Dataset Open Access
All source data is provided as MAT-files, which can be opened in Matlab. In total, the source data contain 270 core data files and 540 processed data files. The data for each individual promoter is stored in a different directory and the 9 promoters are:
pSIP18_mut6 (also referred to as mutant A4)
pSIP18_mut21 (also referred to as mutant D6)
The data for the first 7 promoters was previously reported in (Hansen and O’Shea, 2013), though in an unnormalized form. That is, it was previously reported as a concentration per cell in arbitrary fluorescence units (AU). In the present manuscript, we have calibrated the data to obtain absolute abundances, such that the MAT-files now contain both the old AU concentration as well as absolute abundances, i.e. number of YFP molecules per cell. The calibration was performed as described in (Huang et al., 2016). Similarly, the data for the last 2 promoters (A4 and D6) was previously reported in its unnormalized form in (Hansen and O’Shea, 2015) and it is here also reported in the form of absolute abundances.
The MAT-files containing the raw data have the suffix “_size.mat”. The name of the MAT files describes the experiment. If the file name contains “DM”, then it is a single pulse. Thus, “SIP18_DM_40min_275nM_size.mat” refers to a single 40 min pulse with 275 nM 1-NM-PP1 for the SIP18 promoter. Similarly if the file name contains “FM”, e.g. “RTN2_FM_8_5min_690nM_size.mat” then it refers to eight 5 min pulses separated by 5 min intervals at 690 nM for the RTN2 promoter. Finally, if the file name contains “FM4”, e.g. “TKL2_FM4_15minINT_690nM_size.mat” then the experiment was four 5 min pulses separated by 15 min intervals at 690 nM for the TKL2 promoter. The concentration is the concentration of 1-NM-PP1 that was used and 100 nM, 275 nM, 690 nM and 3mM refers to approximately, 25%, 50%, 75% and 100% Msn2 activation. For full experimental details please see (Hansen and O’Shea, 2013; Hansen et al., 2015).
The “_size.mat” MAT-files contain the following variables:
CFP, CFP_molecules, CFP_raw and YFP, YFP_molecules, YFP_raw are Nx64 matrices, where each row N correspond to a different cell and the 64 columns correspond to the 64 experimentally measured timepoints corresponding to the “time” vector running from -5 min to 152.5 min in increments of 2.5 min and the 1NM-PP1 inhibitor was added at time 0. “CFP_raw” and “YFP_raw” contains raw, uncorrected data, so without photobleaching correction and background subtraction. “CFP” and “YFP” contain corrected data in arbitrary fluorescence units (AU) and report on the concentration (i.e. size normalized). Finally, “CFP_molecules” and “YFP_molecules” contains the total number of CFP and YFP molecules per cell (i.e. this is not a concentration, but the absolute abundance). The area of each cell at each timepoint can be found in the matrix “cell_size_pixels”. Since the cells are live and growing, this will tend to increase during the experiments. Occasionally large fluctuations can occur due to errors in cell segmentation or due to division. For full details on the image analysis and cell segmentation, please see (Hansen and O’Shea, 2013; Hansen et al., 2015).
The variables “inhibitor_conc” and “pulse_parameters” refer to the type of experiment and is also given by the name. “inhibitor_conc” gives the 1NMPP1 concentration: 100 nM, 275 nM, 690 nM or 3000 nM. “pulse_parameters” contains either 2 or 3 elements and given the dynamical pulse sequence parameters. Column 1 contains the number of pulses and column 2 the duration of the pulses. Column 3 gives the interval between the pulses if more than one pulse is used – otherwise column 3 is zero.
Moreover, on a more technical note it should be noted that the signal-to-noise of the CFP reporter is worse than the YFP reporter. Therefore, we always use the YFP reporter for quantitative analysis. Furthermore, the two other MAT-files “…MSN2.mat” and “…YFP.mat” contain processed data. Please see the ReadMe file on the code for a full description and how these were derived.
Finally, Supplementary Table 1 contains the model-inferred parameters for each promoter and condition.
Hansen, A.S., and O’Shea, E.K. (2013). Promoter decoding of transcription factor dynamics involves a trade-off between noise and control of gene expression. Mol. Syst. Biol.
Hansen, A.S., and O’Shea, E.K. (2015). Cis Determinants of Promoter Threshold and Activation Timescale. Cell Rep.
Hansen, A.S., Hao, N., and OShea, E.K. (2015). High-throughput microfluidics to control and measure signaling dynamics in single yeast cells. Nat. Protoc.
Huang, L., Pauleve, L., Zechner, C., Unger, M., Hansen, A.S., and Koeppl, H. (2016). Reconstructing dynamic molecular states from single-cell time series. J. R. Soc. Interface.