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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao PROTEOMICSarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
PROTEOMICS
Article . 2020 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
PROTEOMICS
Article . 2021
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Single‐Cell RNA Sequencing in Hematological Diseases

Authors: Yue Zhu; Yaohui Huang; Yun Tan; Weili Zhao; Qiang Tian;

Single‐Cell RNA Sequencing in Hematological Diseases

Abstract

Abstract Hematological diseases, including leukemia, lymphoma, and multiple myeloma, are characterized by high heterogeneity with diverse cellular subpopulations. Single‐cell RNA sequencing (scRNA‐seq), a transformational technology, provides deep insights into cell‐to‐cell variation in tumor and microenvironment, allows high‐resolution dissection of the pathogenic mechanisms of diseases, and affords potential clinical utilities. Recent developments in single‐cell transcriptomics and associated technologies and their applications in hematological disorders for unraveling cellular subpopulations, disease pathogenesis, patient stratification, and therapeutic responses are summarized.

Related Organizations
Keywords

Proteomics, Base Sequence, Sequence Analysis, RNA, Computational Biology, Humans, Single-Cell Analysis, Hematologic Diseases

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
31
Top 10%
Top 10%
Top 10%
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