Literature DB >> 36147383

Single-cell sequencing: A cutting edge tool in molecular medical research.

Pratibha Misra1, Amruta R Jadhav2, Sharmila A Bapat3.   

Abstract

The rapid development of advanced high throughput technologies and introduction of high resolution "omics" data through analysis of biological molecules has revamped medical research. Single-cell sequencing in recent years, is in fact revolutionising the field by providing a deeper, spatio-temporal analyses of individual cells within tissues and their relevance to disease. Like conventional sequencing, the single-cell approach deciphers the sequence of nucleotides in a given Deoxyribose Nucleic Acid (DNA), Ribose Nucleic Acid (RNA), Micro Ribose Nucleic Acid (miRNA), epigenetically modified DNA or chromatin DNA; however, the unit of analyses is changed to single cells rather than the entire tissue. Further, a large number of single cells analysed from a single tissue generate a unique holistic perception capturing all kinds of perturbations across different cells in the tissue that increases the precision of data. Inherently, execution of the technique generates a large amount of data, which is required to be processed in a specific manner followed by customised bioinformatic analysis to produce meaningful results. The most crucial role of single-cell sequencing technique is in elucidating the inter-cell genetic, epigenetic, transcriptomic and proteomic heterogeneity in health and disease. The current review presents a brief overview of this cutting-edge technology and its applications in medical research.
© 2022 Director General, Armed Forces Medical Services. Published by Elsevier, a division of RELX India Pvt. Ltd.

Entities:  

Keywords:  Epigenome; Genome; Sequencing data analysis; Single-cell sequencing; Transcriptome

Year:  2022        PMID: 36147383      PMCID: PMC9485843          DOI: 10.1016/j.mjafi.2022.08.006

Source DB:  PubMed          Journal:  Med J Armed Forces India        ISSN: 0377-1237


  28 in total

Review 1.  Overview of Next-Generation Sequencing Technologies.

Authors:  Barton E Slatko; Andrew F Gardner; Frederick M Ausubel
Journal:  Curr Protoc Mol Biol       Date:  2018-04

2.  Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.

Authors:  Florian Buettner; Kedar N Natarajan; F Paolo Casale; Valentina Proserpio; Antonio Scialdone; Fabian J Theis; Sarah A Teichmann; John C Marioni; Oliver Stegle
Journal:  Nat Biotechnol       Date:  2015-01-19       Impact factor: 54.908

Review 3.  Single-Cell Proteomics.

Authors:  Luke F Vistain; Savaş Tay
Journal:  Trends Biochem Sci       Date:  2021-02-27       Impact factor: 13.807

4.  CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification.

Authors:  Tamar Hashimshony; Florian Wagner; Noa Sher; Itai Yanai
Journal:  Cell Rep       Date:  2012-08-30       Impact factor: 9.423

Review 5.  Single-cell omics: Overview, analysis, and application in biomedical science.

Authors:  Catarina M Stein; Ralf Weiskirchen; Frederik Damm; Paulina M Strzelecka
Journal:  J Cell Biochem       Date:  2021-08-30       Impact factor: 4.429

6.  FastProNGS: fast preprocessing of next-generation sequencing reads.

Authors:  Xiaoshuang Liu; Zhenhe Yan; Chao Wu; Yang Yang; Xiaomin Li; Guangxin Zhang
Journal:  BMC Bioinformatics       Date:  2019-06-17       Impact factor: 3.169

7.  Single-cell microRNA-mRNA co-sequencing reveals non-genetic heterogeneity and mechanisms of microRNA regulation.

Authors:  Nayi Wang; Ji Zheng; Zhuo Chen; Yang Liu; Burak Dura; Minsuk Kwak; Juliana Xavier-Ferrucio; Yi-Chien Lu; Miaomiao Zhang; Christine Roden; Jijun Cheng; Diane S Krause; Ye Ding; Rong Fan; Jun Lu
Journal:  Nat Commun       Date:  2019-01-09       Impact factor: 14.919

8.  Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model.

Authors:  F William Townes; Stephanie C Hicks; Martin J Aryee; Rafael A Irizarry
Journal:  Genome Biol       Date:  2019-12-23       Impact factor: 13.583

Review 9.  Single cell genomics: advances and future perspectives.

Authors:  Iain C Macaulay; Thierry Voet
Journal:  PLoS Genet       Date:  2014-01-30       Impact factor: 5.917

Review 10.  Introduction to Single-Cell DNA Methylation Profiling Methods.

Authors:  Jongseong Ahn; Sunghoon Heo; Jihyun Lee; Duhee Bang
Journal:  Biomolecules       Date:  2021-07-10
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