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Single-Cell Omics

Guest Editors: Sarah Teichmann and Sten Linnarsson

Advances in single-cell approaches are now enabling us to study differences between cell types and subpopulations, at the level of the genome, transcriptome and epigenome. Single-cell technologies are being used to study diverse areas of biology and disease, such as development, microbial population composition, and cancer evolution.

Genome Biology highlights the emergence of this field with a special issue focused on single-cell methods and their applications.

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EDITORIAL
Single-cell genomics: coming of age
Sten Linnarsson and Sarah A. Teichmann

Genome Biology 2016 17:97
Published on: 10 May 2016

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RESEARCH HIGHLIGHT
Computing tumor trees from single cells
Computational methods have been developed to reconstruct evolutionary lineages from tumors using single-cell genomic data. The resulting tumor trees have important applications in cancer research and clinical ...
Alexander Davis and Nicholas E. Navin

Genome Biology 2016 17:113
Published on: 26 May 2016

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RESEARCH HIGHLIGHT
Single-cell profiling of lncRNAs in the developing human brain
Single-cell RNA-seq in samples from the human neocortex demonstrate that long noncoding RNAs (lncRNAs) are abundantly expressed in specific individual brain cells, despite being hard to detect in bulk samples....
Qing Ma and Howard Y. Chang

Genome Biology 2016 17:68
Published on: 14 April 2016

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Review

REVIEW
Single-cell sequencing in stem cell biology
Cell-to-cell variation and heterogeneity are fundamental and intrinsic characteristics of stem cell populations, but these differences are masked when bulk cells are used for omic analysis. Single-cell sequenc...
Lu Wen and Fuchou Tang

Genome Biology 2016 17:71
Published on: 15 April 2016

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REVIEW
Design and computational analysis of single-cell RNA-sequencing experiments
Single-cell RNA-sequencing (scRNA-seq) has emerged as a revolutionary tool that allows us to address scientific questions that eluded examination just a few years ago. With the advantages of scRNA-seq come com...
Rhonda Bacher and Christina Kendziorski

Genome Biology 2016 17:63
Published on: 7 April 2016

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REVIEW
4D nucleomes in single cells: what can computational modeling reveal about spatial chromatin conformation?
Genome-wide sequencing technologies enable investigations of the structural properties of the genome in various spatial dimensions. Here, we review computational techniques developed to model the three-dimensi...
Monika Sekelja, Jonas Paulsen and Philippe Collas

Genome Biology 2016 17:54
Published on: 7 April 2016

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OPINION
Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity
Emerging single-cell epigenomic methods are being developed with the exciting potential to transform our knowledge of gene regulation. Here we review available techniques and future possibilities, arguing that...
Stephen J. Clark, Heather J. Lee, Sébastien A. Smallwood, Gavin Kelsey and Wolf Reik

Genome Biology 2016 17:72
Published on: 18 April 2016

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OPINION
The potential of single-cell profiling in plants
Single-cell transcriptomics has been employed in a growing number of animal studies, but the technique has yet to be widely used in plants. Nonetheless, early studies indicate that single-cell RNA-seq protocol...
Idan Efroni and Kenneth D. Birnbaum

Genome Biology 2016 17:65
Published on: 5 April 2016

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Interaction

OPEN LETTER
AirLab: a cloud-based platform to manage and share antibody-based single-cell research
Single-cell analysis technologies are essential tools in research and clinical diagnostics. These methods include flow cytometry, mass cytometry, and other microfluidics-based technologies. Most laboratories t...
Raúl Catena, Alaz Özcan, Andrea Jacobs, Stephane Chevrier and Bernd Bodenmiller

Genome Biology 2016 17:142
Published on: 29 June 2016

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Research

RESEARCH
Tracing the origin of disseminated tumor cells in breast cancer using single-cell sequencing
Single-cell micro-metastases of solid tumors often occur in the bone marrow. These disseminated tumor cells (DTCs) may resist therapy and lay dormant or progress to cause overt bone and visceral metastases. Th...
Jonas Demeulemeester, Parveen Kumar, Elen K. Møller, Silje Nord, David C. Wedge, April Peterson, Randi R. Mathiesen, Renathe Fjelldal, Masoud Zamani Esteki, Koen Theunis, Elia Fernandez Gallardo, A. Jason Grundstad, Elin Borgen, Lars O. Baumbusch, Anne-Lise Børresen-Dale, Kevin P. White…

Genome Biology 2016 17:250
Published on: 9 December 2016

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RESEARCH
Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm
Human pluripotent stem cells offer the best available model to study the underlying cellular and molecular mechanisms of human embryonic lineage specification. However, it is not fully understood how individua...
Li-Fang Chu, Ning Leng, Jue Zhang, Zhonggang Hou, Daniel Mamott, David T. Vereide, Jeea Choi, Christina Kendziorski, Ron Stewart and James A. Thomson

Genome Biology 2016 17:173
Published on: 17 August 2016

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RESEARCH
Single-cell genome-wide bisulfite sequencing uncovers extensive heterogeneity in the mouse liver methylome
Transmission fidelity of CpG DNA methylation patterns is not foolproof, with error rates from less than 1 to well over 10 % per CpG site, dependent on preservation of the methylated or unmethylated state and t...
Silvia Gravina, Xiao Dong, Bo Yu and Jan Vijg

Genome Biology 2016 17:150
Published on: 5 July 2016

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RESEARCH
Single-cell whole genome sequencing reveals no evidence for common aneuploidy in normal and Alzheimer’s disease neurons
Alzheimer’s disease (AD) is a neurodegenerative disease of the brain and the most common form of dementia in the elderly. Aneuploidy, a state in which cells have an abnormal number of chromosomes, has been pro...
Hilda van den Bos, Diana C. J. Spierings, Aaron Taudt, Bjorn Bakker, David Porubský, Ester Falconer, Carolina Novoa, Nancy Halsema, Hinke G. Kazemier, Karina Hoekstra-Wakker, Victor Guryev, Wilfred F. A. den Dunnen, Floris Foijer, Maria Colomé-Tatché, Hendrikus W. G. M. Boddeke and Peter M. Lansdorp

Genome Biology 2016 17:116
Published on: 31 May 2016
The Erratum to this article has been published in Genome Biology 2016 17:143

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RESEARCH
Single-cell sequencing reveals karyotype heterogeneity in murine and human malignancies
Chromosome instability leads to aneuploidy, a state in which cells have abnormal numbers of chromosomes, and is found in two out of three cancers. In a chromosomal instable p53 deficient mouse model with accel...
Bjorn Bakker, Aaron Taudt, Mirjam E. Belderbos, David Porubsky, Diana C. J. Spierings, Tristan V. de Jong, Nancy Halsema, Hinke G. Kazemier, Karina Hoekstra-Wakker, Allan Bradley, Eveline S. J. M. de Bont, Anke van den Berg, Victor Guryev, Peter M. Lansdorp, Maria Colomé-Tatché and Floris Foijer

Genome Biology 2016 17:115
Published on: 31 May 2016

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RESEARCH
Single-cell analysis of CD4+ T-cell differentiation reveals three major cell states and progressive acceleration of proliferation
Differentiation of lymphocytes is frequently accompanied by cell cycle changes, interplay that is of central importance for immunity but is still incompletely understood. Here, we interrogate and quantitativel...
Valentina Proserpio, Andrea Piccolo, Liora Haim-Vilmovsky, Gozde Kar, Tapio Lönnberg, Valentine Svensson, Jhuma Pramanik, Kedar Nath Natarajan, Weichao Zhai, Xiuwei Zhang, Giacomo Donati, Melis Kayikci, Jurij Kotar, Andrew N. J. McKenzie, Ruddy Montandon, Oliver Billker…

Genome Biology 2016 17:103
Published on: 12 May 2016
The Erratum to this article has been published in Genome Biology 2016 17:133

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RESEARCH
Exploiting single-cell expression to characterize co-expression replicability
Co-expression networks have been a useful tool for functional genomics, providing important clues about the cellular and biochemical mechanisms that are active in normal and disease processes. However, co-expr...
Megan Crow, Anirban Paul, Sara Ballouz, Z. Josh Huang and Jesse Gillis

Genome Biology 2016 17:101
Published on: 6 May 2016

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RESEARCH
Single-cell profiling of human megakaryocyte-erythroid progenitors identifies distinct megakaryocyte and erythroid differentiation pathways
Recent advances in single-cell techniques have provided the opportunity to finely dissect cellular heterogeneity within populations previously defined by “bulk” assays and to uncover rare cell types. In human ...
Bethan Psaila, Nikolaos Barkas, Deena Iskander, Anindita Roy, Stacie Anderson, Neil Ashley, Valentina S. Caputo, Jens Lichtenberg, Sandra Loaiza, David M. Bodine, Anastasios Karadimitris, Adam J. Mead and Irene Roberts

Genome Biology 2016 17:83
Published on: 3 May 2016

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RESEARCH
Application of single-cell RNA sequencing in optimizing a combinatorial therapeutic strategy in metastatic renal cell carcinoma
Intratumoral heterogeneity hampers the success of marker-based anticancer treatment because the targeted therapy may eliminate a specific subpopulation of tumor cells while leaving others unharmed. Accordingly...
Kyu-Tae Kim, Hye Won Lee, Hae-Ock Lee, Hye Jin Song, Da Eun Jeong, Sang Shin, Hyunho Kim, Yoojin Shin, Do-Hyun Nam, Byong Chang Jeong, David G. Kirsch, Kyeung Min Joo and Woong-Yang Park

Genome Biology 2016 17:80
Published on: 29 April 2016

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RESEARCH
In silico lineage tracing through single cell transcriptomics identifies a neural stem cell population in planarians
The planarian Schmidtea mediterranea is a master regenerator with a large adult stem cell compartment. The lack of transgenic labeling techniques in this animal has hindered the study of lineage progression and h...
Alyssa M. Molinaro and Bret J. Pearson

Genome Biology 2016 17:87
Published on: 27 April 2016

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RESEARCH
Single-cell analysis of long non-coding RNAs in the developing human neocortex
Long non-coding RNAs (lncRNAs) comprise a diverse class of transcripts that can regulate molecular and cellular processes in brain development and disease. LncRNAs exhibit cell type- and tissue-specific expres...
Siyuan John Liu, Tomasz J. Nowakowski, Alex A. Pollen, Jan H. Lui, Max A. Horlbeck, Frank J. Attenello, Daniel He, Jonathan S. Weissman, Arnold R. Kriegstein, Aaron A. Diaz and Daniel A. Lim

Genome Biology 2016 17:67
Published on: 14 April 2016

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METHOD
A statistical approach for identifying differential distributions in single-cell RNA-seq experiments
The ability to quantify cellular heterogeneity is a major advantage of single-cell technologies. However, statistical methods often treat cellular heterogeneity as a nuisance. We present a novel method to char...
Keegan D. Korthauer, Li-Fang Chu, Michael A. Newton, Yuan Li, James Thomson, Ron Stewart and Christina Kendziorski

Genome Biology 2016 17:222
Published on: 25 October 2016

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METHOD
Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction
We present a scalable, integrated strategy for coupled protein and RNA detection from single cells. Our approach leverages the DNA polymerase activity of reverse transcriptase to simultaneously perform proximi...
Alex S Genshaft, Shuqiang Li, Caroline J. Gallant, Spyros Darmanis, Sanjay M. Prakadan, Carly G. K. Ziegler, Martin Lundberg, Simon Fredriksson, Joyce Hong, Aviv Regev, Kenneth J. Livak, Ulf Landegren and Alex K. Shalek

Genome Biology 2016 17:188
Published on: 19 September 2016

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METHOD
GiniClust: detecting rare cell types from single-cell gene expression data with Gini index
High-throughput single-cell technologies have great potential to discover new cell types; however, it remains challenging to detect rare cell types that are distinct from a large population. We present a novel...
Lan Jiang, Huidong Chen, Luca Pinello and Guo-Cheng Yuan

Genome Biology 2016 17:144
Published on: 1 July 2016

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METHOD
Fast and accurate single-cell RNA-seq analysis by clustering of transcript-compatibility counts
Current approaches to single-cell transcriptomic analysis are computationally intensive and require assay-specific modeling, which limits their scope and generality. We propose a novel method that compares and...
Vasilis Ntranos, Govinda M. Kamath, Jesse M. Zhang, Lior Pachter and David N. Tse

Genome Biology 2016 17:112
Published on: 26 May 2016

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METHOD
SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data
Single cell experiments provide an unprecedented opportunity to reconstruct a sequence of changes in a biological process from individual “snapshots” of cells. However, nonlinear gene expression changes, genes...
Joshua D. Welch, Alexander J. Hartemink and Jan F. Prins

Genome Biology 2016 17:106
Published on: 23 May 2016

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METHOD
Simultaneous profiling of transcriptome and DNA methylome from a single cell
Single-cell transcriptome and single-cell methylome technologies have become powerful tools to study RNA and DNA methylation profiles of single cells at a genome-wide scale. A major challenge has been to under...
Youjin Hu, Kevin Huang, Qin An, Guizhen Du, Ganlu Hu, Jinfeng Xue, Xianmin Zhu, Cun-Yu Wang, Zhigang Xue and Guoping Fan

Genome Biology 2016 17:88
Published on: 5 May 2016

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METHOD
Tree inference for single-cell data
Understanding the mutational heterogeneity within tumors is a keystone for the development of efficient cancer therapies. Here, we present SCITE, a stochastic search algorithm to identify the evolutionary hist...
Katharina Jahn, Jack Kuipers and Niko Beerenwinkel

Genome Biology 2016 17:86
Published on: 5 May 2016

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METHOD
CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq
Single-cell transcriptomics requires a method that is sensitive, accurate, and reproducible. Here, we present CEL-Seq2, a modified version of our CEL-Seq method, with threefold higher sensitivity, lower costs,...
Tamar Hashimshony, Naftalie Senderovich, Gal Avital, Agnes Klochendler, Yaron de Leeuw, Leon Anavy, Dave Gennert, Shuqiang Li, Kenneth J. Livak, Orit Rozenblatt-Rosen, Yuval Dor, Aviv Regev and Itai Yanai

Genome Biology 2016 17:77
Published on: 28 April 2016

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METHOD
Pooling across cells to normalize single-cell RNA sequencing data with many zero counts
Normalization of single-cell RNA sequencing data is necessary to eliminate cell-specific biases prior to downstream analyses. However, this is not straightforward for noisy single-cell data where many counts a...
Aaron T. L. Lun, Karsten Bach and John C. Marioni

Genome Biology 2016 17:75
Published on: 27 April 2016

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METHOD
Beyond comparisons of means: understanding changes in gene expression at the single-cell level
Traditional differential expression tools are limited to detecting changes in overall expression, and fail to uncover the rich information provided by single-cell level data sets. We present a Bayesian hierarc...
Catalina A. Vallejos, Sylvia Richardson and John C. Marioni

Genome Biology 2016 17:70
Published on: 15 April 2016

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METHOD
OncoNEM: inferring tumor evolution from single-cell sequencing data
Single-cell sequencing promises a high-resolution view of genetic heterogeneity and clonal evolution in cancer. However, methods to infer tumor evolution from single-cell sequencing data lag behind methods dev...
Edith M. Ross and Florian Markowetz

Genome Biology 2016 17:69
Published on: 15 April 2016

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