Robust, reproducible and quantitative analysis of thousands of proteomes by micro-flow LC-MS/MS

Feed injection in liquid chromatography: Reducing the effect of large-volume injections from purely organic diluents in reversed-phase liquid chromatography

In liquid chromatography (LC), discrepancies in liquid properties such as elution strength and viscosity lead to a mismatch between the sample diluent and mobile phase. This mismatch can result in peak deformation, including peak splitting or even breakthrough, particularly when large sample volumes are injected. The formation of a T-junction between sample solution and mobile phase flow stream, a technique previously used in supercritical fluid chromatography, is the key enabler of feed injection in LC. This T-junction allows the injection needle to infuse the sample directly into the mobile phase. It ensures that the diluent is continuously mixed with the mobile phase before introduced onto the column, thereby reducing the initial solvent mismatch. The degree of dilution depends on the ratio between mobile phase flow rate (Qmp) and feed rate (Qfeed) at which the sample is infused. Our study examined the effect of several parameters on the feed injection of large sample volumes from purely organic diluents in reversed-phase LC. These parameters included the type of diluent, compound retention factor (k), injected sample volume (Vinj), and Qmp. With varied Qfeed, all compounds revealed a similar range of optimal values for Qr = (Qmp-Qfeed)/Qfeed between 2 and 5, a range unaffected by Vinj and Qmp. For Qr > 5, the slope of the plate height curves (H vs. Qr) decreases with increasing k, potentially extending the range of optimal Qr-values. However, the best Qr-value for a separation is determined by the compound with the smallest k, simplifying optimization. Using feed injection, we were able to reduce plate heights by up to a factor of 8 compared to classic flow-through injection of large sample volumes.

Mitochondrial lipidomes are tissue specific - low cholesterol contents relate to UCP1 activity

Lipid composition is conserved within sub-cellular compartments to maintain cell function. Lipidomic analyses of liver, muscle, white and brown adipose tissue (BAT) mitochondria revealed substantial differences in their glycerophospholipid (GPL) and free cholesterol (FC) contents. The GPL to FC ratio was 50-fold higher in brown than white adipose tissue mitochondria. Their purity was verified by comparison of proteomes with ER and mitochondria-associated membranes. A lipid signature containing PC and FC, calculated from the lipidomic profiles, allowed differentiation of mitochondria from BAT of mice housed at different temperatures. Elevating FC in BAT mitochondria prevented uncoupling protein (UCP) 1 function, whereas increasing GPL boosted it. Similarly, STARD3 overexpression facilitating mitochondrial FC import inhibited UCP1 function in primary brown adipocytes, whereas a knockdown promoted it. We conclude that the mitochondrial GPL/FC ratio is key for BAT function and propose that targeting it might be a promising strategy to promote UCP1 activity.

Rapid Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry

Noninvasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography-mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method that integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein corona enrichment for high-throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 min collection time, which enables a potential throughput of approximately 1000 samples daily. The identified proteins are involved in valuable pathways, and 44 of the proteins are FDA-approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation of 17%. Moreover, different protein corona profiles were observed among various NPs based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichment. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.

Metabolic profiling of Citrus maxima L. seedlings in response to cadmium stress using UPLC-QTOF-MS

Cadmium (Cd) pollution significantly reduces agricultural crop yields. In our research, metabolomic changes in Citrus maxima L. subjected to Cd stress were investigated using ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) in tandem with multivariate analytical techniques. This integrative method, coupled with physiological evaluations, aimed to elucidate the core adaptive mechanisms to Cd stress. We found that under Cd stress, C. maxima seedlings exhibited elevated levels of reactive oxygen species, malondialdehyde, and electrolyte leakage. Furthermore, principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) demonstrated distinct a separation of the metabolome among the different treatment groups under Cd stress, indicating dynamic metabolic changes. Metabolic analysis suggested that genes involved are initially induced by Cd treatment, followed by the activation of the flavonoid biosynthesis pathway. This investigation provides new insights into the complex metabolic responses of C. maxima seedlings to Cd exposure.

High-Throughput Proteomics and Phosphoproteomics of Rat Tissues Using Microflow Zeno SWATH

High-throughput tissue proteomics has great potential in the advancement of precision medicine. Here, we investigated the combined sensitivity of trap-elute microflow liquid chromatography with a ZenoTOF for DIA proteomics and phosphoproteomics. Method optimization was conducted on HEK293T cell lines to determine the optimal variable window size, MS2 accumulation time and gradient length. The ZenoTOF 7600 was then compared to the previous generation TripleTOF 6600 using eight rat organs, finding up to 23% more proteins using a fifth of the sample load and a third of the instrument time. Spectral reference libraries generated from Zeno SWATH data in FragPipe (MSFragger-DIA/DIA-NN) contained 4 times more fragment ions than the DIA-NN only library and quantified more proteins. Replicate single-shot phosphopeptide enrichments of 50-100 μg of rat tryptic peptide were analyzed by microflow HPLC using Zeno SWATH without fractionation. Using Spectronaut we quantified a shallow phosphoproteome containing 1000-3000 phosphoprecursors per organ. Promisingly, clear hierarchical clustering of organs was observed with high Pearson correlation coefficients >0.95 between replicate enrichments and median CV of 20%. The combined sensitivity of microflow HPLC with Zeno SWATH allows for the high-throughput quantitation of an extensive proteome and shallow phosphoproteome from small tissue samples.

Hyphenation of microflow chromatography with electrospray ionization mass spectrometry for bioanalytical applications focusing on low molecular weight compounds: A tutorial review

Benefits of miniaturized chromatography with various detection modes, such as increased sensitivity, chromatographic efficiency, and speed, were recognized nearly 50 years ago. Over the past two decades, this approach has experienced rapid growth, driven by the emergence of mass spectrometry applications serving -omics sciences and the need for analyzing minute volumes of precious samples with ever higher sensitivity. While nanoscale liquid chromatography (flow rates <1 μL/min) has gained widespread recognition in proteomics, the adoption of microscale setups (flow rates ranging from 1 to 100 μL/min) for low molecular weight compound applications, including metabolomics, has been surprisingly slow, despite the inherent advantages of the approach. Highly heterogeneous matrices and chemical structures accompanied by a relative lack of options for both selective sample preparation and user-friendly equipment are usually reported as major hindrances. To facilitate the wider implementation of microscale analyses, we present here a comprehensive tutorial encompassing important theoretical and practical considerations. We provide fundamental principles in micro-chromatography and guide the reader through the main elements of a microflow workflow, from LC pumps to ionization devices. Finally, based on both our literature overview and experience, illustrated by some in-house data, we highlight the critical importance of the ionization source design and its careful optimization to achieve significant sensitivity improvement.

Vacancy-Driven High-Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression

Depression is one of the most common mental illnesses and is a well-known risk factor for suicide, characterized by low overall efficacy (<50%) and high relapse rate (40%). A rapid and objective approach for screening and prognosis of depression is highly desirable but still awaits further development. Herein, a high-performance metabolite-based assay to aid the diagnosis and therapeutic evaluation of depression by developing a vacancy-engineered cobalt oxide (Vo-Co3O4) assisted laser desorption/ionization mass spectrometer platform is presented. The easy-prepared nanoparticles with optimal vacancy achieve a considerable signal enhancement, characterized by favorable charge transfer and increased photothermal conversion. The optimized Vo-Co3O4 allows for a direct and robust record of plasma metabolic fingerprints (PMFs). Through machine learning of PMFs, high-performance depression diagnosis is achieved, with the areas under the curve (AUC) of 0.941-0.980 and an accuracy of over 92%. Furthermore, a simplified diagnostic panel for depression is established, with a desirable AUC value of 0.933. Finally, proline levels are quantified in a follow-up cohort of depressive patients, highlighting the potential of metabolite quantification in the therapeutic evaluation of depression. This work promotes the progression of advanced matrixes and brings insights into the management of depression.

Single cell proteomics by mass spectrometry reveals deep epigenetic insight into the actions of an orphan histone deacetylase inhibitor

Epigenetic programming has been shown to play a role in nearly every human system and disease where anyone has thought to look. However, the levels of heterogeneity at which epigenetic or epiproteomic modifications occur at single cell resolution across a population remains elusive. While recent advances in sequencing technology have allowed between 1 and 3 histone post-translational modifications to be analyzed in each single cell, over twenty separate chemical PTMs are known to exist, allowing thousands of possible combinations. Single cell proteomics by mass spectrometry (SCP) is an emerging technology in which hundreds or thousands of proteins can be directly quantified in typical human cells. As the proteins detected and quantified by SCP are heavily biased toward proteins of highest abundance, chromatin proteins are an attractive target for analysis. To this end, I applied SCP to the analysis of cancer cells treated with mocetinostat, a class specific histone deacetylase inhibitor. I find that 16 PTMs can be confidently identified and localized with high site specificity in single cells. In addition, the high abundance of histone proteins allows higher throughput methods to be utilized for SCP than previously described. While quantitative accuracy suffers when analyzing more than 700 cells per day, 9 histone proteins can be measured in single cells analyzed at even 3,500 cells per day, a throughput 10-fold greater than any previous report. In addition, the unbiased global approach utilized herein identifies a previously uncharacterized response to this drug through the S100-A8/S100-A9 protein complex partners. This response is observed in nearly every cell of the over 1,000 analyzed in this study, regardless of the relative throughput of the method utilized. While limitations exist in the methods described herein, current technologies can easily improve upon the results presented here to allow comprehensive analysis of histone PTMs to be performed in any mass spectrometry lab. All raw and processed data described in this study has been made publicly available through the ProteomeXchange/MASSIVE repository system as MSV000093434.

Decoding the molecular interplay in the central dogma: An overview of mass spectrometry-based methods to investigate protein-metabolite interactions

With the emergence of next-generation nucleotide sequencing and mass spectrometry-based proteomics and metabolomics tools, we have comprehensive and scalable methods to analyze the genes, transcripts, proteins, and metabolites of a multitude of biological systems. Despite the fascinating new molecular insights at the genome, transcriptome, proteome and metabolome scale, we are still far from fully understanding cellular organization, cell cycles and biology at the molecular level. Significant advances in sensitivity and depth for both sequencing as well as mass spectrometry-based methods allow the analysis at the single cell and single molecule level. At the same time, new tools are emerging that enable the investigation of molecular interactions throughout the central dogma of molecular biology. In this review, we provide an overview of established and recently developed mass spectrometry-based tools to probe metabolite-protein interactions-from individual interaction pairs to interactions at the proteome-metabolome scale.

The One Hour Human Proteome

We describe deep analysis of the human proteome in less than 1 h. We achieve this expedited proteome characterization by leveraging state-of-the-art sample preparation, chromatographic separations, and data analysis tools, and by using the new Orbitrap Astral mass spectrometer equipped with a quadrupole mass filter, a high-field Orbitrap mass analyzer, and an asymmetric track lossless (Astral) mass analyzer. The system offers high tandem mass spectrometry acquisition speed of 200 Hz and detects hundreds of peptide sequences per second within data-independent acquisition or data-dependent acquisition modes of operation. The fast-switching capabilities of the new quadrupole complement the sensitivity and fast ion scanning of the Astral analyzer to enable narrow-bin data-independent analysis methods. Over a 30-min active chromatographic method consuming a total analysis time of 56 min, the Q-Orbitrap-Astral hybrid MS collects an average of 4319 MS1 scans and 438,062 tandem mass spectrometry scans per run, producing 235,916 peptide sequences (1% false discovery rate). On average, each 30-min analysis achieved detection of 10,411 protein groups (1% false discovery rate). We conclude, with these results and alongside other recent reports, that the 1-h human proteome is within reach.

High-Throughput Nanoflow Proteomics Using a Dual-Column Electrospray Source

With current trends in proteomics, especially regarding clinical and low input (to single cell) samples, it is increasingly important to both maximize the throughput of the analysis and maintain as much sensitivity as possible. The new generation of mass spectrometers (MS) are taking a huge leap in sensitivity, allowing analysis of samples with shorter liquid chromatography (LC) methods while digging as deep in the proteome. However, the throughput can be doubled by implementing a dual column nano-LC-MS configuration. For this purpose, we used a dual-column setup with a two-outlet electrospray source and compared it to a classic dual-column setup with a single-outlet source.

Proteomics-The State of the Field: The Definition and Analysis of Proteomes Should Be Based in Reality, Not Convenience

With growing recognition and acknowledgement of the genuine complexity of proteomes, we are finally entering the post-proteogenomic era. Routine assessment of proteomes as inferred correlates of gene sequences (i.e., canonical 'proteins') cannot provide the necessary critical analysis of systems-level biology that is needed to understand underlying molecular mechanisms and pathways or identify the most selective biomarkers and therapeutic targets. These critical requirements demand the analysis of proteomes at the level of proteoforms/protein species, the actual active molecular players. Currently, only highly refined integrated or integrative top-down proteomics (iTDP) enables the analytical depth necessary to provide routine, comprehensive, and quantitative proteome assessments across the widest range of proteoforms inherent to native systems. Here we provide a broad perspective of the field, taking in historical and current realities, to establish a more balanced understanding of where the field has come from (in particular during the ten years since Proteomes was launched), current issues, and how things likely need to proceed if necessary deep proteome analyses are to succeed. We base this in our firm belief that the best proteomic analyses reflect, as closely as possible, the native sample at the moment of sampling. We also seek to emphasise that this and future analytical approaches are likely best based on the broad recognition and exploitation of the complementarity of currently successful approaches. This also emphasises the need to continuously evaluate and further optimize established approaches, to avoid complacency in thinking and expectations but also to promote the critical and careful development and introduction of new approaches, most notably those that address proteoforms. Above all, we wish to emphasise that a rigorous focus on analytical quality must override current thinking that largely values analytical speed; the latter would certainly be nice, if only proteoforms could thus be effectively, routinely, and quantitatively assessed. Alas, proteomes are composed of proteoforms, not molecular species that can be amplified or that directly mirror genes (i.e., 'canonical'). The problem is hard, and we must accept and address it as such, but the payoff in playing this longer game of rigorous deep proteome analyses is the promise of far more selective biomarkers, drug targets, and truly personalised or even individualised medicine.

Multinozzle Emitter for Improved Negative Mode Analysis of Reduced Native N-Glycans by Microflow Porous Graphitized Carbon Liquid Chromatography Mass Spectrometry

Microflow porous graphitized carbon liquid chromatography (PGC-LC) combined with negative mode ionization mass spectrometry (MS) provides high resolution separation and identification of reduced native N-glycan structural isomers. However, insufficient spray quality and low ionization efficiency of N-glycans present challenges for negative mode electrospray. Here, we evaluated the performance of a recently developed multinozzle electrospray source (MnESI) and accompanying M3 emitter for microflow PGC-LC-MS analysis of N-glycans in negative mode. In comparison to a standard electrospray ionization source, the MnESI with an M3 emitter improves signal intensity, identification, quantification, and resolution of structural isomers to accommodate low-input samples.

Enrichment of Phosphopeptides Based on Zirconium Phthalocyanine-Modified Magnetic Nanoparticles

Highly selective extraction of phosphopeptides is necessary before mass spectrometry (MS) analysis. Herein, zirconium phthalocyanine-modified magnetic nanoparticles were prepared through a simple method. The Fe-O groups on Fe3O4 and the zirconium ions on phthalocyanine had a strong affinity for phosphopeptides based on immobilized metal ion affinity chromatography (IMAC). The enrichment platform exhibited low detection limit (0.01 fmol), high selectivity (α-/β-casein/bovine serum albumin, 1/1/5000), good reusability (10 circles), and recovery (91.1 ± 1.1%) toward phosphopeptides. Nonfat milk, human serum, saliva, and A549 cell lysate were employed as actual samples to assess the applicability of the enrichment protocol. Metallo-phthalocyanine will be a competitive compound for designing highly efficient adsorbents and offers a new approach to phosphopeptide analysis.

A Microfluidic-Fabricated Rod Sprayer for Nanoelectrospray Mass Spectrometry

The nanoelectrosprayer is a key device in the hyphenation of nanoLC-ESI-MS, and its development plays a crucial role in pushing forward the mining depth of biological discovery and industrialization of omics science. In this work, a new type of nanoelectrospray emitter, a rod sprayer, was developed based on microfluidic manufacture. Due to its porous silica structure, the rod sprayer in effect worked as a multinozzle sprayer, which is composed of a bunch of micrometer sized spray channels. Without the need for sophisticated microfabrication equipment, a superclean environment, or a complicated assembling process, such sprayer rods can be facilely fabricated in a mass production style: 3,600 rods with excellent monodispersity have been fabricated in 1 h, and rod sprayers thus made have demonstrated excellent intraday, interday, and interbatch reproducibilities: RSD = 1.9, 4.9, and 6.1%, respectively. The rod sprayer can generate stable electrospray in a wide voltage range from 2.6 to 3.2 kV and flow rates from 50 to 1000 nL/min, covering typical flow rates of subnanoLC, nanoLC, to microLC, and work steadily even under complex matrix environments (e.g., Hank's balanced salt solution containing sodium, magnesium, and calcium ions) without clogging. Meanwhile, the rod sprayers exhibited 200-1800% ionization efficiency enhancement in comparison with commonly used tapered tip emitters, for small molecule drugs, peptides, and proteins, respectively, and provided a broadened linear dynamic range of 4 orders of magnitude. The excellent characteristics of the rod sprayer, together with its small size and mass production capacity, should provide a high quality, high durability, high consistency, and disposable use-supported nanoelectrospray solution for MS-based bioanalyses.

AlphaPept: a modern and open framework for MS-based proteomics

In common with other omics technologies, mass spectrometry (MS)-based proteomics produces ever-increasing amounts of raw data, making efficient analysis a principal challenge. A plethora of different computational tools can process the MS data to derive peptide and protein identification and quantification. However, during the last years there has been dramatic progress in computer science, including collaboration tools that have transformed research and industry. To leverage these advances, we develop AlphaPept, a Python-based open-source framework for efficient processing of large high-resolution MS data sets. Numba for just-in-time compilation on CPU and GPU achieves hundred-fold speed improvements. AlphaPept uses the Python scientific stack of highly optimized packages, reducing the code base to domain-specific tasks while accessing the latest advances. We provide an easy on-ramp for community contributions through the concept of literate programming, implemented in Jupyter Notebooks. Large datasets can rapidly be processed as shown by the analysis of hundreds of proteomes in minutes per file, many-fold faster than acquisition. AlphaPept can be used to build automated processing pipelines with web-serving functionality and compatibility with downstream analysis tools. It provides easy access via one-click installation, a modular Python library for advanced users, and via an open GitHub repository for developers.

Characterization of Adeno-Associated Virus Capsid Proteins by Microflow Liquid Chromatography Coupled with Mass Spectrometry

Adeno-associated virus (AAV) has been widely used to treat various human diseases as an important delivery vector for gene therapy due to its low immunogenicity and safety. AAV capsids proteins are comprised of three capsid viral proteins (VP; VP1, VP2, VP3). The capsid proteins play a key role in viral vector infectivity and transduction efficiency. To ensure the safety and efficacy of AAV gene therapy products, the quality of AAV vector capsid proteins during development and production should be carefully monitored and controlled. Microflow liquid chromatography coupled with mass spectrometry provides superior sensitivity and fast analysis capability. It showed significant advantages in the analysis of low- concentration and large numbers of AAV samples. The intact mass of capsid protein can be accurately determined using high-resolution mass spectrometry (MS). And MS also provides highly confident confirmation of sequence coverage and post-translational modifications site identification and quantitation. In this study, we used microflow liquid chromatography-tandem mass spectrometry (LC-MS/MS) for the characterization of AAV2 capsid protein. we obtained nearly 100% sequence coverage of low-concentration AAV2 capsid protein (8 × 1011 GC/mL). More than 30 post-translational modifications (PTMs) sites were identified, the PTMs types included deamidation, oxidation and acetylation. From this study, the proposed microflow LC-MS/MS method provides a sensitive and high throughput approach in the characterization of AAVs and other biological products with low abundance.

Robust Glycoproteomics Platform Reveals a Tetra-Antennary Site-Specific Glycan Capping with Sialyl-Lewis Antigen for Early Detection of Gastric Cancer

The lack of efficient biomarkers for the early detection of gastric cancer (GC) contributes to its high mortality rate, so it is crucial to discover novel diagnostic targets for GC. Recent studies have implicated the potential of site-specific glycans in cancer diagnosis, yet it is challenging to perform highly reproducible and sensitive glycoproteomics analysis on large cohorts of samples. Here, a highly robust N-glycoproteomics (HRN) platform comprising an automated enrichment method, a stable microflow LC-MS/MS system, and a sensitive glycopeptide-spectra-deciphering tool is developed for large-scale quantitative N-glycoproteome analysis. The HRN platform is applied to analyze serum N-glycoproteomes of 278 subjects from three cohorts to investigate glycosylation changes of GC. It identifies over 20 000 unique site-specific glycans from discovery and validation cohorts, and determines four site-specific glycans as biomarker candidates. One candidate has branched tetra-antennary structure capping with sialyl-Lewis antigen, and it significantly outperforms serum CEA with AUC values > 0.89 compared against < 0.67 for diagnosing early-stage GC. The four-marker panel can provide improved diagnostic performances. Besides, discrimination powers of four candidates are also testified with a verification cohort using PRM strategy. This findings highlight the value of this strong tool in analyzing aberrant site-specific glycans for cancer detection.

Cellular uptake and cytotoxicity of PEGylated MXene nanomaterials mediated by protein corona

A stringent analysis of the biocompatibility of MXene is a necessary condition for assessing the biological risk of MXene. Owing to high surface free energy, MXene is capable of adsorbing a large amount of blood proteins to form MXene-protein corona complexes, however, a comprehensive understanding of the relationship between MXene and cellular physiological systems remains limited. Therefore, we investigated the cellular uptake and cytotoxicity effect of MXene Ti3C2Tx and PEGylation Ti3C2Tx mediated by human serum protein corona in THP-1 cells. It was found that PEGylation can alter the interaction between Ti3C2Tx and serum proteins, inducing a significant transformation in the fingerprint of the protein corona. Following protein corona formation, both Ti3C2Tx and PEGylated Ti3C2Tx predominantly accumulated at lysosomal sites within THP-1 cells. Further analysis revealed that clathrin-mediated endocytosis was the primary mechanism of Ti3C2Tx internalization by THP-1 cells. There was no significant effect on cell viability. However, we found that Ti3C2Tx plays a dual role as both a stimulus and scavenger of ROS within THP-1 cells, influenced by its PEGylation and the formation of a protein corona. This study provides important insights for biocompatibility evaluation and rational design of nanoproducts based on Ti3C2Tx in the future.

1.4 min Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry

Non-invasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method, which integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein coronas enrichment for high throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 minutes collection time, which enables a potential throughput of approximately 1,000 samples daily. The identified proteins are involved in valuable pathways and 44 of the proteins are FDA approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation at 17%. Moreover, different protein corona profiles were observed among various nanoparticles based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichments. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.

High-Throughput Glycan Profiling of Human Serum IgG Subclasses Using Parallel Reaction Monitoring Peptide Bond Fragmentation of Glycopeptides and Microflow LC-MS

LC-MS-based N-glycosylation profiling in four human serum IgG subclasses (IgG1, IgG2, IgG3, and IgG4) often requires additional affinity-based enrichment of specific IgG subclasses, owing to the high amino acid sequence similarity of Fc glycopeptides among subclasses. Notably, for IgG4 and the major allotype of IgG3, the glycopeptide precursors share identical retention time and mass and therefore cannot be distinguished based on precursor or glycan fragmentation. Here, we developed a parallel reaction monitoring (PRM)-based method for quantifying Fc glycopeptides through combined transitions generated from both glycosidic and peptide bond fragmentation. The latter enables the subpopulation of IgG3 and IgG4 to be directly distinguished according to mass differences without requiring further enrichment of specific IgG subclasses. In addition, a multinozzle electrospray emitter coupled to a capillary flow liquid chromatograph was used to increase the robustness and detection sensitivity of the method for low-yield peptide backbone fragment ions. The gradient was optimized to decrease the overall run time and make the method compatible with high-throughput analysis. We demonstrated that this method can be used to effectively monitor the relative levels of 13 representative glycoforms, with a good limit of detection for individual IgG subclasses.

High-Throughput Microbore LC-MS Lipidomics to Investigate APOE Phenotypes

Microflow liquid chromatography interfaced with mass spectrometry (μLC-MS/MS) is increasingly applied for high-throughput profiling of biological samples and has been proven to have an acceptable trade-off between sensitivity and reproducibility. However, lipidomics applications are scarce. We optimized a μLC-MS/MS system utilizing a 1 mm inner diameter × 100 mm column coupled to a triple quadrupole mass spectrometer to establish a sensitive, high-throughput, and robust single-shot lipidomics workflow. Compared to conventional lipidomics methods, we achieve a ∼4-fold increase in response, facilitating quantification of 351 lipid species from a single iPSC-derived cerebral organoid during a 15 min LC-MS analysis. Consecutively, we injected 303 samples over ∼75 h to prove the robustness and reproducibility of the microflow separation. As a proof of concept, μLC-MS/MS analysis of Alzheimer's disease patient-derived iPSC cerebral organoid reveals differential lipid metabolism depending on APOE phenotype (E3/3 vs E4/4). Microflow separation proves to be an environmentally friendly and cost-effective method as it reduces the consumption of harmful solvents. Also, the data demonstrate robust, in-depth, high-throughput performance to enable routine clinical or biomedical applications.

Multicenter Collaborative Study to Optimize Mass Spectrometry Workflows of Clinical Specimens

The foundation for integrating mass spectrometry (MS)-based proteomics into systems medicine is the development of standardized start-to-finish and fit-for-purpose workflows for clinical specimens. An essential step in this pursuit is to highlight the common ground in a diverse landscape of different sample preparation techniques and liquid chromatography-mass spectrometry (LC-MS) setups. With the aim to benchmark and improve the current best practices among the proteomics MS laboratories of the CLINSPECT-M consortium, we performed two consecutive round-robin studies with full freedom to operate in terms of sample preparation and MS measurements. The six study partners were provided with two clinically relevant sample matrices: plasma and cerebrospinal fluid (CSF). In the first round, each laboratory applied their current best practice protocol for the respective matrix. Based on the achieved results and following a transparent exchange of all lab-specific protocols within the consortium, each laboratory could advance their methods before measuring the same samples in the second acquisition round. Both time points are compared with respect to identifications (IDs), data completeness, and precision, as well as reproducibility. As a result, the individual performances of participating study centers were improved in the second measurement, emphasizing the effect and importance of the expert-driven exchange of best practices for direct practical improvements.

Fast proteomics with dia-PASEF and analytical flow-rate chromatography

Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs, and facilitate new approaches in systems biology and biomedical research. Here we propose combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition, and data analysis with the DIA-NN software suite, to achieve high-quality proteomic experiments from limited sample amounts, at a throughput of up to 400 samples per day. For instance, when benchmarking our workflow using a 500-μL/min flow rate and 3-min chromatographic gradients, we report the quantification of 5211 proteins from 2 μg of a mammalian cell-line standard at high quantitative accuracy and precision. We further used this platform to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-min chromatographic gradient and alternating column regeneration on a dual pump system. The method delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates.

Windows Scanning Multiomics: Integrated Metabolomics and Proteomics

Metabolomics and proteomics offer significant advantages in understanding biological mechanisms at two hierarchical levels. However, conventional single omics analysis faces challenges due to the high demand for specimens and the complexity of intrinsic associations. To obtain comprehensive and accurate system biological information, we developed a multiomics analytical method called Windows Scanning Multiomics (WSM). In this method, we performed simultaneous extraction of metabolites and proteins from the same sample, resulting in a 10% increase in the coverage of the identified biomolecules. Both metabolomics and proteomics analyses were conducted by using ultrahigh-performance liquid chromatography mass spectrometry (UPLC-MS), eliminating the need for instrument conversions. Additionally, we designed an R-based program (WSM.R) to integrate mathematical and biological correlations between metabolites and proteins into a correlation network. The network created from simultaneously extracted biomolecules was more focused and comprehensive compared to those from separate extractions. Notably, we excluded six pairs of false-positive relationships between metabolites and proteins in the network established using simultaneously extracted biomolecules. In conclusion, this study introduces a novel approach for multiomics analysis and data processing that greatly aids in bioinformation mining from multiomics results. This method is poised to play an indispensable role in systems biology research.

A region-resolved proteomic map of the human brain enabled by high-throughput proteomics

Substantial efforts are underway to deepen our understanding of human brain morphology, structure, and function using high-resolution imaging as well as high-content molecular profiling technologies. The current work adds to these approaches by providing a comprehensive and quantitative protein expression map of 13 anatomically distinct brain regions covering more than 11,000 proteins. This was enabled by the optimization, characterization, and implementation of a high-sensitivity and high-throughput microflow liquid chromatography timsTOF tandem mass spectrometry system (LC-MS/MS) capable of analyzing more than 2,000 consecutive samples prepared from formalin-fixed paraffin embedded (FFPE) material. Analysis of this proteomic resource highlighted brain region-enriched protein expression patterns and functional protein classes, protein localization differences between brain regions and individual markers for specific areas. To facilitate access to and ease further mining of the data by the scientific community, all data can be explored online in a purpose-built R Shiny app (https://brain-region-atlas.proteomics.ls.tum.de).

Preparation of high-efficiency titanium ion immobilized magnetic graphite nitride nanocomposite for phosphopeptide enrichment

BACKGROUND

Protein phosphorylation has been implicated in life processes including molecular interaction, protein structure transformation, and malignant disease. An in-depth study of protein phosphorylation may provide vital information for the discovery of early biomarkers. Mass spectrometry (MS)-based techniques have become an important method for phosphopeptide identification. Nevertheless, direct detection remains challenging because of the low ionization efficiency of phosphopeptides and serious interference from non-phosphopeptides. There is a great need for an efficient enrichment strategy to analyze protein phosphorylation prior to MS analysis.

RESULTS

In this study, a novel nanocomposite was prepared by introducing titanium ions into two-dimensional magnetic graphite nitride. The nanocomposite was combined with immobilized metal ion affinity chromatography (IMAC) and anion-exchange chromatography mechanisms for phosphoproteome research. The nanocomposite had the advantages of a large specific surface (412.9 m2 g-1), positive electricity (175.44 mV), and excellent magnetic property (35.7 emu g-1). Moreover, it presented satisfactory selectivity (α-casein:β-casein:bovine serum albumin = 1:1:5000), a low detection limit (0.02 fmol), great recyclability (10 cycles), and high recovery (92.8%). The nanocomposite demonstrated great practicability for phosphopeptides from non-fat milk, human serum, and saliva. Further, the nanocomposite was applied to enrich phosphopeptides from a more complicated specimen, A549 cell lysate. A total of 890 phosphopeptides mapping to 564 phosphoproteins were successfully detected with nano LC-MS.

SIGNIFICANCE

We successfully designed and developed an efficient analysis platform for phosphopeptides, which includes protein digestion, phosphopeptide enrichment, and MS detection. The MS-based enrichment platform was further used to analyze phosphopeptides from complicated bio-samples. This work paves the way for the design and preparation of graphite nitride-based IMAC materials for phosphoproteome analysis.

Deep topographic proteomics of a human brain tumour

The spatial organisation of cellular protein expression profiles within tissue determines cellular function and is key to understanding disease pathology. To define molecular phenotypes in the spatial context of tissue, there is a need for unbiased, quantitative technology capable of mapping proteomes within tissue structures. Here, we present a workflow for spatially-resolved, quantitative proteomics of tissue that generates maps of protein abundance across tissue slices derived from a human atypical teratoid-rhabdoid tumour at three spatial resolutions, the highest being 40 µm, to reveal distinct abundance patterns of thousands of proteins. We employ spatially-aware algorithms that do not require prior knowledge of the fine tissue structure to detect proteins and pathways with spatial abundance patterns and correlate proteins in the context of tissue heterogeneity and cellular features such as extracellular matrix or proximity to blood vessels. We identify PYGL, ASPH and CD45 as spatial markers for tumour boundary and reveal immune response-driven, spatially-organised protein networks of the extracellular tumour matrix. Overall, we demonstrate spatially-aware deep proteo-phenotyping of tissue heterogeneity, to re-define understanding tissue biology and pathology at the molecular level.

Clinical Proteomics for Solid Organ Tissues

The evaluation of biopsied solid organ tissue has long relied on visual examination using a microscope. Immunohistochemistry is critical in this process, labeling and detecting cell lineage markers and therapeutic targets. However, while the practice of immunohistochemistry has reshaped diagnostic pathology and facilitated improvements in cancer treatment, it has also been subject to pervasive challenges with respect to standardization and reproducibility. Efforts are ongoing to improve immunohistochemistry, but for some applications, the benefit of such initiatives could be impeded by its reliance on monospecific antibody-protein reagents and limited multiplexing capacity. This perspective surveys the relevant challenges facing traditional immunohistochemistry and describes how mass spectrometry, particularly liquid chromatography-tandem mass spectrometry, could help alleviate problems. In particular, targeted mass spectrometry assays could facilitate measurements of individual proteins or analyte panels, using internal standards for more robust quantification and improved interlaboratory reproducibility. Meanwhile, untargeted mass spectrometry, showcased to date clinically in the form of amyloid typing, is inherently multiplexed, facilitating the detection and crude quantification of 100s to 1000s of proteins in a single analysis. Further, data-independent acquisition has yet to be applied in clinical practice, but offers particular strengths that could appeal to clinical users. Finally, we discuss the guidance that is needed to facilitate broader utilization in clinical environments and achieve standardization.

Collision Induced Unfolding Enables the Quantitation of Isomass Biotherapeutics in Complex Biological Matrices

Quantitative mass spectrometry has been widely used to evaluate the concentrations of molecules within a variety of biological matrices. Typically, such quantitative mass spectrometry analyses are predicated upon the production of mass-resolved precursor or fragment ions, leading to challenges surrounding the quantification of isomeric or conformationally distinct analytes. As such, new approaches are required for the label-free quantification of isomass proteins. Native ion-mobility MS (nIM-MS) in combination with collision induced unfolding (CIU) is a potentially enabling approach for such quantitative mass spectrometry methods as the technique can rapidly separate and detect many biomacromolecule isoforms. CIU uses collisional activation to capture the unfolding trajectory of ions in the gas phase, producing different intermediate structures that can be leveraged to distinguish protein structures that exhibit identical sizes at lower energies. Here we describe the deployment of quantitative CIU methodology to measure the concentrations of isomass pairs of biotherapeutics and sequence homologues in both standard and biological matrices. Our results cover three antibody pairs and include examples of mixed therapies where multiple biologics are commonly provided to patients. In all cases, CIU enables the production of resolved features for each antibody mixture probed, producing calibration curves with correlation coefficients ranging from 0.92 to 0.99, limits of detection ranging from 300 to 5000 nM and sensitivities ranging from 8.7 × 10-5 nM-1 to 6 × 10-3 μM-1. We conclude our report by projecting the future utility of CIU-enabled quantitative MS methods.

Proteomics evaluation of five economical commercial abundant protein depletion kits for enrichment of diseases-specific biomarkers from blood serum

Blood serum is arguably the most analyzed biofluid for disease prediction and diagnosis. Herein, we benchmarked five different serum abundant protein depletion (SAPD) kits with regard to the identification of disease-specific biomarkers in human serum using bottom-up proteomics. As expected, the IgG removal efficiency among the SAPD kits is highly variable, ranging from 70% to 93%. A pairwise comparison of database search results showed a 10%-19% variation in protein identification among the kits. Immunocapturing-based SAPD kits against IgG and albumin outperformed the others in the removal of these two abundant proteins. Conversely, non-antibody-based methods (i.e., kits using ion exchange resins) and kits leveraging a multi-antibody approach were proven to be less efficient in depleting IgG/albumin from samples but led to the highest number of identified peptides. Notably, our results indicate that different cancer biomarkers could be enriched up to 10% depending on the utilized SAPD kit compared with the undepleted sample. Additionally, functional analysis of the bottom-up proteomic results revealed that different SAPD kits enrich distinct disease- and pathway-specific protein sets. Overall, our study emphasizes that a careful selection of the appropriate commercial SAPD kit is crucial for the analysis of disease biomarkers in serum by shotgun proteomics.

Recent applications of quantitative mass spectrometry in biopharmaceutical process development and manufacturing

Biopharmaceutical products have seen rapid growth over the past few decades and continue to dominate the global pharmaceutical market. Aligning with the quality by design (QbD) framework and realization, recent advances in liquid chromatography-mass spectrometry (LC-MS) instrumentation and related techniques have enhanced biopharmaceutical characterization capabilities and have supported an increased development of biopharmaceutical products. Beyond its routine qualitative characterization, the quantitative feature of LC-MS has unique applications in biopharmaceutical process development and manufacturing. This review describes the recent applications and implications of the advancement of quantitative MS methods in biopharmaceutical process development, and characterization of biopharmaceutical product, product-related variants, and process-related impurities. We also provide insights on the emerging applications of quantitative MS in the lifecycle of biopharmaceutical product development including quality control in the Good Manufacturing Practice (GMP) environment and process analytical technology (PAT) practices during process development and manufacturing. Through collaboration with instrument and software vendors and regulatory agencies, we envision broader adoption of phase-appropriate quantitative MS-based methods for the analysis of biopharmaceutical products, which in turn has the potential to enable manufacture of higher quality products for patients.

On-Chip Comparison of the Performance of First- and Second-Generation Micropillar Array Columns

Because of its dimensions, the recently introduced micropillar array columns are most suited for high-efficiency liquid chromatography separations in proteomics. Unlike the packed bed columns and capillary-based column formats, the micropillar array concept still has significant room to progress in terms of the reduction of its characteristic size (i.e., pillar diameter and interpillar distance) to open the road to even higher-efficiency separations and their applications. We report here on the on-chip comparison between first-generation (Gen 1) and second-generation (Gen 2) micropillar array columns wherein the pillar and interpillar size have been halved. Because of the on-chip measurements, the observed plate heights H represent the fundamental band broadening, devoid of any extra-column band-broadening effects. The observed reduction of H with a factor of 2 around the uopt-velocity and with a factor of 4 in the C-term dominated regime of the van Deemter-curve is in full agreement with the theoretically expected gain. This shows the pillar and interpillar size reduction could be effectuated without affecting the theoretical separation potential of the micropillar arrays. Compared to Gen 1, Gen 2 offers a 4-fold reduction of the required analysis time around the optimal velocity and about a 16-fold reduction in the C-term-dominated range.

Quantitative multiorgan proteomics of fatal COVID-19 uncovers tissue-specific effects beyond inflammation

SARS-CoV-2 may directly and indirectly damage lung tissue and other host organs, but there are few system-wide, untargeted studies of these effects on the human body. Here, we developed a parallelized mass spectrometry (MS) proteomics workflow enabling the rapid, quantitative analysis of hundreds of virus-infected FFPE tissues. The first layer of response to SARS-CoV-2 in all tissues was dominated by circulating inflammatory molecules. Beyond systemic inflammation, we differentiated between systemic and true tissue-specific effects to reflect distinct COVID-19-associated damage patterns. Proteomic changes in the lungs resembled those of diffuse alveolar damage (DAD) in non-COVID-19 patients. Extensive organ-specific changes were also evident in the kidneys, liver, and lymphatic and vascular systems. Secondary inflammatory effects in the brain were related to rearrangements in neurotransmitter receptors and myelin degradation. These MS-proteomics-derived results contribute substantially to our understanding of COVID-19 pathomechanisms and suggest strategies for organ-specific therapeutic interventions.

High-Throughput Screening and Proteomic Characterization of Compounds Targeting Myeloid-Derived Suppressor Cells

Myeloid-derived suppressor cells (MDSC) are a heterogeneous cell population of incompletely differentiated immune cells. They are known to suppress T cell activity and are implicated in multiple chronic diseases, which make them an attractive cell population for drug discovery. Here, we characterized the baseline proteomes and phospho-proteomes of mouse MDSC differentiated from a progenitor cell line to a depth of 7000 proteins and phosphorylation sites. We also validated the cellular system for drug discovery by recapitulating and identifying known and novel molecular responses to the well-studied MDSC drugs entinostat and mocetinostat. We established a high-throughput drug screening platform using a MDSC/T cell coculture system and assessed the effects of ∼21,000 small molecule compounds on T cell proliferation and IFN-γ secretion to identify novel MDSC modulator. The most promising candidates were validated in a human MDSC system, and subsequent proteomic experiments showed significant upregulation of several proteins associated with the reduction of reactive oxygen species (ROS). Proteome-wide solvent-induced protein stability assays identified Acyp1 and Cd74 as potential targets, and the ROS-reducing drug phenotype was validated by measuring ROS levels in cells in response to compound, suggesting a potential mode of action. We anticipate that the data and chemical tools developed in this study will be valuable for further research on MDSC and related drug discovery.

Unified Workflow for the Rapid and In-Depth Characterization of Bacterial Proteomes

Bacteria are the most abundant and diverse organisms among the kingdoms of life. Due to this excessive variance, finding a unified, comprehensive, and safe workflow for quantitative bacterial proteomics is challenging. In this study, we have systematically evaluated and optimized sample preparation, mass spectrometric data acquisition, and data analysis strategies in bacterial proteomics. We investigated workflow performances on six representative species with highly different physiologic properties to mimic bacterial diversity. The best sample preparation strategy was a cell lysis protocol in 100% trifluoroacetic acid followed by an in-solution digest. Peptides were separated on a 30-min linear microflow liquid chromatography gradient and analyzed in data-independent acquisition mode. Data analysis was performed with DIA-NN using a predicted spectral library. Performance was evaluated according to the number of identified proteins, quantitative precision, throughput, costs, and biological safety. With this rapid workflow, over 40% of all encoded genes were detected per bacterial species. We demonstrated the general applicability of our workflow on a set of 23 taxonomically and physiologically diverse bacterial species. We could confidently identify over 45,000 proteins in the combined dataset, of which 30,000 have not been experimentally validated before. Our work thereby provides a valuable resource for the microbial scientific community. Finally, we grew Escherichia coli and Bacillus cereus in replicates under 12 different cultivation conditions to demonstrate the high-throughput suitability of the workflow. The proteomic workflow we present in this manuscript does not require any specialized equipment or commercial software and can be easily applied by other laboratories to support and accelerate the proteomic exploration of the bacterial kingdom.

Plasma metabolic fingerprints for large-scale screening and personalized risk stratification of metabolic syndrome

Direct diagnosis and accurate assessment of metabolic syndrome (MetS) allow for prompt clinical interventions. However, traditional diagnostic strategies overlook the complex heterogeneity of MetS. Here, we perform metabolomic analysis in 13,554 participants from the natural cohort and identify 26 hub plasma metabolic fingerprints (PMFs) associated with MetS and its early identification (pre-MetS). By leveraging machine-learning algorithms, we develop robust diagnostic models for pre-MetS and MetS with convincing performance through independent validation. We utilize these PMFs to assess the relative contributions of the four major MetS risk factors in the general population, ranked as follows: hyperglycemia, hypertension, dyslipidemia, and obesity. Furthermore, we devise a personalized three-dimensional plasma metabolic risk (PMR) stratification, revealing three distinct risk patterns. In summary, our study offers effective screening tools for identifying pre-MetS and MetS patients in the general community, while defining the heterogeneous risk stratification of metabolic phenotypes in real-world settings.

Vacuum Insulated Probe Heated Electrospray Ionization Source Enhances Microflow Rate Chromatography Signals in the Bruker timsTOF Mass Spectrometer

By far the largest contribution to ion detectability in liquid chromatography-driven mass spectrometry-based proteomics is the efficient generation of peptide molecular ions by the electrospray source. To maximize the transfer of peptides from the liquid to gaseous phase and allow molecular ions to enter the mass spectrometer at microspray flow rates, an efficient electrospray process is required. Here we describe the superior performance of newly design vacuum insulated probe heated electrospray ionization (VIP-HESI) source coupled to a Bruker timsTOF PRO mass spectrometer operated in microspray mode. VIP-HESI significantly improves chromatography signals in comparison to electrospray ionization (ESI) and nanospray ionization using the captivespray (CS) source and provides increased protein detection with higher quantitative precision, enhancing reproducibility of sample injection amounts. Protein quantitation of human K562 lymphoblast samples displayed excellent chromatographic retention time reproducibility (<10% coefficient of variation (CV)) with no signal degradation over extended periods of time, and a mouse plasma proteome analysis identified 12% more plasma protein groups allowing large-scale analysis to proceed with confidence (1,267 proteins at 0.4% CV). We show that the Slice-PASEF VIP-HESI mode is sensitive in identifying low amounts of peptide without losing quantitative precision. We demonstrate that VIP-HESI coupled with microflow rate chromatography achieves a higher depth of coverage and run-to-run reproducibility for a broad range of proteomic applications. Data and spectral libraries are available via ProteomeXchange (PXD040497).

A Flexible Kenics Mixer for Applications in Liquid Chromatography

Miniaturization of liquid chromatography could help enhance sensitivity, reduce solvent usage, and detect small quantities of peptides. However, it demands better sample homogenization of the mobile phase. We here developed a mixer design based on the inline Kenics geometry, consisting of a periodic arrangement of twisted blades placed inside a cylindrical capillary that repeatedly cut and stack fluid elements to achieve rapid mixing in laminar flow regimes. The mixer design was optimized with respect to the twist angle and aspect ratio of the mixing units to achieve complete mixing at minimum pressure load cost. Results suggest that for optimal designs, for a mixer volume of ~70 μL, complete mixing is achieved within a distance smaller than 4 cm for a broad set of flow rate conditions ranging from 75 μL·min^-1 to 7.5 mL·min^-1. A salient feature that we introduce and test for the first time is the physical flexibility of the cylindrical capillary. The performance of the design remained robust when the mixing section was not rigid and bent in different topologies, as well as when changing the chemical composition of the mobile phase used.

Enhanced Pharmacokinetic Bioanalysis of Antibody-drug Conjugates using Hybrid Immunoaffinity Capture and Microflow LC-MS/MS

The increasing complexity and diversity of antibody-drug conjugates (ADCs) have led to a need for comprehensive and informative bioanalytical methods to enhance pharmacokinetic (PK) understanding. This study aimed to evaluate the feasibility of a hybrid immunoaffinity (IA) capture microflow LC-MS/MS (μLC-MS/MS) method for ADC analysis, utilizing a minimal sample volume for PK assessments in a preclinical study. A robust workflow was established for the quantitative analysis of ADCs by the implementation of solid-phase extraction (SPE) and semi-automation in µLC-MS/MS. Utilizing the µLC-MS/MS approach in conjunction with 1 µL of ADC-dosed mouse plasma sample volume, standard curves of two representative surrogate peptides for total antibody (heavy chain, HC) and intact antibody (light chain, LC) ranged from 1.00 ng/mL (LLOQ) to 5000 ng/mL with correlation coefficients (r2) values of > 0.99. The linear range of the standard curve for payload as a surrogate for the concentration of total ADC was from 0.5 ng/mL (LLOQ) to 2000 ng/mL with high accuracy and precision (< 10% CV at all concentrations). Moreover, a high correlation of concentrations of total antibody between two assay approaches (µLC-MS and ELISA) was achieved with less than 20% difference at all time points, indicating that the two methods are comparable in quantitation of total antibody in plasma samples. The µLC-MS platform demonstrated a greater dynamic range, sensitivity, robustness, and good reproducibility. These findings demonstrated that the cost-effective µLC-MS method can reduce reagent consumption and minimize the use of mice plasma samples while providing more comprehensive information about ADCs being analyzed, including the total antibody, intact antibody, and total ADC.

A single protein to multiple peptides: Investigation of protein-peptide correlations using targeted alpha-2-macroglobulin analysis

Recent advances in proteomics technologies have enabled the analysis of thousands of proteins in a high-throughput manner. Mass spectrometry (MS) based proteomics uses a peptide-centric approach where biological samples undergo specific proteolytic digestion and then only unique peptides are used for protein identification and quantification. Considering the fact that a single protein may have multiple unique peptides and a number of different forms, it becomes essential to understand dynamic protein-peptide relationships to ensure robust and reliable peptide-centric protein analysis. In this study, we investigated the correlation between protein concentration and corresponding unique peptide responses under a conventional proteolytic digestion condition. Protein-peptide correlation, digestion efficiency, matrix-effect, and concentration-effect were evaluated. Twelve unique peptides of alpha-2-macroglobulin (A2MG) were monitored using a targeted MS approach to acquire insights into protein-peptide dynamics. Although the peptide responses were reproducible between replicates, protein-peptide correlation was moderate in protein standards and low in complex matrices. The results suggest that reproducible peptide signal could be misleading in clinical studies and a peptide selection could dramatically change the outcome at protein level. This is the first study investigating quantitative protein-peptide correlations in biological samples using all unique peptides representing the same protein and opens a discussion on peptide-based proteomics.

Systemic Blood Proteome Patterns Reflect Disease Phenotypes in Neovascular Age-Related Macular Degeneration

There is early evidence of extraocular systemic signals effecting function and morphology in neovascular age-related macular degeneration (nAMD). The prospective, cross-sectional BIOMAC study is an explorative investigation of peripheral blood proteome profiles and matched clinical features to uncover systemic determinacy in nAMD under anti-vascular endothelial growth factor intravitreal therapy (anti-VEGF IVT). It includes 46 nAMD patients stratified by the level of disease control under ongoing anti-VEGF treatment. Proteomic profiles in peripheral blood samples of every patient were detected with LC-MS/MS mass spectrometry. The patients underwent extensive clinical examination with a focus on macular function and morphology. In silico analysis includes unbiased dimensionality reduction and clustering, a subsequent annotation of clinical features, and non-linear models for recognition of underlying patterns. The model assessment was performed using leave-one-out cross validation. The findings provide an exploratory demonstration of the link between systemic proteomic signals and macular disease pattern using and validating non-linear classification models. Three main results were obtained: (1) Proteome-based clustering identifies two distinct patient subclusters with the smaller one (n = 10) exhibiting a strong signature for oxidative stress response. Matching the relevant meta-features on the individual patient's level identifies pulmonary dysfunction as an underlying health condition in these patients. (2) We identify biomarkers for nAMD disease features with Aldolase C as a putative factor associated with superior disease control under ongoing anti-VEGF treatment. (3) Apart from this, isolated protein markers are only weakly correlated with nAMD disease expression. In contrast, applying a non-linear classification model identifies complex molecular patterns hidden in a high number of proteomic dimensions determining macular disease expression. In conclusion, so far unconsidered systemic signals in the peripheral blood proteome contribute to the clinically observed phenotype of nAMD, which should be examined in future translational research on AMD.

A proteome-wide atlas of drug mechanism of action

Defining the cellular response to pharmacological agents is critical for understanding the mechanism of action of small molecule perturbagens. Here, we developed a 96-well-plate-based high-throughput screening infrastructure for quantitative proteomics and profiled 875 compounds in a human cancer cell line with near-comprehensive proteome coverage. Examining the 24-h proteome changes revealed ligand-induced changes in protein expression and uncovered rules by which compounds regulate their protein targets while identifying putative dihydrofolate reductase and tankyrase inhibitors. We used protein-protein and compound-compound correlation networks to uncover mechanisms of action for several compounds, including the adrenergic receptor antagonist JP1302, which we show disrupts the FACT complex and degrades histone H1. By profiling many compounds with overlapping targets covering a broad chemical space, we linked compound structure to mechanisms of action and highlighted off-target polypharmacology for molecules within the library.

Accurate label-free quantification by directLFQ to compare unlimited numbers of proteomes

Recent advances in mass spectrometry (MS)-based proteomics enable the acquisition of increasingly large datasets within relatively short times, which exposes bottlenecks in the bioinformatics pipeline. Whereas peptide identification is already scalable, most label-free quantification (LFQ) algorithms scale quadratic or cubic with the sample numbers, which may even preclude the analysis of large-scale data. Here we introduce directLFQ, a ratio-based approach for sample normalization and the calculation of protein intensities. It estimates quantities via aligning samples and ion traces by shifting them on top of each other in logarithmic space. Importantly, directLFQ scales linearly with the number of samples, allowing analyses of large studies to finish in minutes instead of days or months. We quantify 10,000 proteomes in 10 minutes and 100,000 proteomes in less than two hours - a thousand-fold faster than some implementations of the popular LFQ algorithm MaxLFQ. In-depth characterization of directLFQ reveals excellent normalization properties and benchmark results, comparing favorably to MaxLFQ for both data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, directFQ provides normalized peptide intensity estimates for peptide-level comparisons. It is an important part of an overall quantitative proteomic pipeline that also needs to include high sensitive statistical analysis leading to proteoform resolution. Available as an open-source Python package and a GUI with a one-click installer, it can be used in the AlphaPept ecosystem as well as downstream of most common computational proteomics pipelines.

MS-based proteomics of body fluids: The end of the beginning

Accurate biomarkers are a crucial and necessary precondition for precision medicine, yet existing ones are often unspecific and new ones have been very slow to enter the clinic. Mass spectrometry (MS)-based proteomics excels by its untargeted nature, specificity of identification and quantification making it an ideal technology for biomarker discovery and routine measurement. It has unique attributes compared to affinity binder technologies, such as OLINK Proximity Extension Assay and SOMAscan. In a previous review we described technological and conceptual limitations that had held back success (Geyer et al., 2017). We proposed a 'rectangular strategy' to better separate true biomarkers by minimizing cohort-specific effects. Today, this has converged with advances in MS-based proteomics technology, such as increased sample throughput, depth of identification and quantification. As a result, biomarker discovery studies have become more successful, producing biomarker candidates that withstand independent verification and, in some cases, already outperform state-of-the-art clinical assays. We summarize developments over the last years, including the benefits of large and independent cohorts, which are necessary for clinical acceptance. They are also required for machine learning or deep learning. Shorter gradients, new scan modes and multiplexing are about to drastically increase throughput, cross-study integration, and quantification, including proxies for absolute levels. We have found that multi-protein panels are inherently more robust than current single analyte tests and better capture the complexity of human phenotypes. Routine MS measurement in the clinic is fast becoming a viable option. The full set of proteins in a body fluid (global proteome) is the most important reference and the best process control. Additionally, it increasingly has all the information that could be obtained from targeted analysis although the latter may be the most straightforward way to enter into regular use. Many challenges remain, not least of a regulatory and ethical nature, but the outlook for MS-based clinical applications has never been brighter.

Optimization of Targeted Plant Proteomics Using Liquid Chromatography with Tandem Mass Spectrometry (LC-MS/MS)

This study was conducted to optimize a targeted plant proteomics approach from signature peptide selection and liquid chromatography with tandem mass spectrometry (LC-MS/MS) analytical method development and optimization to sample preparation method optimization. Three typical protein extraction and precipitation methods, including trichloroacetic acid (TCA)/acetone method, phenol method, and TCA/acetone/phenol method, and two digestion methods, including trypsin digestion and LysC/trypsin digestion, were evaluated for selected proteins related to the impact of engineered nanomaterials (ENMs) on wheat (Triticum aestivum) plant growth. In addition, we compared two plant tissue homogenization methods: grinding freeze-dried tissue and fresh tissue into a fine powder using a mortar and pestle aided with liquid nitrogen. Wheat plants were grown under a 16 h photoperiod (light intensity 150 μmol·m^-2·s^-1) for 4 weeks at 22 °C with a relative humidity of 60% and were watered daily to maintain a 70-90% water content in the soil. Processed samples were analyzed with an optimized LC-MS/MS method. The concentration of selected signature peptides for the wheat proteins of interest indicated that the phenol extraction method using fresh plant tissue, coupled with trypsin digestion, was the best sample preparation method for the targeted proteomics study. Overall, the optimized approach yielded the highest total peptide concentration (68,831 ng/g, 2.4 times the lowest concentration) as well as higher signature peptide concentrations for most peptides (19 out of 28). In addition, three of the signature peptides could only be detected using the optimized approach. This study provides a workflow for optimizing targeted proteomics studies.

The proteomic landscape of genome-wide genetic perturbations

Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.

Performance comparison of narrow-bore and capillary liquid-chromatography for non-targeted metabolomics profiling by HILIC-QTOF-MS

Clinical metabolomics studies often have to cope with limited sample amounts, thus miniaturized liquid chromatography (LC) systems are a promising alternative. Their applicability has already been demonstrated in various fields, including a few metabolomics studies that mainly used reversed-phase chromatography. However, hydrophilic interaction chromatography (HILIC), which is widely used in metabolomics due to its particular suitability for the analysis of polar molecules, has rarely been tested for miniaturized LC-MS analysis of small molecules. In the present work, the suitability of a capillary HILIC (CapHILIC)-QTOF-MS system for non-targeted metabolomics was evaluated based on extracts of porcine formalin-fixed, paraffin-embedded (FFPE) tissue samples. The performance was assessed with respect to the number and retention time span of metabolic features as well as the analytical repeatability, the signal-to-noise ratio and the signal intensity of 16 annotated metabolites from different compound classes. The results were compared with a well established narrow-bore HILIC-QTOF-MS system. Both platforms have detected a similar number of features and performed excellent with respect to retention time stability (median RT span <0.05 min) and analytical repeatability (>75% of features with CV < 20%). The signal areas of all metabolites assessed were increased up to 18-fold by the use of CapHILIC, although the signal-to-noise ratio was only improved for 50% of the metabolites. An even better reproducibility (median CV = 5.2%) and up to 80-fold increase in signal intensity were observed after optimization of CapHILIC conditions for analysis of bile acid standard solutions. Even though the observed improvement for specific bile acids (e.g. taurocholic acid) in biological matrix needs to be evaluated, the platform comparison indicates, that the tested CapHILIC system is particularly suitable for analyses of a less broad metabolite spectrum, and specifically optimized chromatography.

Decrypting drug actions and protein modifications by dose- and time-resolved proteomics

Although most cancer drugs modulate the activities of cellular pathways by changing posttranslational modifications (PTMs), little is known regarding the extent and the time- and dose-response characteristics of drug-regulated PTMs. In this work, we introduce a proteomic assay called decryptM that quantifies drug-PTM modulation for thousands of PTMs in cells to shed light on target engagement and drug mechanism of action. Examples range from detecting DNA damage by chemotherapeutics, to identifying drug-specific PTM signatures of kinase inhibitors, to demonstrating that rituximab kills CD20-positive B cells by overactivating B cell receptor signaling. DecryptM profiling of 31 cancer drugs in 13 cell lines demonstrates the broad applicability of the approach. The resulting 1.8 million dose-response curves are provided as an interactive molecular resource in ProteomicsDB.