The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Embryo-scale, single-cell spatial transcriptomics
Open accessAbstract
Localizing gene expression: Single-cell RNA sequencing can provide information about cellular relationships based on shared transcriptomes, but most methods lose spatial information. Those methods that do retain spatial information can be limited to a specific set of genes and/or a small area. Srivatsan et al. introduce sci-Space, a spatial transcriptomic method that uses a grid of barcoded oligos on a slide that can be transferred to nuclei of an overlaid frozen tissue section to obtain both the spatial origin and the transcriptome of thousands of single cells per slide. The researchers used sci-Space to create a spatial atlas of mouse E14 sagittal sections, revealing spatially expressed genes across cell types. This application illustrates how sci-Space complements existing approaches in spatial genomics. Science , abb9536, this issue p. 111
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
Fiji: an open-source platform for biological-image analysis
Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2019 · Cited by 66,890
Comprehensive Integration of Single-Cell Data
Stuart, T., Butler, A., Hoffman, P., Hafemeister, C., Papalexi, E., Mauck, W. M., Hao, Y., Stoeckius, M., Smibert, P., & Satija, R. (2019) · Cell · doi:10.1016/j.cell.2019.05.031 · Cited by 15,453
Reversed graph embedding resolves complex single-cell trajectories
Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070
Self-citationThe single-cell transcriptional landscape of mammalian organogenesis
Cao, J., Spielmann, M., Qiu, X., Huang, X., Ibrahim, D. M., Hill, A. J., Zhang, F., Mundlos, S., Christiansen, L., Steemers, F. J., et al. (2019) · Nature · doi:10.1038/s41586-019-0969-x · Cited by 4,490
Self-citationVisualization and analysis of gene expression in tissue sections by spatial transcriptomics
Ståhl, P. L., Salmén, F., Vickovic, S., Lundmark, A., Navarro, J. F., Magnusson, J., Giacomello, S., Asp, M., Westholm, J. O., Huss, M., et al. (2016) · Science · doi:10.1126/science.aaf2403 · Cited by 3,834
A pyramid approach to subpixel registration based on intensity
Thevenaz, P., Ruttimann, U. E., & Unser, M. (1998) · IEEE Transactions on Image Processing · doi:10.1109/83.650848 · Cited by 3,078
Spatially resolved, highly multiplexed RNA profiling in single cells
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S., & Zhuang, X. (2015) · Science · doi:10.1126/science.aaa6090 · Cited by 2,969
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2018) · doi:10.1101/357368 · Cited by 2,758
Scrublet: computational identification of cell doublets in single-cell transcriptomic data
Wolock, S. L., Lopez, R., & Klein, A. M. (2019) · Cell Systems · doi:10.1016/j.cels.2018.11.005 · Cited by 2,758
Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution
Rodriques, S. G., Stickels, R. R., Goeva, A., Martin, C. A., Murray, E., Vanderburg, C. R., Welch, J., Chen, L. M., Chen, F., & Macosko, E. Z. (2019) · Science · doi:10.1126/science.aaw1219 · Cited by 2,424
Cited by
Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays
Chen, A., Liao, S., Cheng, M., Ma, K., Wu, L., Lai, Y., Qiu, X., Yang, J., Xu, J., Hao, S., et al. (2022) · Cell · doi:10.1016/j.cell.2022.04.003 · Cited by 1,784
Museum of spatial transcriptomics
Moses, L., & Pachter, L. (2022) · Nature Methods · doi:10.1038/s41592-022-01409-2 · Cited by 1,255
Museum of Spatial Transcriptomics
Moses, L., & Pachter, L. (2021) · doi:10.1101/2021.05.11.443152 · Cited by 1,255
An introduction to spatial transcriptomics for biomedical research
Williams, C. G., Lee, H. J., Asatsuma, T., Vento-Tormo, R., & Haque, A. (2022) · Genome Medicine · doi:10.1186/s13073-022-01075-1 · Cited by 848
Pham, D., Tan, X., Balderson, B., Xu, J., Grice, L. F., Yoon, S., Willis, E. F., Tran, M., Lam, P. Y., Raghubar, A., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43120-6 · Cited by 730
Metadata
- Authors
- Srivatsan, S. R., Regier, M. C., Barkan, E., Franks, J. M., Packer, J. S., Grosjean, P., Duran, M., Saxton, S., Ladd, J. J., Spielmann, M., Lois, C., Lampe, P. D., Shendure, J., Stevens, K. R., & Trapnell, C.
- Venue
- Science
- Published
- 2021-07-02
- Volume
- 373
- Issue
- 6550
- Pages
- 111-117
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W3176766013
- Cited by
- 267
- References
- 39
- FWCI
- 13.22
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Embryo-scale, single-cell spatial transcriptomics. https://app.cochat.ai/papers/10.1126/science.abb9536
- The paper
- Srivatsan, S. R., Regier, M. C., Barkan, E., Franks, J. M., Packer, J. S., Grosjean, P., Duran, M., Saxton, S., Ladd, J. J., Spielmann, M., Lois, C., Lampe, P. D., Shendure, J., Stevens, K. R., & Trapnell, C. (2021). Embryo-scale, single-cell spatial transcriptomics. Science. https://doi.org/10.1126/science.abb9536
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.
