July 1, 2013 (Vol. 33, No. 13)

From arrays to microfluidics, researchers are exploiting a variety of tools and techniques to gain insight into the complexities of single cells.

“We now have procedures that permit analysis of the transcriptome of single cells using antisense RNA and single-cell PCR—both developed in my lab—[as well as] genomic DNA sequence, single-cell proteomics—using mass spec or various antibody approaches—and metabolomics—using mass spec or various types of biosensors,” says James Eberwine, Ph.D., professor of pharmacology at the University of Pennsylvania Perelman School of Medicine.

“Technology development to study single cells revolves around increased speed of analysis, increasing sample numbers, improving sensitivity and insuring that all molecules are captured and analyzed.”

Emerging tools and techniques to perform single-cell analysis will be discussed at Select Biosciences’ annual summit on the topic to be held in San Diego in September. Dr. Eberwine is among those chosen to discuss their work at the upcoming meeting.

In 2011, Dr. Eberwine’s team developed an antisense RNA (aRNA) amplification method as an alternative to other gene expression analysis techniques, such as RT-PCR. The technique enables the linear amplification of polyadenylated RNA starting with only femtograms of material and yielding micrograms of aRNA. Further, the approach has shown to yield more accurate amplification of the components of the transcriptome of the isolated cell than PCR.

The procedure entails two rounds of amplification: a T7 RNA polymerase promoter site incorporated into double-stranded cDNA created from the mRNA transcripts, followed by an overnight in vitro transcription (IVT) reaction in which T7 RNA polymerase produces several antisense transcripts from the double-stranded cDNA.

After three rounds of amplification, the last round an IVT reaction using biotinylated nucleoside triphosphates, the resulting antisense RNA is hybridized and detected on a microarray. A paper describing this work, “Transcriptome analysis of single cells,” was published in the Journal of Visualized Experiments.

“Variability in cellular biology as reflected by the transcriptome and proteome underlies the ability of cells to properly function and respond to disease,” Dr. Eberwine explains. “Understanding single cell variability will certainly provide significant insight into the capacity of cells to respond to challenges whether they are from normal signaling pathways or induced by disease processes.”

“The ability to quantify all of the RNAs in a single cell or cellular subregion and the ability to functionally analyze this transcriptome using a multigenic functional genomics approach is yielding unprecedented insight into the real biology of cells, namely how gene products interact to produce the biology of the cell,” he adds.

Harvest of the mRNA complement in a 5-HT neuron from its natural microenvironment: Fluorescently labeled 5-HT neuron was pipette harvested from an acute midbrain slice. Top row: Yellow fluorescent protein (YFP) images before and after harvest. Bottom row: Differential interference contrast, or DIC, images before and after harvest. (Scale bar: 20 µm) [Drs. Jennifer Spaethling, Sheryl Beck, and James Eberwine, University of Pennsylvania]

Development and Aging

As cells develop, they inevitably evolve from a seemingly homogeneous population into a diverse array of types.

Studies that have blended different cell populations to examine population averages have the disadvantage of missing interesting heterogeneities that can only be known by studying single cells.

Like many scientists in the field, S. Steven Potter, Ph.D., professor of developmental biology at Cincinnati Children’s Hospital Medical Center, is using a mix of arrays and sequencing to profile gene expression in single cells.

“In one application we are performing microarray and RNA-seq gene expression profiling of single cells to better understand the earliest steps in making developmental decisions,” Dr. Potter says. “In another application we use single-cell analysis to create a high-resolution atlas of the gene expression patterns that drive organogenesis. For this, we disassemble developing organs to single cells, perform gene expression profiling, and reassemble the data to generate a fine structure picture of the gene expression programs that create the distinct differentiated cell type lineages of the adult organ.”

Meanwhile, the Buck Institute for Research on Aging’s James M. Flynn, Ph.D., research associate, is examining single cells as they age.

“We are examining the transcriptional profile of cells as they progress from presenescent to senescent stages, with significant implications in aging and cancer,” Dr. Flynn tells GEN. “We are currently looking into a number of bone disease models to help define cell types within the bone matrix.”

He and his colleagues have developed a method to extract single cortical osteoblasts from a small volume of compact bone and have identified rare cell populations responsible for generating new bone.

Using fluorescence-activated cell sorting (FACS) and single-cell transcriptomics, Dr. Flynn et al., have delved into the heterogeneity of osteoblast lineage cells derived in vivo from translational disease models. “We are now actively pursuing this approach to understand how gene expression in these cell populations shifts from a normal to disease state,” he says.

Once derived, storing rare cells can be a challenge. To address this, Dr. Flynn and his colleagues have developed sample storage methods that allow them to analyze cells down the line without significant loss of RNA integrity. Taking this approach, samples can be easily transported without loss of signal, or reliance on specialized equipment, he says.

“A second motivation when developing our approach is the ability to easily modify the target cell populations using FACS as our understanding of the cell biology changes,” Dr. Flynn explains. “I think this kind of iterative selection process will help pinpoint novel cell populations, which have previously been masked as we’ve only been studying the average from many thousands of cells in bulk studies of gene expression profiling.”

Workflow schematic for the identification and gene expression profiling of rare cell types from a complex cell population: Cells are initially selected via fluorescence-activated cell sorting (FACS) for specific lineage markers and profiled via single-cell quantitative real-time polymerase chain reaction (qPCR). The single cells are characterized into groups based on their gene expression profiles providing novel marker candidates for further study of these subpopulations. [Buck Institute for Research on Aging]

New Normalization

Along those same lines, researchers have also run into challenges translating standard data normalization methods for bulk nucleic acid preparations to single-cell data.

For instance, using “housekeeping” genes to normalize qPCR expression data does not make sense at the single-cell level.

In addition to expression within single cells, Dr. Flynn is interested in studying variations in expression among cells. Being able to discern technical variance from biological variance is a major concern for many of the amplification strategies prior to assay measurement, Dr. Flynn says.

Dr. Flynn and his colleagues are using large expression datasets from nanofluidic qPCR arrays and homogenous reference samples from entire cell populations to normalize data. This approach is somewhat analogous to microarray formats that normalize the signal from each probe spot to the array itself combined with spiked in controls. Overall, this strategy has been fairly successful in validating the single-cell measurements.

Another concern when analyzing single-cell data is that many standard statistical approaches are unusable because much of the single-cell data violates the basic assumptions within these tests.

“The all-too popular Student’s t-test is not appropriate for gene expression comparisons at the single-cell level. We are taking the stance that data analysis must be non-parametric and should be the new standard in single-cell analysis,” Dr. Flynn says.

Data interpretation can indeed be difficult. According to Cincinnati Children’s Hospital’s Dr. Potter, the limitations relate to existence of fewer than ten transcripts per expressed gene, and gene expression occurring in a burst mode, not as a steady state process. “We have made much progress, but there is still considerable room for improvement,” he says.

As Dr. Flynn at the Buck Institute puts it: “What I think will be of broad interest to the biotech community is how single-cell biology will change our approach to the development of disease treatments,” he says. “What is really an unknown at the moment is how diseases progress on a cell-by-cell basis.”

While it is established that cancers can begin with a single cell escaping cell cycle control, it is not so clear if the progression of other maladies come from similar stochastic changes. Single-cell analysis is a potent tool in identifying not only the disease pathogenesis, but also in the development of targeted cell therapies. Current approaches are based on highly sensitive single-cell tests to diagnose diseases prior to the development of clinical symptoms.

Further, single-cell analysis allows for sample sizes of hundreds if not thousands of individual cells in order to detect disease in a normal cell haystack.

Overall, single-cell technology will drive both the sensitivity and specificity of future clinical assays up, Dr. Flynn says.

Clinical Utility

Of course, academic investigators aren’t alone in their enthusiasm about the clinical promise of single-cell analysis.

Several companies, like Genome Data Systems, also seek to harness the power of cellular heterogeneity as potential biomarkers.

“Our vision is that in a few years every clinical trial will include single-cell analysis,” says the firm’s founder and president Rajan Kumar, M.D., Ph.D.

Genome Data Systems’ CytoSnap™ tool can be used to perform multiplexed single-cell protein expression analysis in a wide range of biological samples. Monitoring changes in protein expression could have clinical utility in terms of diagnostic biomarkers.

“CytoSnap can simultaneously detect as well as characterize at the molecular level rare cells such as circulating tumor cells (CTCs) in blood and disseminated tumor cells in bone marrow biopsies,” Dr. Kumar says.

The technology, which has been used in highly multiplexed analysis for label-free screening of CTCs in a patients’ blood, has the potential to analyze a single-cell sample for 100 or more proteins. Beyond identifying and characterizing the expression patterns of individual CTCs in blood samples from breast cancer patients, “using our CytoSnap technology, we have compared protein expression patterns of several breast cancer cell lines cultured in vitro and samples from xenograft tumors,” Dr. Kumar adds.

“We distinguished luminal, Her2+, and basal-like cells from breast cancer cell lines based on differential protein expression profiles with greater than 95% sensitivity and more than 99% specificity,” he explains. “CytoSnap functions by measuring transient cell interactions with immobilized antibodies in a microfluidic channel. Expression of the proteins of interest reduces cell velocities on patches coated with cognate antibodies compared with a pooled IgG patch as a control.”

According to Dr. Kumar, the technology is also nondestructive: “Essentially unaltered cells can be isolated for additional analysis, for example, using RT-PCR.” He points to pharmacogenomics as an entry point for the technology’s use in personalized medicine, though he notes that potential applications range from diagnostic assays to patient stratification in clinical trials, measuring response to treatment, and development of companion diagnostics.

“In the future, we plan to seek FDA approval for the instrument and for clinically relevant companion diagnostic tests in collaboration with academic researchers, CROs, and pharmaceutical companies,” says Dr. Kumar.

A comparison of immunophenotyping technologies: Cytology is best for analysis of a few cells, but is limited in multiplexing due to fluorescence spillover. Flow cytometry is very useful for multiplexing up to 10–15 proteins, but requires a large number of cells and multiplexing reduces single cell sensitivity. According to Genome Data Systems, its label-free CytoSnap™ method will allow highly multiplexed analysis at a single-cell resolution.

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