Early studies of gene expression in single cells revealed a greater than expected amount of heterogeneity among cells from supposedly homogeneous populations. It is now well accepted that, for example, not every tumor cell, liver cell, or stem cell isolated from a distinct population will be identical in terms of which genes are expressed and at what levels.
Simply averaging the transcription levels measured across multiple individual cells will not accurately convey what is taking place in any one cell. Furthermore, this approach may result in an especially interesting and biologically or clinically relevant characteristic of one or more of the cell subtypes present in a cell population to be missed if their genomic heterogeneity is not analyzed and understood.
The desire to characterize this variability in gene expression across cells and to acquire genomic sequence data on a single-cell level is driving advances in the field of single-cell genomics, which is part of a broader field of single-cell analysis that is benefiting from an emerging trend to develop and apply tools and technologies for robust, high-throughput, and reproducible analysis of biological function at the single-cell level.
Initial focus areas include developmental biology and cancer and stem cell research; early commercial applications relate to embryo screening for in vitro fertilization (IVF) procedures, characterization and drug-response testing of circulating tumor cells, and infectious-disease diagnostics. Single-cell genomic analysis was a key area of discussion at Select Biosciences’ “Single Cell Analysis Summit” held recently in San Diego.
In reality, “most cells have very few transcripts, and a few cells have a lot of gene expression,” explained Mikael Kubista, Ph.D., CEO of Tataa Biocenter. Gene expression is a highly dynamic process, occurring in bursts of active transcription followed by less active periods during which existing mRNA transcripts decay.
“We have found that these bursts generally do not correlate from cell to cell,” Dr. Kubista said. This led the company to develop its single-cell transcription correlation platform, in which identification of a cell type is not based on the level of any particular transcript, but rather on correlations of transcription.
Using single-cell expression correlation, it is possible to distinguish between two or more subtypes of cells within a population that express the same transcripts. They differ not in the presence or absence of a particular transcript, but in the pattern of gene-expression levels. One way Tataa is applying this technology is to study circulating tumor cells, with a clinical goal of identifying what clones are present in a patient’s blood and to which chemotherapeutic agents they may/may not respond.
Tataa developed a technique for measuring intracellular mRNA gradients using qPCR and applies it to the study of gene-expression heterogeneity at the single-cell and subcellular level. To capture mRNA transcripts from a single cell without significant loss of material, Tataa formulated a set of detergents to facilitate cell lysis and mRNA removal without the need for washing steps. It licensed the detergents to Roche, which incorporated them into its RealTime Ready Cell Lysis Kit.
Dr. Kubista identified three main challenges at present for single-cell gene-expression analysis including the need for robust single-cell isolation techniques, increased throughput, and improved tools for data mining and multivariate data analysis.
Steven Bodovitz, Ph.D., principal at Bioperspectives, defined the opportunity in this field as the ability to “transform cellular heterogeneity from a source of noise into a source of new discoveries.”
The reasons to do so are “compelling,” he said. For example, if an easy and more reliable method was available to identify the different cell subtypes in a tumor, could a more effective, multitargeted chemotherapeutic approach be developed?
Dr. Bodovitz identified two main drivers of single-cell omics research: the potential biological significance of understanding cell heterogeneity, and the enabling technological advances including miniaturization, microfluidics, and whole-genome amplification (WGA) techniques that yield enough DNA from a single cell to enable genomic analysis using available gene-expression analysis or next-generation sequencing (NGS) methods.
When asked to identify the main challenge the field of single-cell genomics currently faces, Dr. Bodovitz pointed to the need for improved methods of isolating single cells from tissue samples. “One tends to destroy the cell to analyze it.”
Whereas perturbations to a cell are unlikely to affect the results of genetic or epigenetic analysis, as these characteristics should remain stable, cellular disruption could affect gene-expression analysis and other types of omics studies. Intentional perturbation of a single cell represents an “elegant system” for studying biological pathways and intracellular networks based on the up- or downregulation of gene expression, added Dr. Bodovitz. This knowledge could be used to guide the design of drugs capable of interfering in a particular pathway.