The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis
Open accessPublished version of a preprint
Abstract
Single-cell and spatial technologies that profile gene expression across a whole tissue are revolutionizing the resolution of molecular states in clinical samples. Current commercially available technologies provide whole transcriptome single-cell, whole transcriptome spatial, or targeted in situ gene expression analysis. Here, we combine these technologies to explore tissue heterogeneity in large, FFPE human breast cancer sections. This integrative approach allowed us to explore molecular differences that exist between distinct tumor regions and to identify biomarkers involved in the progression towards invasive carcinoma. Further, we study cell neighborhoods and identify rare boundary cells that sit at the critical myoepithelial border confining the spread of malignant cells. Here, we demonstrate that each technology alone provides information about molecular signatures relevant to understanding cancer heterogeneity; however, it is the integration of these technologies that leads to deeper insights, ushering in discoveries that will progress oncology research and the development of diagnostics and therapeutics.
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Citations
Cites
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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
Cellpose: a generalist algorithm for cellular segmentation
Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021) · Nature Methods · doi:10.1038/s41592-020-01018-x · Cited by 4,369
The ever‐increasing importance of cancer as a leading cause of premature death worldwide
Bray, F., Laversanne, M., Weiderpass, E., & Soerjomataram, I. (2021) · Cancer · doi:10.1002/cncr.33587 · Cited by 2,591
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A single-cell and spatially resolved atlas of human breast cancers
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Self-citationA single–cell type transcriptomics map of human tissues
Karlsson, M., Zhang, C., Méar, L., Zhong, W., Digre, A., Katona, B., Sjöstedt, E., Butler, L., Odeberg, J., Dusart, P., et al. (2021) · Science Advances · doi:10.1126/sciadv.abh2169 · Cited by 1,768
Robust decomposition of cell type mixtures in spatial transcriptomics
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Research works printed but not linked: 2
- Rao, N., Clark, S. & Habern, O. Bridging Genomics and Tissue Pathology: 10x Genomics explores new frontiers with the Visium Spatial Gene Expression Solution. Genet. Eng. Biotechnol. N. 40, 50–51 (2020). · doi:10.1089/gen.40.02.16
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Cited by
Methods and applications for single-cell and spatial multi-omics
Vandereyken, K., Sifrim, A., Thienpont, B., & Voet, T. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00580-2 · Cited by 1,244
Unsupervised spatially embedded deep representation of spatial transcriptomics
Xu, H., Fu, H., Long, Y., Ang, K. S., Sethi, R., Chong, K., Li, M., Uddamvathanak, R., Lee, H. K., Ling, J., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01283-x · Cited by 391
High-definition spatial transcriptomic profiling of immune cell populations in colorectal cancer
Oliveira, M. F. D., Romero, J. P., Chung, M., Williams, S. R., Gottscho, A. D., Gupta, A., Pilipauskas, S. E., Mohabbat, S., Raman, N., Sukovich, D. J., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02193-3 · Cited by 321
“The cell type annotation for Visium HD, similar to other spatial transcriptomics methods, is based on deconvolution.”
Self-citationProfiling cell identity and tissue architecture with single-cell and spatial transcriptomics
Gulati, G. S., D’Silva, J. P., Liu, Y., Wang, L., & Newman, A. M. (2025) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00768-2 · Cited by 293
LIANA+ Provides an All-in-One Framework for Cell–Cell Communication Inference
Dimitrov, D., Schäfer, P. S. L., Farr, E., Rodriguez-Mier, P., Lobentanzer, S., Badia-i-Mompel, P., Dugourd, A., Tanevski, J., Ramirez Flores, R. O., & Saez-Rodriguez, J. (2024) · Nature Cell Biology · doi:10.1038/s41556-024-01469-w · Cited by 263
Metadata
- Authors
- Janesick, A., Shelansky, R., Gottscho, A. D., Wagner, F., Williams, S. R., Rouault, M., Beliakoff, G., Morrison, C. A., Oliveira, M. F., Sicherman, J. T., Kohlway, A., Abousoud, J., Drennon, T. Y., Mohabbat, S. H., Teams, 1. D., & Taylor, S. E. B.
- Venue
- Nature Communications
- Published
- 2023-12-19
- Volume
- 14
- Issue
- 1
- Pages
- 8353-8353
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W4389938094
- Cited by
- 909
- References
- 42
- FWCI
- 67.77
- Type
- journal article
- Language
- en
- Open access
- gold
- Licence
- creativecommons.org/licenses/by/4.0 (tdm)
- creativecommons.org/licenses/by/4.0 (vor)
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- Janesick, A., Shelansky, R., Gottscho, A. D., Wagner, F., Williams, S. R., Rouault, M., Beliakoff, G., Morrison, C. A., Oliveira, M. F., Sicherman, J. T., Kohlway, A., Abousoud, J., Drennon, T. Y., Mohabbat, S. H., Teams, 1. D., & Taylor, S. E. B. (2023). High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nature Communications. https://doi.org/10.1038/s41467-023-43458-x
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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.
