Insulator Nanostructure Desorption Ionization Mass Spectrometry.

Surface-assisted laser desorption ionization (SALDI) is an approach for gas-phase ion generation for mass spectrometry using laser excitation on typically conductive or semiconductive nanostructures. Here, we introduce insulator nanostructure desorption ionization mass spectrometry (INDI-MS), a nanostructured polymer substrate for SALDI-MS analysis of small molecules and peptides. INDI-MS surfaces are produced through the self-assembly of a perfluoroalkyl silsesquioxane nanostructures in a single chemical vapor deposition silanization-step. We find that surfaces formed from the perfluorooctyltrichlorosilane monomer assemble semielliptical features with a 10 nm height, diameters between 10 and 50 nm, and have attomole-femtomole sensitivities for selected analytes. Surfaces prepared with silanes that either lack the trichloro or perfluoro groups, lack sensitivity. Further, we demonstrate that hydrophobic INDI regions can be micropatterned onto hydrophilic surfaces to perform on-chip self-desalting in an array format.

Morphology-Driven Control of Metabolite Selectivity Using Nanostructure-Initiator Mass Spectrometry.

Nanostructure-initiator mass spectrometry (NIMS) is a laser desorption/ionization analysis technique based on the vaporization of a nanostructure-trapped liquid "initiator" phase. Here we report an intriguing relationship between NIMS surface morphology and analyte selectivity. Scanning electron microscopy and spectroscopic ellipsometry were used to characterize the surface morphologies of a series of NIMS substrates generated by anodic electrochemical etching. Mass spectrometry imaging was applied to compare NIMS sensitivity of these various surfaces toward the analysis of diverse analytes. The porosity of NIMS surfaces was found to increase linearly with etching time where the pore size ranged from 4 to 12 nm with corresponding porosities estimated to be 7-70%. Surface morphology was found to significantly and selectively alter NIMS sensitivity. The small molecule (<2k Da) sensitivity was found to increase with increased porosity, whereas low porosity had the highest sensitivity for the largest molecules examined. Estimation of molecular sizes showed that this transition occurs when the pore size is <3× the maximum of molecular dimensions. While the origins of selectivity are unclear, increased signal from small molecules with increased surface area is consistent with a surface area restructuring-driven desorption/ionization process where signal intensity increases with porosity. In contrast, large molecules show highest signal for the low-porosity and small-pore-size surfaces. We attribute this to strong interactions between the initiator-coated pore structures and large molecules that hinder desorption/ionization by trapping large molecules. This finding may enable us to design NIMS surfaces with increased specificity to molecules of interest.

MAGI: A Method for Metabolite Annotation and Gene Integration.

Metabolomics is a widely used technology for obtaining direct measures of metabolic activities from diverse biological systems. However, ambiguous metabolite identifications are a common challenge and biochemical interpretation is often limited by incomplete and inaccurate genome-based predictions of enzyme activities (that is, gene annotations). Metabolite Annotation and Gene Integration (MAGI) generates a metabolite-gene association score using a biochemical reaction network. This is calculated by a method that emphasizes consensus between metabolites and genes via biochemical reactions. To demonstrate the potential of this method, we applied MAGI to integrate sequence data and metabolomics data collected from Streptomyces coelicolor A3(2), an extensively characterized bacterium that produces diverse secondary metabolites. Our findings suggest that coupling metabolomics and genomics data by scoring consensus between the two increases the quality of both metabolite identifications and gene annotations in this organism. MAGI also made biochemical predictions for poorly annotated genes that were consistent with the extensive literature on this important organism. This limited analysis suggests that using metabolomics data has the potential to improve annotations in sequenced organisms and also provides testable hypotheses for specific biochemical functions. MAGI is freely available for academic use both as an online tool at https://magi.nersc.gov and with source code available at https://github.com/biorack/magi .

Observing Biosynthetic Activity Utilizing Next Generation Sequencing and the DNA Linked Enzyme Coupled Assay.

Currently, the identification of new genes drastically outpaces current experimental methods for determining their enzymatic function. This disparity necessitates the development of high-throughput techniques that operate with the same scalability as modern gene synthesis and sequencing technologies. In this paper, we demonstrate the versatility of the recently reported DNA-Linked Enzyme-Coupled Assay (DLEnCA) and its ability to support high-throughput data acquisition through next-generation sequencing (NGS). Utilizing methyltransferases, we highlight DLEnCA's ability to rapidly profile an enzyme's substrate specificity, determine relative enzyme kinetics, detect biosynthetic formation of a target molecule, and its potential to benefit from the scales and standardization afforded by NGS. This improved methodology minimizes the effort in acquiring biosynthetic knowledge by tying biochemical techniques to the rapidly evolving abilities in sequencing and synthesizing DNA.

Peptide vectors for gene delivery: from single peptides to multifunctional peptide nanocarriers.

The therapeutic use of nucleic acids relies on the availability of sophisticated delivery systems for targeted and intracellular delivery of these molecules. Such a gene delivery should possess essential characteristics to overcome several extracellular and intracellular barriers. Peptides offer an attractive platform for nonviral gene delivery, as several functional peptide classes exist capable of overcoming these barriers. However, none of these functional peptide classes contain all the essential characteristics required to overcome all of the barriers associated with successful gene delivery. Combining functional peptides into multifunctional peptide vectors will be pivotal for improving peptide-based gene delivery systems. By using combinatorial strategies and high-throughput screening, the identification of multifunctional peptide vectors will accelerate the optimization of peptide-based gene delivery systems.