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scTAM-seq enables targeted high-confidence analysis of DNA methylation in single cells

Bianchi, Agostina, Scherer, Michael, Zaurin, Roser, Quililan, Kimberly, Velten, Lars, Beekman, Renée·2022·Genome Biology
DOI 10.1186/s13059-022-02796-7PMC PMC9615163

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Abstract

Single-cell DNA methylation profiling currently suffers from excessive noise and/or limited cellular throughput. We developed scTAM-seq, a targeted bisulfite-free method for profiling up to 650 CpGs in up to 10,000 cells per experiment, with a dropout rate as low as 7%. We demonstrate that scTAM-seq can resolve DNA methylation dynamics across B-cell differentiation in blood and bone marrow, identifying intermediate differentiation states that were previously masked. scTAM-seq additionally queries surface-protein expression, thus enabling integration of single-cell DNA methylation information with cell atlas data. In summary, scTAM-seq is a high-throughput, high-confidence method for analyzing DNA methylation at single-CpG resolution across thousands of single cells.

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Fig. 1 ScTAM-seq identifies cellular subtypes in B cells from peripheral blood. a Overview of the scTAM-seq workflow. b Per amplicon comparison of fraction of cells with at least one sequencing read in the undigested and digested bone marrow sample. c False-negative rate (estimated on B-cell differentiation amplicons, n=424, undigested bone marrow control) and false-positive rate (estimated on the constitutively unmethylated amplicons, n=32, digested bone marrow sample) across all amplicons of the respective class. d Heatmap (binary distance, Ward’s method) of binarized, single-cell DNAm values of 9583 cells across 313 high-performance amplicons in the peripheral blood sample. e Comparison of pseudo-bulk and bulk DNAm for the 424 B-cell differentiation-related amplicons. r: Pearson’s correlation coefficient. The colors correspond to the DNAm clusters in d
Fig. 1 ScTAM-seq identifies cellular subtypes in B cells from peripheral blood. a Overview of the scTAM-seq workflow. b Per amplicon comparison of fraction of cells with at least one sequencing read in the undigested and digested bone marrow sample. c False-negative rate (estimated on B-cell differentiation amplicons, n=424, undigested bone marrow control) and false-positive rate (estimated on the constitutively unmethylated amplicons, n=32, digested bone marrow sample) across all amplicons of the respective class. d Heatmap (binary distance, Ward’s method) of binarized, single-cell DNAm values of 9583 cells across 313 high-performance amplicons in the peripheral blood sample. e Comparison of pseudo-bulk and bulk DNAm for the 424 B-cell differentiation-related amplicons. r: Pearson’s correlation coefficient. The colors correspond to the DNAm clusters in dCC BY · PubMed Central
Fig. 2 scTAM-seq identifies cellular states associated with proliferation. a Heatmap showing the binarized, single-cell DNAm matrix for 4100 ns-memory B cells in the 313 high-performance amplicons. Five clusters of ns-memory B cells and six CpG clusters were defined based on a hierarchical clustering (binary distance, Ward’s method). b Low-dimensional representation of the binarized data matrix for all cells (naive, cs- and ns-memory B cells) using UMAP. The pseudotime was inferred with Monocle3. c Surface-protein expression within the UMAP-space. The surface-protein expression data was binarized using a cutoff of 1 for the CLR-normalized counts. d Surface-protein expression for the different clusters (ordered by increasing pseudotime) identified in a as barplots. Shown is the mean and two times the standard error within each of the clusters. e Average DNAm value per CpG- and cell cluster were estimated by computing the fraction of all methylated amplicons in a given CpG and cell cluster. The error bar indicates two times the standard error across all cells of a cell cluster. The pie chart indicates the genomic distribution of the CpGs within each CpG cluster according to chromatin states of naive, germinal center, ns-, and cs-memory B cells defined in Beekman et al.
Fig. 2 scTAM-seq identifies cellular states associated with proliferation. a Heatmap showing the binarized, single-cell DNAm matrix for 4100 ns-memory B cells in the 313 high-performance amplicons. Five clusters of ns-memory B cells and six CpG clusters were defined based on a hierarchical clustering (binary distance, Ward’s method). b Low-dimensional representation of the binarized data matrix for all cells (naive, cs- and ns-memory B cells) using UMAP. The pseudotime was inferred with Monocle3. c Surface-protein expression within the UMAP-space. The surface-protein expression data was binarized using a cutoff of 1 for the CLR-normalized counts. d Surface-protein expression for the different clusters (ordered by increasing pseudotime) identified in a as barplots. Shown is the mean and two times the standard error within each of the clusters. e Average DNAm value per CpG- and cell cluster were estimated by computing the fraction of all methylated amplicons in a given CpG and cell cluster. The error bar indicates two times the standard error across all cells of a cell cluster. The pie chart indicates the genomic distribution of the CpGs within each CpG cluster according to chromatin states of naive, germinal center, ns-, and cs-memory B cells defined in Beekman et al.CC BY · PubMed Central
Fig. 3 scTAM-seq captures the B-cell differentiation process in bone marrow. a Heatmap of single-cell DNAm values for 5340 cells and 313 amplicons in the digested bone marrow sample. b–d Visualization of DNAm data in low-dimensional space (UMAP), with labels inferred from bulk DNAm (b), across differentiation pseudotime (inferred with Monocle3, c), or with labels transferred from CITE-seq data (d). e Visualization of surface-protein expression for cells embedded in the DNAm UMAP. The value shows the CLR-normalized expression values (see “Methods”). f, g Relationship between DNAm and log-normalized gene expression across B-cell differentiation showing a positive (f, CXCR5, CpG located in promoter region) and negative correlation (g, SMARCA4, CpG located in intronic region), respectively
Fig. 3 scTAM-seq captures the B-cell differentiation process in bone marrow. a Heatmap of single-cell DNAm values for 5340 cells and 313 amplicons in the digested bone marrow sample. b–d Visualization of DNAm data in low-dimensional space (UMAP), with labels inferred from bulk DNAm (b), across differentiation pseudotime (inferred with Monocle3, c), or with labels transferred from CITE-seq data (d). e Visualization of surface-protein expression for cells embedded in the DNAm UMAP. The value shows the CLR-normalized expression values (see “Methods”). f, g Relationship between DNAm and log-normalized gene expression across B-cell differentiation showing a positive (f, CXCR5, CpG located in promoter region) and negative correlation (g, SMARCA4, CpG located in intronic region), respectivelyCC BY · PubMed Central

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Cites

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Research works printed but not linked: 35

  1. Smallwood SA, Lee HJ, Angermueller C, Krueger F, Saadeh H, Peat J, et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat Methods. 2014;11:817–20. · DOI · Paper page
  2. Guo H, Zhu P, Wu X, Li X, Wen L, Tang F. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res. 2013;23:2126–35. · DOI · Paper page
  3. Clark SJ, Argelaguet R, Kapourani C-A, Stubbs TM, Lee HJ, Alda-Catalinas C, et al. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat Commun. 2018;9:781. · DOI · Paper page
  4. Hui T, Cao Q, Wegrzyn-Woltosz J, O’Neill K, Hammond CA, Knapp DJHF, et al. High-resolution single-cell DNA methylation measurements reveal epigenetically distinct hematopoietic stem cell subpopulations. Stem Cell Rep. 2018;11:578–92. · DOI · Paper page
  5. Durek P, Nordström K, Gasparoni G, Salhab A, Kressler C, de Almeida M, et al. Epigenomic profiling of human CD4+ T cells supports a linear differentiation model and highlights molecular regulators of memory development. Immunity. 2016;45:1148–61. · DOI
  6. Kulis M, Merkel A, Heath S, Queirós AC, Schuyler RP, Castellano G, et al. Whole-genome fingerprint of the DNA methylome during human B cell differentiation. Nat Genet. 2015;47:746–56. · DOI
  7. Stunnenberg HG, Abrignani S, Adams D, de Almeida M, Altucci L, Amin V, et al. The International Human Epigenome Consortium: a blueprint for scientific collaboration and discovery. Cell. 2016;167:1145–9. · DOI
  8. Scherer M, Nebel A, Franke A, Walter J, Lengauer T, Bock C, et al. Quantitative comparison of within-sample heterogeneity scores for DNA methylation data. Nucleic Acids Res. 2020;48:e46. · DOI
  9. Zhu P, Guo H, Ren Y, Hou Y, Dong J, Li R, et al. Single-cell DNA methylome sequencing of human preimplantation embryos. Nat Genet. 2018;50:12–9. · DOI
  10. Cheow LF, Quake SR, Burkholder WF, Messerschmidt DM. Multiplexed locus-specific analysis of DNA methylation in single cells. Nat Protoc. 2015;10:619–31. · DOI
  11. Cheow LF, Courtois ET, Tan Y, Viswanathan R, Xing Q, Tan RZ, et al. Single-cell multimodal profiling reveals cellular epigenetic heterogeneity. Nat Methods. 2016;13:833–6. · DOI
  12. Lorthongpanich C, Cheow LF, Balu S, Quake SR, Knowles BB, Burkholder WF, et al. Single-cell DNA-methylation analysis reveals epigenetic chimerism in preimplantation embryos. Science. 2013;341:1110–2. · DOI · Paper page
  13. Pellegrino M, Sciambi A, Treusch S, Durruthy-Durruthy R, Gokhale K, Jacob J, et al. High-throughput single-cell DNA sequencing of acute myeloid leukemia tumors with droplet microfluidics. Genome Res. 2018;28:1345–52. · DOI
  14. Miles LA, Bowman RL, Merlinsky TR, Csete IS, Ooi AT, Durruthy-Durruthy R, et al. Single-cell mutation analysis of clonal evolution in myeloid malignancies. Nature. 2020;587:477–82. · DOI · Paper page
  15. Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, et al. Simultaneous epitope and transcriptome measurement in single cells. Nat Methods. 2017;14:865–8. · DOI · Paper page
  16. Triana S, Vonficht D, Jopp-Saile L, Raffel S, Lutz R, Leonce D, et al. Single-cell proteo-genomic reference maps of the hematopoietic system enable the purification and massive profiling of precisely defined cell states. Nat Immunol. 2021;22:1577–89. · DOI · Paper page
  17. Queirós AC, Beekman R, Vilarrasa-Blasi R, Duran-Ferrer M, Clot G, Merkel A, et al. Decoding the DNA methylome of mantle cell lymphoma in the light of the entire B cell lineage. Cancer Cell. 2016;30:806–21. · DOI
  18. Grimsholm O, Piano Mortari E, Davydov AN, Shugay M, Obraztsova AS, Bocci C, et al. The interplay between CD27dull and CD27bright B cells ensures the flexibility, stability, and resilience of human B cell memory. Cell Rep. 2020;30:2963–2977.e6. · DOI
  19. Carsetti R, Terreri S, Conti MG, Fernandez Salinas A, Corrente F, Capponi C, et al. Comprehensive phenotyping of human peripheral blood B lymphocytes in healthy conditions. Cytometry A. 2022;101:131–9. · DOI
  20. Beekman R, Chapaprieta V, Russiñol N, Vilarrasa-Blasi R, Verdaguer-Dot N, Martens JHA, et al. The reference epigenome and regulatory chromatin landscape of chronic lymphocytic leukemia. Nat Med. 2018;24:868–80. · DOI
  21. Duran-Ferrer M, Clot G, Nadeu F, Beekman R, Baumann T, Nordlund J, et al. The proliferative history shapes the DNA methylome of B-cell tumors and predicts clinical outcome. Nat Can. 2020;1:1066–81. · DOI
  22. Golinski M-L, Demeules M, Derambure C, Riou G, Maho-Vaillant M, Boyer O, et al. CD11c+ B cells are mainly memory cells, precursors of antibody secreting cells in healthy donors. Front Immunol. 2020;11:32. · DOI
  23. Karnell JL, Kumar V, Wang J, Wang S, Voynova E, Ettinger R. Role of CD11c + T-bet + B cells in human health and disease. Cell Immunol. 2017;321:40–5. · DOI
  24. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol. 2014;32:381–6. · DOI · Paper page
  25. Roberts RJ, Vincze T, Posfai J, Macelis D. REBASE—a database for DNA restriction and modification: enzymes, genes and genomes. Nucleic Acids Res. 2015;43:D298–9. · DOI · Paper page
  26. Zou H, Hastie T. Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol. 2005;67:301–20. · DOI · Paper page
  27. Court F, Tayama C, Romanelli V, Martin-Trujillo A, Iglesias-Platas I, Okamura K, et al. Genome-wide parent-of-origin DNA methylation analysis reveals the intricacies of human imprinting and suggests a germline methylation-independent mechanism of establishment. Genome Res. 2014;24:554–69. · DOI
  28. Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal. 2011;17:10. · DOI · Paper page
  29. Li H, Durbin R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics. 2010;26:589–95. · DOI · Paper page
  30. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25:2078–9. · DOI · Paper page
  31. Mölder F, Jablonski KP, Letcher B, Hall MB, Tomkins-Tinch CH, Sochat V, et al. Sustainable data analysis with Snakemake. F1000Research. 2021;10:33. · DOI · Paper page
  32. Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33:495–502. · DOI · Paper page
  33. Stuart T, Srivastava A, Madad S, Lareau CA, Satija R. Single-cell chromatin state analysis with Signac. Nat Methods. 2021;18:1333–41. · DOI · Paper page
  34. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016;32:2847–9. · DOI
  35. Bianchi A., et al, scTAM-seq enables targeted high-confidence analysis of DNA methylation in single cells. GSE198019. Gene Expression Omnibus. 2022. https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE198019. · DOI

Cited by

Metadata

Authors
Bianchi, A., Scherer, M., Zaurin, R., Quililan, K., Velten, L., & Beekman, R.
Venue
Genome Biology
Publisher
Springer Science and Business Media LLC
Published
2022-10-28
Volume
23
Issue
1
ISSN
1474-760X
DOI
10.1186/s13059-022-02796-7
PMC
PMC9615163
Cited by
36
References
5
Language
en
Licence
Funders
  • Spanish Ministry of Science and Innovation (RTI2018-096359-A-I00, PRE2019-087574)
  • European Hematology Association (Advanced research grant)

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