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
Flagged first, then by citations
Cited by
Sorted by citations
Items 301–400 of 1,033
T cell immunotherapies engage neutrophils to eliminate tumor antigen escape variants
Hirschhorn, D., Budhu, S., Kraehenbuehl, L., Gigoux, M., Schröder, D., Chow, A. T., Ricca, J. M., Gasmi, B., Henau, O. D., Mangarin, L. M. B., et al. (2023) · Cell · doi:10.1016/j.cell.2023.03.007 · Cited by 311
A reference human induced pluripotent stem cell line for large-scale collaborative studies
Pantazis, C. B., Yang, A., Lara, E., McDonough, J. A., Blauwendraat, C., Peng, L., Oguro, H., Kanaujiya, J., Zou, J., Sebesta, D., et al. (2022) · Cell Stem Cell · doi:10.1016/j.stem.2022.11.004 · Cited by 310
T cell–induced CSF1 promotes melanoma resistance to PD1 blockade
Neubert, N. J., Schmittnaegel, M., Bordry, N., Nassiri, S., Wald, N., Martignier, C., Tillé, L., Homicsko, K., Damsky, W., Maby-El Hajjami, H., et al. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aan3311 · Cited by 308
Singh, M., Al-Eryani, G., Carswell, S., Ferguson, J. M., Blackburn, J., Barton, K., Roden, D., Luciani, F., Giang Phan, T., Junankar, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11049-4 · Cited by 308
Yang, C., Siebert, J., Burns, R., Gerbec, Z. J., Bonacci, B. B., Rymaszewski, A. L., Rau, M. J., Riese, M. J., Rao, S., Carlson, K.-S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11947-7 · Cited by 306
Tombor, L., John, D., Glaser, S.-F., Luxán, G., Forte, E., Furtado, M. B., Rosenthal, N. A., Baumgarten, N., Schulz, M. H., Wittig, J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-20905-1 · Cited by 306
Rodriguez-Meira, A., Buck, G., Clark, S.-A., Povinelli, B. J., Alcolea, V., Louka, E., McGowan, S., Hamblin, A., Sousos, N., Barkas, N., et al. (2019) · Molecular Cell · doi:10.1016/j.molcel.2019.01.009 · Cited by 305
Davis, R. T., Blake, K., Ma, D., Gabra, M. B. I., Hernandez, G. A., Phung, A. T., Yang, Y., Maurer, D., Lefebvre, A. E. Y. T., Alshetaiwi, H. S., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-0477-0 · Cited by 301
Lee, J., Shklovskaya, E., Lim, S. Y., Carlino, M. S., Menzies, A. M., Stewart, A., Pedersen, B., Irvine, M., Alavi, S., Yang, J. Y. H., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-15726-7 · Cited by 300
Human prostate cancer bone metastases have an actionable immunosuppressive microenvironment
Kfoury, Y. S., Baryawno, N., Sévère, N., Mei, S., Gustafsson, K. U., Hirz, T., Brouse, T., Scadden, E. W., Igolkina, A. A., Kokkaliaris, K. D., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.09.005 · Cited by 303
Statistical and integrative system-level analysis of DNA methylation data
Teschendorff, A. E., & Relton, C. L. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.86 · Cited by 297
Advancing Cancer Research and Medicine with Single-Cell Genomics
Lim, B., Lin, Y., & Navin, N. E. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.03.008 · Cited by 297
Liu, Y., He, S., Wang, X., Peng, W., Chen, Q., Chi, D., Chen, J., Han, B., Lin, G., Li, Y., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21043-4 · Cited by 297
Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface
Hunter, M. V., Moncada, R. M., Weiss, J. M., Yanai, I., & White, R. M. (2021) · Nature Communications · doi:10.1038/s41467-021-26614-z · Cited by 295
Towards targeting of shared mechanisms of cancer metastasis and therapy resistance
Weiß, F., Lauffenburger, D. A., & Friedl, P. (2022) · Nature reviews. Cancer · doi:10.1038/s41568-021-00427-0 · Cited by 295
Precision Oncology: Who, How, What, When, and When Not?
Schwartzberg, L. S., Kim, E. S., Liu, D., & Schrag, D. (2017) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_174176 · Cited by 294
Song, H., Weinstein, H. N. W., Allegakoen, P., Wadsworth, M. H., Xie, J., Yang, H., Castro, E. A., Lu, K. L., Stohr, B. A., Feng, F. Y.-C., et al. (2022) · Nature Communications · doi:10.1038/s41467-021-27322-4 · Cited by 294
Self-citationJoint analysis of heterogeneous single-cell RNA-seq dataset collections
Barkas, N., Petukhov, V., Nikolaeva, D., Lozinsky, Y., Demharter, S., Khodosevich, K., & Kharchenko, P. V. (2019) · Nature Methods · doi:10.1038/s41592-019-0466-z · Cited by 292
Many ways to resistance: How melanoma cells evade targeted therapies
Kozar, I., Margue, C., Rothengatter, S., Haan, C., & Kreis, S. (2019) · Biochimica et Biophysica Acta (BBA) - Reviews on Cancer · doi:10.1016/j.bbcan.2019.02.002 · Cited by 289
McLellan, M. A., Skelly, D. A., Dona, M. S. I., Squiers, G. T., Farrugia, G. E., Gaynor, T. L., Cohen, C. D., Pandey, R., Diep, H., Vinh, A., et al. (2020) · Circulation · doi:10.1161/circulationaha.119.045115 · Cited by 289
Hirz, T., Mei, S., Sarkar, H., Kfoury, Y. S., Wu, S., Verhoeven, B. M., Subtelny, A. O., Zlatev, D. V., Wszolek, M. W., Salari, K., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36325-2 · Cited by 303
Schenkel, J. M., Herbst, R. H., Canner, D. A., Li, A., Hillman, M., Shanahan, S.-L., Gibbons, G., Smith, O. C. G., Kim, J. Y., Westcott, P. M. K., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.08.026 · Cited by 297
Self-citationWang, T., Li, B., Nelson, C. E., & Nabavi, S. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2599-6 · Cited by 286
Regenerative potential of prostate luminal cells revealed by single-cell analysis
Karthaus, W. R., Hofree, M., Choi, D., Linton, E. L., Turkekul, M., Bejnood, A., Carver, B., Gopalan, A., Abida, W. M., Laudone, V. P., et al. (2020) · Science · doi:10.1126/science.aay0267 · Cited by 285
Self-citationZou, Y., Ye, F., Kong, Y., Hu, X., Deng, X., Xie, J., Song, C., Ou, X., Wu, S., Wu, L., et al. (2022) · Advanced Science · doi:10.1002/advs.202203699 · Cited by 285
Lu, Y., Shiau, F., Yi, W., Lü, S., Wu, Q., Pearson, J. D., Kallman, A., Zhong, S., Hoang, T. V., Zuo, Z., et al. (2019) · Developmental Cell · doi:10.1016/j.devcel.2020.04.009 · Cited by 285
Robust gene expression programs underlie recurrent cell states and phenotype switching in melanoma
Wouters, J., Atak, Z. K., Minnoye, L., Spanier, K. I., Waegeneer, M. D., González‐Blas, C. B., Mauduit, D., Davie, K., Hulselmans, G. J., Najem, A., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-0547-3 · Cited by 284
Comprehensive cell atlas of the first-trimester developing human brain
Braun, E., Danan-Gotthold, M., Borm, L. E., Vinsland, E., Lee, K. W., Lönnerberg, P., Hu, L., Li, X., He, X., Andrusivová, Ž., et al. (2023) · Science · doi:10.1126/science.adf1226 · Cited by 300
Gideon, H. P., Hughes, T. K., Tzouanas, C. N., Wadsworth, M. H., Tu, A. A., Gierahn, T. M., Peters, J. M., Hopkins, F. F., Wei, J.-R., Kummerlowe, C., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.04.004 · Cited by 282
Self-citationApplying high-dimensional single-cell technologies to the analysis of cancer immunotherapy
Gohil, S. H., Iorgulescu, J. B., Braun, D. A., Keskin, D. B., & Livak, K. J. (2021) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-020-00449-x · Cited by 282
BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis
Singhal, V., Chou, N., Lee, J., Yue, Y., Liu, J., Chock, W. K., Lin, L., Chang, Y.-C., Teo, E. M. L., Aow, J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01664-3 · Cited by 282
Estimation of immune cell content in tumour tissue using single-cell RNA-seq data
Schelker, M., Feau, S., Du, J., Ranu, N., Klipp, E., MacBeath, G., Schoeberl, B., & Raue, A. (2017) · Nature Communications · doi:10.1038/s41467-017-02289-3 · Cited by 282
CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing
de Kanter, J. K., Lijnzaad, P., Candelli, T., Margaritis, T., & Holstege, F. C. P. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz543 · Cited by 283
Zhang, Z., Wang, Z., Chen, Y., Wu, H., Yin, L., Zhao, Q., Luo, H., Zeng, Z.-L., Qiu, M., & Xu, R. (2022) · Genome Medicine · doi:10.1186/s13073-022-01050-w · Cited by 282
Distinct cellular dynamics associated with response to CAR-T therapy for refractory B cell lymphoma
Haradhvala, N. J., Leick, M. B., Maurer, K., Gohil, S. H., Larson, R. C., Yao, N., Gallagher, K., Katsis, K., Frigault, M. J., Southard, J., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01959-0 · Cited by 290
Zhou, Y., Bian, S., Zhou, X., Cui, Y., Wang, W., Wen, L., Guo, L., Fu, W., & Tang, F. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.09.015 · Cited by 278
Wang, R., Dang, M., Harada, K., Han, G., Wang, F., Pizzi, M. P., Zhao, M., Tatlonghari, G., Zhang, S., Hao, D., et al. (2021) · Nature Medicine · doi:10.1038/s41591-020-1125-8 · Cited by 278
Dong, R., Yang, R., Zhan, Y., Lai, H., Ye, C., Yao, X.-Y., Luo, W., Cheng, X., Miao, J., Wang, J., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.08.014 · Cited by 276
Mudd, P. A., Crawford, J. C., Turner, J. S., Souquette, A. H., Reynolds, D., Bender, D. E., Bosanquet, J. P., Anand, N. J., Striker, D. A., Martin, R. S., et al. (2020) · Science Advances · doi:10.1126/sciadv.abe3024 · Cited by 276
Morarach, K., Mikhailova, A., Knoflach, V., Memic, F., Kumar, R., Li, W., Ernfors, P., & Marklund, U. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00736-x · Cited by 274
Novel antigen-presenting cell imparts Treg-dependent tolerance to gut microbiota
Akagbosu, B., Tayyebi, Z., Shibu, G., Paucar Iza, Y. A., Deep, D., Parisotto, Y. F., Fisher, L., Pasolli, H. A., Thevin, V., Elmentaite, R., et al. (2022) · Nature · doi:10.1038/s41586-022-05309-5 · Cited by 273
Ramaswamy, A., Brodsky, N. N., Sumida, T. S., Comi, M., Asashima, H., Hoehn, K. B., Li, N., Liu, Y., Shah, A. S., Ravindra, N. G., et al. (2021) · Immunity · doi:10.1016/j.immuni.2021.04.003 · Cited by 276
Aissa, A. F., Islam, A. B. M. M. K., Ariss, M., Go, C. C., Rader, A. E., Conrardy, R. D., Gajda, A. M., Rubio-Pérez, C., Valyi‐Nagy, K., Pasquinelli, M. M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21884-z · Cited by 278
Sublethal cytochrome c release generates drug-tolerant persister cells
Kalkavan, H., Chen, J., Crawford, J. C., Quarato, G., FITZGERALD, P. J., Tait, S. W. G., Goding, C. R., & Green, D. R. (2022) · Cell · doi:10.1016/j.cell.2022.07.025 · Cited by 271
A graph neural network model to estimate cell-wise metabolic flux using single-cell RNA-seq dataPreprint
Alghamdi, N., Chang, W., Dang, P., Lu, X., Wan, C., Gampala, S., Huang, Z., Wang, J., Ma, Q., Zang, Y., et al. (2021) · Genome Research · doi:10.1101/gr.271205.120 · Cited by 271
Single-cell sequencing techniques from individual to multiomics analyses
Kashima, Y., Sakamoto, Y., Kaneko, K., Seki, M., Suzuki, Y., & Suzuki, A. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-00499-2 · Cited by 270
Cancer-Associated Fibroblasts: Tumorigenicity and Targeting for Cancer Therapy
Glabman, R. A., Choyke, P. L., & Sato, N. (2022) · Cancers · doi:10.3390/cancers14163906 · Cited by 270
Hypoxia drives CD39-dependent suppressor function in exhausted T cells to limit antitumor immunity
Vignali, P. D. A., DePeaux, K., Watson, M. J., Ye, C., Ford, B. R., Lontos, K., McGaa, N. K., Scharping, N. E., Menk, A. V., Robson, S. C., et al. (2023) · Nature Immunology · doi:10.1038/s41590-022-01379-9 · Cited by 269
An Immune-Related Gene Prognostic Index for Head and Neck Squamous Cell Carcinoma
Chen, Y., Li, Z., Zhou, G., & Sun, Y. H. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-2166 · Cited by 269
Single-cell RNA sequencing of human kidney
Liao, J., Yu, Z., Yang, C., Bao, M., Zou, C., Zhang, H., Liu, D., Li, T., Zhang, Q., Li, J., et al. (2020) · Scientific Data · doi:10.1038/s41597-019-0351-8 · Cited by 269
Stem-like intestinal Th17 cells give rise to pathogenic effector T cells during autoimmunity
Schnell, A., Huang, L., Singer, M., Singaraju, A., Barilla, R. M., Regan, B. M. L., Bollhagen, A., Thakore, P. I., Dionne, D., Delorey, T. M., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.018 · Cited by 268
Self-citationHotspot identifies informative gene modules across modalities of single-cell genomics
DeTomaso, D., & Yosef, N. (2021) · Cell Systems · doi:10.1016/j.cels.2021.04.005 · Cited by 267
Beckmann, J. S., & Lew, D. (2016) · Genome Medicine · doi:10.1186/s13073-016-0388-7 · Cited by 267
Improved reconstruction of single-cell developmental potential with CytoTRACE 2
Kang, M., Armenteros, J. J. A., Gulati, G. S., Gleyzer, R., Avagyan, S., Brown, E. L., Zhang, W., Usmani, A., Earland, N. J., Wu, Z., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02857-2 · Cited by 266
Workel, H. H., Lubbers, J. M., Arnold, R. R., Prins, T. M., van der Vlies, P., de Lange, K., Bosse, T., van Gool, I. C., Eggink, F. A., Wouters, M. C. A., et al. (2019) · Cancer Immunology Research · doi:10.1158/2326-6066.cir-18-0517 · Cited by 266
Single cell dissection of plasma cell heterogeneity in symptomatic and asymptomatic myeloma
Ledergor, G., Weiner, A., Zada, M., Wang, S.-Y., Cohen, Y. C., Gatt, M. E., Snir, N., Magen, H., Koren-Michowitz, M., Herzog-Tzarfati, K., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0269-2 · Cited by 263
Host tissue determinants of tumour immunity
Salmon, H., Remark, R., Gnjatic, S., & Mérad, M. (2019) · Nature reviews. Cancer · doi:10.1038/s41568-019-0125-9 · Cited by 263
Lee, A., Sun, L., Mochizuki, A. Y., Reynoso, J. G., Orpilla, J. R., Chow, F. E., Kienzler, J. C., Everson, R. G., Nathanson, D. A., Bensinger, S. J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26940-2 · Cited by 262
Molecular Markers and Targets in Melanoma
Teixidó, C., Castillo, P., Martínez-Vila, C., Arance, A. M., & Alós, L. (2021) · Cells · doi:10.3390/cells10092320 · Cited by 262
Mosaicism in health and disease — clones picking up speed
Forsberg, L. A., Gisselsson, D., & Dumanski, J. P. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.145 · Cited by 261
A cellular hierarchy in melanoma uncouples growth and metastasis
Karras, P., Bordeu, I., Poźniak, J., Nowosad, A., Pazzi, C., Raemdonck, N. V., Landeloos, E., Herck, Y. V., Pedri, D., Bervoets, G., et al. (2022) · Nature · doi:10.1038/s41586-022-05242-7 · Cited by 261
A spatially resolved single-cell genomic atlas of the adult human breast
Kumar, T., Nee, K., Wei, R., He, S., Nguyen, Q., Bai, S., Blake, K., Pein, M., Gong, Y., Sei, E., et al. (2023) · Nature · doi:10.1038/s41586-023-06252-9 · Cited by 260
Falletta, P., Sánchez‐del‐Campo, L., Chauhan, J. S., Effern, M., Kenyon, A., Kershaw, C. J., Siddaway, R., Lisle, R., Freter, R., Daniels, M. J., et al. (2017) · Genes & Development · doi:10.1101/gad.290940.116 · Cited by 260
Early emergence of cortical interneuron diversity in the mouse embryo
Mi, D., Li, Z., Lim, L., Li, M., Moissidis, M., Yang, Y., Gao, T., Hu, T. X., Pratt, T., Price, D. J., et al. (2018) · Science · doi:10.1126/science.aar6821 · Cited by 259
Kim, H., Song, G., Park, S., Kim, M. H., Kim, M. H., Kang, H. J., Yoo, C., Kim, K. H., Kim, K. H., Eo, S.-K., et al. (2018) · Gastroenterology · doi:10.1053/j.gastro.2018.08.030 · Cited by 258
Ma, Z., Li, X., Mao, Y., Chen, W., Huang, Z., Li, G., Yin, J., Liang, X., & Liu, Z. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.11.005 · Cited by 256
Hinohara, K., Wu, H.-J., Vigneau, S., McDonald, T. O., Igarashi, K. J., Yamamoto, K. N., Madsen, T., Fassl, A., Egri, S. B., Papanastasiou, M., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.10.014 · Cited by 255
Self-citationImmune Escape in Breast Cancer During In Situ to Invasive Carcinoma Transition
del Alcazar, C. R. G., Huh, S. J., Ekram, M. B., Trinh, A. C., Liu, L. L., de Beça, F. F., Zi, X., Kwak, M., Bergholtz, H., Su, Y., et al. (2017) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-0222 · Cited by 255
An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas
Li, G., Li, L., Li, Y., Qian, Z., Wu, F., He, Y., Jiang, H., Li, R., Wang, D., Zhai, Y., et al. (2021) · Brain · doi:10.1093/brain/awab340 · Cited by 254
Tumor immune microenvironment lncRNAs
Park, E.-G., Pyo, S.-J., Cui, Y., Yoon, S.-H., & Nam, J. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab504 · Cited by 253
Post-infusion CAR TReg cells identify patients resistant to CD19-CAR therapy
Good, Z., Spiegel, J. Y., Sahaf, B., Malipatlolla, M. B., Ehlinger, Z. J., Kurra, S., Desai, M. H., Reynolds, W. D., Lin, A. W., Vandris, P., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01960-7 · Cited by 253
Zhu, Q., Shah, S., Dries, R., Cai, L., & Yuan, G. (2018) · Nature Biotechnology · doi:10.1038/nbt.4260 · Cited by 250
Derivation of Airway Basal Stem Cells from Human Pluripotent Stem Cells
Hawkins, F. J., Suzuki, S., Beermann, M. L., Barillà, C., Wang, R., Villacorta-Martín, C., Berical, A. C., Jean, J., Suer, J. A. L., Matte, T. M., et al. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.09.017 · Cited by 250
Mechanisms of immune activation and regulation: lessons from melanoma
Kalaora, S., Nagler, A., Wargo, J. A., & Samuels, Y. (2022) · Nature reviews. Cancer · doi:10.1038/s41568-022-00442-9 · Cited by 252
Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer
Li, R., Ferdinand, J. R., Loudon, K. W., Bowyer, G., Laidlaw, S., Muyas, F., Mamanova, L., Neves, J. B., Bolt, L., Fasouli, E. S., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.11.001 · Cited by 254
Identifying cell populations with scRNASeq
Andrews, T. S., & Hemberg, M. (2017) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2017.07.002 · Cited by 247
Epigenetic encoding, heritability and plasticity of glioma transcriptional cell states
Chaligné, R., Gaiti, F., Silverbush, D., Schiffman, J. S., Weisman, H. R., Kluegel, L., Gritsch, S., Deochand, S. D., Castro, L. N. G., Richman, A. R., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00927-7 · Cited by 247
Self-citationHuman neural tube morphogenesis in vitro by geometric constraints
Karzbrun, E., Khankhel, A. H., Megale, H. C., Glasauer, S. M. K., Wyle, Y., Britton, G., Warmflash, A., Kosik, K. S., Siggia, E. D., Shraiman, B. I., et al. (2021) · Nature · doi:10.1038/s41586-021-04026-9 · Cited by 246
Revolutionizing immunology with single-cell RNA sequencing
Chen, H., Ye, F., & Guo, G. (2019) · Cellular and Molecular Immunology · doi:10.1038/s41423-019-0214-4 · Cited by 244
Jin, S., Li, R., Chen, M., Chao, Y., Tang, L., Liu, Y.-M., Li, J.-P., Liu, Y.-N., Luo, Y., Zhao, Y., et al. (2020) · Cell Research · doi:10.1038/s41422-020-00402-8 · Cited by 244
Frequent aneuploidy in primary human T cells after CRISPR–Cas9 cleavage
Nahmad, A. D., Reuveni, E., Goldschmidt, E., Tenne, T., Liberman, M., Horovitz‐Fried, M., Khosravi, R., Kobo, H., Reinstein, E., Madi, A., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01377-0 · Cited by 246
Decoding the development of the human hippocampus
Zhong, S., Ding, W., Sun, L., Lu, Y., Dong, H., Fan, X., Liu, Z., Chen, R., Zhang, S., Ma, Q., et al. (2020) · Nature · doi:10.1038/s41586-019-1917-5 · Cited by 242
Che, L., Liu, J., Huo, J.-P., Luo, R., Xu, R.-M., He, C., Li, Y.-Q., Zhou, A., Huang, P., Chen, Y., et al. (2021) · Cell Discovery · doi:10.1038/s41421-021-00312-y · Cited by 241
Unraveling B cell trajectories at single cell resolution
Morgan, D., & Tergaonkar, V. (2022) · Trends in Immunology · doi:10.1016/j.it.2022.01.003 · Cited by 239
Single-cell analysis targeting the proteome
Labib, M., & Kelley, S. O. (2020) · Nature Reviews Chemistry · doi:10.1038/s41570-020-0162-7 · Cited by 238
Wang, W., Li, T., Cheng, Y., Li, F., Qi, S., Mao, M., Wu, J., Liu, Q., Zhang, X., Li, X., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2024.03.013 · Cited by 246
Fallahi‐Sichani, M., Becker, V., Izar, B., Baker, G. J., Lin, J., Boswell, S. A., Shah, P., Rotem, A., Garraway, L. A., & Sorger, P. K. (2017) · Molecular Systems Biology · doi:10.15252/msb.20166796 · Cited by 237
Self-citationAbelin, J. G., Harjanto, D., Malloy, M., Suri, P., Colson, T. R., Goulding, S. P., Creech, A. L., Serrano, L. R., Nasir, G., Nasrullah, Y., et al. (2019) · Immunity · doi:10.1016/j.immuni.2019.08.012 · Cited by 237
Kusnadi, A., Ramírez-Suástegui, C., Fajardo, V., Chee, S., Meckiff, B. J., Simon, H. A., Pelosi, E., Seumois, G., Ay, F., Vijayanand, P., et al. (2021) · Science Immunology · doi:10.1126/sciimmunol.abe4782 · Cited by 237
Gerhardt, L. M. S., Liu, J., Koppitch, K. A., Cippà, P. E., & McMahon, A. P. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2026684118 · Cited by 237
Chen, K., Wang, Q., Li, M., Guo, H., Liu, W., Wang, F., Tian, X., & Yang, Y. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2021.103315 · Cited by 236
Single-cell transcriptome analysis of lineage diversity in high-grade glioma
Yuan, J., Levitin, H. M., Frattini, V., Bush, E. C., Boyett, D. M., Samanamud, J. L., Ceccarelli, M., Dovas, A., Zanazzi, G. J., Canoll, P., et al. (2018) · Genome Medicine · doi:10.1186/s13073-018-0567-9 · Cited by 235
Divergent clonal differentiation trajectories of T cell exhaustion
Dániel, B., Yost, K. E., Hsiung, S., Sándor, K., Xia, Y., Qi, Y., Hiam-Galvez, K. J., Black, M., Raposo, C. J., Shi, Q., et al. (2022) · Nature Immunology · doi:10.1038/s41590-022-01337-5 · Cited by 241
Allué‐Guardia, A., García, J. I., & Torrelles, J. B. (2021) · Frontiers in Microbiology · doi:10.3389/fmicb.2021.612675 · Cited by 233
Yang, M., Lu, J., Zhang, G., Wang, Y., He, M., Xu, Q., Xu, C., & Liu, H. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2020-001136 · Cited by 232
Enhanced safety and efficacy of protease-regulated CAR-T cell receptors
Labanieh, L., Majzner, R. G., Klysz, D. D., Sotillo, E., Fisher, C., Vilches-Moure, J. G., Pacheco, K. Z., Malipatlolla, M. B., Xu, P., Hui, J. H., et al. (2022) · Cell · doi:10.1016/j.cell.2022.03.041 · Cited by 232
Sun, Y., Wu, L. L., Liu, S., Jiang, M., Hu, B., Zhou, K., Guo, W., Xu, Y., Zhong, Y., Zhou, X., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24386-0 · Cited by 230
Boshuizen, J., Koopman, L. A., Krijgsman, O., Shahrabi, A., van den Heuvel, E. G., Ligtenberg, M. A., Vredevoogd, D. W., Kemper, K., Kuilman, T., Song, J.-Y., et al. (2018) · Nature Medicine · doi:10.1038/nm.4472 · Cited by 229
Tang-Huau, T.-L., Gueguen, P., Goudot, C., Durand, M., Bohec, M., Baulande, S., Pasquier, B., Amigorena, S., & Segura, É. (2018) · Nature Communications · doi:10.1038/s41467-018-04985-0 · Cited by 228
Clarke, Z. A., Andrews, T. S., Atif, J., Pouyabahar, D., Innes, B. T., MacParland, S. A., & Bader, G. D. (2021) · Nature Protocols · doi:10.1038/s41596-021-00534-0 · Cited by 234
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
