 455 · see citing works
Spatiotemporal dynamics of molecular pathology in amyotrophic lateral sclerosis
Maniatis, S., Äijö, T., Vickovic, S., Braine, C., Kang, K., Mollbrink, A., Fagegaltier, D., Andrusivová, Ž., Saarenpää, S., Saiz-Castro, G., et al. (2019) · Science · doi:10.1126/science.aav9776 · Cited by 429 · see citing works
Delile, J., Rayon, T., Melchionda, M., Edwards, A., Briscoe, J., & Sagner, A. (2019) · Development · doi:10.1242/dev.173807 · Cited by 356 · see citing works
An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome
Zhu, C., Yu, M., Huang, H., Juric, I., Abnousi, A., Hu, R., Lucero, J., Behrens, M. M., Hu, M., & Ren, B. (2019) · Nature Structural & Molecular Biology · doi:10.1038/s41594-019-0323-x · Cited by 344 · see citing works
Single-Cell Transcriptomics Meets Lineage Tracing
Kester, L., & van Oudenaarden, A. (2018) · Cell Stem Cell · doi:10.1016/j.stem.2018.04.014 · Cited by 375 · see citing works
Microbial single-cell RNA sequencing by split-pool barcoding
Kuchina, A., Brettner, L. M., Paleologu, L., Roco, C. M., Rosenberg, A. B., Carignano, A., Kibler, R., Hirano, M., DePaolo, R. W., & Seelig, G. (2021) · Science · doi:10.1126/science.aba5257 · Cited by 340 · see citing works
Self-citationInterpretable dimensionality reduction of single cell transcriptome data with deep generative models
Ding, J., Condon, A., & Shah, S. P. (2018) · Nature Communications · doi:10.1038/s41467-018-04368-5 · Cited by 337 · 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
Singh, M., Al-Eryani, G., Carswell, S., Ferguson, J. M., Blackburn, J., Barton, K., Roden, D., Luciani, F., Giang Phan, T., Junankar, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11049-4 · Cited by 308 · see citing works
A new era in functional genomics screens
Przybyla, L., & Gilbert, L. A. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-021-00409-w · Cited by 281 · see citing works
Ren, J., Isakova, A., Friedmann, D., Zeng, J., Grutzner, S. M., Pun, A., Zhao, G. Q., Kolluru, S. S., Wang, R., Lin, R., et al. (2019) · eLife · doi:10.7554/elife.49424 · Cited by 304 · see citing works
Joint profiling of histone modifications and transcriptome in single cells from mouse brain
Zhu, C., Zhang, Y., Li, Y. E., Lucero, J., Behrens, M. M., & Ren, B. (2021) · Nature Methods · doi:10.1038/s41592-021-01060-3 · Cited by 301 · see citing works
Challenges in measuring and understanding biological noise
Eling, N., Morgan, M. D., & Marioni, J. C. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0130-6 · Cited by 247 · see citing works
Targeted Perturb-seq enables genome-scale genetic screens in single cells
Schraivogel, D., Gschwind, A. R., Milbank, J. H., Leonce, D. R., Jakob, P., Mathur, L., Korbel, J. O., Merten, C. A., Velten, L., & Steinmetz, L. M. (2020) · Nature Methods · doi:10.1038/s41592-020-0837-5 · Cited by 245 · see citing works
A Common Embryonic Origin of Stem Cells Drives Developmental and Adult Neurogenesis
Berg, D. A., Su, Y., Jimenez-Cyrus, D., Patel, A., Huang, N., Morizet, D., Lee, S., Shah, R., Ringeling, F. R., Jain, R., et al. (2019) · Cell · doi:10.1016/j.cell.2019.02.010 · Cited by 267 · see citing works
Cell type prioritization in single-cell data
Skinnider, M. A., Squair, J. W., Kathe, C., Anderson, M. A., Gautier, M., Matson, K. J. E., Milano, M., Hutson, T. H., Barraud, Q., Phillips, A. A., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0605-1 · Cited by 274 · 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
A harmonized atlas of mouse spinal cord cell types and their spatial organization
Russ, D. E., Cross, R. B. P., Li, L., Koch, S. C., Matson, K. J. E., Yadav, A., Alkaslasi, M. R., Lee, D. I., Le Pichon, C. E., Menon, V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25125-1 · Cited by 255 · see citing works
Blum, J. A., Klemm, S., Shadrach, J. L., Guttenplan, K. A., Nakayama, L., Kathiria, A., Hoang, P. T., Gautier, O., Kaltschmidt, J. A., Greenleaf, W. J., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00795-0 · Cited by 210 · see citing works
Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies
Lafzi, A., Moutinho, C., Picelli, S., & Heyn, H. (2018) · Nature Protocols · doi:10.1038/s41596-018-0073-y · Cited by 218 · see citing works
Cell fixation and preservation for droplet-based single-cell transcriptomics
Alles, J., Karaiskos, N., Praktiknjo, S. D., Grosswendt, S., Wahle, P., Ruffault, P.-L., Ayoub, S., Schreyer, L., Boltengagen, A., Birchmeier, C., et al. (2017) · BMC Biology · doi:10.1186/s12915-017-0383-5 · Cited by 218 · see citing works
Transformer for one stop interpretable cell type annotation
Chen, J., Xu, H., Tao, W., Chen, Z., Zhao, Y., & Han, J.-D. J. (2023) · Nature Communications · doi:10.1038/s41467-023-35923-4 · Cited by 189 · see citing works
Absence of microglia promotes diverse pathologies and early lethality in Alzheimer’s disease mice
Kiani Shabestari, S., Morabito, S., Danhash, E. P., McQuade, A., Sanchez, J. R., Miyoshi, E., Chadarevian, J. P., Claes, C., Coburn, M. A., Hasselmann, J., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110961 · Cited by 167 · see citing works
Alkaslasi, M. R., Piccus, Z. E., Hareendran, S., Silberberg, H., Chen, L., Zhang, Y., Petros, T. J., & Le Pichon, C. E. (2021) · Nature Communications · doi:10.1038/s41467-021-22691-2 · Cited by 163 · see citing works
The evolving concept of cell identity in the single cell era
Morris, S. A. (2019) · Development · doi:10.1242/dev.169748 · Cited by 154 · see citing works
Carter, B., Ku, W. L., Kang, J. Y., Hu, G., Perrie, J., Tang, Q., & Zhao, K. (2019) · Nature Communications · doi:10.1038/s41467-019-11559-1 · Cited by 148 · see citing works
Han, B., Zhou, S., Zhang, Y., Chen, S., Xi, W., Liu, C., Zhou, X., Yuan, M., Yu, X., Li, L., et al. (2024) · Science Translational Medicine · doi:10.1126/scitranslmed.adg1323 · Cited by 143 · see citing works
Joint single-cell measurements of nuclear proteins and RNA in vivo
Chung, H., Parkhurst, C. N., Magee, E. M., Phillips, D., Habibi, E., Chen, F., Yeung, B. Z., Waldman, J., Artis, D., & Regev, A. (2021) · Nature Methods · doi:10.1038/s41592-021-01278-1 · Cited by 144 · see citing works
A scalable platform for the development of cell-type-specific viral drivers
Hrvatin, S., Tzeng, C. P., Nagy, M. A., Stroud, H., Koutsioumpa, C., Wilcox, O. F., Assad, E. G., Green, J., Harvey, C. D., Griffith, E. C., et al. (2019) · eLife · doi:10.7554/elife.48089 · Cited by 118 · see citing works
scMatch: a single-cell gene expression profile annotation tool using reference datasets
Hou, R., Denisenko, E., & Forrest, A. R. R. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz292 · Cited by 130 · see citing works
Resolving organoid brain region identities by mapping single-cell genomic data to reference atlases
Fleck, J. S., Sanchís-Calleja, F., He, Z., Santel, M., Boyle, M. J., Camp, J. G., & Treutlein, B. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2021.02.015 · Cited by 118 · see citing works
Determining sequencing depth in a single-cell RNA-seq experiment
Zhang, M. J., Ntranos, V., & Tse, D. (2020) · Nature Communications · doi:10.1038/s41467-020-14482-y · Cited by 112 · see citing works
Single-cell transcriptomics for the assessment of cardiac disease
Miranda, A. M. A., Janbandhu, V., Maatz, H., Kanemaru, K., Cranley, J., Teichmann, S. A., Hübner, N., Schneider, M. D., Harvey, R. P., & Noseda, M. (2023) · Nature Reviews Cardiology · doi:10.1038/s41569-022-00805-7 · Cited by 117 · see citing works
Gerbino, V., Kaunga, E., Ye, J., Canzio, D., O’Keeffe, S., Rudnick, N. D., Guarnieri, P., Lutz, C. M., & Maniatis, T. (2020) · Neuron · doi:10.1016/j.neuron.2020.03.005 · Cited by 102 · see citing works
From tissues to cell types and back: single-cell gene expression analysis of tissue architecture
Chen, X., Teichmann, S. A., & Meyer, K. B. (2018) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-080917-013452 · Cited by 112 · 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
Self-citationInto the multiverse: advances in single-cell multiomic profiling
Ogbeide, S., Giannese, F., Mincarelli, L., & Macaulay, I. C. (2022) · Trends in Genetics · doi:10.1016/j.tig.2022.03.015 · Cited by 82 · see citing works
Network modeling of single-cell omics data: challenges, opportunities, and progresses
Blencowe, M., Arneson, D., Ding, J., Chen, Y.-W., Saleem, Z., & Yang, X. (2019) · Emerging Topics in Life Sciences · doi:10.1042/etls20180176 · Cited by 73 · see citing works
Spinal Interneurons as Gatekeepers to Neuroplasticity after Injury or Disease
Zholudeva, L. V., Abraira, V. E., Satkunendrarajah, K., McDevitt, T. C., Goulding, M. D., Magnuson, D. S. K., & Lane, M. A. (2021) · The Journal of Neuroscience · doi:10.1523/jneurosci.1654-20.2020 · Cited by 74 · see citing works
Trellis tree-based analysis reveals stromal regulation of patient-derived organoid drug responses
Ramos Zapatero, M., Tong, A., Opzoomer, J. W., O’Sullivan, R., Cardoso Rodriguez, F., Sufi, J., Vlckova, P., Nattress, C., Qin, X., Claus, J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.11.005 · Cited by 72 · see citing works
Deterministic scRNA-seq captures variation in intestinal crypt and organoid composition
Bues, J., Biočanin, M., Pezoldt, J., Dainese, R., Chrisnandy, A., Rezakhani, S., Saelens, W., Gardeux, V., Gupta, R., Sarkis, R., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01391-1 · Cited by 71 · see citing works
Exploring single cells in space and time during tissue development, homeostasis and regeneration
Mayr, U., Serra, D., & Liberali, P. (2019) · Development · doi:10.1242/dev.176727 · Cited by 63 · see citing works
Single-cell transcriptomic landscape of the developing human spinal cord
Andersen, J., Thom, N., Shadrach, J. L., Chen, X., Onesto, M. M., Amin, N. D., Yoon, S.-J., Li, L., Greenleaf, W. J., Müller, F., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01311-w · Cited by 61 · see citing works
Brown, A. J., Snapkov, I., Akbar, R., Pavlović, M., Miho, E., Sandve, G. K., & Greiff, V. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00071b · Cited by 59 · see citing works
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2018) · doi:10.1101/357368 · Cited by 2,758 · see citing works
“, 2017) and well-based (Cao et al., 2017; Gierahn et al., 2017; Han et al., 2018; Rosenberg et al., 2018) technologies now enable the relatively inexpensive, highthroughput isolation and barcoding of cell transcriptomes.”
BackgroundCardiac splicing as a diagnostic and therapeutic target
Gotthardt, M., Badillo-Lisakowski, V., Parikh, V. N., Ashley, E., Furtado, M., Carmo-Fonseca, M., Schudy, S., Meder, B., Grosch, M., Steinmetz, L., et al. (2023) · Nature Reviews Cardiology · doi:10.1038/s41569-022-00828-0 · Cited by 46 · see citing works
Estimating the frequency of multiplets in single-cell RNA sequencing from cell-mixing experiments
Bloom, J. D. (2018) · PeerJ · doi:10.7717/peerj.5578 · Cited by 46 · see citing works
Postnatal maturation of spinal dynorphin circuits and their role in somatosensation
Brewer, C. L., Styczynski, L. M., Serafin, E. K., & Baccei, M. L. (2020) · Pain · doi:10.1097/j.pain.0000000000001884 · Cited by 38 · see citing works
Sample-multiplexing approaches for single-cell sequencing
Zhang, Y., Xu, S., Wen, Z., Gao, J., Li, S., Weissman, S. M., & Pan, X. (2022) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-022-04482-0 · Cited by 40 · see citing works
Encoding Method of Single-cell Spatial Transcriptomics Sequencing
Zhou, Y., Jia, E., Pan, M., Zhao, X., & Ge, Q. (2020) · International Journal of Biological Sciences · doi:10.7150/ijbs.43887 · Cited by 32 · 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
Single-Nucleus RNA-Seq: Open the Era of Great Navigation for FFPE Tissue
Guo, Y., Wang, W., Ye, K., He, L., Ge, Q., Huang, Y., & Zhao, X. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241813744 · Cited by 27 · see citing works
Decoding cell type diversity within the spinal cord
Dobrott, C. I., Sathyamurthy, A., & Levine, A. J. (2019) · Current Opinion in Physiology · doi:10.1016/j.cophys.2018.11.006 · Cited by 26 · see citing works
Thurman, A. L., Ratcliff, J. A., Chimenti, M. S., & Pezzulo, A. A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab337 · Cited by 27 · see citing works
Senescence in aging and disorders of the central nervous system
Swenson, B. L., Meyer, C. F., Bussian, T. J., & Baker, D. J. (2019) · Translational Medicine of Aging · doi:10.1016/j.tma.2019.01.002 · Cited by 24 · see citing works
Transcriptional profile of spinal dynorphin-lineage interneurons in the developing mouse
Serafin, E. K., Chamessian, A., Li, J., Zhang, X., McGann, A., Brewer, C. L., Berta, T., & Baccei, M. (2019) · Pain · doi:10.1097/j.pain.0000000000001636 · Cited by 24 · see citing works
Single-cell parallel analysis of DNA damage and transcriptome reveals selective genome vulnerability
Bai, D., Cao, Z., Attada, N., Song, J., & Zhu, C. (2025) · Nature Methods · doi:10.1038/s41592-025-02632-3 · Cited by 16 · see citing works
Han, X., Maharjan, S., Chen, J., Zhao, Y., Qi, Y., White, L. E., Johnson, G. A., & Wang, N. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120734 · Cited by 15 · see citing works
- Authors
- Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L. T., Peeler, D. J., Mukherjee, S., Chen, W., Pun, S. H., Sellers, D. L., Tasic, B., & Seelig, G.
- Affiliations
- University of Washington (US)
- Allen Institute for Brain Science (US)
- Corresponding author
- Rosenberg, Alexander B.
- Roco, Charles M.
- Seelig, Georg
- ORCID
- Venue
- Science
- Published
- 2018-04-13
- Volume
- 360
- Issue
- 6385
- Pages
- 176-182
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC7643870
- OpenAlex
- W2794083495
- Cited by
- 1,422
- References
- 98
- Citations by year
- 2026: 112 · 2025: 172 · 2024: 248 · 2023: 161 · 2022: 154 · 2021: 189 · 2020: 177 · 2019: 127 · 2018: 70 · 2017: 11
- FWCI
- 51.05
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (CCF-1317653)
- National Institutes of Health (TL1 TR002318)
- National Institute of General Medical Sciences (R01CA207029)
- National Institute of Neurological Disorders and Stroke (R01NS064404, R21NS086500)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- single-cell RNA sequencing, combinatorial barcoding, single-nucleus transcriptomics, mouse brain development, spinal cord development, postnatal development, central nervous system, cell type annotation, developmental lineage, pseudotime, sample multiplexing
- MeSH terms
- Animals, Brain, Cell Nucleus, Humans, Neurons, Spinal Cord, Sequence Analysis, RNA, Gene Expression Regulation, Developmental, Gene Expression Profiling, NIH 3T3 Cells, Mice, HEK293 Cells, Single-Cell Analysis, 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