Thermodynamic and kinetic approaches for evaluation of monoclonal antibody - Lipoprotein interactions

Raman spectroscopy combined with comprehensive gas chromatography for label-free characterization of plasma-derived extracellular vesicle subpopulations

Raman spectroscopy together with comprehensive two-dimensional gas chromatography-time-of-flight mass spectrometry (GCxGC-TOFMS) was employed to characterize exomere- (<50 nm) and exosome-sized (50-80 nm) EVs isolated from human plasma by the novel on-line immunoaffinity chromatography - asymmetric flow field-flow fractionation method. CD9⁺, CD63⁺, and CD81⁺ EVs were selected to represent general EV subpopulations secreted into plasma, while CD61+EVs represented the specific EV subset derived from platelets. Raman spectroscopy could distinguish EVs from non-EV particles, including apolipoprotein B-100-containing lipoproteins, signifying its potential in EV purity assessment. Moreover, platelet-derived (CD61⁺) EVs of both exomere and exosome sizes were discriminated from other EV subpopulations due to different biochemical compositions. Further investigations demonstrated composition differences between exomere- and exosome-sized EVs, confirming the applicability of Raman spectroscopy in distinguishing EVs, not only from different origins but also sizes. In addition, fatty acids that act as building blocks for lipids and membranes in EVs were studied by GCxGC-TOF-MS. The results achieved highlighted differences in EV fatty acid compositions in both esterified (membrane lipids) and non-esterified (free fatty acids) fractions, indicating possible differences in membrane structures, biological functions, and roles in cell-to-cell communications of EV subpopulations.

Kinetics and interaction studies of anti-tetraspanin antibodies and ICAM-1 with extracellular vesicle subpopulations using continuous flow quartz crystal microbalance biosensor

Continuous flow quartz crystal microbalance (QCM) was utilized to study binding kinetics between EV subpopulations (exomere- and exosome-sized EVs) and four affinity ligands: monoclonal antibodies against tetraspanins (anti-CD9, anti-CD63, and anti-CD81) and recombinant intercellular adhesion molecule-1 (ICAM-1) or CD54 protein). High purity CD9⁺, CD63⁺, and CD81⁺ EV subpopulations of <50 nm exomeres and 50-80 nm exosomes were isolated and fractionated using our recently developed on-line coupled immunoaffinity chromatography - asymmetric flow field-flow fractionation system. Adaptive Interaction Distribution Algorithm (AIDA), specifically designed for the analysis of complex biological interactions, was used with a four-step procedure for reliable estimation of the degree of heterogeneity in rate constant distributions. Interactions between exomere-sized EVs and anti-tetraspanin antibodies demonstrated two interaction sites with comparable binding kinetics and estimated dissociation constants K_d ranging from nM to fM. Exomeres exhibited slightly higher affinity compared to exosomes. The highest affinity with anti-tetraspanin antibodies was achieved with CD63⁺ EVs. The interaction of EV subpopulations with ICAM-1 involved in cell internalization of EVs was also investigated. EV - ICAM-1 interaction was also of high affinity (nM to pM range) with overall lower affinity compared to the interactions of anti-tetraspanin antibodies and EVs. Our findings proved that QCM is a valuable label-free tool for kinetic studies with limited sample concentration, and that advanced algorithms, such as AIDA, are crucial for proper determination of kinetic heterogeneity. To the best of our knowledge, this is the first kinetic study on the interaction between plasma-derived EV subpopulations and anti-tetraspanin antibodies and ICAM-1.

Automated On-Line Isolation and Fractionation System for Nanosized Biomacromolecules from Human Plasma

An automated on-line isolation and fractionation system including controlling software was developed for selected nanosized biomacromolecules from human plasma by on-line coupled immunoaffinity chromatography-asymmetric flow field-flow fractionation (IAC-AsFlFFF). The on-line system was versatile, only different monoclonal antibodies, anti-apolipoprotein B-100, anti-CD9, or anti-CD61, were immobilized on monolithic disk columns for isolation of lipoproteins and extracellular vesicles (EVs). The platelet-derived CD61-positive EVs and CD9-positive EVs, isolated by IAC, were further fractionated by AsFlFFF to their size-based subpopulations (e.g., exomeres and exosomes) for further analysis. Field-emission scanning electron microscopy elucidated the morphology of the subpopulations, and 20 free amino acids and glucose in EV subpopulations were identified and quantified in the ng/mL range using hydrophilic interaction liquid chromatography-tandem mass spectrometry (HILIC-MS/MS). The study revealed that there were significant differences between EV origin and size-based subpopulations. The on-line coupled IAC-AsFlFFF system was successfully programmed for reliable execution of 10 sequential isolation and fractionation cycles (37–80 min per cycle) with minimal operator involvement, minimal sample losses, and contamination. The relative standard deviations (RSD) between the cycles for human plasma samples were 0.84–6.6%.

Advanced Analysis of Biosensor Data for SARS-CoV-2 RBD and ACE2 Interactions

The traditional approach for analyzing interaction data from biosensors instruments is based on the simplified assumption that also larger biomolecules interactions are homogeneous. It was recently reported that the human receptor angiotensin-converting enzyme 2 (ACE2) plays a key role for capturing SARS-CoV-2 into the human target body, and binding studies were performed using biosensors techniques based on surface plasmon resonance and bio-layer interferometry. The published affinity constants for the interactions, derived using the traditional approach, described a single interaction between ACE2 and the SARS-CoV-2 receptor binding domain (RBD). We reanalyzed these data sets using our advanced four-step approach based on an adaptive interaction distribution algorithm (AIDA) that accounts for the great complexity of larger biomolecules and gives a two-dimensional distribution of association and dissociation rate constants. Our results showed that in both cases the standard assumption about a single interaction was erroneous, and in one of the cases, the value of the affinity constant K_D differed more than 300% between the reported value and our calculation. This information can prove very useful in providing mechanistic information and insights about the mechanism of interactions between ACE2 and SARS-CoV-2 RBD or similar systems.

Rapid affinity chromatographic isolation method for LDL in human plasma by immobilized chondroitin-6-sulfate and anti-apoB-100 antibody monolithic disks in tandem

Low-density lipoprotein (LDL) is considered the major risk factor for the development of atherosclerotic cardiovascular diseases (ASCVDs). A novel and rapid method for the isolation of LDL from human plasma was developed utilising affinity chromatography with monolithic stationary supports. The isolation method consisted of two polymeric monolithic disk columns, one immobilized with chondroitin-6-sulfate (C6S) and the other with apolipoprotein B-100 monoclonal antibody (anti-apoB-100 mAb). The first disk with C6S was targeted to remove chylomicrons, very-low-density lipoprotein (VLDL) particles, and their remnants including intermediate-density lipoprotein (IDL) particles, thus allowing the remaining major lipoprotein species, i.e. LDL, lipoprotein(a) (Lp(a)), and high-density lipoprotein (HDL) to flow to the anti-apoB-100 disk. The second disk captured LDL particles via the anti-apoB-100 mAb attached on the disk surface in a highly specific manner, permitting the selective LDL isolation. The success of LDL isolation was confirmed by different techniques including quartz crystal microbalance. In addition, the method developed gave comparable results with ultracentrifugation, conventionally used as a standard method. The reliable results achieved together with a short isolation time (less than 30 min) suggest the method to be suitable for clinically relevant LDL functional assays.

Affinity capillary electrophoresis for studying interactions in life sciences

Affinity capillary electrophoresis (ACE) analyzes noncovalent interactions between ligands and analytes based on changes in their electrophoretic mobility. This technique has been widely used to investigate various biomolecules, mainly proteins, polysaccharides and hormones. ACE is becoming a technique of choice to validate high throughput screening results, since it is very predictively working in realistic and relevant media, e.g. in body fluids. It is highly recommended to incorporate ACE as a powerful analytical tool to properly prepare animal testing and preclinical studies. The interacting molecules can be found free in solution or can be immobilized to a solid support. Thus, ACE is classified in two modes, free solution ACE and immobilized ACE. Every ACE mode has advantages and disadvantages. Each can be used for a variety of applications. This review covers literature of scopus and SciFinder data base in the period from 2016 until beginning 2018, including the keywords "affinity capillary electrophoresis", "immunoaffinity capillary electrophoresis", "immunoassay capillary electrophoresis" and "immunosorbent capillary electrophoresis". More than 200 articles have been found and 112 have been selected and thoroughly discussed. During this period, the data processing and the underlying calculations in mobility shift ACE (ms ACE), frontal analysis ACE (FA ACE) and plug-plug kinetic capillary electrophoresis (ppKCE) as mostly applied free solution techniques have substantially improved. The range of applications in diverse free solution and immobilized ACE techniques has been considerably broadened.

Reliable Strategy for Analysis of Complex Biosensor Data

When using biosensors, analyte biomolecules of several different concentrations are percolated over a chip with immobilized ligand molecules that form complexes with analytes. However, in many cases of biological interest, e.g., in antibody interactions, complex formation steady-state is not reached. The data measured are so-called sensorgram, one for each analyte concentration, with total complex concentration vs time. Here we present a new four-step strategy for more reliable processing of this complex kinetic binding data and compare it with the standard global fitting procedure. In our strategy, we first calculate a dissociation graph to reveal if there are any heterogeneous interactions. Thereafter, a new numerical algorithm, AIDA, is used to get the number of different complex formation reactions for each analyte concentration level. This information is then used to estimate the corresponding complex formation rate constants by fitting to the measured sensorgram one by one. Finally, all estimated rate constants are plotted and clustered, where each cluster represents a complex formation. Synthetic and experimental data obtained from three different QCM biosensor experimental systems having fast (close to steady-state), moderate, and slow kinetics (far from steady-state) were evaluated using the four-step strategy and standard global fitting. The new strategy allowed us to more reliably estimate the number of different complex formations, especially for cases of complex and slow dissociation kinetics. Moreover, the new strategy proved to be more robust as it enables one to handle system drift, i.e., data from biosensor chips that deteriorate over time.

Correlation between Cholesterol, Triglycerides, Calculated, and Measured Lipoproteins: Whether Calculated Small Density Lipoprotein Fraction Predicts Cardiovascular Risks

Background

Recent literature in lipidology has identified LDL-fractions to be more atherogenic. In this regard, small density LDL-cholesterol (sdLDLc) has been considered to possess more atherogenicity than other LDL-fractions like large buoyant LDL-cholesterol (lbLDLc). Recently, Srisawasdi et al. have developed a method for calculating sdLDLc and lbLDLc based upon a regression equation. Using that in developing world may provide us with a valuable tool for ASCVD risk prediction.

Objective

(1) To correlate directly measured and calculated lipid indices with insulin resistance, UACR, glycated hemoglobin, anthropometric indices, and blood pressure. (2) To evaluate these lipid parameters in subjects with or without metabolic syndrome, nephropathy, and hypertension and among various groups based upon glycated hemoglobin results.

Design

Cross-sectional study. Place and Duration of Study. From Jan 2016 to 15 April 2017.

Subjects and Methods

Finally enrolled subjects (male: 110, female: 122) were evaluated for differences in various lipid parameters, including measured LDL-cholesterol (mLDLc), HDLc and calculated LDL-cholesterol (cLDLc), non-HDLc, sdLDLC, lbLDLC, and their ratio among subjects with or without metabolic syndrome, nephropathy, glycation index, anthropometric indices, and hypertension.

Results

Significant but weak correlation was mainly observed between anthropometric indices, insulin resistance, blood pressure, and nephropathy for non-HDLc, sdLDLc, and sdLDLc/lbLDLc. Generally lipid indices were higher among subjects with metabolic syndrome [{sdLDLc: 0.92 + 0.33 versus 0.70 + 0.29 (p < 0.001)}, {sdLDLc/lbLDLc: 0.55 + 0.51 versus 0.40 + 0.38 (p = 0.010)}, {non-HDLc: 3,63 + 0.60 versus 3.36 + 0.65 (p = 0.002)}]. The fact that the sdLDLc levels provided were insignificant in Kruskall Wallis Test indicated a sharp increase in subjects with HbA1c > 7.0%. Subjects having nephropathy (UACR > 2.4 mg/g) had higher concentration of non-HDLc levels in comparison to sdLDLc [{non-HDLc: 3.68 + 0.59 versus 3.36 + 0.43} (p = 0.007), {sdLDLc: 0.83 + 0.27 versus 0.75 + 0.35 (p = NS)}].

Conclusion

Lipid markers including cLDLc and mLDLc are less associated with traditional ASCVD markers than non-HDLc, sdLDLc, and sdLDLc/lbLDLc in predicting metabolic syndrome, nephropathy, glycation status, and hypertension.