Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells
Abstract
Expression from both alleles is generally observed in analyses of diploid cell populations, but studies addressing allelic expression patterns genome-wide in single cells are lacking. Here, we present global analyses of allelic expression across individual cells of mouse preimplantation embryos of mixed background (CAST/EiJ × C57BL/6J). We discovered abundant (12 to 24%) monoallelic expression of autosomal genes and that expression of the two alleles occurs independently. The monoallelic expression appeared random and dynamic because there was considerable variation among closely related embryonic cells. Similar patterns of monoallelic expression were observed in mature cells. Our allelic expression analysis also demonstrates the de novo inactivation of the paternal X chromosome. We conclude that independent and stochastic allelic transcription generates abundant random monoallelic expression in the mammalian cell.
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The first single-cell RNA-seq study of ASE used early-stage mouse embryos and adult tissues, observing a high rate of monoallelic expression (12-25%) for even highly expressed autosomal genes.
“The first single‐cell RNA‐seq study of ASE used early‐stage mouse embryos (up to the blastocyst stage) and adult tissues (Deng et al, 2014), observing a high rate of monoallelic expression (12–25%) for even highly expressed autosomal genes.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Deng et al, 2014
Cells in the same embryo expressed different genes monoallelically.
“Cells in the same embryo expressed different genes monoallelically, implicating chance in deciding which alleles are expressed in individual cells.”
From Using single-cell genomics to understand developmental processes and cell fate decisions (2018) · cites this paper as Deng et al, 2014
Cites
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
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
Smart-seq2 for sensitive full-length transcriptome profiling in single cells
Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2639 · Cited by 2,358 · see citing works
Self-citationGenomic imprinting: parental influence on the genome
Reik, W., & Walter, J. (2001) · Nature Reviews Genetics · doi:10.1038/35047554 · Cited by 2,288 · see citing works
Fast and SNP-tolerant detection of complex variants and splicing in short reads
Wu, T. D., & Nacu, S. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq057 · Cited by 1,894 · 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
Self-citationMouse genomic variation and its effect on phenotypes and gene regulation.
Keane, T. M., Goodstadt, L., Danecek, P., White, M. A., Wong, K., Yalcin, B., Heger, A., Agam, A., Slater, G., Goodson, M., et al. (2011) · Nature · doi:10.1038/nature10413 · Cited by 1,580 · see citing works
Epigenetic Reprogramming in Plant and Animal Development
Feng, S., Jacobsen, S. E., & Reik, W. (2010) · Science · doi:10.1126/science.1190614 · Cited by 1,171 · see citing works
Allelic inactivation regulates olfactory receptor gene expression
Chess, A. J., Simon, I., Cedar, H., & Axel, R. (1994) · Cell · doi:10.1016/s0092-8674(94)90562-2 · Cited by 1,066 · 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
Self-citationEpigenetic Dynamics of Imprinted X Inactivation During Early Mouse Development
Okamoto, I., Otte, A. P., Allis, C. D., Reinberg, D., & Heard, É. (2003) · Science · doi:10.1126/science.1092727 · Cited by 811 · see citing works
Widespread Monoallelic Expression on Human Autosomes
Gimelbrant, A., Hutchinson, J. N., Thompson, B. R., & Chess, A. (2007) · Science · doi:10.1126/science.1148910 · Cited by 655 · see citing works
Variability in gene expression underlies incomplete penetrance
Raj, A., Rifkin, S. A., Andersen, E. C., & van Oudenaarden, A. (2010) · Nature · doi:10.1038/nature08781 · Cited by 642 · see citing works
High-Resolution Analysis of Parent-of-Origin Allelic Expression in the Mouse Brain
Gregg, C., Zhang, J., Weissbourd, B., Luo, S., Schroth, G. P., Haig, D., & Dulac, C. (2010) · Science · doi:10.1126/science.1190830 · Cited by 591 · see citing works
Pernis, B., Chiappino, G., Kelus, A. S., & Gell, P. G. H. (1965) · The Journal of Experimental Medicine · doi:10.1084/jem.122.5.853 · Cited by 436 · see citing works
Variability in gene expression underlies incomplete penetrance
van Oudenaarden, A., Rifkin, S. A., Raj, A., & Andersen, E. C. (2010) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 424 · see citing works
Quantitative changes in total RNA, total poly(A), and ribosomes in early mouse embryos
Pikó, L., & Clegg, K. B. (1982) · Developmental Biology · doi:10.1016/0012-1606(82)90325-6 · Cited by 374 · see citing works
Inheritance of a pre-inactivated paternal X chromosome in early mouse embryos
Huynh, K. D., & Lee, J. T. (2003) · Nature · doi:10.1038/nature02222 · Cited by 374 · see citing works
Regulation of TCR α and β gene allelic exclusion during T-cell development
Malissen, M., Trucy, J., Jouvin‐Marche, E., et al. (1992) · Immunology Today · doi:10.1016/0167-5699(92)90044-8 · Cited by 284 · see citing works
Independent and Epigenetic Regulation of the Interleukin-4 Alleles in CD4 + T Cells
Bix, M., & Locksley, R. M. (1998) · Science · doi:10.1126/science.281.5381.1352 · Cited by 246 · see citing works
Control of ground-state pluripotency by allelic regulation of Nanog
Miyanari, Y., & Torres‐Padilla, M. (2012) · Nature · doi:10.1038/nature10807 · Cited by 231 · see citing works
Pathology of tumors of the peripheral nerve sheath in type 1 neurofibromatosis
Woodruff, J. M. (1999) · American Journal of Medical Genetics · doi:10.1002/(sici)1096-8628(19990326)89:1 · Cited by 229 · see citing works
Critical Evaluation of Imprinted Gene Expression by RNA–Seq: A New Perspective
DeVeale, B., van der Kooy, D., & Babak, T. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002600 · Cited by 223 · see citing works
Gracefully ageing at 50, X-chromosome inactivation becomes a paradigm for RNA and chromatin control
Lee, J. T. (2011) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3231 · Cited by 221 · see citing works
Evidence for de novo imprinted X-chromosome inactivation independent of meiotic inactivation in mice
Okamoto, I., Arnaud, D., Baccon, P. L., Otte, A. P., Distèche, C. M., Avner, P. R., & Heard, É. (2005) · Nature · doi:10.1038/nature04155 · Cited by 179 · see citing works
Regulation of IL-4 Expression by Activation of Individual Alleles
Rivière, I., Sunshine, M. J., & Littman, D. R. (1998) · Immunity · doi:10.1016/s1074-7613(00)80604-4 · Cited by 164 · see citing works
Global Survey of Genomic Imprinting by Transcriptome Sequencing
Babak, T., DeVeale, B., Armour, C. D., Raymond, C. K., Cleary, M. A., van der Kooy, D., Johnson, J. M., & Lim, L. P. (2008) · Current Biology · doi:10.1016/j.cub.2008.09.044 · Cited by 163 · see citing works
Evidence of Xist RNA-independent initiation of mouse imprinted X-chromosome inactivation
Kalantry, S., Purushothaman, S., Bowen, R. B., Starmer, J., & Magnuson, T. (2009) · Nature · doi:10.1038/nature08161 · Cited by 127 · see citing works
Muenke, M., Gurrieri, F., Bay, C., et al. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.17.8102 · Cited by 116 · see citing works
Noncoding RNAs and Intranuclear Positioning in Monoallelic Gene Expression
Yang, P. K., & Kuroda, M. I. (2007) · Cell · doi:10.1016/j.cell.2007.01.032 · Cited by 116 · see citing works
Autosomal monoallelic expression in the mouse
Zwemer, L. M., Zak, A., Thompson, B. R., Kirby, A. W., Daly, M. J., Chess, A. J., & Gimelbrant, A. A. (2012) · Genome biology · doi:10.1186/gb-2012-13-2-r10 · Cited by 112 · see citing works
The van der Woude syndrome in a large kindred: Variability, penetrance, genetic risks
Janku, P., Robinow, M., Kelly, T. E., et al. (1980) · American Journal of Medical Genetics · doi:10.1002/ajmg.1320050203 · Cited by 98 · see citing works
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Keren-Shaul, H., Spinrad, A., Weiner, A., Matcovitch-Natan, O., Dvir-Szternfeld, R., Ulland, T. K., David, E., Baruch, K., Lara-Astaiso, D., Toth, B., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.018 · Cited by 5,493 · see citing works
The biogenesis, biology and characterization of circular RNAs
Kristensen, L. S., Andersen, M. S., Stagsted, L. V. W., Ebbesen, K. K., Hansen, T. B., & Kjems, J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0158-7 · Cited by 4,823 · see citing works
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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
Mapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597 · see citing works
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Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., & Enard, W. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.01.023 · Cited by 1,494 · see citing works
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Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348 · see citing works
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Gulati, G. S., Sikandar, S. S., Wesche, D. J., Manjunath, A., Bharadwaj, A., Berger, M. J., Ilagan, F., Kuo, A. H., Hsieh, R. W., Cai, S., et al. (2020) · Science · doi:10.1126/science.aax0249 · Cited by 1,571 · see citing works
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Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing
Cusanovich, D. A., Daza, R., Adey, A., Pliner, H. A., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C., & Shendure, J. (2015) · Science · doi:10.1126/science.aab1601 · Cited by 1,248 · see citing works
Landscape of X chromosome inactivation across human tissues
GTEx Consortium, Tukiainen, T., Villani, A.-C., Yen, A., Rivas, M. A., Marshall, J. L., Satija, R., Aguirre, M., Gauthier, L., Fleharty, M., et al. (2017) · Nature · doi:10.1038/nature24265 · Cited by 1,138 · see citing works
Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity
Smallwood, S. A., Lee, H. J., Angermueller, C., Krueger, F., Saadeh, H., Peat, J., Andrews, S. R., Stegle, O., Reik, W., & Kelsey, G. (2014) · Nature Methods · doi:10.1038/nmeth.3035 · Cited by 1,125 · 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
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Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170 · see citing works
Single-Cell RNA-Seq Reveals Lineage and X Chromosome Dynamics in Human Preimplantation Embryos
Petropoulos, S., Edsgärd, D., Reinius, B., Deng, Q., Panula, S. P., Codeluppi, S., Plaza Reyes, A., Linnarsson, S., Sandberg, R., & Lanner, F. (2016) · Cell · doi:10.1016/j.cell.2016.03.023 · Cited by 1,063 · see citing works
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L. Lun, A. T., Bach, K., & Marioni, J. C. (2016) · Genome Biology · doi:10.1186/s13059-016-0947-7 · Cited by 1,108 · see citing works
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Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945 · see citing works
Kukurba, K. R., & Montgomery, S. B. (2015) · Cold Spring Harbor Protocols · doi:10.1101/pdb.top084970 · Cited by 924 · see citing works
Pollen, A. A., Nowakowski, T. J., Shuga, J., Wang, X., Leyrat, A. A., Lui, J. H., Li, N., Szpankowski, L., Fowler, B., Chen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2967 · Cited by 920 · 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
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
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Soneson, C., & Robinson, M. D. (2018) · Nature Methods · doi:10.1038/nmeth.4612 · Cited by 770 · see citing works
Single-cell RNA counting at allele and isoform resolution using Smart-seq3
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Self-citationRevealing the vectors of cellular identity with single-cell genomics
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A general and flexible method for signal extraction from single-cell RNA-seq data
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Missing data and technical variability in single-cell RNA-sequencing experiments
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Identification of cell types from single-cell transcriptomes using a novel clustering method
Xu, C., & Su, Z. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv088 · Cited by 559 · see citing works
Single-cell genomics unveils critical regulators of Th17 cell pathogenicity
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Design and Analysis of Single-Cell Sequencing Experiments
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A test metric for assessing single-cell RNA-seq batch correction
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Heterogeneity in Oct4 and Sox2 Targets Biases Cell Fate in 4-Cell Mouse Embryos
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Integrated genome and transcriptome sequencing of the same cell
Dey, S. S., Kester, L., Spanjaard, B., Bienko, M., & van Oudenaarden, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3129 · Cited by 502 · see citing works
Dynamics of the epigenetic landscape during the maternal-to-zygotic transition
Eckersley-Maslin, M. A., Alda-Catalinas, C., & Reik, W. (2018) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-018-0008-z · Cited by 431 · see citing works
Control of Transcript Variability in Single Mammalian Cells
Battich, N., Stoeger, T., & Pelkmans, L. (2015) · Cell · doi:10.1016/j.cell.2015.11.018 · Cited by 324 · see citing works
Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells
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Single-cell isoform RNA sequencing characterizes isoforms in thousands of cerebellar cells
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Highly multiplexed simultaneous detection of RNAs and proteins in single cells
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Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape
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Computational approaches for interpreting sc<scp>RNA</scp>‐seq data
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Delmans, M., & Hemberg, M. (2015) · doi:10.1101/020735 · Cited by 111 · see citing works
Evaluating stably expressed genes in single cells
Lin, Y., Ghazanfar, S., Strbenac, D., Wang, A., Patrick, E., Lin, D. M., Speed, T., Yang, J. Y. H., & Yang, P. (2019) · GigaScience · doi:10.1093/gigascience/giz106 · Cited by 84 · see citing works
Alignment of single-cell trajectories to compare cellular expression dynamics
Alpert, A., Moore, L. S., Dubovik, T., & Shen-Orr, S. S. (2018) · Nature Methods · doi:10.1038/nmeth.4628 · Cited by 96 · see citing works
Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench
Chazarra-Gil, R., van Dongen, S., Kiselev, V. Y., & Hemberg, M. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab004 · Cited by 93 · see citing works
Discovery and characterization of variance QTLs in human induced pluripotent stem cells
Sarkar, A. K., Tung, P.-Y., Blischak, J. D., Burnett, J. E., Li, Y. I., Stephens, M., & Gilad, Y. (2019) · PLOS Genetics · doi:10.1371/journal.pgen.1008045 · Cited by 72 · 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
FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data
DeTomaso, D., & Yosef, N. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1176-5 · Cited by 58 · see citing works
A Dynamic Role of TBX3 in the Pluripotency Circuitry
Russell, R., Ilg, M., Lin, Q., Wu, G., Lechel, A., Bergmann, W., Eiseler, T., Linta, L., Kumar P., P., Klingenstein, M., et al. (2015) · Stem Cell Reports · doi:10.1016/j.stemcr.2015.11.003 · Cited by 66 · 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
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
Single-cell transcriptogenomics reveals transcriptional exclusion of ENU-mutated alleles
Li, W., Calder, R. B., Mar, J. C., & Vijg, J. (2015) · Mutation Research - Fundamental and Molecular Mechanisms of Mutagenesis · doi:10.1016/j.mrfmmm.2015.01.002 · Cited by 34 · see citing works
Chen, W., Wang, X., Wei, G., Huang, Y., Shi, Y., Li, D., Qiu, S., Zhou, B., Cao, J., Chen, M., et al. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00867 · Cited by 26 · see citing works
Lotfollahi, M., Wolf, F. A., & Theis, F. J. (2018) · doi:10.1101/478503 · Cited by 21 · see citing works
- Authors
- Deng, Q., Ramsköld, D., Reinius, B., & Sandberg, R.
- Affiliations
- Ludwig Cancer Research (SE)
- Karolinska Institutet (SE)
- Corresponding author
- Deng, Qiaolin
- Ramsköld, Daniel
- Sandberg, Rickard
- Venue
- Science
- Published
- 2014-01-10
- Volume
- 343
- Issue
- 6167
- Pages
- 193-196
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2023887100
- Cited by
- 1,345
- References
- 32
- Citations by year
- 2026: 54 · 2025: 111 · 2024: 115 · 2023: 110 · 2022: 109 · 2021: 138 · 2020: 142 · 2019: 127 · 2018: 108 · 2017: 107 · 2016: 86 · 2015: 81 · 2014: 57
- FWCI
- 72.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Syndromes and Imprinting
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Keywords
- monoallelic expression, allelic expression, single-cell transcriptomics, preimplantation embryo, X chromosome inactivation, stochastic gene expression, diploid cells, autosomal genes
- MeSH terms
- Alleles, Animals, Female, Male, Mice, Inbred C57BL, X Chromosome, Sequence Analysis, RNA, Gene Expression Regulation, Developmental, RNA, Messenger, Stored, Embryonic Development, X Chromosome Inactivation, Mice, Single-Cell Analysis
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