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. 2011 Apr;22(4):773-83.
doi: 10.1007/s13361-011-0075-2. Epub 2011 Feb 15.

Improving proteome coverage on a LTQ-Orbitrap using design of experiments

Genna L Andrews  1 , Ralph A Dean, Adam M Hawkridge, David C Muddiman
Affiliations

Improving proteome coverage on a LTQ-Orbitrap using design of experiments

Genna L Andrews et al. J Am Soc Mass Spectrom. 2011 Apr.

Abstract

Design of experiments (DOE) was used to determine improved settings for a LTQ-Orbitrap XL to maximize proteome coverage of Saccharomyces cerevisiae. A total of nine instrument parameters were evaluated with the best values affording an increase of approximately 60% in proteome coverage. Utilizing JMP software, 2 DOE screening design tables were generated and used to specify parameter values for instrument methods. DOE 1, a fractional factorial design, required 32 methods fully resolving the investigation of six instrument parameters involving only half the time necessary for a full factorial design of the same resolution. It was advantageous to complete a full factorial design for the analysis of three additional instrument parameters. Measured with a maximum of 1% false discovery rate, protein groups, unique peptides, and spectral counts gauged instrument performance. Randomized triplicate nanoLC-LTQ-Orbitrap XL MS/MS analysis of the S. cerevisiae digest demonstrated that the following five parameters significantly influenced proteome coverage of the sample: (1) maximum ion trap ionization time; (2) monoisotopic precursor selection; (3) number of MS/MS events; (4) capillary temperature; and (5) tube lens voltage. Minimal influence on the proteome coverage was observed for the remaining four parameters (dynamic exclusion duration, resolving power, minimum count threshold to trigger a MS/MS event, and normalized collision energy). The DOE approach represents a time- and cost-effective method for empirically optimizing MS-based proteomics workflows including sample preparation, LC conditions, and multiple instrument platforms.

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Figures

Figure 1
Figure 1
The experimental workflow investigating LTQ-Orbitrap MS/MS instrument parameters anticipating the increase of proteome coverage. JMP software afforded the generation of experiments for nanoLC-LTQ-Orbitrap MS/MS investigation of the tryptic digest of S. cerevisiae. Following data processing with the MASCOT and ProteoIQ platform, the results were provided for DOE analysis with JMP demonstrating significant variables
Figure 2
Figure 2
Half normal quantile probability plots and corresponding bar graphs for each response for the determination of significant variables for DOE 1 FracFD. The half normal quantile curves (blue line) in each plot represent the normal distribution, and data points greatly deviating from the curves and with the appropriate statistical measurements indicate a significant variable. Those factors in the red hashed circle are significant variables. The contrast, Lenth t-ratio, and individual p-Value are different statistical measurements concluding the significance of each factor. The Lenth t-ratio is calculated by dividing the contrast value by the Lenth pseudo-standard error which is an estimate of error based on the inactive effects and generated with each half normal plot [43]. The vertical blue lines on the bar graphs represent the default cutoff p-Value (0.05) for indication of the degree of significance of each parameter. The plots and bar graphs are organized by response as follows: (a) # protein groups; (b) # unique peptides; and (c) # spectral counts
Figure 3
Figure 3
ProteoIQ output of peptide discriminant value distributions for the instrument method that generated the most protein identifications. The number of peptides is plotted versus the discriminant value or the measurement of peptide assignment accuracy. The observed values, predicted positive, and predicted negative data are included in each plot. (a) All peptides, (b) 2+ charge-state peptides, (c) 3+ charge-state peptides, and (d) 4+ charge-state peptides are included for comparison as a function of confident peptide identification. It is desirable to have the greatest area overlap of the observed and predicted positive values
Figure 4
Figure 4
Half normal quantile probability plots and corresponding bar graphs for each response demonstrating the significance of each factor in DOE 2 FullFD organized equivalent to Figure 2. Those factors that deviate from the half normal quantile curve (blue line) and within the red hashed circle are significant variables
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References

    1. Rudnick PA, Clauser KR, Kilpatrick LE, Tchekhovskoi DV, Neta P, Blonder N, Billheimer DD, Blackman RK, Bunk DM, Cardasis HL, Ham AJL, Jaffe JD, Kinsinger CR, Mesri M, Neubert TA, Schilling B, Tabb DL, Tegeler TJ, Vega-Montoto L, Variyath AM, Wang M, Wang P, Whiteaker JR, Zimmerman LJ, Carr SA, Fisher SJ, Gibson BW, Paulovich AG, Regnier FE, Rodriguez H, Spiegelman C, Tempst P, Liebler DC, Stein SE. Performance metrics for liquid chromatography-tandem mass spectrometry systems in proteomics analyses. Mol. Cell Proteom. 2010;9(2):225–241. - PMC - PubMed
    1. Paulovich AG, Billheimer D, Ham AJL, Vega-Montoto L, Rudnick PA, Tabb DL, Wang P, Blackman RK, Bunk DM, Cardasis HL, Clauser KR, Kinsinger CR, Schilling B, Tegeler TJ, Variyath AM, Wang M, Whiteaker JR, Zimmerman LJ, Fenyo D, Carr SA, Fisher SJ, Gibson BW, Mesri M, Neubert TA, Regnier FE, Rodriguez H, Spiegelman C, Stein SE, Tempst P, Liebler DC. Interlaboratory study characterizing a yeast performance standard for benchmarking LC-MS platform performance. Mol. Cell Proteom. 2010;9(2):242–254. - PMC - PubMed
    1. Tabb DL, Vega-Montoto L, Rudnick PA, Variyath AM, Ham AJL, Bunk DM, Kilpatrick LE, Billheimer DD, Blackman RK, Cardasis HL, Carr SA, Clauser KR, Jaffe JD, Kowalski KA, Neubert TA, Regnier FE, Schilling B, Tegeler TJ, Wang M, Wang P, Whiteaker JR, Zimmerman LJ, Fisher SJ, Gibson BW, Kinsinger CR, Mesri M, Rodriguez H, Stein SE, Tempst P, Paulovich AG, Liebler DC, Spiegelman C. Repeatability and reproducibility in proteomic identifications by liquid chromatography-tandem mass spectrometry. J. Proteome Res. 2010;9(2):761–776. - PMC - PubMed
    1. Oberacher H, Walcher W, Huber CG. Effect of instrument tuning on the detectability of biopolymers in electrospray ionization mass spectrometry. J. Mass Spectrom. 2003;38(1):10–116. - PubMed
    1. Soule MCK, Longnecker K, Giovannoni SJ, Kujawinski EB. Impact of instrument and experiment parameters on reproducibility of ultrahigh resolution ESI FT-ICR mass spectra of natural organic matter. Org. Geochem. 2010;41(8):725–733.

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