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      Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues

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      Annual Review of Genetics
      Annual Reviews

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          Abstract

          <p class="first" id="P1">Recent increases in scale and decline in cost of single-cell RNA-seq now permit a repetition of cell sampling that increases the power to detect rare cell states, reconstruct developmental trajectories, and measure phenotype in new terms like cellular variance. The characterization of anatomy and developmental dynamics have not had an equivalent breakthrough since groundbreaking advances in live fluorescent microscopy. The new resolution is a boon to genetics, as the novel description of phenotype offers the opportunity to refine gene function and dissect pleiotropy. In addition, the recent pairing of high-throughput genetic perturbation with single-cell RNA-seq has made practical a scale of genetic screening not previously possible. </p>

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          Most cited references30

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          Comparative Analysis of Single-Cell RNA Sequencing Methods.

          Single-cell RNA sequencing (scRNA-seq) offers new possibilities to address biological and medical questions. However, systematic comparisons of the performance of diverse scRNA-seq protocols are lacking. We generated data from 583 mouse embryonic stem cells to evaluate six prominent scRNA-seq methods: CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2. While Smart-seq2 detected the most genes per cell and across cells, CEL-seq2, Drop-seq, MARS-seq, and SCRB-seq quantified mRNA levels with less amplification noise due to the use of unique molecular identifiers (UMIs). Power simulations at different sequencing depths showed that Drop-seq is more cost-efficient for transcriptome quantification of large numbers of cells, while MARS-seq, SCRB-seq, and Smart-seq2 are more efficient when analyzing fewer cells. Our quantitative comparison offers the basis for an informed choice among six prominent scRNA-seq methods, and it provides a framework for benchmarking further improvements of scRNA-seq protocols.
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            Is Open Access

            A Single-Cell Transcriptome Atlas of the Human Pancreas

            Summary To understand organ function, it is important to have an inventory of its cell types and of their corresponding marker genes. This is a particularly challenging task for human tissues like the pancreas, because reliable markers are limited. Hence, transcriptome-wide studies are typically done on pooled islets of Langerhans, obscuring contributions from rare cell types and of potential subpopulations. To overcome this challenge, we developed an automated platform that uses FACS, robotics, and the CEL-Seq2 protocol to obtain the transcriptomes of thousands of single pancreatic cells from deceased organ donors, allowing in silico purification of all main pancreatic cell types. We identify cell type-specific transcription factors and a subpopulation of REG3A-positive acinar cells. We also show that CD24 and TM4SF4 expression can be used to sort live alpha and beta cells with high purity. This resource will be useful for developing a deeper understanding of pancreatic biology and pathophysiology of diabetes mellitus.
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              Accounting for technical noise in single-cell RNA-seq experiments.

              Single-cell RNA-seq can yield valuable insights about the variability within a population of seemingly homogeneous cells. We developed a quantitative statistical method to distinguish true biological variability from the high levels of technical noise in single-cell experiments. Our approach quantifies the statistical significance of observed cell-to-cell variability in expression strength on a gene-by-gene basis. We validate our approach using two independent data sets from Arabidopsis thaliana and Mus musculus.
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                Author and article information

                Journal
                Annual Review of Genetics
                Annu. Rev. Genet.
                Annual Reviews
                0066-4197
                1545-2948
                November 23 2018
                November 23 2018
                : 52
                : 1
                : 203-221
                Affiliations
                [1 ]Center for Genomics and Systems Biology, New York University, New York, NY 10003, USA;
                Article
                10.1146/annurev-genet-120417-031247
                6314027
                30192636
                5bfe659f-6d87-4620-b60e-4d9d776d7b21
                © 2018
                History

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