Brandon Hunt

Label-Free Detection of Differential Protein Expression by LC/MALDI Mass Spectrometry

Protein abundance changes during disease or experimental perturbation are increasingly analyzed by label-free LC/MS approaches. Here we demonstrate the use of LC/MALDI MS for label-free detection of protein expression differences using Escherichia coli cultures grown on arabinose, fructose or glucose as a carbon source. The advantages of MALDI, such as detection of only singly charged ions, and MALDI plate archiving to facilitate retrospective MS/MS data collection are illustrated. MALDI spectra from RP chromatography of tryptic digests of the E. coli lysates were aligned and quantitated using the Rosetta Elucidator system. Approximately 5000 peptide signals were detected in all LC/MALDI runs spanning over 3 orders of magnitude of signal intensity. The average coefficients of variation for all signals across the entire intensity range in all technical replicates were found to be <25%. Pearson correlation coefficients from 0.93 to 0.98 for pairwise comparisons illustrate high replicate reproducibility. Expression differences determined by Analysis of Variance highlighted over 500 isotope clusters ( p < 0.01), which represented candidates for targeted peptide identification using MS/MS. Biologically interpretable protein identifications that could be derived underpin the general utility of this label-free LC/MALDI strategy.

Application of an End-to-End Biomarker Discovery Platform to Identify Target Engagement Markers in Cerebrospinal Fluid by High Resolution Differential Mass Spectrometry

The rapid identification of protein biomarkers in biofluids is important to drug discovery and development. Here, we describe a general proteomic approach for the discovery and identification of proteins that exhibit a statistically significant difference in abundance in cerebrospinal fluid (CSF) before and after pharmacological intervention. This approach, differential mass spectrometry (dMS), is based on the analysis of full scan mass spectrometry data. The dMS workflow does not require complex mixing and pooling strategies, or isotope labeling techniques. Accordingly, clinical samples can be analyzed individually, allowing the use of longitudinal designs and within-subject data analysis in which each subject acts as its own control. As a proof of concept, we performed multifactorial dMS analyses on CSF samples drawn at 6 time points from n = 6 cisterna magna ported (CMP) rhesus monkeys treated with 2 potent gamma secretase inhibitors (GSI) or comparable vehicle in a 3-way crossover study that included a total of 108 individual CSF samples. Using analysis of variance and statistical filtering on the aligned and normalized LC-MS data sets, we detected 26 features that were significantly altered in CSF by drug treatment. Of those 26 features, which belong to 10 distinct isotopic distributions, 20 were identified by MS/MS as 7 peptides from CD99, a cell surface protein. Six features from the remaining 3 isotopic distributions were not identified. A subsequent analysis showed that the relative abundance of these 26 features showed the same temporal profile as the ELISA measured levels of CSF A beta 42 peptide, a known pharmacodynamic marker for gamma-secretase inhibition. These data demonstrate that dMS is a promising approach for the discovery, quantification, and identification of candidate target engagement biomarkers in CSF.