Abstract
Recent efforts have succeeded in surveying open chromatin at the single-cell level, but high-throughput, single-cell assessment of heterochromatin and its underlying genomic determinants remains challenging. We engineered a hybrid transposase including the chromodomain (CD) of the heterochromatin protein-1α (HP-1α), which is involved in heterochromatin assembly and maintenance through its binding to trimethylation of the lysine 9 on histone 3 (H3K9me3), and developed a single-cell method, single-cell genome and epigenome by transposases sequencing (scGET-seq), that, unlike single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq), comprehensively probes both open and closed chromatin and concomitantly records the underlying genomic sequences. We tested scGET-seq in cancer-derived organoids and human-derived xenograft (PDX) models and identified genetic events and plasticity-driven mechanisms contributing to cancer drug resistance. Next, building upon the differential enrichment of closed and open chromatin, we devised a method, Chromatin Velocity, that identifies the trajectories of epigenetic modifications at the single-cell level. Chromatin Velocity uncovered paths of epigenetic reorganization during stem cell reprogramming and identified key transcription factors driving these developmental processes. scGET-seq reveals the dynamics of genomic and epigenetic landscapes underlying any cellular processes.
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Data availability
Fastq files and raw count matrices have been deposited to the Array Express platform (https://www.ebi.ac.uk/arrayexpress/) with the following IDs: E-MTAB-9648, E-MTAB-10218, E-MTAB-2020, E-MTAB-10219, E-MTAB-9650, E-MTAB-9651 and E-MTAB-9659. Source data are provided with this paper.
Code availability
Code necessary to preprocess scGET-seq data is available at https://github.com/leomorelli/scGET (ref. 102) and https://github.com/dawe/scatACC (ref. 103). Illustrative code snippets for postprocessing are reported in Supplementary Data 2.
References
- 1.
McGranahan, N. & Swanton, C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168, 613–628 (2017).
CAS
PubMed
Article
PubMed CentralGoogle Scholar
- 2.
Greaves, M. Evolutionary determinants of cancer. Cancer Discov. 5, 806–821 (2015).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 3.
Liau, B. B. et al. Adaptive chromatin remodeling drives glioblastoma stem cell plasticity and drug tolerance. Cell Stem Cell 20, 233–246 (2017).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 4.
Hangauer, M. J. et al. Drug-tolerant persister cancer cells are vulnerable to GPX4 inhibition. Nature 551, 247–250 (2017).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 5.
Brock, A., Chang, H. & Huang, S. Non-genetic heterogeneity—a mutation-independent driving force for the somatic evolution of tumours. Nat. Rev. Genet. 10, 336–342 (2009).
CAS
PubMed
Article
PubMed CentralGoogle Scholar
- 6.
Shaffer, S. M. et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431–435 (2017).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 7.
Sharma, S. V. et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell 141, 69–80 (2010).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 8.
Flavahan, W. A., Gaskell, E. & Bernstein, B. E. Epigenetic plasticity and the hallmarks of cancer. Science 357, eaal2380 (2017).
PubMed
PubMed Central
Article
CASGoogle Scholar
- 9.
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 10.
Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 11.
Tatarakis, A., Behrouzi, R. & Moazed, D. Evolving models of heterochromatin: from foci to liquid droplets. Mol. Cell 67, 725–727 (2017).
CAS
PubMed
ArticleGoogle Scholar
- 12.
Ninova, M., Tóth, K. F. & Aravin, A. A. The control of gene expression and cell identity by H3K9 trimethylation. Development 146, dev181180 (2019).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 13.
Nicetto, D. et al. H3K9me3-heterochromatin loss at protein-coding genes enables developmental lineage specification. Science 363, 294–297 (2019).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 14.
Nakayama, J., Rice, J. C., Strahl, B. D., Allis, C. D. & Grewal, S. I. Role of histone H3 lysine 9 methylation in epigenetic control of heterochromatin assembly. Science 292, 110–113 (2001).
CAS
PubMed
ArticleGoogle Scholar
- 15.
Peters, A., O’Carroll, D. & Scherthan, H. Loss of the Suv39h histone methyltransferases impairs mammalian heterochromatin and genome stability. Cell 107, 323–337 (2001).
CAS
PubMed
ArticleGoogle Scholar
- 16.
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 17.
Aldridge, S. & Teichmann, S. A. Single cell transcriptomics comes of age. Nat. Commun. 11, 4307 (2020).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 18.
Henikoff, S., Henikoff, J., Kaya-Okur, H. & Ahmad, K. Efficient chromatin accessibility mapping in situ by nucleosome-tethered tagmentation. eLife 9, e63274 (2020).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 19.
Jacobs, S. A. & Khorasanizadeh, S. Structure of HP1 chromodomain bound to a lysine 9-methylated histone H3 tail. Science 295, 2080–2083 (2002).
CAS
PubMed
Article
PubMed CentralGoogle Scholar
- 20.
Lachner, M., O’Carroll, D., Rea, S., Mechtler, K. & Jenuwein, T. Methylation of histone H3 lysine 9 creates a binding site for HP1 proteins. Nature 410, 116–120 (2001).
CAS
PubMed
ArticleGoogle Scholar
- 21.
Bannister, A. J. et al. Selective recognition of methylated lysine 9 on histone H3 by the HP1 chromo domain. Nature 410, 120–124 (2001).
CAS
ArticleGoogle Scholar
- 22.
Satpathy, A. T. et al. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37, 925–936 (2019).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 23.
Cross, W. et al. The evolutionary landscape of colorectal tumorigenesis. Nat. Ecol. Evol. 2, 1661–1672 (2018).
PubMed
PubMed Central
ArticleGoogle Scholar
- 24.
Cross, W. et al. Stabilising selection causes grossly altered but stable karyotypes in metastatic colorectal cancer. Preprint at bioRxiv https://doi.org/10.1101/2020.03.26.007138 (2020).
- 25.
Gézsi, A. et al. VariantMetaCaller: automated fusion of variant calling pipelines for quantitative, precision-based filtering. BMC Genomics 16, 875 (2015).
PubMed
PubMed Central
ArticleGoogle Scholar
- 26.
Misale, S. et al. Vertical suppression of the EGFR pathway prevents onset of resistance in colorectal cancers. Nat. Commun. 6, 8305 (2015).
CAS
PubMed
ArticleGoogle Scholar
- 27.
Lupo, B. et al. Colorectal cancer residual disease at maximal response to EGFR blockade displays a druggable Paneth cell-like phenotype. Sci. Transl. Med. 12, eaax8313 (2020).
CAS
PubMed
ArticleGoogle Scholar
- 28.
Laurent-Puig, P., Lievre, A. & Blons, H. Mutations and response to epidermal growth factor receptor Inhibitors. Clin. Cancer Res. 15, 1133–1139 (2009).
CAS
PubMed
ArticleGoogle Scholar
- 29.
Wang, C. et al. Acquired resistance to EGFR TKIs mediated by TGFβ1/integrin β3 signaling in EGFR-mutant lung cancer. Mol. Cancer Ther. 18, 2357–2367 (2019).
CAS
PubMed
ArticleGoogle Scholar
- 30.
Hu, T. & Li, C. Convergence between Wnt-β-catenin and EGFR signaling in cancer. Mol. Cancer 9, 236 (2010).
PubMed
PubMed Central
Article
CASGoogle Scholar
- 31.
Sondka, Z. et al. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat. Rev. Cancer 18, 696–705 (2018).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 32.
Rondinelli, B. et al. Histone demethylase JARID1C inactivation triggers genomic instability in sporadic renal cancer. J. Clin. Invest. 125, 4625–4637 (2015).
PubMed
PubMed Central
ArticleGoogle Scholar
- 33.
Peric-Hupkes, D. et al. Molecular maps of the reorganization of genome–nuclear lamina interactions during differentiation. Mol. Cell 38, 603–613 (2010).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 34.
Hiratani, I. et al. Global reorganization of replication domains during embryonic stem cell differentiation. PLoS Biol. 6, 2220–2236 (2008).
CAS
ArticleGoogle Scholar
- 35.
Marchal, C. et al. Genome-wide analysis of replication timing by next-generation sequencing with E/L Repli-seq. Nat. Protoc. 13, 819–839 (2018).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 36.
Rondinelli, B. et al. H3K4me3 demethylation by the histone demethylase KDM5C/JARID1C promotes DNA replication origin firing. Nucleic Acids Res. 43, 2560–2574 (2015).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 37.
Wong, R. C. B. et al. L1TD1 is a marker for undifferentiated human embryonic stem cells. PLoS ONE 6, e19355 (2011).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 38.
Wong, Y. H. et al. Protogenin defines a transition stage during embryonic neurogenesis and prevents precocious neuronal differentiation. J. Neurosci. 30, 4428–4439 (2010).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 39.
Setty, M. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37, 451–460 (2019).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 40.
Wang, C. et al. Reprogramming of H3K9me3-dependent heterochromatin during mammalian embryo development. Nat. Cell Biol. 20, 620–631 (2018).
CAS
ArticleGoogle Scholar
- 41.
Nicetto, D. & Zaret, K. S. Role of H3K9me3 heterochromatin in cell identity establishment and maintenance. Curr. Opin. Genet. Dev. 55, 1–10 (2019).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 42.
Burton, A. et al. Heterochromatin establishment during early mammalian development is regulated by pericentromeric RNA and characterized by non-repressive H3K9me3. Nat. Cell Biol. 22, 767–778 (2020).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 43.
Novo, C. L. et al. The pluripotency factor Nanog regulates pericentromeric heterochromatin organization in mouse embryonic stem cells. Genes Dev. 30, 1101–1115 (2016).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 44.
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
PubMed
PubMed Central
Article
CASGoogle Scholar
- 45.
Wold, S., Sjöström, M. & Eriksson, L. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 58, 109–130 (2001).
CAS
ArticleGoogle Scholar
- 46.
Eferl, R. et al. Development of pulmonary fibrosis through a pathway involving the transcription factor Fra-2/AP-1. Proc. Natl Acad. Sci. USA 105, 10525–10530 (2008).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 47.
Soares, E. & Zhou, H. Master regulatory role of p63 in epidermal development and disease. Cell. Mol. Life Sci. 75, 1179–1190 (2018).
CAS
PubMed
Article
PubMed CentralGoogle Scholar
- 48.
Zhu, M. & Zernicka-Goetz, M. Principles of self-organization of the mammalian embryo. Cell 183, 1467–1478 (2020).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 49.
Begley, C. G. et al. Molecular characterization of NSCL, a gene encoding a helix–loop–helix protein expressed in the developing nervous system. Proc. Natl Acad. Sci. USA 89, 38–42 (1992).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 50.
Lombardi, L. M. et al. MECP2 disorders: from the clinic to mice and back. J. Clin. Invest. 125, 2914–2923 (2015).
PubMed
PubMed Central
ArticleGoogle Scholar
- 51.
Martin Caballero, I., Hansen, J., Leaford, D., Pollard, S. & Hendrich, B. D. The methyl-CpG binding proteins Mecp2, Mbd2 and Kaiso are dispensable for mouse embryogenesis, but play a redundant function in neural differentiation. PLoS ONE 4, e4315 (2009).
PubMed
PubMed Central
Article
CASGoogle Scholar
- 52.
Li, C. H. et al. MeCP2 links heterochromatin condensates and neurodevelopmental disease. Nature 586, 440–444 (2020).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 53.
Van Der Raadt, J., Van Gestel, S. H. C., Kasri, N. N. & Albers, C. A. ONECUT transcription factors induce neuronal characteristics and remodel chromatin accessibility. Nucleic Acids Res. 47, 5587–5602 (2019).
PubMed
PubMed Central
Article
CASGoogle Scholar
- 54.
Rhee, H. S. et al. Expression of terminal effector genes in mammalian neurons is maintained by a dynamic relay of transient enhancers. Neuron 92, 1252–1265 (2016).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 55.
Cardoso-Moreira, M. et al. Gene expression across mammalian organ development. Nature 571, 505–509 (2019).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 56.
Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).
PubMed
PubMed Central
Article
CASGoogle Scholar
- 57.
Wu, S. J. et al. Single-cell analysis of chromatin silencing programs in development and tumor progression. Preprint at bioRxiv https://doi.org/10.1101/2020.09.04.282418 (2020).
- 58.
Stadhouders, R. et al. Transcription factors orchestrate dynamic interplay between genome topology and gene regulation during cell reprogramming. Nat. Genet. 50, 238–249 (2018).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 59.
Soufi, A., Donahue, G. & Zaret, K. S. Facilitators and impediments of the pluripotency reprogramming factors’ initial engagement with the genome. Cell 151, 994–1004 (2012).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 60.
Chen, J. Perspectives on somatic reprogramming: spotlighting epigenetic regulation and cellular heterogeneity. Curr. Opin. Genet. Dev. 64, 21–25 (2020).
CAS
PubMed
ArticleGoogle Scholar
- 61.
Li, D. et al. Chromatin accessibility dynamics during iPSC reprogramming. Cell Stem Cell 21, 819–833 (2017).
CAS
PubMed
ArticleGoogle Scholar
- 62.
Schwarz, B. A. et al. Prospective isolation of poised iPSC intermediates reveals principles of cellular reprogramming. Cell Stem Cell 23, 289–305 (2018).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 63.
Zviran, A. et al. Deterministic somatic cell reprogramming involves continuous transcriptional changes governed by Myc and epigenetic-driven modules. Cell Stem Cell 24, 328–341 (2019).
CAS
PubMed
ArticleGoogle Scholar
- 64.
Lin, C., Ding, J. & Bar-Joseph, Z. Inferring TF activation order in time series scRNA-Seq studies. PLoS Comput. Biol. 16, e1007644 (2020).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 65.
Picelli, S. et al. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res. 24, 2033–2040 (2014).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 66.
Machida, S. et al. Structural basis of heterochromatin formation by human HP1. Mol. Cell 69, 385–397 (2018).
CAS
PubMed
ArticleGoogle Scholar
- 67.
Reznikoff, W. S. Transposon Tn5. Annu. Rev. Genet. 42, 269–286 (2008).
CAS
PubMed
ArticleGoogle Scholar
- 68.
Zhu, Q. et al. BRCA1 tumour suppression occurs via heterochromatin-mediated silencing. Nature 477, 179–184 (2011).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 69.
Bertotti, A. et al. A molecularly annotated platform of patient-derived xenografts (‘xenopatients’) identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. Cancer Discov. 1, 508–523 (2011).
CAS
PubMed
ArticleGoogle Scholar
- 70.
Reinhardt, P. et al. Derivation and expansion using only small molecules of human neural progenitors for neurodegenerative disease modeling. PLoS ONE 8, e59252 (2013).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 71.
Smith, T., Heger, A. & Sudbery, I. UMI-tools: modeling sequencing errors in unique molecular identifiers to improve quantification accuracy. Genome Res. 27, 491–499 (2017).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 72.
Lassmann, T. TagDust2: a generic method to extract reads from sequencing data. BMC Bioinformatics 16, 24 (2015).
PubMed
PubMed Central
Article
CASGoogle Scholar
- 73.
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at arXiv https://arxiv.org/abs/1303.3997 (2013).
- 74.
Faust, G. G. & Hall, I. M. SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics 30, 2503–2505 (2014).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 75.
Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A. & Manke, T. DeepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, 187–191 (2014).
Article
CASGoogle Scholar
- 76.
Zhang, Y. et al. Model-based analysis of ChIP–seq (MACS). Genome Biol. 9, R137 (2008).
PubMed
PubMed Central
Article
CASGoogle Scholar
- 77.
Breeze, C. E. et al. Atlas and developmental dynamics of mouse DNase I hypersensitive sites. Preprint at bioRxiv https://doi.org/10.1101/2020.06.26.172718 (2020).
- 78.
Giansanti, V., Tang, M. & Cittaro, D. Fast analysis of scATAC-seq data using a predefined set of genomic regions. F1000Res. 9, 199 (2020).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 79.
Meuleman, W. et al. Index and biological spectrum of human DNase I hypersensitive sites. Nature 584, 244–251 (2020).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 80.
Quinlan, A. R. BEDTools: the Swiss-Army tool for genome feature analysis. Curr. Protoc. Bioinformatics https://doi.org/10.1002/0471250953.bi1112s47 (2014).
- 81.
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
PubMed
PubMed Central
ArticleGoogle Scholar
- 82.
Polański, K. et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36, 964–965 (2020).
PubMed
PubMed CentralGoogle Scholar
- 83.
Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 84.
Morelli, L., Giansanti, V. & Cittaro, D. Nested stochastic block models applied to the analysis of single cell data. Preprint at bioRxiv https://doi.org/10.1101/2020.06.28.176180 (2020).
- 85.
Žitnik, M. & Zupan, B. Data fusion by matrix factorization. IEEE Trans. Pattern Anal. Mach. Intell. 37, 41–53 (2015).
PubMed
Article
PubMed CentralGoogle Scholar
- 86.
Cho, S. W. et al. Promoter of lncRNA gene PVT1 is a tumor-suppressor DNA boundary element. Cell 173, 1398–1412 (2018).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 87.
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2009).
PubMed
PubMed Central
Article
CASGoogle Scholar
- 88.
Zhao, H. et al. CrossMap: a versatile tool for coordinate conversion between genome assemblies. Bioinformatics 30, 1006–1007 (2014).
PubMed
Article
CAS
PubMed CentralGoogle Scholar
- 89.
Karimzadeh, M., Ernst, C., Kundaje, A. & Hoffman, M. M. Umap and Bismap: quantifying genome and methylome mappability. Nucleic Acids Res. 46, e120 (2018).
PubMed
PubMed Central
Article
CASGoogle Scholar
- 90.
Favero, F. et al. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann. Oncol. 26, 64–70 (2015).
CAS
ArticleGoogle Scholar
- 91.
Househam, J., Cross, W. C. H. & Caravagna, G. A fully automated approach for quality control of cancer mutations in the era of high-resolution whole genome sequencing. Preprint at bioRxiv https://doi.org/10.1101/2021.02.13.429885 (2021).
- 92.
Caravagna, G., Sanguinetti, G., Graham, T. A. & Sottoriva, A. The MOBSTER R package for tumour subclonal deconvolution from bulk DNA whole-genome sequencing data. BMC Bioinformatics 21, 531 (2020).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 93.
Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at arXiv https://arxiv.org/abs/1207.3907 (2012).
- 94.
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 6, 80–92 (2012).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 95.
Forbes, S. A. et al. COSMIC: mining complete cancer genomes in the catalogue of somatic mutations in cancer. Nucleic Acids Res. 39, 945–950 (2011).
Article
CASGoogle Scholar
- 96.
Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 97.
Kaminow, B., Yunusov, D. & Dobin, A. STARsolo: accurate, fast and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data. Preprint at bioRxiv https://doi.org/10.1101/2021.05.05.442755 (2021).
- 98.
Harrow, J. et al. GENCODE: the reference human genome annotation for the ENCODE project. Genome Res. 22, 1760–1774 (2012).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 99.
Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291 (2019).
CAS
PubMed
PubMed Central
ArticleGoogle Scholar
- 100.
Kulakovskiy, I. V. et al. HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP–seq analysis. Nucleic Acids Res. 46, D252–D259 (2018).
CAS
ArticleGoogle Scholar
- 101.
Molineris, I., Grassi, E., Ala, U., Di Cunto, F. & Provero, P. Evolution of promoter affinity for transcription factors in the human lineage. Mol. Biol. Evol. 28, 2173–2183 (2011).
CAS
PubMed
ArticleGoogle Scholar
- 102.
Morelli, L. & Cittaro, D. scGET: pre-release of scGET repository. Zenodo https://doi.org/10.5281/zenodo.5095040 (2021).
- 103.
Cittaro, D. scatACC: version 0.1. Zenodo https://doi.org/10.5281/zenodo.5095157 (2021).
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Acknowledgements
We thank all the members of the COSR and Tonon laboratory for discussions, support and critical reading of the manuscript. We are grateful to E. Brambilla and F. Ruffini for preparation of the iPSCs and NPCs and A. Mira for assistance in the preparation of the organoids. We would like to thank S. de Pretis for the thoughtful discussions about chromatin velocity. We are grateful to G. Bucci for providing raw exome sequencing data and P. Dellabona for the coordination of the metastatic colon cancer sample collection and analysis. We also thank D. Gabellini, M. E. Bianchi, A. Agresti and S. Biffo for helpful discussions and for reviewing the manuscript. A.B. and L.T. are members of the EurOPDX Consortium. This work was partially supported by the Italian Ministry of Health with Ricerca Corrente and 5 × 1000 funds (S.M. and S.P.), by Associazione Italiana per la Ricerca sul Cancro (AIRC) investigator grants 20697 (to A.B.) and 22802 (to L.T.), AIRC 5 × 1000 grant 21091 (to A.B. and L.T.), AIRC/CRUK/FC AECC Accelerator Award 22795 (to L.T.), European Research Council Consolidator Grant 724748 BEAT (to A.B.), H2020 grant agreement 754923 COLOSSUS (to L.T.), H2020 INFRAIA grant agreement 731105 EDIReX (to A.B.), Fondazione Piemontese per la Ricerca sul Cancro-ONLUS, 5 × 1000 Ministero della Salute 2014, 2015 and 2016 (to L.T.), AIRC investigator grants (to G.T.) and by the Italian Ministry of Health with 5 × 1000 funds, Fiscal Year 2014 (to G.T.), AIRC 5 × 1000 ID 22737 (to G.T.) and the AIRC/CRUK/FC AECC Accelerator Award ‘Single Cell Cancer Evolution in the Clinic’ A26815 (AIRC number program 2279) (to G.T.).
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Competing interests
M.T., F.G., D.L., S.P., D.C. and G.T. have submitted a patent application, pending, covering TnH.
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Peer review information Nature Biotechnology thanks Kun Zhang and the other, anonymous, reviewer(s) for their contribution to the peer review of this work
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Tn5 transposase is able to tagment compacted chromatin featuring H3K9me3.
a, General scheme of TAM-ChIP technique (created with BioRender.com). A primary antibody (ChIP-validated antibody, dark grey) binds to a specific histone modification (light grey) over the genome (blue-red). A secondary antibody (TAM-ChIP conjugate, blue) is linked to the Tn5 transposon, which is made of Tn5 transposase (yellow) and the respective barcoded adapters (green). Upon the binding of the secondary antibody to the primary antibody, the linked Tn5 transposase targets and cuts the genomic regions flanking the histone modification, adding the barcoded adapters. TAM-ChIP was performed on two biological replicates for each condition (H3K4me3, H3K9me3 and NoAb). b, H3K4me3 (green) and H3K9me3 (red) enrichment profiles obtained either by ChIP-seq or TAM-ChIP-seq, compared with Input ChIP control (violet). c, Enrichment profile of heterochromatic genes FAM5B, NTF3, CACNA1E obtained from TAM-ChIP libraries assessed by Real Time-qPCR confirms Tn5 is able to target heterochromatic loci when redirected by H3K9me3 antibody. For each biological replicate three technical replicates were analyzed by Real-Time qPCR; one of the two H3K4me3 biological replicate was excluded because no appreciable signal was detected for any condition. Whiskers represent standard deviations (n = 3 technical replicates). Data shown in b and c refer to experiments performed on Caki-1 cell line.
Source data
Extended Data Fig. 2 Hybrid CD (HP1α)-Tn5 targets H3K9me3 chromatin regions.
a, Two different lengths of HP1α polypeptide (spanning amino acids 1-93 and 1-112) were linked to Tn5, using either a 3 or 5 poly-tyrosine–glycine–serine (TGS) linker, resulting in four hybrid construct, TnH#1-4. TnH#1 made of 1-93aa (HP1α) – 3x(TGS) – Tn5; TnH#2 made of 1-93aa (HP1α) – 5x(TGS) – Tn5; TnH#3 made of 1-112aa (HP1α) – 3x(TGS) – Tn5; TnH#4 made of 1-112aa (HP1α) – 5x(TGS) – Tn5. The 1-93 or 1-112aa spanning regions of HP1α include 1-75aa of CD followed by 18 or 37aa of natural linker, respectively (Created with BioRender.com). b-c, Tagmentation profiles relative to the four hybrid constructs (TnH#1-4) showing no difference in tagmentation efficiency relative to the native Tn5 enzyme (Nextera and Tn5 in-house produced) when targeting either genomic DNA, panel b, or native chromatin on permeabilized nuclei, panel c. d, Enrichment profiles relative to ATAC-seq performed with the four hybrid constructs (TnH#1-4, red) compared with native Tn5 enzyme (Nextera and Tn5 in-house produced) and with H3K4me3 and H3K9me3 ChIP-seq signals (green). e, Distribution of the enrichment of four TnH hybrid constructs (TnH#1-4) relative to genomic background in regions enriched for H3K4me3 (orange) or H3K9me3 (blue) expressed as log2(ratio) of the signal over the genomic Input. Enrichment over the same regions for native Tn5 enzyme (Nextera and Tn5 in-house produced), H3K4me3 and H3K9me3 ChIP-seq are reported as reference. Ec: global enrichment over H3K9me3-marked regions; Eo: global enrichment over H3K4me3-marked regions; Mc: modal enrichment over H3K9me3-marked regions; Mo: modal enrichment over H3K4me3-marked regions. Data shown in b, c and d refer to experiments performed on Caki-1 cell line.
Extended Data Fig. 3 Optimization of ATAC-seq protocol introducing a combination of Tn5 and TnH transposases.
a, Effect of altering Tn5 (green) to TnH (red) ratio on tagmentation profiles when adding both enzymes simultaneously at the beginning of the 60 minutes of the transposition reaction. b, Sequential addition of the same quantity of Tn5 and then TnH enzyme after 30 minutes of the transposition reaction results in a balanced distribution of enrichment signals between the two enzymes. Experiments performed on Caki-1 cell line.
Extended Data Fig. 4 Characteristic of scGET-seq data.
a Abundance of unique cell barcodes retrieved by scATAC-seq performed on Caki-1 cells using the provided ATAC transposition enzyme (10X Tn5; 10X Genomics) (blue) compared to cell barcodes countable by TnH (orange) or Tn5 (green) alone. scGET-seq performance (Tn5 + TnH) is represented in red. The curves are largely overlapping, indicating no evident bias in single cell identification; b Distribution of per-cell normalized coverage over fixed-size genomic bins (5 kb) is reported for 10X Tn5 (blue) and for signal obtained by TnH (orange) and Tn5 (green). While Tn5 is comparable to 10X Tn5, TnH returns higher and less overdispersed per-bin coverages. White dot in boxplots reprents the median, boxes span between the 25th and 75th percentiles, whiskers extend 1.5 times the interquartile range. n = 3363, 1281 and 1537 cells in one experiment; c Saturation analysis for selected libraries. Dotted lines show the fitted incomplete Gamma functions on subsampled data; red solid lines show subsampling data from the same libraries; d Tn5 (green) and TnH (red) enrichment profiles obtained from scGET-seq (pseudo-bulk) or from ATAC-seq performed by using the two enzymes separately, compared with H3K4me3 (green) and H3K9me3 (red) ChIP-seq data. Data shown refer to experiments performed on Caki-1 cells.
Extended Data Fig. 5 Copy Number analysis at multiple resolutions.
a, Segmentation profiles in individual cells profiled by 10X Tn5 (scATAC-seq) (left panel) or TnH scGET-seq (right panel) at 500 kb. b, Segmentation profiles in individual cells profiled by 10X Tn5 (scATAC-seq) (left panel) or TnH scGET-seq (right panel) at 1 Mb. c, Segmentation profiles in individual cells profiled by 10X Tn5 (scATAC-seq) (left panel) or TnH scGET-seq (right panel) at 10 Mb. On top of each heatmap the genome-wide coverage of bulk sequencing of corresponding cell lines is represented. Centromeric regions and gaps (in white) have been excluded from the analysis.
Extended Data Fig. 6 Characterization of Patient Derived Organoids.
a, evaluation of clonal structure of two PDO (CRC6 and CRC17) by exome sequencing; the histogram show the distribution of the cancer cell fraction estimated from the analysis of somatic mutations; in both organoids we observe a monoclonal structure b, 5X (left panel) and 10X (right panel) magnification contrast phase images of PDO #CRC17 obtained from a liver metastasis of a CRC patient (n>5); c absolute copy number of CRC17 and CRC6 as revealed by whole exome sequencing; data in panel c are equivalent to barplots over heatmaps in Fig. 3a.
Extended Data Fig. 7 scGET-seq analysis on PDX samples.
a, UMAP embedding of individual cells as in Fig. 3, colored by the time PDX were harvested. b, Segmentation profiles in individual cells profiled by scGET-seq at 1 Mb resolution expressed as log2(ratio) over the median signal. Cells are clustered according to genetic clones. Red: positive values; Blue: negative values. Centromeric regions (white) have been excluded from the analysis because they correspond to low mapping and not fully characterized regions.
Extended Data Fig. 8 scGET-seq profiling of NIH-3T3 cells knocked-down for Kdm5c.
a, Distribution of early-to-late ratio of 2-stage Repli-seq data for NIH-3T3 cells. Violin plots represent the value of log2(E/L) values over DHS regions which are differential in the high-vs-low coverage cells in Fig. 4a (Mann-Whitney U= 36169.5, p = 1.403e-84). White dot in boxplots represents the median, boxes span between the 25th and 75th percentiles, whiskers extend 1.5 times the interquartile range. n = 35438 regions. b, Distribution of lamin-B1 DamID scores for NIH-3T3 cells. Violin plots represent the value of DamID scores over DHS regions which are differential in the high-vs-low coverage cells in Fig. 4a (Mann-Whitney U = 723.0, p = 4.621e-6). White dot in boxplots represents the median, boxes span between the 25th and 75th percentiles, whiskers extend 1.5 times the interquartile range. n = 35438 regions. c, UMAP embedding of individual cells coloured by cell groups, identified by Leiden algorithm with resolution parameter set to 0.2. d, Results of the linear model calculating the group-wise differences between TnH and Tn5 enrichment. For each group we reported the coefficient of the model, the p-value and the Benjamini-Hochberg corrected p-value. Values are reported for the two genomic regions including the Major primers (see text). Barplot indicates the proportion of shScr-treated for each cell group.
Extended Data Fig. 9 scGET-seq profiling of a developmental model of iPSC.
a, UMAP embedding of individual cells colored by the probability of being included in a trajectory branch estimated by Palantir. Three major branches have been identified, roughly corresponding to the three cell types profiled in this study. b, Schematic representation of the phase portraits underlying Chromatin Velocity. In RNA-velocity, the time derivative of the unspliced/spliced RNA is used to estimate synthesis or degradation of RNA; in Chromatin Velocity, the same procedure is applied on Tn5/TnH data to estimate chromatin relaxation or compaction. d, UMAP embedding of individual cells colored by cell clusters. e, Heatmap shows average expression profiles of TF with the top 10 most negative on PLS2 during the early brain development. Darker color indicates higher expression. w.p.c.: weeks post conception.
Supplementary information
Supplementary Table 1
Counts of cells from organoid CRC6 or CRC7 found in different clones identified using TnH (above) or Tn5 (below).
Supplementary Table 2
Enrichment analysis over KEGG pathways and Reactome pathways of genes associated with DHS sites that are found to be differentially enriched in epigenetic clones. Enrichment was performed using the Enrichr platform.
Supplementary Table 3
Mutations: list of somatic mutations of the primary tumor as a result of exome sequencing data. scGET-seq mutations: list of mutations profiled by scGET-seq. Only variants that have an impact on protein sequence have been reported.
Supplementary Table 4
Analysis of differential Tn5 signal enrichment according to different cell types. For each cell type, we report log fold change, P value and adjusted P value as a result of a t-test over each region. For each region, we report the closest genes (GENCODE v36) and the distance. We also report the log fold change, P value and adjusted P value of differential expression of the associated genes in each cell type
Supplementary Table 5
Analysis of differential Tn5 signal enrichment with respect to the cell entropy as estimated by Palantir. Regions are sorted for decreasing coefficient of the generalized linear model. Genes associated with regions by proximity are also reported.
Supplementary Table 6
Enrichment analysis of genes associated with top DHS regions with the dynamical profile. Analysis was performed using gProfiler.
Supplementary Table 7
Analysis of global transcription factor activity. HOCOMOCO v11 ID, PWM identification code; Gene Symbol, associated gene symbol; PLS1 and PLS2, loading of the TF after PLS regression, corresponds to the horizontal/vertical displacement of the TF arrows in Fig. 6e.
Supplementary Table 8
Sequencing statistics for all scGET-seq experiments presented in the manuscript. n_reads, number of sequencing fragments; n_reads_in_cell, number of fragments associated to a cell; n_duplicated, number of PCR duplicates; target cells, number of target cells in the experiment; PF cells, number of cells passing the initial processing filters (coverage by cell and by region); Compound Coverage, coverage estimate as number of mapped reads in cells (without duplicates) by read length divided by genome size; Per cell Coverage, average per cell coverage as Compound Coverage divided by the number of PF cells.
Supplementary Data 1
Amino acid sequences of TnH constructs (TGS residues underlined; H stands for histidine residue that is an artifact introduced as a consequence of the cloning strategy); Modified Tn5ME-A and TnHMe-A sequences with Tn- or TnH-associated barcode are underlined.
Supplementary Data 2
Representative code snippets to postprocess scGET-seq data.
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Tedesco, M., Giannese, F., Lazarević, D. et al. Chromatin Velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin.
Nat Biotechnol (2021). https://doi.org/10.1038/s41587-021-01031-1
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DOI: https://doi.org/10.1038/s41587-021-01031-1