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. 2017 Mar 27:7:45178.
doi: 10.1038/srep45178.

Towards reproducible MRM based biomarker discovery using dried blood spots

Sureyya Ozcan  1 , Jason D Cooper  1 , Santiago G Lago  1 , Diarmuid Kenny  2 , Nitin Rustogi  1 , Pawel Stocki  2 , Sabine Bahn  1
Affiliations

Towards reproducible MRM based biomarker discovery using dried blood spots

Sureyya Ozcan et al. Sci Rep. .

Abstract

There is an increasing interest in the use of dried blood spot (DBS) sampling and multiple reaction monitoring in proteomics. Although several groups have explored the utility of DBS by focusing on protein detection, the reproducibility of the approach and whether it can be used for biomarker discovery in high throughput studies is yet to be determined. We assessed the reproducibility of multiplexed targeted protein measurements in DBS compared to serum. Eighty-two medium to high abundance proteins were monitored in a number of technical and biological replicates. Importantly, as part of the data analysis, several statistical quality control approaches were evaluated to detect inaccurate transitions. After implementing statistical quality control measures, the median CV on the original scale for all detected peptides in DBS was 13.2% and in Serum 8.8%. We also found a strong correlation (r = 0.72) between relative peptide abundance measured in DBS and serum. The combination of minimally invasive sample collection with a highly specific and sensitive mass spectrometry (MS) technique allows for targeted quantification of multiple proteins in a single MS run. This approach has the potential to fundamentally change clinical proteomics and personalized medicine by facilitating large-scale studies.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. Detailed experimental workflow for targeted DBS protein quantification.
The protocol consists of the following stages; (i) Automated sample preparation containing protein extraction and digestion, (ii) Multiplex MRM method development including protein/peptide selection, charge-state/transition filtering and endogenous/internal peptide ratio adjustment, (iii) Statistical analysis.
Figure 2
Figure 2. The ‘single-sample’ set CVs for 86 peptide-transitions (58 proteins) after abundance ratio filter exclusions applied, common to both DBS and serum.
(a) The CV for peptide-transition abundance ratios measured in ten DBS sample preparations from the same healthy volunteer. (b) The CV for peptide-transition abundance ratios measured in ten serum sample preparations from reference Sigma Serum. Note that the CVs were based on eight injections for each sample preparation. Pool – pooled sample of all ten sample preparations.
Figure 3
Figure 3. The ‘ten-sample’ set CVs for 81 peptide-transitions (56 proteins) after abundance ratio filter exclusions applied, common to both DBS and serum.
(a) The CV for peptide-transition abundance ratios measured in DBS samples collected from ten healthy volunteers. (b) The CV for peptide-transition abundance ratios measured in serum samples from ten healthy volunteers. Note that the ten donors provided both DBS and serum samples and that the CV was based on three injections for each sample preparation.
Figure 4
Figure 4. The ‘ten-sample’ set correlation between DBS and serum for 81 peptide-transitions (56 proteins) after abundance ratio filter exclusions applied, common to both DBS and serum.
The correlation is based on the mean relative abundance ratios between DBS and serum samples from ten healthy volunteers. The DBS = b0 + b1serum regression line is plotted. The boxplots in the margins show the distribution of the DBS (y-axis) and serum (x-axis) abundance ratios. The figure was plotted using the R package car.
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