Optimizing Mass Spectrometry Analyses: A Tailored Review on the Utility of Design of Experiments

Room-temperature mL-to-μL quantitative liquid concentration device for cyclone flow

Highly sensitive quantitative analysis of liquids is required in various fields. Analytical instruments and devices such as chromatography, spectroscopic analysis, DNA sequencers, immunoassay, mass spectrometry, and microfluidic devices are utilized for this purpose. Typically, the sample volume is at the milliliter scale, while the analysis volume is at the microliter scale. Consequently, most of the sample is discarded. Therefore, a universal volume interface is required to quantitatively concentrate samples from milliliter to microliter volume. This study introduces a liquid quantitative function to the cyclone concentration method using a millimeter-scale channel, which is highly suitable for controlling liquids at the microliter scale due to its high fluidic resistance against cyclone flow. This method enables the effective control of liquid concentration by cyclone flow. The optimum channel structure is investigated, and a 33-fold concentration of aqueous solutions is demonstrated. Finally, the concentration device is applied to measure molybdenum ions in a river.

Optimal conditions for carrying out trypsin digestions on complex proteomes: From bulk samples to single cells

To identify proteins by the bottom-up mass spectrometry workflow, enzymatic digestion is essential to break down proteins into smaller peptides amenable to both chromatographic separation and mass spectrometric analysis. Trypsin is the most extensively used protease due to its high cleavage specificity and generation of peptides with desirable positively charged N- and C-terminal amino acid residues that are amenable to reverse phase HPLC separation and MS/MS analyses. However, trypsin can yield variable digestion profiles and its protein cleavage activity is interdependent on trypsin source and quality, digestion time and temperature, pH, denaturant, trypsin and substrate concentrations, composition/complexity of the sample matrix, and other factors. There is therefore a need for a more standardized, general-purpose trypsin digestion protocol. Based on a review of the literature we delineate optimal conditions for carrying out trypsin digestions of complex proteomes from bulk samples to limiting amounts of protein extracts. Furthermore, we highlight recent developments and technological advances used in digestion protocols to quantify complex proteomes from single cells. SIGNIFICANCE: Currently, bottom-up MS-based proteomics is the method of choice for global proteome analysis. Since trypsin is the most utilized protease in bottom-up MS proteomics, delineating optimal conditions for carrying out trypsin digestions of complex proteomes in samples ranging from tissues to single cells should positively impact a broad range of biomedical research.

OptiMS: An Accessible Program for Automating Mass Spectrometry Parameter Optimization and Configuration

Mass spectrometers have an enormous number of user-changeable parameters that drastically affect the observed mass spectrum. Using optimal parameters can significantly improve mass spectrometric data by increasing signal stability and signal-to-noise ratio, which decreases the limit of detection, thus revealing previously unobservable species. However, ascertaining optimal parameters is time-consuming, tedious, and made further challenging by the fact that parameters can act dependently on each other. Consequently, suboptimal parameters are frequently used during characterization, reducing the quality of results. OptiMS, an open-source, cross-platform program, was developed to simplify, accelerate, and more accurately determine optimal mass spectrometer parameters for a given system. It addresses common difficulties associated with existing software such as slow performance, high costs, and limited functionality. OptiMS efficacy was demonstrated through its application to multiple systems, quickly and successfully optimizing instrument parameters unassisted to maximize a user-defined metric, such as the intensity of a particular analyte. Additionally, among other features, OptiMS allows running of a sequence of predefined parameter configurations, reducing the workload of users wishing to obtain mass spectra under multiple sets of conditions.

Critical Design Strategies Supporting Optimized Drug Release from Polymer-Drug Conjugates

The importance of an adequate linking moiety design that allows controlled drug(s) release at the desired site of action is extensively studied for polymer-drug conjugates (PDCs). Redox-responsive self-immolative linkers bearing disulfide moieties (SS-SIL) represent a powerful strategy for intracellular drug delivery; however, the influence of drug structural features and linker-associated spacers on release kinetics remains relatively unexplored. The influence of drug/spacer chemical structure and the chemical group available for conjugation on drug release and the biological effect of resultant PDCs is evaluated. A "design of experiments" tool is implemented to develop a liquid chromatography-mass spectrometry method to perform the comprehensive characterization required for this systematic study. The obtained fit-for-purpose analytical protocol enables the quantification of low drug concentrations in drug release studies and the elucidation of metabolite presence. and provides the first data that clarifies how drug structural features influence the drug release from SS-SIL and demonstrates the non-universal nature of the SS-SIL. The importance of rigorous linker characterization in understanding structure-function correlations between linkers, drug chemical functionalities, and in vitro release kinetics from a rationally-designed polymer-drug nanoconjugate, a critical strategic crafting methodology that should remain under consideration when using a reductive environment as an endogenous drug release trigger.

Top-Down Proteomics and the Challenges of True Proteoform Characterization

Top-down proteomics (TDP) aims to identify and profile intact protein forms (proteoforms) extracted from biological samples. True proteoform characterization requires that both the base protein sequence be defined and any mass shifts identified, ideally localizing their positions within the protein sequence. Being able to fully elucidate proteoform profiles lends insight into characterizing proteoform-unique roles, and is a crucial aspect of defining protein structure-function relationships and the specific roles of different (combinations of) protein modifications. However, defining and pinpointing protein post-translational modifications (PTMs) on intact proteins remains a challenge. Characterization of (heavily) modified proteins (>∼30 kDa) remains problematic, especially when they exist in a population of similarly modified, or kindred, proteoforms. This issue is compounded as the number of modifications increases, and thus the number of theoretical combinations. Here, we present our perspective on the challenges of analyzing kindred proteoform populations, focusing on annotation of protein modifications on an "average" protein. Furthermore, we discuss the technical requirements to obtain high quality fragmentation spectral data to robustly define site-specific PTMs, and the fact that this is tempered by the time requirements necessary to separate proteoforms in advance of mass spectrometry analysis.

Design of experiments for the automated development of a multicellular cardiac model for high-throughput screening

Cardiovascular toxicity remains a major cause of drug attrition in early drug development, clinical trials, and post-market surveillance. In vitro assessment of cardiovascular liabilities often relies on single cell type-based model systems coupled with functional assays, like calcium flux and multielectrode arrays. Although these models offer high-throughput capabilities and demonstrate good predictivity for functional cardiotoxicities, they fail to consider the vital contribution of non-myocyte cells, thus limiting the potential for integrated risk assessment. Complex 3D hPSC-derived multicellular cardiac model systems have been growing in popularity; however, many of these models are limited to low-throughput with lengthy development timelines and high costs, which hampers their suitability to drug discovery. To optimize the development of an in vitro multicellular model system containing human-induced pluripotent stem-cell derived cardiomyocytes, endothelial cells and cardiac fibroblasts, we employed the Synthace platform, which enables scientists to express complex experimental intent in a simple format (e.g. Design of Experiments) and to translate this to automation protocols using no-code. Utilizing this approach, we systematically screened the impact of multiple cell culture parameters, including the co-culture of three cell types, on cardiac contractility, with minimal hands-on time. Our platform accelerates the assay development process, providing users with an efficient means to explore and optimize the experimental space for the development of multicellular models. This is particularly valuable in scenarios involving variable biological responses and limited understanding of underling mechanisms. Moreover, users can make better use of resources, streamline their workflows, and drive data-driven decision-making throughout the assay development journey.

Intensity-dependent mass search for improving metabolite database matches in chemical isotope labeling LC-QTOF-MS-based metabolomics

Liquid chromatography mass spectrometry (LC-MS) has been increasingly used for metabolome analysis. One of the critical steps in the LC-MS metabolome analysis workflow is related to metabolite identification. Among the measured parameters, peak mass is commonly used to search against a database for potential metabolite matches. Higher accuracy mass measurement allows the use of a narrower mass tolerance window for mass search. While various types of mass analyzers can routinely measure a peak mass with an error of less than a few ppm, mass measurement accuracy is not uniform for peaks with different intensities, particularly for quadrupole time-of-flight (QTOF) MS. Herein we present a simple and convenient method to determine the relation between peak intensity and mass error in LC-QTOF-MS-based metabolome analysis, followed by intensity-dependent mass search (IDMS) of a database for metabolite matches. This method is based on running a series of sodium formate mass calibrants, as part of the standard operating procedure (SOP) in LC-MS data acquisition, and then curve-fitting the measured mass errors and peak intensities. We show that, in two different quadrupole time-of-flight (QTOF) mass analyzers, mass accuracy is generally reduced as peak intensity decreases, which is independent of m/z values in the range commonly used for metabolite detection (e.g., m/z < 1000). We demonstrate the improvement in metabolite matches using IDMS in the analyses of dansyl labeled standards and human urine samples. We have implemented the IDMS method in the freely available MCID database at www.mycompoundid.org, which is composed of 8021 known human endogenous metabolites and their predicted metabolic products (375,809 compounds from one metabolic reaction and 10,583,901 compounds from two reactions).

Simultaneous monitoring of multiple hormones from human islets of Langerhans using solid-phase extraction-mass spectrometry

Islets of Langerhans release peptide hormones in controlled amounts and patterns to ensure proper maintenance of blood glucose levels. The overall release of the hormones is shaped by external factors and by autocrine and paracrine interactions occurring within the islets. To better understand what controls the secretion of islet-secreted peptides, and how these processes go awry in diabetes, methods to monitor the release of multiple hormones simultaneously are needed. While antibody-based assays are typically used, they are most often applied to quantification of a single hormone. Mass spectrometry (MS), on the other hand, is well suited for quantifying multiple hormones simultaneously but typically requires time-consuming separation steps with biological samples. In this report, response surface methodology was used to identify a set of optimal solid-phase extraction (SPE) conditions for the islet-secreted peptides, insulin, C-peptide, glucagon, and somatostatin. The optimized SPE method was used with multiple reaction monitoring and isotopically labeled standards to quantify secretion levels. Calibrations were linear from 0.5 to 50 nM with < 15% RSD peak area ratios. A microfluidic system was used to perfuse 30 human islets with different glucose conditions, and fractions were collected every 2 min for SPE-MS analysis. Results showed the release dynamics of the individual peptides, as well as patterns, such as positively and negatively correlated release and oscillations. This rapid SPE-MS method is expected to be useful for examining other peptide and small-molecule secretions from islets and could be applied to a number of other biological systems for investigating cellular communication.

Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides

Method optimization is crucial for successful mass spectrometry (MS) analysis. However, extensive method assessments, altering various parameters individually, are rarely performed due to practical limitations regarding time and sample quantity. To maximize sample space for optimization while maintaining reasonable instrumentation requirements, a definitive screening design (DSD) is leveraged for systematic optimization of data-independent acquisition (DIA) parameters to maximize crustacean neuropeptide identifications. While DSDs require several injections, a library-free methodology enables surrogate sample usage for comprehensive optimization of MS parameters to assess biomolecules from limited samples. We identified several parameters contributing significant first- or second-order effects to method performance, and the DSD model predicted ideal values to implement. These increased reproducibility and detection capabilities enabled the identification of 461 peptides, compared to 375 and 262 peptides identified through data-dependent acquisition (DDA) and a published DIA method for crustacean neuropeptides, respectively. Herein, we demonstrate a DSD optimization workflow, using standard material, not reliant on spectral libraries for the analysis of any low abundance molecules from previous samples of limited availability. This extends the DIA method to low abundance isoforms dysregulated or only detectable in disease samples, thus improving characterization of previously inaccessible biomolecules, such as neuropeptides. Data are available via ProteomeXchange with identifier PXD038520.

A sDOE (Simple Design-of-Experiment) Approach for Parameter Optimization in Mass Spectrometry. Part 1. Parameter Selection and Interference Effects in Top-Down ETD Fragmentation of Proteins in a UHR-QTOF Instrument

Design-of-experiment (DOE) approaches, originally conceived by Fischer, are widely applied in industry, particularly in the context of production for which they have been greatly expended. In a research and development context, DOE can be of great use for method development. Specifically, DOE can greatly speed up instrument parameter optimization by first identifying parameters that are critical to a given outcome, showing parameter interdependency where it occurs and accelerating optimization of said parameters using matrices of experimental conditions. While DOE approaches have been applied in mass spectrometry experiments, they have so far failed to gain widespread adoption. This could be attributed to the fact that DOE can get quite complex and daunting to the everyday user. Here we make the case that a subset of DOE tools, hereafter called SimpleDOE (sDOE), can make DOE accessible and useful to the Mass Spectrometry community at large. We illustrate the progressive gains from a purely manual approach to sDOE through a stepwise optimization of parameters affecting the efficiency of top-down ETD fragmentation of proteins on a high-resolution Q-TOF mass spectrometer, where the aim is to maximize sequence coverage of fragmentation events.

The development and application of matrix assisted laser desorption electrospray ionization: The teenage years

In the past 15 years, ambient ionization techniques have witnessed a significant incursion into the field of mass spectrometry imaging, demonstrating their ability to provide complementary information to matrix-assisted laser desorption ionization. Matrix-assisted laser desorption electrospray ionization is one such technique that has evolved since its first demonstrations with ultraviolet lasers coupled to Fourier transform-ion cyclotron resonance mass spectrometers to extensive use with infrared lasers coupled to orbitrap-based mass spectrometers. Concurrently, there have been transformative developments of this imaging platform due to the high level of control the principal group has retained over the laser technology, data acquisition software (RastirX), instrument communication, and image processing software (MSiReader). This review will discuss the developments of MALDESI since its first laboratory demonstration in 2005 to the most recent advances in 2021.

Optimising cell-based bioassays via integrated design of experiments (ixDoE) - A practical guide

For process optimisation Design of Experiments (DoE) has long been established as a more powerful strategy than a One Factor at a Time approach. Nevertheless, DoE is not widely used especially in the field of cell-based bioassay development although it is known that complex interactions often exist. We believe that biopharmaceutical manufacturers are reluctant to move beyond standard practices due to the perceived costs, efforts, and complexity. We therefore introduce the integrated DoE (ixDoE) approach to target a smarter use of DoEs in the bioassay setting, specifically in optimising resources and time. Where in a standard practice 3 to 4 separate DoEs would be performed, our ixDoE approach includes the necessary statistical inference from only a single experimental set. Hence, we advocate for an innovative, ixDoE approach accompanied by a suitable statistical analysis strategy and present this as a practical guide for a typical bioassay development from basic research to biopharmaceutical industry.

Next-Generation Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Source for Mass Spectrometry Imaging and High-Throughput Screening

Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) is a hybrid, ambient ionization source that combines the advantages of electrospray ionization and matrix-assisted laser desorption/ionization, making it a versatile tool for both high-throughput screening (HTS) and mass spectrometry imaging (MSI) studies. To expand the capabilities of the IR-MALDESI source, an entirely new architecture was designed to overcome the key limitations of the previous source. This next-generation (NextGen) IR-MALDESI source features a vertically mounted IR-laser, a planar translation stage with computerized sample height control, an aluminum enclosure, and a novel mass spectrometer interface plate. The NextGen IR-MALDESI source has improved user-friendliness, improved overall versatility, and can be coupled to numerous Orbitrap mass spectrometers to accommodate more research laboratories. In this work, we highlight the benefits of the NextGen IR-MALDESI source as an improved platform for MSI and direct analysis. We also optimize the NextGen MALDESI source component geometries to increase target ion abundances over a wide m/z range. Finally, documentation is provided for each NextGen IR-MALDESI part so that it can be replicated and incorporated into any lab space.

Extraction and analysis of an organophosphate salt nucleating agent from irradiated polypropylene resin

Although it is well-established that irradiation of produce can reduce food-borne pathogens and spoilage organisms, data on the effect of irradiation on polymer additives in food packaging materials are limited, particularly for those additives used in packaging leafy greens or in current food packaging materials. We investigated the effects of irradiating a nucleating agent, aluminium, hydroxybis[2,4,8,10-tetrakis(1,1-dimethylethyl)-6-hydroxy-12H-dibenzo [d,g][1,3,2]dioxaphosphocin 6-oxidato]- (CAS Reg. No. 151841-65-5), at doses of 1-20 kGy in polypropylene. That nucleating agent was then extracted using accelerated solvent extraction and analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), liquid chromatography-photodiode array detection (LC-PDA), and solid-state nuclear magnetic resonance (SSNMR) spectroscopy. We found this nucleating agent was not significantly affected by radiation treatment up to 20 kGy. Therefore, this nucleating agent could potentially be useful in food packaging materials that will be irradiated at doses of 20 kGy or less. Establishing which additives are stable under anticipated irradiation doses will help support safety evaluation of food packaging materials.

Optimization of LC-MS2 Data Acquisition Parameters for Molecular Networking Applied to Marine Natural Products

Since the introduction of the online open-source GNPS, molecular networking has quickly become a widely applied tool in the field of natural products chemistry, with applications from dereplication, genome mining, metabolomics, and visualization of chemical space. Studies have shown that data dependent acquisition (DDA) parameters affect molecular network topology but are limited in the number of parameters studied. With an aim to optimize LC-MS² parameters for integrating GNPS-based molecular networking into our marine natural products workflow, a design of experiment (DOE) was used to screen the significance of the effect that eleven parameters have on both Classical Molecular Networking workflow (CLMN) and the new Feature-Based Molecular Networking workflow (FBMN). Our results indicate that four parameters (concentration, run duration, collision energy and number of precursors per cycle) are the most significant data acquisition parameters affecting the network topology. While concentration and the LC duration were found to be the two most important factors to optimize for CLMN, the number of precursors per cycle and collision energy were also very important factors to optimize for FBMN.

Design of Experiments for Matrix-Assisted Laser Desorption/Ionization of Amphiphilic Poly(Ethylene Oxide)-b-Polystyrene Block Copolymers

Matrix-assisted laser/desorption ionization (MALDI) has become a very popular ionization technique for mass spectrometry of synthetic polymers because it allows high throughput analysis of low amounts of sample while avoiding the complexity introduced by extensive multiple charging of electrospray ionization. Yet, fundamental mechanisms underlying this ionization process are not fully understood, so development of sample preparation methods remains empirical. Reliable prediction for the optimal matrix/analyte/salt system is indeed still not possible for homopolymers and it becomes even more challenging in the case of amphiphilic block copolymers where conditions dictated by one block are not compatible with MALDI requirements of the second block. In order to perform MALDI of copolymers composed of poly (ethylene oxide) (PEO) and polystyrene (PS) blocks, it was postulated here that experimental conditions suitable for both species would also be successful for PEO-b-PS. Accordingly, designs of experiments based on Quantitative Structure Activity Relationship (QSAR) analysis were first implemented, studying the influence of 19 matrices and 26 salts on the laser fluence requested for successful MALDI. This analysis first permitted to highlight correlations between the investigated 10 descriptors of matrices and salts and the analytical response, and then to construct models that permits reliable predictions of matrix/salt couples to be used for one or the other homopolymer. Selected couples were then used for MALDI of a PEO-b-PS copolymer but no general trend was observed: experimental conditions expected to work often failed whereas ionic adducts of the copolymer were clearly detected with some matrix/salt systems that were shown to badly perform for constituting homopolymers. Overall, this rules out the working assumption stating that the MALDI behavior of chains composed of PEO and PS segments should combine the behavior of the two polymeric species. Yet, although requiring a dedicated design of experiments, MALDI of the amphiphilic PEO-b-PS copolymer was achieved for the first time.

Robust optimization of SWATH-MS workflow for human blood serum proteome analysis using a quality by design approach

Background

It is not enough to optimize proteomics assays. It is critical those assays are robust to operating conditions. Without robust assays, proteomic biomarkers are unlikely to translate readily into the clinic. This study outlines a structured approach to the identification of a robust operating window for proteomics assays and applies that method to Sequential Window Acquisition of all Theoretical Spectra Mass Spectroscopy (SWATH-MS).

Methods

We used a sequential quality by design approach exploiting a fractional screening design to first identify critical SWATH-MS parameters, then using response surface methods to identify a robust operating window with good reproducibility, before validating those settings in a separate validation study.

Results

The screening experiment identified two critical SWATH-MS parameters. We modelled the number of proteins and reproducibility as a function of those parameters identifying an operating window permitting robust maximization of the number of proteins quantified in human serum. In a separate validation study, these settings were shown to give good proteome-wide coverage and high quantification reproducibility.

Conclusions

Using design of experiments permits identification of a robust operating window for SWATH-MS. The method gives a good understanding of proteomics assays and greater data-driven confidence in SWATH-MS performance.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12014-021-09323-z.

Design of experiments for the optimization of SOFI super-resolution microscopy imaging

Super-resolution optical fluctuation imaging (SOFI) is a well-known super-resolution technique appreciated for its versatility and broad applicability. However, even though an extended theoretical description is available, it is still not fully understood how the interplay between different experimental parameters influences the quality of a SOFI image. We investigated the relationship between five experimental parameters (measurement time, on-time t _on, off-time t _off, probe brightness, and out of focus background) and the quality of the super-resolved images they yielded, expressed as Signal to Noise Ratio (SNR). Empirical relationships were modeled for second- and third-order SOFI using data simulated according to a D-Optimal design of experiments, which is an ad-hoc design built to reduce the experimental load when the total number of trials to be conducted becomes too high for practical applications. This approach proves to be more reliable and efficient for parameter optimization compared to the more classical parameter by parameter approach. Our results indicate that the best image quality is achieved for the fastest emitter blinking (lowest t _on and t _off), lowest background level, and the highest measurement duration, while the brightness variation does not affect the quality in a statistically significant way within the investigated range. However, when the ranges spanned by the parameters are constrained, a different set of optimal conditions may arise. For example, for second-order SOFI, we identified situations in which the increase of t _off can be beneficial to SNR, such as when the measurement duration is long enough. In general, optimal values of t _on and t _off have been found to be highly dependent from each other and from the measurement duration.

HPLC-MS-based design of experiments approach on cocoa roasting

Modern statistical methods, such as the design of experiments and response surface methodology, are widely used to describe changes in multiparameter processes during the processing of food in both science and technology contexts. However, these approaches are described to a lesser degree in the case of cocoa roasting than other foods and processes. Our study aimed to use the design of experiments to establish a model of cocoa roasting for relevant flavor-related constituents. We have used HPLC-MS techniques to link standard process parameters with chemical compounds changing in concentration during cocoa roasting. Influence of time, temperature, the addition of water, acid, and base, on relative concentrations of procyanidin monomers, dimers, and trimers, an Amadori compound, and a peptide, was shown. High-quality models for each compound were established and validated, displaying good prediction accuracy. Such an approach could be used to optimize processing conditions for cocoa roasting in order to influence the concentration of certain chemical compounds, and in turn, improving the flavor of chocolate products.

Design of experiments for development and optimization of a liquid chromatography coupled to tandem mass spectrometry bioanalytical assay

Design of experiments (DoE) is a valuable tool for the optimization of quantitative bioanalytical methods utilizing liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Liquid chromatography mass spectrometry (LC-MS) is composed of several processes, including, liquid introduction and analyte ionization. The goal is to transfer analytes from atmospheric pressure to vacuum and maintain conditions that are compatible for both LC and MS. These processes involve many experimental factors which need to be simultaneously optimized to obtain maximum sensitivity and resolution at minimum retention time. In this tutorial, the basic concepts of DoE will be explained with focus on practical use of DoE. Three case studies optimized with DoE for liquid chromatography tandem mass spectrometry (LC-MS/MS) quantitative assays will then be presented.

HPLC-electrospray ionization-mass spectrometry optimization by high-performance design of experiments for astrocyte glutamine measurement

The amino acid glutamine (Gln) is a likely source of energy in the brain during neuroglucopenia. Effects of glucose deficiency on astrocyte Gln homeostasis remain unclear, as analytical tools of requisite sensitivity for quantification of intracellular levels of this molecule are not currently available. Here, a primary hypothalamic astrocyte culture model was used in conjunction with design of experiments (DOE)-refined high-performance liquid chromatography-electrospray ionization-mass spectrometry (LC-ESI-MS) methodology to investigate the hypothesis that glucoprivation alters astrocyte Gln content in a sex-specific manner. Critical mass spectrometric parameters for Gln derivative chromatographic response were identified by comparing the performance of central composite design, Box-Behnken design, and Optimal Design (OD)-A, -D, -I, -Distance, and -Modified Distance DOE models. The outcomes showed that the OD-A-generated response was superior relative to other design outcomes. Forecasted surface plot critical mass spectrometric parameters were maximized by OD-A, OD-Distance, and OD-Modified Distance designs. OD-A produced a high-performance method that yielded experimental run and forecasted surface plot maximal responses. Optimized mass spectrometric analysis of male versus female astrocyte Gln content provides novel evidence that glucoprivation significantly depletes this amino acid in female, but not in male, and that this sex-specific response may involve differential sensitivity to estrogen receptor signaling. This technological advance will facilitate efforts to ascertain how distinctive physiological and pathophysiological stimuli impact astrocyte Gln metabolism in each sex.

Statistical Design of Experiments for Synthetic Biology

The design and optimization of biological systems is an inherently complex undertaking that requires careful balancing of myriad synergistic and antagonistic variables. However, despite this complexity, much synthetic biology research is predicated on One Factor at A Time (OFAT) experimentation; the genetic and environmental variables affecting the activity of a system of interest are sequentially altered while all other variables are held constant. Beyond being time and resource intensive, OFAT experimentation crucially ignores the effect of interactions between factors. Given the ubiquity of interacting genetic and environmental factors in biology this failure to account for interaction effects in OFAT experimentation can result in the development of suboptimal systems. To address these limitations, an increasing number of studies have turned to Design of Experiments (DoE), a suite of methods that enable efficient, systematic exploration and exploitation of complex design spaces. This review provides an overview of DoE for synthetic biologists. Key concepts and commonly used experimental designs are introduced, and we discuss the advantages of DoE as compared to OFAT experimentation. We dissect the applicability of DoE in the context of synthetic biology and review studies which have successfully employed these methods, illustrating the potential of statistical experimental design to guide the design, characterization, and optimization of biological protocols, pathways, and processes.

Improving sensitivity for the targeted LC-MS/MS analysis of the peptide bradykinin using a design of experiments approach

The nonapeptide bradykinin is endogenously present only in low picomolar plasma concentrations, subsequently making reliable detection using liquid chromatography coupled to mass spectrometry (LC-MS/MS) challenging. Furthermore, non-specific adsorption during sample preparation and storage can lead to unpredictable peptide losses. To overcome these issues, a design of experiments (DoE) approach was applied, which consisted of a screening to identify impacting factors, optimisation and confirmation runs. On the one hand, different injection solvent compositions and sample collection materials were investigated in order to decrease non-specific adsorption. On the other hand, the addition of modifiers, which are known to enhance the signal intensity in LC-MS/MS, to the chromatographic mobile phase was examined. Polypropylene was the most suitable material among those investigated and resulted in a factor increase of 12.0 compared to LC-MS glass. The advantages of protein low-binding polypropylene versus standard polypropylene were fully compensated by the optimisation of the injection solvent. The latter substantially contributed to a decrease of non-specific adsorption of bradykinin. In this regard, bradykinin further benefitted from an organic fraction and a high amount of formic acid. Based on the DoE results, the final optimised injection solvent-consisting of 8.7% formic acid in 49.4/5.3/36.6 water/methanol/dimethyl sulfoxide (v/v/v)-was established. Furthermore, optimisation of the mobile phase composition yielded a signal intensity increase by a factor of 7.7. The transferability of the optimisation results conducted in neat solutions were successfully confirmed in human plasma. The applicability of this approach was further supported by the successful determination of low-abundance endogenous bradykinin levels in human plasma using LC-MS/MS.

Optimization of Ultra-High-Performance Liquid Chromatography-Electrospray Ionization-Mass Spectrometry Detection of Glutamine-FMOC Ad-Hoc Derivative by Central Composite Design

Glutamine (Gln) is converted to excitatory (glutamate, aspartate) and inhibitory (γ-amino butyric acid) amino acid neurotransmitters in brain, and is a source of energy during glucose deprivation. Current research utilized an Analytical Quality by Design approach to optimize levels and combinations of critical gas pressure (sheath, auxiliary, sweep) and temperature (ion transfer tube, vaporizer) parameters for high-sensitivity mass spectrometric quantification of brain tissue glutamine. A Design of Experiments (DOE) matrix for evaluation of relationships between these multiple independent variables and a singular response variable, e.g. glutamine chromatogram area, was developed by statistical response surface methodology using central composite design. A second-order polynomial equation was generated to identify and predict singular versus combinatory effects of synergistic and antagonistic factors on chromatograph area. Predicted versus found outcomes overlapped, with enhanced area associated with the latter. DOE methodology was subsequently used to evaluate liquid chromatographic variable effects, e.g. flow rate, column temperature, and mobile phase composition on the response variable. Results demonstrate that combinatory AQbD-guided mass spectrometric/liquid chromatographic optimization significantly enhanced analytical sensitivity for Gln, thus enabling down-sized brain tissue sample volume procurement for quantification of this critical amino acid.

Solid phase microextraction techniques used for gas chromatography: a review

In the last decade, the development and adoption of greener and sustainable microextraction techniques have been proved to be an effective alternative to classical sample preparation procedures. In this review, 10 commercially available solid-phase microextraction systems are presented, with special attention to the appraisal of their analytical, bioanalytical, and environmental engineering. This review provides an overview of the challenges and achievements in the application of fully automated miniaturized sample preparation methods in analytical laboratories. Both theoretical and practical aspects of these environment-friendly preparation approaches are discussed. The application of chemometrics in method development is also discussed. We are convinced that green analytical chemistry will be really useful in the years ahead. The application of cheap, fast, automated, “clever”, and environmentally safe procedures to environmental, clinical, and food analysis will improve significantly the quality of the analytical data.

Therapeutic potential of polypeptide-based conjugates: Rational design and analytical tools that can boost clinical translation

The clinical success of polypeptides as polymeric drugs, covered by the umbrella term "polymer therapeutics," combined with related scientific and technological breakthroughs, explain their exponential growth in the development of polypeptide-drug conjugates as therapeutic agents. A deeper understanding of the biology at relevant pathological sites and the critical biological barriers faced, combined with advances regarding controlled polymerization techniques, material bioresponsiveness, analytical methods, and scale up-manufacture processes, have fostered the development of these nature-mimicking entities. Now, engineered polypeptides have the potential to combat current challenges in the advanced drug delivery field. In this review, we will discuss examples of polypeptide-drug conjugates as single or combination therapies in both preclinical and clinical studies as therapeutics and molecular imaging tools. Importantly, we will critically discuss relevant examples to highlight those parameters relevant to their rational design, such as linking chemistry, the analytical strategies employed, and their physicochemical and biological characterization, that will foster their rapid clinical translation.

Detection, identification, and quantification of oxidative protein modifications

Exposure of biological molecules to oxidants is inevitable and therefore commonplace. Oxidative stress in cells arises from both external agents and endogenous processes that generate reactive species, either purposely (e.g. during pathogen killing or enzymatic reactions) or accidentally (e.g. exposure to radiation, pollutants, drugs, or chemicals). As proteins are highly abundant and react rapidly with many oxidants, they are highly susceptible to, and major targets of, oxidative damage. This can result in changes to protein structure, function, and turnover and to loss or (occasional) gain of activity. Accumulation of oxidatively-modified proteins, due to either increased generation or decreased removal, has been associated with both aging and multiple diseases. Different oxidants generate a broad, and sometimes characteristic, spectrum of post-translational modifications. The kinetics (rates) of damage formation also vary dramatically. There is a pressing need for reliable and robust methods that can detect, identify, and quantify the products formed on amino acids, peptides, and proteins, especially in complex systems. This review summarizes several advances in our understanding of this complex chemistry and highlights methods that are available to detect oxidative modifications-at the amino acid, peptide, or protein level-and their nature, quantity, and position within a peptide sequence. Although considerable progress has been made in the development and application of new techniques, it is clear that further development is required to fully assess the relative importance of protein oxidation and to determine whether an oxidation is a cause, or merely a consequence, of injurious processes.

Accelerating Lipidomic Method Development through in Silico Simulation

Judicious selection of mass spectrometry (MS) acquisition parameters is essential for effectively profiling the broad diversity and dynamic range of biomolecules. Typically, acquisition parameters are individually optimized to maximally characterize analytes from each new sample matrix. This time-consuming process often ignores the synergistic relationship between MS method parameters, producing suboptimal results. Here we detail the creation of an algorithm which accurately simulates LC-MS/MS lipidomic data acquisition performance for a benchtop quadrupole-Orbitrap MS system. By coupling this simulation tool with a genetic algorithm for constrained parameter optimization, we demonstrate the efficient identification of LC-MS/MS method parameter sets individually suited for specific sample matrices. Finally, we utilize the in silico simulation to examine how continued developments in MS acquisition speed and sensitivity will further increase the power of MS lipidomics as a vital tool for impactful biochemical analysis.

Mixture designs to investigate adverse effects upon co-exposure to environmental cyanotoxins

The goal of this study was to implement powerful mixture design techniques, commonly used in process optimization, to investigate enhanced adverse effects upon co-exposure to environmental cyanotoxins. Exposure to cyanobacteria, which are found ubiquitously in environmental water reservoirs, have been linked to several neurodegenerative diseases. Despite the known co-occurrence of various cyanotoxins, the majority of studies investigating this link have focused on the investigation of a single cyanotoxin, a noncanonical amino acid called β-methylamino-L-alanine (BMAA), which poorly recapitulates an actual environmental exposure. Interactions amongst cyanotoxic compounds is an area of great concern and remains poorly understood. To this end, we describe the use of a simplex axial mixture design to screen for interactive adverse effects of cyanotoxic mixtures. Using a combination of basic toxicity assays coupled with contemporary proteomic techniques, our results show the existence of a significant (p ≤ 0.01) interaction between BMAA and its isomers aminoethyl glycine (AEG) and 2,4-diaminobutyric acid (2,4DAB). Cyanotoxic mixtures significantly decreased cell viability by an average of 19% and increased caspases 3/7 activities by an average of 110% when compared to individual cyanotoxins (p ≤ 0.05). Cyanotoxic mixtures perturbed various biological pathways associated with neurodegeneration, including inhibition of protective autophagy and activation of mitochondrial dysfunction (z-score >|2|). Additionally, exposure to mixtures perturbed important upstream regulators involved in cellular dysfunction, morbidity, and development. Taken together, our results highlight: (1) the need to study combinations of cyanotoxins when investigating the link between cyanobacteria and neurodegenerative pathologies and (2) the application of design of experiment (DoE) as an efficient methodology to study mixtures of relevant environmental toxins.

Chemometrics-assisted optimization of liquid chromatography-quadrupole-time-of-flight mass spectrometry analysis for targeted metabolomics

Mass spectrometry-based metabolomics is characterized by a vast number of variables leading to a great degree of complexity. In this work, we aimed to simplify this process with a stepped chemometric optimization of the both funnel technology (funnel exit DC, FDC; funnel RF LP, FLC; funnel RF HP, FRP) and ion source parameters (Octopolo, Oct; and Fragmentor, Frag) of a quadrupole-time of flight (qTOF) for a human urinary metabolites. The workflow comprised a Box-Behnken experimental design with 47 experiments followed by the identification and quantification of a set of metabolites using high-resolution full-scan MS mode and feature extraction with an inclusion list. Metabolite peak areas were grouped according to abundance (high and low) and modeled by Random Forest regression (variance explained >85%). The full three-level factorial design consisting in 243 experiments was predicted and top 10 solutions for desirability function and those comprising the Pareto front were extracted and investigated. To guarantee the quality of results, we compared the Pareto front solutions with those achieved by standard instrumental parameters suggested by the manufacturer. A set of five solutions were identified that increased the mean peak area by 56-59% and 17%, for high- and low-abundance metabolites, respectively. The optimal parameters were determined to be: FLP, 100 V; FDC, 40 and 30 V; Frag, 275 and 400 V; and Oct, 600 and 800 V. The methodology applied throughout this work represents a flexible strategy to optimize instrumental parameters and exploit the performance of a qTOF MS detector.

Demonstration of hydrazide tagging for O-glycans and a central composite design of experiments optimization using the INLIGHT™ reagent

The INLIGHT™ strategy for N-linked glycan derivatization has been shown to overcome many of the challenges associated with glycan analysis. The hydrazide tag reacts efficiently with the glycans, increasing their non-polar surface area, allowing for reversed-phase separations and increased ionization efficiency. We have taken the INLIGHT™ strategy and adopted it for use with O-linked glycans. A central composite design was utilized to find optimized tagging conditions (45% acetic acid, 0.1 μg/μL tag concentration, 37 C, 1.75 h). Derivatization at optimized conditions was much quicker than any hydrazide derivatization strategy used previously. Human immunoglobulin A (IgA) and bovine submaxillary mucin (BSM) were then deglycosylated through hydrazinolysis and the removed glycans were tagged under optimum conditions. XIC of tagged glycans and MS2 data show successful hydrazide tagging of O-linked glycans for the first time. Graphical abstract The INLIGHT™ hydrazide tag was optimized using a central composite design for derivatization of O-linked glycans. Two glycoprotein standards were deglycosylated through hydrazinolysis and tagged at the optimized conditions. MS/MS data shows INLIGHT™ derivatization of glycans demonstrating successful hydrazide tagging of O-glycans for the first time.

Direct screening of enzyme activity using infrared matrix-assisted laser desorption electrospray ionization

RATIONALE

High-throughput screening (HTS) is a critical step in the drug discovery process. However, most mass spectrometry (MS)-based HTS methods require sample cleanup steps prior to analysis. In this work we present the utility of infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) for monitoring an enzymatic reaction directly from a biological buffer system with no sample cleanup and at high throughput.

METHODS

IR-MALDESI was used to directly analyze reaction mixtures from a well plate at different time points after reaction initiation. The percent conversion of precursors to products was used to screen the enzyme activity. The reaction was performed with two different concentrations of precursors and enzyme in order to assess the dynamic range of the assay. Eventually, a pseudo-HTS study was designed to investigate the utility of IR-MALDESI screening enzyme activity in a high-throughput manner.

RESULTS

IR-MALDESI was able to readily monitor the activity of IDH1 over time at two different concentrations of precursors and enzyme. The calculated Z-factors of 0.65 and 0.41 confirmed the suitability of the developed method for screening enzyme activity in HTS manner. Finally, in a single-blind pseudo-HTS analysis IR-MALDESI was able to correctly predict the identity of all samples, where 8/10 samples were identified with high confidence and the other two samples with lower confidence.

CONCLUSIONS

The enzymatic activity of IDH1 was screened by directly analyzing the reaction content from the buffer in well plates with no sample cleanup steps. This proof-of-concept study demonstrates the robustness of IR-MALDESI for direct analysis of enzymatic reactions from biological buffers with no sample cleanup and its immense potential for HTS applications.

Optimization of mass spectrometric parameters improve the identification performance of capillary zone electrophoresis for single-shot bottom-up proteomics analysis

The effects of MS1 injection time, MS2 injection time, dynamic exclusion time, intensity threshold, and isolation width were investigated on the numbers of peptide and protein identifications for single-shot bottom-up proteomics analysis using CZE-MS/MS analysis of a Xenopus laevis tryptic digest. An electrokinetically pumped nanospray interface was used to couple a linear-polyacrylamide coated capillary to a Q Exactive HF mass spectrometer. A sensitive method that used a 1.4 Th isolation width, 60,000 MS2 resolution, 110 ms MS2 injection time, and a top 7 fragmentation produced the largest number of identifications when the CZE loading amount was less than 100 ng. A programmable autogain control method (pAGC) that used a 1.4 Th isolation width, 15,000 MS2 resolution, 110 ms MS2 injection time, and top 10 fragmentation produced the largest number of identifications for CZE loading amounts greater than 100 ng; 7218 unique peptides and 1653 protein groups were identified from 200 ng by using the pAGC method. The effect of mass spectrometer conditions on the performance of UPLC-MS/MS was also investigated. A fast method that used a 1.4 Th isolation width, 30,000 MS2 resolution, 45 ms MS2 injection time, and top 12 fragmentation produced the largest number of identifications for 200 ng UPLC loading amount (6025 unique peptides and 1501 protein groups). This is the first report where the identification number for CZE surpasses that of the UPLC at the 200 ng loading level. However, more peptides (11476) and protein groups (2378) were identified by using UPLC-MS/MS when the sample loading amount was increased to 2 μg with the fast method. To exploit the fast scan speed of the Q-Exactive HF mass spectrometer, higher sample loading amounts are required for single-shot bottom-up proteomics analysis using CZE-MS/MS.

DRILL: An Electrospray Ionization-Mass Spectrometry Interface for Improved Sensitivity via Inertial Droplet Sorting and Electrohydrodynamic Focusing in a Swirling Flow

We describe the DRILL (dry ion localization and locomotion) device, which is an interface for electrospray ionization (ESI)-mass spectrometry (MS) that exploits a swirling flow to enable the use of inertial separation to prescribe different fates for electrosprayed droplets based on their size. This source adds a new approach to charged droplet trajectory manipulation which, when combined with hydrodynamic drag forces and electric field forces, provides a rich range of possible DRILL operational modes. Here, we experimentally demonstrate sensitivity improvement obtained via vortex-induced inertial sorting of electrosprayed droplets/ions: one possible mode of DRILL operation. In this mode, DRILL removes larger droplets while accelerating the remainder of the ESI plume, producing a high velocity stream of gas-enriched spray with small, highly charged droplets and ions and directing it toward the MS inlet. The improved signal-to-noise ratio (10-fold enhancement) in the detection of angiotensin I is demonstrated using the DRILL interface coupled to ESI-MS along with an improved limit of detection (10-fold enhancement, 100 picomole) in the detection of angiotensin II. The utility of DRILL has also been demonstrated by liquid chromatography (LC)-MS: a stable isotope labeled peptide cocktail was spiked into a complex native tissue extract and quantified by unscheduled multiple reaction monitoring on a TSQ Vantage. DRILL demonstrated improved signal strength (up to a 700-fold) for 8 out of 9 peptides and had no effects on the peak shape of the transitions.

DART-MS analysis of inorganic explosives using high temperature thermal desorption

An ambient mass spectrometry (MS) platform coupling resistive Joule heating thermal desorption (JHTD) and direct analysis in real time (DART) was implemented for the analysis of inorganic nitrite, nitrate, chlorate, and perchlorate salts. The resistive heating component generated discrete and rapid heating ramps and elevated temperatures, up to approximately 400 °C s^-1 and 750 °C, by passing a few amperes of DC current through a nichrome wire. JHTD enhanced the utility and capabilities of traditional DART-MS for the trace detection of previously difficult to detect inorganic compounds. A partial factorial design of experiments (DOE) was implemented for the systematic evaluation of five system parameters. A base set of conditions for JHTD-DART-MS was derived from this evaluation, demonstrating sensitive detection of a range of inorganic oxidizer salts, down to single nanogram levels. DOE also identified JHTD filament current and in-source collision induced dissociation (CID) energy as inducing the greatest effect on system response. Tuning of JHTD current provided a method for controlling the relative degrees of thermal desorption and thermal decomposition. Furthermore, in-source CID provided manipulation of adduct and cluster fragmentation, optimizing the detection of molecular anion species. Finally, the differential thermal desorption nature of the JHTD-DART platform demonstrated efficient desorption and detection of organic and inorganic explosive mixtures, with each desorbing at its respective optimal temperature.

Optimization of LC-Orbitrap-HRMS acquisition and MZmine 2 data processing for nontarget screening of environmental samples using design of experiments

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a well-established technique for nontarget screening of contaminants in complex environmental samples. Automatic peak detection is essential, but its performance has only rarely been assessed and optimized so far. With the aim to fill this gap, we used pristine water extracts spiked with 78 contaminants as a test case to evaluate and optimize chromatogram and spectral data processing. To assess whether data acquisition strategies have a significant impact on peak detection, three values of MS cycle time (CT) of an LTQ Orbitrap instrument were tested. Furthermore, the key parameter settings of the data processing software MZmine 2 were optimized to detect the maximum number of target peaks from the samples by the design of experiments (DoE) approach and compared to a manual evaluation. The results indicate that short CT significantly improves the quality of automatic peak detection, which means that full scan acquisition without additional MS² experiments is suggested for nontarget screening. MZmine 2 detected 75-100 % of the peaks compared to manual peak detection at an intensity level of 10⁵ in a validation dataset on both spiked and real water samples under optimal parameter settings. Finally, we provide an optimization workflow of MZmine 2 for LC-HRMS data processing that is applicable for environmental samples for nontarget screening. The results also show that the DoE approach is useful and effort-saving for optimizing data processing parameters. Graphical Abstract ᅟ.