Genetic Engineering & Biotechnology News

JUL 2017

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22 | JULY 2017 | GENengnews.com | Genetic Engineering & Biotechnology News cell turnover and translation rates differed, resulting in differences in the level of gene expression downstream. Also, these rates did not change to a great extent for most genes. "One thing that fascinates us are the amounts of noncoding transcripts that ap- pear in cells," says Dr. Glasmacher. "From a biological perspective, it is of interest to find out what they are doing. From a technical point of view, it is still challenging to map all the sequences with certainty." Revisiting Immune Cell Taxonomies Cellular markers can be trusted only so far, suggests Nir Hacohen, Ph.D., associate professor of medicine at Harvard Medical School and member of the Broad Institute. They are, he explains, of uncertain utility as markers of pure cell types. Over the years, many of the cellular mark- ers that have been identified have been used to divide cell types into subtypes. "But every time we use one of these markers, we cannot know for sure whether the cellular subtype we found is pure or not," complains Dr. Hacohen. Even if markers are used that appear to be ideal for separating cellular populations into subpopulations, it is often challenging to de- termine whether a subpopulation consists of one cell type (and is "pure"), or whether it represents a mixture of multiple cell types. "We have been using these markers for years—not because we thought the practice was perfect, but because this was the way we could do it," notes Dr. Hacohen. The classification of cells has been par- ticularly challenging when the cells to be subtyped are dendritic cells or monocytes. Historically, these cells were defined based on morphology, physiology, localization, de- velopment, and surface markers. Rather than rely on marker-based assays to identify cellular subpopulations, Dr. Haco- hen and colleagues tried a different approach. They used deep sequencing at the single-cell level and unbiased clustering. This approach, the scientists found, proved capable of reveal- ing new types of human blood dendritic cells, monocytes, and progenitors. The scientists performed single-cell RNA- seq for about 2,400 cells enriched for HLA- DR + cells isolated from healthy blood do- nors. As a result of this work, Dr. Hacohen and colleagues defined six dendritic cell and four monocyte populations. In addition, they found that one of the dendritic cell subtypes, DC5, exists in a continuum, or a set of states that are similar to each other, but not identical. "I look at this approach as a new micro- scope that allows us to see structures that we did not see before," declares Dr. Haco- hen. In that sense, single-cell RNA-seq dif- fers markedly from bulk RNA-seq. "Bulk RNA-seq provides biomarkers and hypoth- eses for pathways," he points out, "whereas single-cell RNA-seq basically takes away the ambiguity from bulk RNA-seq, where one assumes that everybody is doing the same thing because there is no other choice." Even though the single-cell RNA-seq ap- proach is unbiased, and even though it is ca- pable of identifying rare cell types as well as the relationships between cell types, it may still miss some cell types. "There could be very low-level RNAs that are not captured even with single-cell RNA-seq," admits Dr. Hacohen, "and that would indicate that more complexity exists." Additionally, this approach would miss cell types that are de- fined by metabolites or non-RNA molecules. "In the future, it will be important to un- derstand and settle differences between cel- lular states and their functional relevance," predicts Dr. Hacohen. "While this will take a lot of work, we imagine that it will happen in the coming years." A better understanding of the cellular sub- populations will help us visualize the contin- uum of states that can exist. It may also, Dr. Hacohen suggests, help us link continuums of states with different disease states and patho- genicity. Tackling these "fundamental ques- tions" was hard even to imagine, he notes, "before these methods existed." RNA-Seq Continued from page 20 OMICS RNA-seq has become one of the standard tools for gene expression, displacing legacy methods such as hybridization arrays. Single-cell RNA-seq has recently evolved as a powerful method to resolve sample heterogeneity and reveal hidden biology that may be missed in bulk RNA- seq. However, single-cell experiments re- quire considerable expertise for effective experimental design, execution, and data analysis. Initial single-cell RNA-seq experiments were costly, often 10-fold more expensive than traditional bulk methods. A number of strategies have been employed to cre- ate cost-effective, high-throughput meth- ods to drastically bring down the cost of single-cell experiments. "Fluorescence-activated cell sorting (FACS) has emerged as a leading platform for cell-sorting and capture because it is fast and enables high-throughput processing of a heterogeneous mixture of cells to enrich the most important cells," says Eleen Shum, Ph.D., a scientist at BD Genomics. "The workflow of RNA sequencing using BD FACS to isolate cells starts from a single-cell suspension, which is then interrogated with either BD Precise plates or the Resolve system." Researchers sort single cells of desired phenotypes into specific wells of a Precise 96-well plate, enabling correlation of pro- tein and RNA expression of the same cell, explains Dr. Shum, adding that with the Resolve system, up to 10,000 sorted cells can be simultaneously interrogated. Both options provide methods for defining cell- to-cell variation, either within a cell popu- lation or across different cell types. "With both Precise and Resolve, single cells are first lysed, then each mRNA mol- ecule is barcoded during cDNA synthesis," according to Dr. Shum. The resulting cDNA is pooled into a single tube for final library creation. "This technology fits perfectly for single-cell mRNA sequencing of hundreds to thousand cells with a simplified work- flow and resolution of single cells at an affordable price," she says. "The power of a streamlined workflow from single-cell preparation to targeted or whole tran- scriptome analysis provides a complete solution for high-throughput single-cell gene expression." n Single-Cell RNA-Seq Researchers based at Helmholtz Zentrum München used genome-wide real-time expression analysis to characterize T-cell activation. By combining the time courses of 4sU-seq, RNA-seq, ribosome profiling, and RNA polymerase II (RNA Pol II) ChIP-seq, the researchers determined that T cells change their functional program by rapid de novo recruitment of RNA Pol II and coupled changes in transcription and translation. Insights Genomics & Proteomics Amazon Web Services (AWS) recently an- nounced that Ancestry, a genealogy and consumer genomics company, will move all its applications and data to AWS. An- cestry is confident that they will be able to achieve superior scalability, performance, reliability, security, and privacy by choos- ing functionality with a wide range of ana- lytics and machine learning capabilities. "We're providing consumers with insights that can transform their lives," explained Nat Natarajan, executive vice president of product and technology at Ancestry. "By enabling people to dive deeper into themselves and the lives of the people and cultures that led to them, we help customers change their perspective on who they are and how they fit into the world that surrounds them. Accomplishing that requires that we securely manage and analyze an incredible amount of unique, personal data on a daily basis." Ancestry analyzes and compares bil- lions of historical records, tens of millions of family trees, and millions of existing cus- tomer DNA profiles to deliver data-driven insights that help customers develop a new sense of self. The company requires a highly scalable and secure IT infrastructure on which to store and analyze large vol- umes of sensitive information. Ancestry's goal is to empower people to take their journey of self-discovery, providing ac- tionable insights that have a meaningful impact. It offers customers a deeper view into their ethnic backgrounds, hints about their family history, and connections to possible genetic cousins. "With our continuing growth, migrat- ing to the cloud provides us with clear scalability and security advantages," Natarajan noted. "AWS also provides us with the flexibility we need to stay at the forefront of consumer genomics, as the science and technology in the space con- tinue to evolve rapidly." "Because AWS offers much more func- tionality than any other infrastructure pro- vider, Ancestry can easily move existing apps, develop any new app their builders dream up, and leverage AWS's expansive analytics and machine learning offerings to understand their data better and infuse their applications with more intelligence," added Mike Clayville, vice president, worldwide commercial sales at AWS. n Ancestry Migrates to Amazon Web Services

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