Fractional factorial design for optimization of the SELDI protocol for human adipose tissue culture media.
The early factors inducing insulin resistance are not known. Therefore, we are interested in studying the secretome of the human visceral adipose tissue as a potential source of unknown peptides and proteins inducing insulin resistance. Surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) mass spectrometry is a high-throughput proteomics technology to generate peptide and protein profiles (MS spectra). To obtain good quality and reproducible data from SELDI-TOF, many factors in the sample pretreatment and SELDI protocol should be optimized. To identify the optimal combination of factors resulting in the best and the most reproducible spectra, we designed an experiment where factors were varied systematically according to a fractional factorial design. In this study, seven protein chip preparation protocol factors were tested in 32 experiments. The main effects of these factors and their interactions contributing to the best quality spectra were identified by ANOVA. To assess the reproducibility, in a subsequent experiment the eight protocols generating the highest quality spectra were applied to samples in quadruplicates on different chips. This approach resulted in the development of an improved chip protocol, yielding higher quality peaks and more reproducible spectra.
Szalowska
Ewa
E
Department of Medical Biomics, University Medical Center Groningen, University of Groningen, Post Office Box 30001, 9700 RR Groningen, The Netherlands. e.szalowska@med.umcg.nl
van Hijum
Sacha A F T
SA
Roelofsen
Han
H
Hoek
Annemiek
A
Vonk
Roel J
RJ
te Meerman
Gerard J
GJ
D000465:Algorithms; D005163:Factor Analysis, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009924:Organ Culture Techniques; D020543:Proteome / Q000032:analysis* / Q000378:metabolism*; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods*; D046888:Tissue Array Analysis / Q000379:methods*
D020543:Proteome