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Integrated multi-level quality control for proteomic profiling studies using mass spectrometry.

Cairns DA, Perkins DN, Stanley AJ, Thompson D, Barrett JH, Selby PJ, Banks RE - BMC Bioinformatics (2008)

Bottom Line: Manual inspection of those spectral data that were identified as being of poor quality confirmed the efficacy of the algorithms.Variance components analysis demonstrated the relatively small amount of technical variance attributable to day of profile generation and experimental array.The removal of these spectra at the initial stages of the analysis substantially improves the confidence of putative biomarker identification and allows inter-experimental comparisons to be carried out with greater confidence.

View Article: PubMed Central - HTML - PubMed

Affiliation: Cancer Research UK Clinical Centre, Leeds Institute of Molecular Medicine, St James's University Hospital, Leeds, UK. d.a.cairns@leeds.ac.uk

ABSTRACT

Background: Proteomic profiling using mass spectrometry (MS) is one of the most promising methods for the analysis of complex biological samples such as urine, serum and tissue for biomarker discovery. Such experiments are often conducted using MALDI-TOF (matrix-assisted laser desorption/ionisation time-of-flight) and SELDI-TOF (surface-enhanced laser desorption/ionisation time-of-flight) MS. Using such profiling methods it is possible to identify changes in protein expression that differentiate disease states and individual proteins or patterns that may be useful as potential biomarkers. However, the incorporation of quality control (QC) processes that allow the identification of low quality spectra reliably and hence allow the removal of such data before further analysis is often overlooked. In this paper we describe rigorous methods for the assessment of quality of spectral data. These procedures are presented in a user-friendly, web-based program. The data obtained post-QC is then examined using variance components analysis to quantify the amount of variance due to some of the factors in the experimental design.

Results: Using data from a SELDI profiling study of serum from patients with different levels of renal function, we show how the algorithms described in this paper may be used to detect systematic variability within and between sample replicates, pooled samples and SELDI chips and spots. Manual inspection of those spectral data that were identified as being of poor quality confirmed the efficacy of the algorithms. Variance components analysis demonstrated the relatively small amount of technical variance attributable to day of profile generation and experimental array.

Conclusion: Using the techniques described in this paper it is possible to reliably detect poor quality data within proteomic profiling experiments undertaken by MS. The removal of these spectra at the initial stages of the analysis substantially improves the confidence of putative biomarker identification and allows inter-experimental comparisons to be carried out with greater confidence.

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Selected QC tool output for section of study of patients with different levels of renal function using CM10 ProteinChips. Panel (A) is similar to Figure 1 panel (C). Panel (B) shows the mean spectra obtained from samples in the reference set and also individual spectra which appear to be on the edge of the PC space in the PC plots in panel (A).
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Figure 5: Selected QC tool output for section of study of patients with different levels of renal function using CM10 ProteinChips. Panel (A) is similar to Figure 1 panel (C). Panel (B) shows the mean spectra obtained from samples in the reference set and also individual spectra which appear to be on the edge of the PC space in the PC plots in panel (A).

Mentions: Figure 5 shows results for the CM10 ProteinChip part of the study of renal function. These plots are similar to those in Figure 1 which were described in Section 3.1 in that they are the results of the chip-to-chip QC. The equivalents figures for these results as Figure 1(A) and Figure 1(B) fail to show any systematic bias according to chip and spot. However, Figure 4(A) is different to the previously described experiment (c.f. Figure 1(C)) in that there are a number of projections of spectra which can be found on the edge of the three dimensional PCs space denoted by this panel of plots. In particular the QC spectra from chips 3, 5, 8, 9, 19 and 27 appear to be on the extremes of the space in all three plots. Additionally, these plots were shown to have Mahalanobis distances significantly different from zero in 6 dimensional PC space (p < 0.05, data not shown). In Figure 5(B) each of these spectra is shown along with the mean spectra formed by taking the mean over all data points from the spectra which make up the reference set (referred to in the legend as the reference spectrum). Spectra 3 and 5 quite clearly show a decrease in intensity to that observed in the reference spectrum in some of the larger peaks in conjunction with an increase in sinusoidal noise in the region between 2 and 4 kDa, making it apparent why these have been flagged by QC. Spectra 8, 9 and 19 are interesting in that they show a possibility of a drift in the calibration of the machine, evident in the larger peaks between 8 and 10 kDa. In spectra 9 and 19 this is exacerbated by a drop in the intensity in the 6–7 kDa region. Spectrum 27 similarly has a decreased intensity in some peaks, but not in the same manner as those in the other examples shown here. This demonstrates the flexibility of the PCA approach to QC identifying many different kinds of aberrant behaviour in the spectra through one device.


Integrated multi-level quality control for proteomic profiling studies using mass spectrometry.

Cairns DA, Perkins DN, Stanley AJ, Thompson D, Barrett JH, Selby PJ, Banks RE - BMC Bioinformatics (2008)

Selected QC tool output for section of study of patients with different levels of renal function using CM10 ProteinChips. Panel (A) is similar to Figure 1 panel (C). Panel (B) shows the mean spectra obtained from samples in the reference set and also individual spectra which appear to be on the edge of the PC space in the PC plots in panel (A).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2657802&req=5

Figure 5: Selected QC tool output for section of study of patients with different levels of renal function using CM10 ProteinChips. Panel (A) is similar to Figure 1 panel (C). Panel (B) shows the mean spectra obtained from samples in the reference set and also individual spectra which appear to be on the edge of the PC space in the PC plots in panel (A).
Mentions: Figure 5 shows results for the CM10 ProteinChip part of the study of renal function. These plots are similar to those in Figure 1 which were described in Section 3.1 in that they are the results of the chip-to-chip QC. The equivalents figures for these results as Figure 1(A) and Figure 1(B) fail to show any systematic bias according to chip and spot. However, Figure 4(A) is different to the previously described experiment (c.f. Figure 1(C)) in that there are a number of projections of spectra which can be found on the edge of the three dimensional PCs space denoted by this panel of plots. In particular the QC spectra from chips 3, 5, 8, 9, 19 and 27 appear to be on the extremes of the space in all three plots. Additionally, these plots were shown to have Mahalanobis distances significantly different from zero in 6 dimensional PC space (p < 0.05, data not shown). In Figure 5(B) each of these spectra is shown along with the mean spectra formed by taking the mean over all data points from the spectra which make up the reference set (referred to in the legend as the reference spectrum). Spectra 3 and 5 quite clearly show a decrease in intensity to that observed in the reference spectrum in some of the larger peaks in conjunction with an increase in sinusoidal noise in the region between 2 and 4 kDa, making it apparent why these have been flagged by QC. Spectra 8, 9 and 19 are interesting in that they show a possibility of a drift in the calibration of the machine, evident in the larger peaks between 8 and 10 kDa. In spectra 9 and 19 this is exacerbated by a drop in the intensity in the 6–7 kDa region. Spectrum 27 similarly has a decreased intensity in some peaks, but not in the same manner as those in the other examples shown here. This demonstrates the flexibility of the PCA approach to QC identifying many different kinds of aberrant behaviour in the spectra through one device.

Bottom Line: Manual inspection of those spectral data that were identified as being of poor quality confirmed the efficacy of the algorithms.Variance components analysis demonstrated the relatively small amount of technical variance attributable to day of profile generation and experimental array.The removal of these spectra at the initial stages of the analysis substantially improves the confidence of putative biomarker identification and allows inter-experimental comparisons to be carried out with greater confidence.

View Article: PubMed Central - HTML - PubMed

Affiliation: Cancer Research UK Clinical Centre, Leeds Institute of Molecular Medicine, St James's University Hospital, Leeds, UK. d.a.cairns@leeds.ac.uk

ABSTRACT

Background: Proteomic profiling using mass spectrometry (MS) is one of the most promising methods for the analysis of complex biological samples such as urine, serum and tissue for biomarker discovery. Such experiments are often conducted using MALDI-TOF (matrix-assisted laser desorption/ionisation time-of-flight) and SELDI-TOF (surface-enhanced laser desorption/ionisation time-of-flight) MS. Using such profiling methods it is possible to identify changes in protein expression that differentiate disease states and individual proteins or patterns that may be useful as potential biomarkers. However, the incorporation of quality control (QC) processes that allow the identification of low quality spectra reliably and hence allow the removal of such data before further analysis is often overlooked. In this paper we describe rigorous methods for the assessment of quality of spectral data. These procedures are presented in a user-friendly, web-based program. The data obtained post-QC is then examined using variance components analysis to quantify the amount of variance due to some of the factors in the experimental design.

Results: Using data from a SELDI profiling study of serum from patients with different levels of renal function, we show how the algorithms described in this paper may be used to detect systematic variability within and between sample replicates, pooled samples and SELDI chips and spots. Manual inspection of those spectral data that were identified as being of poor quality confirmed the efficacy of the algorithms. Variance components analysis demonstrated the relatively small amount of technical variance attributable to day of profile generation and experimental array.

Conclusion: Using the techniques described in this paper it is possible to reliably detect poor quality data within proteomic profiling experiments undertaken by MS. The removal of these spectra at the initial stages of the analysis substantially improves the confidence of putative biomarker identification and allows inter-experimental comparisons to be carried out with greater confidence.

Show MeSH
Related in: MedlinePlus