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Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq
Open accessAbstract
Single-cell expression profiles of melanoma: Tumors harbor multiple cell types that are thought to play a role in the development of resistance to drug treatments. Tirosh et al. used single-cell sequencing to investigate the distribution of these differing genetic profiles within melanomas. Many cells harbored heterogeneous genetic programs that reflected two different states of genetic expression, one of which was linked to resistance development. Following drug treatment, the resistance-linked expression state was found at a much higher level. Furthermore, the environment of the melanoma cells affected their gene expression programs. Science , this issue p. 189
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“SALL4-regulated genes showed a significant enrichment and positive correlation with published invasiveness signatures40–42, while they significantly anti-correlated with their corresponding proliferation signatures40–42”
From Epigenetic control of melanoma cell invasiveness by the stem cell factor SALL4 (2021) · cites this paper as [42]
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“among the SALL4-HDAC2 targets upregulated upon SALL4 knockdown, we found integrins (ITGB1, ITGA6, ITGA4), N-cadherin (CDH2), TGFBR2, VEGFR-1, PDGFC, LOXL2, MAPK8, and many others which are associated with invasive melanoma phenotypes40–42”
From Epigenetic control of melanoma cell invasiveness by the stem cell factor SALL4 (2021) · cites this paper as [42]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
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“Consistent with previous reports in mice 43 , the proportions of HSCs expressing G2/M/S-phase genes were enhanced upon IFNa treatment ( Fig. 2h,i) .”
From Type 1 interferon perturbates clonal competition by reshaping human blood development (2026) · cites this paper as [43]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
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“As IFNa has been shown to induce HSC and progenitors into cell cycle entry 43 , we posited that differential induction of progenitor subtypes into proliferation may underlie the shifts in differentiation.”
From Type 1 interferon perturbates clonal competition by reshaping human blood development (2026) · cites this paper as [43]
Experimental — accuracy not yet measuredIn the citing paper
“Almost all previous work quantifying differences between single-cell datasets relies on discrete partitioning of the data before downstream analysis9–16.”
From Quantifying the effect of experimental perturbations at single-cell resolution (2021) · cites this paper as [11]
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“Cell cycle genes56 were excluded from this feature set and the dataset was subsetted to the resulting gene set.”
From Inferring and perturbing cell fate regulomes in human brain organoids (2023) · cites this paper as [56]
In the citing paper
“We used the union of the selected genes and TFs and further excluded cell-cycle-related genes56 from the set.”
From Inferring and perturbing cell fate regulomes in human brain organoids (2023) · cites this paper as [56]
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“Through deconvolution of complex samples30, such as tumor biopsies35, one may infer their cellular milieu, and characterize the rare5, functionally important1, and unknown36 cell types they contain.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [35]
In the citing paper
“Several studies inferred the cell cycle phase of individual cells from single-cell RNA-seq data30,35,97,99, relying on transcriptional signatures of discrete phases from earlier bulk profiling experiments146.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [35]
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“We performed latent variable regression to mitigate heterogeneity resulting from cell-cycle state10,11 (Extended Data Fig. 1), preventing subsequent analysis from being dominated by mitotic phase-specific gene expression”
From Developmental diversification of cortical inhibitory interneurons (2018) · cites this paper as [11]
In the citing paper
“In parallel, methods to measure the transcriptomes of single cells have been used to discover new cell types11 and new functional cell states12,13, and provide additional motivation for the development of tools to measure chromatin regulation at single-cell resolution14.”
From Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility (2019) · cites this paper as [12]
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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
Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520
Understanding the tumor immune microenvironment (TIME) for effective therapy
Binnewies, M., Roberts, E. W., Kersten, K., Chan, V., Fearon, D. F., Merad, M., Coussens, L. M., Gabrilovich, D. I., Ostrand-Rosenberg, S., Hedrick, C. C., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0014-x · Cited by 6,145
Determining cell type abundance and expression from bulk tissues with digital cytometry
Newman, A. M., Steen, C. B., Liu, C. L., Gentles, A. J., Chaudhuri, A. A., Scherer, F., Khodadoust, M. S., Esfahani, M. S., Luca, B. A., Steiner, D. F., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0114-2 · Cited by 4,586
Metadata
- Authors
- Tirosh, I., Izar, B., Prakadan, S. M., Wadsworth, M. H., Treacy, D., Trombetta, J. J., Rotem, A., Rodman, C., Lian, C., Murphy, G., Fallahi-Sichani, M., Dutton-Regester, K., Lin, J.-R., Cohen, O., Shah, P., Lu, D., Genshaft, A. S., Hughes, T. K., Ziegler, C. G. K., Kazer, S. W., Gaillard, A., Kolb, K. E., Villani, A.-C., Johannessen, C. M., Andreev, A. Y., Van Allen, E. M., Bertagnolli, M., Sorger, P. K., Sullivan, R. J., Flaherty, K. T., Frederick, D. T., Jané-Valbuena, J., Yoon, C. H., Rozenblatt-Rosen, O., Shalek, A. K., Regev, A., & Garraway, L. A.
- Affiliations
- Broad Institute (US)
- Brigham and Women's Hospital (US)
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.
- Program in Therapeutic Sciences, Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
- Harvard University (US)
- Massachusetts General Hospital (US)
- Corresponding author
- Tirosh, Itay
- Izar, Benjamin
- Yoon, Charles H.
- Shalek, Alex K.
- Regev, Aviv
- Garraway, Levi A.
- ORCID
- Tirosh, Itay
- Izar, Benjamin
- Prakadan, Sanjay M.
- Wadsworth, Marc H.
- Rotem, Asaf
- Rodman, Christopher
- Lian, Christine
- Lin, Jia-Ren
- Cohen, Ofir
- Shah, Parin
- Genshaft, Alex S.
- Hughes, Travis K.
- Ziegler, Carly G. K.
- Kazer, Samuel W.
- Kolb, Kellie E.
- Villani, Alexandra-Chloé
- Johannessen, Cory M.
- Bertagnolli, Monica
- Sorger, Peter K.
- Sullivan, Ryan J.
- Flaherty, Keith T.
- Frederick, Dennie T.
- Yoon, Charles H.
- Shalek, Alex K.
- Regev, Aviv
- Garraway, Levi A.
- Venue
- Science
- Published
- 2016-04-08
- Volume
- 352
- Issue
- 6282
- Pages
- 189-196
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC4944528
- OpenAlex
- W2332292689
- Cited by
- 4,944
- References
- 70
- Citations by year
- 2026: 546 · 2025: 629 · 2024: 633 · 2023: 606 · 2022: 582 · 2021: 608 · 2020: 486 · 2019: 336 · 2018: 265 · 2017: 195 · 2016: 41
- FWCI
- 145.13
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- PMC
- Funders
- National Cancer Institute (NCI) (P01CA163222, R35CA197737)
- Dr. Miriam and Sheldon Adelson Medical Research Foundation
- Melanoma Research Alliance
- Ludwig Center at Harvard Medical School
- Howard Hughes Medicine Institute
- Klarman Cell Observatory
- STARR Cancer Consortium
- Broad Institute
- Searle Scholars Program
- Beckman Young Investigator Program
- Dana-Farber Cancer Institute
- Human Frontier Science Program
- Integrative Cancer Biology Program (U54CA112962)
- NCI (1U24CA180922, P30-CA14051, K99CA194163)
- NIH (DP2 OD020839, P50GM107618)
- Ludwig Center at Harvard
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Keywords
- metastatic melanoma, tumor microenvironment, tumor heterogeneity, MITF, AXL kinase, drug resistance, tumor-infiltrating T cells, T cell exhaustion, clonal expansion, tumor stroma, endothelial cells, cell-cell interactions, immunotherapy
- MeSH terms
- Base Sequence, Cell Communication, Cell Cycle, Humans, Immunotherapy, Lymphocyte Activation, Melanoma, Neoplasm Metastasis, RNA, Skin Neoplasms, T-Lymphocytes, Stromal Cells, Sequence Analysis, RNA, Drug Resistance, Neoplasm, Genomics, Endothelial Cells, Microphthalmia-Associated Transcription Factor, Single-Cell Analysis, Tumor Microenvironment, Transcriptome
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 ›
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