Literature DB >> 28893418

The promise of single-cell RNA sequencing for kidney disease investigation.

Haojia Wu1, Benjamin D Humphreys2.   

Abstract

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.
Copyright © 2017 International Society of Nephrology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  gene expression; single cell; transcription regulation

Mesh:

Substances:

Year:  2017        PMID: 28893418      PMCID: PMC5696024          DOI: 10.1016/j.kint.2017.06.033

Source DB:  PubMed          Journal:  Kidney Int        ISSN: 0085-2538            Impact factor:   10.612


  53 in total

1.  Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development.

Authors:  Sean C Bendall; Kara L Davis; El-Ad David Amir; Michelle D Tadmor; Erin F Simonds; Tiffany J Chen; Daniel K Shenfeld; Garry P Nolan; Dana Pe'er
Journal:  Cell       Date:  2014-04-24       Impact factor: 41.582

2.  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

3.  Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons.

Authors:  Naomi Habib; Yinqing Li; Matthias Heidenreich; Lukasz Swiech; Inbal Avraham-Davidi; John J Trombetta; Cynthia Hession; Feng Zhang; Aviv Regev
Journal:  Science       Date:  2016-07-28       Impact factor: 47.728

4.  Single-cell RNA-sequence analysis of mouse glomerular mesangial cells uncovers mesangial cell essential genes.

Authors:  Yuqiu Lu; Yuting Ye; Qianqian Yang; Shaolin Shi
Journal:  Kidney Int       Date:  2017-03-18       Impact factor: 10.612

5.  Single cell RNA sequencing to dissect the molecular heterogeneity in lupus nephritis.

Authors:  Evan Der; Saritha Ranabothu; Hemant Suryawanshi; Kemal M Akat; Robert Clancy; Pavel Morozov; Manjunath Kustagi; Mareike Czuppa; Peter Izmirly; H Michael Belmont; Tao Wang; Nicole Jordan; Nicole Bornkamp; Janet Nwaukoni; July Martinez; Beatrice Goilav; Jill P Buyon; Thomas Tuschl; Chaim Putterman
Journal:  JCI Insight       Date:  2017-05-04

6.  Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis.

Authors:  Fuchou Tang; Catalin Barbacioru; Siqin Bao; Caroline Lee; Ellen Nordman; Xiaohui Wang; Kaiqin Lao; M Azim Surani
Journal:  Cell Stem Cell       Date:  2010-05-07       Impact factor: 24.633

7.  Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors.

Authors:  Alexandra-Chloé Villani; Rahul Satija; Gary Reynolds; Siranush Sarkizova; Karthik Shekhar; James Fletcher; Morgane Griesbeck; Andrew Butler; Shiwei Zheng; Suzan Lazo; Laura Jardine; David Dixon; Emily Stephenson; Emil Nilsson; Ida Grundberg; David McDonald; Andrew Filby; Weibo Li; Philip L De Jager; Orit Rozenblatt-Rosen; Andrew A Lane; Muzlifah Haniffa; Aviv Regev; Nir Hacohen
Journal:  Science       Date:  2017-04-21       Impact factor: 47.728

8.  Bayesian approach to single-cell differential expression analysis.

Authors:  Peter V Kharchenko; Lev Silberstein; David T Scadden
Journal:  Nat Methods       Date:  2014-05-18       Impact factor: 28.547

9.  MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data.

Authors:  Greg Finak; Andrew McDavid; Masanao Yajima; Jingyuan Deng; Vivian Gersuk; Alex K Shalek; Chloe K Slichter; Hannah W Miller; M Juliana McElrath; Martin Prlic; Peter S Linsley; Raphael Gottardo
Journal:  Genome Biol       Date:  2015-12-10       Impact factor: 13.583

10.  Wishbone identifies bifurcating developmental trajectories from single-cell data.

Authors:  Manu Setty; Michelle D Tadmor; Shlomit Reich-Zeliger; Omer Angel; Tomer Meir Salame; Pooja Kathail; Kristy Choi; Sean Bendall; Nir Friedman; Dana Pe'er
Journal:  Nat Biotechnol       Date:  2016-05-02       Impact factor: 54.908

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  30 in total

Review 1.  Machine learning, the kidney, and genotype-phenotype analysis.

Authors:  Rachel S G Sealfon; Laura H Mariani; Matthias Kretzler; Olga G Troyanskaya
Journal:  Kidney Int       Date:  2020-04-01       Impact factor: 10.612

2.  MAIT Cells as Drivers of Renal Fibrosis and CKD.

Authors:  Birgit Sawitzki
Journal:  J Am Soc Nephrol       Date:  2019-06-11       Impact factor: 10.121

Review 3.  Recent Insights into Kidney Injury and Repair from Transcriptomic Analyses.

Authors:  Yuhei Kirita; Monica Chang-Panesso; Benjamin D Humphreys
Journal:  Nephron       Date:  2019-05-21       Impact factor: 2.847

4.  Dietary Effects on Dahl Salt-Sensitive Hypertension, Renal Damage, and the T Lymphocyte Transcriptome.

Authors:  Justine M Abais-Battad; Ammar J Alsheikh; Xiaoqing Pan; Daniel J Fehrenbach; John Henry Dasinger; Hayley Lund; Michelle L Roberts; Alison J Kriegel; Allen W Cowley; Srividya Kidambi; Theodore A Kotchen; Pengyuan Liu; Mingyu Liang; David L Mattson
Journal:  Hypertension       Date:  2019-09-03       Impact factor: 10.190

Review 5.  The kidney transcriptome, from single cells to whole organs and back.

Authors:  Shizheng Huang; Xin Sheng; Katalin Susztak
Journal:  Curr Opin Nephrol Hypertens       Date:  2019-05       Impact factor: 2.894

6.  Single Cell Sequencing and Kidney Organoids Generated from Pluripotent Stem Cells.

Authors:  Haojia Wu; Benjamin D Humphreys
Journal:  Clin J Am Soc Nephrol       Date:  2020-01-28       Impact factor: 8.237

7.  Systems Biology and Kidney Disease.

Authors:  Jennifer A Schaub; Habib Hamidi; Lalita Subramanian; Matthias Kretzler
Journal:  Clin J Am Soc Nephrol       Date:  2020-01-28       Impact factor: 8.237

8.  Representation and relative abundance of cell-type selective markers in whole-kidney RNA-Seq data.

Authors:  Jevin Z Clark; Lihe Chen; Chung-Lin Chou; Hyun Jun Jung; Jae Wook Lee; Mark A Knepper
Journal:  Kidney Int       Date:  2019-02-27       Impact factor: 10.612

Review 9.  Kidney and organoid single-cell transcriptomics: the end of the beginning.

Authors:  Parker C Wilson; Benjamin D Humphreys
Journal:  Pediatr Nephrol       Date:  2019-01-04       Impact factor: 3.714

Review 10.  Understanding the kidney one cell at a time.

Authors:  Jihwan Park; Chang Linda Liu; Junhyong Kim; Katalin Susztak
Journal:  Kidney Int       Date:  2019-07-26       Impact factor: 10.612

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