Signal processing was performed on raw spectra and peak data were normalized using four methods. Feature selection was performed using Bayesian Network Analysis and a classifier was tested on withheld data. Identification of candidate biomarkers was pursued.

Results: Integrated peak intensities were resolved over full spectra. Normalization using local noise values was superior to global methods in reducing peak correlations, reducing replicate variability

and improving feature selection stability. For the leukemia data set, potential disease biomarkers were detected and were found to be predictive for withheld data. Preliminary AZD0156 research buy assignments of protein IDs were consistent with published results and LC-MS/MS identification. No prostate-specific-antigen-independent biomarkers were detected in BMS-777607 manufacturer the prostate cancer data set.

Conclusions and clinical relevance: Signal processing,

local signal-to-noise (SNR) normalization and Bayesian Network Analysis feature selection facilitate robust detection and identification of biomarker proteins in broad-mass-range clinical TOF-MS data.”
“Purpose: The aim of this study was to use on-tissue reduction followed by MALDI-MS imaging (MSI) to identify an m/z 5812.85 peak, which is over-expressed in healthy human pancreatic tissue compared with type one Diabetes (T1D) tissue.

Experimental design: A major constraint of MALDI-MSI is identification of compounds with m/z >= 4000. On-tissue reduction using tris (2-carboxyethyl) phosphine (TCEP) breaks the inter-domain disulphide bonds generating low-molecular-weight peptides amenable to direct MS/MS analysis. Pancreatic tissues from healthy (n=4) and diabetic subjects

(n=4) were profiled by MALDI-MSI with/without reduction.

Results: On-tissue reduction resulted in the loss of the over-expressed 5812.85 m/z peak and the simultaneous Bupivacaine appearance of a 3430.664 m/z peak in healthy tissues. The latter peak presumably derived from the 5812.85 m/z peak was identified as the insulin B chain by MS/MS. MALDI-MSI images show that both the 5812.85 insulin peak before reduction and the 3430.664 peak after reduction co-localized with the healthy pancreatic islets.

Conclusion and clinical relevance: On-tissue reduction followed by MALDI-MSI resulted in the identification of insulin and localization of pancreatic islets of langerhans. The approach will be useful in the future identification of novel therapeutic molecular targets to beta-cells lost during type one diabetes.