Genetic Engineering & Biotechnology News

JUL 2017

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18 | JULY 2017 | GENengnews.com | Genetic Engineering & Biotechnology News cell types, characterize tumor heterogeneity, and follow the cell-fate decisions that shape development. If, however, sin- gle-cell RNA-seq is to deliver all these advantages, it must be backed by specialized methods of data analysis. Normalizing Single-Cell Results "Like many other groups, up until about two years ago, our group was using a method from bulk analysis for RNA- seq normalization," says Christina Kendziorski, Ph.D., pro- fessor of biostatistics and medical informatics, University of Wisconsin–Madison. "We eventually noticed that this method did not work well for normalizing single-cell RNA-seq data." One of the goals in data normalization is to remove techni- cal artifacts, which include features such as sequencing depth, gene length, or GC content. In bulk RNA-seq, an increase in sequencing depth leads to an almost directly proportional in- crease in expression, and this relationship between sequencing depth and expression is common across all genes. Consider a weakly expressed gene. Here, a doubling of the sequencing depth would lead to an average doubling in expression. Moreover, for a highly expressed gene, the same relationship would be maintained. "For single-cell RNA-seq, we noticed that this relation- ship was not common across genes," recalls Dr. Kendziorski. In the case of single-cell RNA-seq, highly expressed genes appeared similar to the genes captured by bulk sequencing methods in terms of the count-depth relationship, which expresses how the sequencing counts behave as sequencing depth is increased. "However, the moderately and weakly expressed genes," Dr. Kendziorski continues, "did not track with sequencing depth as expected." Therefore, when methods from bulk normalization were used, highly expressed genes were normalized correctly, but moderately and weakly expressed genes were over-normal- ized. "Consequently, for these genes," notes Dr. Kendziorski, "normalization methods from bulk RNA-seq applied in the single-cell setting introduced artifacts." In a recent study, Dr. Kendziorski and colleagues intro- duced SCnorm, a computational method that estimates the dependence of transcript expression on sequencing depth for every gene and allows single-cell RNA-seq data to be nor- malized accurately and efficiently. "We expect that SCnorm will lead to substantial improvements in downstream infer- ence in a number of areas," asserts Dr. Kendziorski. What's next for computational methods in single-cell RNA-seq? "There is a big opportunity to develop methods for network analysis in single-cell data," remarks Dr. Kend- ziorski, who concedes that such analysis is still uncommon. "The methods from bulk RNA-seq, even if we tweak them a little bit, will lose some information—potentially a lot of information." There is also a critical need for methods that integrate data from multiple single-cell technologies, such as those that profile DNA, expression, methylation, and so on. "The tech- nologies are here," observes Dr. Kendziorski, "but we do not yet have robust computational methods to integrate single- cell data across multiple sources." Capturing Morphological Context Although single-cell RNA-seq offers more finely grained information than does bulk RNA-seq, the newer technology poses unique technical difficulties. For example, there is po- tential for error in the isolation of single cells. Even if single- cell RNA-seq succeeds in identifying distinct cell types, it may fail to preserve spatial information, that is, information about where cells of a given type occur within intact tissue. To correlate transcripts of interest with morphological OMICS Feature See RNA-Seq on page 20 RNA-Seq: Less Lumping, More Splitting Continued from page 1 RNAscope, a multiplex nucleic acid in situ hybridization tech- nology developed by Advanced Cell Diagnostics, can simultaneously amplify target-specific signals and suppress background noise from nonspecific hybridization. The technology's ability to capture gene expression at a single-cell level has been demonstrated in various intact tissues. For example, as shown in this image, RNAscope has been used to identify and localize intestinal cell populations according to their respective markers. Human pluripotent stem cells (hPSCs), with their ability for infinite self-renewal, have the potential to be an unlimited source of cells for a variety of biomedical applications, ranging from drug screening to cell-replacement therapy and in vitro organogenesis. Many of these applications typically require reproducible generation of a large number of target cells. This is where research efforts hit a speed bump, as stem cell cultures are often heterogeneous. Maroof Adil, Ph.D., a postdoctoral re- searcher in Professor David Schaffer's lab at UC Berkeley, uses single-cell RNA-seq to better understand population heteroge- neity within hPSCs cultured in vitro. His lab develops 3D biomaterials to increase the efficiency of the large-scale production of clinical-grade stem cells and the mature cells that are derived from them. These biomaterials could help expand and differ- entiate hPSCs into neurons, ultimately to decipher the complicated process of neu- rodevelopment to treat central nervous system diseases such as Parkinson's and Huntington's. Single-cell RNA-seq is a powerful tech- nology that provides a high-resolution snapshot of the population distribu- tion within a biological sample. Using a single-cell sequencing technology jointly developed by Illumina and Bio-Rad, Dr. Adil's team explores heterogeneity of hPSCs. The system isolates and barcodes thousands of single cells in a matter of minutes using a droplet-partitioning technology. The cells are then directed to downstream sequencing. The time from a single-cell suspension to single cells and barcoded beads in droplets is about five minutes, allowing researchers to handle sensitive primary cells that can die quickly during separation. Conventionally, cells are isolated by flow cytometry, RNA extracted individu- ally, and sometimes even sequenced indi- vidually as well. By contrast, the Berkeley team says it drastically reduces time spent in sample preparation and data collection. Dr. Adil hopes that using this technol- ogy to better understand the popula- tion heterogeneity and dynamics within pluripotent stem cell cultures may allow enhanced control over stem cell fate, and facilitate biomedical applications of stem cell technology. n Parallel Single-Cell Sequencing

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