Chromatin Rush displays epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin

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Chromatin Rush displays epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin
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Summary

Recent efforts absorb succeeded in surveying open chromatin at the one-cell level, but excessive-throughput, single-cell review of heterochromatin and its underlying genomic determinants stays irritating. We engineered a hybrid transposase including the chromodomain (CD) of the heterochromatin protein-1α (HP-1α), which is interested by heterochromatin assembly and maintenance through its binding to trimethylation of the lysine 9 on histone 3 (H3K9me3), and developed a single-cell diagram, single-cell genome and epigenome by transposases sequencing (scGET-seq), that, now not like 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 examined scGET-seq in most cancers-derived organoids and human-derived xenograft (PDX) units and identified genetic events and plasticity-driven mechanisms contributing to most cancers drug resistance. Subsequent, constructing upon the differential enrichment of closed and open chromatin, we devised a strategy, Chromatin Rush, that identifies the trajectories of epigenetic changes at the one-cell level. Chromatin Rush uncovered paths of epigenetic reorganization all the very best diagram through stem cell reprogramming and identified key transcription factors riding these developmental processes. scGET-seq displays the dynamics of genomic and epigenetic landscapes underlying any cell processes.

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Records availability

Fastq files and raw depend matrices absorb been deposited to the Array State 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. Offer files are equipped with this paper.

Code availability

Code wanted to preprocess scGET-seq files is accessible 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 Records 2.

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Acknowledgements

We thank all of the members of the COSR and Tonon laboratory for discussions, toughen and serious studying of the manuscript. We are grateful to E. Brambilla and F. Ruffini for preparation of the iPSCs and NPCs and A. Mira for assistance within the preparation of the organoids. We would pick to thank S. de Pretis for the considerate discussions about chromatin trail. We are grateful to G. Bucci for offering raw exome sequencing files and P. Dellabona for the coordination of the metastatic colon most cancers sample collection and evaluation. We furthermore 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 used to be in part 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 Analysis Council Consolidator Grant 724748 BEAT (to A.B.), H2020 grant settlement 754923 COLOSSUS (to L.T.), H2020 INFRAIA grant settlement 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 within the Health center’ A26815 (AIRC number program 2279) (to G.T.).

Creator files

Creator notes

  1. Dalia Rosano

    Present tackle: Division of Surgical treatment and Cancer, Imperial College London, London, UK

  2. These authors contributed equally: Martina Tedesco, Francesca Giannese.

Affiliations

  1. Università Vita-Salute San Raffaele, Milano, Italy

    Martina Tedesco, Paola Panina Bordignon & Gianvito Martino

  2. Purposeful Genomics of Cancer Unit, Division of Experimental Oncology, IRCCS San Raffaele Scientific Institute, Milano, Italy

    Martina Tedesco, Dalia Rosano, Oronza A. Botrugno & Giovanni Tonon

  3. Heart for Omics Sciences, IRCCS San Raffaele Institute, Milano, Italy

    Francesca Giannese, Dejan Lazarević, Valentina Giansanti, Leonardo Morelli, Davide Cittaro & Giovanni Tonon

  4. Division of Informatics, Programs and Dialog, University of Milano-Bicocca, Milano, Italy

    Valentina Giansanti

  5. Biochemistry and Structural Biology Unit, Division of Experimental Oncology, IEO, IRCCS European Institute of Oncology, Milano, Italy

    Silvia Monzani & Sebastiano Pasqualato

  6. Division of Oncology, University of Torino College of Drugs, Candiolo, Torino, Italy

    Irene Catalano, Elena Grassi, Andrea Bertotti & Livio Trusolino

  7. Candiolo Cancer Institute FPO- IRCCS, Candiolo, Torino, Italy

    Irene Catalano, Elena Grassi, Eugenia R. Zanella, Andrea Bertotti & Livio Trusolino

  8. Neuroimmunology Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Health center, Milano, Italy

    Paola Panina Bordignon & Gianvito Martino

  9. Division of Arithmetic and Geosciences, University of Trieste, Trieste, Italy

    Giulio Caravagna

  10. Hepatobiliary Surgical treatment Division, IRCCS San Raffaele Health center, Milano, Italy

    Luca Aldrighetti

Contributions

M.T. performed experiments and analyzed the records. F.G. devised the methodology and experimental create, performed experiments and analyzed files. D.L. devised the methodology. V.G. performed bioinformatic evaluation. D.R. performed experiments and offered experimental assistance and skills. L.M. performed bioinformatic evaluation. S.M. performed cloning and transposase production. I.C. and E.R.Z. performed in vivo experiments. O.A.B. performed experiments connected to culturing and maintenance of organoids. E.G. performed bioinformatic evaluation. G.C. performed evaluation on complete-exome files. P.P.B. designed and supervised the FIB reprogramming and iPSC differentiation experiments. A.B. designed and supervised in vivo experiments and reviewed the records. G.M. designed and supervised the FIB reprogramming and iPSC differentiation experiments and reviewed the records. L.A. offered potentially the most valuable samples primitive for the organoid experiments. S.P. designed and supervised transposase production and reviewed the records. L.T. designed and supervised in vivo experiments and reviewed files. D.C. designed the gaze, performed bioinformatic evaluation and wrote the manuscript. G.T. designed the gaze, analyzed files and wrote the manuscript.

Corresponding authors

Correspondence to
Davide Cittaro or Giovanni Tonon.

Ethics declarations

Competing pursuits

M.T., F.G., D.L., S.P., D.C. and G.T. absorb submitted a patent utility, pending, covering TnH.

Extra files

Discover about evaluate files Nature Biotechnology thanks Kun Zhang and the plenty of, nameless, reviewer(s) for their contribution to the survey evaluate of this work

Creator’s demonstrate Springer Nature stays neutral with regards to jurisdictional claims in published maps and institutional affiliations.

Prolonged files

Prolonged Records Fig. 1 Tn5 transposase is in a position to tagment compacted chromatin featuring H3K9me3.

a, Routine plot of TAM-ChIP strategy (created with BioRender.com). A main antibody (ChIP-validated antibody, darkish grey) binds to a particular histone modification (gentle grey) over the genome (blue-crimson). 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 (inexperienced). Upon the binding of the secondary antibody to potentially the most valuable antibody, the linked Tn5 transposase targets and cuts the genomic areas flanking the histone modification, adding the barcoded adapters. TAM-ChIP used to be performed on two biological replicates for every condition (H3K4me3, H3K9me3 and NoAb). b, H3K4me3 (inexperienced) and H3K9me3 (crimson) enrichment profiles got both by ChIP-seq or TAM-ChIP-seq, when compared with Input ChIP management (violet). c, Enrichment profile of heterochromatic genes FAM5B, NTF3, CACNA1E got from TAM-ChIP libraries assessed by Precise Time-qPCR confirms Tn5 is in a position to center of attention on heterochromatic loci when redirected by H3K9me3 antibody. For every biological replicate three technical replicates had been analyzed by Precise-Time qPCR; one amongst the two H3K4me3 biological replicate used to be excluded as a result of no in actuality intensive signal used to be detected for any condition. Whiskers signify odd deviations (n = 3 technical replicates). Records shown in b and c search advice from experiments performed on Caki-1 cell line.

Offer files

Prolonged Records Fig. 2 Hybrid CD (HP1α)-Tn5 targets H3K9me3 chromatin areas.

a, Two assorted lengths of HP1α polypeptide (spanning amino acids 1-93 and 1-112) had been linked to Tn5, the exercise of both a 3 or 5 poly-tyrosine–glycine–serine (TGS) linker, ensuing in four hybrid rep, 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 areas of HP1α consist of 1-75aa of CD adopted 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) exhibiting no inequity in tagmentation efficiency relative to the native Tn5 enzyme (Nextera and Tn5 in-rental produced) when focused on both 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, crimson) when compared with native Tn5 enzyme (Nextera and Tn5 in-rental produced) and with H3K4me3 and H3K9me3 ChIP-seq indicators (inexperienced). e, Distribution of the enrichment of four TnH hybrid constructs (TnH#1-4) relative to genomic background in areas enriched for H3K4me3 (orange) or H3K9me3 (blue) expressed as log2(ratio) of the signal over the genomic Input. Enrichment over the identical areas for native Tn5 enzyme (Nextera and Tn5 in-rental produced), H3K4me3 and H3K9me3 ChIP-seq are reported as reference. Ec: international enrichment over H3K9me3-marked areas; Eo: international enrichment over H3K4me3-marked areas; Mc: modal enrichment over H3K9me3-marked areas; Mo: modal enrichment over H3K4me3-marked areas. Records shown in b, c and d search advice from experiments performed on Caki-1 cell line.

Prolonged Records Fig. 3 Optimization of ATAC-seq protocol introducing a mix of Tn5 and TnH transposases.

a, Develop of altering Tn5 (inexperienced) to TnH (crimson) ratio on tagmentation profiles when adding both enzymes simultaneously initially of the 60 minutes of the transposition reaction. b, Sequential addition of the identical quantity of Tn5 after which TnH enzyme after 30 minutes of the transposition reaction ends in a balanced distribution of enrichment indicators between the two enzymes. Experiments performed on Caki-1 cell line.

Prolonged Records Fig. 4 Attribute of scGET-seq files.

a Abundance of irregular cell barcodes retrieved by scATAC-seq performed on Caki-1 cells the exercise of the offered ATAC transposition enzyme (10X Tn5; 10X Genomics) (blue) when compared with cell barcodes countable by TnH (orange) or Tn5 (inexperienced) on my own. scGET-seq efficiency (Tn5 + TnH) is represented in crimson. The curves are largely overlapping, indicating no evident bias in single cell identification; b Distribution of per-cell normalized coverage over fixed-dimension genomic boxes (5 kb) is reported for 10X Tn5 (blue) and for signal got by TnH (orange) and Tn5 (inexperienced). While Tn5 is expounded to 10X Tn5, TnH returns higher and now no more overdispersed per-bin coverages. White dot in boxplots reprents the median, boxes span between the 25th and 75th percentiles, whiskers lengthen 1.5 situations the interquartile vary. n = 3363, 1281 and 1537 cells in one experiment; c Saturation evaluation for selected libraries. Dotted strains instruct the fitted incomplete Gamma capabilities on subsampled files; crimson stable strains instruct subsampling files from the identical libraries; d Tn5 (inexperienced) and TnH (crimson) enrichment profiles got from scGET-seq (pseudo-bulk) or from ATAC-seq performed by the exercise of the two enzymes one after the other, when compared with H3K4me3 (inexperienced) and H3K9me3 (crimson) ChIP-seq files. Records shown search advice from experiments performed on Caki-1 cells.

Prolonged Records Fig. 5 Reproduction Number evaluation at a couple of resolutions.

a, Segmentation profiles namely person cells profiled by 10X Tn5 (scATAC-seq) (left panel) or TnH scGET-seq (unprejudiced panel) at 500 kb. b, Segmentation profiles namely person cells profiled by 10X Tn5 (scATAC-seq) (left panel) or TnH scGET-seq (unprejudiced panel) at 1 Mb. c, Segmentation profiles namely person cells profiled by 10X Tn5 (scATAC-seq) (left panel) or TnH scGET-seq (unprejudiced panel) at 10 Mb. On top of every heatmap the genome-huge coverage of bulk sequencing of corresponding cell strains is represented. Centromeric areas and gaps (in white) absorb been excluded from the evaluation.

Prolonged Records Fig. 6 Characterization of Patient Derived Organoids.

a, evaluate of clonal constructing of two PDO (CRC6 and CRC17) by exome sequencing; the histogram instruct the distribution of the most cancers cell fragment estimated from the evaluation of somatic mutations; in both organoids we stare a monoclonal constructing b, 5X (left panel) and 10X (unprejudiced panel) magnification inequity section pictures of PDO #CRC17 got from a liver metastasis of a CRC patient (n>5); c absolute copy different of CRC17 and CRC6 as revealed by complete exome sequencing; files in panel c are a lot like barplots over heatmaps in Fig. 3a.

Prolonged Records Fig. 7 scGET-seq evaluation on PDX samples.

a, UMAP embedding of particular person cells as in Fig. 3, coloured by the time PDX had been harvested. b, Segmentation profiles namely person cells profiled by scGET-seq at 1 Mb possibility expressed as log2(ratio) over the median signal. Cells are clustered in accordance with genetic clones. Crimson: certain values; Blue: damaging values. Centromeric areas (white) absorb been excluded from the evaluation as a result of they correspond to low mapping and no longer fully characterized areas.

Prolonged Records Fig. 8 scGET-seq profiling of NIH-3T3 cells knocked-down for Kdm5c.

a, Distribution of early-to-slack ratio of 2-stage Repli-seq files for NIH-3T3 cells. Violin plots signify the worth of log2(E/L) values over DHS areas which is inclined to be differential within the excessive-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 lengthen 1.5 situations the interquartile vary. n = 35438 areas. b, Distribution of lamin-B1 DamID ratings for NIH-3T3 cells. Violin plots signify the worth of DamID ratings over DHS areas which is inclined to be differential within the excessive-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 lengthen 1.5 situations the interquartile vary. n = 35438 areas. c, UMAP embedding of particular person cells coloured by cell groups, identified by Leiden algorithm with possibility parameter blueprint to 0.2. d, Outcomes of the linear model calculating the neighborhood-luminous variations between TnH and Tn5 enrichment. For every neighborhood we reported the coefficient of the model, the p-payment and the Benjamini-Hochberg corrected p-payment. Values are reported for the two genomic areas including the Essential primers (glimpse text). Barplot signifies the percentage of shScr-handled for every cell neighborhood.

Prolonged Records Fig. 9 scGET-seq profiling of a developmental model of iPSC.

a, UMAP embedding of particular person cells coloured by the possibility of being incorporated in a trajectory department estimated by Palantir. Three main branches absorb been identified, roughly a lot just like the three cell kinds profiled on this gaze. b, Schematic illustration of the section portraits underlying Chromatin Rush. In RNA-trail, the time derivative of the unspliced/spliced RNA is primitive to estimate synthesis or degradation of RNA; in Chromatin Rush, the identical plot is applied on Tn5/TnH files to estimate chromatin relaxation or compaction. d, UMAP embedding of particular person cells coloured by cell clusters. e, Heatmap exhibits reasonable expression profiles of TF with the pinnacle 10 most damaging on PLS2 all the very best diagram through the early mind kind. Darker color signifies higher expression. w.p.c.: weeks publish idea.

Supplementary files

Supplementary Table 1

Counts of cells from organoid CRC6 or CRC7 instruct in assorted clones identified the exercise of TnH (above) or Tn5 (under).

Supplementary Table 2

Enrichment evaluation over KEGG pathways and Reactome pathways of genes associated with DHS websites that are learned to be differentially enriched in epigenetic clones. Enrichment used to be performed the exercise of the Enrichr platform.

Supplementary Table 3

Mutations: list of somatic mutations of potentially the most valuable tumor as a result of exome sequencing files. scGET-seq mutations: list of mutations profiled by scGET-seq. Entirely variants which absorb an influence on protein sequence absorb been reported.

Supplementary Table 4

Diagnosis of differential Tn5 signal enrichment in accordance with assorted cell kinds. For every cell kind, we account log fold trade, P payment and adjusted P payment as a result of a t-test over every situation. For every situation, we account the closest genes (GENCODE v36) and the distance. We furthermore account the log fold trade, P payment and adjusted P payment of differential expression of the associated genes in every cell kind

Supplementary Table 5

Diagnosis of differential Tn5 signal enrichment with respect to the cell entropy as estimated by Palantir. Areas are sorted for reducing coefficient of the generalized linear model. Genes associated with areas by proximity are furthermore reported.

Supplementary Table 6

Enrichment evaluation of genes associated with top DHS areas with the dynamical profile. Diagnosis used to be performed the exercise of gProfiler.

Supplementary Table 7

Diagnosis of international transcription part project. HOCOMOCO v11 ID, PWM identification code; Gene Image, associated gene image; 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 offered within the manuscript. n_reads, different of sequencing fragments; n_reads_in_cell, different of fragments associated to a cell; n_duplicated, different of PCR duplicates; target cells, different of target cells within the experiment; PF cells, different of cells passing the initial processing filters (coverage by cell and by situation); Compound Coverage, coverage estimate as different of mapped reads in cells (with out duplicates) by learn length divided by genome dimension; Per cell Coverage, reasonable per cell coverage as Compound Coverage divided by the different of PF cells.

Supplementary Records 1

Amino acid sequences of TnH constructs (TGS residues underlined; H stands for histidine residue that’s an artifact launched on account of the cloning strategy); Modified Tn5ME-A and TnHMe-A sequences with Tn- or TnH-associated barcode are underlined.

Supplementary Records 2

Consultant code snippets to postprocess scGET-seq files.

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Tedesco, M., Giannese, F., Lazarević, D. et al. Chromatin Rush displays 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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