Mass Spectrometry Measurements of Neuropeptides: From Identification to Quantitation

Neuropeptides (NPs), a unique class of neuronal signaling molecules, participate in a variety of physiological processes and diseases. Quantitative measurements of NPs provide valuable information regarding how these molecules are differentially regulated in a multitude of neurological, metabolic, and mental disorders. Mass spectrometry (MS) has evolved to become a powerful technique for measuring trace levels of NPs in complex biological tissues and individual cells using both targeted and exploratory approaches. There are inherent challenges to measuring NPs, including their wide endogenous concentration range, transport and postmortem degradation, complex sample matrices, and statistical processing of MS data required for accurate NP quantitation. This review highlights techniques developed to address these challenges and presents an overview of quantitative MS-based measurement approaches for NPs, including the incorporation of separation methods for high-throughput analysis, MS imaging for spatial measurements, and methods for NP quantitation in single neurons.

Automated high throughput pKa and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge

The goal of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) challenge is to improve the accuracy of current computational models to estimate free energy of binding, deprotonation, distribution and other associated physical properties that are useful for the design of new pharmaceutical products. New experimental datasets of physicochemical properties provide opportunities for prospective evaluation of computational prediction methods. Here, aqueous pK_a and a range of bi-phasic logD values for a variety of pharmaceutical compounds were determined through a streamlined automated process to be utilized in the SAMPL8 physical property challenge. The goal of this paper is to provide an in-depth review of the experimental methods utilized to create a comprehensive data set for the blind prediction challenge. The significance of this work involves the use of high throughput experimentation equipment and instrumentation to produce acid dissociation constants for twenty-three drug molecules, as well as distribution coefficients for eleven of those molecules.

The First Comprehensive LC-MS/MS Method Allowing Dissection of the Thiamine Pathway in Plants

Arabidopsis thaliana serves as a model plant for genetic research, including vitamin research. When aiming at engineering the thiamine (vitamin B1) pathway in plants, the availability of tools that allow the quantitative determination of different intermediates in the biosynthesis pathway is of pivotal importance. This is a challenge, given the nature of the compounds and the minute quantities of genetically engineered material that may be available for analysis. Here, we report on the first LC-MS/MS method for the simultaneous quantification of thiamine, its mono- and diphosphate derivatives and its precursors 4-methyl-5-(2-hydroxyethyl) thiazole (HET) and 4-amino-2-methyl-5-hydroxymethylpyrimidine (HMP). This method was optimized and validated for the quantitative determination of these analytes in Arabidopsis thaliana. All analytes were chromatographically separated within less than 2.5 min during an 8 min run. No unacceptable interferences were found. The method was fully validated based on international guidelines. Accuracy (%bias) and total imprecision (%CV) were within preset acceptance criteria for all analytes in both QC and real samples. All analytes were stable in extracted samples when stored for 48 h at 4 °C (autosampler stability) and when reanalyzed after storage at -80 °C and -20 °C for 2 weeks (freeze/thaw stability). We demonstrated the start material should be stored at -80 °C to ensure stability of all analytes during short- and long-term storage (up to 3 months). The validity and applicability of the developed procedure was demonstrated via its successful application on Arabidopsis lines, genetically engineered to enhance thiamine content.