The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Single-cell RNA-seq reveals the diversity of trophoblast subtypes and patterns of differentiation in the human placenta
Open accessAbstract
The placenta is crucial for a successful pregnancy and the health of both the fetus and the pregnant woman. However, how the human trophoblast lineage is regulated, including the categorization of the placental cell subtypes is poorly understood. Here we performed single-cell RNA sequencing (RNA-seq) on sorted placental cells from first- and second-trimester human placentas. New subtypes of cells of the known cytotrophoblast cells (CTBs), extravillous trophoblast cells (EVTs), Hofbauer cells, and mesenchymal stromal cells were identified and cell-type-specific gene signatures were defined. Functionally, this study revealed many previously unknown functions of the human placenta. Notably, 102 polypeptide hormone genes were found to be expressed by various subtypes of placental cells, which suggests a complex and significant role of these hormones in regulating fetal growth and adaptations of maternal physiology to pregnancy. These results document human placental trophoblast differentiation at single-cell resolution and thus advance our understanding of human placentation during the early stage of pregnancy.
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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
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464
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
Maltepe, E., & Fisher, S. J. (2015) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-100814-125620 · Cited by 592
Resolving early mesoderm diversification through single-cell expression profiling
Scialdone, A., Tanaka, Y., Jawaid, W., Moignard, V., Wilson, N. K., Macaulay, I. C., Marioni, J. C., & Göttgens, B. (2016) · Nature · doi:10.1038/nature18633 · Cited by 293
Chen, E. H., Grote, E., Mohler, W., & Vignery, A. (2007) · FEBS Letters · doi:10.1016/j.febslet.2007.03.033 · Cited by 263
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Single-cell reconstruction of the early maternal–fetal interface in humans
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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
Ashary, N., Bhide, A., Chakraborty, P., Colaco, S., Mishra, A., Chhabria, K., Jolly, M. K., & Modi, D. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.00783 · Cited by 121
Transcriptomics and Other Omics Approaches to Investigate Effects of Xenobiotics on the Placenta
Rosenfeld, C. S. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.723656 · Cited by 43
Metadata
- Authors
- Liu, Y., Fan, X., Wang, R., Lu, X., Dang, Y.-L., Wang, H., Lin, H.-Y., Zhu, C., Ge, H., Cross, J. C., & Wang, H.
- Venue
- Cell Research
- Published
- 2018-07-24
- Volume
- 28
- Issue
- 8
- Pages
- 819-832
- ISSN
- 1001-0602 (ISSN-L)
- 1748-7838
- OpenAlex
- W2883778176
- Cited by
- 411
- References
- 6
- FWCI
- 10.12
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Liu, Y., Fan, X., Wang, R., Lu, X., Dang, Y.-L., Wang, H., Lin, H.-Y., Zhu, C., Ge, H., Cross, J. C., & Wang, H. (2018). Single-cell RNA-seq reveals the diversity of trophoblast subtypes and patterns of differentiation in the human placenta. Cell Research. https://doi.org/10.1038/s41422-018-0066-y
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