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ABSTRACT: Using replicated human serum samples, we applied an error model for proteomic differential expression profiling for a high-resolution liquid chromatography-mass spectrometry (LC-MS) platform. The detailed noise analysis presented here uses an experimental design that separates variance caused by sample preparation from variance due to analytical equipment. An analytic approach based on a two-component error model was applied, and in combination with an existing data driven technique that utilizes local sample averaging, we characterized and quantified the noise variance as a function of mean peak intensity. The results indicate that for processed LC-MS data a constant coefficient of variation is dominant for high intensities, whereas a model for low intensities explains Poisson-like variations. This result leads to a quadratic variance model which is used for the estimation of sample preparation noise present in LC-MS data.
Bioinformatics 01/2005; 20(18):3575-82. · 5.47 Impact Factor
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ABSTRACT: There is a well-recognized but unmet need for biological markers to characterize disease type, status, progression, and response to therapy in autoimmune diseases. We are developing and applying an integrated bioanalytical platform and clinical research program to facilitate comprehensive differential phenotyping of patient samples and enable the discovery of biomarkers. Our measurement platform includes microvolume laser scanning cytometry for the quantification of hundreds of cellular parameters in whole blood and other samples, liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry for the quantification of proteins and low molecular weight biomolecules in serum and other fluids or tissues, and specific immunoassays for the quantification of trace proteins in serum. We describe the technologies and discuss initial applications to the analysis of subjects with rheumatoid arthritis (RA) and healthy controls.
Clinical Immunology 06/2004; 111(2):186-95. · 4.05 Impact Factor
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ABSTRACT: A new method is presented for quantifying proteomic and metabolomic profile data by liquid chromatography-mass spectrometry (LC-MS) with electrospray ionization. This biotechnology provides differential expression measurements and enables the discovery of biological markers (biomarkers). Work presented here uses human serum but is applicable to any fluid or tissue. The approach relies on linearity of signal versus molecular concentration and reproducibility of sample processing. There is no use of isotopic labeling or chemically similar standard materials. Linear standard curves are reported for a variety of compounds introduced into human serum. As a measure of analytical reproducibility for proteome and metabolome sampling, median coefficients of variation of 25.7 and 23.8%, respectively, were determined for approximately 3400 molecular ions (not counting their numerous isotopes) from 25 independently processed human serum samples, corresponding to a total of 85000 individual molecular ion measurements.
Analytical Chemistry 10/2003; 75(18):4818-26. · 5.86 Impact Factor
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ABSTRACT: A liquid chromatography-mass spectrometry (LC-MS) proteomics and metabolomics platform is presented for quantitative differential expression analysis. Proteome profiles obtained from 1.5 μL of human serum show ∼5000 de-isotoped and quantifiable molecular ions. Approximately 1500 metabolites are observed from 100 μL of serum. Quantification is based on reproducible sample preparation and linear signal intensity as a function of concentration. The platform is validated using human serum, but is generally applicable to all biological fluids and tissues. The median coefficient of variation (CV) for ∼5000 proteomic and ∼1500 metabolomic molecular ions is approximately 25%. For the case of C-reactive protein, results agree with quantification by immunoassay. The independent contributions of two sources of variance, namely sample preparation and LC-MS analysis, are respectively quantified as 20.4 and 15.1% for the proteome, and 19.5 and 13.5% for the metabolome, for median CV values. Furthermore, biological diversity for ∼20 healthy individuals is estimated by measuring the variance of ∼6500 proteomic and metabolomic molecular ions in sera for each sample; the median CV is 22.3% for the proteome and 16.7% for the metabolome. Finally, quantitative differential expression profiling is applied to a clinical study comparing healthy individuals and rheumatoid arthritis (RA) patients.
International Journal of Mass Spectrometry.
