Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling

Abstract

Natural mitochondrial DNA (mtDNA) mutations enable the inference of clonal relationships among cells. mtDNA can be profiled along with measures of cell state, but has not yet been combined with the massively parallel approaches needed to tackle the complexity of human tissue. Here, we introduce a high-throughput, droplet-based mitochondrial single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq), a method that combines high-confidence mtDNA mutation calling in thousands of single cells with their concomitant high-quality accessible chromatin profile. This enables the inference of mtDNA heteroplasmy, clonal relationships, cell state and accessible chromatin variation in individual cells. We reveal single-cell variation in heteroplasmy of a pathologic mtDNA variant, which we associate with intra-individual chromatin variability and clonal evolution. We clonally trace thousands of cells from cancers, linking epigenomic variability to subclonal evolution, and infer cellular dynamics of differentiating hematopoietic cells in vitro and in vivo. Taken together, our approach enables the study of cellular population dynamics and clonal properties in vivo.

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Fig. 1: Optimization of a high-throughput single-cell mtDNA genotyping platform with concomitant accessible chromatin measurements.
Fig. 2: Pathogenic mtDNA variability and clonal evolution in cells derived from a patient with MERRF.
Fig. 3: Identification of high-confidence variants and subclonal structure in TF1 cells.
Fig. 4: Clonal and functional heterogeneity in human malignancies resolved by somatic mtDNA mutations.
Fig. 5: Clonal lineage tracing across accessible chromatin landscapes and time in an in vitro model of hematopoiesis.
Fig. 6: Cellular population dynamics in native hematopoiesis in vivo resolved by mtDNA mutations.

Data availability

Data associated with this work is available at GEO accession GSE142745.

Code availability

Software and documentation for mitochondrial variant calling via mgatk are available at http://github.com/caleblareau/mgatk. Custom code to reproduce all analyses and figures is available at https://github.com/caleblareau/mtscATACpaper_reproducibility.

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Acknowledgements

We are grateful to E. Bao, J. Ulirsch, E. Fiskin and other members of the Sankaran and Regev laboratories for helpful discussion. We acknowledge support from the Broad Institute and the Whitehead Institute Flow Cytometry Core facilities. This research was supported by National Institutes of Health grants no. F31 CA232670 (C.A.L.), no. R01 CA208756 (N.H.), no. P01 CA206978 (C.J.W. and G.G.), no. U10 CA180861 (C.J.W.), no. R01 DK103794 (V.G.S.) and no. R33 HL120791 (V.G.S.); a gift from Arthur, Sandra, and Sarah Irving (N.H.); a gift from the Lodish Family to Boston Children’s Hospital (V.G.S.); the New York Stem Cell Foundation (NYSCF, V.G.S.); and the Howard Hughes Medical Institute and Klarman Cell Observatory (A.R.). S.H.G. is supported by funding from the Kay Kendall Leukaemia Fund. K.P. is supported by a research fellowship of the German Research Foundation (DFG) and a Stand Up To Cancer Peggy Prescott Early Career Scientist Award in Colorectal Cancer Research. G.G. is supported by the Paul C. Zamecnick chair. C.J.W. is a scholar of the Leukemia and Lymphoma Society. F.C. and J.D.B were supported by the Allen Distinguished Investigator Program. V.G.S. is an NYSCF-Robertson Investigator. We are grateful to the patients who made this work possible.

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Authors

Contributions

C.A.L. and L.S.L. conceived and designed the project with guidance from A.R. and V.G.S. C.A.L. developed the software and led data analysis. L.S.L. and C.M. developed the mtscATAC-seq experimental protocol. L.S.L. led, designed and performed experiments with assistance from C.M., W.L. and E.C. S.H.G. processed CLL patient samples with L.S.L. T.Z. performed the in situ genotyping experiments. Z.C. and J.M.V. analyzed data. K.P. processed the colorectal cancer specimen. D.R. and G.G. aided with exome sequencing. F.C., J.D.B., M.J.A., G.M.B., N.H., C.J.W., A.R. and V.G.S. each supervised various aspects of this work. A.R. and V.G.S. provided overall project oversight and acquired funding. C.A.L., L.S.L., A.R. and V.G.S. wrote the manuscript with input from all authors.

Corresponding authors

Correspondence to Caleb A. Lareau or Leif S. Ludwig or Aviv Regev or Vijay G. Sankaran.

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Competing interests

The Broad Institute has filed for a patent related to lineage tracing using mtDNA mutations where C.A.L., L.S.L., C.M., J.D.B., A.R. and V.G.S. are named inventors. J.D.B. holds patents related to ATAC-seq. N.H. and C.J.W. are co-founders, equity holders and SAB members of Neon Therapeutics, Inc., and receive research funding from Pharmacyclics. G.G. receives research funding from IBM and Pharmacyclics. A.R. is a founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas Therapeutics and an SAB member of Syros Pharmaceuticals, Neogene Therapeutics, Asimov and ThermoFisher Scientific.

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Extended data

Extended Data Fig. 1 Additional validation of biotechnological and computational basis for single-cell mtDNA genotyping.

(a) Comparison of chromatin library complexity (estimated number of unique fragments) across screened lysis conditions as shown in Fig. 1. (b) The same variable lysis conditions showing the TSS rate per cell. (c) BioAnalyzer traces of mtscATAC-seq library fragment size distribution for regular conditions and mtDNA-enriched conditions. (d) Heteroplasmy heatmap of single cells (columns) for 43 private homoplasmic mutations (rows) in the TF1 or GM11906 cell lines with (left) and without (right) FA treatment. Color bar, heteroplasmy (% allele frequency). (e) Comparison of mtDNA fragment complexity and chromatin complexity between the original regular 10x scATAC protocol and modified lysis conditions with and without formaldehyde (FA) treatment. (f) Heteroplasmy of sum of single-cell ATAC-seq libraries with variable FA treatment. (g) Schematic, method, and results of improving mtDNA genome coverage via hard-masking the reference genome (Methods). (h) Comparison of % reads mapping to mtDNA and (i) chromatin complexity with (red) and without (blue) the hard masking. (j) Comparison of average coverage of mtscATAC-seq (y axis) and GC content (x axis) at each 50 bp bin (dot) in the mtDNA genome. (k) Accessible chromatin landscapes aggregated from single cells near the ETV2 locus for both cell lines as assayed via regular scATAC-seq and mtscATAC-seq. For boxplots in (a,b,e,h,i), each condition represents the top 1,000 cells (based on chromatin complexity) for one experiment. Boxplots: center line, median; box limits, first and third quartiles; whiskers, 1.5x interquartile range.

Extended Data Fig. 2 Further inferences in analysis of the GM11906 (MERRF) lymphoblastoid cell line.

(a) Alternative field of view for GM11906 in situ genotyping imaging experiment. Representative image selected from one of seven fields of view for one experiment. Pseudo bulk accessibility track plots are shown for the (b) ETV2 and (c) CD19 loci. Pseudo-bulk groups represent 0-10% (low), 10-60% (mid), and 60-100% (high) m.8344 A > G heteroplasmy. (d) Spearman correlation of heteroplasmy against the ChIP-seq deviation scores computed via chromVAR. Each bar is a single transcription factor with selected factors highlighted. (e) Depiction of MEF2C deviation scores from chromVAR for m.8344 A > G heteroplasmy bins, corresponding to 0-10% (Low), 10-60% (Mid), and 60-100% (High). Boxplots: center line, median; box limits, first and third quartiles; whiskers, 1.5x interquartile range. Bins contain single cells collected over one experiment where bins correspond to high (>60%; n = 273), intermediate (10-60%; n = 228), and low (<10%; n = 313) heteroplasmy (see Fig. 2c).

Extended Data Fig. 3 Supporting information for somatic mtDNA mutation calling via mgatk.

(a) Venn diagrams depicting comparisons of heteroplasmic mutations identified by mgatk, samtools/ bcftools, and (b) FreeBayes. (c) Comparison of heteroplasmy estimated from reads aligned to either strand. The top row are three variants called specifically by mgatk; 3549 C > A was identified only by FreeBayes. 7399 C > G and 546 A > C were called specifically by bcftools. (d) Identification of 67 and (e) 36 heteroplasmic variants from previously published Smart-seq2 hematopoietic colony data. Blue variants represent known RNA-editing events. (f) Comparison of population heteroplasmy values for variants replicated by mgatk from a previous supervised approach. Boxplots: center line, median; box limits, first and third quartiles; whiskers, 1.5x interquartile range. Statistical test: two-sided Mann-Whitney U Test. (g) Concordance between discerning cells sharing a clonal origin based on colony-specific mtDNA mutations and their unsupervised identification using indicated algorithms (mgatk, bcftools, FreeBayes) and previously described supervised approach6. Receiver operating characteristic (ROC) using the per cell pair mtDNA similarity metric to identify pairs of cells sharing a clonal origin based on sets of mtDNA variants. The number of variants in each set is also depicted. (h) Area under the ROC (AUROC) is denoted for each donor group and indicated variant caller as depicted in (g). Each bar represents the statistic from one evaluation per donor per tool. (i) Estimated sensitivity (y axis, left), positive predictive value (y axis, right), and (j) estimated % dropout (y axis) for mtscATAC-seq at different simulated levels of heteroplasmy (x axis; Methods). Vertical line: 5% heteroplasmy for a subclonal mutation. The in-graph numbers indicate the values from the curve at a single-cell heteroplasmy of 5% with colors corresponding to different per-cell coverage values in the simulation.

Extended Data Fig. 4 Supporting information for clonal and functional heterogeneity in malignant populations revealed by mtDNA mutations.

(a) Flow cytometry gating strategy of CLL patient derived PBMCs showing expansion of CD19 + cells. (b) Identification of high-confidence variants for Patient 1 (top) and Patient 2 (bottom). The number of variants n is indicated. (c) Inference of subclonal structure from somatic mtDNA mutations for patient 2. Cells (columns) are clustered based on mitochondrial genotypes (rows). Colors at the top of the heatmap represent clusters or putative subclones. Color bar, heteroplasmy (% allele frequency). (d) Dot plots showing the mitochondrial genome coverage (log10; y-axis) for the top 500 cells per technology for four indicated scRNA-seq technologies. (e) The mean per-position mitochondrial genome coverage for the same 500 cells as in (d). (f) Volcano plot showing differential gene expression analysis from major and minor clonotypes defined by BCR sequence. Immunoglobulin (IG) genes are shown in purple; all other genes with an FDR < 0.05 are shown in blue. (g) Results for per-peak chi-squared association with sub-clonal group. Each dot is a peak rank-sorted by the chi-squared statistic. (h) Heteroplasmy from the sum of single-cells in the CD19 + and CD19- mtscATAC-seq experiments for indicated mutations and patients. (i) Histograms showing the distribution of heteroplasmy across the profiled population of cells for six selected variants, four from Patient 1 (left) and two from Patient 2 (right). The number of variants in the top heteroplasmy bin (>90%) are shown in red. (j) Allele frequency from the sum of single cells from the 5’ CD19 + and CD19- scRNA-seq libraries for two indicated variants - chr4:109,084,804A > C (‘LEF1’) and chr19:36,394,730G > A (‘HSCT’). (k) Corroboration of T cells based on gene expression signatures and carrying indicated somatic nuclear and mtDNA mutations (Patient 2). (l) Gene activity scores supporting cell type annotations in Fig. 4n. Arrows: cluster enriched for respective gene score. (m) All mtDNA mutations (rows) by cells (columns) observed in the CRC tumor. Columns are colored by defined chromatin cell state defined as in Fig. 4n. (n,o) Chromatin-derived UMAP with cells marked by select mtDNA mutations enriched in (n) epithelial and (o) immune cells. Color bar: heteroplasmy (% allele frequency).

Extended Data Fig. 5 Supporting information for clonal lineage tracing across accessible chromatin landscapes and time in an in vitro model of hematopoiesis.

(a) Depiction of single-cell UMAP embedding showing the original distribution of cells for each library/ time point, (b) relative cell density, (c) Louvain cluster, and (d) mitochondrial DNA coverage per single cell. (e) Overlap of variants called for each of the two datasets. (f) Comparison of log2 fold change in heteroplasmy from day 14 to day 8 for 19 overlapping variants. The p-value shown is for the beta 1 coefficient of the depicted linear regression model. (g) Proportion of cells (%) at day 8 of the 500 cell (x axis) and 800 cell (y axis) input culture carrying shared mtDNA variants as derived from panel (e) suggests limited clonal overlap. (h) Known pathogenic mtDNA mutations detected from a healthy donor. Each dot is a cell separated by the sampled library. All cells with a heteroplasmy of at least 2% are shown. (i) Depiction of unsupervised clustering of groups of cells based on shared somatic mtDNA mutations (y-axis) with corresponding individual mtDNA mutations (x-axis) associated with each cluster for the 500 cell input and (j) 800 cell input culture. Color bar, heteroplasmy (% allele frequency). (k) Fraction of cells (y-axis) carrying number of somatic mtDNA variants (x-axis) above indicated thresholds (≥1%, ≥5%, ≥10% heteroplasmy; red, black, and blue lines, respectively) for indicated cultures.

Extended Data Fig. 6 Support information for cellular population dynamics in native hematopoiesis in vivo resolved by mtDNA based tracing.

(a) Assignment probabilities (%, colorbar) of scRNA-seq data derived transfer labels (rows) across mtscATAC-seq derived Louvian data clusters (columns) as identified in Fig. 6d. (b) Distribution of percent mitochondrial reads derived from mtscATAC-seq data (y axis) across PBMC populations (x axis). (c) Percent mitochondrial counts (y axis) in FACS sorted populations (x axis) from bulk RNA-seq data. (d) Identification of high confidence variants from CD34 + HSPC and PBMC cell populations. Number of variants passing both thresholds (dotted lines) is indicated. A Venn diagram depicts the overlap of shared mutations. (e) Percent duplicates of sequenced mtDNA fragments, mean mtDNA coverage and percent mitochondrial reads for CD34 + HSPC and PBMC cell populations as derived from mtscATAC-seq data. Boxplots: center line, median; box limits, first and third quartiles; whiskers, 1.5x interquartile range. (f) Distribution of maximum level of heteroplasmy of mgatk derived variants from (d) in individual cells. (g) Unsupervised clustering of groups of cells based on shared somatic mtDNA mutations (y-axis) with corresponding individual mtDNA mutations (x-axis) associated with each cluster/clone. (h) Fold-change (observed over expected) of identified rare mutations (y axis) in each class of mononucleotide and trinucleotide change from the CD34 + HSPC data. (i) Comparison of pseudobulk allele frequencies from mgatk identified variants (blue) and rare variants (green). Boxplots for (b,c,e): center line, median; box limits, first and third quartiles; whiskers, 1.5x interquartile range. Bounds are contained within the data range shown. Sample sizes exceed 100 single cells from one experiment.

Supplementary information

Reporting Summary

Supplementary Table 1

Summary of conditions and statistics for mtscATAC-seq optimization related to Fig. 1b.

Supplementary Table 2

Heteroplasmy statistics across fields of view for m.8344 in GM11906 cells.

Supplementary Table 3

Quality control statistics, thresholds and hyperparameter values for populations and mutation calling for all applications.

Supplementary Table 4

Data sources for public datasets analyzed with this work.

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Lareau, C.A., Ludwig, L.S., Muus, C. et al. Massively parallel single-cell mitochondrial DNA genotyping and chromatin profiling. Nat Biotechnol (2020). https://doi.org/10.1038/s41587-020-0645-6

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