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
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
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?
SALL4-regulated genes showed significant enrichment and positive correlation with published invasiveness signatures, while significantly anti-correlated with their corresponding proliferation signatures.
“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]
In “Heterogeneity in the abundance of a dormant, drug-resistant melanoma subpopulation” (page unavailable), the source defines opposing MITF-high and AXL-high melanoma gene programs, supporting its citation as a source of published signatures. Our copy does not explicitly identify those programs as invasiveness and proliferation signatures. The SALL4 enrichment and correlation results are the citing authors’ own analysis, not findings attributed to this source. (LLM, conf=0.74)
Experimental — accuracy not yet measuredAmong SALL4-HDAC2 targets upregulated upon SALL4 knockdown were integrins (ITGB1, ITGA6, ITGA4), N-cadherin (CDH2), TGFBR2, VEGFR-1, PDGFC, LOXL2, MAPK8, and others associated with invasive melanoma phenotypes.
“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]
The section on dormant, drug-resistant melanoma subpopulations describes MITF-high and AXL-high expression programs, but does not link the listed genes to an invasive melanoma phenotype. Searches for the named genes and invasive-phenotype terminology found no passage establishing that association. The SALL4-knockdown result is presented as the citing authors’ own finding, rather than a finding attributed to this source. (LLM, conf=0.73)
Experimental — accuracy not yet measuredThe proportions of HSCs expressing G2/M/S-phase genes were enhanced upon IFNa treatment.
“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]
The section on malignant-cell cell cycles (page unavailable) describes G1/S and G2/M signatures in melanoma cells, not HSCs. Searches for IFNa treatment, hematopoietic stem cells, and mouse HSC proliferation found no passage addressing the cited prior report. (LLM, conf=0.76)
Experimental — accuracy not yet measuredIFNa has been shown to induce HSC and progenitors into cell cycle entry.
“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]
The section on cell-cycle heterogeneity describes cycling malignant melanoma cells, not hematopoietic stem cells or progenitors. Searches for IFN-alpha, HSCs, and hematopoietic progenitor cell-cycle entry found no passage in our copy addressing whether IFN-alpha induces those cells to enter the cell cycle; no page numbers were supplied. (LLM, conf=0.76)
Experimental — accuracy not yet measuredNearly all prior work comparing single-cell datasets first partitions the data discretely before downstream analysis.
“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]
Cell cycle genes were excluded from the set of highly variable features used for RNA velocity analysis.
“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]
Cell-cycle-related genes were excluded from the set of genes and transcription factors used for integration.
“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]
Through deconvolution of complex samples, one may infer their cellular milieu and characterize rare, functionally important, and unknown cell types they contain.
“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]
Several studies inferred individual cells' cell cycle phase from scRNA-seq using transcriptional signatures of discrete phases from earlier bulk profiling.
“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]
Latent variable regression was used to mitigate cell-cycle heterogeneity, preventing subsequent analysis from being dominated by mitotic phase-specific gene expression.
“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]
Single-cell transcriptome methods have discovered new cell types and functional cell states, which motivates tools for single-cell chromatin regulation.
“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.
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 · see citing works
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 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
(1996) · Cited by 20,179 · see citing works
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 15,314 · see citing works
Improved Survival with Ipilimumab in Patients with Metastatic Melanoma
Hodi, F. S., O'Day, S. J., McDermott, D. F., Weber, R. W., Sosman, J. A., Haanen, J. B., Gonzalez, R., Robert, C., Schadendorf, D., Hassel, J. C., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa1003466 · Cited by 15,155 · see citing works
Safety, activity, and immune correlates of anti–PD-1 antibody in cancer
Topalian, S. L., Hodi, F. S., Brahmer, J. R., Gettinger, S. N., Smith, D. C., McDermott, D. F., Powderly, J. D., Carvajal, R. D., Sosman, J. A., Atkins, M. B., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1200690 · Cited by 12,628 · see citing works
Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer
Borghaei, H., Paz-Ares, L., Horn, L., Spigel, D. R., Steins, M., Ready, N. E., Chow, L. Q., Vokes, E. E., Felip, E., Holgado, E., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1507643 · Cited by 9,335 · see citing works
Combined Nivolumab and Ipilimumab or Monotherapy in Untreated Melanoma
Larkin, J., Chiarion-Sileni, V., Gonzalez, R., Grob, J. J., Cowey, C. L., Lao, C. D., Schadendorf, D., Dummer, R., Smylie, M., Rutkowski, P., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1504030 · Cited by 8,157 · see citing works
The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950 · see citing works
Self-citationSafety and activity of anti-PD-L1 antibody in patients with advanced cancer
Brahmer, J. R., Tykodi, S. S., Chow, L. Q. M., Hwu, W.-J., Topalian, S. L., Hwu, P., Drake, C. G., Camacho, L. H., Kauh, J., Odunsi, K., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1200694 · Cited by 7,945 · see citing works
Mutational landscape determines sensitivity to PD-1 blockade in non–small cell lung cancer
Rizvi, N. A., Hellmann, M. D., Snyder, A., Kvistborg, P., Makarov, V., Havel, J. J., Lee, W., Yuan, J., Wong, P., Ho, T. S., et al. (2015) · Science · doi:10.1126/science.aaa1348 · Cited by 7,802 · see citing works
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · see citing works
Self-citationIntegrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368 · see citing works
Nivolumab versus Everolimus in Advanced Renal-Cell Carcinoma
Motzer, R. J., Escudier, B., McDermott, D. F., George, S., Hammers, H. J., Srinivas, S., Tykodi, S. S., Sosman, J. A., Procopio, G., Plimack, E. R., et al. (2015) · New England Journal of Medicine · doi:10.1056/nejmoa1510665 · Cited by 5,807 · see citing works
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Patel, A. P., Tirosh, I., Trombetta, J. J., Shalek, A. K., Gillespie, S. M., Wakimoto, H., Cahill, D. P., Nahed, B. V., Curry, W. T., Martuza, R. L., et al. (2014) · Science · doi:10.1126/science.1254257 · Cited by 5,063 · see citing works
Self-citationThe immune contexture in human tumours: impact on clinical outcome
Fridman, W. H., Pagès, F., Sautès-Fridman, C., & Galon, J. (2012) · Nature reviews. Cancer · doi:10.1038/nrc3245 · Cited by 4,981 · see citing works
Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples
Cibulskis, K., Lawrence, M. S., Carter, S. L., Sivachenko, A., Jaffe, D. B., Sougnez, C. L., Gabriel, S., Meyerson, M. M., Lander, E. S., & Getz, G. A. (2013) · Nature Biotechnology · doi:10.1038/nbt.2514 · Cited by 4,838 · see citing works
Genetic Basis for Clinical Response to CTLA-4 Blockade in Melanoma
Snyder, A., Makarov, V., Merghoub, T., Yuan, J., Zaretsky, J. M., Desrichard, A., Walsh, L. A., Postow, M. A., Wong, P., Ho, T. S., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1406498 · Cited by 4,286 · see citing works
Wherry, E. J. (2011) · Nature Immunology · doi:10.1038/ni.2035 · Cited by 4,062 · cited 1× · see citing works
“However, this function can be hampered by tumor-mediated T cell exhaustion, such that T cells fail to activate cytotoxic effector functions (40).”
Safety and Tumor Responses with Lambrolizumab (Anti–PD-1) in Melanoma
Hamid, O., Robert, C., DAUD, A. I., Hodi, F. S., Hwu, W., Kefford, R. F., Wolchok, J. D., Hersey, P., Joseph, R. W., Weber, J. S., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1305133 · Cited by 3,373 · see citing works
Genomic classification of cutaneous melanoma
Akbani, R., Akdemir, K. C., Aksoy, B. A., Albert, M., Ally, A., Amin, S. B., Arachchi, H., Arora, A., Auman, J. T., Ayala, B., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.044 · Cited by 3,151 · see citing works
Self-citationMolecular mechanisms of T cell co-stimulation and co-inhibition
Chen, L., & Flies, D. B. (2013) · Nature Reviews Immunology · doi:10.1038/nri3405 · Cited by 3,114 · see citing works
Brahmer, J. R., Drake, C. G., WOLLNER, I. S., Powderly, J. D., Picus, J., Sharfman, W. H., Stankevich, E., Pons, A., Salay, T. M., McMiller, T. L., et al. (2010) · Journal of Clinical Oncology · doi:10.1200/jco.2009.26.7609 · Cited by 2,967 · see citing works
Genomic correlates of response to CTLA-4 blockade in metastatic melanoma
Van Allen, E. M., Miao, D., Schilling, B., Shukla, S. A., Blank, C., Zimmer, L., Sucker, A., Hillen, U., Geukes Foppen, M. H., Goldinger, S. M., et al. (2015) · Science · doi:10.1126/science.aad0095 · Cited by 2,839 · see citing works
Self-citationSmart-seq2 for sensitive full-length transcriptome profiling in single cells
Picelli, S., Björklund, Å. K., Faridani, O. R., Sagasser, S., Winberg, G., & Sandberg, R. (2013) · Nature Methods · doi:10.1038/nmeth.2639 · Cited by 2,358 · see citing works
Tumour heterogeneity and cancer cell plasticity
Meacham, C., & Morrison, S. (2013) · Nature · doi:10.1038/nature12624 · Cited by 2,350 · see citing works
Melanomas acquire resistance to B-RAF(V600E) inhibition by RTK or N-RAS upregulation
Nazarian, R., Shi, H., Wang, Q., Kong, X., Koya, R. C., Lee, H., Chen, Z., Lee, M., Attar, N., Sazegar, H., et al. (2010) · Nature · doi:10.1038/nature09626 · Cited by 2,198 · see citing works
Molecular Signature of CD8+ T Cell Exhaustion during Chronic Viral Infection
Wherry, E. J., Ha, S.-J., Kaech, S. M., Haining, W. N., Sarkar, S., Kalia, V., Subramaniam, S., Blattman, J. N., Barber, D. L., & Ahmed, R. (2007) · Immunity · doi:10.1016/j.immuni.2007.09.006 · Cited by 2,103 · see citing works
Absolute quantification of somatic DNA alterations in human cancer
Carter, S. L., Cibulskis, K., Helman, E., McKenna, A., Shen, H., Zack, T. I., Laird, P. W., Onofrio, R. C., Winckler, W., Weir, B. A., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2203 · Cited by 2,003 · see citing works
Strelka: accurate somatic small-variant calling from sequenced tumor–normal sample pairs
Saunders, C. T., Wong, W. S. W., Swamy, S., Becq, J., Murray, L. J., & Cheetham, R. K. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts271 · Cited by 1,589 · see citing works
Garraway, L. A., Widlund, H. R., Rubin, M. A., Getz, G., Berger, A. J., Ramaswamy, S., Beroukhim, R., Milner, D. A., Granter, S. R., Du, J., et al. (2005) · Nature · doi:10.1038/nature03664 · Cited by 1,548 · see citing works
Self-citationWhitfield, M. L., Sherlock, G., Saldanha, A. J., Murray, J. I., Ball, C. A., Alexander, K. E., Matese, J. C., Perou, C. M., Hurt, M. M., Brown, P. O., et al. (2002) · Molecular Biology of the Cell · doi:10.1091/mbc.02-02-0030 · Cited by 1,527 · see citing works
Molecular Signature of CD8+ T Cell Exhaustion during Chronic Viral Infection
Wherry, E. J., Ha, S., Kaech, S. M., Haining, W. N., Sarkar, S., Kalia, V., Subramaniam, S., Blattman, J. N., Barber, D. L., & Ahmed, R. (2007) · Immunity · doi:10.1016/j.immuni.2007.11.006 · Cited by 1,355 · see citing works
Activation of the AXL kinase causes resistance to EGFR-targeted therapy in lung cancer
Zhang, Z., Lee, J. C., Lin, L., Olivas, V., Au, V., LaFramboise, T., Abdel-Rahman, M., Wang, X., Levine, A. D., Rho, J. K., et al. (2012) · Nature Genetics · doi:10.1038/ng.2330 · Cited by 1,224 · see citing works
The genomic complexity of primary human prostate cancer
Berger, M. F., Lawrence, M. S., Demichelis, F., Drier, Y., Cibulskis, K., Sivachenko, A., Sboner, A., Esgueva, R., Pflueger, D., Sougnez, C. L., et al. (2011) · Nature · doi:10.1038/nature09744 · Cited by 1,213 · see citing works
Self-citationSingle-cell transcriptomics reveals bimodality in expression and splicing in immune cells
Shalek, A. K., Satija, R., Adiconis, X., Gertner, R. S., Gaublomme, J. T., Raychowdhury, R., Schwartz, S., Yosef, N., Malboeuf, C., Lu, D., et al. (2013) · Nature · doi:10.1038/nature12172 · Cited by 1,190 · see citing works
Self-citationRoesch, A., Fukunaga‐Kalabis, M., Schmidt, E. C., Zabierowski, S. E., Brafford, P. A., Vultur, A., Basu, D., Gimotty, P. A., VOGT, T. M., & Herlyn, M. (2010) · Cell · doi:10.1016/j.cell.2010.04.020 · Cited by 1,162 · see citing works
Single-cell RNA-seq reveals dynamic paracrine control of cellular variation
Shalek, A. K., Satija, R., Shuga, J., Trombetta, J. J., Gennert, D., Lu, D., Chen, P., Gertner, R. S., Gaublomme, J. T., Yosef, N., et al. (2014) · Nature · doi:10.1038/nature13437 · Cited by 1,000 · see citing works
Self-citationDissecting Therapeutic Resistance to RAF Inhibition in Melanoma by Tumor Genomic Profiling
Wagle, N., Emery, C., Berger, M. F., Davis, M. J., Sawyer, A. M., Pochanard, P., Kehoe, S. M., Johannessen, C. M., MacConaill, L. E., Hahn, W. C., et al. (2011) · Journal of Clinical Oncology · doi:10.1200/jco.2010.33.2312 · Cited by 996 · see citing works
Self-citationA faster circular binary segmentation algorithm for the analysis of array CGH data
Venkatraman, E. S., & Olshen, A. B. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btl646 · Cited by 963 · see citing works
The Genetic Landscape of Clinical Resistance to RAF Inhibition in Metastatic Melanoma
Allen, E. M. V., Wagle, N., Sucker, A., Treacy, D. J., Johannessen, C. M., Goetz, E. M., Place, C. S., Taylor-Weiner, A. N., Whittaker, S. R., Kryukov, G. V., et al. (2013) · Cancer Discovery · doi:10.1158/2159-8290.cd-13-0617 · Cited by 904 · see citing works
Self-citationThe Transcription Factor NFAT Promotes Exhaustion of Activated CD8 + T Cells
Martinez, G. J., Pereira, R. M., Äijö, T., Kim, E. Y., Marangoni, F., Pipkin, M. E., Togher, S., Heissmeyer, V., Zhang, Y. C., Crotty, S., et al. (2015) · Immunity · doi:10.1016/j.immuni.2015.01.006 · Cited by 857 · cited 1× · see citing works
“Another variable gene of interest was the transcription factor NFATC1; previously implicated in T cell exhaustion (49).”
Exhaustion of tumor-specific CD8+ T cells in metastases from melanoma patients
Baitsch, L., Baumgaertner, P., Devêvre, E., Raghav, S. K., Legat, A., Barba, L., Wieckowski, S., Bouzourene, H., Deplancke, B., Romero, P., et al. (2011) · Journal of Clinical Investigation · doi:10.1172/jci46102 · Cited by 845 · see citing works
Kowalczyk, M. S., Tirosh, I., Heckl, D., Rao, T. N., Dixit, A., Haas, B. J., Schneider, R. K., Wagers, A. J., Ebert, B. L., & Regev, A. (2015) · Genome Research · doi:10.1101/gr.192237.115 · Cited by 778 · see citing works
Self-citationModulation of the antitumor immune response by complement
Markiewski, M. M., DeAngelis, R. A., Benencia, F., Ricklin-Lichtsteiner, S. K., Koutoulaki, A., Gerard, C. J., Coukos, G., & Lambris, J. D. (2008) · Nature Immunology · doi:10.1038/ni.1655 · Cited by 770 · see citing works
<i>In vivo</i> Switching of Human Melanoma Cells between Proliferative and Invasive States
Hoek, K. S., Eichhoff, O. M., Schlegel, N. C., Döbbeling, U., Kobert, N., Schaerer, L., Hemmi, S., & Dummer, R. (2008) · Cancer Research · doi:10.1158/0008-5472.can-07-2491 · Cited by 743 · see citing works
Human melanoma-initiating cells express neural crest nerve growth factor receptor CD271
Boiko, A. D., Razorenova, O. V., van de Rijn, M., Swetter, S. M., Johnson, D. L., Ly, D. P., Butler, P. D., Yang, G. P., Joshua, B., Kaplan, M. J., et al. (2010) · Nature · doi:10.1038/nature09161 · Cited by 714 · see citing works
Common markers of proliferation
Whitfield, M. L., George, L. K., Grant, G. D., & Perou, C. M. (2006) · Nature Reviews Cancer · doi:10.1038/nrc1802 · Cited by 650 · cited 1× · see citing works
“To characterize cycling cells more precisely, we used gene signatures previously shown to denote G1/S or G2/M phases in both synchronization (20) and single-cell (16) experiments in cell lines.”
Low MITF/AXL ratio predicts early resistance to multiple targeted drugs in melanoma
Müller, J., Krijgsman, O., Tsoi, J., Robert, L., Hugo, W., Song, C., Kong, X., Possik, P. A., Cornelissen-Steijger, P. D. M., Foppen, M. H. G., et al. (2014) · Nature Communications · doi:10.1038/ncomms6712 · Cited by 639 · see citing works
Non-genomic and Immune Evolution of Melanoma Acquiring MAPKi Resistance
Hugo, W., Shi, H., Sun, L., Piva, M., Song, C., Kong, X., Moriceau, G., Hong, A., Dahlman, K. B., Johnson, D. B., et al. (2015) · Cell · doi:10.1016/j.cell.2015.07.061 · Cited by 604 · see citing works
Selective expansion of a subset of exhausted CD8 T cells by αPD-L1 blockade
Blackburn, S. D., Shin, H., Freeman, G. J., & Wherry, E. J. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0801497105 · Cited by 575 · see citing works
Weber, J. S., Kudchadkar, R. R., Yu, B., Gallenstein, D., Horak, C. E., Inzunza, H. D., Zhao, X., Martinez, A. J., Wang, W., Gibney, G., et al. (2013) · Journal of Clinical Oncology · doi:10.1200/jco.2013.51.4802 · Cited by 535 · see citing works
A Melanoma Cell State Distinction Influences Sensitivity to MAPK Pathway Inhibitors
Konieczkowski, D. J., Johannessen, C. M., Abudayyeh, O. O., Kim, J. W., Cooper, Z. A., Piris, A., Frederick, D. T., Barzily-Rokni, M., Straussman, R., Haq, R., et al. (2014) · Cancer Discovery · doi:10.1158/2159-8290.cd-13-0424 · Cited by 526 · see citing works
Self-citationOncotator: Cancer Variant Annotation Tool
Ramos, A. H., Lichtenstein, L., Gupta, M., Lawrence, M. S., Pugh, T. J., Saksena, G., Meyerson, M. M., & Getz, G. A. (2015) · Human Mutation · doi:10.1002/humu.22771 · Cited by 485 · see citing works
A melanocyte lineage program confers resistance to MAP kinase pathway inhibition
Johannessen, C. M., Johnson, L. A., Piccioni, F., Townes, A., Frederick, D. T., Donahue, M. K., Narayan, R., Flaherty, K. T., Wargo, J. A., Root, D. E., et al. (2013) · Nature · doi:10.1038/nature12688 · Cited by 452 · see citing works
Self-citationPreparation of Single‐Cell RNA‐Seq Libraries for Next Generation Sequencing
Trombetta, J. J., Gennert, D. G., Lu, D. Y., Satija, R., Shalek, A. K., & Regev, A. (2014) · Current Protocols in Molecular Biology · doi:10.1002/0471142727.mb0422s107 · Cited by 293 · see citing works
Self-citationInhibitory Receptors Beyond T Cell Exhaustion
Marraco, S. A. F., Neubert, N. J., Verdeil, G., & Speiser, D. E. (2015) · Frontiers in Immunology · doi:10.3389/fimmu.2015.00310 · Cited by 242 · see citing works
AXL kinase as a novel target for cancer therapy
Wu, X., Liu, X., Koul, S., Lee, C. Y., Zhang, Z., & Halmos, B. (2014) · Oncotarget · doi:10.18632/oncotarget.2542 · Cited by 191 · see citing works
Phenotype Switching in Melanoma: Implications for Progression and Therapy
Li, F. Z., Dhillon, A. S., Anderson, R. L., McArthur, G. A., & Ferrao, P. T. (2015) · Frontiers in Oncology · doi:10.3389/fonc.2015.00031 · Cited by 164 · see citing works
Campton, D. E., Ramirez, A. B., Nordberg, J. J., Drovetto, N., Clein, A. C., Varshavskaya, P., Friemel, B. H., Quarre, S., Breman, A. M., Dorschner, M. O., et al. (2015) · BMC Cancer · doi:10.1186/s12885-015-1383-x · Cited by 156 · see citing works
Prognostic and predictive markers for the new immunotherapies.
Mahoney, K. M., & Atkins, M. B. (2014) · PubMed · Cited by 136 · see citing works
Konsti, J., Lundin, M., Joensuu, H., Lehtimäki, T., Sihto, H. J., Holli, K., Turpeenniemi-Hujanen, T. M., Kataja, V. V., Sailas, L., Isola, J. J., et al. (2011) · BMC Clinical Pathology · doi:10.1186/1472-6890-11-3 · Cited by 88 · see citing works
Shapiro, G., Rosen, L. S., Tolcher, A. W., Goldman, J. W., Gandhi, L., Papadopoulos, K. P., Tolaney, S. M., Beeram, M., Rasco, D. W., Kulanthaivel, P., et al. (2013) · Journal of Clinical Oncology · doi:10.1200/jco.2013.31.15_suppl.2500 · Cited by 67 · see citing works
Self-citationSkaland, I., Øvestad, I. T., Janssen, E. A. M., Kłos, J., Kjellevold, K. H., Helliesen, T., & Baak, J. P. A. (2007) · Journal of Clinical Pathology · doi:10.1136/jcp.2007.046763 · Cited by 62 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
xCell: digitally portraying the tissue cellular heterogeneity landscape
Aran, D., Hu, Z., & Butte, A. J. (2017) · Genome Biology · doi:10.1186/s13059-017-1349-1 · Cited by 4,397 · see citing works
Clonal 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 · see citing works
Puram, S. V., Tirosh, I., Parikh, A. S., Patel, A. P., Yizhak, K., Gillespie, S., Rodman, C., Luo, C. L., Mroz, E. A., Emerick, K. S., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.044 · Cited by 2,557 · see citing works
Self-citationTertiary lymphoid structures improve immunotherapy and survival in melanoma
Cabrita, R., Lauss, M., Sanna, A., Donia, M., Skaarup Larsen, M., Mitra, S., Johansson, I., Phung, B., Harbst, K., Vallon-Christersson, J., et al. (2020) · Nature · doi:10.1038/s41586-019-1914-8 · Cited by 2,374 · see citing works
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · see citing works
Self-citationThe immune contexture in cancer prognosis and treatment
Fridman, W. H., Zitvogel, L., Sautès–Fridman, C., & Kroemer, G. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.101 · Cited by 2,263 · see citing works
Landscape of Infiltrating T Cells in Liver Cancer Revealed by Single-Cell Sequencing
Zheng, C., Zheng, L., Yoo, J.-K., Guo, H., Zhang, Y., Guo, X., Kang, B., Hu, R., Huang, J. Y., Zhang, Q., et al. (2017) · Cell · doi:10.1016/j.cell.2017.05.035 · Cited by 2,152 · see citing works
Defining T Cell States Associated with Response to Checkpoint Immunotherapy in Melanoma
Sade-Feldman, M., Yizhak, K., Bjorgaard, S. L., Ray, J. P., de Boer, C. G., Jenkins, R. W., Lieb, D. J., Chen, J. H., Frederick, D. T., Barzily-Rokni, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.10.038 · Cited by 2,053 · see citing works
Self-citationDependency of a therapy-resistant state of cancer cells on a lipid peroxidase pathway
Viswanathan, V. S., Ryan, M. J., Dhruv, H. D., Gill, S., Eichhoff, O. M., Seashore-Ludlow, B., Kaffenberger, S. D., Eaton, J. K., Shimada, K., Aguirre, A. J., et al. (2017) · Nature · doi:10.1038/nature23007 · Cited by 2,011 · see citing works
Siddiqui, I., Schaeuble, K., Chennupati, V., Fuertes Marraco, S. A., Calderon-Copete, S., Pais Ferreira, D., Carmona, S. J., Scarpellino, L., Gfeller, D., Pradervand, S., et al. (2019) · Immunity · doi:10.1016/j.immuni.2018.12.021 · Cited by 1,679 · see citing works
Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma
Zhang, Q., He, Y., Luo, N., Patel, S. J., Han, Y., Gao, R., Modak, M., Carotta, S., Haslinger, C., Kind, D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.10.003 · Cited by 1,624 · see citing works
Exploring tissue architecture using spatial transcriptomics
Rao, A., Barkley, D., França, G. S., & Yanai, I. (2021) · Nature · doi:10.1038/s41586-021-03634-9 · Cited by 1,700 · see citing works
Comparative Analysis of Single-Cell RNA Sequencing Methods
Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., & Enard, W. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.01.023 · Cited by 1,494 · see citing works
Lineage tracking reveals dynamic relationships of T cells in colorectal cancer
Zhang, L., Yu, X., Zheng, L., Zhang, Y., Li, Y., Fang, Q., Gao, R., Kang, B., Zhang, Q., Huang, J. Y., et al. (2018) · Nature · doi:10.1038/s41586-018-0694-x · Cited by 1,505 · see citing works
Clonal replacement of tumor-specific T cells following PD-1 blockade
Yost, K. E., Satpathy, A. T., Wells, D. K., Qi, Y., Wang, C., Kageyama, R., McNamara, K. L., Granja, J. M., Sarin, K. Y., Brown, R. A., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0522-3 · Cited by 1,534 · see citing works
Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402 · see citing works
Blank, C. U., Haining, W. N., Held, W., Hogan, P. G., Kallies, A., Lugli, E., Lynn, R. C., Philip, M., Rao, A., Restifo, N. P., et al. (2019) · Nature Reviews Immunology · doi:10.1038/s41577-019-0221-9 · Cited by 1,603 · see citing works
Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding
Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L. T., Peeler, D. J., Mukherjee, S., Chen, W., et al. (2018) · Science · doi:10.1126/science.aam8999 · Cited by 1,422 · see citing works
Integrative single-cell analysis
Stuart, T., & Satija, R. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0093-7 · Cited by 1,460 · see citing works
Benchmarking atlas-level data integration in single-cell genomics
Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438 · see citing works
A Cancer Cell Program Promotes T Cell Exclusion and Resistance to Checkpoint Blockade
Jerby-Arnon, L., Shah, P., Cuoco, M. S., Rodman, C., Su, M.-J., Melms, J. C., Leeson, R., Kanodia, A., Mei, S., Lin, J.-R., et al. (2018) · Cell · doi:10.1016/j.cell.2018.09.006 · Cited by 1,429 · see citing works
Self-citationMultiplexed droplet single-cell RNA-sequencing using natural genetic variation
Kang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., Wan, E., Wong, S., Byrnes, L., Lanata, C. M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4042 · Cited by 1,339 · see citing works
Diversity and biology of cancer-associated fibroblasts
Biffi, G., & Tuveson, D. A. (2021) · Physiological Reviews · doi:10.1152/physrev.00048.2019 · Cited by 1,265 · see citing works
Thommen, D. S., Koelzer, V. H., Herzig, P., Roller, A., Trefny, M., Dimeloe, S., Kiialainen, A., Hanhart, J., Schill, C., Hess, C., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0057-z · Cited by 1,161 · see citing works
“5 (2D1), -CD56 PE (CMSSB), - CD3 APC-eFluor780 (SK7), -Lag-3 FITC (3DS223H), -TIGIT PE (MBSA43), -2B4 FITC (eBioDM244), -CD137 FITC (4B4), -IL-2 PE (MQ1-17H12), -TNF-α APC (MAb11), - CD26 FITC (2A6), -CXCR5 Biotin (MU5UBEE), -ICOS FITC (ISA-3), -CD38 APC (HIT2), -Eomes eFluor660 (WD1928), -T-bet PE (eBio4B10), -pSTAT3…”
InfluentialRare cell variability and drug-induced reprogramming as a mode of cancer drug resistance
Shaffer, S. M., Dunagin, M. C., Torborg, S. R., Torre, E. A., Emert, B., Krepler, C., Beqiri, M., Sproesser, K., Brafford, P. A., Xiao, M., et al. (2017) · Nature · doi:10.1038/nature22794 · Cited by 1,218 · see citing works
Massively parallel single-nucleus RNA-seq with DroNc-seq
Habib, N., Avraham-Davidi, I., Basu, A., Burks, T., Shekhar, K., Hofree, M., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4407 · Cited by 1,069 · see citing works
Self-citationMultimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma
Ji, A. L., Rubin, A. J., Thrane, K., Jiang, S., Reynolds, D. L., Meyers, R. M., Guo, M. G., George, B. M., Mollbrink, A., Bergenstråhle, J., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.039 · Cited by 1,117 · see citing works
Li, H., Courtois, E. T., Sengupta, D., Tan, Y., Chen, K. H., Goh, J. J. L., Kong, S. L., Chua, C., Hon, L. K., Tan, W. S., et al. (2017) · Nature Genetics · doi:10.1038/ng.3818 · Cited by 1,080 · see citing works
Li, H., van der Leun, A. M., Yofe, I., Lubling, Y., Gelbard-Solodkin, D., van Akkooi, A. C. J., van den Braber, M., Rozeman, E. A., Haanen, J. B. A. G., Blank, C. U., et al. (2019) · Cell · doi:10.1016/j.cell.2018.11.043 · Cited by 1,101 · see citing works
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071 · see citing works
UCell: robust and scalable single-cell gene signature scoring
Andreatta, M., & Carmona, S. J. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.06.043 · Cited by 1,104 · see citing works
Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer
Chung, W., Eum, H. H., Lee, H.-O., Lee, K.-M., Lee, H.-B., Kim, K.-T., Ryu, H. S., Kim, S., Lee, J. E., Park, Y. H., et al. (2017) · Nature Communications · doi:10.1038/ncomms15081 · Cited by 992 · see citing works
“T lymphocytes in four patient tumours manifested distinct patterns for naive, costimulatory, regulatory, exhaustion and cytotoxicity expression signatures 39–41 .”
cGAS–STING drives ageing-related inflammation and neurodegeneration
Gulen, M. F., Samson, N., Keller, A., Schwabenland, M., Liu, C., Glück, S., Thacker, V. V., Favre, L., Mangeat, B., Kroese, L. J., et al. (2023) · Nature · doi:10.1038/s41586-023-06373-1 · Cited by 1,074 · see citing works
Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics
Longo, S. K., Guo, M. G., Ji, A. L., & Khavari, P. A. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00370-8 · Cited by 1,059 · see citing works
Genetic and transcriptional evolution alters cancer cell line drug response
Ben-David, U., Siranosian, B., Ha, G., Tang, H., Oren, Y., Hinohara, K., Strathdee, C. A., Dempster, J., Lyons, N. J., Burns, R., et al. (2018) · Nature · doi:10.1038/s41586-018-0409-3 · Cited by 931 · see citing works
The bone marrow microenvironment at single-cell resolution
Tikhonova, A. N., Dolgalev, I., Hu, H., Sivaraj, K. K., Hoxha, E., Cuesta-Domínguez, Á., Pinho, S., Akhmetzyanova, I., Gao, J., Witkowski, M., et al. (2019) · Nature · doi:10.1038/s41586-019-1104-8 · Cited by 936 · see citing works
Satpathy, A. T., Granja, J. M., Yost, K. E., Qi, Y., Meschi, F., McDermott, G. P., Olsen, B. N., Mumbach, M. R., Pierce, S. E., Corces, M. R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0206-z · Cited by 980 · see citing works
Self-citationOrchestrating single-cell analysis with Bioconductor
Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945 · see citing works
Monaco, G., Lee, B., Xu, W., Mustafah, S., Hwang, Y. Y., Carré, C., Burdin, N., Visan, L., Ceccarelli, M., Poidinger, M., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.01.041 · Cited by 958 · see citing works
Multiplexed quantification of proteins and transcripts in single cells
Peterson, V. M., Zhang, K. X., Kumar, N., Wong, J., Li, L., Wilson, D. C., Moore, R., McClanahan, T. K., Sadekova, S., & Klappenbach, J. A. (2017) · Nature Biotechnology · doi:10.1038/nbt.3973 · Cited by 832 · see citing works
Self-Renewing Resident Cardiac Macrophages Limit Adverse Remodeling Following Myocardial Infarction
Dick, S. A., Macklin, J. A., Nejat, S., Momen, A., Clemente-Casares, X., Althagafi, M. G., Chen, J., Kantores, C., Hosseinzadeh, S., Aronoff, L., et al. (2019) · Nature Immunology · doi:10.1038/s41590-018-0272-2 · Cited by 883 · see citing works
Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer.
Kieffer, Y., Hocine, H. R., Gentric, G., Pelon, F., Bernard, C., Bourachot, B., Lameiras, S., Albergante, L., Bonneau, C., Guyard, A., et al. (2020) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-1384 · Cited by 910 · see citing works
NR4A transcription factors limit CAR T cell function in solid tumours
Chen, J., López-Moyado, I. F., Seo, H., Lio, C.-W. J., Hempleman, L. J., Sekiya, T., Yoshimura, A., Scott-Browne, J. P., & Rao, A. (2019) · Nature · doi:10.1038/s41586-019-0985-x · Cited by 874 · see citing works
Toward Minimal Residual Disease-Directed Therapy in Melanoma
Rambow, F., Rogiers, A., Marin-Bejar, O., Aibar, S., Femel, J., Dewaele, M., Karras, P., Brown, D., Chang, Y. H., Debiec-Rychter, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.025 · Cited by 771 · see citing works
Self-citationScaling single-cell genomics from phenomenology to mechanism
Tanay, A., & Regev, A. (2017) · Nature · doi:10.1038/nature21350 · Cited by 792 · see citing works
Self-citationZhang, Y., Chen, H., Mo, H., Hu, X., Gao, R., Zhao, Y., Liu, B., Niu, L., Sun, X., Yu, X., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.09.010 · Cited by 783 · see citing works
A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex
Zhong, S., Zhang, S., Fan, X., Wu, Q., Yan, L., Dong, J., Zhang, H., Li, L., Sun, L., Pan, N., et al. (2018) · Nature · doi:10.1038/nature25980 · Cited by 689 · see citing works
Intratumoral CD4+ T Cells Mediate Anti-tumor Cytotoxicity in Human Bladder Cancer
Oh, D. Y., Kwek, S. S., Raju, S. S., Li, T., McCarthy, E., Chow, E., Aran, D., Ilano, A., Pai, C.-C. S., Rancan, C., et al. (2020) · Cell · doi:10.1016/j.cell.2020.05.017 · Cited by 734 · see citing works
Revealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632 · see citing works
Self-citationChakravarthy, A., Khan, L., Bensler, N. P., Bose, P., & De Carvalho, D. D. (2018) · Nature Communications · doi:10.1038/s41467-018-06654-8 · Cited by 656 · see citing works
Denisenko, E., Guo, B. B., Jones, M., Hou, R., de Kock, L., Lassmann, T., Poppe, D., Clément, O., Simmons, R. K., Lister, R., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-02048-6 · Cited by 686 · see citing works
Checkpoint Blockade Immunotherapy Induces Dynamic Changes in PD-1-CD8+ Tumor-Infiltrating T Cells
Kurtulus, S., Madi, A., Escobar, G., Klapholz, M., Nyman, J., Christian, E., Pawlak, M., Dionne, D., Xia, J., Rozenblatt-Rosen, O., et al. (2019) · Immunity · doi:10.1016/j.immuni.2018.11.014 · Cited by 636 · see citing works
Self-citationZhou, Y., Yang, D., Yang, Q., Lv, X., Huang, W., Zhou, Z., Wang, Y., Zhang, Z., Yuan, T., Ding, X., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-20059-6 · Cited by 672 · see citing works
From bulk, single-cell to spatial RNA sequencing
Li, X., & Wang, C.-Y. (2021) · International Journal of Oral Science · doi:10.1038/s41368-021-00146-0 · Cited by 605 · see citing works
The Human Cell Atlas: from vision to reality
Rozenblatt-Rosen, O., Stubbington, M. J. T., Regev, A., & Teichmann, S. A. (2017) · Nature · doi:10.1038/550451a · Cited by 638 · see citing works
Self-citationMicroenvironment drives cell state, plasticity, and drug response in pancreatic cancer
Raghavan, S., Winter, P. S., Navia, A. W., Williams, H. L., DenAdel, A., Lowder, K. E., Galvez-Reyes, J., Kalekar, R. L., Mulugeta, N., Kapner, K. S., et al. (2021) · Cell · doi:10.1016/j.cell.2021.11.017 · Cited by 592 · see citing works
Self-citationTumour heterogeneity and metastasis at single-cell resolution
Lawson, D. A., Kessenbrock, K., Davis, R. T., Pervolarakis, N., & Werb, Z. (2018) · Nature Cell Biology · doi:10.1038/s41556-018-0236-7 · Cited by 606 · see citing works
<i>Ex Vivo</i> Profiling of PD-1 Blockade Using Organotypic Tumor Spheroids
Jenkins, R. W., Aref, A. R., Lizotte, P. H., Ivanova, E., Stinson, S., Zhou, C. W., Bowden, M., Deng, J., Liu, H., Miao, D., et al. (2018) · Cancer Discovery · doi:10.1158/2159-8290.cd-17-0833 · Cited by 594 · see citing works
Self-citationWang, S., Mustafa, M., Yuede, C. M., Salazar, S. V., Kong, P., Long, H., Ward, M., Siddiqui, O., Paul, R., Gilfillan, S., et al. (2020) · Journal of Experimental Medicine · doi:10.1084/jem.20200785 · Cited by 549 · see citing works
Metabolic landscape of the tumor microenvironment at single cell resolution
Xiao, Z., Dai, Z., & Locasale, J. W. (2019) · Nature Communications · doi:10.1038/s41467-019-11738-0 · Cited by 524 · see citing works
Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours
Gavish, A., Tyler, M., Greenwald, A. C., Hoefflin, R., Simkin, D., Tschernichovsky, R., Galili Darnell, N., Somech, E., Barbolin, C., Antman, T., et al. (2023) · Nature · doi:10.1038/s41586-023-06130-4 · Cited by 556 · see citing works
Self-citationBenci, J. L., Johnson, L. R., Choa, R., Xu, Y., Qiu, J., Zhou, Z., Xu, B., Ye, D., Nathanson, K. L., June, C. H., et al. (2019) · Cell · doi:10.1016/j.cell.2019.07.019 · Cited by 541 · see citing works
Lakins, M. A., Ghorani, E., Munir, H., Martins, C. P., & Shields, J. D. (2018) · Nature Communications · doi:10.1038/s41467-018-03347-0 · Cited by 578 · see citing works
Barkley, D., Moncada, R., Pour, M., Liberman, D. A., Dryg, I., Werba, G., Wang, W., Baron, M., Rao, A., Xia, B., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01141-9 · Cited by 532 · see citing works
Guerrero-Juarez, C. F., Dedhia, P. H., Jin, S., Ruiz-Vega, R., Ma, D., Liu, Y., Yamaga, K., Shestova, O., Gay, D. L., Yang, Z., et al. (2019) · Nature Communications · doi:10.1038/s41467-018-08247-x · Cited by 521 · see citing works
Developmental diversification of cortical inhibitory interneurons
Mayer, C., Hafemeister, C., Bandler, R. C., Machold, R., Batista Brito, R., Jaglin, X., Allaway, K., Butler, A., Fishell, G., & Satija, R. (2018) · Nature · doi:10.1038/nature25999 · Cited by 492 · see citing works
Ganesan, A.-P., Clarke, J., Wood, O., Garrido-Martin, E. M., Chee, S. J., Mellows, T., Samaniego-Castruita, D., Singh, D., Seumois, G., Alzetani, A., et al. (2017) · Nature Immunology · doi:10.1038/ni.3775 · Cited by 528 · see citing works
Single-cell multiomics: technologies and data analysis methods
Lee, J., Hyeon, D. Y., & Hwang, D. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0420-2 · Cited by 524 · see citing works
A noninflammatory mRNA vaccine for treatment of experimental autoimmune encephalomyelitis
Krienke, C., Kolb, L., Diken, E., Streuber, M., Kirchhoff, S., Bukur, T., Akilli-Öztürk, Ö., Kranz, L. M., Berger, H., Petschenka, J., et al. (2021) · Science · doi:10.1126/science.aay3638 · Cited by 520 · see citing works
Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility
Lareau, C. A., Duarte, F. M., Chew, J. G., Kartha, V. K., Burkett, Z. D., Kohlway, A. S., Pokholok, D., Aryee, M. J., Steemers, F. J., Lebofsky, R., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0147-6 · Cited by 444 · see citing works
Pan-cancer single-cell RNA-seq identifies recurring programs of cellular heterogeneity
Kinker, G. S., Greenwald, A. C., Tal, R., Orlova, Z., Cuoco, M. S., McFarland, J. M., Warren, A., Rodman, C., Roth, J. A., Bender, S. A., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-00726-6 · Cited by 445 · see citing works
Self-citationFibroblast-macrophage reciprocal interactions in health, fibrosis, and cancer
Buechler, M. B., Fu, W., & Turley, S. J. (2021) · Immunity · doi:10.1016/j.immuni.2021.04.021 · Cited by 494 · see citing works
Single-cell multiomics: multiple measurements from single cells
Macaulay, I. C., Ponting, C. P., & Voet, T. (2017) · Trends in Genetics · doi:10.1016/j.tig.2016.12.003 · Cited by 451 · see citing works
Nutrition, inflammation and cancer
Zitvogel, L., Pietrocola, F., & Kroemer, G. (2017) · Nature Immunology · doi:10.1038/ni.3754 · Cited by 446 · see citing works
Induction and transcriptional regulation of the co-inhibitory gene module in T cells
Chihara, N., Madi, A., Kondo, T., Zhang, H., Acharya, N., Singer, M., Nyman, J., Marjanovic, N. D., Kowalczyk, M. S., Wang, C., et al. (2018) · Nature · doi:10.1038/s41586-018-0206-z · Cited by 438 · see citing works
Self-citationMulticlonal Invasion in Breast Tumors Identified by Topographic Single Cell Sequencing
Casasent, A. K., Schalck, A., Gao, R., Sei, E., Long, A., Pangburn, W., Casasent, T., Meric-Bernstam, F., Edgerton, M. E., & Navin, N. E. (2018) · Cell · doi:10.1016/j.cell.2017.12.007 · Cited by 411 · see citing works
Andersson, A., Larsson, L., Stenbeck, L., Salmén, F., Ehinger, A., Wu, S. Z., Al-Eryani, G., Roden, D., Swarbrick, A., Borg, Å., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26271-2 · Cited by 404 · see citing works
Massively multiplex chemical transcriptomics at single-cell resolution
Srivatsan, S. R., McFaline-Figueroa, J. L., Ramani, V., Saunders, L., Cao, J., Packer, J., Pliner, H. A., Jackson, D. L., Daza, R. M., Christiansen, L., et al. (2020) · Science · doi:10.1126/science.aax6234 · Cited by 401 · see citing works
Single-nucleus profiling of human dilated and hypertrophic cardiomyopathy
Chaffin, M., Papangeli, I., Simonson, B., Akkad, A.-D., Hill, M. C., Arduini, A., Fleming, S. J., Melanson, M., Hayat, S., Kost-Alimova, M., et al. (2022) · Nature · doi:10.1038/s41586-022-04817-8 · Cited by 418 · see citing works
Cell type transcriptome atlas for the planarian <i>Schmidtea mediterranea</i>
Fincher, C. T., Wurtzel, O., de Hoog, T., Kravarik, K. M., & Reddien, P. W. (2018) · Science · doi:10.1126/science.aaq1736 · Cited by 436 · see citing works
Marsh, S. E., Walker, A. J., Kamath, T., Dissing-Olesen, L., Hammond, T. R., de Soysa, T. Y., Young, A. M. H., Murphy, S., Abdulraouf, A., Nadaf, N., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01022-8 · Cited by 432 · see citing works
B lymphocytes and cancer: a love-hate relationship
Yuen, G. J., Demissie, E., & Pillai, S. (2016) · Trends in cancer · doi:10.1016/j.trecan.2016.10.010 · Cited by 433 · see citing works
The progress of gut microbiome research related to brain disorders
Zhu, S., Jiang, Y., Xu, K., Cui, M., Ye, W., Zhao, G., Jin, L., & Chen, X. (2020) · Journal of Neuroinflammation · doi:10.1186/s12974-020-1705-z · Cited by 419 · see citing works
Exosomes in the tumor microenvironment as mediators of cancer therapy resistance
Li, I., & Nabet, B. Y. (2019) · Molecular Cancer · doi:10.1186/s12943-019-0975-5 · Cited by 416 · see citing works
Sadick, J. S., O’Dea, M. R., Hasel, P., Dykstra, T., Faustin, A., & Liddelow, S. A. (2022) · Neuron · doi:10.1016/j.neuron.2022.03.008 · Cited by 415 · see citing works
Giustacchini, A., Thongjuea, S., Barkas, N., Woll, P. S., Povinelli, B. J., Booth, C. A. G., Sopp, P., Norfo, R., Rodriguez-Meira, A., Ashley, N., et al. (2017) · Nature Medicine · doi:10.1038/nm.4336 · Cited by 403 · see citing works
Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome
Teschendorff, A. E., & Enver, T. (2017) · Nature Communications · doi:10.1038/ncomms15599 · Cited by 346 · see citing works
Single-Cell Transcriptomics Meets Lineage Tracing
Kester, L., & van Oudenaarden, A. (2018) · Cell Stem Cell · doi:10.1016/j.stem.2018.04.014 · Cited by 375 · see citing works
Phenotype plasticity as enabler of melanoma progression and therapy resistance
Arozarena, I., & Wellbrock, C. (2019) · Nature Reviews Cancer · doi:10.1038/s41568-019-0154-4 · Cited by 381 · see citing works
A practical solution to pseudoreplication bias in single-cell studies
Zimmerman, K. D., Espeland, M. A., & Langefeld, C. D. (2021) · Nature Communications · doi:10.1038/s41467-021-21038-1 · Cited by 362 · see citing works
Wang, T., Li, B., Nelson, C. E., & Nabavi, S. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2599-6 · Cited by 286 · see citing works
Understanding the tumor microenvironment for effective immunotherapy
Sadeghi Rad, H., Monkman, J., Warkiani, M. E., Ladwa, R., O'Byrne, K., Rezaei, N., & Kulasinghe, A. (2021) · Medicinal Research Reviews · doi:10.1002/med.21765 · Cited by 334 · see citing works
Single-cell analysis defines a pancreatic fibroblast lineage that supports anti-tumor immunity
Hutton, C., Heider, F., Blanco-Gomez, A., Banyard, A., Kononov, A., Zhang, X., Karim, S., Paulus-Hock, V., Watt, D., Steele, N., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.06.017 · Cited by 337 · see citing works
Single-cell multiregion dissection of Alzheimer’s disease
Mathys, H., Boix, C. A., Akay, L. A., Xia, Z., Davila-Velderrain, J., Ng, A. P., Jiang, X., Abdelhady, G., Galani, K., Mantero, J., et al. (2024) · Nature · doi:10.1038/s41586-024-07606-7 · Cited by 343 · see citing works
Cancer cells deploy lipocalin-2 to collect limiting iron in leptomeningeal metastasis.
Chi, Y., Remsik, J., Kiseliovas, V., Derderian, C., Sener, U., Alghader, M., Saadeh, F., Nikishina, K., Bale, T., Iacobuzio-Donahue, C., et al. (2020) · Science · doi:10.1126/science.aaz2193 · Cited by 320 · see citing works
Inferring and perturbing cell fate regulomes in human brain organoids
Fleck, J. S., Jansen, S. M. J., Wollny, D., Zenk, F., Seimiya, M., Jain, A., Okamoto, R., Santel, M., He, Z., Camp, J. G., et al. (2023) · Nature · doi:10.1038/s41586-022-05279-8 · Cited by 315 · see citing works
- 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 · 2015: 1 · 2014: 1 · 2013: 1 · 2012: 2
- 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
