Caitlin Smith has a B.A. in biology from Reed College, a Ph.D. in neuroscience from Yale University, and completed postdoctoral work at the Vollum Institute.
Just as all the people in a city are different in various ways, so too are the cells in a population. Because of this, an individual cell might show phenotypes that are vastly different from those of the cell population as a whole. Although population averages are useful, they do fail to preserve the interesting biological noise—the wide range of phenotypes expressed at different times or in response to different stimuli. Sometimes these differences in expression contain important information about a cell’s recent past or its imminent future. Scientists are peering further into cells than ever before thanks to rapid developments in single-cell analysis, such as cell isolation, single-cell sequencing, single-cell proteomics and transcriptomics and high-content imaging.
Isolating a single cell
Before you can study a single cell, you need the ability to isolate it reliably. Several vendors offer technology for this, including the microfluidics-based C1 Single-Cell Analysis System from Fluidigm and DEPArray System from Silicon Biosystems, laser-capture microdissection and fluorescence-activated cell sorting (FACS). Joseph Beecham, senior vice president of research and development at NanoString Technologies, says cell isolation is particularly important when studying rare cells. For example, with rare circulating tumor cells (CTCs) that move through the bloodstream, it’s wise to sort out the many types of blood cells first, rather than analyze all the cells. “Do you really want to look through every cell and blood cell to find the CTCs?’ he says. “No, nobody wants to do that.” He recommends sorting cells by FACS first, followed by single-cell analysis.
Nicholas Navin, an assistant professor in the department of genetics at The University of Texas MD Anderson Cancer Center, uses FACS among other techniques for isolating single CTCs. “We do flow sorting a lot for isolating single cells—it's a gold standard for us—but we also use a lot of other techniques, too,” he explains. Navin’s lab is exploring both the C1 Single-Cell Analysis System and the DEPArray System to isolate CTCs to study the genomics of single cells. FACS alone "can isolate rare cells down to about 0.1% of the population but beyond that you need a system like DEP Array which can isolate about 1 in 100,000 cells.” says Navin.
Single-cell genomics
Navin’s lab sequences the genomes of single cells from breast tumors to understand how tumors change during tumorigenesis, chemoresistance and metastasis. Navin says that despite a recent explosion in methods for sequencing transcriptomes (RNA-seq) or genomes (DNA-seq) of single cells, the field is grappling with important technical issues. “A major challenge in the single-cell field is to distinguish extensive variation from extensive technical error,” he says.
Sources of error can creep into the workflow during amplification of the genetic material. Unfortunately, at least a few cycles of amplification are usually necessary when studying the tiny amounts of RNA contained in a single cell. This could happen, for example, in RNA amplification when studying single-RNA transcriptomes; the different RNA levels that result could mistakenly be interpreted as cell-to-cell differences. Furthermore, false positives can pop up during DNA amplification, because DNA polymerases are inherently error-prone. Therefore, says Navin, “it is critically important to have good knowledge of the error rates associated with a particular method prior to investigating and reporting on biological variation.” He hopes that in the future, improvements in single-molecule sequencing will enable us to measure mutations in single cells directly, without amplification.
Single-cell transcriptomics and proteomics
Scientists at NanoString Technologies are finding that the average RNA expression of a group of cells is not necessarily the same as the RNA expression of a single cell from the same population. For instance, in NanoString’s system, some genes were expressed continuously at a low level, while others were expressed strongly for only a brief period of time. Such patterns of expression become averaged out in macroscopic measurements. “Our feeling is that there’s some very interesting biology happening here,” says Beecham.
The company’s unique digital system counts individual RNA molecules directly—as many as 800 different molecules at once. It accomplishes this using tiny optical barcodes that recognize specific RNA molecules in NanoString’s nCounter Single Cell Gene Expression Assay. The barcodes, consisting of six-color fluorescent tags, are read by NanoString’s nCounter® Analysis System.
Hongkun Park’s group at Harvard, in close collaboration with Aviv Regev's group at the Broad Institute of MIT and Harvard is also finding heterogeneity in gene expression patterns in the heterogeneity observed among apparently homogeneous cells. Park, Regev and colleagues used a technique for sequencing RNA in single cells, known as Smart-Seq (originally developed by Rickard Sandberg at the Ludwig Institute in Sweden), for better read coverage of RNA transcripts [1]. Compared to original RNA-seq methods, Smart-Seq gives researchers more details about isoforms resulting from alternative splicing of mRNA, as well as better identification of single-nucleotide polymorphisms.
Using correlative computer analyses, Regev and Park’s found that the cell-to-cell differences in gene-expression patterns they observed among cells could be explained by the cells existing in distinct cell states. The researchers also use other techniques to complement single-cell RNA Seq—for example, single-cell fluorescence in situ hybridization (single-cell FISH) to test for the presence of specific RNA transcripts or isoforms, FACS and single-cell qPCR. Now they are broadening their sample types. “We are currently applying our single-cell RNA-Seq pipeline to a variety of immune cells, neurons and cancer cells in an effort to understand the factors that drive cell-to-cell variability, the dynamics of this variation and [the] biological implications,” Park says.
The Park-Regev team is also exploring another new technique, originally developed by Garry Nolan’s lab at Stanford University, known as mass cytometry or CyTOF. This method enables researchers to assay for tens, or more than 100, different mRNAs or proteins expressed by single cells. “We believe that single-cell genomics [such as DNA-seq and RNA-seq] approaches and mass cytometry are exciting developments,” says Park. “When paired with the right computational methodologies, the two techniques provide a powerful single-cell discovery pipeline.”
Another recent advance in RNA sequencing is known as RNA-capture-seq, which uses tiling arrays to “zoom in” on part of the transcriptome prior to sequencing. This gives better resolution and focuses the sequencing only on the area of interest. Agilent Technologies offers such target-enrichment tools for RNA sequencing; so do Illumina and Roche Nimblegen.
Sheila Purim, head of strategic marketing for the Diagnostics and Genomics Group at Agilent Technologies, says “there is great potential for the use of Agilent's current next-generation sequencing target-enrichment technologies, such as SureSelect and HaloPlex, with single cells.” She believes new technologies like these are overcoming barriers for work in single-cell analysis. Agilent’s senior product manager in the Diagnostics and Genomics Group, Anniek De Witte, says target-enrichment strategies—such as RNA-capture-seq—are especially suited to clinical-research applications, in which cells may be extremely rare, as in “for example, circulating tumor cells, or single cells biopsied from embryos. Researchers are also interested in heterogeneous tumor samples, where it can be very important to look at one cell at a time.”
High-content imaging
Microscopists have begun to turn their attention to single cells as the resolution of imaging systems and reagents has improved. Thermo Fisher Scientific offers tools for fluorescence-based high-content imaging and analysis of single cells, as well as populations of cells, whether live or previously fixed. Their instrumentation options include the Thermo Scientific™ ArrayScan™ and CellInsight™ high-content imagers; they also offer reagents and antibodies for multiplexing during high-content imaging.
High-content instrumentation is designed to extract large amounts of information from single cells. As such, Thermo Fisher Scientific systems are equipped with sensitive detectors that are especially good for “... researchers with dim samples, or live samples where low light levels are needed to curtail phototoxic effects,” says Suk Hong, senior scientist at Thermo Fisher Scientific. One example is the new Thermo Scientific ArrayScan Infinity High Content platform, whose confocal module includes resolution-enhancing features such as high-speed Nipkow spinning-disk technology. Hong agrees that single-cell analysis will continue to be important in cancer research: “Understanding the phenotypic differences and similarities between tumors and tumor cells will help close the gap between research and diagnostics to improve patient care in the future.”
The future of single-cell analysis looks busy. “In the single-cell world right now, the holy grail is to be able to get both the RNA and DNA data from one cell,” says Navin. A tall order, but with single-cell technologies advancing at such a rapid pace, this goal may soon be within reach.
Reference
[1] Shalek, AK, Satija, R, Adiconis, X, Gertner, RS, Gaublomme, JT, Raychowdhury, R, Schwartz, S, Yosef, N, Malboeuf, C, Lu, D, Trombetta, JJ, Gennert, D, Gnirke, A, Goren, A, Hacohen, N, Levin, JZ, Park, H, Regev, A, “Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells,” Nature, 498(7453):236-240, 2013.