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

Harnessing the Clinical Power of Single Cells

  • Click Image To Enlarge +
    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]

    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.

  • Development and Aging

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    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]

    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.”


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