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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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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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Bianchi, A., Scherer, M., Zaurin, R., Quililan, K., Velten, L., & Beekman, R. (2022). scTAM-seq enables targeted high-confidence analysis of DNA methylation in single cells. Genome Biology. https://doi.org/10.1186/s13059-022-02796-7