Dr. Stephen Quake

Exploring novel insights into cancer’s molecular origins

Dr. Stephen Quake's lab at Stanford University is among the first to pioneer the use of microfluidic tools to study single-cell genomics. The lab is using the Biomark HD System and the C1 Single-Cell Auto Prep System to explore the molecular identity of minority cancer cell populations, including "cancer stem cells." Discovery of cancer stem cells is a first step toward a better understanding of cancer biology and improving cancer therapies.

Tumors are composed of heterogeneous populations of cancer cells that contribute to cancer's transcriptional heterogeneity. To achieve a better understanding of colorectal cancer using single-cell methods, the Quake lab collaborates with the laboratory of Michael Clarke at Stanford's Institute for Stem Cell Biology and Regenerative Medicine. In their paper, "Single-cell dissection of transcriptional heterogeneity in human colon tumors," the Quake and Clarke teams demonstrated that transcriptional heterogeneity in colon tumors can be explained by the coexistence of multiple cell types that mirror the various differentiation lineages found in the normal colon epithelium.1 Among these different cell types are one or more subpopulations of cancer stem cells. The Quake lab is using single-cell methods to better define properties of cancer stem cells. Identifying the gene-expression profile of cancer stem cells could provide novel insights into the molecular origins of cancer and could lead to novel biochemical targets for anti-tumor drug design.

A postdoctoral fellow in Dr. Clarke's lab, Dr. Piero Dalerba, uses single-cell methods, because bulk mRNA methods do not allow to distinguish the various mRNA signatures from different cell types in the tumor, especially signatures from minority cell populations. Markers of cancer stem cells, for example, can be difficult to identify by bulk mRNA analysis, because cancer stem cells are admixed with other tumor cell types. As Dalerba says about using bulk mRNA methods,

“We remain blind to the molecular identity of the minority cell populations.” 

Single-cell gene expression analysis of tumor cell populations by real-time PCR (qPCR) with the Biomark HD System, however, allows more in-depth profiling of gene expression. Quake's team uses qPCR to study mRNA expression and identify rare subpopulations of cells by clustering analysis. As a next step, the Quake lab is trying to fully characterize key tumor cell lineages in order to identify subpopulations containing cancer stem cells.

“This goal requires the lab to drill down the major tumor cell populations and progressively enrich for rarer subpopulations of cell types that may be cancer stem cells”

says Dr. Angela Wu, a postdoctoral fellow in Quake's lab. Enriching for tumorigenic subtypes, however, demands concerted and iterative use of single-cell technologies.

Single-cell gene expression analysis, based on single-cell sorting and real-time PCR with the Biomark HD System, relies on postulating in advance which genes need to be analyzed. To screen for unknown genes that may be important, mRNA sequencing of the entire transcriptome of a single cell is a powerful technique. So, Quake's team plans to use the Fluidigm C1 Single-Cell Auto Prep System. By using Fluidigm technology, the lab can identify novel cell surface markers that can then be used to sort new cell populations for cancer stem cells.

“I believe that the validation of [our] observations,” Dr. Dalerba says, “comes from its reproducibility across independent patient samples.”

In an effort to qualify single-cell mRNA-sequencing methods, Wu and Quake recently published a comparative evaluation of multiple single-cell mRNA amplification methods in Nature Methods. In their publication, Quantitative assessment of single-cell RNA-sequencing methods, Wu et. al. find that cell processing with the C1 System for single-cell mRNA sequencing can accurately quantitate transcriptomes.

With the assistance of new single-cell tools, the Quake lab hopes to gain deeper insights into the transcriptional heterogeneity of cancer and complement larger gene expression profiling studies.