RNA-sequencing from single nuclei
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Significance: One of the central goals of developmental biology and medicine is to ascertain the relationships between the genotype and phenotype of cells. Single-cell transcriptome analysis represents a powerful strategy to reach this goal. We advance these strategies to single nuclei from neural progenitor cells and dentate gyrus tissue, from which it is very difficult to recover intact cells. This provides a unique means to carry out RNA sequencing from individual neurons that avoids requiring isolation of single-cell suspensions, eliminating potential changes in gene expression due to enzymatic-cell dissociation methods. This method will be useful for analysis of processes occurring in the nucleus and for gene-expression studies of highly interconnected cells such as neurons.
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The central nervous system of older mice is more complex than that of younger mice, making it harder to dissociate it into single cells, and as a result some cell types can get over- or underrepresented.
“The central nervous system of older mice is more complex than that of younger mice, making it harder to dissociate it into single cells, and as a result some cell types can get over-or underrepresented (Darmanis et al., 2015;Grindberg et al., 2013;Krishnaswami et al., 2016;Lake et al., 2016;Lake et al., 2017;Tasic et al., 2018).”
From Single-cell resolution analysis of the crosstalk between chemogenically activated astrocytes and microglia (2020) · cites this paper as Grindberg et al., 2013
The Discussion (page unavailable) says that intact single cells are difficult to isolate from complex CNS tissue, supporting the dissociation difficulty. It does not compare older and younger mice or say that dissociation over- or underrepresents particular cell types. (LLM, conf=0.88)
Experimental — accuracy not yet measuredsnRNA-seq generates transcriptional profiles with high similarity to scRNA-seq while offering advantages: nuclei are promptly released during homogenization, minimizing cell type biases and artifactual transcriptional alteration; it is compatible with cryopreserved samples; and it can study cell types less amenable to scRNA-seq such as adipocytes and cardiomyocytes.
“While generating transcriptional profiles with high similarity to those by scRNA-seq (Grindberg et al., 2013; Habib et al., 2017), snRNA-seq offers advantages. First, this method utilizes nuclei that are promptly released from tissues during homogenization, obviating the need for an extended digestion process and thereby minimizing cell type biases and artifactual transcriptional alteration.”
From Robust single-nucleus RNA sequencing reveals depot-specific cell population dynamics in adipose tissue remodeling during obesity (2025) · cites this paper as Grindberg et al., 2013
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Experimental — accuracy not yet measuredSingle-nucleus RNA-seq was developed to analyze RNA in nuclei from fresh, frozen, or lightly fixed tissues.
“To address this challenge, we3 and others4–6 developed single nucleus RNA-seq (snRNA-seq) for analysis of RNA in single nuclei from fresh, frozen or lightly fixed tissues.”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [6]
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Cites
Mapping and quantifying mammalian transcriptomes by RNA-Seq
Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008) · Nature Methods · doi:10.1038/nmeth.1226 · Cited by 13,696 · see citing works
RNA-Seq: a revolutionary tool for transcriptomics
Wang, Z., Gerstein, M., & Snyder, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2484 · Cited by 13,174 · see citing works
Stochastic Gene Expression in a Single Cell
Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441 · see citing works
Functional Demarcation of Active and Silent Chromatin Domains in Human HOX Loci by Noncoding RNAs
Rinn, J. L., Kertesz, M., Wang, J. K., Squazzo, S. L., Xu, X. H., Brugmann, S. A., Goodnough, L. H., Helms, J. A., Farnham, P., Segal, E., et al. (2007) · Cell · doi:10.1016/j.cell.2007.05.022 · Cited by 4,246 · see citing works
mRNA-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
Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences
Raj, A., & van Oudenaarden, A. (2008) · Cell · doi:10.1016/j.cell.2008.09.050 · Cited by 2,593 · see citing works
Cai, X., Hagedorn, C. H., & Cullen, B. R. (2004) · RNA · doi:10.1261/rna.7135204 · Cited by 1,874 · see citing works
lincRNAs act in the circuitry controlling pluripotency and differentiation
Guttman, M., Donaghey, J., Carey, B. W., Garber, M. G., Grenier, J. K., Munson, G., Young, G., Lucas, A. B., Ach, R. A., Bruhn, L., et al. (2011) · Nature · doi:10.1038/nature10398 · Cited by 1,845 · see citing works
Noise in eukaryotic gene expression
Blake, W. J., Kærn, M., Cantor, C. R., & Collins, J. J. (2003) · Nature · doi:10.1038/nature01546 · Cited by 1,636 · see citing works
Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells
Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609 · see citing works
Hayashi, S., & McMahon, A. P. (2002) · Developmental Biology · doi:10.1006/dbio.2002.0597 · Cited by 1,347 · see citing works
The highways and byways of mRNA decay
Garneau, N. L., Wilusz, J., & Wilusz, C. J. (2007) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm2104 · Cited by 1,294 · 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
Transcriptional Maps of 10 Human Chromosomes at 5-Nucleotide Resolution
Cheng, J., Kapranov, P., Drenkow, J., Dike, S., Brubaker, S., Patel, S. A., Long, J. C., Stern, D., Tammana, H., Helt, G. A., et al. (2005) · Science · doi:10.1126/science.1108625 · Cited by 1,175 · see citing works
Genome-wide Identification of Polycomb-Associated RNAs by RIP-seq
Zhao, J. C., Ohsumi, T. K., Kung, J. T., Ogawa, Y., Grau, D., Sarma, K., Song, J. J., Kingston, R. E., Borowsky, M. L., & Lee, J. T. (2010) · Molecular Cell · doi:10.1016/j.molcel.2010.12.011 · Cited by 974 · see citing works
Self-citationCharacterization of the single-cell transcriptional landscape by highly multiplex RNA-seq
Islam, S., Kjällquist, U., Moliner, A., Zajac, P., Fan, J.-B., Lönnerberg, P., & Linnarsson, S. (2011) · Genome Research · doi:10.1101/gr.110882.110 · Cited by 949 · see citing works
Mammalian genes are transcribed with widely different bursting kinetics
Suter, D. M., Molina, N., Gatfield, D., Schneider, K., Schibler, U., & Naef, F. (2011) · Science · doi:10.1126/science.1198817 · Cited by 927 · see citing works
An 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
The Genetic Signatures of Noncoding RNAs
Mattick, J. S. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000459 · Cited by 704 · see citing works
Regulation of cytoplasmic mRNA decay
Schoenberg, D. R., & Maquat, L. E. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3160 · Cited by 656 · see citing works
Gringhuis, S. I., Kaptein, T. M., Wevers, B. A., Theelen, B., van der Vlist, M., Boekhout, T., & Geijtenbeek, T. B. H. (2012) · Nature Immunology · doi:10.1038/ni.2222 · Cited by 612 · see citing works
Sox18 induces development of the lymphatic vasculature in mice
François, M., Caprini, A., Hosking, B. M., Orsenigo, F., Wilhelm, D., Browne, C. M., Paavonen, K., Karnezis, T., Shayan, R., Downes, M., et al. (2008) · Nature · doi:10.1038/nature07391 · Cited by 554 · see citing works
Tang, F., Bárbácioru, C., Bao, S., Lee, C. G. L., Nordman, E., Wang, X., Lao, K., & Surani, M. A. (2010) · Cell stem cell · doi:10.1016/j.stem.2010.03.015 · Cited by 499 · see citing works
RNA-Seq analysis to capture the transcriptome landscape of a single cell
Tang, F., Bárbácioru, C., Nordman, E., Li, B., Xu, N., Bashkirov, V. I., Lao, K., & Surani, M. A. (2010) · Nature Protocols · doi:10.1038/nprot.2009.236 · Cited by 487 · see citing works
Tilgner, H., Knowles, D. G., Johnson, R., Davis, C., Chakrabortty, S. K., Djebali, S., Curado, J., Snyder, M. P., Gingeras, T., & Guigó, R. (2012) · Genome Research · doi:10.1101/gr.134445.111 · Cited by 463 · see citing works
Deal, R. B., & Henikoff, S. (2010) · Nature Protocols · doi:10.1038/nprot.2010.175 · Cited by 387 · see citing works
Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR
Warren, L. A., Bryder, D., Weissman, I., & Quake, S. (2006) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0608512103 · Cited by 373 · see citing works
Trypsin-induced proteome alteration during cell subculture in mammalian cells
Huang, H.-L., Hsing, H.-W., Lai, T.-C., Chen, Y.-W., Lee, T., Chan, H.-T., Lyu, P. C., Wu, C., Lu, Y.-C., Lin, S., et al. (2010) · Journal of Biomedical Science · doi:10.1186/1423-0127-17-36 · Cited by 314 · see citing works
Sequentially acting Sox transcription factors in neural lineage developmentPreprint
Bergsland, M., Ramsköld, D., Zaouter, C., Klum, S., Sandberg, R., & Muhr, J. (2011) · Genes & Development · doi:10.1101/gad.176008.111 · Cited by 305 · see citing works
Highly multiplexed and strand-specific single-cell RNA 5′ end sequencing
Islam, S., Kjällquist, U., Moliner, A., Zajac, P., Fan, J.-B., Lönnerberg, P., & Linnarsson, S. (2012) · Nature Protocols · doi:10.1038/nprot.2012.022 · Cited by 272 · see citing works
Lein, E. S., Zhao, X., & Gage, F. H. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.4710-03.2004 · Cited by 270 · see citing works
Self-citationSequencing technology does not eliminate biological variability
Hansen, K. D., Wu, Z., Irizarry, R. A., & Leek, J. T. (2011) · Nature Biotechnology · doi:10.1038/nbt.1910 · Cited by 226 · see citing works
Regulatory activity revealed by dynamic correlations in gene expression noise
Dunlop, M. J., Cox, R. S., Levine, J. H., Murray, R. M., & Elowitz, M. B. (2008) · Nature Genetics · doi:10.1038/ng.281 · Cited by 219 · see citing works
Housekeeping and tissue-specific genes in mouse tissues
Kouadjo, K. E., Nishida, Y., Cadrin-Girard, J.-F., Yoshioka, M., & St‐Amand, J. (2007) · BMC Genomics · doi:10.1186/1471-2164-8-127 · Cited by 205 · see citing works
Kurimoto, K., Yabuta, Y., Ohinata, Y., & Saitou, M. (2007) · Nature Protocols · doi:10.1038/nprot.2007.79 · Cited by 186 · see citing works
Okoniewski, M. J., & Miller, C. J. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-276 · Cited by 185 · see citing works
RNA traffic control of chromatin complexes
Koziol, M. J., & Rinn, J. L. (2010) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2010.03.003 · Cited by 167 · see citing works
Deterministic and Stochastic Allele Specific Gene Expression in Single Mouse Blastomeres
Tang, F., Bárbácioru, C., Nordman, E., Bao, S., Lee, C. G. L., Wang, X., Tuch, B. B., Heard, É., Lao, K., & Surani, M. A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021208 · Cited by 150 · see citing works
Łabaj, P. P., Leparc, G. G., Linggi, B. E., Markillie, L. M., Wiley, H. S., & Kreil, D. P. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr247 · Cited by 141 · see citing works
Zhang, C., Barthelson, R. A., Lambert, G. M., & Galbraith, D. W. (2008) · PLANT PHYSIOLOGY · doi:10.1104/pp.107.115246 · Cited by 126 · see citing works
Self-citationGene Expression Changes in the Course of Neural Progenitor Cell Differentiation
Gurok, U., Steinhoff, C., Lipkowitz, B., Ropers, H., Scharff, C., & Nuber, U. A. (2004) · Journal of Neuroscience · doi:10.1523/jneurosci.0809-04.2004 · Cited by 112 · see citing works
The color of mice: in the light of GFP-variant reporters
Hadjantonakis, A., & Nagy, A. (2000) · Histochemistry and Cell Biology · doi:10.1007/s004180000233 · Cited by 108 · see citing works
Single-cell gene expression profiling using reverse transcription quantitative real-time PCR
Ståhlberg, A., & Bengtsson, M. (2010) · Methods · doi:10.1016/j.ymeth.2010.01.002 · Cited by 101 · see citing works
Structure, expression and chromosomal location of the Oct-4 gene
YOUNGILYEOM, HA, H., Balling, R., et al. (1991) · Mechanisms of Development · doi:10.1016/0925-4773(91)90016-y · Cited by 100 · see citing works
Barthelson, R. A., Lambert, G. M., Vanier, C. H., Lynch, R. M., & Galbraith, D. W. (2007) · BMC Genomics · doi:10.1186/1471-2164-8-340 · Cited by 91 · see citing works
Self-citationTranscriptome Analysis of Single Cells
Morris, J. F., Singh, J. M., & Eberwine, J. H. (2011) · Journal of Visualized Experiments · doi:10.3791/2634 · Cited by 68 · see citing works
Senner, C. E., Nesterova, T. B., Norton, S., et al. (2011) · Development · doi:10.1242/dev.056812 · Cited by 49 · see citing works
Microarrays and the Gene Expression Profile of a Single Cell
Kawasaki, E. S. (2004) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1310.010 · Cited by 43 · see citing works
mRNA-sequencing whole transcriptome analysis of a single cell on the SOLiD system.
Lao, K. Q., Tang, F., Bárbácioru, C., et al. (2009) · PubMed · Cited by 33 · see citing works
Age-related changes in neuronal RNA content in rhesus monkeys (Macaca mulatta)
Uemura, E. (1980) · Brain Research Bulletin · doi:10.1016/0361-9230(80)90182-3 · Cited by 32 · see citing works
Roozemond, R. C. (1976) · The Histochemical Journal · doi:10.1007/bf01003963 · Cited by 31 · see citing works
Trask, H. W., Sallari, R. C., Sartor, M. A., Gui, J., Heath, C. V., Renuka, J., Higgins, A.-J., Andrews, P. C., Korc, M., Moore, J. H., et al. (2009) · RNA · doi:10.1261/rna.1677409 · Cited by 30 · see citing works
Schwanekamp, J. A., Sartor, M. A., Karyala, S. V., Halbleib, D., Medvedovic, M., & Tomlinson, C. R. (2006) · Biochimica et Biophysica Acta (BBA) - Gene Structure and Expression · doi:10.1016/j.bbaexp.2006.07.005 · Cited by 21 · see citing works
Mobile MicroRNAs Hit the Target
Gursanscky, N. R., Searle, I. R., & Carroll, B. S. (2011) · Traffic · doi:10.1111/j.1600-0854.2011.01253.x · Cited by 14 · see citing works
Transcriptome Analysis of Single Cells
Morris, J. F., Singh, J. M., & Eberwine, J. H. (2011) · Journal of Visualized Experiments · doi:10.3791/2634-v · Cited by 14 · see citing works
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The Technology and Biology of Single-Cell RNA Sequencing
Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C., & Teichmann, S. A. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.04.005 · Cited by 1,487 · see citing works
Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402 · see citing works
Massively parallel single-nucleus RNA-seq with DroNc-seq
Habib, N., Avraham-Davidi, I., Basu, A., Burks, T., Shekhar, K., Hofree, M., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4407 · Cited by 1,069 · 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
Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain
Lake, B. B., Chen, S., Sos, B. C., Fan, J., Kaeser, G. E., Yung, Y. C., Duong, T. E., Gao, D., Chun, J., Kharchenko, P. V., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4038 · Cited by 983 · see citing works
Single-cell RNA-seq: advances and future challenges
Saliba, A.-E., Westermann, A. J., Gorski, S. A., & Vogel, J. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku555 · Cited by 847 · see citing works
In vivo interrogation of gene function in the mammalian brain using CRISPR-Cas9
Swiech, L., Heidenreich, M., Banerjee, A., Habib, N., Li, Y., Trombetta, J., Sur, M., & Zhang, F. (2015) · Nature Biotechnology · doi:10.1038/nbt.3055 · Cited by 772 · 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
Nagy, C., Maitra, M., Tanti, A., Suderman, M., Théroux, J.-F., Davoli, M. A., Perlman, K., Yerko, V., Wang, Y. C., Tripathy, S. J., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0621-y · Cited by 532 · see citing works
Determinants of resident tissue macrophage identity and function
Blériot, C., Chakarov, S., & Ginhoux, F. (2020) · Immunity · doi:10.1016/j.immuni.2020.05.014 · Cited by 507 · 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
Multiclonal Invasion in Breast Tumors Identified by Topographic Single Cell Sequencing
Casasent, A. K., Schalck, A., Gao, R., Sei, E., Long, A., Pangburn, W., Casasent, T., Meric-Bernstam, F., Edgerton, M. E., & Navin, N. E. (2018) · Cell · doi:10.1016/j.cell.2017.12.007 · Cited by 411 · see citing works
Single Cell Genomics: Advances and Future Perspectives
Macaulay, I. C., & Voet, T. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004126 · Cited by 384 · see citing works
Sathyamurthy, 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
Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type
Boldog, E., Bakken, T. E., Hodge, R. D., Novotny, M., Aevermann, B. D., Baka, J., Bordé, S., Close, J. L., Diez-Fuertes, F., Ding, S.-L., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-018-0205-2 · Cited by 291 · see citing works
Self-citationLake, B. B., Codeluppi, S., Yung, Y. C., Gao, D., Chun, J., Kharchenko, P. V., Linnarsson, S., & Zhang, K. (2017) · Scientific Reports · doi:10.1038/s41598-017-04426-w · Cited by 271 · see citing works
The neurons that restore walking after paralysis
Kathe, C., Skinnider, M. A., Hutson, T. H., Regazzi, N., Gautier, M., Demesmaeker, R., Komi, S., Ceto, S., James, N. D., Cho, N., et al. (2022) · Nature · doi:10.1038/s41586-022-05385-7 · Cited by 304 · 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
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
Molecular signatures underlying neurofibrillary tangle susceptibility in Alzheimer’s disease
Otero-Garcia, M., Mahajani, S. U., Wakhloo, D., Tang, W., Xue, Y.-Q., Morabito, S., Pan, J., Oberhauser, J., Madira, A. E., Shakouri, T., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.06.021 · Cited by 262 · 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
Experimental Considerations for Single-Cell RNA Sequencing Approaches
Nguyen, Q. H., Pervolarakis, N., Nee, K., & Kessenbrock, K. (2018) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2018.00108 · Cited by 170 · see citing works
Single cell transcriptome profiling of the human alcohol-dependent brain
Brenner, E., Tiwari, G. R., Kapoor, M., Liu, Y., Brock, A., & Mayfield, R. D. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa038 · Cited by 118 · 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
Brown, D. M., Hixon, A. M., Oldfield, L. M., Zhang, Y., Novotny, M., Wang, W., Das, S. R., Shabman, R. S., Tyler, K. L., & Scheuermann, R. H. (2018) · mBio · doi:10.1128/mbio.01954-18 · Cited by 105 · see citing works
Self-citationAnalytical tools and current challenges in the modern era of neuroepigenomics
Maze, I., Shen, L., Zhang, B., Garcia, B. A., Shao, N., Mitchell, A., Sun, H., Akbarian, S., Allis, C. D., & Nestler, E. J. (2014) · Nature Neuroscience · doi:10.1038/nn.3816 · Cited by 100 · see citing works
Perspectives on single-nucleus RNA sequencing in different cell types and tissues
Kim, N., Kang, H., Jo, A., Yoo, S.-A., & Lee, H.-O. (2023) · Journal of Pathology and Translational Medicine · doi:10.4132/jptm.2022.12.19 · Cited by 97 · see citing works
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Richter, M. L., Deligiannis, I. K., Yin, K., Danese, A., Lleshi, E., Coupland, P., Vallejos, C. A., Matchett, K. P., Henderson, N. C., Colome-Tatche, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24543-5 · Cited by 75 · see citing works
Comparative analysis of antibody- and lipid-based multiplexing methods for single-cell RNA-seq
Mylka, V., Matetovici, I., Poovathingal, S., Aerts, J., Vandamme, N., Seurinck, R., Verstaen, K., Hulselmans, G., Van den Hoecke, S., Scheyltjens, I., et al. (2022) · Genome Biology · doi:10.1186/s13059-022-02628-8 · Cited by 53 · see citing works
Miller, J. A., Menon, V., Goldy, J., Kaykas, A., Lee, C.-K., Smith, K. A., Shen, E. H., Phillips, J. W., Lein, E. S., & Hawrylycz, M. J. (2014) · BMC Genomics · doi:10.1186/1471-2164-15-154 · Cited by 48 · see citing works
McFarland, K. N., & Chakrabarty, P. (2022) · Neurotherapeutics · doi:10.1007/s13311-021-01179-3 · Cited by 48 · 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
So, J., Strobel, O., Wann, J., Kim, K., Paul, A., Acri, D. J., Dabin, L. C., Kim, J., Peng, G., & Roh, H. C. (2025) · eLife · doi:10.7554/elife.97981 · Cited by 18 · see citing works
Philtjens, S., Turnbull, M. T., Thedy, B. P., Moon, Y., & Kim, J. (2020) · doi:10.1101/2020.04.27.064881 · Cited by 7 · see citing works
“…is more complex than that of younger mice, making it harder to dissociate it into single cells, and as a result some cell types can get over- or underrepresented (Darmanis et al., 2015; Grindberg et al., 2013; Krishnaswami et al., 2016; Lake et al., 2016; Lake et al., 2017; Tasic et al., 2018).”
Background
- Authors
- Grindberg, R. V., Yee-Greenbaum, J. L., McConnell, M. J., Novotny, M., O’Shaughnessy, A. L., Lambert, G. M., Araúzo-Bravo, M. J., Lee, J., Fishman, M., Robbins, G. E., Lin, X., Venepally, P., Badger, J. H., Galbraith, D. W., Gage, F. H., & Lasken, R. S.
- Affiliations
- J. Craig Venter Institute (US)
- University of Virginia (US)
- J. Craig Venter Institute, San Diego, CA 92121;
- University of Arizona (US)
- Max Planck Institute for Molecular Biomedicine (DE)
- LeGene Biosciences, San Diego, CA 92126;
- Applied Biosystems, Life Technologies, Foster City, CA 94404;
- Salk Institute for Biological Studies (US)
- ORCID
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2013-11-18
- Volume
- 110
- Issue
- 49
- Pages
- 19802-19807
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3856806
- OpenAlex
- W2112630596
- Cited by
- 422
- References
- 55
- Citations by year
- 2026: 23 · 2025: 29 · 2024: 39 · 2023: 67 · 2022: 56 · 2021: 44 · 2020: 44 · 2019: 34 · 2018: 23 · 2017: 18 · 2016: 14 · 2015: 6 · 2014: 21 · 2013: 3 · 2012: 1
- FWCI
- 6.56
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- James S. McDonnell Foundation
- JPB Foundation
- G. Harold and Leila Y. Mathers Foundation
- National Institutes of Health
- College of Agriculture and Life Sciences, University of Arizona
- National Institute of Mental Health (TRO1MH095741, R01MH095741)
- National Human Genome Research Institute (NIH-1 RC1 HG005471, NIH 2R01 HG003647, R01HG003647, RC1HG005471)
- National Cancer Institute (NIH-NCI R21-CA164690, R21CA164690)
- 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 › RNA Research and Splicing
- Keywords
- single-nucleus RNA sequencing, single-nucleus transcriptomics, single-cell transcriptomics, gene expression quantification, hippocampus
- MeSH terms
- Animals, Cell Nucleus, Flow Cytometry, Micromanipulation, Microscopy, Fluorescence, Dentate Gyrus, Gene Expression Profiling, Mice, Embryonic Stem Cells, Neural Stem Cells, High-Throughput Nucleotide Sequencing
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