Estimation of fluorescence-tagged RNA numbers from spot intensities

MOTIVATION Present research on gene expression using live cell imaging and fluorescent proteins or tagged RNA requires accurate automated methods of quantification of these molecules from the images. Here, we propose a novel automated method for classifying pixel intensities of fluorescent spots to RNA numbers. RESULTS The method relies on a new model of intensity distributions of tagged RNAs, for which we estimated parameter values in maximum likelihood sense from measurement data, and constructed a maximum a posteriori classifier to estimate RNA numbers in fluorescent RNA spots. We applied the method to estimate the number of tagged RNAs in individual live Escherichia coli cells containing a gene coding for an RNA with MS2-GFP binding sites. We tested the method using two constructs, coding for either 96 or 48 binding sites, and obtained similar distributions of RNA numbers, showing that the method is adaptive. We further show that the results agree with a method that uses time series data and with quantitative polymerase chain reaction measurements. Lastly, using simulated data, we show that the method is accurate in realistic parameter ranges. This method should, in general, be applicable to live single-cell measurements of low-copy number fluorescence-tagged molecules. AVAILABILITY AND IMPLEMENTATION MATLAB extensions written in C for parameter estimation and finding decision boundaries are available under Mozilla public license at http://www.cs.tut.fi/%7ehakkin22/estrna/ CONTACT: andre.ribeiro@tut.fi.

[1]  O. Sliusarenko,et al.  Spatial organization of the flow of genetic information in bacteria , 2010, Nature.

[2]  M Kandhavelu,et al.  Single-molecule dynamics of transcription of the lar promoter , 2012, Physical biology.

[3]  D. Peabody,et al.  The RNA binding site of bacteriophage MS2 coat protein. , 1993, The EMBO journal.

[4]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[5]  Olli Yli-Harja,et al.  Cell segmentation by multi-resolution analysis and maximum likelihood estimation (MAMLE) , 2013, BMC Bioinformatics.

[6]  X. Xie,et al.  Probing Gene Expression in Live Cells, One Protein Molecule at a Time , 2006, Science.

[7]  Thomas D. Schmittgen,et al.  Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. , 2001, Methods.

[8]  T. Elston,et al.  Stochasticity in gene expression: from theories to phenotypes , 2005, Nature Reviews Genetics.

[9]  Olli Yli-Harja,et al.  Regulation of mean and noise of the in vivo kinetics of transcription under the control of the lac/ara‐1 promoter , 2012, FEBS letters.

[10]  Ertugrul M. Ozbudak,et al.  Regulation of noise in the expression of a single gene , 2002, Nature Genetics.

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

[12]  Arkady B. Khodursky,et al.  Global analysis of mRNA decay and abundance in Escherichia coli at single-gene resolution using two-color fluorescent DNA microarrays , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Paul J. Choi,et al.  Quantifying E. coli Proteome and Transcriptome with Single-Molecule Sensitivity in Single Cells , 2010, Science.

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

[15]  Ido Golding,et al.  RNA dynamics in live Escherichia coli cells. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[16]  Antti Häkkinen,et al.  Asymmetric Disposal of Individual Protein Aggregates in Escherichia coli, One Aggregate at a Time , 2012, Journal of bacteriology.

[17]  New York Dover,et al.  ON THE CONVERGENCE PROPERTIES OF THE EM ALGORITHM , 1983 .

[18]  S. S. Wilks.  The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses , 1938 .

[19]  Olli Yli-Harja,et al.  Dynamics of transcription driven by the tetA promoter, one event at a time, in live Escherichia coli cells , 2012, Nucleic acids research.

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