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The promise of single-cell RNA sequencing for kidney disease investigation
Author(s) -
Hao Wu,
Benjamin D. Humphreys
Publication year - 2017
Publication title -
kidney international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.499
H-Index - 276
eISSN - 1523-1755
pISSN - 0085-2538
DOI - 10.1016/j.kint.2017.06.033
Subject(s) - computational biology , identification (biology) , disease , transcriptome , transformative learning , biology , computer science , rna , data science , medicine , genetics , gene , gene expression , psychology , botany , pedagogy , pathology
Recent techniques for single-cell RNA sequencing (scRNA-seq) at high throughput are leading to profound new discoveries in biology. The ability to generate vast amounts of transcriptomic data at cellular resolution represents a transformative advance, allowing the identification of novel cell types, states, and dynamics. In this review, we summarize the development of scRNA-seq methodologies and highlight their advantages and drawbacks. We discuss available software tools for analyzing scRNA-Seq data and summarize current computational challenges. Finally, we outline ways in which this powerful technology might be applied to discovery research in kidney development and disease.

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