BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes

High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows

Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in healthy and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in a whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ∼3500 proteins at a spatial resolution of 50 µm and the highest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables the identification of protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.

Robust assessment of sample preparation protocols for proteomics of cells and tissues

In proteomic studies, the reliability and reproducibility of results hinge on well-executed protein extraction and digestion protocols. Here, we systematically compared three established digestion methods for macrophages, namely filter-assisted sample preparation (FASP), in-solution, and in-gel digestion protocols. We also compared lyophilization and manual lysis for liver tissue protein extraction, each of them tested using either sodium deoxycholate (SDC)- or RIPA-based lysis buffer. For the macrophage cell line, FASP using passivated filter units outperformed the other tested methods regarding the number of identified peptides and proteins. However, a careful standardization has shown that all three methods can yield robust results across a wide range of starting material (even starting with 1 μg of proteins). Importantly, inter and intra-day coefficients of variance (CVs) were determined for all sample preparation protocols. Thus, the median inter-day CVs for in-solution, in-gel and FASP protocols were respectively 10, 8 and 9%, very similar to the median CVs obtained for the intra-day analysis (9, 8 and 8%, respectively). Moreover, FASP digestion presented 80% of proteins with a CV lower than 25%, followed closely by in-gel digestion (78%) and in-solution sample preparation (72%) protocols. For tissue proteomics, both manual lysis and lyophilization presented similar proteome coverage and reproducibility, but the efficiency of protein extraction depended on the lysis buffer used, with RIPA buffer showing better results. In conclusion, although each sample preparation method has its own particularity, they are all suited for successful proteomic experiments if a careful standardization of the sample preparation workflow is carried out.

Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning

Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

Improved Data Acquisition Settings on Q Exactive HF-X and Fusion Lumos Tribrid Orbitrap-Based Mass Spectrometers for Proteomic Analysis of Limited Samples

Deep proteomic profiling of complex biological and medical samples available at low nanogram and subnanogram levels is still challenging. Thorough optimization of settings, parameters, and conditions in nanoflow liquid chromatography-tandem mass spectrometry (MS)-based proteomic profiling is crucial for generating informative data using amount-limited samples. This study demonstrates that by adjusting selected instrument parameters, e.g., ion injection time, automated gain control, and minimally altering the conditions for resuspending or storing the sample in solvents of different compositions, up to 15-fold more thorough proteomic profiling can be achieved compared to conventionally used settings. More specifically, the analysis of 1 ng of the HeLa protein digest standard by Q Exactive HF-X Hybrid Quadrupole-Orbitrap and Orbitrap Fusion Lumos Tribrid mass spectrometers yielded an increase from 1758 to 5477 (3-fold) and 281 to 4276 (15-fold) peptides, respectively, demonstrating that higher protein identification results can be obtained using the optimized methods. While the instruments applied in this study do not belong to the latest generation of mass spectrometers, they are broadly used worldwide, which makes the guidelines for improving performance desirable to a wide range of proteomics practitioners.

Increasing Proteome Coverage Through a Reduction in Analyte Complexity in Single-Cell Equivalent Samples

The advancement of sophisticated instrumentation in mass spectrometry has catalyzed an in-depth exploration of complex proteomes. This exploration necessitates a nuanced balance in experimental design, particularly between quantitative precision and the enumeration of analytes detected. In bottom-up proteomics, a key challenge is that oversampling of abundant proteins can adversely affect the identification of a diverse array of unique proteins. This issue is especially pronounced in samples with limited analytes, such as small tissue biopsies or single-cell samples. Methods such as depletion and fractionation are suboptimal to reduce oversampling in single cell samples, and other improvements on LC and mass spectrometry technologies and methods have been developed to address the trade-off between precision and enumeration. We demonstrate that by using a monosubstrate protease for proteomic analysis of single-cell equivalent digest samples, an improvement in quantitative accuracy can be achieved, while maintaining high proteome coverage established by trypsin. This improvement is particularly vital for the field of single-cell proteomics, where single-cell samples with limited number of protein copies, especially in the context of low-abundance proteins, can benefit from considering analyte complexity. Considerations about analyte complexity, alongside chromatographic complexity, integration with data acquisition methods, and other factors such as those involving enzyme kinetics, will be crucial in the design of future single-cell workflows.

LED Junction Temperature Measurement: From Steady State to Transient State

In this review, we meticulously analyze and consolidate various techniques used for measuring the junction temperature of light-emitting diodes (LEDs) by examining recent advancements in the field as reported in the literature. We initiate our exploration by delineating the evolution of LED technology and underscore the criticality of junction temperature detection. Subsequently, we delve into two key facets of LED junction temperature assessment: steady-state and transient measurements. Beginning with an examination of innovations in steady-state junction temperature detection, we cover a spectrum of approaches ranging from traditional one-dimensional methods to more advanced three-dimensional techniques. These include micro-thermocouple, liquid crystal thermography (LCT), temperature sensitive optical parameters (TSOPs), and infrared (IR) thermography methods. We provide a comprehensive summary of the contributions made by researchers in this domain, while also elucidating the merits and demerits of each method. Transitioning to transient detection, we offer a detailed overview of various techniques such as the improved T3ster method, an enhanced one-dimensional continuous rectangular wave method (CRWM), and thermal reflection imaging. Additionally, we introduce novel methods leveraging high-speed camera technology and reflected light intensity (h-SCRLI), as well as micro high-speed transient imaging based on reflected light (μ_HSTI). Finally, we provide a critical appraisal of the advantages and limitations inherent in several transient detection methods and offer prognostications on future developments in this burgeoning field.

Harnessing the power of proteomics in precision diabetes medicine

Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.

Tear Proteomics in Infants at Risk of Retinopathy of Prematurity: A Feasibility Study

Purpose

This feasibility study investigated the practicability of collecting and analyzing tear proteins from preterm infants at risk of retinopathy of prematurity (ROP). We sought to identify any tear proteins which might be implicated in the pathophysiology of ROP as well as prognostic markers.

Methods

Schirmer's test was used to obtain tear samples from premature babies, scheduled for ROP screening, after parental informed consent. Mass spectrometry was used for proteomic analysis.

Results

Samples were collected from 12 infants, which were all adequate for protein analysis. Gestational age ranged from 25 + 6 to 31 + 1 weeks. Postnatal age at sampling ranged from 19 to 66 days. One infant developed self-limiting ROP. Seven hundred one proteins were identified; 261 proteins identified in the majority of tear samples, including several common tear proteins, were used for analyses. Increased risk of ROP as determined by the postnatal growth ROP (G-ROP) criteria was associated with an increase in lactate dehydrogenase B chain in tears. Older infants demonstrated increased concentration of immunoglobulin complexes within their tear samples and two sets of twins in the cohort showed exceptionally similar proteomes, supporting validity of the analysis.

Conclusions

Tear sampling by Schirmer test strips and subsequent proteomic analysis by mass spectrometry in preterm infants is feasible. A larger study is required to investigate the potential use of tear proteomics in identification of ROP.

Translational Relevance

Tear sampling and subsequent mass spectrometry in preterm infants is feasible. Investigation of the premature tear proteome may increase our understanding of retinal development and provide noninvasive biomarkers for identification of treatment-warranted ROP.

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.

Proteomics of prostate cancer serum and plasma using low and high throughput approaches

Despite progress, MS-based proteomics in biofluids, especially blood, faces challenges such as dynamic range and throughput limitations in biomarker and disease studies. In this work, we used cutting-edge proteomics technologies to construct label-based and label-free workflows, capable of quantifying approximately 2,000 proteins in biofluids. With 70µL of blood and a single depletion strategy, we conducted an analysis of a homogenous cohort (n = 32), comparing medium-grade prostate cancer patients (Gleason score: 7(3 + 4); TNM stage: T2cN0M0, stage IIB) to healthy donors. The results revealed dozens of differentially expressed proteins in both plasma and serum. We identified the upregulation of Prostate Specific Antigen (PSA), a well-known biomarker for prostate cancer, in the serum of cancer cohort. Further bioinformatics analysis highlighted noteworthy proteins which appear to be differentially secreted into the bloodstream, making them good candidates for further exploration.

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.

Plasma proteome profiling reveals metabolic and immunologic differences between Anorexia Nervosa subtypes

AIMS/HYPOTHESIS

Anorexia Nervosa (AN) is a severe psychiatric disorder of an unknown etiology with a crude mortality rate of about 5 % per decade, making it one of the deadliest of all psychiatric illnesses. AN is broadly classified into two main subtypes, restricting and binge/purging disorder. Despite extensive research efforts during several decades, the underlying pathophysiology of AN remains poorly understood. In this study, we aimed to identify novel protein biomarkers for AN by performing a proteomics analysis of fasting plasma samples from 78 females with AN (57 restrictive and 21 binge/purge type) and 70 healthy controls.

METHODS

Using state-of-the-art mass spectrometry-based proteomics technology in conjunction with an advanced bioinformatics pipeline, we quantify >500 plasma proteins.

RESULTS

Differential expression analysis and correlation of proteomics data with clinical variables led to identification of a panel of novel protein biomarkers with potential pathophysiological significance for AN. Our findings demonstrate evidence of a humoral immune system response, altered lipid metabolism and potential alteration of plasma cells in AN patients. Additionally, we stratified AN patients based on the quantified proteins and suggest a potential autoimmune nature in the restrictive subtype of AN.

CONCLUSIONS/INTERPRETATION

In summary, on top of biomarkers of AN subtypes, this study provides a comprehensive map of plasma proteins that constitute a resource for further studies of the pathophysiology of AN.

Enhancing Top-Down Analysis of Proteins by Combining Ultraviolet Photodissociation (UVPD), Proton-Transfer Charge Reduction (PTCR), and Gas-Phase Fractionation to Alleviate the Impact of Nondissociated Precursor Ions

Recent advances in top-down mass spectrometry strategies continue to improve the analysis of intact proteins. 193 nm ultraviolet photodissociation (UVPD) is one method well-suited for top-down analysis. UVPD is often performed using relatively low photon flux in order to limit multiple-generation dissociation of fragment ions and maximize sequence coverage. Consequently, a large portion of the precursor ion survives the UVPD process, dominates the spectrum, and may impede identification of fragment ions. Here, we explore the isolation of subpopulations of fragment ions lower and higher than the precursor ion after UVPD as a means to eliminate the impact of the surviving precursor ion on the detection of low abundance fragment ions. This gas-phase fractionation method improved sequence coverage harvested from fragment ions found in the m/z regions lower and higher than the precursor by an average factor of 1.3 and 2.3, respectively. Combining this gas-phase fractionation method with proton transfer charge reduction (PTCR) further increased the sequence coverage obtained from these m/z regions by another factor of 1.3 and 1.4, respectively. Implementing a post-UVPD fractionation + PTCR strategy with six fractionation events resulted in a sequence coverage of 75% for enolase, the highest reported for 193 nm UVPD.

Full Mass Range ΦSDM Orbitrap Mass Spectrometry for DIA Proteome Analysis

Optimizing data-independent acquisition methods for proteomics applications often requires balancing spectral resolution and acquisition speed. Here, we describe a real-time full mass range implementation of the phase-constrained spectrum deconvolution method (ΦSDM) for Orbitrap mass spectrometry that increases mass resolving power without increasing scan time. Comparing its performance to the standard enhanced Fourier transformation signal processing revealed that the increased resolving power of ΦSDM is beneficial in areas of high peptide density and comes with a greater ability to resolve low-abundance signals. In a standard 2 h analysis of a 200 ng HeLa digest, this resulted in an increase of 16% in the number of quantified peptides. As the acquisition speed becomes even more important when using fast chromatographic gradients, we further applied ΦSDM methods to a range of shorter gradient lengths (21, 12, and 5 min). While ΦSDM improved identification rates and spectral quality in all tested gradients, it proved particularly advantageous for the 5 min gradient. Here, the number of identified protein groups and peptides increased by >15% in comparison to enhanced Fourier transformation processing. In conclusion, ΦSDM is an alternative signal processing algorithm for processing Orbitrap data that can improve spectral quality and benefit quantitative accuracy in typical proteomics experiments, especially when using short gradients.

Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023

Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.

Bidirectional de novo peptide sequencing using a transformer model

In proteomics, a crucial aspect is to identify peptide sequences. De novo sequencing methods have been widely employed to identify peptide sequences, and numerous tools have been proposed over the past two decades. Recently, deep learning approaches have been introduced for de novo sequencing. Previous methods focused on encoding tandem mass spectra and predicting peptide sequences from the first amino acid onwards. However, when predicting peptides using tandem mass spectra, the peptide sequence can be predicted not only from the first amino acid but also from the last amino acid due to the coexistence of b-ion (or a- or c-ion) and y-ion (or x- or z-ion) fragments in the tandem mass spectra. Therefore, it is essential to predict peptide sequences bidirectionally. Our approach, called NovoB, utilizes a Transformer model to predict peptide sequences bidirectionally, starting with both the first and last amino acids. In comparison to Casanovo, our method achieved an improvement of the average peptide-level accuracy rate of approximately 9.8% across all species.

Biological mass spectrometry enables spatiotemporal 'omics: From tissues to cells to organelles

Biological processes unfold across broad spatial and temporal dimensions, and measurement of the underlying molecular world is essential to their understanding. Interdisciplinary efforts advanced mass spectrometry (MS) into a tour de force for assessing virtually all levels of the molecular architecture, some in exquisite detection sensitivity and scalability in space-time. In this review, we offer vignettes of milestones in technology innovations that ushered sample collection and processing, chemical separation, ionization, and 'omics analyses to progressively finer resolutions in the realms of tissue biopsies and limited cell populations, single cells, and subcellular organelles. Also highlighted are methodologies that empowered the acquisition and analysis of multidimensional MS data sets to reveal proteomes, peptidomes, and metabolomes in ever-deepening coverage in these limited and dynamic specimens. In pursuit of richer knowledge of biological processes, we discuss efforts pioneering the integration of orthogonal approaches from molecular and functional studies, both within and beyond MS. With established and emerging community-wide efforts ensuring scientific rigor and reproducibility, spatiotemporal MS emerged as an exciting and powerful resource to study biological systems in space-time.

Integrative Proteogenomics for Differential Expression and Splicing Variation in a DM1 Mouse Model

Dysregulated mRNA splicing is involved in the pathogenesis of many diseases including cancer, neurodegenerative diseases, and muscular dystrophies such as myotonic dystrophy type 1 (DM1). Comprehensive assessment of dysregulated splicing on the transcriptome and proteome level has been methodologically challenging, and thus investigations have often been targeting only few genes. Here, we performed a large-scale coordinated transcriptomic and proteomic analysis to characterize a DM1 mouse model (HSALR) in comparison to wild type. Our integrative proteogenomics approach comprised gene- and splicing-level assessments for mRNAs and proteins. It recapitulated many known instances of aberrant mRNA splicing in DM1 and identified new ones. It enabled the design and targeting of splicing-specific peptides and confirmed the translation of known instances of aberrantly spliced disease-related genes (e.g., Atp2a1, Bin1, Ryr1), complemented by novel findings (Flnc and Ywhae). Comparative analysis of large-scale mRNA and protein expression data showed quantitative agreement of differentially expressed genes and splicing patterns between disease and wild type. We hence propose this work as a suitable blueprint for a robust and scalable integrative proteogenomic strategy geared toward advancing our understanding of splicing-based disorders. With such a strategy, splicing-based biomarker candidates emerge as an attractive and accessible option, as they can be efficiently asserted on the mRNA and protein level in coordinated fashion.

Evaluating Proteomics Imputation Methods with Improved Criteria

Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. Missing values hinder reproducibility, reduce statistical power, and make it difficult to compare across samples or experiments. Although many methods exist for imputing missing values, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error such as the mean-squared error between imputed and held-out values. Here we evaluate the performance of commonly used imputation methods using three practical, "downstream-centric" criteria. These criteria measure the ability to identify differentially expressed peptides, generate new quantitative peptides, and improve the peptide lower limit of quantification. Our evaluation comprises several experiment types and acquisition strategies, including data-dependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides but that it can identify new quantitative peptides and improve the peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that existing imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do.

Mass spectrometry in the discovery of peptides involved in intercellular communication: From targeted to untargeted peptidomics approaches

Endogenous peptide hormones represent an essential class of biomolecules, which regulate cell-cell communications in diverse physiological processes of organisms. Mass spectrometry (MS) has been developed to be a powerful technology for identifying and quantifying peptides in a highly efficient manner. However, it is difficult to directly identify these peptide hormones due to their diverse characteristics, dynamic regulations, low abundance, and existence in a complicated biological matrix. Here, we summarize and discuss the roles of targeted and untargeted MS in discovering peptide hormones using bioassay-guided purification, bioinformatics screening, or the peptidomics-based approach. Although the peptidomics approach is expected to discover novel peptide hormones unbiasedly, only a limited number of successful cases have been reported. The critical challenges and corresponding measures for peptidomics from the steps of sample preparation, peptide extraction, and separation to the MS data acquisition and analysis are also discussed. We also identify emerging technologies and methods that can be integrated into the discovery platform toward the comprehensive study of endogenous peptide hormones.

Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape

The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.

Quantitative Time-Course Analysis of Osmotic and Salt Stress in Arabidopsis thaliana Using Short Gradient Multi-CV FAIMSpro BoxCar DIA

A major limitation when undertaking quantitative proteomic time-course experimentation is the tradeoff between depth-of-analysis and speed-of-analysis. In high complexity and high dynamic range sample types, such as plant extracts, balance between resolution and time is especially apparent. To address this, we evaluate multiple compensation voltage (CV) high field asymmetric waveform ion mobility spectrometry (FAIMSpro) settings using the latest label-free single-shot Orbitrap-based DIA acquisition workflows for their ability to deeply quantify the Arabidopsis thaliana seedling proteome. Using a BoxCarDIA acquisition workflow with a -30 -50 -70 CV FAIMSpro setting, we were able to consistently quantify >5000 Arabidopsis seedling proteins over a 21-min gradient, facilitating the analysis of ∼42 samples per day. Utilizing this acquisition approach, we then quantified proteome-level changes occurring in Arabidopsis seedling shoots and roots over 24 h of salt and osmotic stress, to identify early and late stress response proteins and reveal stress response overlaps. Here, we successfully quantify >6400 shoot and >8500 root protein groups, respectively, quantifying nearly ∼9700 unique protein groups in total across the study. Collectively, we pioneer a short gradient, multi-CV FAIMSpro BoxCarDIA acquisition workflow that represents an exciting new analysis approach for undertaking quantitative proteomic time-course experimentation in plants.

Unraveling adipose tissue proteomic landscapes in severe obesity: insights into metabolic complications and potential biomarkers

In this study, we aimed to comprehensively characterize the proteomic landscapes of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in patients with severe obesity, to establish their associations with clinical characteristics, and to identify potential serum protein biomarkers indicative of tissue-specific alterations or metabolic states. We conducted a cross-sectional analysis of 32 patients with severe obesity (16 males and 16 females) of Central European descent who underwent bariatric surgery. Clinical parameters and body composition were assessed using dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance, with 15 patients diagnosed with type 2 diabetes (T2D) and 17 with hypertension. Paired SAT and VAT samples, along with serum samples, were subjected to state-of-the-art proteomics liquid chromatography-mass spectrometry (LC-MS). Our analysis identified 7,284 proteins across SAT and VAT, with 1,249 differentially expressed proteins between the tissues and 1,206 proteins identified in serum. Correlation analyses between differential protein expression and clinical traits suggest a significant role of SAT in the pathogenesis of obesity and related metabolic complications. Specifically, the SAT proteomic profile revealed marked alterations in metabolic pathways and processes contributing to tissue fibrosis and inflammation. Although we do not establish a definitive causal relationship, it appears that VAT might respond to SAT metabolic dysfunction by potentially enhancing mitochondrial activity and expanding its capacity. However, when this adaptive response is exceeded, it could possibly contribute to insulin resistance (IR) and in some cases, it may be associated with the progression to T2D. Our findings provide critical insights into the molecular foundations of SAT and VAT in obesity and may inform the development of targeted therapeutic strategies.NEW & NOTEWORTHY This study provides insights into distinct proteomic profiles of subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and serum in patients with severe obesity and their associations with clinical traits and body composition. It underscores SAT's crucial role in obesity development and related complications, such as insulin resistance (IR) and type 2 diabetes (T2D). Our findings emphasize the importance of understanding the SAT and VAT balance in energy homeostasis, proteostasis, and the potential role of SAT capacity in the development of metabolic disorders.

Mass Spectrometry-Based Proteomic Technology and Its Application to Study Skeletal Muscle Cell Biology

Voluntary striated muscles are characterized by a highly complex and dynamic proteome that efficiently adapts to changed physiological demands or alters considerably during pathophysiological dysfunction. The skeletal muscle proteome has been extensively studied in relation to myogenesis, fiber type specification, muscle transitions, the effects of physical exercise, disuse atrophy, neuromuscular disorders, muscle co-morbidities and sarcopenia of old age. Since muscle tissue accounts for approximately 40% of body mass in humans, alterations in the skeletal muscle proteome have considerable influence on whole-body physiology. This review outlines the main bioanalytical avenues taken in the proteomic characterization of skeletal muscle tissues, including top-down proteomics focusing on the characterization of intact proteoforms and their post-translational modifications, bottom-up proteomics, which is a peptide-centric method concerned with the large-scale detection of proteins in complex mixtures, and subproteomics that examines the protein composition of distinct subcellular fractions. Mass spectrometric studies over the last two decades have decisively improved our general cell biological understanding of protein diversity and the heterogeneous composition of individual myofibers in skeletal muscles. This detailed proteomic knowledge can now be integrated with findings from other omics-type methodologies to establish a systems biological view of skeletal muscle function.

Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition

Single-cell resolution analysis of complex biological tissues is fundamental to capture cell-state heterogeneity and distinct cellular signaling patterns that remain obscured with population-based techniques. The limited amount of material encapsulated in a single cell however, raises significant technical challenges to molecular profiling. Due to extensive optimization efforts, single-cell proteomics by Mass Spectrometry (scp-MS) has emerged as a powerful tool to facilitate proteome profiling from ultra-low amounts of input, although further development is needed to realize its full potential. To this end, we carry out comprehensive analysis of orbitrap-based data-independent acquisition (DIA) for limited material proteomics. Notably, we find a fundamental difference between optimal DIA methods for high- and low-load samples. We further improve our low-input DIA method by relying on high-resolution MS1 quantification, thus enhancing sensitivity by more efficiently utilizing available mass analyzer time. With our ultra-low input tailored DIA method, we are able to accommodate long injection times and high resolution, while keeping the scan cycle time low enough to ensure robust quantification. Finally, we demonstrate the capability of our approach by profiling mouse embryonic stem cell culture conditions, showcasing heterogeneity in global proteomes and highlighting distinct differences in key metabolic enzyme expression in distinct cell subclusters.

A preliminary study about the potential risks of the UV-weathered microplastic: The proteome-level changes in the brain in response to polystyrene derived weathered microplastics

The growing use of plastic materials has resulted in a constant increase in the risk associated with microplastics (MPs). Ultra-violet (UV) light and wind break down modify MPs in the environment into smaller particles known as weathered MPs (WMPs) and these processes increase the risk of MP toxicity. The neurotoxicity of weathered polystyrene-MPs remains unclear. Therefore, it is important to understand the risks posed by WMPs. We evaluated the chemical changes of WMPs generated under laboratory-synchronized environmentally mimetic conditions and compared them with virgin MPs (VMPs). We found that WMP had a rough surface, slight yellow color, reduced molecular weight, and structural alteration compared with those of VMP. Next, 2 μg of ∼100 μm in size of WMP and VMP were orally administered once a day for one week to C57BL/6 male mice. Proteomic analysis revealed that the WMP group had significantly increased activation of immune and neurodegeneration-related pathways compared with that of the VMP group. Consistently, in in vitro experiments, the human brain-derived microglial cell line (HMC-3) also exhibited a more severe inflammatory response to WMP than to VMP. These results show that WMP is a more profound inflammatory factor than VMP. In summary, our findings demonstrate the toxicity of WMPs and provide theoretical insights into their potential risks to biological systems and even humans in the ecosystem.

Mouse Model of Fragile X Syndrome Analyzed by Quantitative Proteomics: A Comparison of Methods

Multiple methods for quantitative proteomics are available for proteome profiling. It is unclear which methods are most useful in situations involving deep proteome profiling and the detection of subtle distortions in the proteome. Here, we compared the performance of seven different strategies in the analysis of a mouse model of Fragile X Syndrome, involving the knockout of the fmr1 gene that is the leading cause of autism spectrum disorder. Focusing on the cerebellum, we show that data-independent acquisition (DIA) and the tandem mass tag (TMT)-based real-time search method (RTS) generated the most informative profiles, generating 334 and 329 significantly altered proteins, respectively, although the latter still suffered from ratio compression. Label-free methods such as BoxCar and a conventional data-dependent acquisition were too noisy to generate a reliable profile, while TMT methods that do not invoke RTS showed a suppressed dynamic range. The TMT method using the TMTpro reagents together with complementary ion quantification (ProC) overcomes ratio compression, but current limitations in ion detection reduce sensitivity. Overall, both DIA and RTS uncovered known regulators of the syndrome and detected alterations in calcium signaling pathways that are consistent with calcium deregulation recently observed in imaging studies. Data are available via ProteomeXchange with the identifier PXD039885.

Detection of Known and Novel Small Proteins in Pseudomonas stutzeri Using a Combination of Bottom-Up and Digest-Free Proteomics and Proteogenomics

Small proteins of around 50 aa in length have been largely overlooked in genetic and biochemical assays due to the inherent challenges with detecting and characterizing them. Recent discoveries of their critical roles in many biological processes have led to an increased recognition of the importance of small proteins for basic research and as potential new drug targets. One example is CcoM, a 36 aa subunit of the cbb3-type oxidase that plays an essential role in adaptation to oxygen-limited conditions in Pseudomonas stutzeri (P. stutzeri), a model for the clinically relevant, opportunistic pathogen Pseudomonas aeruginosa. However, as no comprehensive data were available in P. stutzeri, we devised an integrated, generic approach to study small proteins more systematically. Using the first complete genome as basis, we conducted bottom-up proteomics analyses and established a digest-free, direct-sequencing proteomics approach to study cells grown under aerobic and oxygen-limiting conditions. Finally, we also applied a proteogenomics pipeline to identify missed protein-coding genes. Overall, we identified 2921 known and 29 novel proteins, many of which were differentially regulated. Among 176 small proteins 16 were novel. Direct sequencing, featuring a specialized precursor acquisition scheme, exhibited advantages in the detection of small proteins with higher (up to 100%) sequence coverage and more spectral counts, including sequences with high proline content. Three novel small proteins, uniquely identified by direct sequencing and not conserved beyond P. stutzeri, were predicted to form an operon with a conserved protein and may represent de novo genes. These data demonstrate the power of this combined approach to study small proteins in P. stutzeri and show its potential for other prokaryotes.

Insight on physicochemical properties governing peptide MS1 response in HPLC-ESI-MS/MS: A deep learning approach

Accurate and absolute quantification of peptides in complex mixtures using quantitative mass spectrometry (MS)-based methods requires foreground knowledge and isotopically labeled standards, thereby increasing analytical expenses, time consumption, and labor, thus limiting the number of peptides that can be accurately quantified. This originates from differential ionization efficiency between peptides and thus, understanding the physicochemical properties that influence the ionization and response in MS analysis is essential for developing less restrictive label-free quantitative methods. Here, we used equimolar peptide pool repository data to develop a deep learning model capable of identifying amino acids influencing the MS1 response. By using an encoder-decoder with an attention mechanism and correlating attention weights with amino acid physicochemical properties, we obtain insight on properties governing the peptide-level MS1 response within the datasets. While the problem cannot be described by one single set of amino acids and properties, distinct patterns were reproducibly obtained. Properties are grouped in three main categories related to peptide hydrophobicity, charge, and structural propensities. Moreover, our model can predict MS1 intensity output under defined conditions based solely on peptide sequence input. Using a refined training dataset, the model predicted log-transformed peptide MS1 intensities with an average error of 9.7 ± 0.5% based on 5-fold cross validation, and outperformed random forest and ridge regression models on both log-transformed and real scale data. This work demonstrates how deep learning can facilitate identification of physicochemical properties influencing peptide MS1 responses, but also illustrates how sequence-based response prediction and label-free peptide-level quantification may impact future workflows within quantitative proteomics.

Dear-DIAXMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics

Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIA^XMBD, for direct analysis of DIA data. Dear-DIA^XMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and peptides. We show that Dear-DIA^XMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIA^XMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.

Single-Cell Proteomics with Spatial Attributes: Tools and Techniques

Now-a-days, the single-cell proteomics (SCP) concept is attracting interest, especially in clinical research, because it can identify the proteomic signature specific to diseased cells. This information is very essential when dealing with the progression of certain diseases, such as cancer, diabetes, Alzheimer's, etc. One of the major drawbacks of conventional destructive proteomics is that it gives an average idea about the protein expression profile in the disease condition. During the extraction of the protein from a biopsy or blood sample, proteins may come from both diseased cells and adjacent normal cells or any other cells from the disease environment. Again, SCP along with spatial attributes is utilized to learn about the heterogeneous function of a single protein. Before performing SCP, it is necessary to isolate single cells. This can be done by various techniques, including fluorescence-activated cell sorting (FACS), magnetic-activated cell sorting (MACS), laser capture microdissection (LCM), microfluidics, manual cell picking/micromanipulation, etc. Among the different approaches for proteomics, mass spectrometry-based proteomics tools are widely used for their high resolution as well as sensitivity. This Review mainly focuses on the mass spectrometry-based approaches for the study of single-cell proteomics.

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.

Technological development of multidimensional liquid chromatography-mass spectrometry in proteome research

Liquid chromatography-mass spectrometry (LC-MS) is the method of choice for high-throughput proteomic research. Limited by the peak capacity, the separation performance of conventional single-dimensional LC hampers the development of proteomics. Combining different separation modes orthogonally, multidimensional liquid chromatography (MDLC) with high peak capacity was developed to address this challenge. MDLC has evolved rapidly since its establishment, and the progress of proteomics has been greatly facilitated by the advent of novel MDLC-MS-based methods. In this paper, we will review the advances of MDLC-MS-based methodologies and technologies in proteomics studies, from different perspectives including novel application scenarios and proteomic targets, automation, miniaturization, and the improvement of the classic methods in recent years. In addition, attempts regarding new MDLC-MS models are also mentioned together with the outlook of MDLC-MS-based proteomics methods.

Robust and High-Throughput Analytical Flow Proteomics Analysis of Cynomolgus Monkey and Human Matrices with Zeno SWATH Data Independent Acquisition

Modern mass spectrometers routinely allow deep proteome coverage in a single experiment. These methods are typically operated at nano and micro flow regimes, but they often lack throughput and chromatographic robustness, which is critical for large-scale studies. In this context, we have developed, optimized and benchmarked LC-MS methods combining the robustness and throughput of analytical flow chromatography with the added sensitivity provided by the Zeno trap across a wide range of cynomolgus monkey and human matrices of interest for toxicological studies and clinical biomarker discovery. SWATH data independent acquisition (DIA) experiments with Zeno trap activated (Zeno SWATH DIA) provided a clear advantage over conventional SWATH DIA in all sample types tested with improved sensitivity, quantitative robustness and signal linearity as well as increased protein coverage by up to 9-fold. Using a 10-min gradient chromatography, up to 3,300 proteins were identified in tissues at 2 μg peptide load. Importantly, the performance gains with Zeno SWATH translated into better biological pathway representation and improved the ability to identify dysregulated proteins and pathways associated with two metabolic diseases in human plasma. Finally, we demonstrate that this method is highly stable over time with the acquisition of reliable data over the injection of 1,000+ samples (14.2 days of uninterrupted acquisition) without the need for human intervention or normalization. Altogether, Zeno SWATH DIA methodology allows fast, sensitive and robust proteomic workflows using analytical flow and is amenable to large-scale studies. This work provides detailed method performance assessment on a variety of relevant biological matrices and serves as a valuable resource for the proteomics community.

A basic phosphoproteomic-DIA workflow integrating precise quantification of phosphosites in systems biology

Phosphorylation is one of the most important post-translational modifications (PTMs) of proteins, governing critical protein functions. Most human proteins have been shown to undergo phosphorylation, and phosphoproteomic studies have been widely applied due to recent advancements in high-resolution mass spectrometry technology. Although the experimental workflow for phosphoproteomics has been well-established, it would be useful to optimize and summarize a detailed, feasible protocol that combines phosphoproteomics and data-independent acquisition (DIA), along with follow-up data analysis procedures due to the recent instrumental and bioinformatic advances in measuring and understanding tens of thousands of site-specific phosphorylation events in a single experiment. Here, we describe an optimized Phos-DIA protocol, from sample preparation to bioinformatic analysis, along with practical considerations and experimental configurations for each step. The protocol is designed to be robust and applicable for both small-scale phosphoproteomic analysis and large-scale quantification of hundreds of samples for studies in systems biology and systems medicine.

Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology

There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.

Sampling the proteome by emerging single-molecule and mass spectrometry methods

Mammalian cells have about 30,000-fold more protein molecules than mRNA molecules, which has major implications in the development of proteomics technologies. We review strategies that have been helpful for counting billions of protein molecules by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and suggest that these strategies can benefit single-molecule methods, especially in mitigating the challenges of the wide dynamic range of the proteome.

Recent advances in top-down proteome sample processing ahead of MS analysis

Top-down proteomics is emerging as a preferred approach to investigate biological systems, with objectives ranging from the detailed assessment of a single protein therapeutic, to the complete characterization of every possible protein including their modifications, which define the human proteoform. Given the controlling influence of protein modifications on their biological function, understanding how gene products manifest or respond to disease is most precisely achieved by characterization at the intact protein level. Top-down mass spectrometry (MS) analysis of proteins entails unique challenges associated with processing whole proteins while maintaining their integrity throughout the processes of extraction, enrichment, purification, and fractionation. Recent advances in each of these critical front-end preparation processes, including minimalistic workflows, have greatly expanded the capacity of MS for top-down proteome analysis. Acknowledging the many contributions in MS technology and sample processing, the present review aims to highlight the diverse strategies that have forged a pathway for top-down proteomics. We comprehensively discuss the evolution of front-end workflows that today facilitate optimal characterization of proteoform-driven biology, including a brief description of the clinical applications that have motivated these impactful contributions.

Methods for quantification of glycopeptides by liquid separation and mass spectrometry

Recent advances in analytical techniques provide the opportunity to quantify even low-abundance glycopeptides derived from complex biological mixtures, allowing for the identification of glycosylation differences between healthy samples and those derived from disease states. Herein, we discuss the sample preparation procedures and the mass spectrometry (MS) strategies that have facilitated glycopeptide quantification, as well as the standards used for glycopeptide quantification. For sample preparation, various glycopeptide enrichment methods are summarized including the columns used for glycopeptide separation in liquid chromatography separation. For MS analysis strategies, MS1 level-based quantification and MS2 level-based quantification are described, either with or without labeling, where we have covered isotope labeling, TMT/iTRAQ labeling, data dependent acquisition, data independent acquisition, multiple reaction monitoring, and parallel reaction monitoring. The strengths and weaknesses of these methods are compared, particularly those associated with the figures of merit that are important for clinical biomarker studies and the pathological and functional studies of glycoproteins in various diseases. Possible future developments for glycopeptide quantification are discussed.

Risk control of host cell proteins in one therapeutic antibody produced by concentrated fed-batch (CFB) mode

Multiple control strategies, including a downstream purification process with well-controlled parameters and a comprehensive release or characterization for intermediates or drug substances, were implemented to mitigate the potential risk of host cell proteins (HCPs) in one concentrated fed-batch (CFB) mode manufactured product. A host cell process specific enzyme-linked immunosorbent assay (ELISA) method was developed for the quantitation of HCPs. The method was fully validated and showed good performance including high antibody coverage. This was confirmed by 2D Gel-Western Blot analysis. Furthermore, a LC-MS/MS method with non-denaturing digestion and a long gradient chromatographic separation coupled with data dependent acquisition (DDA) on a Thermo/QE-HF-X mass spectrometer was developed as an orthogonal method to help identify the specific types of HCPs in this CFB product. Because of the high sensitivity, selectivity and adaptability of the new developed LC-MS/MS method, significantly more species of HCP contaminants were able to be identified. Even though high levels of HCPs were observed in the harvest bulk of this CFB product, the development of multiple processes and analytical control strategies may greatly mitigate potential risks and reduce HCPs contaminants to a very low level. No high-risk HCP was identified and the total amount of HCPs was very low in the CFB final product.

Proteomic Discovery of Plasma Protein Biomarkers and Development of Models Predicting Prognosis of High-Grade Serous Ovarian Carcinoma

Ovarian cancer is one of the most lethal female cancers. For accurate prognosis prediction, this study aimed to investigate novel, blood-based prognostic biomarkers for high-grade serous ovarian carcinoma (HGSOC) using mass spectrometry-based proteomics methods. We conducted label-free liquid chromatography-tandem mass spectrometry using frozen plasma samples obtained from patients with newly diagnosed HGSOC (n = 20). Based on progression-free survival (PFS), the samples were divided into two groups: good (PFS ≥18 months) and poor prognosis groups (PFS <18 months). Proteomic profiles were compared between the two groups. Referring to proteomics data that we previously obtained using frozen cancer tissues from chemotherapy-naïve patients with HGSOC, overlapping protein biomarkers were selected as candidate biomarkers. Biomarkers were validated using an independent set of HGSOC plasma samples (n = 202) via enzyme-linked immunosorbent assay (ELISA). To construct models predicting the 18-month PFS rate, we performed stepwise selection based on the area under the receiver operating characteristic curve (AUC) with 5-fold cross-validation. Analysis of differentially expressed proteins in plasma samples revealed that 35 and 61 proteins were upregulated in the good and poor prognosis groups, respectively. Through hierarchical clustering and bioinformatic analyses, GSN, VCAN, SND1, SIGLEC14, CD163, and PRMT1 were selected as candidate biomarkers and were subjected to ELISA. In multivariate analysis, plasma GSN was identified as an independent poor prognostic biomarker for PFS (adjusted hazard ratio, 1.556; 95% confidence interval, 1.073-2.256; p = 0.020). By combining clinical factors and ELISA results, we constructed several models to predict the 18-month PFS rate. A model consisting of four predictors (FIGO stage, residual tumor after surgery, and plasma levels of GSN and VCAN) showed the best predictive performance (mean validated AUC, 0.779). The newly developed model was converted to a nomogram for clinical use. Our study results provided insights into protein biomarkers, which might offer clues for developing therapeutic targets.

Using the Proteomics Toolbox to Resolve Topology and Dynamics of Compartmentalized cAMP Signaling

cAMP is a second messenger that regulates a myriad of cellular functions in response to multiple extracellular stimuli. New developments in the field have provided exciting insights into how cAMP utilizes compartmentalization to ensure specificity when the message conveyed to the cell by an extracellular stimulus is translated into the appropriate functional outcome. cAMP compartmentalization relies on the formation of local signaling domains where the subset of cAMP signaling effectors, regulators and targets involved in a specific cellular response cluster together. These domains are dynamic in nature and underpin the exacting spatiotemporal regulation of cAMP signaling. In this review, we focus on how the proteomics toolbox can be utilized to identify the molecular components of these domains and to define the dynamic cellular cAMP signaling landscape. From a therapeutic perspective, compiling data on compartmentalized cAMP signaling in physiological and pathological conditions will help define the signaling events underlying disease and may reveal domain-specific targets for the development of precision medicine interventions.

The genetic background shapes the susceptibility to mitochondrial dysfunction and NASH progression

Non-alcoholic steatohepatitis (NASH) is a global health concern without treatment. The challenge in finding effective therapies is due to the lack of good mouse models and the complexity of the disease, characterized by gene-environment interactions. We tested the susceptibility of seven mouse strains to develop NASH. The severity of the clinical phenotypes observed varied widely across strains. PWK/PhJ mice were the most prone to develop hepatic inflammation and the only strain to progress to NASH with extensive fibrosis, while CAST/EiJ mice were completely resistant. Levels of mitochondrial transcripts and proteins as well as mitochondrial function were robustly reduced specifically in the liver of PWK/PhJ mice, suggesting a central role of mitochondrial dysfunction in NASH progression. Importantly, the NASH gene expression profile of PWK/PhJ mice had the highest overlap with the human NASH signature. Our study exposes the limitations of using a single mouse genetic background in metabolic studies and describes a novel NASH mouse model with features of the human NASH.

Multi-Cell Line Analysis of Lysosomal Proteomes Reveals Unique Features and Novel Lysosomal Proteins

Lysosomes, the main degradative organelles of mammalian cells, play a key role in the regulation of metabolism. It is becoming more and more apparent that they are highly active, diverse, and involved in a large variety of processes. The essential role of lysosomes is exemplified by the detrimental consequences of their malfunction, which can result in lysosomal storage disorders, neurodegenerative diseases, and cancer. Using lysosome enrichment and mass spectrometry, we investigated the lysosomal proteomes of HEK293, HeLa, HuH-7, SH-SY5Y, MEF, and NIH3T3 cells. We provide evidence on a large scale for cell type-specific differences of lysosomes, showing that levels of distinct lysosomal proteins are highly variable within one cell type, while expression of others is highly conserved across several cell lines. Using differentially stable isotope-labeled cells and bimodal distribution analysis, we furthermore identify a high confidence population of lysosomal proteins for each cell line. Multi-cell line correlation of these data reveals potential novel lysosomal proteins, and we confirm lysosomal localization for six candidates. All data are available via ProteomeXchange with identifier PXD020600.

A streamlined tandem tip-based workflow for sensitive nanoscale phosphoproteomics

Effective phosphoproteome of nanoscale sample analysis remains a daunting task, primarily due to significant sample loss associated with non-specific surface adsorption during enrichment of low stoichiometric phosphopeptide. We develop a tandem tip phosphoproteomics sample preparation method that is capable of sample cleanup and enrichment without additional sample transfer, and its integration with our recently developed SOP (Surfactant-assisted One-Pot sample preparation) and iBASIL (improved Boosting to Amplify Signal with Isobaric Labeling) approaches provides a streamlined workflow enabling sensitive, high-throughput nanoscale phosphoproteome measurements. This approach significantly reduces both sample loss and processing time, allowing the identification of >3000 (>9500) phosphopeptides from 1 (10) µg of cell lysate using the label-free method without a spectral library. It also enables precise quantification of ~600 phosphopeptides from 100 sorted cells (single-cell level input for the enriched phosphopeptides) and ~700 phosphopeptides from human spleen tissue voxels with a spatial resolution of 200 µm (equivalent to ~100 cells) in a high-throughput manner. The new workflow opens avenues for phosphoproteome profiling of mass-limited samples at the low nanogram level.

Rapid Multi-Omics Sample Preparation for Mass Spectrometry

Multi-omics analysis is a powerful and increasingly utilized approach to gain insight into complex biological systems. One major hindrance with multi-omics, however, is the lengthy and wasteful sample preparation process. Preparing samples for mass spectrometry (MS)-based multi-omics involves extraction of metabolites and lipids with organic solvents, precipitation of proteins, and overnight digestion of proteins. These existing workflows are disparate and laborious. Here, we present a simple, efficient, and unified approach to prepare lipids, metabolites, and proteins for MS analysis. Our approach, termed the Bead-enabled Accelerated Monophasic Multi-omics (BAMM) method, combines an n-butanol-based monophasic extraction with unmodified magnetic beads and accelerated protein digestion. We demonstrate that the BAMM method affords comparable depth, quantitative reproducibility, and recovery of biomolecules as state-of-the-art multi-omics methods (e.g., Matyash extraction and overnight protein digestion). However, the BAMM method only requires about 3 h to perform, which saves 11 steps and 19 h on average compared to published multi-omics methods. Furthermore, we validate the BAMM method for multiple sample types and formats (biofluid, culture plate, and pellet) and show that in all cases, it produces high biomolecular coverage and data quality.

Proteomics to study cancer immunity and improve treatment

Cancer survival and progression depend on the ability of tumor cells to avoid immune recognition. Advances in the understanding of cancer immunity and tumor immune escape mechanisms enabled the development of immunotherapeutic approaches. In patients with otherwise incurable metastatic cancers, immunotherapy resulted in unprecedented response rates with the potential for durable complete responses. However, primary and acquired resistance mechanisms limit the efficacy of immunotherapy. Further therapeutic advances require a deeper understanding of the interplay between immune cells and tumors. Most high-throughput studies within the past decade focused on an omics characterization at DNA and RNA level. However, proteins are the molecular effectors of genomic information; therefore, the study of proteins provides deeper understanding of cellular functions. Recent advances in mass spectrometry (MS)-based proteomics at a system-wide scale may allow translational and clinical discoveries by enabling the analysis of understudied post-translational modifications, subcellular protein localization, cell signaling, and protein-protein interactions. In this review, we discuss the potential contribution of MS-based proteomics to preclinical and clinical research findings in the context of tumor immunity and cancer immunotherapies.