Missing Value Monitoring Enhances the Robustness in Proteomics Quantitation

In global proteomic analysis, it is estimated that proteins span from millions to less than 100 copies per cell. The challenge of protein quantitation by classic shotgun proteomic techniques relies on the presence of missing values in peptides belonging to low-abundance proteins that lowers intraruns reproducibility affecting postdata statistical analysis. Here, we present a new analytical workflow MvM (missing value monitoring) able to recover quantitation of missing values generated by shotgun analysis. In particular, we used confident data-dependent acquisition (DDA) quantitation only for proteins measured in all the runs, while we filled the missing values with data-independent acquisition analysis using the library previously generated in DDA. We analyzed cell cycle regulated proteins, as they are low abundance proteins with highly dynamic expression levels. Indeed, we found that cell cycle related proteins are the major components of the missing values-rich proteome. Using the MvM workflow, we doubled the number of robustly quantified cell cycle related proteins, and we reduced the number of missing values achieving robust quantitation for proteins over ∼50 molecules per cell. MvM allows lower quantification variance among replicates for low abundance proteins with respect to DDA analysis, which demonstrates the potential of this novel workflow to measure low abundance, dynamically regulated proteins

Wiring diagram of possible reactions leading to APC/C^MCC2 (inhibited APC/C) formation.

APC/CCdc20 is the active form of the APC/C that initiates anaphase. Reaction (i) indicates APC/CMCC2 formation by MCC1 binding to APC/CCdc20. Reactions marked with (ii) indicate APC/CMCC2 formation by MCC2 binding to APC/C.</p

Different networks for APC/C^MCC2 formation.

Free Mad2 and Mad3 were combined in the variable ‘Mad’. (i) Sequential inhibition network, which is characterized by MCC1 binding to APC/CCdc20. (ii) Competitive inhibition network, which is characterized by competition between MCC2 and Cdc20 for APC/C binding. (iii) Different representations of the same mixed network, containing the reactions of both (i) and (ii). Network (iii) can be generated either by adding reaction 4 and 5 (MCC2 formation and MCC2 binding to APC/C) to the sequential inhibition network, or by adding reaction 3 (MCC1 binding to APC/CCdc20) to the competitive inhibition network. Note that in networks (ii) and (iii), some molecular species are listed more than once at different positions in order to simplify the depiction of the overall network structure.</p

Timescale Separation in the Model of the G₂/M Network

<p>The exact solution (black lines) is compared with the QSSA, blue lines in (A), and to the tQSSA, red lines in B. Arrows indicate the direction of time, whereas the distance between consecutive dots on the lines is 1 min. Equations in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030045#pcbi-0030045-t004" target="_blank">Table 4</a>, parameter values in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030045#pcbi-0030045-t002" target="_blank">Table 2</a>.</p

Missing Value Monitoring Enhances the Robustness in Proteomics Quantitation

In global proteomic analysis, it is estimated that proteins span from millions to less than 100 copies per cell. The challenge of protein quantitation by classic shotgun proteomic techniques relies on the presence of missing values in peptides belonging to low-abundance proteins that lowers intraruns reproducibility affecting postdata statistical analysis. Here, we present a new analytical workflow MvM (missing value monitoring) able to recover quantitation of missing values generated by shotgun analysis. In particular, we used confident data-dependent acquisition (DDA) quantitation only for proteins measured in all the runs, while we filled the missing values with data-independent acquisition analysis using the library previously generated in DDA. We analyzed cell cycle regulated proteins, as they are low abundance proteins with highly dynamic expression levels. Indeed, we found that cell cycle related proteins are the major components of the missing values-rich proteome. Using the MvM workflow, we doubled the number of robustly quantified cell cycle related proteins, and we reduced the number of missing values achieving robust quantitation for proteins over ∼50 molecules per cell. MvM allows lower quantification variance among replicates for low abundance proteins with respect to DDA analysis, which demonstrates the potential of this novel workflow to measure low abundance, dynamically regulated proteins

Numerical solutions for all models.

(A) Steady state concentration of each species for varying total Cdc20 concentration and for networks (i), (ii), and (iii). All concentrations are normalized to the total concentration of Mad. APC/C is present at a Mad-normalized value of 0.5 (see S4 Fig for APC/Ctotal = 1). The molecular species are organized into three groups: those that include Cdc20, APC/C, or Mad. The plots show how these three species are distributed among different complexes. While total APC/C and total Mad remain constant, total Cdc20 increases along the x-axis. MCC2 and APC/CMCC2 include two molecules of Cdc20, thus their concentration is counted twice in the plots representing the Cdc20-species. The vertical white dashed lines in the panels for APC/C indicate the physiological Cdc20 range based on reported measurements. Full model descriptions in S1 Text. (B) Wiring diagram and simulations analogous to (A) for a variation of network (iii) including reactions 1, 2, 3 and 4, but not reaction 5 (shaded). By increasing the MCC2 association rate (reaction 4, marked in grey), the network switches from a sequential inhibition network behavior (association rate = 0) to a competitive inhibition network behavior (association rate > 0). The dissociation rate of reaction 4 remained unchanged, while the association rate was modified.</p

Approximations for limiting Cdc20 (Cdc20_totaltotal, APC/C_total) or high Cdc20 (Cdc20_total>Mad_total, APC/C_total) concentrations.

(A) At limiting Cdc20 concentrations, APC/CMCC2 exceeds APC/CCdc20 for APC/CCdc20>k. The values for k are illustrative and not meant to be physiologically relevant. (B) Schematic representation of the funneling effect (grey path), which results in preferred APC/CMCC2 formation over APC/CCdc20. In both sequential and competitive inhibition three binding reactions are required for APC/CMCC2 formation (grey), whereas only one single binding reaction (reaction 2) is required for APC/CCdc20 formation. (C) At high Cdc20 concentrations, the sequential inhibition model reduces to reaction 3 (boxed region). In this regime, the ratio of APC/CCdc20 to APC/CMCC2 does not depend on Cdc20 concentration (bottom graph, S1 Text, eq. 29). (D) At high Cdc20 concentrations, the competitive inhibition model reduces to reactions 2 and 5 (boxed region) and the ratio of APC/CCdc20 to APC/CMCC2 increases linearly with Cdc20 concentration (bottom graph, S1 Text, eq. 64).</p

Sensitivity of the mitotic checkpoint response.

(A) Steady state values for APC/CCdc20 (normalized to total Mad) when varying the rate of MCC1 formation for models (i), (ii), and (iii). It is reasonable to assume that the association rate for an active checkpoint lies between 5 and 500 (in units of 1/min*[total Mad]) [14, 42]. (B) By decreasing the association rate kass_AMCC2 of reaction 3 in the sequential inhibition model (top), the funneling effect is weakened, as shown by checkpoint response curves for different values of kass_AMCC2 (bottom). The same effect can be seen in the competitive and combined models, which share the same funnelling effect with the sequential inhibition model.</p