Single-cell analysis of transcription kinetics across the cell cycle

Transcription is a highly stochastic process. To infer transcription kinetics for a gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to the prediction of a theoretical model. However, the reliability of this procedure is limited because the measured mRNA numbers represent integration over the mRNA lifetime, contribution from multiple gene copies, and mixing of cells from different cell-cycle phases. We address these limitations by simultaneously quantifying nascent and mature mRNA in individual cells, and incorporating cell-cycle effects in the analysis of mRNA statistics. We demonstrate our approach on Oct4 and Nanog in mouse embryonic stem cells. Both genes follow similar two-state kinetics. However, Nanog exhibits slower ON/OFF switching, resulting in increased cell-to-cell variability in mRNA levels. Early in the cell cycle, the two copies of each gene exhibit independent activity. After gene replication, the probability of each gene copy to be active diminishes, resulting in dosage compensation. DOI: http://dx.doi.org/10.7554/eLife.12175.001

[1]  D. Gillespie.  Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[2]  B. Barlogie,et al.  Automatic processing and interpretation of DNA distributions: comparison of several techniques. , 1978, Computers and biomedical research, an international journal.

[3]  Yamamura Ken-ichi,et al.  Efficient selection for high-expression transfectants with a novel eukaryotic vector , 1991 .

[4]  H. Niwa,et al.  Efficient selection for high-expression transfectants with a novel eukaryotic vector. , 1991, Gene.

[5]  P. Avner,et al.  X-chromosome inactivation in mammals. , 1997, Annual review of genetics.

[6]  H. Schöler,et al.  Differential expression of the Oct-4 transcription factor during mouse germ cell differentiation , 1998, Mechanisms of Development.

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

[8]  J. Miyazaki,et al.  Quantitative expression of Oct-3/4 defines differentiation, dedifferentiation or self-renewal of ES cells , 2000, Nature Genetics.

[9]  M. Thattai,et al.  Intrinsic noise in gene regulatory networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[10]  N. Hastie,et al.  Transcriptome analysis of human autosomal trisomy. , 2002, Human molecular genetics.

[11]  J. Nichols,et al.  Functional Expression Cloning of Nanog, a Pluripotency Sustaining Factor in Embryonic Stem Cells , 2003, Cell.

[12]  M. Gertsenstein,et al.  Mouse in red: Red fluorescent protein expression in mouse ES cells, embryos, and adult animals , 2004, Genesis.

[13]  P. Swain,et al.  Gene Regulation at the Single-Cell Level , 2005, Science.

[14]  J. Raser,et al.  Noise in Gene Expression: Origins, Consequences, and Control , 2005, Science.

[15]  E. Cox,et al.  Real-Time Kinetics of Gene Activity in Individual Bacteria , 2005, Cell.

[16]  S. Dalton,et al.  LIF/STAT3 controls ES cell self-renewal and pluripotency by a Myc-dependent mechanism , 2005, Development.

[17]  M. Khammash,et al.  The finite state projection algorithm for the solution of the chemical master equation. , 2006, The Journal of chemical physics.

[18]  J. Malley,et al.  Global analysis of X-chromosome dosage compensation , 2006, Journal of biology.

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

[20]  Nir Friedman,et al.  Linking stochastic dynamics to population distribution: an analytical framework of gene expression. , 2006, Physical review letters.

[21]  J. Nichols,et al.  Nanog safeguards pluripotency and mediates germline development , 2007, Nature.

[22]  D. Larson,et al.  Single-RNA counting reveals alternative modes of gene expression in yeast , 2008, Nature Structural &Molecular Biology.

[23]  A. Oudenaarden,et al.  Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences , 2008, Cell.

[24]  Scott A. Rifkin,et al.  Imaging individual mRNA molecules using multiple singly labeled probes , 2008, Nature Methods.

[25]  Vahid Shahrezaei,et al.  Analytical distributions for stochastic gene expression , 2008, Proceedings of the National Academy of Sciences.

[26]  C. Lim,et al.  Regulated Fluctuations in Nanog Expression Mediate Cell Fate Decisions in Embryonic Stem Cells , 2009, PLoS biology.

[27]  Alexei A. Sharov,et al.  Database for mRNA Half-Life of 19 977 Genes Obtained by DNA Microarray Analysis of Pluripotent and Differentiating Mouse Embryonic Stem Cells , 2008, DNA research : an international journal for rapid publication of reports on genes and genomes.

[28]  Ge Guo,et al.  Nanog Is the Gateway to the Pluripotent Ground State , 2009, Cell.

[29]  L. A. Sepúlveda,et al.  Lysogen stability is determined by the frequency of activity bursts from the fate-determining gene , 2010, Molecular systems biology.

[30]  M. Selbach,et al.  Global quantification of mammalian gene expression control , 2011, Nature.

[31]  Sanjay Tyagi,et al.  Single-Molecule Imaging of Transcriptionally Coupled and Uncoupled Splicing , 2011, Cell.

[32]  Richard A Young,et al.  Control of the Embryonic Stem Cell State , 2011, Cell.

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

[34]  Johan Paulsson,et al.  Non-genetic heterogeneity from stochastic partitioning at cell division , 2011, Nature Genetics.

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

[36]  Tao Wang,et al.  Cell-Cycle Control of Developmentally Regulated Transcription Factors Accounts for Heterogeneity in Human Pluripotent Cells , 2013, Stem cell reports.

[37]  Ido Golding,et al.  Measuring mRNA copy number in individual Escherichia coli cells using single-molecule fluorescent in situ hybridization , 2013, Nature Protocols.

[38]  M. Khammash,et al.  Systematic Identification of Signal-Activated Stochastic Gene Regulation , 2013, Science.

[39]  D. Henrique,et al.  Generation and Characterization of a Novel Mouse Embryonic Stem Cell Line with a Dynamic Reporter of Nanog Expression , 2013, PloS one.

[40]  R. Jaenisch,et al.  Single-cell analysis reveals that expression of nanog is biallelic and equally variable as that of other pluripotency factors in mouse ESCs. , 2013, Cell stem cell.

[41]  Ido Golding,et al.  Genetic Determinants and Cellular Constraints in Noisy Gene Expression , 2013, Science.

[42]  P. Cahan,et al.  Origins and implications of pluripotent stem cell variability and heterogeneity , 2013, Nature Reviews Molecular Cell Biology.

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

[44]  M. Kirschner,et al.  Dynamics extracted from fixed cells reveal feedback linking cell growth to cell cycle , 2013, Nature.

[45]  C. J. Zopf,et al.  Cell-Cycle Dependence of Transcription Dominates Noise in Gene Expression , 2013, PLoS Comput. Biol..

[46]  A. van Oudenaarden,et al.  Allele-specific detection of single mRNA molecules in situ , 2013, Nature Methods.

[47]  Jane Kondev,et al.  Regulation of noise in gene expression. , 2013, Annual review of biophysics.

[48]  D. Ish-Horowicz,et al.  Transcript processing and export kinetics are rate-limiting steps in expressing vertebrate segmentation clock genes , 2013, Proceedings of the National Academy of Sciences.

[49]  Heiko Lickert,et al.  Biallelic expression of nanog protein in mouse embryonic stem cells. , 2013, Cell stem cell.

[50]  Jordi Garcia-Ojalvo,et al.  A competitive protein interaction network buffers Oct4-mediated differentiation to promote pluripotency in embryonic stem cells , 2013, Molecular systems biology.

[51]  Petr Svoboda,et al.  Stochastic NANOG fluctuations allow mouse embryonic stem cells to explore pluripotency , 2014, Development.

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

[53]  Tetsushi Sakuma,et al.  Stochastic promoter activation affects Nanog expression variability in mouse embryonic stem cells , 2014, Scientific Reports.

[54]  A. Oudenaarden,et al.  Validation of noise models for single-cell transcriptomics , 2014, Nature Methods.

[55]  Rob Phillips,et al.  Promoter architecture dictates cell-to-cell variability in gene expression , 2014, Science.

[56]  Ludovic Vallier,et al.  The Cell-Cycle State of Stem Cells Determines Cell Fate Propensity , 2014, Cell.

[57]  Brian Munsky,et al.  Transcription Factors Modulate c-Fos Transcriptional Bursts , 2014, Cell reports.

[58]  Michael B. Elowitz,et al.  Dynamic Heterogeneity and DNA Methylation in Embryonic Stem Cells , 2014, Molecular cell.

[59]  M. Torres-Padilla,et al.  Transcription factor heterogeneity in pluripotent stem cells: a stochastic advantage , 2014, Development.

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

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

[62]  H. Ng,et al.  Deterministic Restriction on Pluripotent State Dissolution by Cell-Cycle Pathways , 2015, Cell.

[63]  Heng Xu,et al.  COMBINING PROTEIN AND mRNA QUANTIFICATION TO DECIPHER TRANSCRIPTIONAL REGULATION , 2015, Nature Methods.

[64]  Huiyi Chen,et al.  Genome-wide study of mRNA degradation and transcript elongation in Escherichia coli , 2015, Molecular systems biology.

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

[66]  A. Raj,et al.  Single mammalian cells compensate for differences in cellular volume and DNA copy number through independent global transcriptional mechanisms. , 2015, Molecular cell.

[67]  Jeffrey W. Smith,et al.  Stochastic Gene Expression in a Single Cell , 2022 .