Abstract 5910: Single-cell multiomic analysis of SNV, CNV, and protein expression

Abstract

Abstract Recent advancements in precision medicine, while highly promising, presents a major technical challenge to researchers due to disease heterogeneity. The emergence of single-cell technologies has greatly refined the resolution in which sample diversity can be investigated, enhancing the efficiency of selecting appropriate molecular targets. Additionally, applying multiomic analysis on single cells would further improve the understanding of cell-to-cell heterogeneity by providing unique insights on cellular and genetic composition. Using a two-step droplet microfluidic technology, the Mission Bio Tapestri Platform enables multiplex-PCR based high-throughput targeted DNA sequencing in single cells to obtain single-nucleotide variation (SNV) and copy number variation (CNV) information. By leveraging this technology, a new workflow is developed to detect protein expression in addition to DNA genotype in the same single cells. In this approach, cells are labeled with a pool of oligonucleotide-conjugated antibodies prior to loading the cells into the Tapestri Instrument for targeted DNA analysis. Sequencing libraries are then prepared from both antibody oligonucleotides and the amplified DNA sequences, followed by identification of single-cell DNA genotypes and protein signatures from the sequencing readout. In a mixed population of four cell lines, single-cell SNV and CNV information from 127 targeted amplicons and the protein data from 10 antibodies independently classified the cells into appropriate clusters. This method has been successfully performed on clinical samples with myeloid malignancies. In an acute myeloid leukemia (AML) sample, combined single-cell SNV, CNV, and protein expression data illustrated the heterogeneity within the sample. The data clearly identified CD3+ T cells and CD19+ B cells without pathogenic SNVs and CNVs. CD34hiCD11blo and CD34loCD11bhi subpopulations were also identified within the cells carrying the same pathogenic SNVs and CNVs. We believe that this novel multiomic technology will enable new discoveries in the complex relationship between genotype and phenotype, leading to a better understanding of disease biology, and subsequently better design of diagnostics and therapies. Citation Format: Aik Ooi, Pedro Mendez, Dalia Dhingra, Nigel Beard, David Ruff. Single-cell multiomic analysis of SNV, CNV, and protein expression [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5910.