Single‐cell biomarker detection identifies heterogeneity within cancer populations

Abstract

Understanding a complex biological system such as a cancerous tumor is already a difficult endeavor. Further, this complexity is compounded given the multiple cell subpopulations found in cancer tissue (e.g., cancer stem cells), broadly described as tumor heterogeneity. The significance of tumor heterogeneity is best understood when examined at the cellular level, where distinct differences in gene expression, signaling pathways, nutrient requirements can contribute to a diverse and dynamic microenvironment, which has already been implicated as a key factor in chemotherapy resistance. With so many distinct factors at play performing bulk measurements on a subset of cells will limit the ability to understand key differences which exist at the single cell level.Traditional methods tend to measure RNA expression globally, with cell lysis and fixation steps resulting in a loss of key information about individual cells, making it challenging to properly assay the genetic heterogeneity within cell populations. Analyzing the relationships between gene expression and biological processes involved in the development and progression of cancer requires analysis at single cell resolution. We have developed a single cell biomarker tool for sequence‐specific detection of gene expression in live cells without altering viability or inherent biology. In addition to detection of cellular heterogeneity within researchers’ samples, the live single cell compatibility enables enrichment and further characterization of cell subpopulations based on RNA expression through Fluorescent Activated Cell Sorting (FACS).Here, we describe live single cell detection of cancer biomarkers within heterogeneous cell models. Breast cancer cell lines were co‐cultured in specific ratios to mimic heterogeneous breast cancer populations. Subsequent characterization of the populations was performed by both flow cytometry using fluorescent live‐cell RNA detection probes, and quantitative real‐time PCR. Additionally, we analyzed miR‐221 expression in a mixture of T47D and MDA‐MB‐231 breast cancer cell lines, which allowed us to sort the two cell types based on differential expression of this microRNA. Subsequently after culturing the sorted cells we were able to perform downstream IHC analysis of vimentin and E‐cadherin, two protein markers known to be associated with the epithelial‐to‐mesenchymal transition during tumor progression.Single cell analysis of populations with varied gene expression allows a better understanding of the inherent differences that exist within the population. The ability to identify differences in gene expression within live cells allows scientists to formulate novel questions and design experiments that better address the importance of gene expression heterogeneity in normal and disease states. This is particularly true in cancer where alterations in gene expression within specific cell subpopulations can play a role in resistance to standard drug therapies.