Dataset Information

A proteomics approach to the protein normalization problem

ABSTRACT: The approach suggested here can be used more widely to determine the suitability of proteins or sets of proteins as loading and normalization controls for any biological system. As we have shown here, historically selected ‘household proteins’ may not be the most suitable for this task for technical or biological reasons. Conversely, expression data across diverse systems can inform the selection of proteins to serve as a biomarker.

INSTRUMENT(S): LTQ Orbitrap

ORGANISM(S): Homo Sapiens (human) Mus Musculus (mouse)

TISSUE(S): Brain, Liver, Kidney

SUBMITTER: Jacek Wisniewski

LAB HEAD: Jacek R Wisniewski

PROVIDER: PXD003422 | Pride | 2016-06-20

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	20150622_Orbi2_JRW_SA_K13_L1.raw	Raw
	20150622_Orbi2_JRW_SA_K13_L2.raw	Raw
	20150622_Orbi2_JRW_SA_K13_L3.raw	Raw
	20150622_Orbi2_JRW_SA_K13_T1.raw	Raw
	20150622_Orbi2_JRW_SA_K13_T2.raw	Raw

Items per page:

1 - 5 of 66

Publications

A Proteomics Approach to the Protein Normalization Problem: Selection of Unvarying Proteins for MS-Based Proteomics and Western Blotting.

Wiśniewski Jacek R JR Mann Matthias M

Journal of proteome research 20160623 7

Proteomics and other protein-based analysis methods such as Western blotting all face the challenge of discriminating changes in the levels of proteins of interest from inadvertent changes in the amount loaded for analysis. Mass-spectrometry-based proteomics can now estimate the relative and absolute amounts of thousands of proteins across diverse biological systems. We reasoned that this new technology could prove useful for selection of very stably expressed proteins that could serve as better ...[more]

PMID: 27297043

Similar Datasets

Project description:Profiling miRNA levels in cells with miRNA-microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. Here, we developed a method to select non-changing miRNAs (“invariants”) and used them to compute linear regression normalization coefficients or Variance Stabilizing Normalization (VSN) parameters. We compared the invariant normalizations to normalization by, scaling, quantile and VSN with default parameters as well as to no normalization using samples with strong differential expression of miRNAs (heart-brain comparison) and samples where only few miRNAs are affected (p53 overexpression in SCC13 cells versus GFP vector control transfected cells). All normalization methods performed better than no normalization. Normalizations procedures based on the set of invariants and quantile were the most robust over all experimental conditions tested. Our method of invariant selection and normalization is not limited to Agilent miRNA arrays and can be applied to other datasets from one color miRNA microarray platforms, focused gene expression arrays and gene expression analysis using quantitative PCR. Keywords: miRNA profiling To assay technical reproducibility, 3 technical replicates from brain and heart RNA (Stratagene) were hybridized on Agilent human miRNA microarrays. To determine sensitivity and specificity, heart and brain RNA were mixed in the following ratios: 50% heart 50% brain, 25% heart 75% brain and 5% heart 95% brain. Each of the dilutions was hybridized in a technical duplicate on Agilent human miRNA arrays. To assay the effect of p53 expression on miRNA levels in the human SCC13 cell line, RNA from 3 biological replicates of p53-expressing or control cells were hybridized in technical duplicates on the microarrays, resulting in a total of 12 hybridizations.