A.5. Construction of a cell-cycle time-resolved transcriptome

Away from the base case, a time-resolved transcriptome was estimated with the aim to model the variation of mRNA abundance during the cell cycle. In the absence of absolute genome-level mRNA quantification data for yeast, a simple model was used to combine absolute mRNA counts from a FISH experiment on a subset of genes with relative mRNA percentage values from an RNA-Seq experiment for all genes. This yields individual gene-level estimates for mRNA abundance time courses during the cell cycle.

FISH experiments were conducted for seven genes (SIC1, CLN2, CLB2, CLB5, PCL1, PCL9, SWE1) which are known to change their expression during the cell cycle (Amoussouvi et al. [1]). Data was available in the form of time courses of counts of fluorescence-labelled mRNA molecules for each of seven cell cycle phases (early G1, late G1, S, G2, P/M, Ana, T/C). A total of 500-1000 cells was counted for each gene, yielding a time-resolved frequency distribution of mRNA counts. The average values of these distributions were used as estimators of the absolute number of mRNA transcripts per gene in each cell cycle phase. Time points were mapped to the cell cycle phases with the help of four genetic and morphological markers (presence and size of bud, shape and distribution of the DAPI-stained nucleus, localization of TagGFP-labelled transcription repressor Whi5, number and localization of spindle pole body visualized by mTurquoise-labelled Spc42) (Trcek et al. [52], [53]).

On the other hand, RNA-Seq data was available for 6650 genes as time series with five-minute time steps at which the relative abundance was quantified (normalized to 100% for every time step) (Teufel et al. [51]).

For each of the seven genes of the FISH experiment a time-resolved scaling factor was then derived by dividing the known absolute mRNA values from FISH by the known relative mRNA percentages from RNA-Seq. Depending on the cell cycle phase, the scaling factor varied across the genes so that an aggregation method was sought to combine the seven observed gene-level factors into one. Median, arithmetic mean and geometric mean yielded similar results, with the possible exception of the early G1 phase, where the arithmetic mean was higher due to high values of SIC1 and PCL9 in that phase. On this basis, the median was used for all times.

This yielded a qualitatively and quantitatively plausible candidate for a through-the-cell-cycle transcriptome (Fig. 6) which can be used to conduct time-resolved simulations. It is remarkable that the range of the time-resolved transcript counts (between 73000 in early G1 phase and 15000 in G2 phase) nearly coincides with the ranges given in the literature (Milo et al. [39, IDs 108248, 106763, 103023, 102988]).