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.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Nature Biotechnology Open Access 19 December 2022
Nature Communications Open Access 08 December 2022
Nature Biotechnology Open Access 31 October 2022
Subscribe to Nature+
Get immediate online access to Nature and 55 other Nature journal
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
McGranahan, N. & Swanton, C. Clonal heterogeneity and tumor evolution: past, present, and the future. Cell 168, 613–628 (2017).
Greaves, M. Evolutionary determinants of cancer. Cancer Discov. 5, 806–821 (2015).
Liau, B. B. et al. Adaptive chromatin remodeling drives glioblastoma stem cell plasticity and drug tolerance. Cell Stem Cell 20, 233–246 (2017).
Hangauer, M. J. et al. Drug-tolerant persister cancer cells are vulnerable to GPX4 inhibition. Nature 551, 247–250 (2017).
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).
Shaffer, S. M. et al. Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance. Nature 546, 431–435 (2017).
Sharma, S. V. et al. A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell 141, 69–80 (2010).
Flavahan, W. A., Gaskell, E. & Bernstein, B. E. Epigenetic plasticity and the hallmarks of cancer. Science 357, eaal2380 (2017).
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).
Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).
Tatarakis, A., Behrouzi, R. & Moazed, D. Evolving models of heterochromatin: from foci to liquid droplets. Mol. Cell 67, 725–727 (2017).
Ninova, M., Tóth, K. F. & Aravin, A. A. The control of gene expression and cell identity by H3K9 trimethylation. Development 146, dev181180 (2019).
Nicetto, D. et al. H3K9me3-heterochromatin loss at protein-coding genes enables developmental lineage specification. Science 363, 294–297 (2019).
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).
Peters, A., O’Carroll, D. & Scherthan, H. Loss of the Suv39h histone methyltransferases impairs mammalian heterochromatin and genome stability. Cell 107, 323–337 (2001).
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
Aldridge, S. & Teichmann, S. A. Single cell transcriptomics comes of age. Nat. Commun. 11, 4307 (2020).
Henikoff, S., Henikoff, J., Kaya-Okur, H. & Ahmad, K. Efficient chromatin accessibility mapping in situ by nucleosome-tethered tagmentation. eLife 9, e63274 (2020).
Jacobs, S. A. & Khorasanizadeh, S. Structure of HP1 chromodomain bound to a lysine 9-methylated histone H3 tail. Science 295, 2080–2083 (2002).
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).
Bannister, A. J. et al. Selective recognition of methylated lysine 9 on histone H3 by the HP1 chromo domain. Nature 410, 120–124 (2001).
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).
Cross, W. et al. The evolutionary landscape of colorectal tumorigenesis. Nat. Ecol. Evol. 2, 1661–1672 (2018).
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).
Gézsi, A. et al. VariantMetaCaller: automated fusion of variant calling pipelines for quantitative, precision-based filtering. BMC Genomics 16, 875 (2015).
Misale, S. et al. Vertical suppression of the EGFR pathway prevents onset of resistance in colorectal cancers. Nat. Commun. 6, 8305 (2015).
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).
Laurent-Puig, P., Lievre, A. & Blons, H. Mutations and response to epidermal growth factor receptor Inhibitors. Clin. Cancer Res. 15, 1133–1139 (2009).
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).
Hu, T. & Li, C. Convergence between Wnt-β-catenin and EGFR signaling in cancer. Mol. Cancer 9, 236 (2010).
Sondka, Z. et al. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat. Rev. Cancer 18, 696–705 (2018).
Rondinelli, B. et al. Histone demethylase JARID1C inactivation triggers genomic instability in sporadic renal cancer. J. Clin. Invest. 125, 4625–4637 (2015).
Peric-Hupkes, D. et al. Molecular maps of the reorganization of genome–nuclear lamina interactions during differentiation. Mol. Cell 38, 603–613 (2010).
Hiratani, I. et al. Global reorganization of replication domains during embryonic stem cell differentiation. PLoS Biol. 6, 2220–2236 (2008).
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).
Rondinelli, B. et al. H3K4me3 demethylation by the histone demethylase KDM5C/JARID1C promotes DNA replication origin firing. Nucleic Acids Res. 43, 2560–2574 (2015).
Wong, R. C. B. et al. L1TD1 is a marker for undifferentiated human embryonic stem cells. PLoS ONE 6, e19355 (2011).
Wong, Y. H. et al. Protogenin defines a transition stage during embryonic neurogenesis and prevents precocious neuronal differentiation. J. Neurosci. 30, 4428–4439 (2010).
Setty, M. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37, 451–460 (2019).
Wang, C. et al. Reprogramming of H3K9me3-dependent heterochromatin during mammalian embryo development. Nat. Cell Biol. 20, 620–631 (2018).
Nicetto, D. & Zaret, K. S. Role of H3K9me3 heterochromatin in cell identity establishment and maintenance. Curr. Opin. Genet. Dev. 55, 1–10 (2019).
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).
Novo, C. L. et al. The pluripotency factor Nanog regulates pericentromeric heterochromatin organization in mouse embryonic stem cells. Genes Dev. 30, 1101–1115 (2016).
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
Wold, S., Sjöström, M. & Eriksson, L. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 58, 109–130 (2001).
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).
Soares, E. & Zhou, H. Master regulatory role of p63 in epidermal development and disease. Cell. Mol. Life Sci. 75, 1179–1190 (2018).
Zhu, M. & Zernicka-Goetz, M. Principles of self-organization of the mammalian embryo. Cell 183, 1467–1478 (2020).
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).
Lombardi, L. M. et al. MECP2 disorders: from the clinic to mice and back. J. Clin. Invest. 125, 2914–2923 (2015).
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).
Li, C. H. et al. MeCP2 links heterochromatin condensates and neurodevelopmental disease. Nature 586, 440–444 (2020).
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).
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).
Cardoso-Moreira, M. et al. Gene expression across mammalian organ development. Nature 571, 505–509 (2019).
Kaya-Okur, H. S. et al. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10, 1930 (2019).
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).
Stadhouders, R. et al. Transcription factors orchestrate dynamic interplay between genome topology and gene regulation during cell reprogramming. Nat. Genet. 50, 238–249 (2018).
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).
Chen, J. Perspectives on somatic reprogramming: spotlighting epigenetic regulation and cellular heterogeneity. Curr. Opin. Genet. Dev. 64, 21–25 (2020).
Li, D. et al. Chromatin accessibility dynamics during iPSC reprogramming. Cell Stem Cell 21, 819–833 (2017).
Schwarz, B. A. et al. Prospective isolation of poised iPSC intermediates reveals principles of cellular reprogramming. Cell Stem Cell 23, 289–305 (2018).
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).
Lin, C., Ding, J. & Bar-Joseph, Z. Inferring TF activation order in time series scRNA-Seq studies. PLoS Comput. Biol. 16, e1007644 (2020).
Picelli, S. et al. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res. 24, 2033–2040 (2014).
Machida, S. et al. Structural basis of heterochromatin formation by human HP1. Mol. Cell 69, 385–397 (2018).
Reznikoff, W. S. Transposon Tn5. Annu. Rev. Genet. 42, 269–286 (2008).
Zhu, Q. et al. BRCA1 tumour suppression occurs via heterochromatin-mediated silencing. Nature 477, 179–184 (2011).
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).
Reinhardt, P. et al. Derivation and expansion using only small molecules of human neural progenitors for neurodegenerative disease modeling. PLoS ONE 8, e59252 (2013).
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).
Lassmann, T. TagDust2: a generic method to extract reads from sequencing data. BMC Bioinformatics 16, 24 (2015).
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at arXiv https://arxiv.org/abs/1303.3997 (2013).
Faust, G. G. & Hall, I. M. SAMBLASTER: fast duplicate marking and structural variant read extraction. Bioinformatics 30, 2503–2505 (2014).
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).
Zhang, Y. et al. Model-based analysis of ChIP–seq (MACS). Genome Biol. 9, R137 (2008).
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).
Giansanti, V., Tang, M. & Cittaro, D. Fast analysis of scATAC-seq data using a predefined set of genomic regions. F1000Res. 9, 199 (2020).
Meuleman, W. et al. Index and biological spectrum of human DNase I hypersensitive sites. Nature 584, 244–251 (2020).
Quinlan, A. R. BEDTools: the Swiss-Army tool for genome feature analysis. Curr. Protoc. Bioinformatics https://doi.org/10.1002/0471250953.bi1112s47 (2014).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Polański, K. et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36, 964–965 (2020).
Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).
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).
Žitnik, M. & Zupan, B. Data fusion by matrix factorization. IEEE Trans. Pattern Anal. Mach. Intell. 37, 41–53 (2015).
Cho, S. W. et al. Promoter of lncRNA gene PVT1 is a tumor-suppressor DNA boundary element. Cell 173, 1398–1412 (2018).
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).
Zhao, H. et al. CrossMap: a versatile tool for coordinate conversion between genome assemblies. Bioinformatics 30, 1006–1007 (2014).
Karimzadeh, M., Ernst, C., Kundaje, A. & Hoffman, M. M. Umap and Bismap: quantifying genome and methylome mappability. Nucleic Acids Res. 46, e120 (2018).
Favero, F. et al. Sequenza: allele-specific copy number and mutation profiles from tumor sequencing data. Ann. Oncol. 26, 64–70 (2015).
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).
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).
Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. Preprint at arXiv https://arxiv.org/abs/1207.3907 (2012).
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).
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).
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).
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).
Harrow, J. et al. GENCODE: the reference human genome annotation for the ENCODE project. Genome Res. 22, 1760–1774 (2012).
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).
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).
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).
Morelli, L. & Cittaro, D. scGET: pre-release of scGET repository. Zenodo https://doi.org/10.5281/zenodo.5095040 (2021).
Cittaro, D. scatACC: version 0.1. Zenodo https://doi.org/10.5281/zenodo.5095157 (2021).
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.).
M.T., F.G., D.L., S.P., D.C. and G.T. have submitted a patent application, pending, covering TnH.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Counts of cells from organoid CRC6 or CRC7 found in different clones identified using TnH (above) or Tn5 (below).
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.
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.
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
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.
Enrichment analysis of genes associated with top DHS regions with the dynamical profile. Analysis was performed using gProfiler.
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.
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.
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.
Representative code snippets to postprocess scGET-seq data.
About this article
Cite this article
Tedesco, M., Giannese, F., Lazarević, D. et al. Chromatin Velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin. Nat Biotechnol 40, 235–244 (2022). https://doi.org/10.1038/s41587-021-01031-1
This article is cited by
Nature Reviews Genetics (2023)
Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
Genome Biology (2022)
sciCAN: single-cell chromatin accessibility and gene expression data integration via cycle-consistent adversarial network
npj Systems Biology and Applications (2022)
Nanobody-tethered transposition enables multifactorial chromatin profiling at single-cell resolution
Nature Biotechnology (2022)
Nature Biotechnology (2022)