The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Transcriptomic Profiling Maps Anatomically Patterned Subpopulations among Single Embryonic Cardiac Cells
Open accessAbstract
Summary Embryonic gene expression intricately reflects anatomical context, developmental stage, and cell type. To address whether the precise spatial origins of cardiac cells can be deduced solely from their transcriptional profiles, we established a genome-wide expression database from 118, 949, and 1166 single murine heart cells at embryonic days (e)8.5, 9.5, and 10.5, respectively. We segregated these cells by type using unsupervised bioinformatic analysis and identified chamber-specific genes. Using a random forest algorithm, we reconstructed the spatial origin of single e9.5 and e10.5 cardiomyocytes with 92.0+/-3.2% and 91.2+/-2.8% accuracy respectively (99.4+/-1.0% and 99.1+/-1.1% if a +/-1 zone margin is permitted) and predicted the second heart field distribution of Isl1-lineage descendants. When applied to Nkx2-5-/- cardiomyocytes from murine e9.5 hearts, we showed their transcriptional alteration and lack of ventricular phenotype. Our database and zone classification algorithm will enable the discovery of novel mechanisms in early cardiac development and disease.
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Cites
WGCNA: an R package for weighted correlation network analysis
Langfelder, P., & Horvath, S. (2008) · BMC Bioinformatics · doi:10.1186/1471-2105-9-559 · Cited by 27,252
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
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
Spatial reconstruction of single-cell gene expression data
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F., & Regev, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3192 · Cited by 7,055
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 · cited 2×
“Two uL of RNA for each sample were reverse transcribed and pre-amplified following the protocol described previously (Picelli et al., 2013).”
Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq
Treutlein, B., Brownfield, D. G., Wu, A. R., Neff, N. F., Mantalas, G. L., Espinoza, F. H., Desai, T. J., Krasnow, M. A., & Quake, S. R. (2014) · Nature · doi:10.1038/nature13173 · Cited by 1,342
Self-citationKattman, S. J., Witty, A. D., Gagliardi, M., Dubois, N. C., Niapour, M., Hotta, A., Ellis, J., & Keller, G. (2011) · Cell Stem Cell · doi:10.1016/j.stem.2010.12.008 · Cited by 1,159
Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing
Xue, Z., Huang, K., Cai, C., Cai, L., Jiang, C.-Y., Feng, Y., Liu, Z., Zeng, Q., Cheng, L., Sun, Y. E., et al. (2013) · Nature · doi:10.1038/nature12364 · Cited by 963
Quantitative assessment of single-cell RNA-sequencing methods
Wu, A. R., Neff, N. F., Kalisky, T., Dalerba, P., Treutlein, B., Rothenberg, M. E., Mburu, F. M., Mantalas, G. L., Sim, S., Clarke, M. F., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2694 · Cited by 696
Self-citationIsl1Cre reveals a common Bmp pathway in heart and limb development
Yang, L., Cai, C.-L., Lin, L., Qyang, Y., Chung, C., Monteiro, R. M., Mummery, C. L., Fishman, G. I., Cogen, A., & Evans, S. (2006) · Development · doi:10.1242/dev.02322 · Cited by 267
Cited by
Friedman, C. E., Nguyen, Q., Lukowski, S. W., Helfer, A., Chiu, H. S., Miklas, J., Levy, S., Suo, S., Han, J.-D. J., Osteil, P., et al. (2018) · Cell Stem Cell · doi:10.1016/j.stem.2018.09.009 · Cited by 258
Churko, J. M., Garg, P., Treutlein, B., Venkatasubramanian, M., Wu, H., Lee, J., Wessells, Q. N., Chen, S.-Y., Chen, W.-Y., Chetal, K., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07333-4 · Cited by 207
Mantri, M., Scuderi, G. J., Abedini-Nassab, R., Wang, M. F. Z., McKellar, D., Shi, H., Grodner, B., Butcher, J. T., & De Vlaminck, I. (2021) · Nature Communications · doi:10.1038/s41467-021-21892-z · Cited by 205
Dong, J., Hu, Y., Fan, X., Wu, X., Mao, Y., Hu, B., Guo, H., Wen, L., & Tang, F. (2018) · Genome Biology · doi:10.1186/s13059-018-1416-2 · Cited by 187
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
Modeling Human TBX5 Haploinsufficiency Predicts Regulatory Networks for Congenital Heart Disease
Kathiriya, I. S., Rao, K. S., Iacono, G., Devine, W. P., Blair, A. P., Hota, S. K., Lai, M. H., Garay, B. I., Thomas, R., Gong, H. Z., et al. (2021) · Developmental Cell · doi:10.1016/j.devcel.2020.11.020 · Cited by 116
Gerbin, K. A., Grancharova, T., Donovan-Maiye, R. M., Hendershott, M. C., Anderson, H. G., Brown, J. M., Chen, J., Dinh, S. Q., Gehring, J. L., Johnson, G. R., et al. (2021) · Cell Systems · doi:10.1016/j.cels.2021.05.001 · Cited by 59
Coordination of endothelial cell positioning and fate specification by the epicardium
Quijada, P., Trembley, M. A., Misra, A., Myers, J. A., Baker, C. D., Pérez-Hernández, M., Myers, J. R., Dirkx, R. A., Cohen, E. D., Delmar, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24414-z · Cited by 50
Metadata
- Authors
- Li, G., Xu, A., Sim, S., Priest, J. R., Tian, X., Khan, T., Quertermous, T., Zhou, B., Tsao, P. S., Quake, S. R., & Wu, S. M.
- Venue
- Developmental Cell
- Publisher
- Elsevier BV
- Published
- 2016-11
- Volume
- 39
- Issue
- 4
- Pages
- 491-507
- ISSN
- 1534-5807 (ISSN-L)
- OpenAlex
- W2555626725
- Cited by
- 251
- References
- 11
- FWCI
- 10.83
- Type
- journal article
- Language
- en
- Open access
- bronze
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