A droplet-based microfluidic platform combined with primary template-directed amplification (PTA) enables the analysis of clonal heterogeneity and mosaicism in leukemia at single-cell genome level

Nan Zong1, Kyle Hukari2, Joseph Dahl2, Victor Weigman2, Jay West2, Stavros Stravrakis1 and Andrew deMello1

1 ETH Zurich, Switzerland

2 BioSkryb Genomics, Durham, NC, USA

Mosaicism in leukemia is driven by dynamic genetic instability within a heterogeneous population of cells. This introduces complexity into diagnosis and treatment. The primary challenge is the detection of rare clones within a tumor. A secondary hurdle is detecting point mutations in rare clones that inform treatment regimes.

To address the first challenge, we leverage droplet microfluidics and Primary Template directed Amplification (PTA) to develop a high throughput platform for whole genome amplification. Specifically, we encapsulate single cells within hydrogel beads to allow amplification of each cell’s genome. We demonstrate this using a patient-derived chronic myelogenous leukemia line, K-562, known for its genomic instability. Successful encapsulation of single cells within hydrogel beads allows for the formation of up to 1 million single-cell hydrogel beads/hour. These hydrogel beads were re-encapsulated with the PTA mixture within droplets for whole genome amplification, with real-time fluorescence monitoring of individual bead amplification. Detection of thousands of amplified single-cell genomes occurs in less than 1 hour. We then employ a microfluidic device to fragment genomes and attach unique barcodes to all fragments in the workflow.

To address the secondary challenge, we developed a targeted approach for sample sequencing. First, we assess the performance of libraries at low sequencing depth to determine the presence of discrete barcodes and to ensure accurate insert structure. Highperforming libraries are then sequenced to a greater depth to determine cellular mosaicism through copy number variation (CNV) at approximately 0.5X per cell. To determine singlenucleotide variant (SNV) status, clusters of cells with common aneuploidy structures are bioinformatically grouped to establish sub-clonal populations. The accuracy of CNVclustered SNV calling is verified with deeper whole genome sequencing of a subset of cells. Data delineates the existence of single-cell CNV and SNV mosaicism at a scale that allows detection of rare emergent clones, which could be applied to clinical samples to determine disease evolution and define pathology mitigation strategies.