Single-cell sequencing leads a new era of profiling transcriptomic landscape

Abstract. Understanding the complexity of biological systems requires a comprehensive analysis of their cell populations. Ideally, this should be done at the single cell level, because bulk analysis of the full population obscured many critical details due to artifacts introduced by averaging. Howev...

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Main Authors: Huidan Zhang, PhD, MD, Naiwen Cui, Yamei Cai, PhD, Fengyang Lei, PhD, MD, David A. Weitz, PhD
Format: Article
Language:English
Published: Wolters Kluwer Health 2018-06-01
Series:Journal of Bio-X Research
Online Access:http://journals.lww.com/10.1097/JBR.0000000000000003
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spelling doaj-f6930e13319f4252b13ef69c5d73d4fc2020-11-25T03:38:46ZengWolters Kluwer HealthJournal of Bio-X Research2096-56722577-35852018-06-01112610.1097/JBR.0000000000000003201806000-00002Single-cell sequencing leads a new era of profiling transcriptomic landscapeHuidan Zhang, PhD, MDNaiwen CuiYamei Cai, PhDFengyang Lei, PhD, MDDavid A. Weitz, PhDAbstract. Understanding the complexity of biological systems requires a comprehensive analysis of their cell populations. Ideally, this should be done at the single cell level, because bulk analysis of the full population obscured many critical details due to artifacts introduced by averaging. However, this has been technically challenging due to the cumbersome procedure, low throughput, and high costs of performing analysis on a single-cell basis. Excitingly, technical improvements in single-cell RNA sequencing are making it economically practical to profile the transcriptomics of large populations of cells at the single-cell level, and have yielded numerous results that address important biological and medical questions. Further development of the technology and data analysis will significantly benefit the biomedical field by unraveling the function of individual cells in their microenvironments and modeling their transcriptional dynamics.http://journals.lww.com/10.1097/JBR.0000000000000003
collection DOAJ
language English
format Article
sources DOAJ
author Huidan Zhang, PhD, MD
Naiwen Cui
Yamei Cai, PhD
Fengyang Lei, PhD, MD
David A. Weitz, PhD
spellingShingle Huidan Zhang, PhD, MD
Naiwen Cui
Yamei Cai, PhD
Fengyang Lei, PhD, MD
David A. Weitz, PhD
Single-cell sequencing leads a new era of profiling transcriptomic landscape
Journal of Bio-X Research
author_facet Huidan Zhang, PhD, MD
Naiwen Cui
Yamei Cai, PhD
Fengyang Lei, PhD, MD
David A. Weitz, PhD
author_sort Huidan Zhang, PhD, MD
title Single-cell sequencing leads a new era of profiling transcriptomic landscape
title_short Single-cell sequencing leads a new era of profiling transcriptomic landscape
title_full Single-cell sequencing leads a new era of profiling transcriptomic landscape
title_fullStr Single-cell sequencing leads a new era of profiling transcriptomic landscape
title_full_unstemmed Single-cell sequencing leads a new era of profiling transcriptomic landscape
title_sort single-cell sequencing leads a new era of profiling transcriptomic landscape
publisher Wolters Kluwer Health
series Journal of Bio-X Research
issn 2096-5672
2577-3585
publishDate 2018-06-01
description Abstract. Understanding the complexity of biological systems requires a comprehensive analysis of their cell populations. Ideally, this should be done at the single cell level, because bulk analysis of the full population obscured many critical details due to artifacts introduced by averaging. However, this has been technically challenging due to the cumbersome procedure, low throughput, and high costs of performing analysis on a single-cell basis. Excitingly, technical improvements in single-cell RNA sequencing are making it economically practical to profile the transcriptomics of large populations of cells at the single-cell level, and have yielded numerous results that address important biological and medical questions. Further development of the technology and data analysis will significantly benefit the biomedical field by unraveling the function of individual cells in their microenvironments and modeling their transcriptional dynamics.
url http://journals.lww.com/10.1097/JBR.0000000000000003
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