Abstract LB-076: Single-cell analysis of transcription factors provides deeper characterization of cancer cells
Authors: Egidio Camila , Durruthy-Durruthy Robert , Gonzales Michael , Ray Manisha , McKinney Jason 
Published: July 2017

Abstract
Single-cell mRNA sequencing has enabled scientists to gain new insight into the diversity of cell populations. However, only approximately 20% of all transcripts are detected in a single cell¹, with low-expressed transcripts affected most. These transcripts can play a critical role in regulating cell function, so detecting their expression levels is key for deeper characterization studies subsequent to atlasing studies that utilize mRNA sequencing. Here we demonstrate the benefit of utilizing single-cell qPCR to explore expression levels of transcription factor (TF) genes involved in pathway activation and inactivation in cell lines derived from breast, lung and bone marrow cancers. In parallel, we have also performed single-cell sequencing to compare the sensitivity of both methods in capturing information on these low-expressed TF genes. Single-cell suspensions were used for capture and processing on the C1™ system (Fluidigm®) using SMART-Seq® v1 (Clontech®) and Ambion® Single Cell-to-CT™ chemistry (Thermo Scientific™). The cDNA product generated by SMART-Seq, which represents all mRNA sequences captured for each cell, was sequenced on the Illumina® NextSeq™ platform. The Single Cell-to-CT kit uses specific target amplification to amplify the 89 TF mRNAs contained in each cell. Detection was done using a high-throughput qPCR platform, the Biomark™ system. Both qPCR and sequencing datasets were analyzed using the expression levels of the TF genes across a total of 610 single cells. Hierarchical clustering and principal component analysis were used to reclassify the three different cell types into their correct groups. Accurate classification was obtained with the qPCR but not with the sequencing data due to stochastic dropout of TF targets in the sequencing data. The stochastic dropout of specific TF targets observed can affect conclusions on mechanisms behind the process of tumorigenesis. For example, MYC, a cancer-specific target involved in approximately 70% of human cancers, was consistently detected in the qPCR data but failed to be detected in a percentage of the cells by sequencing. This result can lead to misinterpretation of the role of MYC in tumor progression and development. Other targets that fall into the same category are ID1, GATA1 and SOX2. Single-cell analysis via the targeted gene expression protocol on C1 followed by Biomark qPCR is a sensitive approach generating high-quality data that can be used for deep characterization of cell subtypes. Sequencing can generate data on single cells to perform atlasing. However, it cannot provide the accurate information obtained via targeted gene expression followed by qPCR to understand the roles of regulatory molecular programs in cell biology and uncover novel tumor markers and actionable targets in cancer disease. 1. Shalek et al. Nature, 2014.
Citation Format: Camila Egidio, Robert Durruthy-Durruthy, Michael Gonzales, Manisha Ray, Jason McKinney. Single-cell analysis of transcription factors provides deeper characterization of cancer cells [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 LB-076. doi:10.1158/1538-7445.AM2017-LB-076