The DOI matches its Crossref record; 51 of the 53 references it cites were found in a registry or an index.
Spatial genomics enables multi-modal study of clonal heterogeneity in tissues
Open accessAbstract
The state and behaviour of a cell can be influenced by both genetic and environmental factors. In particular, tumour progression is determined by underlying genetic aberrations 1-4 as well as the makeup of the tumour microenvironment 5,6. Quantifying the contributions of these factors requires new technologies that can accurately measure the spatial location of genomic sequence together with phenotypic readouts. Here we developed slide-DNA-seq, a method for capturing spatially resolved DNA sequences from intact tissue sections. We demonstrate that this method accurately preserves local tumour architecture and enables the de novo discovery of distinct tumour clones and their copy number alterations. We then apply slide-DNA-seq to a mouse model of metastasis and a primary human cancer, revealing that clonal populations are confined to distinct spatial regions. Moreover, through integration with spatial transcriptomics, we uncover distinct sets of genes that are associated with clone-specific genetic aberrations, the local tumour microenvironment, or both. Together, this multi-modal spatial genomics approach provides a versatile platform for quantifying how cell-intrinsic and cell-extrinsic factors contribute to gene expression, protein abundance and other cellular phenotypes.
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“Very recently, a newly developed spatial technology, slide-DNA-seq, was used to preserve local tumour architecture, enabling the discovery of distinct tumour clones and their copy number variations157.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [157]
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1 retracted
HMGA2 functions as a competing endogenous RNA to promote lung cancer progression
Kumar, M. S., Armenteros-Monterroso, E., East, P., Chakravorty, P., Matthews, N., Winslow, M. M., & Downward, J. (2013) · Nature · doi:10.1038/nature12785 · Cited by 255
RetractedComprehensive molecular characterization of human colon and rectal cancer
The Cancer Genome Atlas Network (2012) · Nature · doi:10.1038/nature11252 · Cited by 8,499
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013) · Nature Methods · doi:10.1038/nmeth.2688 · Cited by 6,422
Self-citationClonal heterogeneity and tumor evolution: past, present, and the future
McGranahan, N., & Swanton, C. (2017) · Cell · doi:10.1016/j.cell.2017.01.018 · Cited by 2,840
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
Self-citationTracking the Evolution of Non–Small-Cell Lung Cancer
Jamal-Hanjani, M., Wilson, G. A., McGranahan, N., Birkbak, N. J., Watkins, T. B. K., Veeriah, S., Shafi, S., Johnson, D. H., Mitter, R., Rosenthal, R., et al. (2017) · New England Journal of Medicine · doi:10.1056/nejmoa1616288 · Cited by 2,349
Concepts of extracellular matrix remodelling in tumour progression and metastasis
Winkler, J., Abisoye-Ogunniyan, A., Metcalf, K. J., & Werb, Z. (2020) · Nature Communications · doi:10.1038/s41467-020-18794-x · Cited by 2,296
Robust decomposition of cell type mixtures in spatial transcriptomics
Cable, D. M., Murray, E., Zou, L. S., Goeva, A., Macosko, E. Z., Chen, F., & Irizarry, R. A. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-00830-w · Cited by 1,487
Self-citationThe Integrative Human Microbiome Project
The Integrative HMP (iHMP) Research Network Consortium, Proctor, L. M., Creasy, H. H., Fettweis, J. M., Lloyd-Price, J., Mahurkar, A., Zhou, W., Buck, G. A., Snyder, M. P., Strauss, J. F., et al. (2019) · Nature · doi:10.1038/s41586-019-1238-8 · Cited by 1,381
Evolution and impact of subclonal mutations in chronic lymphocytic leukemia
Landau, D. A., Carter, S. L., Stojanov, P., McKenna, A., Stevenson, K., Lawrence, M. S., Sougnez, C., Stewart, C., Sivachenko, A., Wang, L., et al. (2013) · Cell · doi:10.1016/j.cell.2013.01.019 · Cited by 1,379
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Sorted by citations
The technological landscape and applications of single-cell multi-omics
Baysoy, A., Bai, Z., Satija, R., & Fan, R. (2023) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-023-00615-w · Cited by 1,037
Breast cancer: pathogenesis and treatments
Xiong, X., Zheng, L., Ding, Y., Chen, Y., Cai, Y., Wang, L., Huang, L., Liu, C., Shao, Z., & Yu, K. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-024-02108-4 · Cited by 1,170
Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma
Ravi, V. M., Will, P., Kueckelhaus, J., Sun, N., Joseph, K., Salié, H., Vollmer, L., Kuliešiūtė, U., von Ehr, J., Benotmane, J. K., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.05.009 · Cited by 686
Bressan, D., Battistoni, G., & Hannon, G. J. (2023) · Science · doi:10.1126/science.abq4964 · Cited by 622
The expanding vistas of spatial transcriptomics
Tian, L., Chen, F., & Macosko, E. Z. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01448-2 · Cited by 539
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Metadata
- Authors
- Zhao, T., Chiang, Z. D., Morriss, J. W., LaFave, L. M., Murray, E. M., Del Priore, I., Meli, K., Lareau, C. A., Nadaf, N. M., Li, J., Earl, A. S., Macosko, E. Z., Jacks, T., Buenrostro, J. D., & Chen, F.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- Massachusetts Institute of Technology (US)
- Corresponding author
- Buenrostro, Jason D.
- Chen, Fei
- ORCID
- Venue
- Nature
- Published
- 2021-12-15
- Volume
- 601
- Issue
- 7891
- Pages
- 85-91
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC9301586
- OpenAlex
- W4200559225
- Cited by
- 268
- References
- 53
- Citations by year
- 2026: 51 · 2025: 51 · 2024: 75 · 2023: 53 · 2022: 37
- FWCI
- 13.34
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- Wellcome Trust
- National Institutes of Health (DP5OD024583)
- National Heart, Lung, and Blood Institute (DP2HL151353)
- National Human Genome Research Institute (R21HG009749, T32HG002295, R01HG010647)
- National Cancer Institute (UH3CA246632, R33CA246455)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomic variations and chromosomal abnormalities
- Keywords
- spatial genomics, tumor microenvironment, copy number alterations, tumor architecture, metastasis, spatial transcriptomics, tumor progression, gene expression regulation
- MeSH terms
- RNA-Seq, Animals, Clone Cells, Humans, Phenotype, Transcription, Genetic, Colorectal Neoplasms, Sequence Analysis, DNA, Genomics, Mice, DNA Copy Number Variations, Transcriptome
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