diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition

On the Road to Accurate Protein Biomarkers in Prostate Cancer Diagnosis and Prognosis: Current Status and Future Advances

Prostate cancer (PC) is a leading cause of morbidity and mortality among men worldwide. Molecular biomarkers work in conjunction with existing clinicopathologic tools to help physicians decide who to biopsy, re-biopsy, treat, or re-treat. The past decade has witnessed the commercialization of multiple PC protein biomarkers with improved performance, remarkable progress in proteomic technologies for global discovery and targeted validation of novel protein biomarkers from clinical specimens, and the emergence of novel, promising PC protein biomarkers. In this review, we summarize these advances and discuss the challenges and potential solutions for identifying and validating clinically useful protein biomarkers in PC diagnosis and prognosis. The identification of multi-protein biomarkers with high sensitivity and specificity, as well as their integration with clinicopathologic parameters, imaging, and other molecular biomarkers, bodes well for optimal personalized management of PC patients.

Data-independent acquisition (DIA): An emerging proteomics technology for analysis of drug-metabolizing enzymes and transporters

Data-independent acquisition (DIA) proteomics is a recently-developed global mass spectrometry (MS)-based proteomics strategy. In a DIA method, precursor ions are isolated into pre-defined isolation windows and fragmented; all fragmented ions in each window are then analyzed by a high-resolution mass spectrometer. DIA proteomics analysis is characterized by a broad protein coverage, high reproducibility, and accuracy, and its combination with advances in other techniques such as sample preparation and computational data analysis could lead to further improvements in assay performances. DIA technology has been increasingly utilized in various proteomics studies, including quantifying drug-metabolizing enzymes and transporters. Quantitative proteomics study of drug-metabolizing enzymes and transporters could lead to a better understanding of pharmacokinetics and pharmacodynamics and facilitate drug development. This review summarizes the application of DIA technology in proteomic analysis of drug-metabolizing enzymes and transporters.

Association of Complement and MAPK Activation With SARS-CoV-2-Associated Myocardial Inflammation

Question

What is the cardiac phenotype of patients with SARS-CoV-2 infection compared with viral and immune-mediated myocarditis and noninflammatory cardiomyopathy?

Findings

In this case series of 19 patients undergoing endomyocardial biopsies, cardiac specimens of patients with SARS-CoV-2 infection had a higher abundance of complement-associated factors and serine/threonine protein kinases, with mitogen-activated protein kinase–associated pathways having the highest abundance. Similarities in the cardiac immune signature were found among those with SARS-CoV-2 infection and viral myocarditis.

Meaning

In this study, the exploratory data, which characterized myocardial inflammation by deep phenotyping, have implications for the development of treatment strategies to reduce SARS-CoV-2–mediated tissue injury; these findings require confirmation in a prospective and extended cohort of patients.

Importance

Myocardial injury is a common feature of patients with SARS-CoV-2 infection. However, the cardiac inflammatory processes associated with SARS-CoV-2 infection are not completely understood.

Objective

To investigate the inflammatory cardiac phenotype associated with SARS-CoV-2 infection compared with viral myocarditis, immune-mediated myocarditis, and noninflammatory cardiomyopathy by integrating histologic, transcriptomic, and proteomic profiling.

Design, Setting, and Participants

This case series was a cooperative study between the Ludwig Maximilian University Hospital Munich and the Cardiopathology Referral Center at the University of Tübingen in Germany. A cohort of 19 patients with suspected myocarditis was examined; of those, 5 patients were hospitalized with SARS-CoV-2 infection between March and May 2020. Cardiac tissue specimens from those 5 patients were compared with specimens from 5 patients with immune-mediated myocarditis, 4 patients with non–SARS-CoV-2 viral myocarditis, and 5 patients with noninflammatory cardiomyopathy, collected from January to August 2019.

Exposures

Endomyocardial biopsy.

Main Outcomes and Measures

The inflammatory cardiac phenotypes were measured by immunohistologic analysis, RNA exome capture sequencing, and mass spectrometry–based proteomic analysis of endomyocardial biopsy specimens.

Results

Among 19 participants, the median age was 58 years (range, 37-76 years), and 15 individuals (79%) were male. Data on race and ethnicity were not collected. The abundance of CD163+ macrophages was generally higher in the cardiac tissue of patients with myocarditis, whereas lymphocyte counts were lower in the tissue of patients with SARS-CoV-2 infection vs patients with non–SARS-CoV-2 virus-associated and immune-mediated myocarditis. Among those with SARS-CoV-2 infection, components of the complement cascade, including C1q subunits (transcriptomic analysis: 2.5-fold to 3.6-fold increase; proteomic analysis: 2.0-fold to 3.4-fold increase) and serine/cysteine proteinase inhibitor clade G member 1 (transcriptomic analysis: 1.7-fold increase; proteomic analysis: 2.6-fold increase), belonged to the most commonly upregulated transcripts and differentially abundant proteins. In cardiac macrophages, the abundance of C1q was highest in SARS-CoV-2 infection. Assessment of important signaling cascades identified an upregulation of the serine/threonine mitogen-activated protein kinase pathways.

Conclusions and Relevance

This case series found that the cardiac immune signature varied in inflammatory conditions with different etiologic characteristics. Future studies are needed to examine the role of these immune pathways in myocardial inflammation.

GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control

Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data independent acquisition (DIA) is an emerging technology with deep proteome coverage and accurate quantitative capability in proteomics studies, but is still in the early stage of development in the field of glycoproteomics. We propose GproDIA, a framework for the proteome-wide characterization of intact glycopeptides from DIA data with comprehensive statistical control by a 2-dimentional false discovery rate approach and a glycoform inference algorithm, enabling accurate identification of intact glycopeptides using wide isolation windows. We further utilize a semi-empirical spectrum prediction strategy to expand the coverage of spectral libraries of glycopeptides. We benchmark our method for N-glycopeptide profiling on DIA data of yeast and human serum samples, demonstrating that DIA with GproDIA outperforms the data-dependent acquisition-based methods for glycoproteomics in terms of capacity and data completeness of identification, as well as accuracy and precision of quantification. We expect that this work can provide a powerful tool for glycoproteomic studies.

Advances in mass spectrometry-based glycoproteomics: An update covering the period 2017-2021

Protein glycosylation is one of the most common posttranslational modifications, and plays an essential role in a wide range of biological processes such as immune response, intercellular signaling, inflammation, host-pathogen interaction, and protein stability. Glycoproteomics is a proteomics subfield dedicated to identifying and characterizing the glycans and glycoproteins in a given cell or tissue. Aberrant glycosylation has been associated with various diseases such as Alzheimer's disease, viral infections, inflammation, immune deficiencies, congenital disorders, and cancers. However, glycoproteomic analysis remains challenging because of the low abundance, site-specific heterogeneity, and poor ionization efficiency of glycopeptides during LC-MS analyses. Therefore, the development of sensitive and accurate approaches to efficiently characterize protein glycosylation is crucial. Methods such as metabolic labeling, enrichment, and derivatization of glycopeptides, coupled with different mass spectrometry techniques and bioinformatics tools, have been developed to achieve sophisticated levels of quantitative and qualitative analyses of glycoproteins. This review attempts to update the recent developments in the field of glycoproteomics reported between 2017 and 2021.

Targeted liquid chromatography-tandem mass spectrometry analysis of proteins: Basic principles, applications, and perspectives

Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is now the main analytical method for the identification and quantification of peptides and proteins in biological samples. In modern research, identification of biomarkers and their quantitative comparison between samples are becoming increasingly important for discovery, validation, and monitoring. Such data can be obtained following specific signals after fragmentation of peptides using multiple reaction monitoring (MRM) and parallel reaction monitoring (PRM) methods, with high specificity, accuracy, and reproducibility. In addition, these methods allow measurement of the amount of post-translationally modified forms and isoforms of proteins. This review article describes the basic principles of MRM assays, guidelines for sample preparation, recent advanced MRM-based strategies, applications and illustrative perspectives of MRM/PRM methods in clinical research and molecular biology.

Narrow Precursor Mass Range for DIA-MS Enhances Protein Identification and Quantification in Arabidopsis

Data independent acquisition-mass spectrometry (DIA-MS) is becoming widely utilised for robust and accurate quantification of samples in quantitative proteomics. Here, we describe the systematic evaluation of the effects of DIA precursor mass range on total protein identification and quantification. We show that a narrow mass range of precursors (~250 m/z) for DIA-MS enables a higher number of protein identifications. Subsequent application of DIA with narrow precursor range (from 400 to 650 m/z) on an Arabidopsis sample with spike-in known proteins identified 34.7% more proteins than in conventional DIA (cDIA) with a wide precursor range of 400-1200 m/z. When combining several DIA-MS analyses with narrow precursor ranges (i.e., 400-650, 650-900 and 900-1200 m/z), we were able to quantify 10,099 protein groups with a median coefficient of variation of <6%. These findings represent a 54.7% increase in the number of proteins quantified than with cDIA analysis. This is particularly important for low abundance proteins, as exemplified by the six-protein mix spike-in. In cDIA only five out of the six-protein mix were quantified while our approach allowed accurate quantitation of all six proteins.

AlphaTims: Indexing Trapped Ion Mobility Spectrometry-TOF Data for Fast and Easy Accession and Visualization

High-resolution MS-based proteomics generates large amounts of data, even in the standard LC-tandem MS configuration. Adding an ion mobility dimension vastly increases the acquired data volume, challenging both analytical processing pipelines and especially data exploration by scientists. This has necessitated data aggregation, effectively discarding much of the information present in these rich datasets. Taking trapped ion mobility spectrometry (TIMS) on a quadrupole TOF (Q-TOF) platform as an example, we developed an efficient indexing scheme that represents all data points as detector arrival times on scales of minutes (LC), milliseconds (TIMS), and microseconds (TOF). In our open-source AlphaTims package, data are indexed, accessed, and visualized by a combination of tools of the scientific Python ecosystem. We interpret unprocessed data as a sparse four-dimensional matrix and use just-in-time compilation to machine code with Numba, accelerating our computational procedures by several orders of magnitude while keeping to familiar indexing and slicing notations. For samples with more than six billion detector events, a modern laptop can load and index raw data in about a minute. Loading is even faster when AlphaTims has already saved indexed data in an HDF5 file, a portable scientific standard used in extremely large-scale data acquisition. Subsequently, data accession along any dimension and interactive visualization happens in milliseconds. We have found AlphaTims to be a key enabling tool to explore high-dimensional LC-TIMS-Q-TOF data and have made it freely available as an open-source Python package with a stand-alone graphical user interface at https://github.com/MannLabs/alphatims or as part of the AlphaPept 'ecosystem'.

High-throughput proteomics and AI for cancer biomarker discovery

Biomarkers are assayed to assess biological and pathological status. Recent advances in high-throughput proteomic technology provide opportunities for developing next generation biomarkers for clinical practice aided by artificial intelligence (AI) based techniques. We summarize the advances and limitations of cancer biomarkers based on genomic and transcriptomic analysis, as well as classical antibody-based methodologies. Then we review recent progresses in mass spectrometry (MS)-based proteomics in terms of sample preparation, peptide fractionation by liquid chromatography (LC) and mass spectrometric data acquisition. We highlight applications of AI techniques in high-throughput clinical studies as compared with clinical decisions based on singular features. This review sets out our approach for discovering clinical biomarkers in studies using proteomic big data technology conjoined with computational and statistical methods.

Use of Linear Ion Traps in Data-Independent Acquisition Methods Benefits Low-Input Proteomics

The need for a better understanding of cellular heterogeneity has pushed mass spectrometry technologies to the analysis of single-cell and single-cell-type proteomes, although several challenges still limit their widespread implementation. Among the efforts toward single-cell and low-input analyses, there is the adoption of data-independent acquisition methods to increase analytical sensitivity. Here, we revisited the use of linear ion traps mass analyzers in data-independent acquisition methods and demonstrate their benefits to boost peptide and protein identifications in low-input proteomes.

Trapped Ion Mobility Spectrometry and Parallel Accumulation-Serial Fragmentation in Proteomics

Recent advances in efficiency and ease of implementation have rekindled interest in ion mobility spectrometry, a technique that separates gas phase ions by their size and shape and that can be hybridized with conventional LC and MS. Here, we review the recent development of trapped ion mobility spectrometry (TIMS) coupled to TOF mass analysis. In particular, the parallel accumulation-serial fragmentation (PASEF) operation mode offers unique advantages in terms of sequencing speed and sensitivity. Its defining feature is that it synchronizes the release of ions from the TIMS device with the downstream selection of precursors for fragmentation in a TIMS quadrupole TOF configuration. As ions are compressed into narrow ion mobility peaks, the number of peptide fragment ion spectra obtained in data-dependent or targeted analyses can be increased by an order of magnitude without compromising sensitivity. Taking advantage of the correlation between ion mobility and mass, the PASEF principle also multiplies the efficiency of data-independent acquisition. This makes the technology well suited for rapid proteome profiling, an increasingly important attribute in clinical proteomics, as well as for ultrasensitive measurements down to single cells. The speed and accuracy of TIMS and PASEF also enable precise measurements of collisional cross section values at the scale of more than a million data points and the development of neural networks capable of predicting them based only on peptide sequences. Peptide collisional cross section values can differ for isobaric sequences or positional isomers of post-translational modifications. This additional information may be leveraged in real time to direct data acquisition or in postprocessing to increase confidence in peptide identifications. These developments make TIMS quadrupole TOF PASEF a powerful and expandable platform for proteomics and beyond.

DIA-Based Proteome Profiling of Nasopharyngeal Swabs from COVID-19 Patients

Since the recent outbreak of COVID-19, there have been intense efforts to understand viral pathogenesis and host immune response to combat SARS-CoV-2. It has become evident that different host alterations can be identified in SARS-CoV-2 infection based on whether infected cells, animal models or clinical samples are studied. Although nasopharyngeal swabs are routinely collected for SARS-CoV-2 detection by RT-PCR testing, host alterations in the nasopharynx at the proteomic level have not been systematically investigated. Thus, we sought to characterize the host response through global proteome profiling of nasopharyngeal swab specimens. A mass spectrometer combining trapped ion mobility spectrometry (TIMS) and high-resolution QTOF mass spectrometer with parallel accumulation-serial fragmentation (PASEF) was deployed for unbiased proteome profiling. First, deep proteome profiling of pooled nasopharyngeal swab samples was performed in the PASEF enabled DDA mode, which identified 7723 proteins that were then used to generate a spectral library. This approach provided peptide level evidence of five missing proteins for which MS/MS spectrum and mobilograms were validated with synthetic peptides. Subsequently, quantitative proteomic profiling was carried out for 90 individual nasopharyngeal swab samples (45 positive and 45 negative) in DIA combined with PASEF, termed as diaPASEF mode, which resulted in a total of 5023 protein identifications. Of these, 577 proteins were found to be upregulated in SARS-CoV-2 positive samples. Functional analysis of these upregulated proteins revealed alterations in several biological processes including innate immune response, viral protein assembly, and exocytosis. To the best of our knowledge, this study is the first to deploy diaPASEF for quantitative proteomic profiling of clinical samples and shows the feasibility of adopting such an approach to understand mechanisms and pathways altered in diseases.

Toward Comprehensive Plasma Proteomics by Orthogonal Protease Digestion

Rapid and consistent protein identification across large clinical cohorts is an important goal for clinical proteomics. With the development of data-independent technologies (DIA/SWATH-MS), it is now possible to analyze hundreds of samples with great reproducibility and quantitative accuracy. However, this technology benefits from empirically derived spectral libraries that define the detectable set of peptides and proteins. Here, we apply a simple and accessible tip-based workflow for the generation of spectral libraries to provide a comprehensive overview on the plasma proteome in individuals with and without active tuberculosis (TB). To boost protein coverage, we utilized nonconventional proteases such as GluC and AspN together with the gold standard trypsin, identifying more than 30,000 peptides mapping to 3309 proteins. Application of this library to quantify plasma proteome differences in TB infection recovered more than 400 proteins in 50 min of MS acquisition, including diagnostic Mycobacterium tuberculosis (Mtb) proteins that have previously been detectable primarily by antibody-based assays and intracellular proteins not previously described to be in plasma.

Evaluation of the Sensitivity of Proteomics Methods Using the Absolute Copy Number of Proteins in a Single Cell as a Metric

Proteomic technology has improved at a staggering pace in recent years, with even practitioners challenged to keep up with new methods and hardware. The most common metric used for method performance is the number of peptides and proteins identified. While this metric may be helpful for proteomics researchers shopping for new hardware, this is often not the most biologically relevant metric. Biologists often utilize proteomics in the search for protein regulators that are of a lower relative copy number in the cell. In this review, I re-evaluate untargeted proteomics data using a simple graphical representation of the absolute copy number of proteins present in a single cancer cell as a metric. By comparing single-shot proteomics data to the coverage of the most in-depth proteomic analysis of that cell line acquired to date, we can obtain a rapid metric of method performance. Using a simple copy number metric allows visualization of how proteomics has developed in both sensitivity and overall dynamic range when using both relatively long and short acquisition times. To enable reanalysis beyond what is presented here, two available web applications have been developed for single- and multi-experiment comparisons with reference protein copy number data for multiple cell lines and organisms.

Fragmentation and Mobility Separation of Peptide and Protein Ions in a Trapped-Ion Mobility Device

Ion mobility separations (IMS) have increasingly been coupled with mass spectrometry to increase peak capacity and deconvolute complex mass spectra in proteomics workflows. IMS separations can be integrated prior to or following the collisional activation step. Post-activation IMS separations have demonstrated many advantages, yet few instrument platforms are capable of this feat. Here, we present the fragmentation of peptide ions within a commercially available trapped-ion mobility spectrometry device. Fragmentation is initiated prior to mobility analysis enabling the separation of generated product ions. The added separation step deconvolutes product ion spectra and permits improved annotation of product ions. Furthermore, we demonstrate the isolation and fragmentation of mobility separated product ions with the downstream quadrupole and collisional cell. When applied to melittin and ubiquitin, this ion mobility assisted pseudo-MS³ fragmentation approach generates sequence coverage ∼50% greater than that of typical MS² analyses. We envision this ion-mobility-assisted fragmentation technique as the foundation of a powerful new pseudo-MS³ workflow for application toward middle- or top-down proteomics.

Ultra-fast proteomics with Scanning SWATH

Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.

The Protein Landscape of Chronic Lymphocytic Leukemia (CLL)

Many functional consequences of mutations on tumor phenotypes in chronic lymphocytic leukemia (CLL) are unknown. This may be in part due to a scarcity of information on the proteome of CLL. We profiled the proteome of 117 CLL patient samples with data-independent acquisition mass spectrometry (DIA-MS) and integrated the results with genomic, transcriptomic, ex vivo drug response and clinical outcome data. We found trisomy 12, IGHV mutational status, mutated SF3B1, trisomy 19, del(17)(p13), del(11)(q22.3), mutated DDX3X, and MED12 to influence protein expression (FDR < 5%). Trisomy 12 and IGHV status were the major determinants of protein expression variation in CLL as shown by principal component analysis (1055 and 542 differentially expressed proteins, FDR=5%). Gene set enrichment analyses of CLL with trisomy 12 implicated BCR/PI3K/AKT signaling as a tumor driver. These findings were supported by analyses of protein abundance buffering and protein complex formation, which identified limited protein abundance buffering and an upregulated protein complex involved in BCR, AKT, MAPK and PI3K signaling in trisomy 12 CLL. A survey of proteins associated with trisomy 12/IGHV-independent drug response linked STAT2 protein expression with response to kinase inhibitors including BTK and MEK inhibitors. STAT2 was upregulated in U-CLL, trisomy 12 CLL and required for chemokine/cytokine signaling (interferon response). This study highlights the importance of protein abundance data as a non-redundant layer of information in tumor biology, and provides a protein expression reference map for CLL.

Systematic detection of functional proteoform groups from bottom-up proteomic datasets

To a large extent functional diversity in cells is achieved by the expansion of molecular complexity beyond that of the coding genome. Various processes create multiple distinct but related proteins per coding gene - so-called proteoforms - that expand the functional capacity of a cell. Evaluating proteoforms from classical bottom-up proteomics datasets, where peptides instead of intact proteoforms are measured, has remained difficult. Here we present COPF, a tool for COrrelation-based functional ProteoForm assessment in bottom-up proteomics data. It leverages the concept of peptide correlation analysis to systematically assign peptides to co-varying proteoform groups. We show applications of COPF to protein complex co-fractionation data as well as to more typical protein abundance vs. sample data matrices, demonstrating the systematic detection of assembly- and tissue-specific proteoform groups, respectively, in either dataset. We envision that the presented approach lays the foundation for a systematic assessment of proteoforms and their functional implications directly from bottom-up proteomic datasets.

Spatial proteomics for understanding the tissue microenvironment

The human body comprises rich populations of cells, which are arranged into tissues and organs with diverse functionalities. These cells exhibit a broad spectrum of phenotypes and are often organized as a heterogeneous but sophisticatedly regulated ecosystem - tissue microenvironment, inside which every cell interacts with and is reciprocally influenced by its surroundings through its life span. Therefore, it is critical to comprehensively explore the cellular machinery and biological processes in the tissue microenvironment, which is best exemplified by the tumor microenvironment (TME). The past decade has seen increasing advances in the field of spatial proteomics, the main purpose of which is to characterize the abundance and spatial distribution of proteins and their post-translational modifications in the microenvironment of diseased tissues. Herein, we outline the achievements and remaining challenges of mass spectrometry-based tissue spatial proteomics. Exciting technology developments along with important biomedical applications of spatial proteomics are highlighted. In detail, we focus on high-quality resources built by scalpel macrodissection-based region-resolved proteomics, method development of sensitive sample preparation for laser microdissection-based spatial proteomics, and antibody recognition-based multiplexed tissue imaging. In the end, critical issues and potential future directions for spatial proteomics are also discussed.

Sequence-Specific Model for Predicting Peptide Collision Cross Section Values in Proteomic Ion Mobility Spectrometry

The contribution of peptide amino acid sequence to collision cross section values (CCS) has been investigated using a dataset of ∼134 000 peptides of four different charge states (1+ to 4+). The migration data were acquired using a two-dimensional liquid chromatography (LC)/trapped ion mobility spectrometry/quadrupole/time-of-flight mass spectrometry (MS) analysis of HeLa cell digests created using seven different proteases and was converted to CCS values. Following the previously reported modeling approaches using intrinsic size parameters (ISP), we extended this methodology to encode the position of individual residues within a peptide sequence. A generalized prediction model was built by dividing the dataset into eight groups (four charges for both tryptic/nontryptic peptides). Position-dependent ISPs were independently optimized for the eight subsets of peptides, resulting in prediction accuracy of ∼0.981 for the entire population of peptides. We find that ion mobility is strongly affected by the peptide's ability to solvate the positively charged sites. Internal positioning of polar residues and proline leads to decreased CCS values as they improve charge solvation; conversely, this ability decreases with increasing peptide charge due to electrostatic repulsion. Furthermore, higher helical propensity and peptide hydrophobicity result in a preferential formation of extended structures with higher than predicted CCS values. Finally, acidic/basic residues exhibit position-dependent ISP behavior consistent with electrostatic interaction with the peptide macrodipole, which affects the peptide helicity. The MS raw data files have been deposited with the ProteomeXchange Consortium via the jPOST partner repository (http://jpostdb.org) with the dataset identifiers PXD021440/JPST000959, PXD022800/JPST001017, and PXD026087/ JPST001176.

A matter of perspective-Cutting-edge technology-driven urine proteome in COVID-19

In a recent issue of Nature Communications, we highlighted in-depth urine proteomic research in which significant immunosuppression was revealed in early SARS-CoV-2- infected patients 1. The application of urine in mapping the landscape of molecular changes closely associated with human diseases has been widely accepted. Herein, we take a systematic review of the published article from the perspective of both methodology and clinical significance.

Genome-wide CRISPRi screening identifies OCIAD1 as a prohibitin client and regulatory determinant of mitochondrial Complex III assembly in human cells

Dysfunction of the mitochondrial electron transport chain (mETC) is a major cause of human mitochondrial diseases. To identify determinants of mETC function, we screened a genome-wide human CRISPRi library under oxidative metabolic conditions with selective inhibition of mitochondrial Complex III and identified ovarian carcinoma immunoreactive antigen (OCIA) domain-containing protein 1 (OCIAD1) as a Complex III assembly factor. We find that OCIAD1 is an inner mitochondrial membrane protein that forms a complex with supramolecular prohibitin assemblies. Our data indicate that OCIAD1 is required for maintenance of normal steady-state levels of Complex III and the proteolytic processing of the catalytic subunit cytochrome c₁ (CYC1). In OCIAD1 depleted mitochondria, unprocessed CYC1 is hemylated and incorporated into Complex III. We propose that OCIAD1 acts as an adaptor within prohibitin assemblies to stabilize and/or chaperone CYC1 and to facilitate its proteolytic processing by the IMMP2L protease.

New insights into the functional role of retrotransposon dynamics in mammalian somatic cells

Retrotransposons are genetic elements present across all eukaryotic genomes. While their role in evolution is considered as a potentially beneficial natural source of genetic variation, their activity is classically considered detrimental due to their potentially harmful effects on genome stability. However, studies are increasingly shedding light on the regulatory function and beneficial role of somatic retroelement reactivation in non-pathological contexts. Here, we review recent findings unveiling the regulatory potential of retrotransposons, including their role in noncoding RNA transcription, as modulators of mammalian transcriptional and epigenome landscapes. We also discuss technical challenges in deciphering the multifaceted activity of retrotransposable elements, highlighting an unforeseen central role of this neglected portion of the genome both in early development and in adult life.

Quantitative Mass Spectrometry-Based Proteomics for Biomarker Development in Ovarian Cancer

Ovarian cancer is the most lethal gynecologic malignancy among women. Approximately 70–80% of patients with advanced ovarian cancer experience relapse within five years and develop platinum-resistance. The short life expectancy of patients with platinum-resistant or platinum-refractory disease underscores the need to develop new and more effective treatment strategies. Early detection is a critical step in mitigating the risk of disease progression from early to an advanced stage disease, and protein biomarkers have an integral role in this process. The best biological diagnostic tool for ovarian cancer will likely be a combination of biomarkers. Targeted proteomics methods, including mass spectrometry-based approaches, have emerged as robust methods that can address the chasm between initial biomarker discovery and the successful verification and validation of these biomarkers enabling their clinical translation due to the robust sensitivity, specificity, and reproducibility of these versatile methods. In this review, we provide background information on the fundamental principles of biomarkers and the need for improved treatment strategies in ovarian cancer. We also provide insight into the ways in which mass spectrometry-based targeted proteomics approaches can provide greatly needed solutions to many of the challenges related to ovarian cancer biomarker development.

Discovery-Versus Hypothesis-Driven Detection of Protein-Protein Interactions and Complexes

Protein complexes are the main functional modules in the cell that coordinate and perform the vast majority of molecular functions. The main approaches to identify and quantify the interactome to date are based on mass spectrometry (MS). Here I summarize the benefits and limitations of different MS-based interactome screens, with a focus on untargeted interactome acquisition, such as co-fractionation MS. Specific emphasis is given to the discussion of discovery- versus hypothesis-driven data analysis concepts and their applicability to large, proteome-wide interactome screens. Hypothesis-driven analysis approaches, i.e., complex- or network-centric, are highlighted as promising strategies for comparative studies. While these approaches require prior information from public databases, also reviewed herein, the available wealth of interactomic data continuously increases, thereby providing more exhaustive information for future studies. Finally, guidance on the selection of interactome acquisition and analysis methods is provided to aid the reader in the design of protein-protein interaction studies.

A New Parallel High-Pressure Packing System Enables Rapid Multiplexed Production of Capillary Columns

Reversed-phase HPLC is the most commonly applied peptide-separation technique in MS-based proteomics. Particle-packed capillary columns are predominantly used in nanoflow HPLC systems. Despite being the broadly applied standard for many years, capillary columns are still expensive and suffer from short lifetimes, particularly in combination with ultra-high-pressure chromatography systems. For this reason, and to achieve maximum performance, many laboratories produce their own in-house packed columns. This typically requires a considerable amount of time and trained personnel. Here, we present a new packing system for capillary columns enabling rapid, multiplexed column packing with pressures reaching up to 3000 bar. Requiring only a conventional gas pressure supply and methanol as the driving fluid, our system replaces the traditional setup of helium-pressured packing bombs. By using 10× multiplexing, we have reduced the production time to just under 2 min for several 50 cm columns with 1.9-µm particle size, speeding up the process of column production 40 to 800 times. We compare capillary columns with various inner diameters and lengths packed under different pressure conditions with our newly designed, broadly accessible high-pressure packing station.

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We present a newly designed high-pressure packing station for capillary columns.

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Detailed part lists and manufacturing instructions included.

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40 to 800 times faster packing of capillary columns for high-performance proteomics.

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Columns produced with the presented setup have state-of-the-art quality metrics.

Proteogenomic interrogation of cancer cell lines: an overview of the field

Introduction: Cancer cell lines (CCLs) have been a major resource for cancer research. Over the past couple of decades, they have been instrumental in omic profiling method development and as model systems to generate new knowledge in cell and cancer biology. More recently, with the increasing amount of genomic, transcriptomic and proteomic data being generated in hundreds of CCLs, there is growing potential for integrative proteogenomic data analyses to be performed.Areas covered: In this review, we first describe the most commonly used proteome profiling methods in CCLs. We then discuss how these proteomics data can be integrated with genomics data for proteogenomics analyses. Finally, we highlight some of the recent biological discoveries that have arisen from proteogenomics analyses of CCLs.Expert opinion: Protegeonomics analyses of CCLs have so far enabled the discovery of novel proteins and proteoforms. It has also improved our understanding of biological processes including post-transcriptional regulation of protein abundance and the presentation of antigens by major histocompatibility complex alleles. With proteomics data to be generated in hundreds to thousands of CCLs in coming years, there will be further potential for large-scale proteogenomics analyses and data integration with the phenotypically well-characterized CCLs.

pDeep3: Toward More Accurate Spectrum Prediction with Fast Few-Shot Learning

Spectrum prediction using deep learning has attracted a lot of attention in recent years. Although existing deep learning methods have dramatically increased the prediction accuracy, there is still considerable space for improvement, which is presently limited by the difference of fragmentation types or instrument settings. In this work, we use the few-shot learning method to fit the data online to make up for the shortcoming. The method is evaluated using ten data sets, where the instruments includes Velos, QE, Lumos, and Sciex, with collision energies being differently set. Experimental results show that few-shot learning can achieve higher prediction accuracy with almost negligible computing resources. For example, on the data set from a untrained instrument Sciex-6600, within about 10 s, the prediction accuracy is increased from 69.7% to 86.4%; on the CID (collision-induced dissociation) data set, the prediction accuracy of the model trained by HCD (higher energy collision dissociation) spectra is increased from 48.0% to 83.9%. It is also shown that, the method is not critical to data quality and is sufficiently efficient to fill the accuracy gap. The source code of pDeep3 is available at http://pfind.ict.ac.cn/software/pdeep3.

Elucidation of host-virus surfaceome interactions using spatial proteotyping

The cellular surfaceome and its residing extracellularly exposed proteins are involved in a multitude of molecular signaling processes across the viral infection cycle. Successful viral propagation, including viral entry, immune evasion, virion release and viral spread rely on dynamic molecular interactions with the surfaceome. Decoding of these viral-host surfaceome interactions using advanced technologies enabled the discovery of fundamental new functional insights into cellular and viral biology. In this review, we highlight recently developed experimental strategies, with a focus on spatial proteotyping technologies, aiding in the rational design of theranostic strategies to combat viral infections.

IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs

Missing values weaken the power of label-free quantitative proteomic experiments to uncover true quantitative differences between biological samples or experimental conditions. Match-between-runs (MBR) has become a common approach to mitigate the missing value problem, where peptides identified by tandem mass spectra in one run are transferred to another by inference based on m/z, charge state, retention time, and ion mobility when applicable. Though tolerances are used to ensure such transferred identifications are reasonably located and meet certain quality thresholds, little work has been done to evaluate the statistical confidence of MBR. Here, we present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant. Using several benchmarking datasets generated on both Orbitrap and timsTOF mass spectrometers, we demonstrate superior performance of IonQuant with FDR-controlled MBR compared with MaxQuant (19-38 times faster; 6-18% more proteins quantified and with comparable or better accuracy). We further illustrate the performance of IonQuant and highlight the need for FDR-controlled MBR, in two single-cell proteomics experiments, including one acquired with the help of high-field asymmetric ion mobility spectrometry separation. Fully integrated in the FragPipe computational environment, IonQuant with FDR-controlled MBR enables fast and accurate peptide and protein quantification in label-free proteomics experiments.

The rise of single‐cell proteomics

Mass spectrometry‐based proteomics comprehensively defines proteome expression patterns in thousands of cells majorly contributing to our current understanding of many biological processes. More recently, single‐cell transcriptome and genome studies, however, have demonstrated overwhelming heterogeneity of tissues and cellular subpopulations. These studies have indicated different cellular functionality and identity, which are mainly driven by proteins and their posttranscriptional modifications. The rapidly emerging field of single‐cell proteomics aims at complementing transcriptome and genome data by generating comparative protein expression profiles from individual cells. Recent developments demonstrated tremendous improvements in sample preparation workflows and MS instrumentation, quantifying over 1000 proteins from a single cell. Efficient and reproducible sample processing in conjunction with sensitive MS acquisition strategies will allow to further increase the proteome coverage of tissues with single‐cell resolution. The required throughput and data reliability of such studies are still subject to further developments. Therefore, we herein discuss recent progress on specialized workflows and instrumentation next to advancements outside the field, which we expect to contribute to the development of comprehensive single‐cell proteomics.

OpenTIMS, TimsPy, and TimsR: Open and Easy Access to timsTOF Raw Data

The Bruker timsTOF Pro is an instrument that couples trapped ion mobility spectrometry (TIMS) to high-resolution time-of-flight (TOF) mass spectrometry (MS). For proteomics, lipidomics, and metabolomics applications, the instrument is typically interfaced with a liquid chromatography (LC) system. The resulting LC-TIMS-MS data sets are, in general, several gigabytes in size and are stored in the proprietary Bruker Tims data format (TDF). The raw data can be accessed using proprietary binaries in C, C++, and Python on Windows and Linux operating systems. Here we introduce a suite of computer programs for data accession, including OpenTIMS, TimsR, and TimsPy. OpenTIMS is a C++ library capable of reading Bruker TDF files. It opens up Bruker's proprietary codebase. TimsPy and TimsR build on top of OpenTIMS, enabling swift and user-friendly data access to the raw data with Python and R. Both programs are available under a GPL3 license on all major platforms, extending the possibility to interact with timsTOF data to macOS. Additionally, OpenTIMS is capable of translating Bruker data into HDF5 files that can be easily analyzed from Python with the vaex module. OpenTIMS and TimsPy therefore provide easy and quick access to Bruker timsTOF raw data.

Deep learning the collisional cross sections of the peptide universe from a million experimental values

The size and shape of peptide ions in the gas phase are an under-explored dimension for mass spectrometry-based proteomics. To investigate the nature and utility of the peptide collisional cross section (CCS) space, we measure more than a million data points from whole-proteome digests of five organisms with trapped ion mobility spectrometry (TIMS) and parallel accumulation-serial fragmentation (PASEF). The scale and precision (CV < 1%) of our data is sufficient to train a deep recurrent neural network that accurately predicts CCS values solely based on the peptide sequence. Cross section predictions for the synthetic ProteomeTools peptides validate the model within a 1.4% median relative error (R > 0.99). Hydrophobicity, proportion of prolines and position of histidines are main determinants of the cross sections in addition to sequence-specific interactions. CCS values can now be predicted for any peptide and organism, forming a basis for advanced proteomics workflows that make full use of the additional information.

Proteomics has been advanced by algorithms that can predict different peptide features, but predicting peptide collisional cross sections (CCS) has remained challenging. Here, the authors measure over one million CCS values of tryptic peptides and develop a deep learning model for peptide CCS prediction.

Use of data-independent acquisition mass spectrometry for comparative proteomics analyses of sera from pregnant women with intrahepatic cholestasis of pregnancy

We used data-independent acquisition (DIA) proteomics technology followed by ELISAs and automated biochemical analyses to identify and validate protein expression levels in Intrahepatic Cholestasis of Pregnancy (ICP) and healthy pregnant controls. We employed bioinformatics to identify metabolic processes associated with differentially expressed proteins.The expression levels of two proteins (S100-A9 and the L-lactate dehydrogenase A chain) were significantly higher in ICP patients than in controls; the areas under the receiver operating characteristic (ROC) curves (AUCs) were 0.774 and 0.828, respectively. The expression levels of two other proteins (apolipoprotein A-I and cholinesterase) were significantly lower in patients, with values of 0.900 and 0.842, respectively. Multiple logistic regression showed that a combination of the levels of the four proteins optimized the AUC (0.962), thus more reliably diagnosing ICP. The levels of all four proteins were positively associated with that of total bile acids. Bioinformatics analyses indicated that the four proteins principally affected neutrophil activation involved in the immune response, cell adhesion, lipoprotein metabolism, and the PPAR signaling pathway. SIGNIFICANCE: This preliminary work improves our understanding of changes in serum levels of protein in pregnant women with ICP. The four proteins may serve as novel noninvasive biomarkers for ICP.