The DOI matches its Crossref record; 70 of the 70 references it cites were found in a registry or an index.
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
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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.
In the citing paper
“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.
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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]
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
Hallmarks of Cancer: The Next Generation
Hanahan, D., & Weinberg, R. A. (2011) · Cell · doi:10.1016/j.cell.2011.02.013 · Cited by 66,590
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
(2008) · Journal of Machine Learning Research · Cited by 33,051
PD-1 and PD-L1 Immune Checkpoint Blockade to Treat Breast Cancer
Hartkopf, A. D., Taran, F., Wallwiener, M., Walter, C. B., Krämer, B., Grischke, E., & Brucker, S. Y. (2016) · Breast Care · doi:10.1159/000453569 · Cited by 30,989
New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1)
Eisenhauer, E. A., Therasse, P., Bogaerts, J., Schwartz, L. H., Sargent, D., Ford, R., Dancey, J., Arbuck, S., Gwyther, S., Mooney, M., et al. (2009) · European Journal of Cancer · doi:10.1016/j.ejca.2008.10.026 · Cited by 29,534
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome
Li, B., & Dewey, C. N. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-323 · Cited by 22,179
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
(1996) · Cited by 20,179
Adverse Renal Effects of Immune Checkpoint Inhibitors: A Narrative Review
Wanchoo, R., Karam, S., Uppal, N. N., Barta, V. S., Deray, G., Devoe, C., Launay‐Vacher, V., Jhaveri, K. D., & on behalf of Cancer and Kidney International Network Workgroup on Immune Checkpoint Inhibitors (2017) · American Journal of Nephrology · doi:10.1159/000455014 · Cited by 15,320
Cited by
Sorted by citations
Items 401–500 of 1,033
Fan, J., Lee, H., Lee, S., Ryu, D., Lee, S., Xue, C., Kim, S. J., Kım, K., Barkas, N., Park, P. J., et al. (2018) · Genome Research · doi:10.1101/gr.228080.117 · Cited by 228
DSTG: deconvoluting spatial transcriptomics data through graph-based artificial intelligence
Song, Q., & Su, J. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa414 · Cited by 229
Quantifying the effect of experimental perturbations at single-cell resolution
Burkhardt, D. B., Stanley, J. S., Tong, A., Perdigoto, A. L., Gigante, S. A., Herold, K. C., Wolf, G., Giraldez, A. J., van Dijk, D., & Krishnaswamy, S. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-00803-5 · Cited by 226
Schlesinger, Y., Yosefov-Levi, O., Kolodkin‐Gal, D., Granit, R. Z., Peters, L., Kalifa, R., Lei, X., Nasereddin, A., Shiff, I., Amran, O., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18207-z · Cited by 226
A signature for pan-cancer prognosis based on neutrophil extracellular traps
Zhang, Y., Guo, L., Dai, Q., Shang, B., Xiao, T., Di, X., Zhang, K., Feng, L., Shou, J., & Wang, Y. (2022) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2021-004210 · Cited by 226
Slide-tags enables single-nucleus barcoding for multimodal spatial genomics
Russell, A. J. C., Weir, J. A., Nadaf, N. M., Shabet, M., Kumar, V., Kambhampati, S., Raichur, R., Marrero, G. J., Liu, S., Balderrama, K. S., et al. (2024) · Nature · doi:10.1038/s41586-023-06837-4 · Cited by 225
Self-citationTumor-associated myeloid cells: diversity and therapeutic targeting
Mantovani, A., Marchesi, F., Jaillon, S., Garlanda, C., & Allavena, P. (2021) · Cellular and Molecular Immunology · doi:10.1038/s41423-020-00613-4 · Cited by 230
Zuo, S., Wei, M., Wang, S., Dong, J., & Wei, J. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.01218 · Cited by 224
Single-cell analysis supports a luminal-neuroendocrine transdifferentiation in human prostate cancer
Dong, B., Miao, J., Wang, Y., Luo, W., Ji, Z., Lai, H., Zhang, M., Cheng, X., Wang, J., Fang, Y., et al. (2020) · Communications Biology · doi:10.1038/s42003-020-01476-1 · Cited by 230
Cellular architecture of human brain metastases
Velozo, H. G., Mei, W., Robles, I., Hagerling, C., Allen, B. M., Okholm, T. L. H., Nanjaraj, A., Verbeek, T. C. A. I., Kalavacherla, S., van Gogh, M., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.043 · Cited by 224
Time-resolved single-cell transcriptomics defines immune trajectories in glioblastoma
Kirschenbaum, D. S., Xie, K., Ingelfinger, F., Katzenelenbogen, Y., Abadie, K., Look, T., Sheban, F., Phan, T. S., Li, B., Zwicky, P., et al. (2023) · Cell · doi:10.1016/j.cell.2023.11.032 · Cited by 224
Rare Pulmonary Neuroendocrine Cells Are Stem Cells Regulated by Rb, p53, and Notch
Ouadah, Y., Rojas, E., Riordan, D. P., Capostagno, S., Kuo, C. S., & Krasnow, M. A. (2019) · Cell · doi:10.1016/j.cell.2019.09.010 · Cited by 223
Autocrine vitamin D signaling switches off pro-inflammatory programs of TH1 cells
Chauss, D., Freiwald, T., McGregor, R., Yan, B., Wang, L., Nova‐Lamperti, E., Kumar, D., Zhang, Z., Teague, H., West, E. E., et al. (2021) · Nature Immunology · doi:10.1038/s41590-021-01080-3 · Cited by 227
Gomez-Roca, C., Italiano, A., Tourneau, C. L., Cassier, P. A., Toulmonde, M., D’Angelo, S. P., Campone, M., WEBER, K. L., Loirat, D., Cannarile, M. A., et al. (2019) · Annals of Oncology · doi:10.1093/annonc/mdz163 · Cited by 223
SARS-CoV-2 drives JAK1/2-dependent local complement hyperactivation
Yan, B., Freiwald, T., Chauss, D., Wang, L., West, E. E., Mirabelli, C., Zhang, C. J., Nichols, E.-M., Malik, N. T., Gregory, R., et al. (2021) · Science Immunology · doi:10.1126/sciimmunol.abg0833 · Cited by 222
Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis
Wang, G., Qiu, M., Xing, X., Zhou, J., Yao, H., Li, M., Yin, R. H., Hou, Y., Li, Y., Pan, S., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abk2756 · Cited by 222
Sathe, A., Mason, K., Grimes, S. M., Zhou, Z., Lau, B. T., Bai, X., Su, A., Tan, X., Lee, H., Suarez, C. J., et al. (2022) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-22-2041 · Cited by 225
Spatial transcriptomic survey of human embryonic cerebral cortex by single-cell RNA-seq analysis
Fan, X., Dong, J., Zhong, S., Wei, Y., Wu, Q., Yan, L., Yong, J., Sun, L., Wang, X., Zhao, Y., et al. (2018) · Cell Research · doi:10.1038/s41422-018-0053-3 · Cited by 221
Ji, Z., He, L., Regev, A., & Struhl, K. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1821068116 · Cited by 220
Self-citationRational Cancer Treatment Combinations: An Urgent Clinical Need
Boshuizen, J., & Peeper, D. S. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.05.031 · Cited by 221
Lopez‐Anido, C., Vatén, A., Smoot, N. K., Sharma, N., Guo, V., Gong, Y., Gil, M. X. A., Weimer, A. K. K., & Bergmann, D. C. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.03.014 · Cited by 223
Comparison of glioblastoma (GBM) molecular classification methods
Lee, E., Yong, R. L. M., Paddison, P. J., & Zhu, J. (2018) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2018.07.006 · Cited by 219
Pizzolato, G., Kaminski, H., Tosolini, M., Franchini, D., Pont, F., Martins, F., Valle, C., Labourdette, D., Cadot, S., Quillet‐Mary, A., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1818488116 · Cited by 219
Mapping cells through time and space with moscot
Klein, D., Palla, G., Lange, M., Klein, M., Piran, Z., Gander, M., Meng-Papaxanthos, L., Sterr, M., Saber, L., Jing, C., et al. (2025) · Nature · doi:10.1038/s41586-024-08453-2 · Cited by 235
Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies
Lafzi, A., Moutinho, C., Picelli, S., & Heyn, H. (2018) · Nature Protocols · doi:10.1038/s41596-018-0073-y · Cited by 218
Single cell transcriptomics of human epidermis identifies basal stem cell transition states
Wang, S., Drummond, M. L., Guerrero-Juarez, C. F., Tarapore, E., MacLean, A. L., Stabell, A. R., Wu, S. C., Gutierrez, G., That, B. T., Benavente, C. A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18075-7 · Cited by 218
T-lymphocyte homing: an underappreciated yet critical hurdle for successful cancer immunotherapy
Sackstein, R., Schatton, T., & Barthel, S. R. (2017) · Laboratory Investigation · doi:10.1038/labinvest.2017.25 · Cited by 220
Understanding tumor ecosystems by single-cell sequencing: promises and limitations
Ren, X., Kang, B., & Zhang, Z. (2018) · Genome biology · doi:10.1186/s13059-018-1593-z · Cited by 220
Melanoma models for the next generation of therapies
Patton, E. E., Mueller, K. L., Adams, D. J., Anandasabapathy, N., Aplin, A. E., Bertolotto, C., Bosenberg, M., Ceol, C. J., Burd, C. E., Chi, P., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.01.011 · Cited by 219
Spatial biology of cancer evolution
Seferbekova, Z., Lomakin, A., Yates, L. R., & Gerstung, M. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00553-x · Cited by 227
Advances in single-cell RNA sequencing and its applications in cancer research
Huang, D., Ma, N., Li, X., Gou, Y., Duan, Y., Liu, B., Xia, J., Zhao, X., Wang, X., Li, Q., et al. (2023) · Journal of Hematology & Oncology · doi:10.1186/s13045-023-01494-6 · Cited by 224
An entropy-based metric for assessing the purity of single cell populations
Liu, B., Li, C., Li, Z., Wang, D., Ren, X., & Zhang, Z. (2020) · Nature Communications · doi:10.1038/s41467-020-16904-3 · Cited by 217
Tan, Y., Flynn, W. F., Sivajothi, S. K., Luo, D., Bozal, S. B., Davé, M., Luciano, A. A., Robson, P., Luciano, D. E., & Courtois, E. T. (2022) · Nature Cell Biology · doi:10.1038/s41556-022-00961-5 · Cited by 223
Tumour heterogeneity and the evolutionary trade-offs of cancer
Hausser, J., & Alon, U. S. (2020) · Nature reviews. Cancer · doi:10.1038/s41568-020-0241-6 · Cited by 216
A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing
Petti, A. A., Williams, S. R., Miller, C. A., Fiddes, I. T., Srivatsan, S. N., Chen, D. Y., Fronick, C. C., Fulton, R. S., Church, D. M., & Ley, T. J. (2019) · Nature Communications · doi:10.1038/s41467-019-11591-1 · Cited by 215
Immune Cell Gene Signatures for Profiling the Microenvironment of Solid Tumors
Nirmal, A. J., Regan, T. W., Shih, B. B.-J., Hume, D. A., Sims, A. H., & Freeman, T. C. (2018) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-18-0342 · Cited by 215
Zhang, S., Fang, W., Zhou, S., Zhu, D., Chen, R., Gao, X., Li, Z., Fu, Y. S., Zhang, Y., Yang, F., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40727-7 · Cited by 214
Future cancer research priorities in the USA: a Lancet Oncology Commission
Jaffee, E. M., Dang, C. V., Agus, D. B., Alexander, B. M., Anderson, K. C., Ashworth, A., Barker, A. D., Bastani, R., Bhatia, S. N., Bluestone, J. A., et al. (2017) · The Lancet Oncology · doi:10.1016/s1470-2045(17)30698-8 · Cited by 212
Self-citationShiao, S. L., Gouin, K. H., Ing, N., Ho, A. Y., Basho, R. K., Shah, A. S., Mebane, R. H., Zitser, D., Martinez, A., Mevises, N.-Y., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2023.12.012 · Cited by 212
Grauel, A. L., Nguyen, B., Ruddy, D., Laszewski, T., Schwartz, S., Chang, J., Chen, J., Piquet, M., Pelletier, M., Yan, Z., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19920-5 · Cited by 211
Subramanian, A., Sidhom, E.-H., Emani, M., Vernon, K., Sahakian, N., Zhou, Y., Kost-Alimova, M., Slyper, M., Waldman, J., Dionne, D., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13382-0 · Cited by 211
Self-citationThe Spatial Landscape of Progression and Immunoediting in Primary Melanoma at Single-Cell Resolution
Nirmal, A. J., Maliga, Z., Vallius, T., Quattrochi, B., Chen, A. A., Jacobson, C. A., Pelletier, R. J., Yapp, C., Arias-Camison, R., Chen, Y., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-21-1357 · Cited by 211
Self-citationThe journey from melanocytes to melanoma
Centeno, P. P., Pavet, V., & Marais, R. (2023) · Nature reviews. Cancer · doi:10.1038/s41568-023-00565-7 · Cited by 211
Understanding tumour endothelial cell heterogeneity and function from single-cell omics
Zeng, Q., Mousa, M., Nadukkandy, A. S., Franssens, L., Alnaqbi, H., Alshamsi, F. Y., AlSafar, H. S., & Carmeliet, P. (2023) · Nature reviews. Cancer · doi:10.1038/s41568-023-00591-5 · Cited by 220
Exuberant fibroblast activity compromises lung function via ADAMTS4
Boyd, D. F., Allen, E. K., Randolph, A. G., Guo, X.-Z. J., Weng, Y., Sanders, C. J., Bajracharya, R., Lee, N. K., Guy, C. S., Vogel, P., et al. (2020) · Nature · doi:10.1038/s41586-020-2877-5 · Cited by 211
Gao, S., Yan, L., Wang, R., Li, J., Yong, J., Zhou, X., Wei, Y., Wu, X., Wang, X., Fan, X., et al. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0105-4 · Cited by 208
DoubletDecon: Deconvoluting Doublets from Single-Cell RNA-Sequencing Data
DePasquale, E. A. K., Schnell, D. J., Van Camp, P.-J., Valiente-Alandí, Í., Blaxall, B. C., Grimes, H. L., Singh, H., & Salomonis, N. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.09.082 · Cited by 207
Applications of Immunogenomics to Cancer
Liu, X. S., & Mardis, E. R. (2017) · Cell · doi:10.1016/j.cell.2017.01.014 · Cited by 207
Dissecting the treatment-naive ecosystem of human melanoma brain metastasis
Biermann, J., Melms, J. C., Amin, A. D., Wang, Y., Caprio, L., Karz, A., Tagore, S., Barrera, I., Ibarra-Arellano, M. A., Andreatta, M., et al. (2022) · Cell · doi:10.1016/j.cell.2022.06.007 · Cited by 207
Self-citationCell-type deconvolution in epigenome-wide association studies: a review and recommendations
Teschendorff, A. E., & Zheng, S. (2017) · Epigenomics · doi:10.2217/epi-2016-0153 · Cited by 206
Tang, Q., Iyer, S., Lobbardi, R., Moore, J. C., Chen, H., Lareau, C. A., Hebert, C. M., Shaw, M. L., Neftel, C., Suvà, M. L., et al. (2017) · The Journal of Experimental Medicine · doi:10.1084/jem.20170976 · Cited by 206
Tumor Functional Heterogeneity Unraveled by scRNA-seq Technologies
González-Silva, L., Quevedo, L., & Varela, I. (2020) · Trends in cancer · doi:10.1016/j.trecan.2019.11.010 · Cited by 205
Balzer, M. S., Doke, T., Yang, Y., Aldridge, D. L., Hu, H., Mai, H., Mukhi, D., Ma, Z., Shrestha, R., Palmer, M. B., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31772-9 · Cited by 213
Van Damme, H., Dombrecht, B., Kiss, M., Roose, H., Allen, E., Van Overmeire, E., Kancheva, D., Martens, L., Murgaski, A., Bardet, P. M. R., et al. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-001749 · Cited by 205
He, J., Babarinde, I. A., Sun, L., Xu, S., Chen, R., Shi, J., Wei, Y., Li, Y., Ma, G., Zhuang, Q., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21808-x · Cited by 205
Landscape of helper and regulatory antitumour CD4+ T cells in melanoma
Oliveira, G., Stromhaug, K., Cieri, N., Iorgulescu, J. B., Klaeger, S., Wolff, J. O., Rachimi, S., Chea, V. A., Krause, K., Freeman, S. S., et al. (2022) · Nature · doi:10.1038/s41586-022-04682-5 · Cited by 205
Self-citationGenome-wide association analysis identifies genetic correlates of immune infiltrates in solid tumors
Siemers, N. O., Holloway, J. L., Han, C., Chasalow, S. D., Ross‐Macdonald, P., Voliva, C. F., & Szustakowski, J. D. (2017) · PLoS ONE · doi:10.1371/journal.pone.0179726 · Cited by 204
Xing, X., Yang, F., Huang, Q., Guo, H., Li, J., Qiu, M., Bai, F., & Wang, J. (2021) · Science Advances · doi:10.1126/sciadv.abd9738 · Cited by 204
Genomic Analysis of Nasopharyngeal Carcinoma Reveals TME-Based Subtypes
Zhang, L., MacIsaac, K. D., Zhou, T., Huang, P., Xin, C., Dobson, J. R., Yu, K., Chiang, D. Y., Fan, Y., Pelletier, M. R., et al. (2017) · Molecular Cancer Research · doi:10.1158/1541-7786.mcr-17-0134 · Cited by 203
Zhao, Q., Eichten, A., Parveen, A. A., Adler, C., Huang, Y., Wang, W., Ding, Y., Adler, A. P., Nevins, T. D., Ni, M., et al. (2018) · Cancer Research · doi:10.1158/0008-5472.can-17-2728 · Cited by 205
Tracing the first hematopoietic stem cell generation in human embryo by single-cell RNA sequencing
Zeng, Y., He, J., Bai, Z., Li, Z., Gong, Y., Liu, C., Ni, Y., Du, J., Ma, C., Bian, L., et al. (2019) · Cell Research · doi:10.1038/s41422-019-0228-6 · Cited by 203
Zhang, Y., Zuo, C., Liu, L., Hu, Y., Yang, B. R., Qiu, S., Li, Y., Cao, D., Ju, Z., Ge, J., et al. (2021) · Journal of Hepatology · doi:10.1016/j.jhep.2021.06.023 · Cited by 207
Reprogramming roadmap reveals route to human induced trophoblast stem cells
Liu, X., Ouyang, J. F., Rossello, F. J., Tan, J. P., Davidson, K. C., Valdes, D. S., Schröder, J., Sun, Y., Chen, J., Knaupp, A. S., et al. (2020) · Nature · doi:10.1038/s41586-020-2734-6 · Cited by 202
Hu, Q., Hong, Y., Qi, P., Lu, G., Mai, X., Xu, S., He, X., Guo, Y., Gao, L., Jing, Z., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22300-2 · Cited by 201
Introduction to Single‐Cell RNA Sequencing
Olsen, T. K., & Baryawno, N. (2018) · Current Protocols in Molecular Biology · doi:10.1002/cpmb.57 · Cited by 201
A phenotypic signature that identifies neoantigen-reactive T cells in fresh human lung cancers
Hanada, K.-I., Zhao, C., Gil-Hoyos, R., Gartner, J. J., Chow-Parmer, C., Lowery, F. J., Krishna, S., Prickett, T. D., Kivitz, S., Parkhurst, M. R., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.03.012 · Cited by 200
Single Cell Multi-Omics Technology: Methodology and Application
Hu, Y., An, Q., Sheu, K. M., Trejo, B. M., Fan, S., & Guo, Y. Q. (2018) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2018.00028 · Cited by 201
The Myeloid Cell Compartment—Cell by Cell
Bassler, K. E., Schulte-Schrepping, J., Warnat‐Herresthal, S., Aschenbrenner, A. C., & Schultze, J. L. (2019) · Annual Review of Immunology · doi:10.1146/annurev-immunol-042718-041728 · Cited by 200
A TCF4-dependent gene regulatory network confers resistance to immunotherapy in melanoma
Pozniak, J., Pedri, D., Landeloos, E., Van Herck, Y., Antoranz, A., Vanwynsberghe, L., Nowosad, A., Roda, N., Makhzami, S., Bervoets, G., et al. (2024) · Cell · doi:10.1016/j.cell.2023.11.037 · Cited by 199
Extensive Remodeling of the Immune Microenvironment in B Cell Acute Lymphoblastic Leukemia
Witkowski, M. T., Dolgalev, I., Evensen, N. A., Ma, C., Chambers, T. M., Roberts, K. G., Sreeram, S., Dai, Y., Tikhonova, A. N., Lasry, A., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.04.015 · Cited by 200
Human Anti-tumor Immunity: Insights from Immunotherapy Clinical Trials
Egen, J. G., Ouyang, W., & Wu, L. C. (2020) · Immunity · doi:10.1016/j.immuni.2019.12.010 · Cited by 198
IRF4 instructs effector Treg differentiation and immune suppression in human cancer
Alvisi, G., Brummelman, J., Puccio, S., Mazza, E. M. C., Tomada, E. P., Losurdo, A., Zanon, V., Peano, C., Colombo, F., Scarpa, A., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci130426 · Cited by 200
Spatiotemporal dissection of the cell cycle with single-cell proteogenomics
Mahdessian, D., Cesnik, A. J., Gnann, C., Danielsson, F., Stenström, L., Arif, M., Zhang, C., Le, T. H., Johansson, F., Schutten, R., et al. (2021) · Nature · doi:10.1038/s41586-021-03232-9 · Cited by 204
Spatially resolved single-cell translatomics at molecular resolution
(2023) · doi:10.1126/science.add3067 · Cited by 198
Precision Oncology: The Road Ahead
Senft, D., Leiserson, M. D. M., Ruppin, E., & Ronai, Z. A. (2017) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2017.08.003 · Cited by 199
Single-Cell Transcriptomic Analysis of Tumor Heterogeneity
Levitin, H. M., Yuan, J., & Sims, P. A. (2018) · Trends in cancer · doi:10.1016/j.trecan.2018.02.003 · Cited by 197
Deciphering cell lineage specification of human lung adenocarcinoma with single-cell RNA sequencing
Wang, Z., Li, Z., Zhou, K., Wang, C., Jiang, L., Zhang, L., Yang, Y., Luo, W., Qiao, W., Wang, G., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26770-2 · Cited by 206
Targeting the Myofibroblastic Cancer-Associated Fibroblast Phenotype Through Inhibition of NOX4
Hanley, C. J., Mellone, M., Ford, K., Thirdborough, S. M., Mellows, T., Frampton, S. J., Smith, D. M., Harden, E., Szyndralewiez, C., Bullock, M., et al. (2018) · JNCI: Journal of the National Cancer Institute · doi:10.1093/jnci/djx121 · Cited by 195
Single-cell transcriptomics in cancer: computational challenges and opportunities
Fan, J., Slowikowski, K., & Zhang, F. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0422-0 · Cited by 195
Olbrecht, S., Busschaert, P., Qian, J., Vanderstichele, A., Loverix, L., Gorp, T. V., Nieuwenhuysen, E. V., Han, S., den Broeck, A. V., Coosemans, A., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00922-x · Cited by 195
Androgen receptor-mediated CD8+ T cell stemness programs drive sex differences in antitumor immunity
Yang, C., Jin, J., Yang, Y., Sun, H., Wu, L., Shen, M., Hong, X., Li, W., Lu, L., Cao, D., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.05.012 · Cited by 195
Kim, S. P., Vale, N. R., Zacharakis, N., Krishna, S., Yu, Z., Gasmi, B., Gartner, J. J., Sindiri, S., Malekzadeh, P., Deniger, D. C., et al. (2022) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-22-0040 · Cited by 195
Luebker, S. A., & Koepsell, S. A. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00268 · Cited by 194
The Pancreatic Cancer Microenvironment
Dougan, S. K. (2017) · The Cancer Journal · doi:10.1097/ppo.0000000000000288 · Cited by 193
Dynamic interplay between tumour, stroma and immune system can drive or prevent tumour progression
Seager, R. J., Hajal, C., Spill, F., Kamm, R. D., & Zaman, M. H. (2017) · Convergent Science Physical Oncology · doi:10.1088/2057-1739/aa7e86 · Cited by 192
Lee, H. W., Chung, W., Lee, H., Jeong, D. E., Jo, A., Lim, J. E., Hong, J. H., Nam, D., Jeong, B. C., Park, S. H., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-00741-6 · Cited by 192
Cillo, A. R., Cardello, C., Shan, F., Karapetyan, L., Kunning, S. R., Sander, C., Rush, E. A., Karunamurthy, A. D., Massa, R. C., Rohatgi, A., et al. (2024) · Cell · doi:10.1016/j.cell.2024.06.036 · Cited by 192
Immunotherapy Resistance by Inflammation-Induced Dedifferentiation
Mehta, A., Kim, Y. J., Robert, L., Tsoi, J., Comı́n-Anduix, B., Berent-Maoz, B., Cochran, A. J., Economou, J. S., Tumeh, P. C., Puig-Saus, C., et al. (2018) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-1178 · Cited by 191
Verginadis, I. I., Avgousti, H., Monslow, J., Skoufos, G., Chinga, F., Kim, K., Leli, N. M., Karagounis, I. V., Bell, B. I., Velalopoulou, A. P., et al. (2022) · Nature Cell Biology · doi:10.1038/s41556-022-00918-8 · Cited by 191
Liu, Y., Feng, W., Dai, Y., Bao, M., Yuan, Z., He, M., Qin, Z., Liao, S., He, J., Huang, Q., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.709210 · Cited by 190
Duraiswamy, J., Turrini, R., Minasyan, A., Barras, D., Crespo, I., Grimm, A. J., Casado, J., Genolet, R., Benedetti, F., Wicky, A. M., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.10.008 · Cited by 191
Self-citationThe B-Side of Cancer Immunity: The Underrated Tune
Largeot, A., Pagano, G., Gonder, S., Moussay, E., & Paggetti, J. (2019) · Cells · doi:10.3390/cells8050449 · Cited by 189
Admin, A., Theocharidis, G., Baltzis, D., Roustit, M., Tellechea, A., Dangwal, S., Khetani, R. S., Shu, B., Zhao, W., Fu, J., et al. (2020) · Diabetes · doi:10.2337/db20-0188 · Cited by 188
High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE
Vahid, M. R., Brown, E. L., Steen, C. B., Zhang, W., Jeon, H. S., Kang, M., Gentles, A. J., & Newman, A. M. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01697-9 · Cited by 187
Dong, J., Hu, Y., Fan, X., Wu, X., Mao, Y., Hu, B., Guo, H., Wen, L., & Tang, F. (2018) · Genome Biology · doi:10.1186/s13059-018-1416-2 · Cited by 187
Roider, T., Seufert, J., Uvarovskii, A., Frauhammer, F., Bordas, M., Abedpour, N., Stolarczyk, M., Mallm, J., Herbst, S. A., Bruch, P., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-0532-x · Cited by 186
Dingler, F. A., Wang, M., Mu, A., Millington, C. L., Oberbeck, N., Watcham, S., Pontel, L. B., Kamimae-Lanning, A. N., Langevin, F., Nadler, C., et al. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.10.012 · Cited by 186
Diverse clonal fates emerge upon drug treatment of homogeneous cancer cells
Goyal, Y., Busch, G. T., Pillai, M., Li, J., Boe, R. H., Grody, E. I., Chelvanambi, M., Dardani, I. P., Emert, B. L., Bodkin, N., et al. (2023) · Nature · doi:10.1038/s41586-023-06342-8 · Cited by 186
The function and dysfunction of memory CD 8+ T cells in tumor immunity
Reading, J. L., Gálvez‐Cancino, F., Swanton, C., Lladser, Á., Peggs, K. S., & Quezada, S. A. (2018) · Immunological Reviews · doi:10.1111/imr.12657 · Cited by 186
Infiltrative and drug‐resistant slow‐cycling cells support metabolic heterogeneity in glioblastoma
Hoang-Minh, L., Siebzehnrübl, F. A., Yang, C., Suzuki‐Hatano, S., Dajac, K., Loche, T., Andrews, N., Massari, M. S., Patel, J., Amin, K., et al. (2018) · The EMBO Journal · doi:10.15252/embj.201798772 · Cited by 185
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 ›
Are you an author, or do you see a problem with this page? Request a review
