Targeted transcript quantification in single disseminated cancer cells after whole transcriptome amplification

PLoS One. 2019 Aug 20;14(8):e0216442. doi: 10.1371/journal.pone.0216442. eCollection 2019.

Abstract

Gene expression analysis of rare or heterogeneous cell populations such as disseminated cancer cells (DCCs) requires a sensitive method allowing reliable analysis of single cells. Therefore, we developed and explored the feasibility of a quantitative PCR (qPCR) assay to analyze single-cell cDNA pre-amplified using a previously established whole transcriptome amplification (WTA) protocol. We carefully selected and optimized multiple steps of the protocol, e.g. re-amplification of WTA products, quantification of amplified cDNA yields and final qPCR quantification, to identify the most reliable and accurate workflow for quantitation of gene expression of the ERBB2 gene in DCCs. We found that absolute quantification outperforms relative quantification. We then validated the performance of our method on single cells of established breast cancer cell lines displaying distinct levels of HER2 protein. The different protein levels were faithfully reflected by transcript expression across the tested cell lines thereby proving the accuracy of our approach. Finally, we applied our method to breast cancer DCCs of a patient undergoing anti-HER2-directed therapy. Here, we were able to measure ERBB2 expression levels in all HER2-protein-positive DCCs. In summary, we developed a reliable single-cell qPCR assay applicable to measure distinct levels of ERBB2 in DCCs.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cell Line, Tumor
  • Gene Expression Profiling*
  • Genes, erbB-2 / genetics
  • Humans
  • RNA, Messenger / genetics
  • Single-Cell Analysis*

Substances

  • RNA, Messenger

Grant support

This work was supported by grants from the Bavarian Research Foundation (Bayerische Forschungsstiftung, DOK-165-13), the ERC (322602) and the Bavarian Ministry for Economy, Media, Energy and Technology. Grant Number: 20‐3410.1‐1‐1.