CoChat
Sign Up
Cited by 258

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

Epigenetic encoding, heritability and plasticity of glioma transcriptional cell states

Chaligne, Ronan, Gaiti, Federico, Silverbush, Dana, Schiffman, Joshua S., Weisman, Hannah R., Kluegel, Lloyd, Gritsch, Simon, Deochand, Sunil D., Gonzalez Castro, L. Nicolas, Richman, Alyssa R., Klughammer, Johanna, Biancalani, Tommaso, Muus, Christoph, Sheridan, Caroline, Alonso, Alicia, Izzo, Franco, Park, Jane, Rozenblatt-Rosen, Orit, Regev, Aviv, Suvà, Mario L., Landau, Dan A.·2021·Nature Genetics
DOI 10.1038/s41588-021-00927-7PMC PMC8675181
Open this paper in CoChat — free account

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41588-021-00927-7

Cites

26 of 26 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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?

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

Show all 26 references

Research works printed but not linked: 87

  1. Puram, S. V. et al. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611–1624 (2017). · DOI · Paper page
  2. Hata, A. N. et al. Tumor cells can follow distinct evolutionary paths to become resistant to epidermal growth factor receptor inhibition. Nat. Med. 22, 262–269 (2016). · DOI · Paper page
  3. Shaffer, S. M. et al. Memory sequencing reveals heritable single-cell gene expression programs associated with distinct cellular behaviors. Cell 182, 947–959 (2020). · DOI
  4. Tirosh, I. et al. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539, 309–313 (2016). · DOI
  5. Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017). · DOI · Paper page
  6. Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars. Nat. Biotechnol. 36, 469–473 (2018). · DOI · Paper page
  7. Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442–450 (2018). · DOI
  8. McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016). · DOI · Paper page
  9. Alemany, A., Florescu, M., Baron, C. S., Peterson-Maduro, J. & van Oudenaarden, A. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112 (2018). · DOI
  10. Lathia, J. D., Mack, S. C., Mulkearns-Hubert, E. E., Valentim, C. L. L. & Rich, J. N. Cancer stem cells in glioblastoma. Genes Dev. 29, 1203–1217 (2015). · DOI · Paper page
  11. Gimple, R. C., Bhargava, S., Dixit, D. & Rich, J. N. Glioblastoma stem cells: lessons from the tumor hierarchy in a lethal cancer. Genes Dev. 33, 591–609 (2019). · DOI · Paper page
  12. Suvà, M. L. et al. Reconstructing and reprogramming the tumor-propagating potential of glioblastoma stem-like cells. Cell 157, 580–594 (2014). · DOI
  13. Suvà, M. L. & Tirosh, I. The glioma stem cell model in the era of single-cell genomics. Cancer Cell 37, 630–636 (2020). · DOI
  14. Bao, S. et al. Glioma stem cells promote radioresistance by preferential activation of the DNA damage response. Nature 444, 756–760 (2006). · DOI
  15. Liau, B. B. et al. Adaptive chromatin remodeling drives glioblastoma stem cell plasticity and drug tolerance. Cell Stem Cell 20, 233–246 (2017). · DOI · Paper page
  16. Filbin, M. G. et al. Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq. Science 360, 331–335 (2018). · DOI
  17. Neftel, C. et al. An integrative model of cellular states, plasticity, and genetics for glioblastoma. Cell 178, 835–849 (2019). · DOI · Paper page
  18. Venteicher, A. S. et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science 355, eaai8478 (2017). · DOI
  19. Garofano, L. et al. Pathway-based classification of glioblastoma uncovers a mitochondrial subtype with therapeutic vulnerabilities. Nat. Cancer 2, 141–156 (2021). · DOI
  20. Richards, L. M. et al. Gradient of developmental and injury response transcriptional states defines functional vulnerabilities underpinning glioblastoma heterogeneity. Nat. Cancer 2, 157–173 (2021). · DOI
  21. Castellan, M. et al. Single-cell analyses reveal YAP/TAZ as regulators of stemness and cell plasticity in glioblastoma. Nat. Cancer 2, 174–188 (2021). · DOI
  22. Latil, M. et al. Cell-type-specific chromatin states differentially prime squamous cell carcinoma tumor-initiating cells for epithelial to mesenchymal transition. Cell Stem Cell 20, 191–204 (2017). · DOI
  23. Meir, Z., Mukamel, Z., Chomsky, E., Lifshitz, A. & Tanay, A. Single-cell analysis of clonal maintenance of transcriptional and epigenetic states in cancer cells. Nat. Genet. 52, 709–718 (2020). · DOI
  24. Guilhamon, P. et al. Single-cell chromatin accessibility profiling of glioblastoma identifies an invasive cancer stem cell population associated with lower survival. eLife 10, e64090 (2021). · DOI
  25. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018). · DOI · Paper page
  26. Fine, H. A. Malignant gliomas: simplifying the complexity. Cancer Discov. 9, 1650–1652 (2019). · DOI
  27. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014). · DOI · Paper page
  28. Morton, A. R. et al. Functional enhancers shape extrachromosomal oncogene amplifications. Cell 179, 1330–1341 (2019). · DOI
  29. Sun, W. et al. The association between copy number aberration, DNA methylation and gene expression in tumor samples. Nucleic Acids Res. 46, 3009–3018 (2018). · DOI
  30. Davoli, T. et al. Cumulative haploinsufficiency and triplosensitivity drive aneuploidy patterns and shape the cancer genome. Cell 155, 948–962 (2013). · DOI · Paper page
  31. Ceccarelli, M. et al. Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma. Cell 164, 550–563 (2016). · DOI
  32. Capper, D. et al. DNA methylation-based classification of central nervous system tumours. Nature 555, 469–474 (2018). · DOI
  33. Pine, A. R. et al. Tumor microenvironment is critical for the maintenance of cellular states found in primary glioblastomas. Cancer Discov. https://doi.org/10.1158/2159-8290.CD-20-0057 (2020). · DOI
  34. Wang, Q. et al. Tumor evolution of glioma-intrinsic gene expression subtypes associates with immunological changes in the microenvironment. Cancer Cell 32, 42–56 (2017). · DOI
  35. Ben-Porath, I. et al. An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors. Nat. Genet. 40, 499–507 (2008). · DOI
  36. Rheinbay, E. et al. An aberrant transcription factor network essential for Wnt signaling and stem cell maintenance in glioblastoma. Cell Rep. 3, 1567–1579 (2013). · DOI
  37. Suvà, M.-L. et al. EZH2 is essential for glioblastoma cancer stem cell maintenance. Cancer Res. 69, 9211–9218 (2009). · DOI
  38. Natsume, A. et al. Chromatin regulator PRC2 is a key regulator of epigenetic plasticity in glioblastoma. Cancer Res. 73, 4559–4570 (2013). · DOI
  39. ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012). · DOI · Paper page
  40. O’Connor, T., Grant, C. E., Bodén, M. & Bailey, T. L. T-Gene: improved target gene prediction. Bioinformatics https://doi.org/10.1093/bioinformatics/btaa227 (2020). · DOI
  41. Reddington, J. P., Sproul, D. & Meehan, R. R. DNA methylation reprogramming in cancer: Does it act by re-configuring the binding landscape of Polycomb repressive complexes? Bioessays 36, 134–140 (2014). · DOI
  42. Douillet, D. et al. Uncoupling histone H3K4 trimethylation from developmental gene expression via an equilibrium of COMPASS, Polycomb and DNA methylation. Nat. Genet. https://doi.org/10.1038/s41588-020-0618-1 (2020). · DOI
  43. Bintu, L. et al. Dynamics of epigenetic regulation at the single-cell level. Science 351, 720–724 (2016). · DOI
  44. Wang, L. et al. The phenotypes of proliferating glioblastoma cells reside on a single axis of variation. Cancer Discov. 9, 1708–1719 (2019). · DOI
  45. Hoffmann, A., Sportelli, V., Ziller, M. & Spengler, D. Switch-like roles for Polycomb proteins from neurodevelopment to neurodegeneration. Epigenomes 1, 21 (2017). · DOI
  46. Lu, F., Liu, Y., Jiang, L., Yamaguchi, S. & Zhang, Y. Role of Tet proteins in enhancer activity and telomere elongation. Genes Dev. https://doi.org/10.1101/gad.248005.114 (2014). · DOI
  47. Hon, G. C. et al. 5mC oxidation by Tet2 modulates enhancer activity and timing of transcriptome reprogramming during differentiation. Mol. Cell 56, 286–297 (2014). · DOI · Paper page
  48. Ginno, P. A. et al. A genome-scale map of DNA methylation turnover identifies site-specific dependencies of DNMT and TET activity. Nat. Commun. 11, 2680 (2020). · DOI
  49. Creyghton, M. P. et al. Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl Acad. Sci. USA 107, 21931–21936 (2010). · DOI · Paper page
  50. Landau, D. A. et al. Locally disordered methylation forms the basis of intratumor methylome variation in chronic lymphocytic leukemia. Cancer Cell 26, 813–825 (2014). · DOI
  51. Pan, H. et al. Epigenomic evolution in diffuse large B-cell lymphomas. Nat. Commun. 6, 6921 (2015). · DOI
  52. Jones, P. A. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 13, 484–492 (2012). · DOI · Paper page
  53. Turcan, S. et al. Mutant-IDH1-dependent chromatin state reprogramming, reversibility, and persistence. Nat. Genet. 50, 62–72 (2018). · DOI
  54. Núñez, F. J. et al. IDH1-R132H acts as a tumor suppressor in glioma via epigenetic up-regulation of the DNA damage response. Sci. Transl. Med. 11, eaaq1427 (2019). · DOI
  55. Brocks, D. et al. Intratumor DNA methylation heterogeneity reflects clonal evolution in aggressive prostate cancer. Cell Rep. 8, 798–806 (2014). · DOI
  56. Roerink, S. F. et al. Intra-tumour diversification in colorectal cancer at the single-cell level. Nature 556, 457–462 (2018). · DOI
  57. Shibata, D. Mutation and epigenetic molecular clocks in cancer. Carcinogenesis 32, 123–128 (2011). · DOI
  58. Moran, P. A. P. Notes on continuous stochastic phenomena. Biometrika 37, 17–23 (1950). · DOI · Paper page
  59. Maddison, W. P., Midford, P. E. & Otto, S. P. Estimating a binary character’s effect on speciation and extinction. Syst. Biol. 56, 701–710 (2007). · DOI
  60. Stadler, T. & Bonhoeffer, S. Uncovering epidemiological dynamics in heterogeneous host populations using phylogenetic methods. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120198 (2013). · DOI
  61. Boyle, A. P. et al. High-resolution mapping and characterization of open chromatin across the genome. Cell 132, 311–322 (2008). · DOI · Paper page
  62. Bell, R. E. et al. Enhancer methylation dynamics contribute to cancer plasticity and patient mortality. Genome Res. 26, 601–611 (2016). · DOI
  63. Pastore, A. et al. Corrupted coordination of epigenetic modifications leads to diverging chromatin states and transcriptional heterogeneity in CLL. Nat. Commun. 10, 1874 (2019). · DOI
  64. Irizarry, R. A. et al. The human colon cancer methylome shows similar hypo- and hypermethylation at conserved tissue-specific CpG island shores. Nat. Genet. 41, 178–186 (2009). · DOI · Paper page
  65. Polak, P. et al. A mutational signature reveals alterations underlying deficient homologous recombination repair in breast cancer. Nat. Genet. 49, 1476–1486 (2017). · DOI · Paper page
  66. Challen, G. A. et al. Dnmt3a is essential for hematopoietic stem cell differentiation. Nat. Genet. 44, 23–31 (2011). · DOI · Paper page
  67. Klughammer, J. et al. The DNA methylation landscape of glioblastoma disease progression shows extensive heterogeneity in time and space. Nat. Med. 24, 1611–1624 (2018). · DOI
  68. Boyer, L. A. et al. Polycomb complexes repress developmental regulators in murine embryonic stem cells. Nature 441, 349–353 (2006). · DOI
  69. Boulard, M., Edwards, J. R. & Bestor, T. H. FBXL10 protects Polycomb-bound genes from hypermethylation. Nat. Genet. 47, 479–485 (2015). · DOI
  70. Domcke, S. et al. A human cell atlas of fetal chromatin accessibility. Science 370, eaba7612 (2020). · DOI
  71. Mohn, F. et al. Lineage-specific Polycomb targets and de novo DNA methylation define restriction and potential of neuronal progenitors. Mol. Cell 30, 755–766 (2008). · DOI
  72. Alcantara Llaguno, S. R. & Parada, L. F. Cell of origin of glioma: biological and clinical implications. Br. J. Cancer 115, 1445–1450 (2016). · DOI
  73. Chaffer, C. L. et al. Normal and neoplastic nonstem cells can spontaneously convert to a stem-like state. Proc. Natl Acad. Sci. USA 108, 7950–7955 (2011). · DOI
  74. Morris, V. et al. Single-cell analysis reveals mechanisms of plasticity of leukemia initiating cells. Preprint at bioRxiv https://doi.org/10.1101/2020.04.29.066332 (2020). · DOI
  75. Lieberman, E., Hauert, C. & Nowak, M. A. Evolutionary dynamics on graphs. Nature 433, 312–316 (2005). · DOI
  76. Lappalainen, T. & Greally, J. M. Associating cellular epigenetic models with human phenotypes. Nat. Rev. Genet. 18, 441–451 (2017). · DOI · Paper page
  77. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013). · DOI · Paper page
  78. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011). · DOI · Paper page
  79. Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S. & Vert, J.-P. A general and flexible method for signal extraction from single-cell RNA-seq data. Nat. Commun. 9, 284 (2018). · DOI · Paper page
  80. Van den Berge, K., Soneson, C., Robinson, M. D. & Clement, L. stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression and differential transcript usage. Genome Biol. 18, 151 (2017). · DOI · Paper page
  81. Ernst, J. & Kellis, M. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9, 215–216 (2012). · DOI
  82. Robinson, D. F. & Foulds, L. R. Comparison of phylogenetic trees. Math. Biosci. 53, 131–147 (1981). · DOI
  83. Gittleman, J. L. & Kot, M. Adaptation: statistics and a null model for estimating phylogenetic effects. Syst. Biol. 39, 227–241 (1990).
  84. Wartenberg, D. Multivariate spatial correlation: a method for exploratory geographical analysis. Geographical Anal. 17, 263–283 (1985). · DOI
  85. FitzJohn, R. G. Diversitree: comparative phylogenetic analyses of diversification in R. Methods Ecol. Evol. 3, 1084–1092 (2012). · DOI
  86. Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012). · DOI
  87. Xiang, Y., Gubian, S., Suomela, B. & Hoeng, J. Generalized simulated annealing for global optimization: the GenSA package. R Journal 5, 13 (2013). · DOI

Cited by

See all 258

Metadata

Authors
Chaligne, R., Gaiti, F., Silverbush, D., Schiffman, J. S., Weisman, H. R., Kluegel, L., Gritsch, S., Deochand, S. D., Gonzalez Castro, L. N., Richman, A. R., Klughammer, J., Biancalani, T., Muus, C., Sheridan, C., Alonso, A., Izzo, F., Park, J., Rozenblatt-Rosen, O., Regev, A., Suvà, M. L., & Landau, D. A.
Venue
Nature Genetics
Publisher
Springer Science and Business Media LLC
Published
2021-09-30
Volume
53
Issue
10
Pages
1469-1479
ISSN
  • 1061-4036
  • 1546-1718
DOI
10.1038/s41588-021-00927-7
PMC
PMC8675181
Cited by
258
References
26
Language
en
Licence

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Epigenetic encoding, heritability and plasticity of glioma transcriptional cell states. https://app.cochat.ai/papers/10.1038/s41588-021-00927-7
The paper
Chaligne, R., Gaiti, F., Silverbush, D., Schiffman, J. S., Weisman, H. R., Kluegel, L., Gritsch, S., Deochand, S. D., Gonzalez Castro, L. N., Richman, A. R., Klughammer, J., Biancalani, T., Muus, C., Sheridan, C., Alonso, A., Izzo, F., Park, J., Rozenblatt-Rosen, O., Regev, A., . . . Landau, D. A. (2021). Epigenetic encoding, heritability and plasticity of glioma transcriptional cell states. Nature Genetics. https://doi.org/10.1038/s41588-021-00927-7