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

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.

[1]  David G. Kirsch,et al.  Application of single-cell RNA sequencing in optimizing a combinatorial therapeutic strategy in metastatic renal cell carcinoma , 2016, Genome Biology.

[2]  P. Linsley,et al.  MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data , 2015, Genome Biology.

[3]  S. Potter,et al.  Single cell dissection of early kidney development: multilineage priming , 2014, Development.

[4]  Feng Zhang,et al.  DroNc-Seq: Deciphering cell types in human archived brain tissues by massively-parallel single nucleus RNA-seq , 2017, bioRxiv.

[5]  N. Navin,et al.  Advances and applications of single-cell sequencing technologies. , 2015, Molecular cell.

[6]  Allon M. Klein,et al.  Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells , 2015, Cell.

[7]  E. Pierson,et al.  ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis , 2015, Genome Biology.

[8]  A. McMahon,et al.  Translational profiles of medullary myofibroblasts during kidney fibrosis. , 2014, Journal of the American Society of Nephrology : JASN.

[9]  Sean C. Bendall,et al.  viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia , 2013, Nature Biotechnology.

[10]  M. Boerries,et al.  Molecular fingerprinting of the podocyte reveals novel gene and protein regulatory networks. , 2013, Kidney international.

[11]  J. C. Love,et al.  Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput , 2017, Nature Methods.

[12]  Åsa K. Björklund,et al.  Smart-seq2 for sensitive full-length transcriptome profiling in single cells , 2013, Nature Methods.

[13]  Evan Z. Macosko,et al.  Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics , 2016, Cell.

[14]  Fabian J Theis,et al.  Diffusion pseudotime robustly reconstructs lineage branching , 2016, Nature Methods.

[15]  Allon M. Klein,et al.  miR-9 regulates basal ganglia-dependent developmental vocal learning and adult vocal performance in songbirds , 2017, bioRxiv.

[16]  Xueqing Yu,et al.  Identification of Genes Associated with Smad3-dependent Renal Injury by RNA-seq-based Transcriptome Analysis , 2015, Scientific Reports.

[17]  Aleksandra A. Kolodziejczyk,et al.  The technology and biology of single-cell RNA sequencing. , 2015, Molecular cell.

[18]  Jae Wook Lee,et al.  Deep Sequencing in Microdissected Renal Tubules Identifies Nephron Segment-Specific Transcriptomes. , 2015, Journal of the American Society of Nephrology : JASN.

[19]  R. Kucherlapati,et al.  Differential gene expression following early renal ischemia/reperfusion. , 2003, Kidney international.

[20]  N. Hacohen,et al.  Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors , 2017, Science.

[21]  H. Yamada,et al.  Molecular Markers of Tubulointerstitial Fibrosis and Tubular Cell Damage in Patients with Chronic Kidney Disease , 2015, PloS one.

[22]  Sean C. Bendall,et al.  Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development , 2014, Cell.

[23]  N. Neff,et al.  Quantitative assessment of single-cell RNA-sequencing methods , 2013, Nature Methods.

[24]  Salah Ayoub,et al.  Cell fixation and preservation for droplet-based single-cell transcriptomics , 2017, BMC Biology.

[25]  P. Kharchenko,et al.  Bayesian approach to single-cell differential expression analysis , 2014, Nature Methods.

[26]  Andrew P McMahon,et al.  Cell-specific translational profiling in acute kidney injury. , 2014, The Journal of clinical investigation.

[27]  T. Hashimshony,et al.  CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. , 2012, Cell reports.

[28]  Sean C. Bendall,et al.  Wishbone identifies bifurcating developmental trajectories from single-cell data , 2016, Nature Biotechnology.

[29]  S. Shi,et al.  Single-cell RNA-sequence analysis of mouse glomerular mesangial cells uncovers mesangial cell essential genes. , 2017, Kidney international.

[30]  Charles H. Yoon,et al.  Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq , 2016, Science.

[31]  G. Genovese,et al.  Discovery of new glomerular disease-relevant genes by translational profiling of podocytes in vivo , 2014, Kidney international.

[32]  Jamie A Davies,et al.  GUDMAP: the genitourinary developmental molecular anatomy project. , 2008, Journal of the American Society of Nephrology : JASN.

[33]  Catalin C. Barbacioru,et al.  Tracing the Derivation of Embryonic Stem Cells from the Inner Cell Mass by Single-Cell RNA-Seq Analysis , 2010, Cell stem cell.

[34]  Cole Trapnell,et al.  The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells , 2014, Nature Biotechnology.

[35]  W. Koh,et al.  Single-cell genome sequencing: current state of the science , 2016, Nature Reviews Genetics.

[36]  Evan Z. Macosko,et al.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets , 2015, Cell.

[37]  I. Amit,et al.  Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types , 2014, Science.

[38]  Kazuki Kurimoto,et al.  SC3-seq: a method for highly parallel and quantitative measurement of single-cell gene expression , 2015, Nucleic acids research.

[39]  Daniel R. Berger,et al.  Cell diversity and network dynamics in photosensitive human brain organoids , 2017, Nature.

[40]  Chen Xu,et al.  Identification of cell types from single-cell transcriptomes using a novel clustering method , 2015, Bioinform..

[41]  F. Piano,et al.  Large scale sorting of C. elegans embryos reveals the dynamics of small RNA expression , 2009, Nature Methods.

[42]  Catalin C. Barbacioru,et al.  mRNA-Seq whole-transcriptome analysis of a single cell , 2009, Nature Methods.

[43]  J. Marioni,et al.  Pooling across cells to normalize single-cell RNA sequencing data with many zero counts , 2016, Genome Biology.

[44]  I. Hellmann,et al.  Comparative Analysis of Single-Cell RNA Sequencing Methods , 2016, bioRxiv.

[45]  S. Linnarsson,et al.  Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. , 2011, Genome research.

[46]  Grace X. Y. Zheng,et al.  Massively parallel digital transcriptional profiling of single cells , 2016, Nature Communications.

[47]  Aleksandra A. Kolodziejczyk,et al.  Accounting for technical noise in single-cell RNA-seq experiments , 2013, Nature Methods.

[48]  Cynthia C. Hession,et al.  Div-Seq: Single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons , 2016, Science.

[49]  Gioele La Manno,et al.  Quantitative single-cell RNA-seq with unique molecular identifiers , 2013, Nature Methods.

[50]  Sean C. Bendall,et al.  Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis , 2015, Cell.

[51]  Samuel L. Wolock,et al.  A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure. , 2016, Cell systems.

[52]  T. Tuschl,et al.  Single cell RNA sequencing to dissect the molecular heterogeneity in lupus nephritis. , 2017, JCI insight.

[53]  Shuqiang Li,et al.  CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq , 2016, Genome Biology.

[54]  R. Sandberg,et al.  Full-Length mRNA-Seq from single cell levels of RNA and individual circulating tumor cells , 2012, Nature Biotechnology.