Historically, the vast majority of experimental studies that have scrutinized biological phenomena have done so by surveying populations of cells. While it is expedient, this approach has a significant shortcoming—it yields measurements that reflect population averages. Such measurements say little or nothing about individual cells.
Nevertheless, it is possible to visualize variability at the single-cell level, even in genetically identical cells grown under identical conditions. This kind of variability becomes relevant in many contexts including cancer evolution, resistance to antimicrobial or chemotherapeutic agents, cell differentiation, and virus-host interactions.
Increasingly, scientists are exploiting advances in biotechnology to assess cell-to-cell variability. Indeed, they are already using single-cell approaches to enrich their understanding of biological processes. Single-cell approaches are even driving a conceptual shift, a reassessment of the notions that have accumulated about cellular and molecular processes. Because these processes shape development and disease, a deeper understanding of them promises to powerfully impact diagnostic and therapeutic strategies.
“We do not have a full understanding of the complexity of various responses in biological processes, and one of the reasons is that there are many different cell types, many different cell states, and thousands of millions of interactions that determine an overall population response,” explains Rahul Satija, Ph.D., postdoctoral fellow at the Broad Institute of MIT and Harvard University. “We hope that single-cell analyses will help us increase the resolution to understand these biological processes.”
In a recent analysis, Dr. Satija and colleagues compared multiple technologies for their ability to amplify low-input and low-quality RNA samples. “We had been thinking about how to expand the use of transcriptome analysis beyond the more traditional applications in expression profiling,” says Dr. Satija.
Extending this work to profile single cells in a pilot project, Dr. Satija and colleagues made a somewhat unanticipated observation. After sequencing 18 single cells, they noticed huge differences from one cell to another in the way they were responding to a stimulus.
“Even though we have long appreciated that cells are different, we have been unable, due to technical challenges, to examine cells individually and deeply,” adds Alex K. Shalek, Ph.D., postdoctoral fellow in the Departments of chemistry and chemical biology at Harvard University. “[But now] there is a wonderful opportunity for genome-scale single-cell analyses to give us new insights into biology.”
In experiments using dendritic cells derived from mouse bone marrow, Drs. Shalek and Satija, together with collaborators, found a bimodal variation in the mRNA abundance in different cells responding to the same stimulus. The investigators found that using dendritic cells for single-cell analyses offered several advantages. These cells are relatively thoroughly characterized. They have a robust response to lipopolysaccharide activation—a response that has been intensely studied at the population level. They are clinical relevant. And they are post-mitotic.
“The first thing we had to explore was whether the differences in the single-cell transcriptomes we measured were just artifacts of the amplification, because when a single cell’s genetic material is amplified to generate a library, there are biases that can be introduced at every step,” recalls Dr. Shalek.
Single-cell RNA sequencing revealed that, while some artifacts existed, the differences that were visualized most likely reflected biologically relevant changes, and RNA-fluorescence in situ hybridization, an imaging approach that does not use sequencing or amplification, conferred additional confidence that the observations are biologically meaningful and not technical artifacts.
“Once we became convinced that these heterogeneities are real, we wanted to understand why the cells were behaving in this way, and what they can teach us in terms of their biology,” states Dr. Satija.
An analysis of the single-cell expression profiles helped identify two distinct subpopulations of cells based on their inflammatory cytokine expression levels, and incorporating an analysis of their maturation markers revealed that the two groups of cells represent two distinct maturity states. This indicated that RNA-Seq is powered to distinguish between closely related but developmentally different cellular states of the same cell type.
Calculations of the correlation in expression profiles between every pair of induced genes in all the single cells helped identify a cluster of over 130 genes that were changing in a correlated way. Many were relevant for the antiviral response, including two master antiviral response regulators, Irf7 and Stat2.
In cells isolated from an Irf7 knockout mouse, the expression of most bimodally expressed transcripts decreased, but that of the remaining ones was not significantly affected, and the Stat2 gene was not affected in this genetic background. These findings suggest that Stat2 may be acting in either a parallel network with (or upstream of) Irf7. They also illustrate the strength of this approach in dissecting cellular regulatory circuits.