A High Throughput Single-Cell Workflow For Paired Genomic And Phenotypic Analysis

Fusion gene detection has long been a focus of cancer research, when combined with mutations found in gDNA, can lead to a better understanding of disease progression. One example, BCR-ABL, a marker for CML and AML stem cells, is a target for tyrosine kinase inhibitor (TKI) treatments; however, there are mutations within BCR-ABL that evade TKIs and are selectively resistant to drug therapy. Single-cell technologies are now able to provide information into genomic DNA content, RNA expression, and protein surface markers, unmasked by the heterogeneity found in bulk data. However, multimodal analysis from the same single cell has not been straightforward to implement in a high throughput single-cell workflow. Here, we report the development of chemistries that enable analyses of both targeted genomic DNA and RNA sequencing from the same cell. This workflow relies on the Tapestri platform, which uses a two-step microfluidic droplet system for the analysis of thousands of cells per run. The first droplet encapsulates each cell and releases the DNA while the second droplet introduces additional reagents for enzymatic manipulations on the cellular analytes. Novel primer designs are leveraged to capture both DNA and RNA and provide for independent barcoded sequence information. We first establish the utility of the Tapestri platform for accurate gene expression assessment with a targeted panel for breast cancer. For fusion gene transcript detection, panels were designed for AML and CML including primers targeting multiple potential variants of BCR-ABL transcripts. Cell lines were used to show high sensitivity and specificity of fusion sequence calls from RNA in cells where the expected SNVs, indels, and CNVs were also detected from gDNA. More complex targeted RNA and DNA panels, such as one for breast cancer with 35 gene expression amplicons and 88 genotyping amplicons, were also tested where we show agreement between cells clustered based on RNA expression and the cell assignment based on SNVs from gDNA. This single-cell multimodal workflow on the Tapestri platform currently has the power to quantitatively link genotypic and phenotypic data from the same cell with future potential in biomarker identification and ultimately, to link to best-fit therapeutics. Citation Format: Dalia Dhingra, Pedro Mendez, Aik Ooi, Shu Wang, Saurabh Gulati, Adam Sciambi, Dave Ruff. A high throughput single-cell workflow for paired genomic and phenotypic analysis [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 219.