Literature DB >> 25016438

Single cell sequencing approaches for complex biological systems.

Timour Baslan1, James Hicks2.   

Abstract

Biological phenotype is the output of complex interactions between heterogeneous cells within a specified niche. These interactions are tightly governed and regulated by the genetic, epigenetic, and transcriptional states of single cells, with deregulation of these states resulting in disease. As such, genome wide single cell investigations are bound to enhance our knowledge of the underlying principles that govern biological systems. Recent technological advances have enabled such investigations in the form of single-cell sequencing. Here, we review the most recent developments in genome wide profiling of single cells, discuss some of the novel biological observations gleaned by such investigations, and touch upon the promise of single cell sequencing in unraveling biological systems.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2014        PMID: 25016438     DOI: 10.1016/j.gde.2014.06.004

Source DB:  PubMed          Journal:  Curr Opin Genet Dev        ISSN: 0959-437X            Impact factor:   5.578


  15 in total

1.  Genome-wide quantification of rare somatic mutations in normal human tissues using massively parallel sequencing.

Authors:  Margaret L Hoang; Isaac Kinde; Cristian Tomasetti; K Wyatt McMahon; Thomas A Rosenquist; Arthur P Grollman; Kenneth W Kinzler; Bert Vogelstein; Nickolas Papadopoulos
Journal:  Proc Natl Acad Sci U S A       Date:  2016-08-15       Impact factor: 11.205

2.  Deletions linked to TP53 loss drive cancer through p53-independent mechanisms.

Authors:  Yu Liu; Chong Chen; Zhengmin Xu; Claudio Scuoppo; Cory D Rillahan; Jianjiong Gao; Barbara Spitzer; Benedikt Bosbach; Edward R Kastenhuber; Timour Baslan; Sarah Ackermann; Lihua Cheng; Qingguo Wang; Ting Niu; Nikolaus Schultz; Ross L Levine; Alea A Mills; Scott W Lowe
Journal:  Nature       Date:  2016-03-16       Impact factor: 49.962

Review 3.  Cerebral cortex assembly: generating and reprogramming projection neuron diversity.

Authors:  Simona Lodato; Ashwin S Shetty; Paola Arlotta
Journal:  Trends Neurosci       Date:  2014-12-17       Impact factor: 13.837

4.  Single-Cell RNA Sequencing Reveals Expanded Clones of Islet Antigen-Reactive CD4+ T Cells in Peripheral Blood of Subjects with Type 1 Diabetes.

Authors:  Karen Cerosaletti; Fariba Barahmand-Pour-Whitman; Junbao Yang; Hannah A DeBerg; Matthew J Dufort; Sara A Murray; Elisabeth Israelsson; Cate Speake; Vivian H Gersuk; James A Eddy; Helena Reijonen; Carla J Greenbaum; William W Kwok; Erik Wambre; Martin Prlic; Raphael Gottardo; Gerald T Nepom; Peter S Linsley
Journal:  J Immunol       Date:  2017-05-31       Impact factor: 5.422

Review 5.  Biophysical technologies for understanding circulating tumor cell biology and metastasis.

Authors:  Derrick W Su; Jorge Nieva
Journal:  Transl Lung Cancer Res       Date:  2017-08

6.  Preparing Single-cell DNA Library Using Nextera for Detection of CNV.

Authors:  Larry Xi; Patrick Leong; Aleksandar Mihajlovic
Journal:  Bio Protoc       Date:  2019-02-20

7.  SCSilicon: a tool for synthetic single-cell DNA sequencing data generation.

Authors:  Xikang Feng; Lingxi Chen
Journal:  BMC Genomics       Date:  2022-05-11       Impact factor: 4.547

8.  Optimizing sparse sequencing of single cells for highly multiplex copy number profiling.

Authors:  Timour Baslan; Jude Kendall; Brian Ward; Hilary Cox; Anthony Leotta; Linda Rodgers; Michael Riggs; Sean D'Italia; Guoli Sun; Mao Yong; Kristy Miskimen; Hannah Gilmore; Michael Saborowski; Nevenka Dimitrova; Alexander Krasnitz; Lyndsay Harris; Michael Wigler; James Hicks
Journal:  Genome Res       Date:  2015-04-09       Impact factor: 9.043

9.  Gene Expression in Single Cells Isolated from the CWR-R1 Prostate Cancer Cell Line and Human Prostate Tissue Based on the Side Population Phenotype.

Authors:  Kalyan J Gangavarapu; Austin Miller; Wendy J Huss
Journal:  Single Cell Biol       Date:  2016-09-12

10.  Predicting Ecological Roles in the Rhizosphere Using Metabolome and Transportome Modeling.

Authors:  Peter E Larsen; Frank R Collart; Yang Dai
Journal:  PLoS One       Date:  2015-09-02       Impact factor: 3.240

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.