The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Spatiotemporal Developmental Trajectories in the Arabidopsis Root Revealed Using High-Throughput Single-Cell RNA Sequencing
Open accessAbstract
High-throughput single-cell RNA sequencing (scRNA-seq) is becoming a cornerstone of developmental research, providing unprecedented power in understanding dynamic processes. Here, we present a high-resolution scRNA-seq expression atlas of the Arabidopsis root composed of thousands of independently profiled cells. This atlas provides detailed spatiotemporal information, identifying defining expression features for all major cell types, including the scarce cells of the quiescent center. These reveal key developmental regulators and downstream genes that translate cell fate into distinctive cell shapes and functions. Developmental trajectories derived from pseudotime analysis depict a finely resolved cascade of cell progressions from the niche through differentiation that are supported by mirroring expression waves of highly interconnected transcription factors. This study demonstrates the power of applying scRNA-seq to plants and provides an unparalleled spatiotemporal perspective of root cell differentiation.
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
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
Love, M. I., Huber, W., & Anders, S. (2014) · Genome Biology · doi:10.1186/s13059-014-0550-8 · Cited by 95,106
Trimmomatic: a flexible trimmer for Illumina sequence data
Bolger, A. M., Lohse, M., & Usadel, B. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu170 · Cited by 65,548
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
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
(2008) · Journal of Machine Learning Research · Cited by 33,051
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
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
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353
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
Cited by
High-Throughput Single-Cell Transcriptome Profiling of Plant Cell Types
Shulse, C. N., Cole, B. J., Ciobanu, D., Lin, J., Yoshinaga, Y., Gouran, M., Turco, G. M., Zhu, Y., O’Malley, R. C., Brady, S. M., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.04.054 · Cited by 432
Shahan, R., Hsu, C.-W., Nolan, T. M., Cole, B. J., Taylor, I. W., Greenstreet, L., Zhang, S., Afanassiev, A., Vlot, A. H. C., Schiebinger, G., et al. (2022) · Developmental Cell · doi:10.1016/j.devcel.2022.01.008 · Cited by 363
Decaestecker, W., Buono, R. A., Pfeiffer, M. L., Vangheluwe, N., Jourquin, J., Karimi, M., Van Isterdael, G., Beeckman, T., Nowack, M. K., & Jacobs, T. B. (2019) · The Plant Cell · doi:10.1105/tpc.19.00454 · Cited by 258
Plant synthetic biology innovations for biofuels and bioproducts
Yang, Y., Chaffin, T. A., Ahkami, A. H., Blumwald, E., & Stewart, C. N. (2022) · Trends in Biotechnology · doi:10.1016/j.tibtech.2022.09.007 · Cited by 59
Platre, M. P., Satbhai, S. B., Brent, L., Gleason, M. F., Cao, M., Grison, M., Glavier, M., Zhang, L., Gaillochet, C., Goeschl, C., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-32167-6 · Cited by 46
Metadata
- Authors
- Denyer, T., Ma, X., Klesen, S., Scacchi, E., Nieselt, K., & Timmermans, M. C. P.
- Venue
- Developmental Cell
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 48
- Issue
- 6
- Pages
- 840-852.e5
- ISSN
- 1534-5807 (ISSN-L)
- OpenAlex
- W2922995994
- Cited by
- 608
- References
- 54
- FWCI
- 14.99
- Type
- journal article
- Language
- en
- Open access
- bronze
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.
