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Native Nano-electrospray Differential Mobility Analyzer (nES GEMMA) Enables Size Selection of Liposomal Nanocarriers Combined with Subsequent Direct Spectroscopic Analysis.

ABSTRACT: Gas-phase electrophoresis employing a nano-electrospray differential mobility analyzer (nES DMA), aka gas-phase electrophoretic mobility molecular analyzer (nES GEMMA), enables nanoparticle separation in the gas-phase according to their surface-dry diameter with number-based concentration detection. Moreover, particles in the nanometer size range can be collected after size selection on supporting materials. It has been shown by subsequent analyses employing orthogonal methods, for instance, microscopic or antibody-based techniques, that the surface integrity of collected analytes remains intact. Additionally, native nES GEMMA demonstrated its applicability for liposome characterization. Liposomes are nanometer-sized, biodegradable, and rather labile carriers (nanoobjects) consisting of a lipid bilayer encapsulating an aqueous lumen. In nutritional and pharmaceutical applications, these vesicles allow shielded, targeted transport and sustained release of bioactive cargo material. To date, cargo quantification is based on bulk measurements after bilayer rupture. In this context, we now compare capillary electrophoresis and spectroscopic characterization of vesicles in solution (bulk measurements) to the possibility of spectroscopic investigation of individual, size-separated/collected liposomes after nES GEMMA. Surface-dried, size-selected vesicles were collected intact on calcium fluoride (CaF₂) substrates and zinc selenide (ZnSe) prisms, respectively, for subsequent spectroscopic investigation. Our proof-of-principle study demonstrates that the off-line hyphenation of gas-phase electrophoresis and confocal Raman spectroscopy allows detection of isolated, nanometer-sized soft material/objects. Additionally, atomic force microscopy-infrared spectroscopy (AFM-IR) as an advanced spectroscopic system was employed to access molecule-specific information with nanoscale lateral resolution. The off-line hyphenation of nES GEMMA and AFM-IR is introduced to enable chemical imaging of single, i.e., individual, liposome particles.

SUBMITTER: Weiss VU

PROVIDER: S-EPMC6427476 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature

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Native Nano-electrospray Differential Mobility Analyzer (nES GEMMA) Enables Size Selection of Liposomal Nanocarriers Combined with Subsequent Direct Spectroscopic Analysis.

Weiss Victor U VU Wieland Karin K Schwaighofer Andreas A Lendl Bernhard B Allmaier Guenter G

Analytical chemistry 20190227 6

Gas-phase electrophoresis employing a nano-electrospray differential mobility analyzer (nES DMA), aka gas-phase electrophoretic mobility molecular analyzer (nES GEMMA), enables nanoparticle separation in the gas-phase according to their surface-dry diameter with number-based concentration detection. Moreover, particles in the nanometer size range can be collected after size selection on supporting materials. It has been shown by subsequent analyses employing orthogonal methods, for instance, mic ...[more]

PMID: 30735037

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Project description:The emerging role of extracellular vesicles (EVs) as biomarkers and their envisioned therapeutic use require advanced techniques for their detailed characterization. In this context, we investigated gas-phase electrophoresis on a nano electrospray gas-phase electrophoretic mobility molecular analyzer (nES GEMMA, aka nES differential mobility analyzer, nES DMA) as an alternative to standard analytical techniques. In gas-phase electrophoresis, single-charged, surface-dry, native, polydisperse, and aerosolized analytes, e.g., proteins or bio-nanoparticles, are separated according to their electrophoretic mobility diameter, i.e., globular size. Subsequently, monodisperse particles are counted after a nucleation step in a supersaturated atmosphere as they pass a focused laser beam. Hence, particle number concentrations are obtained in accordance with recommendations of the European Commission for nanoparticle characterization (2011/696/EU from October 18th, 2011). Smaller sample constituents (e.g., co-purified proteins) can be detected next to larger ones (e.g., vesicles). Focusing on platelet-derived EVs, we compared different vesicle isolation techniques. In all cases, nanoparticle tracking analysis (NTA) confirmed the presence of vesicles. However, nES GEMMA often revealed a significant co-purification of proteins from the sample matrix, precluding gas-phase electrophoresis of less-diluted samples containing higher vesicle concentrations. Therefore, mainly peaks in the protein size range were detected. Mass spectrometry revealed that these main contaminants belonged to the group of globulins and coagulation-related components. An additional size exclusion chromatography (SEC) step enabled the depletion of co-purified, proteinaceous matrix components, while a label-free quantitative proteomics approach revealed no significant differences in the detected EV core proteome. Hence, the future in-depth analysis of EVs via gas-phase electrophoresis appears feasible. Platelet-derived extracellular vesicles (EVs)with/without additional size exclusion chromatographic (SEC) purification were subjected to nanoparticle tracking analysis (NTA) and gas-phase electrophoresis (nES GEMMA). The latter revealed presence of co-purified proteins, targetable via mass spectrometry (MS). MS also revealed that SEC did not influence EV protein content. To conclude, nES GEMMA is a valuable tool for quality control of EV-containing samples under native conditions allowing for detection of co-purified proteins from complex matrices.

Dataset Information

Native Nano-electrospray Differential Mobility Analyzer (nES GEMMA) Enables Size Selection of Liposomal Nanocarriers Combined with Subsequent Direct Spectroscopic Analysis.

Publications

Native Nano-electrospray Differential Mobility Analyzer (nES GEMMA) Enables Size Selection of Liposomal Nanocarriers Combined with Subsequent Direct Spectroscopic Analysis.

Similar Datasets

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