Assessing the utility of SELDI-TOF and model averaging for serum proteomic biomarker discovery

Abstract

The SELDI-TOF technique was used to profile serum proteins from Type 1 diabetes (T1D) patients and healthy autoantibody-negative (AbN) controls. Univariate and multivariate analyses were performed to identify putative biomarkers for T1D and to assess the reproducibility of the SELDI technique. We found 146 protein/peptide peaks (581 total peaks discovered) in human serum showing statistical differences in expression levels between T1D patients and controls, with 84% of these peaks showing technical replication. Because individual proteins did not offer great power for disease prediction, we used our model averaging approach that combines the information from multiple multivariate models to accurately classify T1D and control subjects (88.9% specificity and 90.0% sensitivity). Analyses of a test subset of the data showed less accuracy (82.8% specificity and 76.2% sensitivity), although the results are still positive. Unfortunately, no multivariate model could be replicated using the same samples. This first attempt of high throughput analyses of the human serum proteome in T1D patients suggests that model averaging is a viable method for developing biomarkers; however, the reproducibility of SELDI-TOF is currently not sufficient to be used for classification of complex diseases like T1D.

SELDI‐TOF as a method for biomarker discovery in the urine of aristolochic‐acid‐treated mice

“…An additional problem is the less-effective ionization in the high molecular range resulting with apparently lower sensitivity of the instrument for large proteins. After improvement of both the hardware and software, and thorough investigations of sample storage and preparation, SELDI-TOF MS is again being more frequently used [16][17][18][19]. The big advantage of the SELDI-TOF technology is the simple automation of sample preparation and the measurement process that enables its use for high-throughput screening, which is necessary when dealing with large sample numbers during the biomarker discovery process.…”

Serum protein patterns in newly diagnosed type 2 diabetes mellitus—influence of diabetic environment and family history of diabetes

“…The easy accessibility of blood in contrast to pancreatic islets makes it attractive to study changes of other blood-borne proteins also, which may be related to the progression of the disease. With the development of different methodologies capable of simultaneously measuring large numbers of proteins present in biological samples, blood protein profiling has become an important area for studying the pathophysiology of type 2 diabetes [12][13][14]. We have used the SELDI-platform, which uses chemically defined surfaces to capture proteins [15].…”

Screening of Serum Biomarkers and Establishment of a Decision Tree in Silica-Exposed Populations by Surface-Enhanced Laser Desorption Ionization Time-of-Fly Mass Spectrometry

“…The high technical replication of peaks by SELDI-TOF showed the reliability of this method, and the results obtained in this study confirmed the best condition reported by Purohit's group. 20 According to China's national diagnostic standard for pneumoconiosis (enacted in 2002), the diagnosis of silicosis mainly depends on occupational history and radiological evidence. This definitely lags behind biochemical change and tissue biopsy, while the invasive nature of biopsy limited its application in clinical practice.…”