Resolving lineage relationships between cells is necessary to understand the fundamental mechanisms underlying normal development and the progression of disease. In recent years, new methods have emerged to enable cell lineage tracking with increasing resolution, leading to substantial biological insights (Woodworth et al., 2017). Specifically, genome editing of reporter constructs via CRISPR-Cas9 allowed synthetic reconstruction of cell lineage relationships in model organisms, and has been coupled with transcriptome profiling to inform cell fates (Spanjaard et al., 2018). These prospective ‘mutate-and-record’ methods provide powerful tools to resolve the developmental origin of cells in genetically engineered cells and organisms, but cannot be utilized in living humans, archival clinical samples, or any wild type organism (Woodworth et al., 2017). Given these limitations, retrospective lineage tracing using endogenous genetic markers is an alternative solution. Recent advances in sequencing enable naturally occurring somatic mutations to be used as lineage markers, which usually required single-cell genome sequencing to capture the sparse genetic information (Lee-Six et al., 2018; Wang et al., 2014). Regions with high mutation rates, such as microsatellite repeats, retrotransposons, and copy-number variants, has been used to resolve the lineage relationship for normal or cancerous tissue samples (Evrony et al., 2015; Biezuner et al., 2016). These methods reduce the cost of whole genome sequencing, but still lack information on cell phenotypes.

Simultaneous measurement of the lineage relationship and cell fates is ultimately required to address many biomedical questions. Here we describe EMBLEM (Epigenome and Mitochondrial Barcode of Lineage from Endogenous Mutations), a strategy to track cell lineage using endogenous mitochondrial DNA variants in ATAC-seq data. The end result of EMBLEM is single-cell lineage information and rich global epigenomic profile from the same individual cells (Figure 1A and Figure 1—figure supplement 1).

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We illustrate the utility of EMBLEM in human blood progenitor cells to clarify the process of pre-leukemic clonal evolution and the emerging biology of clonal hematopoiesis.

Assay of Transposase-Accessible Chromatin by sequencing (ATAC-seq) is a sensitive method used to study chromatin accessibility profiles in diverse cell types and organisms (Buenrostro et al., 2013). During DNA transposition and amplification in cells, mitochondrial DNA is also amplified at the same time (Figure 1A). Mitochondrial DNA (mtDNA) is a ~ 16 kb circular genome with ~10 fold higher mutation rate compared to the nuclear genome. Hence, mtDNA incrementally accumulates unique, irreversible genetic mutations that are passed on to daughter cells even in healthy humans and may be used for lineage tracing (Morris et al., 2017), (Fellous et al., 2009). The majority of somatic mtDNA mutations are noncoding and thought to be passenger (Ju et al., 2014). Importantly, the number of mitochondria (and therefore mtDNA) range from several hundreds to >10,000 per cell in different cell types, facilitating robust mtDNA analysis even from a single cell.

We first observed that ATAC-seq effectively enriches for mtDNA. While mtDNA is present in many kinds of DNA sequence libraries, it is substantially enriched in ATAC-seq libraries due to the fact that mtDNA is not chromatinized and is therefore highly accessible (Supplementary file 1). ATAC-seq enables a 17-fold or greater enrichment of mtDNA compared to exome sequencing or whole genome sequencing in GM12878 human B cells (Figure 1B), leading to an average ~18,000X coverage of mtDNA (Figure 1—figure supplement 2A). With this coverage, we detected 27 mitochondrial variants from GM12878 cells (Figure 1C). 13 of these variants have a variant allele frequency (VAF) greater than 90%, which are known as homoplasmic variants (Figure 1—figure supplement 2B). We also detected 14 low frequency mitochondrial DNA variants, with VAFs ranging from 0.1% to 24% (Figure 1C and Figure 1—figure supplement 2C). Similar results for mtDNA enrichment were observed in human K562 cells (Figure 1—figure supplement 3, Supplementary file 1).

The VAF from bulk ATAC-seq data represents the average of the allele frequencies of the cell population. A 25% VAF may arise from 25% of cells in the population with a homoplasmic variant, or alternatively arise from 100% of cells all having a quarter of their mitochondria with the variant allele (Figure 1D). To distinguish between these two models, we analyzed single-cell ATAC-seq data from GM12878. For four mtDNA variants (VAF between 0.5% ~ 24% at population level), we find that a mixture of both models is in action for different variants (Figure 1E). For instance, mtDNA mutation 3082 is widely spread among single cells, but at low frequency per cell. Because it is extremely unlikely (see Materials and method) that the identical mutation arose independently in every single cell, cells sharing the same mitochondrial mutations are inferred to have descended from the same ancestral cell. These results suggest that even low frequency heteroplasmic mtDNA mutations can be exploited for lineage tracing.

To prove the principle that somatic mitochondrial mutations can track cells from the same ancestor and to quantify the power of lineage mapping, we next applied EMBLEM to primary blood cells from patients with acute myeloid leukemia (AML). Human AML is organized as a hierarchy: a hematopoietic stem cell first acquires an initiating mutation in one of a number of chromatin modifier genes, previously termed as ‘pre-leukemic’ hematopoietic stem cell (pHSC) (Jan et al., 2012; Corces-Zimmerman et al., 2014). pHSCs are functionally normal and are not able to transplant AML, but upon accumulation of additional mutations, they give rise to leukemic stem cells (LSCs) that are able to self-renew and recapitulate AML disease upon transplantation (Jan et al., 2012; Thomas and Majeti, 2017). Finally, LSCs give rise to the bulk leukemic blast cells in AML (Thomas and Majeti, 2017). Targeted exome sequencing in these samples have identified somatic mutations in tumor suppressor genes and oncogenes that link the lineage relationship of pHSCs, LSCs and blasts, providing the ground truth for our analyses (Corces-Zimmerman et al., 2014).

We applied EMBLEM to the ATAC-seq profiles of FACS-purified LSCs and leukemic blasts first (Supplementary file 1). Using high-confidence mtDNA mutations, detected both from bulk ATAC-seq and single cell ATAC-seq, we found the LSC and blast populations not only shared the same heteroplasmic variants, but also showed similar distribution and allele frequency at the cellular level (Figure 1—figure supplement 4). These results indicate the two populations are identical at the genetic level, but divergent at the epigenomic level, consistent with previous studies (Corces et al., 2016; Schep et al., 2017). In patient SU353, we identified four diagnostic mtDNA mutations in the same cell (Figure 1F), which indicates these four mitochondrial variants already co-existed in the ancestral cell (see Materials and method). With the assumption that all these LSCs and blasts are clonal, we further quantified the detection rate of each mtDNA variant as a function of allele frequency and sequencing depth (Figure 1G). We found that when a single variant allele has a frequency greater than 20%, the detection rate can be up to 90% with >20X coverage (e.g. site 6776). In contrast, when the variant allele has a frequency lower than 1%, the detection rate drops to 20% when the coverage is below 100X (e.g. site 6705). While high drop-out rate is a common challenge for single-cell technologies (Kharchenko et al., 2014), computational imputation of the missing information from single cell data can address this problem (Zhang and Zhang, 2018). When multiple mtDNA variants are co-detected in multiple single cells, we can infer their origin and linkage in the ancestral cell (see Materials and method). Thus, cells containing any one of these variants will still inform their origin from the same lineage. With any combination of the four variants, 90% (sensitivity) of the cells can be unambiguously assigned to the correct lineage with just 20x mtDNA coverage (Figure 1H). Furthermore, two mtDNA mutations identified in other cells (e.g. pHSC specific site 2967,6268) were never detected (false positive = 0) in LSCs and blasts (Figure 1—figure supplement 4), showing a high specificity of the method. Similar performance of single cell lineage tracing for another patient (SU070) is shown in Figure 1—figure supplement 5. These results demonstrate that somatic DNA mutations in the mitochondrial genome are a powerful endogenous marker to identify clonal cell populations.

To expand on these findings to additional different cell lineages, we applied EMBLEM to bulk ATAC-seq data from sorted blood cells from healthy human donors and patients with AML(Supplementary file 1) (Corces et al., 2016). We identified heteroplasmic mtDNA mutations in multiple cell populations of primary blood cells from healthy donors and all AML patients (Figure 2—figure supplement 1A, Supplementary file 2). The heteroplasmic mtDNA mutations showed a similar mutant spectrum as observed by previous studies using cancer genomic data (Figure 2—figure supplement 1B and C) (Ju et al., 2014).

Furthermore, EMBLEM, not only confirmed the previous lineage hierarchy of AML, but also extended the previous model of pHSC heterogeneity (Figure 2A). In the AML cases with LSCs sequenced by ATAC-seq, the LSCs and their corresponding leukemic blasts have nearly identical heteroplasmic mtDNA mutations (Figure 2B–C and Figure 2—figure supplement 1D), suggesting a direct lineage relationship and short generation history between LSCs and blasts. We then examined whether any of the mtDNA variants present in LSCs can be seen in the pHSCs, where the first leukemia-associated protein-coding mutations have already occurred in functional normal hematopoietic stem cells (Corces-Zimmerman et al., 2014; Corces et al., 2016). We detected blast-associated mtDNA mutations in pHSCs in all 11 cases. Interestingly, we also detected additional heteroplasmic mtDNA mutations present specifically in pHSCs (Figure 2C). In the 11 cases we investigated, seven cases have pHSC-unique heteroplasmic mtDNA mutations (Figure 2—figure supplement 1D and E), a previously unrecognized level of pHSC heterogeneity. pHSCs are capable of long-term self-renewal and possess a clonal growth advantage, allowing them to clonally outcompete normal HSCs. Indeed, the clonal frequency of pHSCs is a poor prognostic factor for overall survival in AML (Corces et al., 2016). Our discovery of pHSCs with distinct heteroplasmic mtDNA mutations suggests the existence of multiple distinct sub-clones of pHSCs in AML patients.

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To validate the heterogeneity of pHSCs inferred from EMBLEM of bulk cell populations, we performed single-cell ATAC-seq of HSCs from AML patient SU353, which exhibited both a high burden of pre-leukemic somatic coding gene mutations and high frequency of pHSC-specific heteroplasmic mtDNA mutations (Corces et al., 2016). We identified the heteroplasmic mtDNA variants from each single cell, which separated the HSCs into three lineages: Two clonal subpopulations termed ‘clone 1’ (18 cells) and ‘clone 2’ (104 cells), and a third population with no mtDNA variants despite sufficient mtDNA coverage (pHSC with WT mtDNA, 31 cells) (Figure 2D). Notably, clone 2 possessed pHSC-specific mtDNA mutations, while clone 1 possessed mtDNA mutations shared with LSCs, indicating clone1 is the lineage precursor of AML. These results confirm that multiple pHSC clones arise in AML patients, and one subclone eventually evolved to become the LSC (Figure 2—figure supplement 2A).

Finally, we related the clonotype of pHSCs to their single-cell chromatin accessibility profiles. We interrogated the patterns of active DNA elements and enriched transcription factor motifs in sequential stages of AML development from the same patient, and contrasted with HSCs from normal donors using ChromVAR (Schep et al., 2017) (Figure 2E and Figure 2—figure supplement 2B). The chromatin accessibility profiles of pHSCs are more similar to HSCs than to LSCs or leukemic blasts. The greatest deviation between HSC and other cell types occurred at DNA binding motifs of the transcription factor Jun/Fos, a known key regulator of HSC biology (Santaguida et al., 2009) (Figure 2F). Furthermore, the three lineages of pHSCs revealed by mtDNA mutations also showed distinctive chromatin profiles (Figure 2G). Clone 1 pHSC, which gives rise to the LSC and AML leukemia, is already more similar to LSCs and blasts in its chromatin accessibility. In contrast, clone 2 that comprises the larger fraction of pHSCs exhibited variable chromatin profiles at the single-cell level that spanned the range of normal HSCs, pHSC with WT mtDNA(WT cells), is also diverged from normal HSCs. Thus, both lineage tracing and single cell epigenomic states indicate clone 1 as the original stem cell of the AML in patient SU353. Supervised comparison of the chromatin accessibility profiles among these clonal sub-populations further identified distinct and significantly enriched transcription factor motifs (Figure 2H and Figure 2—figure supplement 2C–E). These results indicate the heterogeneity of HSCs from AML patients both on a genetic and epigenomic level.

We present a computational strategy to combine cell lineages tracing by endogenous mtDNA mutations and chromatin accessibility profiling in the same cell using single-cell ATAC-seq data. This approach is applicable to any eukaryote, does not require genetic engineering or genome editing, and is cost effective as the lineage information comes ‘for free’ on top of epigenomic insights. The relative merits of mtDNA vs other genetic markers for lineage tracing are outlined in Supplementary file 3. An important advantage of EMBLEM is that we enable clonotype tracing in existing ATAC-seq data sets and hierarchical lineage construction from ATAC-seq that thousands of labs have already generated. All future ATAC-seq data acquired for other inquiries will also have the benefit of lineage information. EMBLEM may also be extended to other single cell technologies, in which mtDNA is sequenced. We show that EMBLEM is successful even with low frequency heteroplasmic mutations, detection of rare clones in a population, and authentic clinical samples. With advances in the throughput and depth of single-cell genomic technologies, we believe EMBLEM may be a powerful tool to bring insight for many biomedical questions, including development, regeneration, immunity, and cancer with integration of genotype and phenotype information from the same cell. During revision of this work, Ludwig et al. (2019) reported the feasibility of using mtDNA and single cell genomics for lineage tracing, which independently validates the potentially broad utility of this approach.

Although powerful and broadly applicable, mtDNA lineage tracing also has its limitations. One limitation of this method is absence of mtDNA mutations in cells and tissues of embryos and young animals, which precluded us from applying EMBLEM to published scATAC-seq data of early animal development. Moreover, the possibility of selective mitochondrial inheritance or intercellular mitochondria transfer may affect the accuracy of inferred lineages (Mishra and Chan, 2014; Moschoi et al., 2016; Marlein et al., 2017; Hayakawa et al., 2016). On the other hand, asymmetric transmission of mitochondria would not necessarily affect cellular lineage tracing, as long as the variant alleles are randomly segregated. Using scATAC-seq data from a mixing experiment with human and mouse cells (Satpathy et al., 2018), we found species-specific mtDNA always paired with species-specific nuclear genomic DNA (Figure 2—figure supplement 4). These results suggest that mitochondrial horizontal transfer is not a confounder of our study and does not universally occur between cells. The aforementioned two scenarios reflect the potential uncoupling of nuclear and mitochondrial genomes, which would be of interest to investigate by EMBLEM in combination with other nucDNA tracing methods.

mtDNA lineage tracing produced new insights concerning the pHSC, the human hematopoietic stem cell that suffers the first oncogenic mutation in AML evolution. Our results add to the evidence that the pHSC population is heterogeneous, with evidence of multiple mtDNA clones. Unexpectedly, the pHSC lineage that gives rise to the subsequent acute myeloid leukemia is not the lineage with the best competitive potential among pHSCs, as the leukemogenic lineage is often in the minority. pHSC burden is a strong poor prognostic predictor of AML survival (Corces et al., 2016). It is widely believed that the association between high pHSC burden and poor AML patient prognosis reflected the enhanced self-renewal and competitive ability of the mutant pHSC. Our analysis suggests that high pHSC burden may reflect the diversity of pHSCs or the underlying mutational processes. These alternative interpretations of the link between pHSC burden and poor clinical prognosis should be addressed in future studies.

Sequencing data have been deposited in GEO under accession codes GSE122576 and GSE122577.

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Author details

1. Jin Xu

   1. Center for Personal Dynamic Regulomes, Stanford, United States
   2. Department of Dermatology, Stanford University School of Medicine, Stanford, United States
   3. Department of Genetics, Stanford University School of Medicine, Stanford, United States

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0944-9835

2. Kevin Nuno

   1. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States
   2. Division of Hematology, Department of Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Resources, Data curation, Investigation, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

3. Ulrike M Litzenburger

   1. Center for Personal Dynamic Regulomes, Stanford, United States
   2. Department of Dermatology, Stanford University School of Medicine, Stanford, United States
   3. Department of Genetics, Stanford University School of Medicine, Stanford, United States

   Contribution

   Resources, Data curation, Investigation, Methodology

   Competing interests

   No competing interests declared

4. Yanyan Qi

   1. Center for Personal Dynamic Regulomes, Stanford, United States
   2. Department of Dermatology, Stanford University School of Medicine, Stanford, United States
   3. Department of Genetics, Stanford University School of Medicine, Stanford, United States

   Contribution

   Resources, Data curation, Investigation

   Competing interests

   No competing interests declared

5. M Ryan Corces

   1. Center for Personal Dynamic Regulomes, Stanford, United States
   2. Department of Dermatology, Stanford University School of Medicine, Stanford, United States
   3. Department of Genetics, Stanford University School of Medicine, Stanford, United States

   Contribution

   Resources, Data curation

   Competing interests

   No competing interests declared

6. Ravindra Majeti

   1. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, United States
   2. Division of Hematology, Department of Medicine, Stanford University School of Medicine, Stanford, United States

   Contribution

   Resources, Data curation, Supervision, Funding acquisition, Methodology, Writing—review and editing

   For correspondence

   rmajeti@stanford.edu

   Competing interests

   is a co-founder, consultant, equity holder, and serves on the Board of Directors of Forty Seven Inc. Ravindra Majeti is Reviewing editor, eLife

7. Howard Y Chang

   1. Center for Personal Dynamic Regulomes, Stanford, United States
   2. Department of Dermatology, Stanford University School of Medicine, Stanford, United States
   3. Department of Genetics, Stanford University School of Medicine, Stanford, United States
   4. Howard Hughes Medical Institute, Stanford University, Stanford, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing—original draft, Writing—review and editing

   For correspondence

   howchang@stanford.edu

   Competing interests

   is a co-founder of Accent Therapeutics and an advisor for 10x Genomics and Spring Discovery. Stanford University has filed a patent on ATAC-seq(US20160060691A1), on which HYC is named as an inventor.

   "This ORCID iD identifies the author of this article:" 0000-0002-9459-4393

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