Accessible high-throughput single-cell whole-genome sequencing with paired chromatin accessibility

Abstract

Single-cell whole-genome sequencing (scWGS) enables the assessment of genome-level molecular differences between individual cells with particular relevance to genetically diverse systems like solid tumors. The application of scWGS was limited due to a dearth of accessible platforms capable of producing high-throughput profiles. We present a technique that leverages nucleosome disruption methodologies with the widely adopted 10× Genomics ATAC-seq workflow to produce scWGS profiles for high-throughput copy-number analysis without new equipment or custom reagents. We further demonstrate the use of commercially available indexed transposase complexes from ScaleBio for sample multiplexing, reducing the per-sample preparation costs. Finally, we demonstrate that sequential indexed tagmentation with an intervening nucleosome disruption step allows for the generation of both ATAC and WGS data from the same cell, producing comparable data to the unimodal assays. By exclusively utilizing accessible commercial reagents, we anticipate that these scWGS and scWGS+ATAC methods can be broadly adopted by the research community.

Keywords: CP: Biotechnology; CP: Genetics; cancer biology; chromatin accessibility; copy-number alterations; single-cell genomics.

Graphical abstract

Figure 1

Single-cell WGS using the 10× Genomics scATAC kit (A) Workflow diagram. (B) Raw TSS enrichment for each condition. PDCL, patient-derived cell line. Boxes indicate 25th and 75th percentile, whiskers represent 90th and 10th percentile. These boxplot settings are used for all subsequent figures. (C) Estimated library complexity. (D) Comparison of MAD scores to assess coverage biases between our technique and the discontinued 10× scCNV kit. (E) Median MAD scores as a function of median read depth. Colors are as in (D). (F) Association between raw coverage bias and HiC compartments. (G) RIDDLER copy-number calls for GM12878 and PDCL samples. (H) Multiplexing workflow diagram. (I) Estimated library complexity. (J) Raw TSS enrichment for each indexed sample. (K) Cells per droplet assessed by the number of unique tagmentation barcodes associated with each droplet barcode.

Figure 2

Double tagmentation for paired ATAC+WGS (A) Workflow diagram. (B) Raw TSS enrichment for each modality for each sample. (C) Estimated library complexity. (D) Log10 unique reads for each cell for ATAC versus WGS modalities.

Figure 3

dTag WGS performance (A) MAD scores for dTag scWGS data. (B) Accumulated fold coverage (top) and percentage of genome covered (bottom) for random subsampled cell increments. (C) RIDDLER copy-number profiles for dTag scWGS cells. (D) Distribution of copy-number calls for GM12878 compared between scWGS and dTag scWGS methods. Copy number = 2 is highlighted.

Figure 4

dTag ATAC performance (A) ArchR TSS enrichment. (B) ATAC modality fragment size distribution. (C) UMAP of standalone scATAC (light red) and dTag scATAC conditions. Respective cell lines group together. (D) ATAC coverage tracks for housekeeping genes shows the same patterning for GM12878 for standalone and dTag assays, with clear differences in the K562 line highlighted. Below each track is the corresponding scWGS track. (E) Called peaks as a function of unique sequence reads for the dTag scATAC conditions as well as four downsampled datasets from the scATAC standalone assay. (F) Percentage of peak calls in GM12878 scATAC standalone and dTag datasets compared to GeneHancer (GH) annotated regions and ENCODE DNase hypersensitivity sites (DHSs).