Mitochondrial single-cell ATAC-seq for high-throughput multi-omic detection of mitochondrial genotypes and chromatin accessibility

Abstract

Natural sequence variation within mitochondrial DNA (mtDNA) contributes to human phenotypes and may serve as natural genetic markers in human cells for clonal and lineage tracing. We recently developed a single-cell multi-omic approach, called 'mitochondrial single-cell assay for transposase-accessible chromatin with sequencing' (mtscATAC-seq), enabling concomitant high-throughput mtDNA genotyping and accessible chromatin profiling. Specifically, our technique allows the mitochondrial genome-wide inference of mtDNA variant heteroplasmy along with information on cell state and accessible chromatin variation in individual cells. Leveraging somatic mtDNA mutations, our method further enables inference of clonal relationships among native ex vivo-derived human cells not amenable to genetic engineering-based clonal tracing approaches. Here, we provide a step-by-step protocol for the use of mtscATAC-seq, including various cell-processing and flow cytometry workflows, by using primary hematopoietic cells, subsequent single-cell genomic library preparation and sequencing that collectively take ~3-4 days to complete. We discuss experimental and computational data quality control metrics and considerations for the extension to other mammalian tissues. Overall, mtscATAC-seq provides a broadly applicable platform to map clonal relationships between cells in human tissues, investigate fundamental aspects of mitochondrial genetics and enable additional modes of multi-omic discovery.

Fig. 1 |. Schematic of the mtscATAC-seq experimental workflow.

To retain mtDNA within their host cells, whole cells are fixed before mild lysis and permeabilization. Permeabilization enables access of the Tn5 transposase for transposition of nuclear accessible chromatin and mtDNA. Tagmented cells are encapsulated into droplets by using the Chromium Next GEM Single Cell ATAC platform by 10x Genomics to achieve single-cell compartmentalization and library generation.

Fig. 2 |. Schematic of the computational mtscATAC-seq pipeline.

mtscATAC-seq library generation is followed by sequencing, data processing and analysis. Cell types and states can be identified on the basis of the chromatin accessibility data. Leveraging mtDNA reads, high-confidence (somatic) mutations and genotypes can be identified and used, for example, for clonal inferences. Both data modalities may be readily integrated for in-depth downstream analysis.

Fig. 3 |. Flow cytometry cell sorting strategies.

a, Human PBMCs were isolated from whole blood by using a density gradient followed by red blood cell lysis and stained with a live or dead cell marker (SYTOX Blue) and anti-CD66b. The gating strategy is shown and aims to exclude cell doublets (forward scatter area (FSC-A):side scatter area (SSC-A)), dead cells (SYTOX Blue:SSC-A) and granulocytes (CD66b:SSC-A). Note that when using Ficoll density-gradient centrifugation, most granulocytes will have been depleted. b, Gating strategy to obtain various (non)-immune cells from a human ovarian tumor. Additional surface markers CD45 and CD3 were stained to enrich indicated cell populations via sorting for the downstream mtscATAC-seq workflow. FSC-H, forward scatter height.

Fig. 4 |. Images of human PBMCs before and after lysis.

PBMCs were stained with trypan blue before fixation and lysis (left) and after lysis (right) to validate viability and successful lysis and permeabilization with no clumping of cells. Scale bar = 100 μm. Images were obtained by using a Leica DFC3000 G microscope (20× magnification).

Fig. 5 |. mtscATAC-seq library size distribution of human blood and immune cells.

a, Typical fragment size distribution of a conventional 10x Genomics scATAC-seq library with prominent peaks indicative of the nucleosomal banding of chromatin. b, Typical fragment size distribution of mtscATAC-seq libraries, with the first peak (nucleosome-free DNA) often, but not always, being the most prominent, given the additional enrichment of mitochondrial DNA. Libraries were run on a high-sensitivity DNA chip and the Agilent Bioanalyzer 2100 system. FU, fluorescence units.

Fig. 6 |. Troubleshooting mtscATAC-seq library size distribution.

a,b, (Residual) granulocytes may significantly alter the ATAC-seq library size distribution as shown in bulk ATAC-seq (a) and mtscATAC-seq (b) libraries, resulting in substantially altered sequencing data quality. Note that bulk ATAC-seq libraries in a were not size selected, leading to larger genomic fragments between 1,000 and 10,000 bp being retained. c, Putative Tn5 adapter dimers may be observed in scATAC-seq and mtscATAC-seq libraries and appear more pronounced when the number of input cells is low. When dimers are present at low fractions within the libraries (as shown), they do not appear to interfere with sequencing.

Fig. 7 |. Overview of mtscATAC-seq computational workflow and quality-control metrics.

a, Schematic of the computational processing workflow of mtscATAC-seq data using CellRanger-ATAC and mgatk. b, Visualization of mitochondrial genome coverage improvements in mtscATAC-seq (red) compared with the original scATAC-seq protocol (blue). The mean coverage per cell as called by CellRanger-ATAC is shown. The dotted line represents 30× coverage. c, Distribution of the number of mtDNA fragments (log₁₀) per cell as a function of library type. The red line shows ~10× coverage and the proportion of cells per library that exceed this minimum coverage. d, Identification of high-confidence variants from high strand concordance in paired-end sequencing data and high variance-mean ratio (VMR). Homoplasmic and likely clonal heteroplasmic variants are noted in blue and red, respectively. e, Comparison of heteroplasmy estimated from reads aligned to either the forward or reverse strand for individual cells (dots). 13677C→G (left) is a low-quality variant (low strand correlation), whereas 7389T→C (right) is a high-quality variant (high strand correlation) as identified by mgatk. f, Mutation signature plot for all called variants from the default mgatk output. Shown is the substitution rate (observed over expected) of mutations (y axis) in each class of mononucleotide and trinucleotide change resolved by the heavy (H) and light (L) strands of the mitochondrial genome. g, ArchR quality-control metrics and thresholds for accessible chromatin data for indicated library types. Key summary metrics are summarized (right). h, Uniform manifold approximation and projection (UMAP) showing an example embedding of PBMCs annotated by accessible chromatin profiles. i, Projection of heteroplasmy of a high-quality variant (7389T→C; from d and e) in specific cell states (as in h), indicating a common clonal origin and showcasing the multi-modal nature of mtscATAC-seq data. DC, dendritic cell; HSPC, hematopoietic stem and progenitor cell; MAIT, mucosa-associated invariant T cell; Mono, monocyte; NK, natural killer.