The DOI matches its Crossref record; 76 of the 76 references it cites were found in a registry or an index.
Defining the three cell lineages of the human blastocyst by single-cell RNA-seq
Open accessAbstract
Here we provide fundamental insights into early human development by single-cell RNA-sequencing of human and mouse preimplantation embryos. We elucidate conserved transcriptional programs along with those that are human-specific. Importantly, we validate our RNA-sequencing findings at the protein level, which further reveals differences in human and mouse embryo gene expression. For example, we identify several genes exclusively expressed in the human pluripotent epiblast including the transcription factor KLF17. Key components of the TGF-β signaling pathway including NODAL, GDF3, TGFBR1/ALK5, LEFTY1, SMAD2, SMAD4 and TDGF1 are also enriched in the human epiblast. Intriguingly, inhibition of TGF-β signaling abrogates NANOG expression in human epiblast cells, consistent with a requirement for this pathway in pluripotency. Although key trophectoderm factors Id2, Elf5, and Eomes are exclusively localized to this lineage in the mouse, the human orthologues are either absent or expressed in alternative lineages. Importantly, we also identify genes with conserved expression dynamics including Foxa2/FOXA2, which we show is restricted to the primitive endoderm in both human and mouse embryos. Comparisons of the human epiblast to existing embryonic stem cells (hESCs) reveals conservation of pluripotency but also additional pathways more enriched in hESCs. Our analysis highlights significant differences in human preimplantation development compared to mouse and provides a molecular blueprint to understand human embryogenesis and its relationship to stem cells.
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., et al. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0506580102 · Cited by 53,625
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581
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
The ground state of embryonic stem cell self-renewal
Ying, Q.-L., Wray, J., Nichols, J., Batlle-Morera, L., Doble, B., Woodgett, J., Cohen, P., & Smith, A. (2008) · Nature · doi:10.1038/nature06968 · Cited by 3,442
New cell lines from mouse epiblast share defining features with human embryonic stem cells
Tesar, P. J., Chenoweth, J. G., Brook, F. A., Davies, T. J., Evans, E. P., Mack, D. L., Gardner, R. L., & McKay, R. D. G. (2007) · Nature · doi:10.1038/nature05972 · Cited by 2,152
Derivation of pluripotent epiblast stem cells from mammalian embryos
Brons, I. G. M., Smithers, L. E., Trotter, M. W. B., Rugg-Gunn, P., Sun, B., Chuva de Sousa Lopes, S. M., Howlett, S. K., Clarkson, A., Ahrlund-Richter, L., Pedersen, R. A., et al. (2007) · Nature · doi:10.1038/nature05950 · Cited by 2,007
Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells
Yan, L., Yang, M., Guo, H., Yang, L., Wu, J., Li, R., Liu, P., Lian, Y., Zheng, X., Yan, J., et al. (2013) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.2660 · Cited by 1,740
Differential expression in RNA-seq: A matter of depth
Tarazona, S., García-Alcalde, F., Dopazo, J., Ferrer, A., & Conesa, A. (2011) · Genome Research · doi:10.1101/gr.124321.111 · Cited by 1,632
Braude, P., Bolton, V., & Moore, S. (1988) · Nature · doi:10.1038/332459a0 · Cited by 1,463
Bayesian approach to single-cell differential expression analysis
Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348
Cited by
Integrating single-cell transcriptomic data across different conditions, technologies, and species
Butler, A., Hoffman, P., Smibert, P., Papalexi, E., & Satija, R. (2018) · Nature Biotechnology · doi:10.1038/nbt.4096 · Cited by 13,174
SC3: consensus clustering of single-cell RNA-seq data
Kiselev, V. Y., Kirschner, K., Schaub, M. T., Andrews, T., Yiu, A., Chandra, T., Natarajan, K. N., Reik, W., Barahona, M., Green, A. R., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4236 · Cited by 1,500
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
Construction of a human cell landscape at single-cell level
Han, X., Zhou, Z., Fei, L., Sun, H., Wang, R., Chen, Y., Chen, H., Wang, J., Tang, H., Ge, W., et al. (2020) · Nature · doi:10.1038/s41586-020-2157-4 · Cited by 1,087
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
A human cell atlas of fetal gene expression
Cao, J., O’Day, D. R., Pliner, H. A., Kingsley, P. D., Deng, M., Daza, R. M., Zager, M. A., Aldinger, K. A., Blecher-Gonen, R., Zhang, F., et al. (2020) · Science · doi:10.1126/science.aba7721 · Cited by 764
Dynamic stem cell states: Naive to primed pluripotency in rodents and humans
Weinberger, L., Ayyash, M., Novershtern, N., & Hanna, J. H. (2016) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm.2015.28 · Cited by 622
Genome editing reveals a role for OCT4 in human embryogenesis
Fogarty, N. M. E., McCarthy, A., Snijders, K. E., Powell, B. E., Kubikova, N., Blakeley, P., Lea, R., Elder, K., Wamaitha, S. E., Kim, D., et al. (2017) · Nature · doi:10.1038/nature24033 · Cited by 392
Self-citationDrImpute: imputing dropout events in single cell RNA sequencing data
Gong, W., Kwak, I.-Y., Pota, P., Koyano-Nakagawa, N., & Garry, D. J. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2226-y · Cited by 319
Epigenetic resetting of human pluripotency
Guo, G., von Meyenn, F., Rostovskaya, M., Clarke, J., Dietmann, S., Baker, D., Sahakyan, A., Myers, S., Bertone, P., Reik, W., et al. (2017) · Development · doi:10.1242/dev.146811 · Cited by 319
Messmer, T., von Meyenn, F., Savino, A., Santos, F., Mohammed, H., Lun, A. T. L., Marioni, J. C., & Reik, W. (2019) · Cell Reports · doi:10.1016/j.celrep.2018.12.099 · Cited by 156
Progress and challenges in large-scale expansion of human pluripotent stem cells
Kropp, C., Massai, D., & Zweigerdt, R. (2017) · Process Biochemistry · doi:10.1016/j.procbio.2016.09.032 · Cited by 167
Principles of signaling pathway modulation for enhancing human naive pluripotency induction
Bayerl, J., Ayyash, M., Shani, T., Manor, Y. S., Gafni, O., Massarwa, R., Kalma, Y., Aguilera-Castrejon, A., Zerbib, M., Amir, H., et al. (2021) · Cell stem cell · doi:10.1016/j.stem.2021.04.001 · Cited by 165
Multi-omic Profiling Reveals Dynamics of the Phased Progression of Pluripotency
Yang, P., Humphrey, S. J., Cinghu, S., Pathania, R., Oldfield, A. J., Kumar, D., Perera, D., Yang, J. Y. H., James, D. E., Mann, M., et al. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.012 · Cited by 146
Integrated analysis of single cell transcriptomic data across conditions, technologies, and species
Butler, A., & Satija, R. (2017) · doi:10.1101/164889 · Cited by 13,200
“(1-7) (1-7) (8) (8) (12-13) (12-13) (18) (18) (9-10) (9-10) (11) (11) (14-15) (14-15) (16-17) (16-17) Er yth Mk Bas DC ono eu Eo LM PP .”
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
RNA-seq as a tool for evaluating human embryo competence
Groff, A. F., Resetkova, N., DiDomenico, F., Sakkas, D., Penzias, A., Rinn, J. L., & Eggan, K. (2019) · Genome Research · doi:10.1101/gr.252981.119 · Cited by 47
Metadata
- Authors
- Blakeley, P., Fogarty, N. M. E., del Valle, I., Wamaitha, S. E., Hu, T. X., Elder, K., Snell, P., Christie, L., Robson, P., & Niakan, K. K.
- Venue
- Development
- Publisher
- The Company of Biologists
- Published
- 2015-01-01
- Volume
- 142
- Issue
- 18
- Pages
- 3151-3613
- ISSN
- 0950-1991 (ISSN-L)
- 1477-9129
- OpenAlex
- W2125336256
- Cited by
- 466
- References
- 76
- FWCI
- 19.68
- Type
- journal article
- Language
- en
- Open access
- hybrid
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.