The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma
Open accessAbstract
Cancer at single-cell resolution: Single-cell sequencing can illuminate the genetic properties of brain cancers and reveal heterogeneity within a tumor. Patel et al. examined the genome sequence of single cells isolated from brain glioblastomas. The findings revealed shared chromosomal changes but also extensive transcription variation, including genes related to signaling, which represent potential therapeutic targets. The authors suggest that the variation in tumor cells reflects neural development and that such variation among cancer cells may prove to have clinical significance. Science , this issue p. 1396
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?
In 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 [9]
In the citing paper
“Recent studies have successfully applied similar approaches to single-cell data, for example, by Patel et al. [36] to identify gene signatures and TFs that correlate with the stemness gradient in glioblastoma”
From Mapping gene regulatory networks from single-cell omics data (2018) · cites this paper as [36]
In the citing paper
“First, in contexts such as development and physiology, some facets of cellular identity are transient in time and space. Temporal processes may, for example, progress along one or more trajectories (e.g., differentiation); oscillate continuously between cellular states (e.g., the cell cycle98,99 or circadian rhythm100); or be influenced by the physical position and neighborhood of the cell71,93.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [99]
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 [99]
In the citing paper
“single-cell profiles provide a near-continuous sampling and show high conservation across cell types and species30,97,99”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [99]
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
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
Radiotherapy Plus Concomitant and Adjuvant Temozolomide for Glioblastoma
Stupp, R., Mason, W. P., van den Bent, M. J., Weller, M., Fisher, B., Taphoorn, M. J. B., Belanger, K., Brandes, A. A., Marosi, C., Bogdahn, U., et al. (2005) · New England Journal of Medicine · doi:10.1056/nejmoa043330 · Cited by 21,807
Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1113205 · Cited by 7,774
Verhaak, R. G. W., Hoadley, K. A., Purdom, E., Wang, V., Qi, Y., Wilkerson, M. D., Miller, C. R., Ding, L., Golub, T., Mesirov, J. P., et al. (2010) · Cancer Cell · doi:10.1016/j.ccr.2009.12.020 · Cited by 7,575
Identification of human brain tumour initiating cells
Singh, S. K., Hawkins, C. E., Clarke, I. D., Squire, J. A., Bayani, J., Hide, T., Henkelman, R. M., Cusimano, M. D., & Dirks, P. B. (2004) · Nature · doi:10.1038/nature03128 · Cited by 7,564
Glioma stem cells promote radioresistance by preferential activation of the DNA damage response
Bao, S., Wu, Q., McLendon, R. E., Hao, Y., Shi, Q., Hjelmeland, A. B., Dewhirst, M. W., Bigner, D. D., & Rich, J. N. (2006) · Nature · doi:10.1038/nature05236 · Cited by 6,517
An Integrated Genomic Analysis of Human Glioblastoma Multiforme
Parsons, D. W., Jones, S., Zhang, X., Lin, J. C.-H., Leary, R. J., Angenendt, P., Mankoo, P., Carter, H., Siu, I.-M., Gallia, G. L., et al. (2008) · Science · doi:10.1126/science.1164382 · Cited by 5,857
The Somatic Genomic Landscape of Glioblastoma
Brennan, C. W., Verhaak, R. G. W., McKenna, A., Campos, B., Noushmehr, H., Salama, S. R., Zheng, S., Chakravarty, D., Sanborn, J. Z., Berman, S. H., et al. (2013) · Cell · doi:10.1016/j.cell.2013.09.034 · Cited by 5,016
Cahoy, J. D., Emery, B., Kaushal, A., Foo, L. C., Zamanian, J. L., Christopherson, K. S., Xing, Y., Lubischer, J. L., Krieg, P. A., Krupenko, S. A., et al. (2008) · The Journal of Neuroscience · doi:10.1523/jneurosci.4178-07.2008 · Cited by 3,129
Flagged first, then by citations
Cited by
Sorted by citations
Items 301–400 of 1,018
Purine metabolism regulates DNA repair and therapy resistance in glioblastoma
Zhou, W., Yao, Y., Scott, A. J., Wilder-Romans, K., Dresser, J., Werner, C. K., Sun, H., Pratt, D. W., Sajjakulnukit, P., Zhao, S. G., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17512-x · Cited by 251
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
Glioblastoma evolution and heterogeneity from a 3D whole-tumor perspective
Mathur, R., Wang, Q., Schupp, P. G., Nikolić, A., Hilz, S., Hong, C., Grishanina, N. R., Kwok, D. W., Stevers, N. O., Jin, Q., et al. (2024) · Cell · doi:10.1016/j.cell.2023.12.013 · Cited by 262
Dong, F., Qin, X., Wang, B., Li, Q., Hu, J., Cheng, X., Guo, D., Cheng, F., Fang, C., Tan, Y., et al. (2021) · Cancer Research · doi:10.1158/0008-5472.can-21-1456 · Cited by 244
Ren, Y., Huang, Z., Zhou, L., Xiao, P., Song, J., He, P., Xie, C., Zhou, R., Li, M., Dong, X., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36707-6 · Cited by 252
Adorno-Cruz, V., Kibria, G., Liu, X., Doherty, M. R., Junk, D. J., Guan, D., Hubert, C., Venere, M., Mulkearns-Hubert, E. E., Sinyuk, M., et al. (2015) · Cancer Research · doi:10.1158/0008-5472.can-14-3225 · Cited by 240
Chiu, I. M., Barrett, L. B., Williams, E. K., Strochlic, D. E., Lee, S., Weyer, A. D., Lou, S., Bryman, G. S., Roberson, D. P., Ghasemlou, N., et al. (2014) · eLife · doi:10.7554/elife.04660 · Cited by 242
MacLeod, G. D. A. H., Bozek, D., Rajakulendran, N., Monteiro, V., Ahmadi, M., Steinhart, Z., Kushida, M. M., Yu, H., Coutinho, F. J., Cavalli, F. M. G., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2019.03.047 · Cited by 246
Fedele, M., Cerchia, L., Pegoraro, S., Sgarra, R., & Manfioletti, G. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20112746 · Cited by 229
Macrophages and microglia: the cerberus of glioblastoma
Buonfiglioli, A., & Hambardzumyan, D. (2021) · Acta Neuropathologica Communications · doi:10.1186/s40478-021-01156-z · Cited by 254
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
Fan, J., Lee, H., Lee, S., Ryu, D., Lee, S., Xue, C., Kim, S. J., Kım, K., Barkas, N., Park, P. J., et al. (2018) · Genome Research · doi:10.1101/gr.228080.117 · Cited by 228
Mapping gene regulatory networks from single-cell omics data
Fiers, M. W. E. J., Minnoye, L., Aibar, S., Bravo González-Blas, C., Kalender Atak, Z., & Aerts, S. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/elx046 · Cited by 235
The specificity of long noncoding RNA expression
Gloss, B., & Dinger, M. E. (2015) · Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms · doi:10.1016/j.bbagrm.2015.08.005 · Cited by 239
Single-cell transcriptome profiling of the stepwise progression of head and neck cancer
Choi, J. H., Lee, B., Jang, J. Y., Lee, Y. S., Kim, H. J., Roh, J., Shin, Y. S., Woo, H. G., & Kim, C. (2023) · Nature Communications · doi:10.1038/s41467-023-36691-x · Cited by 248
Finicle, B. T., Jayashankar, V., & Edinger, A. L. (2018) · Nature reviews. Cancer · doi:10.1038/s41568-018-0048-x · Cited by 241
Single-cell transcriptome sequencing: recent advances and remaining challenges
Liu, S., & Trapnell, C. (2016) · F1000Research · doi:10.12688/f1000research.7223.1 · Cited by 230
Hiscox, L. V., Johnson, C. L., Barnhill, E., McGarry, M., Huston, J., van Beek, E. J. R., Starr, J. M., & Roberts, N. (2016) · Physics in Medicine and Biology · doi:10.1088/0031-9155/61/24/r401 · Cited by 244
Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
Van den Berge, K., Perraudeau, F., Soneson, C., Love, M. I., Risso, D., Vert, J.-P., Robinson, M. D., Dudoit, S., & Clement, L. (2018) · Genome Biology · doi:10.1186/s13059-018-1406-4 · Cited by 228
Combination therapy to checkmate Glioblastoma: clinical challenges and advances
Ghosh, D., Nandi, S., & Bhattacharjee, S. (2018) · Clinical and Translational Medicine · doi:10.1186/s40169-018-0211-8 · Cited by 233
Quantifying the effect of experimental perturbations at single-cell resolution
Burkhardt, D. B., Stanley, J. S., Tong, A., Perdigoto, A. L., Gigante, S. A., Herold, K. C., Wolf, G., Giraldez, A. J., van Dijk, D., & Krishnaswamy, S. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-00803-5 · Cited by 226
Oligodendroglioma: pathology, molecular mechanisms and markers
Wesseling, P., van den Bent, M. J., & Perry, A. (2015) · Acta Neuropathologica · doi:10.1007/s00401-015-1424-1 · Cited by 233
Sun, S., Zhu, J., Ma, Y., & Zhou, X. (2019) · Genome biology · doi:10.1186/s13059-019-1898-6 · Cited by 225
EGFR as a Target for Glioblastoma Treatment: An Unfulfilled Promise
Westphal, M., Maire, C. L., & Lamszus, K. (2017) · CNS Drugs · doi:10.1007/s40263-017-0456-6 · Cited by 233
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
Harnessing the immune system in glioblastoma
Brown, N. F., Carter, T. J., Ottaviani, D., & Mulholland, P. J. (2018) · British Journal of Cancer · doi:10.1038/s41416-018-0258-8 · Cited by 227
Single-Cell Analysis in Cancer Genomics
Saadatpour, A., Lai, S., Guo, G., & Yuan, G. (2015) · Trends in Genetics · doi:10.1016/j.tig.2015.07.003 · Cited by 230
Phenotypic Plasticity of Invasive Edge Glioma Stem-like Cells in Response to Ionizing Radiation
Minata, M., Audia, A., Shi, J., Lu, S., Bernstock, J. D., Pavlyukov, M. S., Das, A., Kim, S.-H., Shin, Y. J., Lee, Y., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.01.076 · Cited by 222
Glioma stem cells and their roles within the hypoxic tumor microenvironment
Boyd, N., Tran, A. N., Bernstock, J. D., Etminan, T., Jones, A. B., Gillespie, G. Y., Friedman, G. K., & Hjelmeland, A. B. (2020) · Theranostics · doi:10.7150/thno.41692 · Cited by 221
Single-cell analysis supports a luminal-neuroendocrine transdifferentiation in human prostate cancer
Dong, B., Miao, J., Wang, Y., Luo, W., Ji, Z., Lai, H., Zhang, M., Cheng, X., Wang, J., Fang, Y., et al. (2020) · Communications Biology · doi:10.1038/s42003-020-01476-1 · Cited by 230
Cellular architecture of human brain metastases
Velozo, H. G., Mei, W., Robles, I., Hagerling, C., Allen, B. M., Okholm, T. L. H., Nanjaraj, A., Verbeek, T. C. A. I., Kalavacherla, S., van Gogh, M., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.043 · Cited by 224
Simultaneous Multiplexed Measurement of RNA and Proteins in Single Cells
Darmanis, S., Gallant, C. J., Marinescu, V. D., Niklasson, M., Segerman, A., Flamourakis, G., Fredriksson, S., Assarsson, E., Lundberg, M., Nelander, S., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2015.12.021 · Cited by 219
Systems biology primer: the basic methods and approaches
Tavassoly, I., Goldfarb, J., & Iyengar, R. (2018) · Essays in Biochemistry · doi:10.1042/ebc20180003 · Cited by 228
Lung cancer scRNA-seq and lipidomics reveal aberrant lipid metabolism for early-stage diagnosis
Wang, G., Qiu, M., Xing, X., Zhou, J., Yao, H., Li, M., Yin, R. H., Hou, Y., Li, Y., Pan, S., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abk2756 · Cited by 222
Cellular adaptation to cancer therapy along a resistance continuum
França, G. S., Baron, M., King, B. R., Bossowski, J. P., Bjornberg, A., Pour, M., Rao, A. N., Patel, A. S., Misirlioğlu, S., Barkley, D., et al. (2024) · Nature · doi:10.1038/s41586-024-07690-9 · Cited by 235
Self-citationJacob, F., Ming, G., & Song, H. (2020) · Nature Protocols · doi:10.1038/s41596-020-0402-9 · Cited by 219
A general approach for detecting expressed mutations in AML cells using single cell RNA-sequencing
Petti, A. A., Williams, S. R., Miller, C. A., Fiddes, I. T., Srivatsan, S. N., Chen, D. Y., Fronick, C. C., Fulton, R. S., Church, D. M., & Ley, T. J. (2019) · Nature Communications · doi:10.1038/s41467-019-11591-1 · Cited by 215
Intratumoral Heterogeneity of the Epigenome
Mazor, T., Pankov, A., Song, J. S., & Costello, J. (2016) · Cancer Cell · doi:10.1016/j.ccell.2016.03.009 · Cited by 217
Comparison of glioblastoma (GBM) molecular classification methods
Lee, E., Yong, R. L. M., Paddison, P. J., & Zhu, J. (2018) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2018.07.006 · Cited by 219
Wang, D., & Gu, J. (2018) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2018.08.003 · Cited by 221
Chlorotoxin-directed CAR T cells for specific and effective targeting of glioblastoma
Wang, D., Starr, R., Chang, W.-C., Aguilar, B., Alizadeh, D., Wright, S. L., Yang, X., Brito, A., Sarkissian, A., Ostberg, J. R., et al. (2020) · Science Translational Medicine · doi:10.1126/scitranslmed.aaw2672 · Cited by 220
PRMT5 inhibition disrupts splicing and stemness in glioblastoma
Sachamitr, P., Ho, J. C., Ciamponi, F. E., Ba-Alawi, W., Coutinho, F. J., Guilhamon, P., Kushida, M. M., Cavalli, F. M. G., Lee, L., Rastegar, N., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21204-5 · Cited by 227
Park, N. I., Guilhamon, P., Desai, K., McAdam, R. F., Langille, E. R., O’Connor, M., Lan, X., Whetstone, H., Coutinho, F. J., Vanner, R. J., et al. (2017) · Cell stem cell · doi:10.1016/j.stem.2017.06.004 · Cited by 218
CAR T cells for brain tumors: Lessons learned and road ahead
Akhavan, D., Alizadeh, D., Wang, D., Weist, M. R., Shepphird, J. K., & Brown, C. E. (2019) · Immunological Reviews · doi:10.1111/imr.12773 · Cited by 217
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
Tumor Heterogeneity in Glioblastomas: From Light Microscopy to Molecular Pathology
Becker, A. P., Sells, B. E., Haque, S., & Chakravarti, A. (2021) · Cancers · doi:10.3390/cancers13040761 · Cited by 234
Mitigation of chromosome loss in clinical CRISPR-Cas9-engineered T cells
Tsuchida, C. A., Brandes, N., Bueno, R., Trinidad, M. I., Mazumder, T., Yu, B., Hwang, B., Chang, C. R., Liu, J., Sun, Y., et al. (2023) · Cell · doi:10.1016/j.cell.2023.08.041 · Cited by 220
Grauel, A. L., Nguyen, B., Ruddy, D., Laszewski, T., Schwartz, S., Chang, J., Chen, J., Piquet, M., Pelletier, M., Yan, Z., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19920-5 · Cited by 211
The biology and mathematical modelling of glioma invasion: a review
Alfonso, J. C. L., Talkenberger, K., Seifert, M., Klink, B., Hawkins-Daarud, A. J., Swanson, K. R., Hatzikirou, H., & Deutsch, A. (2017) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0490 · Cited by 206
Understanding tumor ecosystems by single-cell sequencing: promises and limitations
Ren, X., Kang, B., & Zhang, Z. (2018) · Genome biology · doi:10.1186/s13059-018-1593-z · Cited by 220
Stringer, B. W., Day, B. W., D’Souza, R. C. J., Jamieson, P. R., Ensbey, K. S., Bruce, Z. C., Lim, Y. C., Goasdoué, K., Offenhäuser, C., Akgül, S., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-41277-z · Cited by 213
Weng, Q., Wang, J., Wang, J., He, D., Cheng, Z., Zhang, F., Verma, R., Xu, L., Dong, X., Liao, Y., et al. (2019) · Cell stem cell · doi:10.1016/j.stem.2019.03.006 · Cited by 206
Self-citationClustering and classification methods for single-cell RNA-sequencing data
Qi, R., Ma, A., Ma, Q., & Zou, Q. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz062 · Cited by 218
Glial and myeloid heterogeneity in the brain tumour microenvironment
Andersen, B. M., Akl, C. F., Wheeler, M. A., Chiocca, E. A., Reardon, D. A., & Quintana, F. J. (2021) · Nature reviews. Cancer · doi:10.1038/s41568-021-00397-3 · Cited by 216
Single cell RNA sequencing to dissect the molecular heterogeneity in lupus nephritis
Der, E. B., Ranabothu, S., Suryawanshi, H., Akat, K. M., Clancy, R. M., Morozov, P. S., Kustagi, M., Czuppa, M., Izmirly, P., Belmont, M. H., et al. (2017) · JCI Insight · doi:10.1172/jci.insight.93009 · Cited by 212
Functions and mechanisms of circular RNAs in cancer radiotherapy and chemotherapy resistance
Cui, C., Yang, J., Xiao, L., Liu, D., Fu, L., & Wang, X. (2020) · Molecular Cancer · doi:10.1186/s12943-020-01180-y · Cited by 205
The Evidence of Glioblastoma Heterogeneity
Soeda, A., Hara, A., Kunisada, T., Yoshimura, S., Iwama, T., & Park, D. M. (2015) · Scientific Reports · doi:10.1038/srep07979 · Cited by 211
Xing, X., Yang, F., Huang, Q., Guo, H., Li, J., Qiu, M., Bai, F., & Wang, J. (2021) · Science Advances · doi:10.1126/sciadv.abd9738 · Cited by 204
Adoptive cellular therapy in solid tumor malignancies: review of the literature and challenges ahead
Kirtane, K., Elmariah, H., Chung, C. H., & Abate‐Daga, D. (2021) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2021-002723 · Cited by 205
Single-cell RNA-seq ties macrophage polarization to growth rate of intracellular Salmonella
Saliba, A., Li, L., Westermann, A. J., Appenzeller, S., Stapels, D. A. C., Schulte, L. N., Hélaine, S., & Vogel, J. (2016) · Nature Microbiology · doi:10.1038/nmicrobiol.2016.206 · Cited by 204
The recurrent architecture of tumour initiation, progression and drug sensitivity
Califano, A., & Alvarez, M. J. (2016) · Nature reviews. Cancer · doi:10.1038/nrc.2016.124 · Cited by 203
Tanaka, N., Kanatani, S., Tomer, R., Sahlgren, C., Kronqvist, P., Kaczyńska, D., Louhivuori, L. M., Kis, L. L., Lindh, C., Mitura, P., et al. (2017) · Nature Biomedical Engineering · doi:10.1038/s41551-017-0139-0 · Cited by 202
Tumor Functional Heterogeneity Unraveled by scRNA-seq Technologies
González-Silva, L., Quevedo, L., & Varela, I. (2020) · Trends in cancer · doi:10.1016/j.trecan.2019.11.010 · Cited by 205
Hu, Z., Artibani, M., Alsaadi, A., Wietek, N. C., Morotti, M., Shi, T., Zhong, Z., González, L. S., El-Sahhar, S., Carrami, E. M., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.01.003 · Cited by 195
Cancer stem cells: landscape, challenges and emerging therapeutic innovations
Lee, H., Kim, B., Park, J., Park, S., Yoo, G., Yum, S., Kang, W., Lee, J., Youn, H., & Youn, B. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02360-2 · Cited by 247
Single-cell CUT&Tag analysis of chromatin modifications in differentiation and tumor progression
Wu, S. J., Furlan, S. N., Mihalas, A. B., Kaya-Okur, H. S., Feroze, A. H., Emerson, S. N., Zheng, Y., Carson, K., Cimino, P. J., Keene, C. D., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00865-z · Cited by 201
Self-citationAn Immune-Related Six-lncRNA Signature to Improve Prognosis Prediction of Glioblastoma Multiforme
Zhou, M., Zhang, Z., Zhao, H., Bao, S., Cheng, L., & Sun, J. (2017) · Molecular Neurobiology · doi:10.1007/s12035-017-0572-9 · Cited by 199
Genome organization and DNA accessibility control antigenic variation in trypanosomes
Müller, L. S. M., Cosentino, R. O., Förstner, K., Guizetti, J., Wedel, C., Kaplan, N., Janzen, C. J., Arampatzi, P., Vogel, J., Steinbiss, S., et al. (2018) · Nature · doi:10.1038/s41586-018-0619-8 · Cited by 193
Zhang, Y., Zuo, C., Liu, L., Hu, Y., Yang, B. R., Qiu, S., Li, Y., Cao, D., Ju, Z., Ge, J., et al. (2021) · Journal of Hepatology · doi:10.1016/j.jhep.2021.06.023 · Cited by 207
Hoffman, L. M., DeWire, M. D., Ryall, S. T., Buczkowicz, P., Leach, J., Miles, L., Ramani, A. K., Brudno, M., Kumar, S. S., Drissi, R., et al. (2016) · Acta Neuropathologica Communications · doi:10.1186/s40478-015-0269-0 · Cited by 198
Bakas, S., Sako, C., Akbari, H., Bilello, M., Sotiras, A., Shukla, G., Rudie, J. D., Santamaría, N. A. F., Kazerooni, A. F., Pati, S., et al. (2022) · Scientific Data · doi:10.1038/s41597-022-01560-7 · Cited by 226
Kim, K.-T., Lee, H. W., Lee, H.-O., Song, H. J., Jeong, D. E., Shin, S., Kim, H., Shin, Y., Nam, D.-H., Jeong, B. C., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0945-9 · Cited by 198
Precision Oncology: The Road Ahead
Senft, D., Leiserson, M. D. M., Ruppin, E., & Ronai, Z. A. (2017) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2017.08.003 · Cited by 199
Tumor Heterogeneity: A Great Barrier in the Age of Cancer Immunotherapy
El-Sayes, N., Vito, A., & Mossman, K. L. (2021) · Cancers · doi:10.3390/cancers13040806 · Cited by 208
Single-cell sequencing to multi-omics: technologies and applications
Wu, X., Yang, X., Dai, Y., Zhao, Z., Zhu, J., Guo, H., & Yang, R. (2024) · Biomarker Research · doi:10.1186/s40364-024-00643-4 · Cited by 222
“Furthermore, inferring copy number variations (CNVs) with inferCNV analysis by comparing gene expression levels in cells with those in a reference genome is particularly crucial in cancer biology [15].”
BackgroundMineo, M., Ricklefs, F. L., Rooj, A. K., Lyons, S. M., Ivanov, P. V., Ansari, K. I., Nakano, I., Chiocca, E. A., Godlewski, J. A., & Bronisz, A. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.05.018 · Cited by 198
Targeting pyrimidine synthesis accentuates molecular therapy response in glioblastoma stem cells
Wang, X., Yang, K., Wu, Q., Kim, L. J. Y., Morton, A. R., Gimple, R. C., Prager, B. C., Shi, Y., Zhou, W., Bhargava, S., et al. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aau4972 · Cited by 197
EGFR Mutation Promotes Glioblastoma through Epigenome and Transcription Factor Network Remodeling
Liu, F., Hon, G. C., Villa, G. R., Turner, K. M., Ikegami, S., Yang, H., Ye, Z., Li, B., Kuan, S., Lee, A. Y., et al. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.09.002 · Cited by 194
Single-cell sequencing in stem cell biology
Wen, L., & Tang, F. (2016) · Genome biology · doi:10.1186/s13059-016-0941-0 · Cited by 195
Single-Cell Transcriptomic Analysis of Tumor Heterogeneity
Levitin, H. M., Yuan, J., & Sims, P. A. (2018) · Trends in cancer · doi:10.1016/j.trecan.2018.02.003 · Cited by 197
Introduction to Single‐Cell RNA Sequencing
Olsen, T. K., & Baryawno, N. (2018) · Current Protocols in Molecular Biology · doi:10.1002/cpmb.57 · Cited by 201
Molecular and cellular heterogeneity: the hallmark of glioblastoma
Aum, D. J., Kim, D. H., Beaumont, T., Leuthardt, E. C., Dunn, G. P., & Kim, A. H. (2014) · Neurosurgical FOCUS · doi:10.3171/2014.9.focus14521 · Cited by 200
Single-cell multimodal profiling reveals cellular epigenetic heterogeneity
Cheow, L. F., Courtois, E. T., Tan, Y., Viswanathan, R., Xing, Q. R., Tan, R. Z., Tan, D. S.-W., Robson, P., Loh, Y., Quake, S. R., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3961 · Cited by 193
The linker histone H1.0 generates epigenetic and functional intratumor heterogeneity
Morales, C., Biran, A., Burney, M. J., Patel, H., Henser‐Brownhill, T., Cohen, A.-H., Li, Y., Ben‐Hamo, R., Nye, E. L., Spencer‐Dene, B., et al. (2016) · Science · doi:10.1126/science.aaf1644 · Cited by 193
Olbrecht, S., Busschaert, P., Qian, J., Vanderstichele, A., Loverix, L., Gorp, T. V., Nieuwenhuysen, E. V., Han, S., den Broeck, A. V., Coosemans, A., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00922-x · Cited by 195
The relevance of EMT in breast cancer metastasis: Correlation or causality?
Bill, R., & Christofori, G. (2015) · FEBS Letters · doi:10.1016/j.febslet.2015.05.002 · Cited by 192
Imdahl, F., Vafadarnejad, E., Homberger, C., Saliba, A., & Vogel, J. (2020) · Nature Microbiology · doi:10.1038/s41564-020-0774-1 · Cited by 192
Surveying brain tumor heterogeneity by single-cell RNA-sequencing of multi-sector biopsies
Yu, K., Hu, Y., Wu, F., Guo, Q., Qian, Z., Hu, W., Chen, J., Wang, K., Fan, X., Wu, X., et al. (2020) · National Science Review · doi:10.1093/nsr/nwaa099 · Cited by 190
Single-cell transcriptomics in cancer: computational challenges and opportunities
Fan, J., Slowikowski, K., & Zhang, F. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0422-0 · Cited by 195
Advances in spatial transcriptomics and related data analysis strategies
Du, J., Yang, Y., An, Z.-J., Zhang, M., Fu, X.-H., Huang, Z., Yuan, Y., & Hou, J. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04150-2 · Cited by 199
Hu, J., Wang, S., Hou, Y., Chen, Z., Liu, L., Li, R., Li, N., Zhou, L., Yang, Y., Wang, L., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01662-5 · Cited by 207
The Rational Development of CD133-Targeting Immunotherapies for Glioblastoma
Vora, P., Venugopal, C., Salim, S. K., Tatari, N., Bakhshinyan, D., Singh, M., Seyfrid, M., Upreti, D., Rentas, S., Wong, N., et al. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.04.008 · Cited by 193
The Pancreatic Cancer Microenvironment
Dougan, S. K. (2017) · The Cancer Journal · doi:10.1097/ppo.0000000000000288 · Cited by 193
Hypoxic Induction of Vasorin Regulates Notch1 Turnover to Maintain Glioma Stem-like Cells
Man, J., Yu, X., Huang, H., Zhou, W., Xiang, C., Huang, H., Miele, L., Liu, Z.-G., Bebek, G., Bao, S., et al. (2017) · Cell stem cell · doi:10.1016/j.stem.2017.10.005 · Cited by 188
Liu, Y., Feng, W., Dai, Y., Bao, M., Yuan, Z., He, M., Qin, Z., Liao, S., He, J., Huang, Q., et al. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.709210 · Cited by 190
Famine versus feast: understanding the metabolism of tumors in vivo
Mayers, J. R., & Heiden, M. G. V. (2015) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2015.01.004 · Cited by 190
Infiltrative and drug‐resistant slow‐cycling cells support metabolic heterogeneity in glioblastoma
Hoang-Minh, L., Siebzehnrübl, F. A., Yang, C., Suzuki‐Hatano, S., Dajac, K., Loche, T., Andrews, N., Massari, M. S., Patel, J., Amin, K., et al. (2018) · The EMBO Journal · doi:10.15252/embj.201798772 · Cited by 185
B7-H3-redirected chimeric antigen receptor T cells target glioblastoma and neurospheres
Nehama, D., Ianni, N. D., Musio, S., Du, H., Patanè, M., Pollo, B., Finocchiaro, G., Park, J. J. H., Dunn, D., Edwards, D. S., et al. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.08.030 · Cited by 183
Immunotherapy for Glioblastoma: Current Progress and Challenges
Yu, M. W., & Quail, D. F. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.676301 · Cited by 191
Aldape, K., Amin, S. B., Ashley, D. M., Barnholtz‐Sloan, J. S., Bates, A. J., Beroukhim, R., Bock, C., Brat, D. J., Claus, E. B., Costello, J., et al. (2018) · Neuro-Oncology · doi:10.1093/neuonc/noy020 · Cited by 179
Metadata
- Authors
- 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., Louis, D. N., Rozenblatt-Rosen, O., Suvà, M. L., Regev, A., & Bernstein, B. E.
- Affiliations
- Broad Institute (US)
- Harvard University (US)
- Broad Institute of Harvard and Massachusetts Institute of Techonology (MIT), Cambridge, MA 02142, USA.
- Corresponding author
- Patel, Anoop P.
- Tirosh, Itay
- Suvà, Mario L.
- Regev, Aviv
- Bernstein, Bradley E.
- ORCID
- Venue
- Science
- Published
- 2014-06-20
- Volume
- 344
- Issue
- 6190
- Pages
- 1396-1401
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC4123637
- OpenAlex
- W1792532087
- Cited by
- 5,063
- References
- 47
- Citations by year
- 2026: 437 · 2025: 505 · 2024: 545 · 2023: 514 · 2022: 501 · 2021: 538 · 2020: 521 · 2019: 376 · 2018: 352 · 2017: 306 · 2016: 251 · 2015: 167 · 2014: 43
- FWCI
- 104.76
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc-sa)
- PMC
- Funders
- Howard Hughes Medical Institute
- National Human Genome Research Institute (U54HG006991)
- National Cancer Institute (U24CA180922, P50CA165962)
- National Institute of Neurological Disorders and Stroke (R01NS032677, R25NS065743)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Oncology › Glioma Diagnosis and Treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- glioblastoma, intratumoral heterogeneity, tumor heterogeneity, cancer stemness, transcriptional programs, oncogenic signaling, hypoxia, immune response, glioblastoma subtypes, prognostic factors, single-cell transcriptomics
- MeSH terms
- Brain Neoplasms, Glioblastoma, Humans, Prognosis, RNA, Messenger, Genetic Variation, Sequence Analysis, RNA, Gene Expression Profiling, Single-Cell Analysis
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