High-throughput analysis of single cells reveals cell-to-cell differences

Traditional genetic and epigenetic studies use bulk cell samples that provide useful data but don’t distinguish between cellular subpopulations. Advances in single-cell protocols are now making it possible to analyze cellular heterogeneity within tissues and cell lines, and researchers are discovering differences between individual cells that provide crucial insight for future research and clinical progress.

A paper published online in Nature Methods on August 15, “Single cell multimodal profiling reveals cellular epigenetic heterogeneity,” describes an automated, high-throughput platform that allows simultaneous investigation of genotype, gene expression and DNA methylation at multiple locations in the genomes of single cells. Called Single Cell analysis of Genotype, Expression and Methylation (sc-GEM), it was developed by a team led by researchers from the Institute of Molecular and Cell Biology and Genome Institute of Singapore in collaboration with Paul Robson, Ph.D., director of single cell biology at the The Jackson Laboratory for Genomic Medicine. sc-GEM employs an automated programmable microfluidics chip and previously developed single-cell protocols to process up to 96 cells per chip in fewer than 48 hours. As described in the paper, the research team used sc-GEM to assay human fibroblasts undergoing reprogramming to pluripotency and primary lung adenocarcinoma cells and revealed important cell-to-cell differences in both.

The process of generating induced pluripotent stem cells (iPSCs) from human skin cells is well established, but conversion efficiency remains low, potentially because of epigenetic barriers. Applying sc-GEM, the researchers profiled cells at various stages of reprogramming to assess gene expression and DNA methylation changes. They identified two distinct patterns of DNA methylation, indicating different stages of reprogramming, and showed that individual cells with the later-stage (pluripotency) pattern also expressed late pluripotency genetic markers. The cells reprogrammed at different rates, however, indicating they went through the process in a heterogeneous manner. Understanding the dynamics of gene expression and DNA methylation offers a way to investigate individual cellular states during reprogramming and ways to improve the speed and efficiency of iPSC derivation.

Using sc-GEM with human lung adenocarcinoma cells, the team detected clear cell type differences based on gene expression. A subset of the tumor cells had an epigenetic change—hypermethylation—at tumor-associated locations in the genome, and most cells in the same subset harbored specific mutations in EFGR, a gene commonly mutated in lung cancers. The ability to divide tumor cells with DNA methylation signatures could improve prognosis and treatment efficacy.

Cheow LF et al. Single cell multimodal profiling reveals cellular epigenetic heterogeneity. Nature Methods 2016 doi:10.1038/nmeth.3961