Dave Ruff

Abstract BACKGROUND The comeasurement of both genomic and transcriptomic signatures in single cells is of fundamental importance to accurately assess how the genetic information correlates with the transcriptomic phenotype. However, existing technologies have low throughput and laborious work flows. METHODS We developed a new method for concurrent sequencing of the transcriptome and targeted genomic regions (CORTAD-seq) within the same single cell on an automated microfluidic platform. The method was compatible with the downstream library preparation, allowing easy integration into existing next-generation sequencing work flows. We incorporated a single-cell bioinformatics pipeline for transcriptome and mutation analysis. RESULTS As proof of principle, we applied CORTAD-seq to lung cancer cell lines to dissect the cellular consequences of mutations that result in resistance to targeted therapy. We obtained a mean detection of 6000 expressed genes and an exonic rate of 50%. The targeted DNA-sequencing data achieved a 97.8% detection rate for mutations and allowed for the identification of copy number variations and haplotype construction. We detected expression signatures of tyrosine kinase inhibitor (TKI) resistance, epidermal growth factor receptor (EGFR) amplification, and expansion of the T790M mutation among resistant cells. We also identified characteristics for TKI resistance that were independent of EGFR T790M, indicating that other alterations are required for resistance in this context. CONCLUSIONS CORTAD-seq allows assessment of the interconnection between genetic and transcriptomic changes in single cells. It is operated on an automated, commercially available single-cell isolation platform, making its implementation straightforward.

Abstract Recent improvements in microfluidics and biochemistry have enabled single-cell molecular analysis, providing new insight into the heterogeneity of cell populations. The C1 Single-Cell Auto Prep System is an automated platform that streamlines the isolation and processing of 96 individual, live cells for RNA and DNA analysis. Single-cell protein profiling is a direct complement to genomic analysis as it provides additional insights into key molecular mechanisms and system biology. To enable this, we adapted a highly multiplexed protein detection system (Proseek Multiplex Oncology, Olink Bioscience) for use on the C1 System. We analyzed 192 individual cells from two human promyelocytic leukemia cell lines (HL60 and K562) and compared these result against 16 FACS-sorted cells in tube format. These results indicated that four key proteins (i.e. CASP3, CSTB, CD69 and MPO) are differentially expressed between the two cell lines and vary from cell-to-cell. For example, the human protein MPO, produced by neutrophils for their microbicidal activity, was differentially expression between individual HL60 and K562 cells, suggesting varying levels of neutrophil activation within the cell populations. This work demonstrates highly-multiplexed protein detection at the single-cell level and shows promise to have sufficient sensitivity, reproducibility and scale to effectively interrogate a statistically significant numbers of individual cells.

Abstract 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.

Abstract Resistance to therapy is one of the major causes of cancer-associated death. While a number of mechanisms for the development of resistance to epidermal growth factor receptor, EGFR-targeting drugs in lung cancer have been described, most genomic and transcriptomic studies have focused on analyzing bulk samples that can only provide an average measurement over the entire mixture of cell populations of a tumor. With this approach, it is difficult to resolve cell-to-cell variability of drug resistance within a heterogeneous tumor. In order to accurately describe and eventually delineate the underlying causes of cancer progression, it requires the analysis of single cells on both, the transcriptomic and the genomic level. Only the co-detection of mutations and expression features in individual cells allows to define the connection between the two. We therefore aimed to establish a new methodology for concurrent evaluation of transcriptomic and genomic features within the same single cell on the Fluidigm C1 system. We applied this protocol to an EGFR-mutant lung cancer cell line, PC9, that has developed resistance to tyrosine kinase inhibitor, TKI treatment after prolonged exposure to sublethal doses of Gefitinib. We amplified cDNA and selected genomic EGFR regions of the TKI responsive and resistant PC9 clones from a total of ≈300 single cells. We used our in-house single cell’s analysis pipeline for parallel processing of transcriptome and variants detection. In addition, a novel algorithm, NODES was used to normalize the single cell RNAseq data and detect differentially expressed genes. The differential transcriptome and emergence of the EGFR T790M resistance mutation in relation to the TKI response were evaluated. We observed up-regulation of receptor tyrosine kinase AXL in the resistant cell lines. This observation is in agreement with previous findings that AXL is the key player that promotes TKI resistance in lung cancer. In addition, we found up-regulation of other genes that have not been previously described. Cumulatively, this method allows us to dissect the underpinnings of the TKI resistance mechanism and enables us to identify biomarker for specific cellular features that are connected with resistance and thereby with lung cancer patient’s response to TKI treatment. Citation Format: Say Li Kong, Huipeng Li, Dave Ruff, Joyce An Yi Tai, Elise T. Courtois, Huay Mei Poh, Dawn Pingxi Lau, Audrey Ann Liew, Gek San Tan, Tony Kiat Hon Lim, Daniel Shao Weng Tan, Shyam Prabhakar, Axel M. Hillmer. Transcriptome differences in tyrosine kinase inhibitor-resistant clones of EGFR-mutant lung cancer using a new microfluidic assay for concurrent single-cell RNA and targeted DNA sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3153. doi:10.1158/1538-7445.AM2017-3153