1. Volume 4, Issue 6, Pages 636-644.e9 (June 2017)
Time-Resolved Proteomics Extends Ribosome Profiling-Based Measurements of Protein Synthesis Dynamics 
Tzu-Yu Liu, Hector H. Huang, Diamond Wheeler, Yichen Xu, James A. Wells, Yun S. Song, Arun P. Wiita 
Cell Systems 
Volume 4, Issue 6, Pages e9 (June 2017)
DOI: /j.cels
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2. Figure 1
Direct Monitoring of Protein Synthesis by Targeted pSILAC Mass Spectrometry with Simultaneous Measurement of Transcript Abundance and Ribosome Footprint Density
(A) Example time-course SRM data for peptides from PROF1 and DDX5. Red traces, “light” channel intensity (degraded from baseline); blue traces, “heavy” channel intensity (newly synthesized post-stable isotope pulse). Each trace represents added intensity of all monitored SRM transitions (four per peptide per channel). Inset: intensity values for each channel plotted over time; error bars reflect ±SD from replicate assays.
(B) Relative mRNA abundance and ribosome footprint read density (ratio versus 0 hr [0h], in RPKM) move together over the time course. Changes in protein abundance are not as prominent as transcript-level changes. SRM measurements of total protein here refers to the sum of all peptide intensities in light and heavy channels.
Cell Systems 2017 4, e9DOI: ( /j.cels )
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3. Figure 2
Measuring and Modeling Translational Rate by pSILAC Proteomics and Ribosome Profiling
(A) Early in the time course, before any biochemical evidence of translational inhibition (Figure S2), comparison of average ribosome footprint density on transcript coding sequence and newly synthesized proteins per cell, measured by SRM intensity and extrapolated based on iBAQ estimate of total protein copies per cell, show a strong correlation (Pearson’s R = 0.80 on log-transformed data). Red line is line of best fit, with slope = 0.97 (95% confidence interval 0.86–1.06). Molecules synthesized per cell by SRM represent “heavy” protein copies at 12 hr (12h) minus “heavy” copies at 0 hr (see STAR Methods); footprint data are average RPKM from the 6, 12, and 24 hr time points.
(B) Example plots from proteins EIF6 and YWHAE show proteomic data. Lines represent fits from model fitting based on orthogonal splines with linearity constraints (see STAR Methods). Solid lines, total protein; short dashed lines, degradation; long dashed lines, synthesis.
(C) Differential rate equation model describes protein abundance as a function of protein synthesis and degradation as measured from proteomic experiments and mRNA-seq. The primary free fitting parameter kgs(t) describes the number of proteins synthesized per transcript per unit time, Pgd(t) is abundance of “light” proteins, kgd is protein degradation rate constant derived from single-exponential fit to light channel data, Mg is absolute transcript abundance as derived from mRNA-seq data, and Pgs(t) is abundance of newly synthesized “heavy” protein. The definition of translational efficiency from ribosome profiling literature is denoted by TE per gene: ribosome footprint read density rg(t) divided by mRNA-seq read density mg(t) (both in RPKM). The normalized translational efficiency is denoted by TE˜: ribosome footprint read density normalized to mitochondrial footprints Rg(t) divided by absolute transcript abundance Mg(t).
(D) Baseline comparison of TE and kgs(t) show that they are correlated, but imperfectly (Pearson’s R = 0.58 on log-transformed data).
(E–G) TE (E), kgs (F), and TE˜ (G) plotted across all 272 proteins across the time course, as a ratio to the value at 0 hr (0h), shows no change in TE by standard analysis but similar changes in translational rate as measured by proteomics (F) and mitochondrial-corrected ribosome footprints (G).
Cell Systems 2017 4, e9DOI: ( /j.cels )
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4. Figure 3
Biochemical Measurements of Protein Synthesis and Translational Efficiency Are Consistent with Targeted pSILAC and Allow for Simulations of Protein Synthesis Based on Experimental Data
(A) Puromycin incorporation into nascent protein chains (1 hr pulse of puromycin added at each time point prior to cell harvest; western blot with anti-puromycin antibody) demonstrates decreased global protein synthesis at later time points.
(B) Simulation of protein abundance using the differential equation we proposed with the derived mRNA abundance Mg(t), degradation rate constant kgd and translational rate parameter kgs(t). At each sampling time τ we simulated the protein abundance after 1 hr with the initial conditions that the protein abundance at τ = 0 was 0. The heatmaps were normalized to the first column to show the relative abundance.
(C) Relative intensity in puromycin incorporation shows a slight increase at 6 hr and 12 hr and then a decrease in synthesis at 36 hr and 48 hr. The line represents fits using degree-3 orthogonal natural cubic splines with four knots and linearity constraints (see STAR Methods).
(D) The ratio of total ribosome footprint reads in each sample to the total number of ChrM-mapping reads, an independent measurement of global protein synthesis, also shows a very similar decrease across the time course. Here, the total number of ChrM-mapping reads is the sum of the individual genes in Figure S2C. The line represents the orthogonal natural cubic spline fits.
(E) The computationally estimated changes in global protein synthetic capacity Gr(t) (STAR Methods) demonstrates a very similar pattern to both the puromycin incorporation measurements (C) and the ratio of total ribosome footprint reads to the total number of ChrM-mapping reads (D).
(F) The total mRNA nucleotide abundance Gm(t) per cell is obtained from the biochemical data shown in Figure S1D. The line represents the orthogonal natural cubic spline fits.
(G) The ratio of the relative intensity in puromycin incorporation (C) to the total mRNA nucleotide abundance (F) is used to represent bulk translational efficiency from biochemistry experiments. This ratio shows a global translational repression over the time course. The line represents the orthogonal natural cubic spline fits.
(H) The average normalized translational rate parameter 1N∑g=1Nkgs(t)/kgs(0) derived from pSILAC proteomics also shows a global translational repression, similar to the pattern observed for the independent biochemical measurements in (G). The line represents the orthogonal natural cubic spline fits.
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5. Figure 4
A Dynamic Model Predicts Absolute Protein Synthesis under Conditions of Cellular Stress
(A) Simulation of absolute protein copies synthesized as a function of different levels of translational capacity (denoted by Gr(t)) in MM1.S, with inputs of iBAQ protein copy number, ribosome footprint density, mRNA-seq, β, and kgd from MM1.S data. Three conditions are considered: Gr(t) being constant, as in untreated cells (black); Gr(t) varying as found in low-dose bortezomib (btz)-treated MM1.S (blue); and Gr(t) decreasing toward zero, as in high-dose bortezomib-treated MM1.S (green).
(B) The simulated heavy channel protein abundance Pgs(t) normalized by the heavy channel protein abundance at 48 hr under constant Gr(t), denoted by Pgs∗(48) in MM1.S. Under high-dose btz, protein synthesis is strongly curtailed, consistent with Wiita et al. (2013).
(C) The simulated total protein abundance Pgs(t)+Pgd(t) normalized by the total protein abundance at 48 hr under constant Gr(t), denoted by Pgs∗(48)+Pgd∗(48). High-dose btz again shows a significant decrease in total protein abundance.
(D) Untreated Epstein-Barr virus-immortalized B cells also show a strong correlation (Pearson’s R = 0.84 on log-transformed data) between absolute protein synthesis and footprint RPKM as in Figure 2A. Red line is line of best fit, with slope = 0.93 (95% confidence interval 0.84–1.02).
(E) SRM pSILAC-measured absolute protein copy number synthesis per B cell (left) compared with simulated estimates of total absolute proteins synthesized in untreated B-cells over 48 hr using quantitative model (right), with inputs of iBAQ protein copy number, ribosome footprint density, and mRNA-seq measured in B cells and β and kgd from MM1.S data, show good agreement.
Cell Systems 2017 4, e9DOI: ( /j.cels )
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6. Cell Systems 2017 4, 636-644.e9DOI: (10.1016/j.cels.2017.05.001)
Copyright © 2017 The Authors Terms and Conditions