The applications of single-cell genomics

Lovett, Michael

doi:10.1093/hmg/ddt377

Cited by 25 publications

(12 citation statements)

References 36 publications

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“…The diversity of the topics studied by ascidian biologists and the recent increase in the number of laboratories working on these organisms have led to an expansion of the number of ascidian species with a sequenced genome. In parallel, novel types of genome-wide transcriptomics and epigenetic data sets have recently been developed, sometimes applicable to single cells ( 50 – 53 ), promising a renewed quantitative view of developmental processes. To keep up with this evolution, we refactored ANISEED to facilitate its extension to additional species, non-embryonic developmental processes and data types.…”

Section: Introductionmentioning

confidence: 99%

ANISEED 2015: a digital framework for the comparative developmental biology of ascidians

et al. 2015

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Ascidians belong to the tunicates, the sister group of vertebrates and are recognized model organisms in the field of embryonic development, regeneration and stem cells. ANISEED is the main information system in the field of ascidian developmental biology. This article reports the development of the system since its initial publication in 2010. Over the past five years, we refactored the system from an initial custom schema to an extended version of the Chado schema and redesigned all user and back end interfaces. This new architecture was used to improve and enrich the description of Ciona intestinalis embryonic development, based on an improved genome assembly and gene model set, refined functional gene annotation, and anatomical ontologies, and a new collection of full ORF cDNAs. The genomes of nine ascidian species have been sequenced since the release of the C. intestinalis genome. In ANISEED 2015, all nine new ascidian species can be explored via dedicated genome browsers, and searched by Blast. In addition, ANISEED provides full functional gene annotation, anatomical ontologies and some gene expression data for the six species with highest quality genomes. ANISEED is publicly available at: http://www.aniseed.cnrs.fr.

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Section: Introductionmentioning

confidence: 99%

ANISEED 2015: a digital framework for the comparative developmental biology of ascidians

et al. 2015

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“…Systems biology-driven modeling of drug response will gain more importance with the ever-increasing amounts of genome data becoming available for individual patients. The further decrease in sequencing costs along with the generation of purer materials, such as single-cell omics analyses 76,77 and material adapted closer to the individual patient, such as induced pluripotent stem (iPS) cells, 78,79 enable completely new chances for personalized therapies. Here, in particular the incorporation of mutations and larger human variations, eg, in neuro-oncology, 80,81 and allele-specific protein expression 82,83 will gain further importance.…”

Section: Discussionmentioning

confidence: 99%

Computational modeling of drug response with applications to neuroscience

Herwig

2014

Dialogues in Clinical Neuroscience

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The development of novel high-throughput technologies has opened up the opportunity to deeply characterize patient tissues at various molecular levels and has given rise to a paradigm shift in medicine towards personalized therapies. Computational analysis plays a pivotal role in integrating the various genome data and understanding the cellular response to a drug. Based on that data, molecular models can be constructed that incorporate the known downstream effects of drug-targeted receptor molecules and that predict optimal therapy decisions. In this article, we describe the different steps in the conceptual framework of computational modeling. We review resources that hold information on molecular pathways that build the basis for constructing the model interaction maps, highlight network analysis concepts that have been helpful in identifying predictive disease patterns, and introduce the basic concepts of kinetic modeling. Finally, we illustrate this framework with selected studies related to the modeling of important target pathways affected by drugs.

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“…Single-cell analysis has become an attractive and challenging field of modern molecular biology and medicine, the main goal of which is to study biological questions with single-cell resolution [ 1 , 2 , 3 ]. Such an approach reflects cell heterogeneity and reveals the complex response of an organism to various physiological and pathophysiological stimuli [ 4 , 5 , 6 , 7 , 8 , 9 ]. Another important application is the analysis of rare cells, such as circulating tumor cells (CTC), residual cells relevant to disease or therapy, and stem cells [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Platforms for Single-Cell Collection and Analysis

Valihrach

Androvic

Kubista

2018

IJMS

143

107

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Single-cell analysis has become an established method to study cell heterogeneity and for rare cell characterization. Despite the high cost and technical constraints, applications are increasing every year in all fields of biology. Following the trend, there is a tremendous development of tools for single-cell analysis, especially in the RNA sequencing field. Every improvement increases sensitivity and throughput. Collecting a large amount of data also stimulates the development of new approaches for bioinformatic analysis and interpretation. However, the essential requirement for any analysis is the collection of single cells of high quality. The single-cell isolation must be fast, effective, and gentle to maintain the native expression profiles. Classical methods for single-cell isolation are micromanipulation, microdissection, and fluorescence-activated cell sorting (FACS). In the last decade several new and highly efficient approaches have been developed, which not just supplement but may fully replace the traditional ones. These new techniques are based on microfluidic chips, droplets, micro-well plates, and automatic collection of cells using capillaries, magnets, an electric field, or a punching probe. In this review we summarize the current methods and developments in this field. We discuss the advantages of the different commercially available platforms and their applicability, and also provide remarks on future developments.

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The applications of single-cell genomics

Cited by 25 publications

References 36 publications

ANISEED 2015: a digital framework for the comparative developmental biology of ascidians

ANISEED 2015: a digital framework for the comparative developmental biology of ascidians

Computational modeling of drug response with applications to neuroscience

Platforms for Single-Cell Collection and Analysis

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