Single-cell systems biology: probing the basic unit of information flow.

Gene expression varies across cells in a population or a tissue. This heterogeneity has come into sharp focus in recent years through developments in new imaging and sequencing technologies. However, our ability to measure variation has outpaced our ability to interpret it. Much of the variability may arise from random effects occurring in the processes of gene expression (transcription, RNA processing and decay, translation). The molecular basis of these effects is largely unknown. Likewise, a functional role of this variability in growth, differentiation and disease has only been elucidated in a few cases. In this review, we highlight recent experimental and theoretical advances for measuring and analyzing stochastic variation.

[1]  Hye Yoon Park,et al.  A transgenic mouse for in vivo detection of endogenous labeled mRNA , 2010, Nature Methods.

[2]  D. R. Larson,et al.  Fluctuation Analysis: Dissecting Transcriptional Kinetics with Signal Theory. , 2016, Methods in enzymology.

[3]  A. Coulon,et al.  Kinetic competition during the transcription cycle results in stochastic RNA processing , 2014, eLife.

[4]  Hazen P Babcock,et al.  High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization , 2016, Proceedings of the National Academy of Sciences.

[5]  K. Malmgren,et al.  Classification of Subpopulations of Cells Within Human Primary Brain Tumors by Single Cell Gene Expression Profiling , 2014, Neurochemical Research.

[6]  S. Itzkovitz,et al.  Nuclear Retention of mRNA in Mammalian Tissues , 2015, Cell reports.

[7]  S. P. Fodor,et al.  Combinatorial labeling of single cells for gene expression cytometry , 2015, Science.

[8]  Michelle Girvan,et al.  Interpreting Patterns of Gene Expression: Signatures of Coregulation, the Data Processing Inequality, and Triplet Motifs , 2012, PloS one.

[9]  Shawn M. Gillespie,et al.  Insulator dysfunction and oncogene activation in IDH mutant gliomas , 2015, Nature.

[10]  Eugenio Cinquemani,et al.  What Population Reveals about Individual Cell Identity: Single-Cell Parameter Estimation of Models of Gene Expression in Yeast , 2016, PLoS Comput. Biol..

[11]  F S Fay,et al.  Visualization of single RNA transcripts in situ. , 1998, Science.

[12]  J. Sedat,et al.  Spatial partitioning of the regulatory landscape of the X-inactivation centre , 2012, Nature.

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

[14]  J. Raser,et al.  Control of Stochasticity in Eukaryotic Gene Expression , 2004, Science.

[15]  William Bialek,et al.  Information processing in living systems , 2014, 1412.8752.

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

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

[18]  Marc S. Sherman,et al.  Cell-to-cell variability in the propensity to transcribe explains correlated fluctuations in gene expression. , 2015, Cell systems.

[19]  Lior Pachter,et al.  Estimating intrinsic and extrinsic noise from single-cell gene expression measurements , 2016, Statistical applications in genetics and molecular biology.

[20]  Robert H Singer,et al.  Single-Cell and Single-Molecule Analysis of Gene Expression Regulation. , 2016, Annual review of genetics.

[21]  S. Itzkovitz,et al.  Bursty gene expression in the intact mammalian liver. , 2015, Molecular cell.

[22]  M. Gerstein,et al.  RNA-Seq: a revolutionary tool for transcriptomics , 2009, Nature Reviews Genetics.

[23]  Bin Wu,et al.  Real-Time Observation of Transcription Initiation and Elongation on an Endogenous Yeast Gene , 2011, Science.

[24]  J. Seidman,et al.  Single-Cell Resolution of Temporal Gene Expression during Heart Development. , 2016, Developmental cell.

[25]  Hernan G. Garcia,et al.  Dynamic regulation of eve stripe 2 expression reveals transcriptional bursts in living Drosophila embryos , 2014, Proceedings of the National Academy of Sciences.

[26]  D. Lipsker,et al.  Single-cell gene expression signatures reveal melanoma cell heterogeneity , 2014, Oncogene.

[27]  M. Delbrück,et al.  Mutations of Bacteria from Virus Sensitivity to Virus Resistance. , 1943, Genetics.

[28]  Robert H Singer,et al.  Single-Cell Gene Expression Profiling , 2002, Science.

[29]  Gašper Tkačik,et al.  Noise and information transmission in promoters with multiple internal States. , 2013, Biophysical journal.

[30]  J. Dekker,et al.  The hierarchy of the 3D genome. , 2013, Molecular cell.

[31]  Michael Levine,et al.  Enhancer Control of Transcriptional Bursting , 2016, Cell.

[32]  Kun Zhang,et al.  Fluorescent in situ sequencing (FISSEQ) of RNA for gene expression profiling in intact cells and tissues , 2015, Nature Protocols.

[33]  X. Zhuang,et al.  Spatially resolved, highly multiplexed RNA profiling in single cells , 2015, Science.

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

[35]  R. Singer,et al.  Localization of ASH1 mRNA particles in living yeast. , 1998, Molecular cell.

[36]  R. Segev,et al.  GENERAL PROPERTIES OF THE TRANSCRIPTIONAL TIME-SERIES IN ESCHERICHIA COLI , 2011, Nature Genetics.

[37]  Visualizing adenosine to inosine RNA editing in single mammalian cells , 2017, Nature Methods.

[38]  J. Peccoud,et al.  Markovian Modeling of Gene-Product Synthesis , 1995 .

[39]  D. Tranchina,et al.  Stochastic mRNA Synthesis in Mammalian Cells , 2006, PLoS biology.

[40]  Claire M. McLeod,et al.  Single-cell differences in matrix gene expression do not predict matrix deposition , 2016, Nature Communications.

[41]  T. Lenstra,et al.  Single-Molecule Imaging Reveals a Switch between Spurious and Functional ncRNA Transcription. , 2015, Molecular cell.

[42]  T. Kirchhausen,et al.  Live-cell visualization of pre-mRNA splicing with single-molecule sensitivity. , 2013, Cell reports.

[43]  Anders S Hansen,et al.  Limits on information transduction through amplitude and frequency regulation of transcription factor activity , 2015, eLife.

[44]  P. Swain,et al.  Stochastic Gene Expression in a Single Cell , 2002, Science.

[45]  Jesse R. Dixon,et al.  Topological Domains in Mammalian Genomes Identified by Analysis of Chromatin Interactions , 2012, Nature.

[46]  A. Raj,et al.  Enhancer Regulation of Transcriptional Bursting Parameters Revealed by Forced Chromatin Looping. , 2016, Molecular cell.

[47]  Debora S. Marks,et al.  MicroRNA control of protein expression noise , 2015, Science.

[48]  Wendy A Bickmore,et al.  The Hierarchy of Transcriptional Activation: From Enhancer to Promoter. , 2015, Trends in genetics : TIG.

[49]  Jeffrey A. Chao,et al.  An RNA biosensor for imaging the first round of translation from single cells to living animals , 2015, Science.

[50]  O. Peters,et al.  Ergodicity breaking in geometric Brownian motion. , 2012, Physical review letters.

[51]  E. Ott,et al.  The effect of network topology on the stability of discrete state models of genetic control , 2009, Proceedings of the National Academy of Sciences.

[52]  J. Kondev,et al.  Stochastic models of transcription: from single molecules to single cells. , 2013, Methods.

[53]  Timur Zhiyentayev,et al.  Single-cell in situ RNA profiling by sequential hybridization , 2014, Nature Methods.

[54]  Vasyl Pihur,et al.  Gene expression anti-profiles as a basis for accurate universal cancer signatures , 2012, BMC Bioinformatics.

[55]  Hernan G. Garcia,et al.  The embryo as a laboratory: quantifying transcription in Drosophila. , 2014, Trends in genetics : TIG.

[56]  Hiroshi Ochiai,et al.  Simultaneous live imaging of the transcription and nuclear position of specific genes , 2015, Nucleic acids research.

[57]  Daniel S. Day,et al.  Activation of proto-oncogenes by disruption of chromosome neighborhoods , 2015, Science.

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

[59]  Long Cai,et al.  Single cell systems biology by super-resolution imaging and combinatorial labeling , 2012, Nature Methods.

[60]  Nacho Molina,et al.  Mammalian Genes Are Transcribed with Widely Different Bursting Kinetics , 2011, Science.

[61]  Hannah Dueck,et al.  Variation is function: Are single cell differences functionally important? , 2015, BioEssays : news and reviews in molecular, cellular and developmental biology.

[62]  Thomas Ried,et al.  From Silencing to Gene Expression Real-Time Analysis in Single Cells , 2004, Cell.

[63]  Héctor Corrada Bravo,et al.  Gene Expression Signatures Based on Variability can Robustly Predict Tumor Progression and Prognosis , 2015, Cancer informatics.

[64]  Guillaume Devailly,et al.  Gene expression variability in mammalian embryonic stem cells using single cell RNA-seq data , 2016, Comput. Biol. Chem..

[65]  D. Larson,et al.  Direct observation of frequency modulated transcription in single cells using light activation , 2013, eLife.

[66]  I. Petersen,et al.  Pan-cancer analysis of somatic copy-number alterations implicates IRS4 and IGF2 in enhancer hijacking , 2016, Nature Genetics.

[67]  Julian R. E. Davis,et al.  Dynamic Analysis of Stochastic Transcription Cycles , 2011, PLoS biology.

[68]  Keji Zhao,et al.  CTCF-Mediated Enhancer-Promoter Interaction Is a Critical Regulator of Cell-to-Cell Variation of Gene Expression. , 2017, Molecular cell.

[69]  Russell B. Fletcher,et al.  Deconstructing Olfactory Stem Cell Trajectories at Single-Cell Resolution. , 2017, Cell stem cell.

[70]  Sydney M. Shaffer,et al.  Rare cell variability and drug-induced reprogramming as a mode of cancer drug resistance , 2017, Nature.

[71]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[72]  L. Pelkmans,et al.  Control of Transcript Variability in Single Mammalian Cells , 2015, Cell.

[73]  Jennifer A. Mitchell,et al.  Preferential associations between co-regulated genes reveal a transcriptional interactome in erythroid cells , 2010, Nature Genetics.

[74]  Marshall J. Levesque,et al.  Visualizing SNVs to quantify allele-specific expression in single cells , 2013, Nature Methods.

[75]  Thomas N. Sato,et al.  Whole-mount single molecule FISH method for zebrafish embryo , 2015, Scientific Reports.

[76]  M. Girvan,et al.  A pathway-centric view of spatial proximity in the 3D nucleome across cell lines , 2015, Scientific Reports.

[77]  Maxwell Z. Wilson,et al.  Tracing Information Flow from Erk to Target Gene Induction Reveals Mechanisms of Dynamic and Combinatorial Control. , 2017, Molecular cell.

[78]  Perry Evans,et al.  The BET Protein BRD2 Cooperates with CTCF to Enforce Transcriptional and Architectural Boundaries. , 2017, Molecular cell.

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